Health in Europe : results from 1997-2000 surveys.
Date: 2003-12-05
Institutions: Eurostat
Subjects: EU statistics, health statistics, population statistics

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Health in Europe 

Results from 1997-2000 surveys

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THEME 3
Population  
and social  
conditions

E U R O P E A N
C O M M I S S I O N

 
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ISBN 92-894-6828-9

© European Communities, 2003

Foreword and acknowledgement 

(cid:4) 

Foreword and acknowledgement 

Health  Interview  Surveys  (HIS)  are  very  relevant  sources  of  information  on  health  as  the  persons 
themselves  are  well  informed  people  on  their  own  health  status,  their  behaviours  that  can  affect 
positively  or  negatively  their  health,  and  their  use  of  the  health  care  systems.  Though  they  are  less 
precise and more subjective than health examination surveys for health status or than administrative 
sources on the use of the health care systems, they provide invaluable information as they are broadly 
implemented  and  less  influenced  by  the  differences  between  the  national  health  care  systems. 
However,  the  differences between concepts,  questions, coverage,  etc.,  of  the  national surveys  raise 
comparability issues when analysing HIS results from various countries and even from various surveys 
carried out within a country.  

In 2002 Eurostat launched for the second time a data collection on health items from national health 
and  population  surveys.  The  first  collection  in  1999  on  12  health  items  was  a pilot  exercise  and  the 
data were not published. The second data collection in 2002 included 6 new items, i.e. a total of 18 
items, and was carried out in conjunction with OECD. The 18 items covered various aspects on health 
status,  including  not  only  the  "classical"  self-perceived  health  and body  mass  index  but also chronic 
conditions  and  temporary  reduction  in  usual  activities.  Similarly,  on  health  determinants,  smoking  or 
alcohol  consumption  as  well  as  physical  activity  or  positive  elements  such  as  preventive  care  were 
covered.  Finally,  on  use  of  the  health  care  systems,  consultations  with  physicians  as  well  as 
hospitalisation or use of medicines were covered. The sources were non-harmonised national surveys 
from the EU Member States and EFTA countries. In a large majority of cases they were HIS though 
some  health  examination  surveys  (HES)  were  used  in  a  few  countries  for  some  items.  However, 
countries  were  asked  to  post-harmonise  as  far  as  possible  the  data  before  submission  according  to 
guidelines based on existing international standards. The data reference years varied between 1997 
and 2000 depending on the country. 

The analysis made on these data represent the first attempt of this kind at EU level. The comparability 
issues  explained  above  were  as  far  as  possible  fully  taken  into  consideration.  Moreover,  in  order  to 
assess  the  quality  of  the  data,  an  analysis  was  made  on  the  results  from  the  European  Community 
Household  Panel  (ECHP,  1998  data)  for  the  same  or  similar  items,  when  relevant,  to  compare  the 
post-harmonised  results  with  a  pre-harmonised  European  data  source.  However,  even  when  the 
results were coherent with the ECHP, the potential limitations in the comparability of the data between 
countries (both Member States and EFTA countries) should always be kept in mind. Additionally, for 
some items the data were only available for few countries and this limits the value of the analysis for 
these items.  

However,  the  improvements  compared  to  the  first  round  in  1999,  the  important  set  of  meta  data 
collected in order to assist interpretation, as well as the intensive and very precise work carried out to 
perform the analysis, provide interesting and valuable results justifying publication.  

Additionally,  both  the  efforts  made  by  Member  States  to  comply  with  the  specifications  from  the 
guidelines  and  the  experience  gained  in  the  process  and  analysis  of  the  data  at  EU  level,  are 
contributing to the ongoing work on a set of pre-standardised health modules. This set of modules will 
constitute the European Core Health Interview Survey (ECHIS) together with the Minimum European 
Health  Module  (MEHM)  already  included  in  the  new  Community  Statistics  on  Income  and  Living 
Conditions  (SILC)  survey.  The  development  of  the  ECHIS  was  welcomed  by  the  Directors  of  Social 
Statistics  of  the  European  National  Statistical  Institutes  at  their  meeting  in  September  2002.  The 
ECHIS will be implemented on an harmonised and regular basis in all Member States and Acceding 
and  Candidate  Countries  from  around  2006.  Such  harmonisation  and  standardisation  of  HIS  survey 
modules is necessary to achieve high comparability between EU countries.  

3

                                                       
 
 
 
 
 
 
 
 
(cid:4) 

                                              Foreword and acknowledgement 

In the meantime a next round of data collection on the 18 HIS/HES items will take place in 2004. It will 
be carried out both in the current 15 Member States and in the Acceding and Candidate Countries (in 
particular  via  a  specific  PHARE  project)  as  well  as  in  the  EFTA  countries.  The  guidelines  will  be 
improved  with  recommendations  from the  experience  of  the  2002  data  collection.  It  is  expected  that 
the reliability of the data will be improved further and that this new 18 HIS/HES items data collection 
will constitute again a step forward towards the high quality standards expected in the future ECHIS.  

Finally,  Eurostat  would  like  to  thanks  Renaud  Decoster  and  Aurélie  Jacquin  who  carried  out,  in 
collaboration  with  the  health  and  safety  statistics  section  in  Eurostat,  the  very  detailed  analysis 
presented  in  the  current  publication.  Items  with  quite  comparable  characteristics  are  distinguished 
from those for which additional improvements are still necessary in order to achieve the same quality 
standards. They also draw important methodological conclusions from this exercise both for the next 
round and for the more general work on future harmonised modules. 

We hope this first presentation of a European analysis of health surveys data will provide, despite its 
limitations, useful information for policy departments and any other institutions and people involved in 
European-wide activities on health. 

4

 
 
 
 
Table of contents 

(cid:4) 

Detailed table of contents 

Foreword and acknowledgement  .................................................................................................................3 

List of tables ..................................................................................................................................................7 

List of graphs ...............................................................................................................................................11 

Annexes.......................................................................................................................................................17 

List of symbols used and country abbreviations .........................................................................................19 

1.  Characteristics of the surveys, population samples and statistical methodology......................21 

1.1.  Characteristics of the surveys and population samples .................................................................23 
1.2.  Statistical methodology...................................................................................................................31 

2.  Analysis of health indicators with relatively high level of comparability .....................................35 

2.1.  Perceived health ..........................................................................................................................35 
2.1.1.  Perceived health (national data) .....................................................................................35 
2.1.2.  Perceived health (ECHP) ................................................................................................40 
2.2.  Chronic conditions........................................................................................................................47 
2.3.  Temporary cut-down on usual activities.......................................................................................55 
2.3.1.  Percentage of population having temporarily cut down on usual activities.....................55 

2.3.1.1. Percentage of population having temporarily cut down on usual activities  

(national data)..........................................................................................................55 

2.3.1.2. Percentage of population having temporarily cut down on usual activities  

(ECHP) ....................................................................................................................60 
2.3.2.  Average number of days cut down..................................................................................64 
2.3.3.  Average number of days in bed ......................................................................................67 
2.4.  Body Mass Index..........................................................................................................................71 
2.4.1.  Body Mass Index (national data) ....................................................................................71 
2.4.2.  Body Mass Index (ECHP) ...............................................................................................78 
2.5.  Smoking .......................................................................................................................................85 
2.5.1.  Present smoking..............................................................................................................85 
2.5.1.1. Present smoking (national data)..............................................................................85 
2.5.1.2. Present smoking (ECHP) ........................................................................................90 
2.5.1.  Number of cigarettes smoked per day ............................................................................95 
2.5.2.1. Number of cigarettes smoked per day (national data) ............................................95 
2.5.2.2. Number of cigarettes smoked per day (ECHP) .......................................................99 
2.5.3.  Former smokers ............................................................................................................104 
2.5.3.1. Former smokers (national data) ............................................................................104 
2.5.3.2. Former smokers (ECHP) .......................................................................................107 
2.6.  Alcohol drinking..........................................................................................................................113 
2.7.  Hospitalisation in the past 12 months ........................................................................................119 
2.7.1.  Hospitalisation in the past 12 months (national data) ...................................................119 
2.7.2.  Hospitalisation in the past 12 months (ECHP)..............................................................125 
2.8.  Consultation with a medical doctor ............................................................................................131 
2.8.1.  Consultation with a medical doctor in the past 12 months............................................131 
2.8.2.  Number of visits to medical doctor in the past year ......................................................135 

5

 
 
 
 
(cid:4) 

                                                                                   Table of contents 

2.9.  Consultation with dentists or orthodontists ................................................................................141 
2.9.1.  Consultation with dentists or orthodontists in the past 12 months................................141 
2.9.1.1. Consultation with dentists or orthodontists in the past 12 months (national data) ...141 
2.9.1.2. Consultation with dentists or orthodontists in the past 12 months (ECHP)...........145 
2.9.2.  Average number of consultations with a dentist in one year ........................................150 
2.9.2.1. Average number of consultations with a dentist in one year (national data).........150 
2.9.2.2. Average number of consultations with a dentist in one year (ECHP) ...................154 
2.10.  Preventive care ..........................................................................................................................159 
2.10.1.  Vaccination against influenza........................................................................................159 
2.10.2.  Screening for breast cancer ..........................................................................................163 
2.10.2.1. Percentage of women who ever had a mammography ......................................163 
2.10.2.2. Time since last mammography ...........................................................................166 
2.10.3. Screening for cervical cancer.........................................................................................169 
2.10.3.1. Percentage of women ever having had a cervical cancer test............................169 
2.10.3.2. Time since last screening test for cervical cancer ..............................................173 
2.11.  Use of medicines........................................................................................................................177 

3.  Analysis of health indicators with relatively weak level of comparability ..................................183 

3.1.  Physical and sensory functional limitations................................................................................183 
3.1.1. Limitations as regards walking.........................................................................................183 
3.1.2. Limitations in seeing clearly near.....................................................................................189 
3.1.3. Limitations in seeing clearly far .......................................................................................190 
3.1.4. Limitations in hearing .......................................................................................................191 
3.1.5. Limitations in carrying a shopping bag ............................................................................192 
3.2.  Personal care activities ..............................................................................................................195 
3.2.1. Limitations in feeding oneself...........................................................................................195 
3.2.2. Limitations in getting in and out of bed ............................................................................196 
3.2.3. Limitations in dressing and undressing oneself ...............................................................198 
3.2.4. Limitations in using toilets ................................................................................................199 
3.2.5. Limitations in washing oneself .........................................................................................200 
3.3.  Mental health..............................................................................................................................205 
3.4.  Physical activities .......................................................................................................................209 
3.5.  Drugs used in the past 12 months .............................................................................................213 
3.6.  Diet/food consumption habits.....................................................................................................215 
3.7.  Quality of life ..............................................................................................................................217 

4.  Conclusion: towards better data comparability ............................................................................219 

4.1. 
International comparability of health interview survey data .......................................................219 
4.2.  Recommendations for the future................................................................................................219 

Annexes ...................................................................................................................................................219 

Annex 1 ................................................................................................................................................221 
Annex 2 ................................................................................................................................................287 
Annex 3 ................................................................................................................................................313 

6

 
 
List of tables 

List of tables 

Code   

Title 

(cid:4) 

1.1.   Characteristics of the national surveys and population samples 

Table 1.1.1.  
Table 1.1.2.  
Table 1.1.3.  
Table 1.1.4.  
Table 1.1.5. 
Table 1.1.6.  
Table 1.1.7.  
Table 1.1.8.  
Table 1.1.9.  
Table 1.1.10.  
Table 1.1.11. 
Table 1.1.12.  
Table 1.1.13.  

Table 1.1.14.  

Summary survey methodology 
Background variables, summary 
Differences of response categories requested 
Percentage of male and female population, by survey 
Percentage of national population, by survey and by age 
Percentage of national population, by educational level 
Percentage of national population, by activity status  
Percentage of male and female population, by country 
Percentage of the ECHP population, by country and by age 
Percentage of the ECHP population, by activity status 
Percentage of the ECHP population, by educational level 
Table summarises of data collected (data available in grey) 
Percentage of population with characteristic X, by age  
(all population, adjusted for gender) 
Percentage of population with characteristic X, by age (regression estimates) 

2.1. 

 Perceived health 

Table 2.1.1.1.    
Table 2.1.1.2.    
Table 2.1.1.3.    
Table 2.1.1.4.    
Table 2.1.2.1.    
Table 2.1.2.2.    

Wording of the questions by survey 
Response categories in different countries 
Comparative summary and variable averages 
Analysis of gradients 
Wording of the questions by survey 
Analysis of gradients 

2.2.   Chronic conditions 

Table 2.2.1.  
Table 2.2.2.  
Table 2.2.3.  

Wording of the questions by survey 
Comparative summary and variable averages 
Analysis of gradients 

2.3.   Temporary cut-down on usual activities 

Table 2.3.1.1.1.  
Table 2.3.1.1.2.  
Table 2.3.1.1.3.  
Table 2.3.1.2.1.  
Table 2.3.1.2.2.  
Table 2.3.2.1.    
Table 2.3.2.2.    
Table 2.3.3.  

Wording of the questions by survey 
Comparative summary and variable averages 
Analysis of gradients 
Wording of the questions by survey 
Analysis of gradients 
Wording of the questions by survey 
Analysis of gradients 
Wording of the questions by survey 

2.4.   Body Mass Index (BMI) 
Table 2.4.1.1.    
Table 2.4.1.2.    
Table 2.4.1.3.    
Table 2.4.1.4.    
Table 2.4.2.1.    
Table 2.4.2.2.    
Table 2.4.2.3.    
Table 2.4.2.4.    

Wording of the questions by survey 
Comparative summary and variable averages 
Analysis of gradients (Overweight) 
Analysis of gradients (Severely overweight (obese)) 
Wording of the questions by survey 
Comparative summary and variable averages 
Analysis of gradients (Population overweight (BMI (cid:127) 27))  
Analysis of gradients (Population severely overweight (BMI (cid:127) 30))  

2.5. Smoking 
Table 2.5.1.1.1.  
Table 2.5.1.1.2.  

Wording of the questions by survey 
Comparative summary and variable averages 

7

 
 
 
 
 
   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
List of tables 

(cid:4) 

Table 2.5.1.1.3.  
Table 2.5.1.2.1.  
Table 2.5.1.2.2.  
Table 2.5.1.2.3.  
Table 2.5.2.1.1.  
Table 2.5.2.1.2.  
Table 2.5.2.2.1.  
Table 2.5.2.2.2.  
Table 2.5.2.2.3.  
Table 2.5.3.1.1.  
Table 2.5.3.2.1.  
Table 2.5.3.2.1.  
Table 2.5.3.2.2.  
Table 2.5.3.2.3.  

Analysis of gradients 
Wording of the questions by survey 
Comparative summary and variable averages 
Analysis of gradients 
Wording of the questions by survey 
Analysis of gradients 
Wording of the questions by survey 
Comparative summary and variable averages 
Analysis of gradients 
Wording of the questions by survey 
Analysis of gradients 
Wording of the questions by survey 
Comparative summary and variable averages 
Analysis of gradients 

2.6. Alcohol drinking 
Table 2.6.1.  
Table 2.6.2.  
Table 2.6.3.  

Wording of the questions by survey 
Comparative summary and variable averages 
Analysis of gradients 

2.7. Hospitalisation in the past 12 months 

Table 2.7.1.1a.   
Table 2.7.1.1b.   
Table 2.7.1.2.    
Table 2.7.1.3.    
Table 2.7.2.1.    
Table 2.7.2.2.    
Table 2.7.2.3.     

Wording of the questions by survey 
Wording of the questions by survey 
Comparative summary and variable averages 
Analysis of gradients 
Wording of the question 
Comparative summary and variable averages 
Analysis of gradients 

2.8. Consultation with a medical doctor 
Table 2.8.1.1.    
Table 2.8.1.2.    
Table 2.8.1.3.    
Table 2.8.2.1.    
Table 2.8.2.2.    
Table 2.8.2.3.    

Wording of the questions by survey 
Comparative summary and variable averages 
Analysis of gradients 
Wording of the questions by survey 
Comparative summary and variable averages 
Analysis of gradients 

2.9.  Consultation with dentists or orthodontists 
Table 2.9.1.1.1.  
Table 2.9.1.1.2.  
Table 2.9.1.1.3.  
Table 2.9.1.2.1.  
Table 2.9.1.2.2.  
Table 2.9.1.2.3.  
Table 2.9.2.1.1.  
Table 2.9.2.1.2.  
Table 2.9.2.1.3.  

Wording of the questions by survey 
Comparative summary and variable averages 
Analysis of gradients 
Wording of the questions by survey 
Comparative summary and variable averages 
Analysis of gradients 
Wording of the questions by survey 
Comparative summary and variable averages 
Comparative summary on the average number of consultations among 
countries with data on dentist consultations 
Analysis of gradients 
Wording of the questions by survey 
Comparative summary and variable averages 
Analysis of gradients 

Table 2.9.2.1.4.  
Table 2.9.2.2.1.  
Table 2.9.2.2.2.  
Table 2.9.2.2.3.  

2.10.   Preventive care 
Table 2.10.1.1.   
Table 2.10.1.2.   
Table 2.10.1.3.   
Table 2.10.2.1.1.  
Table 2.10.2.1.2.  
Table 2.10.2.1.3.  

Wording of the questions by survey 
Comparative summary and variable averages 
Analysis of gradients 
Wording of the questions by survey 
Comparative summary and variable averages 
Analysis of gradients 

8

 
 
 
 
 
 
 
 
 
 
(cid:4) 

List of tables 

Table 2.10.2.2.1.  
Table 2.10.2.2.2.  
Table 2.10.2.2.3.  
Table 2.10.3.1.1.  
Table 2.10.3.1.2.  
Table 2.10.3.1.3.  
Table 2.10.3.2.1. 
Table 2.10.3.2.2.  
Table 2.10.3.2.3.  

Wording of the questions by survey 
Comparative summary and variable averages 
Analysis of gradients 
Wording of the questions by survey 
Comparative summary and variable averages 
Analysis of gradients 
 Wording of the questions by survey 
Comparative summary and variable averages 
Analysis of gradients 

2.11.   Use of medicines 

Table 2.11.1.  
Table 2.11.2.  
Table 2.11.3.  

Wording of the questions by survey 
Comparative summary and variable averages 
Analysis of gradients 

3.1.  Physical and sensory functional limitations 

Table 3.1.1.1.    
Table 3.1.1.2.    
Table 3.1.2.1.    
Table 3.1.2.2.    
Table 3.1.3.1.    
Table 3.1.3.2.    
Table 3.1.4.1.    
Table 3.1.4.2.    
Table 3.1.5.1.    
Table 3.1.5.2.    

Wording of the questions by survey 
Comparative summary and variable averages 
Wording of the questions by survey 
Comparative summary and variable averages 
Wording of the questions by survey 
Comparative summary and variable averages 
Wording of the questions by survey 
Comparative summary and variable averages 
Wording of the questions by survey 
Comparative summary and variable averages 

3.2.  Personal care activities 

Table 3.2.1.1.    
Table 3.2.1.2.    
Table 3.2.2.1.    
Table 3.2.2.2.    
Table 3.2.1.3.    
Table 3.2.4.2.    
Table 3.2.4.1.    
Table 3.2.4.2.    
Table 3.2.5.1.    
Table 3.2.5.2.    
Table 3.2.5.3.    
Table 3.2.5.4.    
Table 3.2.5.5.    
Table 3.2.5.6.    

Wording of the questions by survey 
Comparative summary and variable averages 
Wording of the questions by survey 
Comparative summary and variable averages 
Wording of the questions by survey 
Comparative summary and variable averages 
Wording of the questions by survey 
Comparative summary and variable averages 
Wording of the questions by survey 
Comparative summary and variable averages 
Comparative summary and variable averages (1) 
Comparison of the specific details (1) 
Comparative summary and variable averages (2) 
Comparison of the specific details (2) 

3.3.  Mental Health 
Table 3.3.1.  
Table 3.3.2.  
Table 3.3.3.  
Table 3.3.4.  
Table 3.3.5.  

Variable averages 
Variable averages 
Variable averages 
Variable averages 
Variable averages 

3.4.  Physical activity 

Table 3.4.1.  
Table 3.4.2.   
Table 3.4.3.  
Table 3.4.4.  
Table 3.4.5.  

Wording of the questions by survey 
As requested 
’Physically active’ or ’not physically active’ 
Frequencies of physical activity 
Other 

3.5  Drugs used in the past 12 months 

Table 3.5.1.  
Table 3.5.2.  

Wording of the questions by survey 
Comparative summary and variable averages 

9

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

List of tables 

3.6  Diet/food consumption habits 

Table 3.6.1.  
Table 3.6.2.  

Wording of the questions by survey 
Comparative summary and variable averages 

3.7.   Quality of life 

Table 3.7.1.  
Table 3.7.2.  
Table 3.7.2.  

Percentages by response categories 
Percentages by response categories 
Percentages by response categories 

10

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
List of graphs 

List of graphs 

Code  

Title 

(cid:4) 

1.1.   Characteristics of the national surveys and population samples 

Graph 1.1.  

Graph 1.2.  

Percentage of population with characteristic X, by age (all population, adjusted for 
gender) 
Percentage of population with characteristic X, by age (regression estimates) 

2.1. 

 Perceived health 

Graph 2.1.1.1.  
Graph 2.1.1.2.  

Graph 2.1.1.3.  

Graph 2.1.1.4.  

Graph 2.1.1.5.  

Graph 2.1.2.1.  

Graph 2.1.2.2.  

Graph 2.1.2.3.  

Graph 2.1.2.4.  

Graph 2.1.2.5.  

Graph 2.1.2.6.  

Graph 2.1.2.7.  

Percentage of population perceiving their health as 'good' or 'very good', by country 
Percentage of female population perceiving their health as 'good' or 'very good', by 
age 
Percentage of male population perceiving their health as 'good' or 'very good', by 
age 
Percentage  of  population  perceiving  their  health  as  'good'  or  'very  good',  by 
educational level (by country) 
Percentage of population perceiving their health as 'good' or 'very good', by activity 
status 
Percentage  of  population  perceiving  their  health  as  'good'  or  'very  good'  (ECHP, 
1998) 
Percentage of female population perceiving their health as 'good' or 'very good', by 
age (ECHP, 1998) 
Percentage of male population perceiving their health as 'good' or 'very good', by 
age (ECHP, 1998) 
Percentage  of  population  perceiving  their  health  as  'good'  or  'very  good',  by 
educational level (ECHP, 1998) 
Percentage of population perceiving their health as 'good' or 'very good', by activity 
status (ECHP, 1998) 
Comparison of percentage of population perceiving their health as 'good' or 'very 
good': national data vs 1998 ECHP data (all population) 
Comparison of percentage of population perceiving their health as 'good' or 'very 
good': national data vs 1998 ECHP data (by country and age group) 

2.2.   Chronic conditions 
Graph 2.2.1.  
Graph 2.2.2.  

Graph 2.2.3.  

Graph 2.2.4.  

Graph 2.2.5.  

Percentage of population with a longstanding illness or health problem 
Percentage of female population with a longstanding illness or health problem, by 
age 
Percentage  of  male  population  with  a  longstanding  illness  or  health  problem,  by 
age 
Percentage  of  population  with  a  longstanding  illness  or  health  problem,  by 
educational level 
Percentage of population with a longstanding illness or health problem, by activity 
status 

2.3.   Temporary cut-down on usual activities 

Graph 2.3.1.1.1.   Percentage of population having temporarily cut down on usual activities during the 

previous two weeks (national data) 

Graph 2.3.1.1.2.   Percentage  of  female  population  having  temporarily  cut  down  on  usual  activities 

Graph 2.3.1.1.3.   Percentage  of  male  population  having  temporarily  cut  down  on  usual  activities 

during the previous two weeks, by age (national data) 

during the previous two weeks, by age (national data) 

Graph 2.3.1.1.4.   Percentage of population having temporarily cut down on usual activities during the 

Graph 2.3.1.1.5.   Percentage of population having temporarily cut down on usual activities during the 

previous two weeks, by educational level (national data) 

Graph 2.3.1.2.1.   Percentage of population having temporarily cut down on usual activities during the 

previous two weeks, by activity status (national data) 

previous two weeks (ECHP, 1998) 

11

 
 
 
 
 
 
 
 
 
(cid:4) 

List of graphs 

Graph 2.3.1.2.2.  Percentage  of  female  population  having  temporarily  cut  down  on  usual  activities 

Graph 2.3.1.2.3.   Percentage  of  male  population  having  temporarily  cut  down  on  usual  activities 

during the previous two weeks, by age (ECHP, 1998) 

during the previous two weeks, by age (ECHP, 1998) 

Graph 2.3.1.2.4.   Percentage of population having temporarily cut down on usual activities during the 

previous two weeks, by educational level (ECHP, 1998) 

Graph 2.3.1.2.5.   Percentage of population having temporarily cut down on usual activities during the 

previous two weeks, by activity status (ECHP, 1998) 

Graph 2.3.1.2.6.   Percentage of population having temporarily cut down on usual activities during the 

previous two weeks: national data vs 1998 ECHP data 

Graph 2.3.2.5.  

Graph 2.3.2.4.  

Graph 2.3.2.3.  

Graph 2.3.2.2.  

Graph 2.3.2.1.  

Graph 2.3.1.2.7.   Percentage of population having temporarily cut down on usual activities during the 
previous two weeks, by age: national data vs 1998 ECHP data  
Average number of days usual activities have been cut down during the previous 
two weeks: overall population 
Average number of days usual activities have been cut down during the previous 
two weeks: female population, by age 
Average number of days usual activities have been cut down during the previous 
two weeks: male population, by age 
Average number of days usual activities have been cut down during  the  previous 
two weeks, by educational level 
Average number of days usual activities have been cut down during the previous 
two weeks, by activity status 
Average number of days usual activities have been cut down during the previous 
two weeks: overall population 
Average number of days in bed in the overall population having cut down on their 
usual activities during the previous two weeks 
Average number of days in bed in the female population having cut down on their 
usual activities during the previous two weeks, by age 
Average  number  of  days  in  bed  in  the  male  population  having  cut  down  on  their 
usual activities during the previous two weeks, by age 
Average number of days in bed in the population having cut down on their usual 
activities during the previous two weeks, by educational level 
Average number of days in bed in the population having cut down on their usual 
activities during the previous two weeks, by activity status 

Graph 2.3.2.1.  

Graph 2.3.3.  

Graph 2.3.4.  

Graph 2.3.5.  

Graph 2.3.6. 

Graph 2.3.7. 

2.4.   Body Mass Index (BMI) 

Percentage of population overweight (BMI >= 27) 
Percentage of population severely overweight (BMI >= 30)  
Percentage of female population overweight (BMI >= 27), by age 
Percentage of male population overweight (BMI >= 27), by age 
Percentage of female population severely overweight (BMI >= 30), by age 
Percentage of male population severely overweight (BMI >= 30), by age 
Percentage of population overweight (BMI >= 27), by educational level 
Percentage of population severely overweight (BMI >= 30), by educational level 
Percentage of population overweight (BMI >= 27), by activity status 

Graph 2.4.1.1. 
Graph 2.4.1.2. 
Graph 2.4.1.3. 
Graph 2.4.1.4. 
Graph 2.4.1.5. 
Graph 2.4.1.6. 
Graph 2.4.1.7. 
Graph 2.4.1.8. 
Graph 2.4.1.9. 
Graph 2.4.1.10.  Percentage of population severely overweight (BMI >= 30), by activity status 
Graph 2.4.2.1. 
Graph 2.4.2.2. 
Graph 2.4.2.3. 
Graph 2.4.2.4. 

Percentage of population overweight (BMI >= 27) (ECHP, 1998) 
Percentage of population severely overweight (BMI >= 30) (ECHP, 1998) 
Percentage of female population overweight, by age (BMI >= 27) (ECHP, 1998) 
Percentage of female population severely overweight, by age (BMI >= 30) (ECHP, 
1998) 
Percentage of male population overweight, by age (BMI >= 27) (ECHP, 1998) 
Percentage  of  male  population  severely  overweight,  by  age  (BMI  >=  30)  (ECHP, 

Percentage  of  population  overweight  (BMI  >=  27),  by  educational  level  (ECHP, 

 Percentage  of  population  severely  overweight  (BMI  >=  30),  by  educational  level 
(ECHP,1998) 
Percentage of population overweight (BMI >= 27), by activity status (ECHP, 1998) 

Graph 2.4.2.9.  
Graph 2.4.2.10.  Percentage  of  population  severely  overweight  (BMI  >=  30),  by  activity  status 

Graph 2.4.2.11. 

(ECHP, 1998) 
 Percentage of population overweight (BMI >= 27) and severely overweight (BMI >= 
30): national vs 1998 ECHP data 

12

Graph 2.4.2.5. 
Graph 2.4.2.6. 
1998) 
Graph 2.4.2.7. 
1998) 
Graph 2.4.2.8. 

 
 
 
List of graphs 

2.5. Smoking 

(cid:4) 

Graph 2.5.1.1.1.  Percentage of present smokers in the population 
Graph 2.5.1.1.2.  Percentage of present smokers in the female population, by age 
Graph 2.5.1.1.3.   Percentage of present smokers in the male population, by age 
Graph 2.5.1.1.4.  Percentage of present smokers, by educational level 
Graph 2.5.1.1.5.   Percentage of present smokers, by activity status 
Graph 2.5.1.2.1.  Percentage of present smokers in the population (ECHP, 1998) 
Graph 2.5.1.2.2.  Percentage of female population who smoke, by age (ECHP, 1998) 
Graph 2.5.1.2.3.  Percentage of male population who smoke, by age (ECHP, 1998) 
Graph 2.5.1.2.4.   Percentage of population who smoke, by educational level (ECHP, 1998) 
Graph 2.5.1.2.5.   Percentage of population who smoke, by activity status (ECHP, 1998) 
Graph 2.5.1.2.6.   Comparison of percentage of smokers: national data vs 1998 ECHP data  

(all population) 

Graph 2.5.1.2.7.   Comparison of percentage of smokers: national data vs 1998 ECHP data  

(by age adjusted on gender) 

Graph 2.5.2.1.1.   Percentage of present smokers who smoke more than 20 cigarettes per day 
Graph 2.5.2.1.2.  

 Relationship  between  smoking  prevalence  in  the  population  and  heavy  smoking 
among smokers 

Graph 2.5.2.1.3.   Percentage of population who smoke more than 20 cigarettes per day 
Graph 2.5.2.1.4.   Percentage  of  female  smokers  who  smoke  more  than  20  cigarettes  per  day,  by 

age 

Graph 2.5.2.1.5.   Percentage of male smokers who smoke more than 20 cigarettes per day, by age 
Graph 2.5.2.1.6.   Percentage  of  smokers  who  smoke  more  than  20  cigarettes  per  day,  by 

Graph 2.5.2.1.7.   Percentage  of  smokers  who  smoke  more  than  20  cigarettes  per  day,  by  activity 

educational level 

status 

Graph 2.5.2.2.1.   Percentage  of  population  who  smoke  or  smoked  20  or  more  cigarettes  per  day 

(ECHP, 1998) 

Graph 2.5.2.2.2.   Percentage of female population who smoke or smoked 20 or more cigarettes per 

day, by age (ECHP, 1998) 

Graph 2.5.2.2.3.     Percentage  of  male  population  who  smoke  or  smoked  20  or  more  cigarettes  per 

day, by age (ECHP, 1998) 

Graph 2.5.2.2.4.     Percentage of population who smoke or smoked 20 or more cigarettes per day, by 

educational level (ECHP, 1998) 

Graph 2.5.2.2.5.     Percentage of population who smoke or smoked 20 or more cigarettes per day, by 

activity status (ECHP, 1998) 

Graph 2.5.2.2.6.     Comparison of percentage of persons who smoke more than 20 cigarettes a day: 
national  data  (%  of  current  smokers)  and  1998  ECHP  data  (%  of  current  and 
former smokers 

Graph 2.5.3.1.1.   Percentage of former smokers in the population 
Graph 2.5.3.1.2.   Percentage of former smokers among females, by age 
Graph 2.5.3.1.3.   Percentage of former smokers among males, by age 
Graph 2.5.3.1.4.   Percentage of former smokers in the population, by educational level 
Graph 2.5.3.1.5.   Percentage of former smokers in the population, by activity status 
Graph 2.5.3.2.1.   Percentage of former smokers in the population (ECHP, 1998) 
Graph 2.5.3.2.2.   Percentage of former smokers among females, by age (ECHP, 1998) 
Graph 2.5.3.2.3.   Percentage of former smokers among males, by age (ECHP, 1998) 
Graph 2.5.3.2.4.   Percentage  of  former  smokers  in  the  population,  by  educational  level  (ECHP, 
1998) 
Graph 2.5.3.2.5.   Percentage of former smokers in the population, by activity status (ECHP, 1998) 
Graph 2.5.3.2.6.  

 Comparison of percentage of former smokers in the population: national vs 1998 
ECHP data 
 Comparison of percentage of former smokers in the population: national vs 1998 
ECHP data (by country and age group) 

Graph 2.5.3.2.7.  

13

 
 
 
 
 
(cid:4) 

2.6. Alcohol drinking 

List of graphs 

Graph 2.6.1.   
Graph 2.6.2.   

Percentage of population having drunk alcohol in the past 12 months 
Percentage of female population having drunk alcohol in the past 12 months, by 

age 

Graph 2.6.3.   
Graph 2.6.4.   

Graph 2.6.5.   

Percentage of male population having drunk alcohol in the past 12 months, by age 
Percentage  of  population  having  drunk  alcohol  in  the  past  12  months,  by 
educationallevel 
Percentage  of  population  having  drunk  alcohol  in  the  past  12  months,  by  activity 

status 

2.7. Hospitalisation in the past 12 months 
Graph 2.7.1.1.    Percentage of population having been hospitalised as an in-patient in the past 12 

Graph 2.7.1.2.  

Graph 2.7.1.3.  

Graph 2.7.1.4.  

Graph 2.7.1.5.  

Graph 2.7.2.1.   

Graph 2.7.2.2.  

Graph 2.7.2.3.  

Graph 2.7.2.4.  

Graph 2.7.2.5.  

Graph 2.7.2.6.  

Graph 2.7.2.7.  

months 
 Percentage  of  female  population  who  having  been  hospitalised  as  an  in-patient 
during the previous 12 months, by age 
 Percentage  of  male  population  who  having  been  hospitalised  as  an  in-patient 
during the previous 12 months, by age 
Percentage of population having been hospitalised as an in-patient, by educational 
level 
Percentage  of  population  having  been  hospitalised  as  an  in-patient,  by  activity 

status 

 Percentage of population having been hospitalised as an in-patient in the past 12 
months (ECHP, 1998) 
 Percentage  of  female  population  who  having  been  hospitalised  as  an  in-patient 
during the previous 12 months, by age (ECHP, 1998) 
 Percentage  of  male  population  who  having  been  hospitalised  as  an  in-patient 
during the previous 12 months, by age (ECHP, 1998) 
 Percentage of population having been hospitalised as an in-patient, by educational 
level (ECHP, 1998) 
 Percentage  of  population  having  been  hospitalised  as  an  in-patient,  by  activity 
status (ECHP, 1998) 
Comparison of percentage of population having been hospitalised as an in-patient 
in the past 12 months: national vs. 1998 ECHP data 
Comparison of percentage of population having been hospitalised as an in-patient 
in the past 12 months: national vs. 1998 ECHP data, by age 

2.8. Consultation with a medical doctor 

Graph 2.8.1.1.  

Graph 2.8.1.2.  

Graph 2.8.1.3.  

Graph 2.8.1.4.  

Graph 2.8.1.5.  

Graph 2.8.2.1. 
Graph 2.8.2.2. 

Graph 2.8.2.3. 

Graph 2.8.2.4. 

Graph 2.8.2.5. 

Percentage  of  population  who  had  consulted  a  medical  doctor  in  the  past  12 
months 
Percentage of female population who had consulted a medical doctor in the past 
12 months, by age 
Percentage of male population who had consulted a medical doctor in the past 12 
months, by age 
Percentage  of  population  who  had  consulted  a  medical  doctor  in  the  past  12 
months, by educational level 
Percentage  of  population  who  had  consulted  a  medical  doctor  in  the  past  12 
months, by activity status 
Average number of consultations with a medical doctor in one year 
 Average number of consultations with a medical doctor in one year by age, female 
population 
Average number of consultations with a medical doctor in one year by age,  
male population 
Average number of consultations with a medical doctor in one year, by educational 
level 
Average  number  of  consultations  with  a  medical  doctor  in  one  year,  by  activity 

status 

2.9.  Consultation with dentists or orthodontists 

Graph 2.9.1.1.1.   Percentage of population having consulted a dentist or orthodontist in the past 12 

months 

14

 
 
 
 
 
 
List of graphs 

(cid:4) 

Graph 2.9.1.1.2.  Percentage of female population having consulted a dentist or orthodontist in the 

Graph 2.9.1.1.3.  

Graph 2.9.1.1.4.  

Graph 2.9.1.1.5.  

past 12 months, by age 
 Percentage  of  male  population  having  consulted  a  dentist  or  orthodontist  in  the 
past 12 months, by age 
 Percentage of population having consulted a dentist or orthodontist in the past 12 
months, by educational level 
 Percentage of population having consulted a dentist or orthodontist in the past 12 
months, by activity status 

Graph 2.9.1.2.1.   Percentage of population having consulted a dentist in the past 12 months (ECHP, 

Graph 2.9.1.2.2.  

1998) 
 Percentage of female population having consulted a dentist in the past 12 months, 
by age (ECHP, 1998) 

Graph 2.9.1.2.3.   Percentage of male population having consulted a dentist in the past 12 months, by 

Graph 2.9.1.2.4.  

age (ECHP, 1998) 
 Percentage of population having consulted a dentist or orthodontist in the past 12 
months, by educational level (ECHP, 1998) 

Graph 2.9.1.2.5.   Percentage of population having consulted a dentist or orthodontist in the past 12 

Graph 2.9.1.2.6.   Comparison of percentage of population having consulted a dentist in the past 12 

months, by activity status (ECHP, 1998) 

months: national vs 1998 ECHP data 

Graph 2.9.1.2.7.   Comparison of percentage of population having consulted a dentist in the past 12 
months: national vs 1998 ECHP data (by country and age groups) 

Graph 2.9.2.1.1.   Average number of consultations with a dentist or orthodontist in one year 
Graph 2.9.2.1.2.  

Graph 2.9.2.1.3.  

Graph 2.9.2.1.4.  

Graph 2.9.2.1.5.  

 Average  number  of  consultations  with  a  dentist  or  orthodontist  in  one  year  in  the 
female population, by age  
 Average  number  of  consultations  with  a  dentist  or  orthodontist  in  one  year  in  the 
male population, by age  
 Average number of consultations with a dentist or orthodontist in one year among 
those having had at least one consultation, by age 
 Average  number  of  consultations  with  a  dentist  or  orthodontist  in  one  year,  by 
educational level 

Graph 2.9.2.1.6.   Average number of consultations with a dentist or orthodontist in one year, by activity 

status 

Graph 2.9.2.2.1.   Average number of consultations with a dentist in one year (ECHP, 1998) 
Graph 2.9.2.2.2.  

 Average  number  of  consultations  with  a  dentist  in  one  year  in  the  female 
population, by age (ECHP, 1998) 

Graph 2.9.2.2.3.   Average number of consultations with a dentist in one year in the male population, 

Graph 2.9.2.2.4.   Average  number  of  consultations  with  a  dentist  in  one  year,  by  educational  level 

by age (ECHP, 1998) 

(ECHP, 1998) 

Graph 2.9.2.2.5.   Average  number  of  consultations  with  a  dentist  or  orthodontist  in  one  year,  by 

activity status (ECHP, 1998) 

Graph 2.9.2.2.6.   Comparison of average annual number of consultation of a dentist or orthodontist: 

national data vs ECHP 1998 

Graph 2.9.2.2.7.   Comparison of average annual number of consultation of a dentist or orthodontist: 

national data vs ECHP 1998 (by age) 

2.10.   Preventive care 

Graph 2.10.1.1.   Percentage of population vaccinated against influenza 
Graph 2.10.1.2.   Percentage of female population vaccinated against influenza, by age 
Graph 2.10.1.3.   Percentage of male population vaccinated against influenza, by age 
Graph 2.10.1.4.   Percentage of population vaccinated against influenza, by educational level 
Graph 2.10.1.5.   Percentage of population vaccinated against influenza, by activity status 
Graph 2.10.2.1.1.  Percentage of female population ever having had a mammography 
Graph 2.10.2.1.2.  Percentage of female population ever having had a mammography, by age 
Graph 2.10.2.1.3.  Percentage of female population ever having had a mammography, by educational 

level 

Graph 2.10.2.1.4.  Percentage  of  female  population  ever  having  had  a  mammography,  by  activity 

status 

Graph 2.10.2.2.1.  Women  having  had  a  mammography  within  the  past  year  as  a  percentage  of  all 

women ever having had a mammography 

Graph 2.10.2.2.2.  Women  having  had  a  mammography  within  the  past  year  as  a  percentage  of  all 

women ever having had a mammography, by age 

15

 
 
 
(cid:4) 

List of graphs 

Graph 2.10.2.2.3.  Women  having  had  a  mammography  within  the  past  year  as  a  percentage  of  all 
women ever having had a mammography, by educational level 
Graph 2.10.2.2.4.  Women  having  had  a  mammography  within  the  past  year  as  a  percentage  of  all 

women ever having had a mammography, by activity status 

Graph 2.10.3.1.1.  Percentage of female population ever having had a cervical cancer screening test 
Graph 2.10.3.1.2.  Percentage of female population ever having had a cervical cancer screening test, 

by age 

Graph 2.10.3.1.3.  Percentage of female population ever having had a cervical cancer screening test, 

Graph 2.10.3.1.4.  Percentage of female population ever having had a cervical cancer screening test, 

by educational level 

by activity status 

Graph 2.10.3.2.1.  Women  having  had  a  cervical  cancer  screening  test  within  the  past  year  as  a 

percentage of all women ever having had a cervical cancer screening test 

Graph 2.10.3.2.2.  Women  having  had  a  cervical  cancer  screening  test  within  the  past  year  as  a 

percentage of all women ever having had a cervical cancer screening test, by age 

Graph 2.10.3.2.3.  Women  having  had  a  cervical  cancer  screening  test  within  the  past  year  as  a 
percentage  of  all  women  ever  having  had  a  cervical  cancer  screening  test,  by 
educational level 

Graph 2.10.3.2.4.  Women  having  had  a  cervical  cancer  screening  test  within  the  past  year  as  a 
percentage  of  all  women  ever  having  had  a  cervical  cancer  screening  test,  by 
activity status 

2.11.   Use of medicines 

Graph 2.11.1.  
Graph 2.11.2.  

Graph 2.11.3.  

Graph 2.11.4.  

Graph 2.11.5.  

Percentage of population having used prescribed medicines in the past weeks 
Percentage of female population having used prescribed medicines in the past 2 or 
4 weeks, by age 
Percentage of male population having used prescribed medicines in the past 2 or 4 
weeks, by age 
Percentage of male population having used prescribed medicines in the past 2 or 4 
weeks, by educational level 
Percentage of male population having used prescribed medicines in the past 2 or 4 
weeks, by activity status 

3.1.  Physical and sensory functional limitations 

Graph 3.1.1.1.  
Graph 3.1.1.2.  
Graph 3.1.1.3.  
Graph 3.1.1.4.  
Graph 3.1.1.5.  

Percentage of population limited in terms of walking 
Percentage of female population limited in terms of walking, by age 
Percentage of male population limited in terms of walking, by age 
Percentage of population limited in terms of walking, by educational level 
Percentage of population limited in terms of walking, by activity status 

16

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annexes: List of tables 

Annexes: List of tables 

Code   

Title 

(cid:4) 

Annex 1: Items with relatively high level of comparability 

2.1.1. 

2.1.2. 
2.2. 
2.3.1.1. 

2.3.1.2. 
2.3.2. 

2.3.3. 
2.4.1.1. 
2.4.1.2.  
2.4.2.1.  
2.4.2.2.  
2.5.1.1.  
2.5.1.2.  
2.5.2.1. 
2.5.2.2. 
2.5.3.1.  
2.5.3.2.  
2.6.  
2.7.1.    
2.7.2. 
2.8.1.    
2.8.2.    
2.9.1.1.  
2.9.1.2.  
2.9.2.1.  
2.9.2.2.  
2.10.1.   
2.10.2.1.  
2.10.2.2. 

2.10.3.1.  
2.10.3.2.  

2.11.1.  

2.11.2. 

Percentage of population perceiving their health as ‘good’ or ‘very good’ (national 
data) 
Percentage of population perceiving their health as ‘good’ or ‘very good’ (ECHP) 
Percentage of population with chronic conditions (national data) 
Percentage of population having temporarily cut down on usual activities (national 
data) 
Percentage of population having temporarily cut down on usual activities (ECHP) 
Average number of days cut down on usual activities during the past 2 weeks 
(national data) 
Average number of days in bed during the past 2 weeks (national data) 
Percentage of population with a BMI >= 27 (national data) 
Percentage of population with a BMI >= 30 (national data) 
Percentage of population with a BMI >= 27   (ECHP) 
Percentage of population with a BMI >= 30   (ECHP) 
Percentage of smokers (national data) 
Percentage of smokers (ECHP) 
Percentage of smokers of more than 20 cigarettes per day (national data) 
Percentage of smokers of more than 20 cigarettes per day (ECHP) 
Percentage of former smokers (national data) 
Percentage of former smokers (ECHP) 
Percentage of alcohol drinkers in the past 12 months (national data) 
Percentage of population hospitalised in the past 12 months (national data) 
Percentage of population hospitalised in the past 12 months (ECHP) 
Percentage of population who had consulted a medical doctor in the past 12 months  
Average number of consultations with a medical doctor 
Percentage of population who had consulted a dentist in the past year (national data) 
Percentage of population who had consulted a dentist in the past year  (ECHP) 
Average number of consultations with a dentist  (national data) 
Average number of consultations with a dentist  (ECHP) 
Percentage of population vaccinated against influenza (national data) 
Percentage of females ever having had a breast cancer test (national data) 
Percentage of population ever having had a breast cancer test within the past year 
(national data) 
Percentage of population ever having had a cervical cancer test (national data) 
Percentage of population ever having had a cervical cancer test within the past year 
(national data) 
Percentage of population who had used prescribed medicines in the past 2 weeks 
(national data) 
Percentage of population who had used prescribed medicines in the past 4 weeks 
(national data) 

Annex 2: Items with relatively weak level of comparability 

3.1.1.    
3.1.2. 
3.1.3.    
3.1.4.    
3.1.5.    
3.2.1. 
3.2.2.  

3.2.3.  

3.2.4.    

Percentage of population limited in terms of walking (national data) 
Percentage of population limited in terms of seeing near (national data) 
Percentage of population limited in terms of seeing far (national data) 
Percentage of population limited in terms of hearing (national data) 
Percentage of population limited in terms of carrying something (national data) 
Percentage of population with limitations in feeding oneself (national data) 
Percentage of population with limitations in terms of getting in and out of bed (national 
data) 
Percentage of population with limitations in dressing and undressing themselves 
(national data) 
Percentage of population with limitations in using toilets 

17

 
 
 
 
 
 
 
   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

3.2.5.    
3.3.1. 
3.3.2.    
3.3.3.    
3.4.  
3.5.  
3.6. 

                                                             Annexes: List of tables                                   

Percentage of population with limitations in washing their hands and face 
Average summary score – GHQ 
Average summary score – MHI 
Average summary score – EVI 
Percentage of population who had not been physically active in the past 12 months 
Percentage of population having used drug (cannabis) users in the past 12 months: 
Percentage of population who followed a special diet 

18

 
 
 
 
 
 
 
(cid:4) 

Symbols used and country abbreviations 

Symbols used and country abbreviations 

- 
: 
. 
B 
DK 
D 
EL 
E 
F 
IRL 
I 
NL 
A 
P 
S 
UK 
IS 
NO 
CH 

not applicable 
nil or data non available 
unreliable data due to a small size of the sample or partial data 
Belgium 
Denmark 
Germany 
Greece 
Spain 
France 
Ireland 
Italy 
The Netherlands 
Austria 
Portugal 
Sweden 
United Kingdom 
Iceland 
Norway 
Switzerland 

19

 
 
 
 
Introduction 

(cid:4) 

1. Characteristics of the surveys, population samples  

and statistical methodology 

Objectives and methods of the data collection 
In 1999, Eurostat launched a collection of health and health-related data in an aggregated format from 
Europe’s various health and population surveys. The aim was to put the key findings of these surveys, 
whenever  possible,  into  a  common  format  and  disseminate  the  results.  The  proposal  for  the  data 
collection  was  prepared  by  the  Task  Force  on  ‘Health  and  health-related  survey  data’  (TF/HIS)  and 
endorsed  by  the  Working  Group  on  Public  Health  Statistics  (WG/PH).  This  approach  was  also 
approved by the Statistical Programme Committee (SPC) as part of Eurostat’s programme on health 
statistics. A list of 12 different items was set up and data on these items were received from a number 
of European countries. 
A  first  (global)  analysis  of  these  data  resulted  in  the  conclusion  that  international  comparability  was 
quite good for some of the topics, but direct comparability was limited for others. There was a need for 
further evaluations of comparability, further work on ex-post harmonisation, which could improve the 
latter,  and  further  multivariate  analysis  of  the  data.  In  addition,  a  new  round  of  data  collection, 
evaluation  and  analysis  was  planned  (2002/2003)  in  order  to  monitor  trends  and  take  account  of 
additional topics in the surveys. 
The TF/HIS meeting on 3-4 October 2001 proposed a revised list of 18 items – the original list of 12 
items, some of which were modified, and 6 additions, on which new data were collected; the list was 
endorsed by the WG/PH on 22-23 November 2001. 
Guidelines  for  collection  of  data  on  18  HIS  items  were  elaborated  and  contained  detailed  technical 
explanations  and  specifications  of  the  items  for  which  survey  data  were  requested.  The  guidelines 
also  listed  four  background  variables.  For  each  of  the  18  items,  two  tables  with  absolute  weighted 
numbers were requested – one table according to sex, age and education and one table according to 
sex, age and activity status. 
The definitions of the data requested in the guidelines were a compromise between: 

• 

• 

the  latest  European  and  international  recommendations:  from  the  European  Community 
Health Indicators project (ECHI), the EuroHIS  (Harmonisation of Health Interview Surveys in 
Europe) project of the WHO, the Euro-REVES networks, etc. 
the national questionnaires. 

21

 
 
 
 
 
(cid:4) 

List of the 18 items: 

No. 

Topic 

1  Chronic conditions 
2  Self-perceived health 
3  Activity restriction (general question) 
4  Physical and sensory functional limitations 
5  Personal care activities 
6  Mental health 
7  Temporary cut-down on usual activities 
8  Height and Weight 
9  Present and former smoking 

In-patient care 

10  Consumption of alcohol 
11  Physical activity 
12 
13  Out-patient care 
14  Preventive care 
15  Use of medicines 
16  Use of drugs 
17  Diet/food consumption habits 
18  Quality of life 

Source: Eurostat 

Introduction 

Main source of definitions 

EuroHIS 
WHO 19961 & Euro-REVES 
Euro-REVES 
Euro-REVES 
Euro-REVES 
EuroHIS 
WHO 1996 
WHO 1996 
WHO 1996 & 19982 
EuroHIS 
EuroHIS 
EuroHIS 
EuroHIS 
EuroHIS 
EuroHIS 
EMCDDA3 
WHO 1996 
EuroHIS 

To underpin the analysis of the national data, it was decided to also use the fifth wave of the ECHP  
1998  (the  last  set  of  available  data)  when  questions  were  similar  to  the  national  ones  collected. 
Corresponding  questions  were  provided  on  perceived  health,  body  mass  index,  smoking  and  out- 
patient  care  for  Austria,  Belgium,  Denmark,  Greece,  Spain,  France,  Ireland,  Italy,  the  Netherlands, 
Portugal Sweden and the United Kingdom.  

1 WHO (1996)* "Health interview surveys: Towards international harmonization of methods and instruments". 
de Bruin, A.; Picavet, H.S.J. and Nossikov, A., Copenhagen: World Health Organisation. Regional Office 
for Europe. Voorburg: Statistics Netherlands. (WHO Regional Publications. European Series n°58), 1996, 
xiii + 161 pp..  http://www.who.dk/document/E7241A.pdf & http://www.who.dk/document/E7241B.pdf 

2 WHO (1998)  “Guidelines for controlling and monitoring the tobacco epidemic”  WHO, Geneva, 1998, x + 190 

pp. 

3 EMCDDA (2002) “Handbook for Surveys on Drug Use among the General Population - Final Report” 
European Monitoring Centre for Drugs and Drugs Addiction (EMCDDA), Lisbon, Aug. 2002, 151 pp. 
http://www.emcdda.org/multimedia/project_reports/situation/population_survey_handbook.pdf 

22

 
 
 
 
 
                                                           
 
Introduction 

(cid:4) 

1.1 Characteristics of the national surveys and population samples 

(cid:153)  HIS Survey methodology 

The  main  methodological  characteristics  of  the  surveys  from  which  data  have  been  obtained  are 
summarised in the following table. 

Table 1.1.1. Summary survey methodology 

Country 

Survey 
year 

Survey name 
(English) 

Survey 
Code 

Type of 
survey 

Frequency 

Type of 
sample 

Stratification 
variables 

Sample 
size 

Age 
restriction 

Inclusion  
of the 
institutional 
population 

% Non-
response 

B01 

HIS 

Every 3 years

Sample of 
households

Age, geographic 
area, household 
size 

10 221  No restriction 

Yes 

39.5 

DK01 

HIS 

Every 6-7 
years 

Sample of 
individuals 

Geographic area 

16 690 

16+ 

Yes 

22 

Belgium 

1997 

Denmark 

2000 

Germany 

1998 

Health Interview 
Survey 

Danish Health 
and Morbidity 
Survey 

German National 
Health 
Examination and 
Interview Survey 

D05 

HIS/HES 

Every 6-7 
years 

Sample of 
individuals 

Spain 

1997 

National Health 
Survey 

E01 

HIS 

Irregular 

France 

98/00 

Health and 
Social Protection 
Survey 

F03 

Other 

Every 2 years

Sample of 
individuals 

Sample of 
individuals 

Age, sex, degree of 
urbanisation, 
federal state 

Age, sex, 
geographic area, 
degree of 
urbanisation 
Persons born in 
October of an even 
year with a 
particularity of their 
social security 
number 

7 124 

18-79 

No 

38.6 

6 400 

No restriction 

No 

23 000  No restriction 

No 

34 

Ireland 

1998 

Survey of 
Lifestyle, 
Attitudes and 
Nutrition (SLÁN) 

IRL01 

Multi 
purpose 
survey 

Every 4 years

Sample of 
individuals 

Sex, geographic 
area 

6 539 

18+ 

Yes 

37.8 

99/00 

Health 
Conditions and 
the Use of Health 
Services 

Italy 

I01 

HIS 

Every 4 years

Sample of 
households

2000 

Aspects of daily 
life 

I02 

Multi 
purpose 
survey 

Every year 

Sample of 
households

Netherlands 

2000 

Continuous 
Quality of Life 
Survey 

NL01 

HIS/HES 

Continuous 

Austria 

1999  Microcensus 

A01 

HIS 

Irregular 

Sample of 
individuals 

Sample of 
households

Geographic area, 
number of 
residents in the 
municipalities 

Geographic area, 
number of 
residents in the 
municipalities 

Geographic area, 
degree of 
urbanisation 

Geographic area, 
degree of 
urbanisation 

53 000  No restriction 

No 

10 

53 000  No restriction 

No 

10 

16 090  No restriction 

No 

37.7 

60 000  No restriction 

No 

17.8 

Portugal 

1999 

National Health 
Survey 

P01 

HIS 

Every 3 years 
(if possible) 

Sample of 
households

Geographic area 

41 874  No restriction 

No 

27 

Sweden 

2000 

Living Conditions 
Survey 

S01 

United 
Kingdom 

2000 

General 
Household 
Survey 

2000 

The Health 
Survey for 
England 

UK01 

UK12 

Multi 
purpose 
survey 

Multi 
purpose 
survey 

Every year 

Sample of 
individuals 

no 

11 484 

16-84 

Yes 

23.3 

Continuous 

Sample of 
households

Geographic area, 
percentage renting 
privately; 
percentage in local 
authority housing; 
socio-economic 
group 

8 221 

16+ 

No 

12 

No 

7 988 

Iceland 

1998 

Health and Living 
Conditions in 
Iceland 

IS02 

HIS 

Irregular 

Sample of 
individuals 

Not applicable 

1 924 

18-75 

Yes 

31 

Norway 

1998 

Survey on Living 
Conditions 

N01 

Multi 
purpose 
survey 

Every 3-4 
years 

Sample of 
individuals 

Geographic area 

10 000 

16+ 

Switzerland 

1997 

Swiss Health 
Survey 

CH01 

HIS 

Every 5 years

Sample of 
households

Geographic area, 
degree of 
urbanisation 

13 004 

15+ 

No 

No 

27.3 

31 

Source: National data 

23

 
 
 
 
 
 
 
  
  
  
  
  
  
  
 
(cid:4) 

Introduction 

Extensive  use  was  made  of  the  inventory  of  existing  health  interview  surveys  (HIS)  and  health 
examination surveys (HES) of the Institute of Public Health (Belgium). The most recent data collection 
exercises as well as the surveys with the most available items could be identified from their HIS/HES 
database. 
Some countries did not provide new data for the second round of data collection, because these were 
not  yet  available.  As  a  result,  data  from  the  first  data  collection  were  used  for  Spain,  Belgium  and 
Switzerland. 

(cid:153)  Background variables 

Table 1.1.2. Background variables, summary 

Country 

Survey 
year 

Survey 
code 

Age 
restriction

Educational level 

Activity status 

Belgium 

1997 

B01 

15+ 

ISCED 0-1; ISCED 2; ISCED 3; 
ISCED 4-6; no education; missing 

Active; Non-active; Unknown 

Denmark 

2000 

DK01 

16+ 

ISCED 0-2; ISCED 3; ISCED 4-6  Active; Non-active; Unknown 

Germany 

1998 

D05 

17-79 

ISCED 0-1; ISCED 2; ISCED 3; 
ISCED 4-6; missing 

Active; Non-active; Unknown 

Spain 

1997 

E01 

15+ 

ISCED 0-2; ISCED 3; ISCED 4-6; 
no education; missing 

Active; Non-active; Unknown 

France 

98/00 

F03 

15+ 

ISCED 0-1; ISCED 2; ISCED 3; 
ISCED 4-6; foreign; missing 

Active; Non-active; Unknown 

Ireland 

1998 

IRL01 

15+ 

Primary; Secondary; Tertiary 

Active; Non-active 

Italy 

99/00 

I01 

15+ 

ISCED 0-1; ISCED 2; ISCED 3; 
ISCED 4-6 

Active; Non-active 

2000 

I02 

15+ 

ISCED 0-1; ISCED 2; ISCED 3; 
ISCED 4-6 

Active; Non-active 

Netherlands 

1999/ 
2000 

NL01 

15+ 

ISCED 0-1; ISCED 2; ISCED 3; 
ISCED 4-6; missing 

Active; Non-active 

Austria 

1999 

A01 

15+ 

ISCED 0-2; ISCED 3; ISCED 4-6  Active; Non-active 

Portugal 

1999 

P01 

15+ 

ISCED 0-1; ISCED 2; ISCED 3; 
ISCED 4-6; no education; missing 

Active; Non-active; Unknown 

Sweden 

2000 

S01 

16-84 

ISCED 0-1; ISCED 2; ISCED 3; 
ISCED 4-6; missing 

Active; Non-active 

United 
Kingdom 

2000 

UK01 

15+/16+ 

ISCED 0-1; ISCED 2; ISCED 3; 
ISCED 4-6; foreign; missing 

Active; Non-active; Unknown 

2000 

UK12 

16+ 

ISCED 0-1; ISCED 2; ISCED 3; 
ISCED 4-6; foreign; missing 

Active; Non-active 

Iceland 

1998 

IS02 

18-75 

Norway 

1998 

N01 

15+ 

ISCED 0-1; ISCED 2; ISCED 3; 
ISCED 4-6; missing 

ISCED 0-1; ISCED 2; ISCED 3; 
ISCED 4-6; missing 

Active; Non-active; Unknown 

Active; Non-active; Unknown 

Switzerland 

1997 

CH01 

15+ 

ISCED 0-2; ISCED 3; ISCED 4-6  Active; Non-active 

Source: National data 

24

 
 
 
 
 
 
 
(cid:4) 

Introduction 

Age 
The different age groups requested were: 

1.  15-24 
2.  25-34 
3.  35-44 
4.  45-54 
5.  55-64 
6.  65-74 
7.  75-84 
8.  85+ 

•  Differences with the guideline: 

For most of the surveys, Member States provided macro data with the data on age already distributed 
into  the  eight  age  groups.  Germany,  Denmark,  Iceland,  and  Portugal  sent  micro  data  with  the 
individual age of each of the respondents, which could be allocated to the predefined age groups. 
As regards the upper age limit, Germany, Iceland and Sweden did not provide data for the 85 -years- 
and-above age group.  
For the General Household Survey, the United Kingdom provided two different distributions of the age 
groups:  one  according  to  the  educational  levels  and  one  according  to  the  activity  status.  The  age 
groups for activity status table were: 15-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75-84 and 85+, while 
the age groups for the education table were: 16-24, 25-34, 35-44, 45-54, 55-64, 65-69 and 70+. 

Educational level 
It was proposed to use the four ISCED 971 categories, as follows:  

lower secondary or second stage of basic education (ISCED 2) 

1.  pre-primary and primary (ISCED 0-1) 
2. 
3.  upper secondary (ISCED 3) 
4.  post-secondary (ISCED (4-6) 

The use of two rules was specified: 

- 
- 

classification according to highest level completed 
respondents still in full time education: classification according to the level they are 
attaining 

•  Differences with the guideline: 

Table 1.1.3. Differences of response categories requested 

Ireland 

Primary 

Secondary 

Tertiary 

Denmark 

less than 10 years 

10 years 

11-12 years 

13-14 years 

15 years or more 

School attendant 

Foreign, other qualification 

Missing value 

Source: National data 

Denmark provided data by number of years in school and further vocational training or education. In 
the absence of a recommended conversion rule to translate the number of years in school and further 
education into one of the four ISCED levels of education, the following procedure was used:  
= ISCED 0-2 

- 
- 
- 

less than 10 years  
10 years + 11-12 years  = ISCED 3 
13-14 years +15 years and more = ISCED 4-6 

1 See UNESCO (1997) and UNESCO Institute for Statistics (1999). 

25

 
 
 
 
 
 
 
 
 
 
 
 
 
                                                           
 
(cid:4) 

Introduction 

No educational level data were provided for the 70+ population from the General Household Survey of 
the United Kingdom. 

Activity status  
The following two categories were proposed: 

1.  active 
2.  non-active 

… based on the detailed classification as follows: 

1.  active: 

11.  employees, at least 12 hours (LFS) per week 
12.  self-employed persons, at least 12 hours per week 
13.  unemployed, but willing to work at least 12 per week 

2.  non-active: 

21.  employees, less than 12 hours per week 
22.  self-employed persons, less than 12 hours per week 
23.  unemployed, possibly willing to work for at most 12 hours per week 
24.  unpaid (family) workers 
25.  housewives, housemen 
26.  retired persons 
27.  students and other persons in training 

Often, a third class was added by Member States, including the missing responses. 

(cid:153)  Comparative description of the nationally surveyed populations 

Table 1.1.4. Percentage of male and female population, by survey 

B01  DK01  D05  E01  F03 

IRL01 

I01 

I02  NL01 A01  P01 S01  UK01  UK12 

IS02  N01  CH01

Female  53.0 

54.3 

Male 

47.0 

45.7 

51.3 

48.7 

51.2 

48.8 

56.0 

44.0 

53.0 

47.0 

54.4 

45.6 

51.9 

48.1 

53.3 

46.7 

51.1 

54.0 

51.3 

48.9 

46.0 

48.7 

51.3 

48.7 

54.2 

45.8 

55.4 

44.6 

51.4 

48.6 

55.2 

44.8 

Source: National data 

All the surveys have similar ratios.  

Table 1.1.5. Percentage of national population, by survey and by age 

15-24 

25-34 

35-44 

45-54 

55-64 

65-74 

75-84 

85+ 

B01  DK01  D05  E01  F03 
15.5 
9.2 
11.7 

13.0 

19.2 

17.1 

18.4 

17.7 

14.7 

13.1 

5.9 

1.4 

17.4 

17.9 

18.8 

14.7 

9.8 

6.5 

1.9 

20.0 

20.1 

15.9 

18.0 

12.5 

4.4 

: 

19.6 

16.4 

13.5 

14.2 

11.0 

5.3 

0.8 

17.3 

20.7 

17.7 

12.4 

10.5 

5.0 

0.9 

IRL01
16.8 

21.4 

23.1 

13.4 

10.0 

8.7 

5.4 

1.2 

I01
13.0 

17.9 

18.2 

15.7 

14.1 

12.1 

6.9 

2.2 

I02 NL01 A01 P01 S01 UK01  UK12 IS02 N01 CH01
11.0 
13.9 

10.7  17.7 

13.1 

16.1 

13.6 

14.2 

14.6 

13.7 

17.3 

18.5 

16.4 

13.9 

11.6 

6.5 

2.0 

18.0 

18.4 

17.1 

14.1 

11.5 

6.4 

1.4 

19.6 

19.4 

15.0 

13.5 

10.2 

6.0 

2.1 

13.9 

15.4 

15.6 

14.9 

14.2 

8.0 

2.0 

17.2 

17.4 

17.4 

15.3 

10.7 

8.5 

: 

17.9 

19.5 

16.3 

13.1 

10.3 

6.7 

1.7 

18.0  22.6 

19.9  21.6 

16.4  16.9 

14.0  11.0 

11.8  10.0 

7.4 

1.8 

0.2 

: 

18.6 

18.5 

16.6 

11.5 

10.6 

8.1 

2.5 

22.4 

19.4 

15.1 

12.8 

11.5 

6.1 

1.6 

Source: National data 

Differences from the guidelines: 

The 15-24 age groups of DK01, S01 and UK12 cover the age group16-24 year old 
The 15-24 age group of D05 covers the age group 17-24 
The 15-24 age group of IS02 covers the age group 18-24 
D05 and IS02 combined the age groups 75-84 and 85+ into 75+ 

26

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Introduction 

Pre-primary 
and Primary 

Secondary 

Upper 
secondary 
Post-
secondary 

Missing value 

No education 

Foreign, other 
qualification 
School 
attendant 

(cid:4) 

Table 1.1.6. Percentage of national population, by educational level 

B01  DK01  D05  E01  F03  IRL01  I01 

I02  NL01  A01  P01  S01  UK01 UK12  IS02  N01  CH01 

19.7

21.3

19.0 

3.4 

71.5 

53.0 

16.4 

21.0 31.8  30.6 

18.9 

33.8

41.6  15.0

23.7 

30.1  12.2

0.4 

23.8 

35.2 

30.2  31.0 

25.6 

25.6  11.4

23.9 

5.3  28.0 22.9 

29.2

29.4  13.1  24.7  18.9 

30.9  31.4 

33.7  47.9

7.1  46.1

19.2 

34.2  38.9 50.3 

59.5 

49.3

25.7 

48.4 

8.9  13.8  25.3 

29.7 

7.1 

7.0 

21.5  18.3 

8.6  27.4 

27.8 

26.1  17.2  21.1 

16.8 

1.4 

2.7 

: 

: 

0.7 

3.0 

7.0 

1.7 

: 

1.1 

1.5 

: 

: 

: 

1.3 

: 

: 

: 

2.4 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

0.3 

: 

0.1 

0.1 

0.7 

0.5 

3.7 

5.3 

: 

: 

: 

:  17.0 

: 

: 

: 

: 

: 

: 

: 

: 

: 

4.7 

3.8 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

Austria, Switzerland, Denmark and Spain: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Ireland: Primary, Secondary and Tertiary 
Source: National data 

Table 1.1.7. Percentage of national population, by activity status   

B01  DK01  D05  E01  F03  IRL01 

I01 

I02  NL01  A01  P01  S01  UK01  UK12  IS02  N01  CH01 

Active 

44.7 

60.9  53.7  38.1  57.2 

53.0  49.6  50.0 

63.3  58.2 

6.5  65.8 

Non-active  48.1 

38.9  43.4  16.6  42.7 

47.0  50.5  50.0 

36.7  41.8  43.3  34.2 

62.1 

36.3 

54.3 

77.5  34.2 

45.7 

14.1  19.8 

61.0 

39.0 

Unknown 

7.2 

0.2 

2.9  45.3 

0.1 

: 

: 

: 

: 

:  50.2 

: 

1.6 

: 

8.4  46.1 

: 

Source: National data 

In  three  countries  (Spain,  Portugal  and  Norway)  the  percentage  of  ‘unknown  activity  status’  is  very 
high,  which  questions  the  validity  of  disaggregation  by  activity  status  for  these  countries.  In  Portugal, 
information on respondents who could not be contacted during the survey (most of them economically 
active)  was  obtained  from  proxy  interviews.  Since  information  on  activity  status  was  not  asked  from 
proxy  respondents,  the  apparent  percentage  of  economically  active  persons  in  Portugal  is  extremely 
low (6.5%). As a result, data by activity status for Portugal have not been included in the analysis.  
Norway  can  only  supply  the  activity  status  from  the  main  sample  because  respondents  in  the 
supplement sample were not asked questions that allow the data provided to generate  this distinction. 

v  Comparative description of the ECHP populations 

Table 1.1.8. Percentage of male and female population, by country 

Female 

Male 

B  DK 

52.9 

47.1 

50.7 

49.3 

EL 

51.4 

48.6 

E 

51.3 

48.7 

F 

48.6 

51.4 

IRL 

51.3 

48.7 

I 

NL 

A 

51.7 

48.3 

51.6  52.0 

48.4  48.0 

P 

52.4 

47.6 

S  UK 

51.9 

53.0 

48.1 

47.0 

Source: European Community Household Panel, 1998 

Table 1.1.9. Percentage of the ECHP population, by country and by age 

15-24 

25-34 

35-44 

45-54 

55-64 

65-74 

75-84 

85+ 

B 

11.4 

18.8 

19.9 

16.3 

13.2 

12.1 

6.7 

1.6 

DK

11.6

18.2

18.3

18.6

13.7

10.4

6.9

2.2

EL 

13.1 

17.3 

16.7 

15.7 

15.5 

14.1 

5.9 

1.6 

E 

16.8 

19.4 

17.1 

14.4 

12.4 

11.7 

6.4 

1.8 

F 

11.5 

19.5 

19.8 

16.9 

12.4 

11.6 

6.0 

2.4 

Source: European Community Household Panel, 1998 

IRL 

18.0 

19.7 

19.0 

16.1 

11.3 

8.8 

5.8 

1.2 

27 

I 

12.9 

18.9 

17.2 

15.5 

14.3 

12.2 

6.1 

2.9 

NL 

11.1 

20.5 

19.3 

17.8 

12.9 

10.9 

6.2 

1.1 

A 

13.0 

20.5 

18.9 

15.2 

13.5 

11.0 

6.1 

1.9 

P 

16.6 

18.5 

16.9 

15.1 

13.7 

11.8 

6.2 

1.2 

S 

17.6 

17.4 

14.9 

16.5 

12.2 

11.3 

10.1 

: 

UK 

12.5 

18.2 

18.0 

17.5 

12.7 

11.6 

7.2 

2.3 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

Introduction 

Table 1.1.10. Percentage of the ECHP population, by activity status 

B  DK 

EL 

E

F

IRL

I

NL

A

P 

S  UK 

Active 

51.3 

63.4 

52.3 

49.5 

56.9 

56.2 

46.9 

57.4 

57.7 

61.3 

Non-active 

48.4 

36.5 

47.6 

50.4 

40.7 

43.8 

53.0 

39.9 

42.2 

37.9 

: 

: 

57.5 

37.6 

Unknown 

0.3 

0.1 

0.1 

0.1 

2.4 

: 

0.1 

2.8 

0.1 

0.8  100.0 

4.9 

Source: European Community Household Panel, 1998 

Table 1.1.11. Percentage of the ECHP population, by educational level  

B  DK 

EL

E

F

IRL

I

NL

A

P 

S  UK 

ISCED 0-2 

36.5 

30.5 

53.8 

62.9 

ISCED 3 

32.7 

47.0 

30.2 

18.4 

ISCED 4-6 

25.4 

21.4 

14.0 

18.5 

3.9 

1.5 

2.3 

Missing value 

5.4 

1.1 

2.0 

0.1 

92.4 

1.2 

Source: European Community Household Panel, 1998 

(cid:153)  Data collected 

51.4 

62.4 

96.5 

33.4 

79.3 

32.5 

40.4 

34.5 

31.8 

12.9 

5.7 

0.1 

1.6 

1.8 

0.1 

58.2 

12.5 

43.6 

10.2 

6.0 

2.4 

8.1 

0.1 

23.6 

47.7 

0.2 

1.8 

The following table summarises the data collected. Each line corresponds to a topic or sub-topic; each 
column corresponds to a survey. Shaded boxes indicate that the survey (column) includes data on the 
topic or sub-topic (line). The first five columns of the surveys on the left provided data for the previous 
round of data collection. The 13 columns relate to new surveys. 

28

 
 
  
 
 
 
 
 
 
Introduction                                                                   

(cid:4) 

)
y
e
r
g
n

i
e
l
b
a
l
i
a
v
a
a
t
a
d
(
d
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t
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(cid:4) 

1.2. Statistical methodology 

Introduction 

1.2.1. Database 
The  aim  was  to  build  a  data  warehouse  with  all  the  information  provided  by  the  countries  on  their 
population’s state of health and to disseminate the database. With this aim in view, it was necessary to 
simplify  the  structure  of  the  initial  database,  built  in  1999  by  Eurostat,  to  allow  users  to  produce 
specific outputs without difficulty. 
Most  of  the  countries  sent  the  data  files  with  all  the  necessary  documents:  list  of  the  variables 
collected  (name  and  labels  of  the  response  categories),  corresponding  question  numbers  in  the 
questionnaire, programme statements explaining how background variables can be recoded to obtain 
the requested variables, etc.  
Different tables were created in the main library (HIS) for questions, countries, ages, topics, subtopics 
and categories. A code was created for each country, item (topic), subtopic (corresponding to a label), 
age group, gender, and education or activity category and subcategory. These codes have their own 
labels listed in the attached bases. 
A  system  of  coding  was  created  allowing  the  integration  of  different  levels  of  distinction  in  the 
questions  collected.  Because  the  second  round  of  collection  integrated  6  new  additional  topics  and 
because some of the 12 initial topics had been modified, some of the reply categories had changed. 
Topics  were  recoded  with  a  more  detailed  structure  than  those  hitherto  existing.  This  codification 
applies to not only the second but also the first round of data collection. 
With the perspective of the new round, it was necessary to identify each of the surveys for a country 
and a particular year by a specific code. It would be possible to receive two or more different surveys 
for the same country and the same year for different or the same topics. We created a table with the 
code  of  the  country,  the  year,  the  label  of  the  survey  and  a  corresponding  ‘code  source’  containing 
one part for the code of the country listed in the HIS/HES Access Database and the second part for 
the year. This code is the identifier of the survey and was integrated into the final database. 
The main table summarises in a single table all the frequencies by sex, age and educational level and 
all the frequencies by sex, age and activity status for each topic dealt for each country. A system of 
weights was added for the items, which dealt with average scores or average number of days. For this 
specific item, the weights associated are the number of persons replying to the questions. 
This final database includes the category (educational or economic level), the sub-categories (4 levels 
of  education  or  2  categories  of  activity  status  (active  or  non-active),  and  the  name  of  the  countries, 
age, sex, topic, subtopic and frequencies.  
Each row therefore includes the frequency associated with a particular subtopic of a specific topic for a 
particular subcategory of a specific category of a specific country, for men or women of a specific age 
group.  The  frequencies  associated  with  subtopics  are  by  sex*age*education  or  by  sex*age*activity 
status.  
Eight annexed tables have been built under the SAS System to integrate all the information sent so far 
by Member States: 

(cid:190)  3  tables  about  social  aspects  of  the  people  interviewed  such  as  the  age,  the  sex  and  the 

economic category and educational level:  
-  with the different codes used according to the gender, age groups and their economic and 

educational level, 
the English labels associated.  

- 

(cid:190)  1 table for the codes of the countries and their English labels associated.  
(cid:190)  1 table for the list of the 12 topics collected with their codes and English labels.  
(cid:190)  1  table  for  the  subtopics  (response  categories) associated  with  each  topic with  the codes of 

the topics, the codes of the subtopics and the English labels of the subtopics  

(cid:190)  1 table for the questions asked to the interviewee with the code of the country, the year of the 
survey,  the  code  of  the  topic,  the  label  of  the  question  and  other  variables  with  further 
information on the heading of the question.  

(cid:190)  1 table for the sources with the code source, the country and the year, the name of the survey 

in English and the codes of the topics.  

The countries delivered questions in their original language and / or into English. In all cases, English 
questions  were  collected  from  the  HIS/HES  database  and  entered  into  the  database  together  with 
those in the original languages, according to their availability.  

31

 
 
 
 
 
 
Introduction 

1.2.2. Statistical methods and procedures used  

(cid:4) 

Treatment of non-response: 
The  ‘unknown’  response  categories  were  shared  out  between  the  other  response  categories 
according to the distribution of the national samples. 
However, the ‘unknown’ subcategories of educational level or activity status could not be redistributed 
between  the  other  subcategories.  In  Portugal  and  Spain,  the  high  percentage  of  unknown  activity 
status is due to the absence of the question in proxy interviews.  
There were no data with unknown age or unknown gender.  

Multiple Classification Analysis: 
Whenever,  for  example,  the  value  of  a  variable  differs  between  males  and  females,  one  part  of  the 
difference  may  be  due  to  the  difference  in  age  structures  (women  have  better  survival  prospects, 
particularly at older ages). In addition to the raw differences between genders, it is also of interest to 
determine the part of this gender difference which is not due to the age structure. 
Various  techniques  could  be  selected  to  that  effect,  the  best  choice  depending  on  the  type  of  data 
available.  Since  a  wide  variety  of  data  needed  to  be  analysed,  it  was  felt  preferable  to  use  a  single 
robust  method.  To  isolate  the  independent  effect  of  age,  gender,  education  and  activity  status,  use 
was therefore made of the Multiple Classification Analysis (MCA) technique. MCA can be seen either 
as  an  analysis  of  variance  or  as  a  simple  regression  where  the  independent  variable  is  regressed 
against a set of dummy variables. The method had been used earlier by Henk Swinkels (2000).  
For each item, three MCAs were performed on the dependent variable: 

•  a regression on age and sex, 

•  a regression on age, sex and educational level, 
•  a regression on age, sex and activity status.  

Regressing a dichotomous variable for individual respondents is equivalent to performing a weighted 
regression on the frequencies, the weights being proportional to the population size of each group.  
Four sets of estimations were obtained: 

• 

• 

• 

from the first regression, estimations by age adjusted for sex, and sex adjusted for age. 

from the second regression, estimations by educational level adjusted for age and sex  

from the third regression, estimations by activity status adjusted for age and sex. 

The methods also provides: 

•  The  ‘gross  effect’  of  each  of  the  factors  (‘eta’  coefficients);  the  ‘eta’  squared  (also  called 
correlation  ratios)  can  be  interpreted  as  the  proportion  of  variance  ‘explained’  by  the  factor, 
without  adjustment.  For  example,  the  ‘gross  effect’  of  old  age  includes  the  indirect  effect  of 
gender, since more females than males reach old ages.  

•  The net effect of each factor (‘beta’ coefficients), an approximate measure of the relationship 
between  the  factor  and  the  dependent  variable,  while  holding  constant  all  other  factors  (i.e. 
assuming that in each category of a given factor, all other factors are distributed as in the total 
population). For example, the net effect of old age eliminates the indirect effect of gender by 
assuming  that  the  sex  ratio  of  the  old-age  population  is  the  same  as  that  of  the  total 
population.  

For  the  first  regression  on  age  and  sex  weighted  by  the  frequencies,  the  SAS  GLM  procedure 
provided  the  means  (here,  a  percentage)  for  each  gender  and  age  groups  of  people,  say,  who 
perceived  their  health  as  good  (if  ‘good’  is  coded  100  and  ‘not  good’  is  0)  and  the  Least-squares 
Means for sex adjusted for age and age adjusted for sex. 
Variance  analysis  (with  a  GLM  procedure  on  SAS)  allows  to  compute  an  adjusted  percentage  for 
nominal  effects  like  sex  and  age  groups  when  all  other  effects  are  set  to  their  mean  values.  One 
important use of MCA scores is to examine the pattern of changes in the effects of a given variable as 
we introduce more variables as controls. 
In the same way, the two other regressions including the activity status or the educational level can be 
performed using GLM.  

32

 
 
 
 
 
 
 
 
 
(cid:4) 

Introduction 

Gradient study: 
For each topic with a yes/no question, the only variable will be the percentage replying ‘yes’; for items 
with multiple response categories several variables are possible; in some cases, only the most 
significant response category or set of response categories will be considered. 

The information on each variable will be summarised by a set of indicators for each country: 

• 

• 

• 

• 

• 

The  average  level  of  the  variable,  computed  as  the  ratio  of  the  average  value  of  the 
variable for the total population on the median of these average values. The median was 
preferred  to  the  simple  average  so  as  to  minimise  the  effect  of  outlying  or  poorly 
comparable data. 

The ‘gender gradient’, a simple ratio of the average value of the variable for males on the 
average value of the variable for females (taken as reference). The two average values are 
adjusted for differences in age composition between males and females.  

The  ‘age  gradient’  was  obtained  in  two  steps.  [1]  First,  a  median  age  distribution  is 
computed  for  males  and  females  combined,  with,  for  each,  the  age  structure  of  the  total 
population. This distribution is the ‘median age effect’. [2] The distribution of the variable for 
each country is then considered as the addition of a fixed country effect and of the product 
of  the  median  age  effect  by  the  ‘age  gradient’  of  the  country  for  the  variable  considered. 
The ‘age gradient is therefore obtained by simple regression of the age distribution on the 
median age effect. 
For  a  given  variable,  the  comparison  of  age  gradients  among  countries  shows,  for  each 
country,  to  which  degree  the  variable  is  related  to  age,  whatever  the  shape  of  this 
relationship. This assumes that there is a clear median pattern of relationship between the 
variable and age. 

The ‘educational level gradient’, a ratio of the average of the variable for post-secondary 
respondents  on  the  average  value  of  the  variable  for  respondents  at  the  pre-primary  or 
primary level, each one being adjusted for age and gender. 

The ‘activity status gradient’, a ratio of the average of the variable for economically active 
respondents  on  the  average  value  of  the  variable  for  economically  inactive  respondents, 
each one being adjusted for age and gender. 

Example on computation of age gradient 
The following table provides, for countries A to H, the percentage of each age group having a given 
characteristic X. 

Table 1.1.13. Percentage of population with characteristic X, by age  
(all population, adjusted for gender) 

15-24 

25-34 

35-44 

45-54 

55-64 

65-74 

75-84 

85+ 

A 

3 

5 

18 

22 

25 

29 

27 

19 

B

9

12

9

20

31

31

26

22

C

4

11

14

28

32

41

24

31

D

3

3

24

25

32

41

26

35

E

12

8

20

31

43

40

22

27

F

9

19

16

28

33

43

36

41

G

13

24

30

38

47

53

26

39

H Median

19

15

23

30

53

48

32

43

9

12

19

28

33

41

26

33

Source: National data 

Let 

c
ax

 be the percentage of population of country ‘c’ in age group ‘a’ having the characteristic X, and 

am  be the median of 
cα
The age gradient 

 among the countries. 

c
ax
 for country ‘c’ is obtained by the regression of 

c
ax

 on 

am

, with a country-specific 

constant 

cβ

: 

x

c
a

c
α=

m

a

c
β+

. 

33

 
 
 
 
 
 
 
Introduction 

(cid:4) 

The results are provided in the following table: 

Table 1.1.14. Percentage of population with characteristic X, by age (regression estimates) 

15-24 
25-34 
35-44 
45-54 
55-64 
65-74 
75-84 
85+ 
gradient 

A 
2.2 
4.7 
12.4 
21.6 
26.1 
34.8 
19.5 
26.7 
1.02 

B
2.8 
5.5 
13.6 
23.2 
28.0 
37.2 
21.1 
28.6 
1.07 

C
9.0 
11.2 
17.8 
25.8 
29.8 
37.3 
24.0 
30.2 
0.88 

D
11.3 
13.3 
19.0 
25.9 
29.4 
35.9 
24.4 
29.8 
0.77 

E
12.4 
14.5 
20.5 
27.8 
31.4 
38.3 
26.2 
31.8 
0.81 

F
14.8 
16.9 
23.1 
30.6 
34.3 
41.4 
29.0 
34.8 
0.83 

G
21.1 
23.1 
29.0 
36.1 
39.7 
46.4 
34.5 
40.1 
0.79 

H 
21.2 
23.0 
28.5 
35.1 
38.3 
44.5 
33.6 
38.7 
0.73 

Source: National data 

The curve of each country is therefore summarised by a base level
to the ‘median age effect’. The higher the age gradient, the stronger the effect of age for the country. 

and an age effect proportional 

cβ

1.2. Percentage of population w ith 
characteristic X, by age (regression 
estimates)

1.1. Percentage of population w ith 
characteristic X, by age (all population, 
adjusted for gender)

60

50

40

30

20

10

0

60

50

40

30

20

10

0

15-24 25-34 35-44 45-54 55-64 65-74 75-84

85+

A

F

B

G

C

H

D

M edian

15-24 25-34 35-44 45-54 55-64 65-74 75-84

85+

A

B

C

D

E

F

G

Source: National data 

34

 
 
 
 
 
 
Perceived health 

(cid:4) 

2. Analysis of health indicators with relatively high level of 
comparability 

2.1. Perceived health 
Questions  on  the  way  people  perceive  their  own  health  are  commonly  asked  in  health  interview 
surveys. They provide indicators which complement the usual morbidity statistics collected from health 
providers,  since  they  cover  conditions,  which  do  not  lead  to  medical  treatment.  They  are  also  an 
invaluable tool to develop health priorities and policies.  

2.1.1. Perceived health (national data) 

A. Data requested 
Data were requested on the self-assessment of health, obtained through replies to the question: “How 
is your health in general?”, with the five reply categories ‘very good’, ‘good’, ‘fair’, ‘bad’ and ‘very bad’. 
Countries were to provide the number of respondents in each of these categories. 
Countries, which used a similar question or different response categories, were asked to provide the 
corresponding data and to indicate the exact wording of the question and/or the response categories 
used. 

B. Data collected 

Table 2.1.1.1. Wording of the questions by survey 

Country  Year 

Question 

Response categories provided 

B 

DK 

D 

E 

F 

IRL 

I 

NL 

A 

P 

S 

UK 

IS 

NO 

CH 

1997  In general, would you say your health is? 

Very good, good, fair, bad, very bad 

2000  How do you rate your present state of health in 

Really good, good, fair, bad, very bad 

general? 

1998  In general, would you say your health is: 

Excellent, very good, good, fair, bad 

1997  How would you rate the state of your health 

Very good, good, fair, bad, very bad 

generally? 

2000  Can you note your own health status between 0 and 

10?  

1998  In general, would you say your health is? 

(0 = very bad and 10 excellent). 
Recoded as: very good, good, fair, 
Excellent, very good, good, fair, poor 

2000  How is your health in general? 

Very good, good, fair, bad, very bad 

2000  How is your health in general? 

1999  How is your health in general? 

1999  In a general way, how would you consider  

[your/her/his] health condition? 

2000  In your opinion, how is your state of health? 

Very good, good, sometimes good-
sometimes bad, bad 
Very good, good, fair, bad, very bad 

Very good, good, reasonable, bad, 
very bad 
Very good, good, fair, bad, very bad 

2000  How is your health in general? 

Very good, good, fair, bad, very bad 

1998  How do you generally rate your physical health? 

Very good, good, fair, poor 

1998  How would you describe your own general health? 

Very good, good, fair, bad, very bad 

1997  How do you feel now? 

Very good, good, fair, bad, very bad 

Source: National data 

There are some variations among countries in the question asked, all of which may not be reflected in 
their  English  translation.  For  example,  many  countries  ask  “How  is  our  health  in  general?”,  without 
reference to persons of the same age group and gender. In Switzerland, the question "How do you 
feel now?" appears to reflect a general satisfaction with life as well as a state of health. 
More important are differences in response categories. Eleven countries propose five categories, the 
middle one being relatively ‘neutral’: In the Netherlands and Iceland, four categories are proposed, the 
‘middle ground’ lying somewhere between  ‘good and 'fair’. Finally, Germany and Ireland propose five 
categories, but the central one is ‘good’. 
Finally,  French  respondents  are  not  asked  to  select  between  five  categories,  but  among  11  scores, 
from  0  to  11.  These  scores  are  then  recoded  into five categories: the data obtained depend largely 
upon the way data have been recoded. 

35

 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

Countries 

B, DK, E, I, 
A, P, S, UK, 
NO, CH 

F 

IS, 
NL 

Table 2.1.1.2. Response categories in different countries 

Perceived health 

Very good or 
really good or  
excellent 

Good 

Fair or 
reasonable 

Bad 

Very bad 

Very good 
(recode) 

Good 
(recode) 

Fair (recode) 

Bad 
(recode) 

Very bad 
(recode) 

Very good 

Good 

Fair or 
sometimes good-
sometimes fair 

Poor or 
Bad 

D, IRL 

Excellent 

Very good 

Good 

Fair 

Bad or Poor 

Source: National data 

Table 2.1.1.3. Comparative summary and variable averages 

Country 

% of ‘Good’ 

B 

DK 

D 

E 

F 

IRL 

I 

NL 

A 

P 

S 

UK 

IS 

NO 

CH 

Source: National data 

78.3 

77.9 

80.8 

78.4 

69.3 

86.0 

56.2 

77.2 

73.5 

31.9 

74.8 

74.0 

82.1 

79.7 

73.9 

Comparison 
Question 
Same 

Same 

Same 

Same 

Same 

Same 

Same 

Same 

Same 

Same 

Same 

Now 

Remark 
Response categories 
Very good & good 

Really good & good 

Excellent, very good & good 

Very good & good 

Very good & good (recoded) 

Excellent, very good & good 

Very good & good 

Very good & good 

Very good & good 

Very good & good 

Very good & good 

Very good & good 

Very good & good 

Very good & good 

Very good & good 

36

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Perceived health 

(cid:4) 

C. Analysis 
The analysis is restricted to the percentage of the population stating that they perceive their health as 
‘good’ or ‘very good’ (as against ‘fair’, ‘bad’ and ‘very bad’). 

1- Overall level 

2.1.1.1.  Percentage of population perceiving their health as 
'good' or 'very good', by country

100

90

80

70

60

50

40

30

20

10

0

B

DK

D

E

F

IRL

I

NL

A

P

S

UK

IS

NO

CH

Source: National data 

The two countries with ‘good’ as a ‘central’ response category score respectively the highest  (Ireland, 
86%) and the fourth highest (Germany, 82%) levels of satisfaction with perceived health. 
As for countries with a ‘central’ response category between ‘good’ and ‘fair’ scores, Ireland scores the 
third highest level (82%), while the Netherlands has the median score (77%). 
Among  the  countries  with  ‘fair’  as  ‘central’  category,  only  Switzerland  ranks  among  the  first  four 
highest scores (83%). 
While  some  of  the  differences  among  countries  may  be  real,  the  choice  of  different  response 
categories may have influenced the results and their comparability. 
The lowest degree of satisfaction with self-perceived health is found in the three  southern European 
EU  Member  States:  Portugal  (39%),  considerably  below  Italy  (56%)  and  Spain  (68%).  These  three 
countries  are  closely  followed  by  France,  which  shows  a  slightly  higher  degree  of  satisfaction  than 
Spain (69%). Another rule for recoding the scores would have led to a different result. 

2- Age and gender 
After adjustment for  age, women express less satisfaction with self-perceived health than men in all 
countries but two (Ireland and Iceland). 
The three southern European Member States are also those where women display the least degree of 
satisfaction in comparison to men. 

37

 
 
 
 
 
 
 
(cid:4) 

Perceived health 

2.1.1.2.  Percentage of female population perceiving their health 
as 'good' or 'very good', by age

100
90
80
70
60
50
40
30
20
10
0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

A

DK

P

D

S

E

UK

F

IS

IRL

NO

I

CH

NL

Source: National data 

2.1.1.3.  Percentage of male population perceiving their health 
as 'good' or 'very good', by age

100
90
80
70
60
50
40
30
20
10
0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

A

DK

P

D

S

E

UK

F

IS

IRL

NO

I

CH

NL

Source: National data 

Among the countries considered, health is perceived as least good in Portugal at all ages, probably 
because  of  cultural  attitudes  towards  the  question  asked.  Italy  displays  a  similar  pattern,  but  to  a 
lesser  extent.  The  two  other  southern  countries  (Italy  and  Spain)  also  display a   lower  degree  of 
satisfaction than other countries, together with France, whose data depend on the recoding procedure 
used. 
Among the youngest groups, self-perceived health is generally perceived as good, and declines  with 
age  up  to  the  75-84  age  group.  This  decline  varies  among  countries,  being  very  steep  in  Italy  but 
much less so in Switzerland. Several countries display a reversal in perceived health after age 75-84, 
which can be explained by several factors: 

·   With increasing age, individuals increasingly tend  to compare their own health with the health 

of persons of similar age, or in relation to their own survival in their generation. 

·   Several studies have shown that,  with  an  apparently  similar  health  status,  mortality  is  much 
higher  among  those  tending  to  perceive  their  health  as  ‘bad’ or  ‘very  bad’;  with  a  resulting 
higher survival of more optimistic elders. 

38

 
 
 
 
 
 
 
 
Perceived health 

(cid:4) 

·  

In the same way, at a given level of objective health, older people have a higher probability to 
be institutionalised (and excluded from the scope of most surveys) because they tend more 
often to be widowed or because they are deemed to be more at risk than younger individuals. 

3- Educational level 

2.1.1.4.  Percentage of population perceiving their health as 
'good' or 'very good', by educational level (by country)

100

90

80

70

60

50

40

30

20

10

0

B

DK

D

E

F

IRL

I

NL

A

P

S

UK

IS

NO

CH

Pre-primary and Primary (ISCED 0-1)

Secondary (ISCED 2)

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Austria, Switzerland, Denmark and Spain: ISCED 0-2 (Pre-primary and Primary + Secondary 
Ireland: Primary, Secondary (Secondary + Upper secondary), Tertiary 
Source: National data 

In all countries, the higher the formal educational level, the higher the satisfaction with self-perceived 
health. In some cases, the gradient is extremely steep, as in Norway. 

4- Activity status 

2.1.1.5.  Percentage of population perceiving their health as 
'good' or 'very good', by activity status

100

90

80

70

60

50

40

30

20

10

0

B

DK

D

E

F

IRL

I

NL

A

S

UK

IS

NO

CH

Source: National data 

Active

Inactive

39

 
 
 
 
 
 
 
 
 
 
(cid:4) 

Perceived health 

After  adjustment  for  age  and  sex,  economically  active  individuals  tend  to  perceive  their  health  as 
better than the economically inactive, although the opposite is found in Portugal. 

5- Comparative study of countries1 
The gradients for the different countries are reproduced in the following table: 

Table 2.1.1.4. Analysis of gradients 

Level 

Gender 

Age 

  B  DK  D 

E 

F 

IRL 

I 

NL  A 

P 

S  UK 

IS  NO  CH 

1.01  1.01  1.06  0.89  0.90  1.11  0.73  1.00  0.95  0.50  0.97  0.96  1.06  1.03  1.07 

1.07  1.03  1.04  1.09  1.07  0.98  1.15  1.07  1.01  1.39  1.06  1.01  0.99  1.02  1.06 

1.14  1.05  0.90  1.35  1.48  0.97  1.92  0.97  1.26  1.70  1.02  0.89  1.13  0.86  0.59 

Education 

1.35  1.25  1.19  1.21  1.27  1.19  1.43  1.33  1.20  1.85  1.26  1.32  1.31  2.13  1.18 

Activity status 

1.27  1.33  1.10  1.09  1.08  1.12  1.09  1.29  1.08 

. 

1.27  1.34  1.24  1.41  1.12 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: National data 

Several elements appear from the table: 

·  

·   Portugal  appears  as  a  special  case  among  European  countries,  with  the  lowest  overall 
perception of health, the largest difference between genders, the second steepest age effect, 
and the second largest educational effect.  
Ireland, Switzerland and Germany have the highest percentage of persons satisfied with their 
health. However, as noted earlier, the response categories used in Ireland and Germany may 
have  affected  the  comparability  of  their  results.  Satisfaction  with  health  is  lowest  in  two 
southern European countries (Italy and Spain) and in France, where data have been derived 
from a recoding of scores. 

·   After  adjustment  for  age,  men  tend  to  be  more  satisfied  with  their  health  than  women, 
particularly  in  Italy,  France  and  Spain,  although  little  difference  is  found  in  many  countries. 
More  detailed  analysis  show  little  difference  between  genders  among  the  young,  and  an 
increasing relative satisfaction of men as age grows. 

·   The education differential is most pronounced in Norway and lowest in Switzerland. 
·   Norway also has the highest differential between the economically active and the inactive. 

2.1.2. Perceived health (ECHP) 
As indicated in the introduction, some of the data requested from countries had also been collected 
from  the  European  Community  Household  Panel  (ECHP).  Data  from  the  1998  round  are  presented 
below for comparison purposes. 

B. Data collected 
The question asked in each country, and the response categories, were the closest translation of: 

Table 2.1.2.1. Wording of the questions by survey 

Year 

1998 

Question 

Response categories 

How is your health in general? 

Very good, good, fair, bad, very bad 

Source: European Community Household Panel, 1998 

1 See 1.2.2 above for the definition of  gradients. 

40

 
 
 
 
 
 
 
 
 
 
                                                             
Perceived health 

(cid:4) 

C. Analysis 
The variable analysed is the percentage of population with ‘good’ or ‘very good’ perceived health  

1- Overall level 

2.1.2.1. Percentage of population perceiving their health as 
'good' or 'very good' (ECHP, 1998)

90

80

70

60

50

40

30

20

10

0

B

DK

EL

E

F

IRL

I

NL

A

P

S

UK

Source: European Community Household Panel, 1998 

Again, Portugal displays the lowest percentage of the population perceiving their health as  ‘good’ or 
‘very good’, followed by France. The highest level of satisfaction is found in Ireland. 

2- Age and gender 

2.1.2.2. Percentage of female population perceiving their health 
as 'good' or 'very good', by age (ECHP, 1998)

100
90
80
70
60
50
40
30
20
10
0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

A

DK

P

EL

S

E

UK

F

IRL

I

NL

Source: European Community Household Panel, 1998 

41

 
 
 
 
 
 
 
 
 
 
(cid:4) 

Perceived health 

2.1.2.3. Percentage of male population perceiving their health 
as 'good' or 'very good', by age (ECHP, 1998)

100
90
80
70
60
50
40
30
20
10
0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

A

DK

P

EL

S

E

UK

F

IRL

I

NL

Source: European Community Household Panel, 1998 

The percentage of the population perceiving their health as ‘good’ or  ‘very good’ is lower in Portugal 
than in other countries at all ages except the youngest  (15-24). The general decline with age in the 
percentage of persons who perceive their health as ‘good/very good’ is similar to that observed from 
national data, with a large decline with age in Portugal. Among men, Greece displays the most modest 
decrease with age. As with national data, a reversal in trend between those aged 75 to 84 years and 
those aged 85 years and above is found in several countries. 

3- Educational level 

2.1.2.4. Percentage of population perceiving their health as 
'good' or 'very good', by educational level (ECHP, 1998)

100

90

80

70

60

50

40

30

20

10

0

B

DK

EL

E

F

IRL

I

NL

A

P

S

UK

Pre-primary and Primary (ISCED 0-2)

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Source: European Community Household Panel, 1998 

After adjustment for age and sex, the correlation between self-perceived health and educational level 
is similar to that found with national data, except in France, where satisfaction is highest among upper 
secondary level respondents.  

42

 
 
 
 
 
 
 
 
 
(cid:4) 

2.1.2.5. Percentage of population perceiving their health as 
'good' or 'very good', by activity status (ECHP, 1998)

Perceived health 

4- Activity status 

100

90

80

70

60

50

40

30

20

10

0

B

DK

EL

E

F

IRL

I

NL

A

P

UK

Active

Inactive

Source: European Community Household Panel, 1998 

Here also, economic inactivity is associated with lower satisfaction with health, even in Portugal. 

5- Comparative study of countries2 

Table 2.1.2.2. Analysis of gradients 

Level 

Gender 

Age 

Education 

Activity status 

  B 

1.00 

1.11 

0.79 

1.20 

1.16 

DK 

1.04 

1.01 

0.85 

1.36 

1.32 

EL 

0.94 

1.08 

1.23 

1.16 

1.15 

E 

1.04 

1.03 

1.16 

1.21 

1.15 

F 

IRL 

I 

0.77 

1.09 

0.93 

1.10 

1.14 

1.11 

1.03 

0.75 

1.17 

1.18 

0.81 

1.13 

1.19 

1.24 

1.11 

NL 

1.01 

1.10 

0.76 

1.11 

1.17 

A 

1.00 

1.01 

1.13 

1.34 

1.08 

P 

0.64 

1.18 

1.31 

1.63 

1.26 

S 

1.05 

1.04 

0.66 

1.23 

n.a. 

UK 

0.94 

1.05 

0.42 

1.19 

1.14 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: European Community Household Panel, 1998 

The gradients derived from ECHP data are similar to those obtained from national data, but much less 
variation is found among countries, probably reflecting the higher degree of comparability among data. 

A comparison of national and ECHP data by gender and age 
Data are available from the ECHP and from national data for 11 countries. These data generally relate 
to  different  periods,  so  that  they  cannot  be  expected  to  be  identical.  However,  it  is  of  interest  to 
compare them in detail. 

2 See the definition of gradients in Chapter 1. 

43

 
 
 
 
 
 
 
 
 
 
                                                             
(cid:4) 

Perceived health 

2.1.2.6. Comparison of percentage of population perceiving their 
health as 'good' or 'very good': national data vs 1998 ECHP data (all 
population)

a
t
a
d

l
a
n
o
i
a
N

90

80

70

60

50

40

30

30

40

B
IRL
P

50

DK
I
S

60
ECHP data

E
NL
UK

70

80

90

F
A
Diagonal

Source: European Community Household Panel, 1998 & national data 

In  the  graph,  data  on  the  percentage  of  persons  who  perceive  their  health  as  ‘good’ or  ‘very  good’ 
from both surveys are represented by a point. The horizontal line represents the percentage according 
to the ECHP; the vertical line represents the same indicator obtained from the national survey. If the 
percentages in both surveys are identical, the point lies on the diagonal. 
For many countries, the point lies relatively near the diagonal, displaying broad agreement between 
the two surveys. However, a sizeable difference is found in France, Portugal, and, to a lesser extent,  
Spain. 
A more detailed examination by age-group data could shed more light on these differences. 

2.1.2.7.  Comparison of percentage of population perceiving their 
health as 'good' or 'very good': national data vs 1998 ECHP data (by 
country and age group)

a
t
a
d

l
a
n
o
i
t
a
N

100

90

80

70

60

50

40

30

20

10

0

0

10

20

30

40

B

IRL

P

DK

I

S

50
ECHP data
E

NL

UK

60

70

80

90

100

F

A

Diagonal

Source: European Community Household Panel, 1998 & national data 

44

 
 
 
 
 
 
 
 
 
 
Perceived health 

(cid:4) 

In the graph above, each country is represented by a broken line, the points of each line representing 
the ECHP and national data of a given age group (men and women combined).3 Since the percentage 
of persons who perceive their health as ‘good’ or  ‘very good’ tends to decline with age, each country 
line starts from the top right of the graph and moves towards the bottom left as age increases – with, 
as noted earlier, reverse movements between the 75-84 and 85+ age groups in some countries. 
Most countries  are relatively close to the diagonal, showing a large degree of concordance between 
national and ECHP data. In Sweden, very little difference is found between the ECHP data collected in 
1998 and the national data collected in 2000, although the question was very slightly different. Data 
from  Italy,  where  identical  questions  were  asked  in  both  surveys,  are  also  very  similar,  but  slightly 
lower in 2000 than in 1998 at all ages. 
Many  points  and  lines  are  situated  above  the  diagonal,  but  it  cannot  be  ascertained  whether  this  is 
due  to  differences  in  the  questions  asked,  differences  in  the  population  covered  or  actual  changes 
over time. Large discrepancies found in the older age group (especially in Austria) may be due to the 
different  coverage  of  the  elderly  population  in  the  two  surveys  (attrition  in  a  panel  is  higher  for 
populations with poor health) or because of the small sample size. However, the general tendency of 
national data displays an increasingly positive health picture as age increases. 
In  France,  national  data  are  systematically  higher  than  ECHP  data,  by  more  than  10%  in  each  age 
group. It is very doubtful that perceived health could have largely increased for all age groups between 
1998 (ECHP) and 2000 (national survey). It may be that the systematic differences found between the 
two  surveys  are  due  to  the  different  questions  asked:  the  percentage  of  those  satisfied  with  their 
health  (national  survey)  being  larger  than  the  percentage  perceiving  their  health  as  good.  Another 
possibility is that the recoding procedure leads to a systematic overestimation of the population with 
good or very good health. With a 10 to 15% downward adjustment of the national rates, France would 
then  belong  to  the  ‘southern  European  group’  as  regards  perceived  health  which  appeared  in  the 
analysis of national data.  

3 Note that where the national and ECHP data have been conducted in different years, age -specific data refer to slightly 

different cohorts. 

45

 
 
 
                                                             
 
 
Chronic conditions 

2.2. Chronic conditions 

(cid:4) 

A. Data requested 
The  overall  prevalence  of  chronic  conditions  was  requested,  preferably  measured  by  means  of  the 
open-ended question: “Do you have any long-standing illness or health problem?” Yes / No: 

1.  Number of respondents having a long-standing illness or health problem 
2.  Number of respondents not having a long-standing illness or health problem 

Countries which used a different but similar question, or different response categories were asked to 
provide  the  corresponding  data  and  to  indicate  the  wording  of  the  question  and/or  the  response 
categories used. 

B. Data collected 

Table 2.2.1. Wording of the questions by survey 

Country  Year 

Question 

Response 
categories 

B 

1997  Do you suffer from one or more diseases, long-term conditions or handicaps?  

Yes / No 

DK 

2000 

Do you suffer from any long-standing illness, long-standing after effect from 
injury, any disability or other long-standing condition? 

Yes / No 

E 

1997 

During the past 12 months, has chronic illness restricted your normal activities 
in any way? 

Yes / No 

IRL 

1998 

Is your daily activity or work limited by a long-term illness, health problem or 
disability? 

NL 

2000 

Do you suffer from one or more chronic disease(s), affliction(s) or 
handicap(s)? 

A 

S 

1999  Do you have any long-standing illness or health problem (chronic) ? 

2000 

Do you suffer from any long-term illness, after-effects from an accident, 
disability or other ailment? 

UK 

2000  Do you have any long-standing illness, disability or infirmity? 

IS 

1998 

Chronic disease in the past 12 months? (based on a list of 47 conditions that 
are either always classified as chronic [e.g. coronary hearth disease, lung 
cancer, arthritis, alcoholism, mental disorder], or have lasted at least three 
months [e.g. ongoing/repeated lower back pain, bronchitis, or itch/rash for at 
least three months]) 

NO 

1998 

Do you suffer from any illness or disorder of a more long-term nature (for 6 
months or more), any congenital disease or the effect of an injury? 

CH 

1997 

Today there are a number of people who have a physical or a psychological 
problem that limits their daily activities. Do you have such a problem or an 
illness of this type, which you have had for more than one year? 

Yes / No 

Yes / No 

Yes / No 

Yes / No 

Yes / No 

Yes / No 

Yes / No 

Yes / No 

Source: National data 

Iceland used a list of possible long-standing illnesses or health problems1, which were presented to 
respondents.  Compared  to  more  general  questions,  the  procedure  is  known  to  elicit  more  positive 

1 List of the illnesses for Iceland: asthma/ persisting (i.e. repeated or lasting)/ bronchitis over at least a 3-month 
period/ pneumonia/ chronic itching skin (over at least a 3-month period)/ chronic rash on skin (over at least a 3-
month period)/ chronic sinusitis (over at least a 3-month period)/ arthritis/ rheumatism/ varicose veins/ high blood 
pressure/  chest  pain  (angina  pectoris)/  heart  attack/  myocardial  infarction/  thrombosis  in  the  limbs/  apoplexy  or 
cerebral/  haemorrhage/  haemorrhoid/  allergy/  disease  of  the  prostate  gland  (other  than  cancer)/  ulcer/  hernia/ 
disease of the kidneys (not cancer)/ other disease of the urinary system (not cancer)/ chronic constipation (difficult 
to pass stools) over at least a 3-month period / chronic incontinence of bowel movements over at least a 3-month 
period / chronic difficulty urinating (over at least a 3-month period)/ chronic urinary incontinence (over at least a 3-
month    period)/  disease  of  the  reproductive  organs/sexual  organs  (other  than  cancer)/  cancer  of  the  breast/ 
cancer of the lungs/ cancer of the reproductive organs/sexual organs/ cancer of the urinary system / other cancer 
/  disease  of  the  liver  (other  than  cancer)/  diabetes/  disease  of  the  thyroid  gland/  tonsillitis/  illness  of  the  eyes/ 
illness in the ears/ bodily disability/ general debility or lack of strength in the body/ chronic backache (over at least 
a 3-month period)/ epilepsy/ migraine/ Parkinson’s disease/ other disease of the nerves. 

47

 
 
 
 
 
 
 
                                                             
(cid:4) 

Chronic conditions 

responses. For example, persons with periodic itching, skin or allergy may not spontaneously declare 
themselves as having a longstanding health problem. Indeed, Iceland almost systematically appears 
as an outlier and has been omitted from further analysis.  In Austria, interviewers were provided with a 
long  list  of  possible  illnesses  and  chronic  health  problems2.  Since  the  list  was  not  shown  to 
respondents, it is unlikely to have affected replies as it did in Iceland.  
Two sub-groups can be distinguished among other countries, according to the question asked: 
1.  The  questions  asked  in  Ireland,  Spain  and  Switzerland  were  much  more  restrictive,  excluding 
longstanding illnesses or health problems which did not limit daily activities or work. 
2. Norway and Denmark mention ‘injury’ in the phrasing of the question, while Sweden mentions the 
‘after-effects of accidents’. These are not mentioned by other countries3.  

Table 2.2.2. Comparative summary and variable averages 

Country 

Period 

% of Yes 

Remarks 

B 

DK 

E 

IRL 

NL 

A 

S 

UK 

IS 

NO 

CH 

At present 

At present 

1 year 

At present 

At present 

At present 

At present 

At present 

1 year 

6 months or more 

1 year 

27.5 

41.1 

27.8 

13.1 

34.6 

30.2 

48.8 

35.5 

75.9 

37 

17.2 

- 

Accidents included 

Restricting normal activities 

Limiting daily activity or work 

- 

List of 36 illnesses; accidents included 

Includes ‘after effects’ 

- 

List of 47 illnesses 

Accidents included 

Limiting daily activities 

Source: National data 

2  The  Austrian  list  included:  diabetes/  high  blood  pressure/  low  blood  pressure/  cardiac  infarction/  other  heart 
diseases, cerebrovascular diseases (stroke)/ phlebitis, venous thromboses, varicose veins/ diseases of the eye/ 
nose,  ear  and  throat  diseases  (including  impaired  hearing)/  colds,  influenza,  tonsillitis,  acute  bronchitis/  chronic 
bronchitis,  pulmonary  emphysema/  asthma/  pneumonia/  gastric  or  peptic  ulcers  and  duodenal  ulcers/  other 
gastric  disorders    (e.g.  gastritis)/  intestinal  diseases/  cholecystitis,  gall  stones/  hepatic  diseases/  kidney  stones, 
nephritis/ metabolic disorders (e.g. gout)/ skin allergies/ other skin diseases/ damage to the spine/ diseases of the 
joints  (hip,  leg)/  diseases  of  the  joints  (shoulder,  arm)/  vascular  disorders  of  the  legs/  rheumatism  of  the  joints/ 
neuritis, neuralgia, sciatica/ gynaecological diseases/ diseases of the prostate/ children‘s diseases (e.g. rubella, 
measles, scarlet fever, whooping cough, chicken pox, mumps)/ broken bones/ contusions, sprains, bruising/ other 
injuries/ other diseases. 
3 ‘Injury’ was also mentioned in the Austrian list, but this did not affect the replies since the list was not shown to 
respondents. 

48

 
 
 
 
 
                                                             
Chronic conditions 

C. Analysis 
Analysis relates to the percentage of persons with chronic conditions. 

1- Overall level 

2.2.1. Percentage of total population with a longstanding illness 
or health problem

(cid:4) 

50

45

40

35

30

25

20

15

10

5

0

IRL

CH

E

B

A

NL

UK

NO

DK

S

Activity restriction                                    Other                                 Incl. injury/accident

Source: National data 

In the graph, countries have been allocated to three groups: 
The first group includes three countries (Ireland, Switzerland and Spain) which asked questions on the 
existence of longstanding illness or health problems limiting daily activities or work. Since a number of 
illnesses  or  health  problems  do  not  significantly  limit  daily  activities,  it  is  not  surprising  that  these 
countries display the lowest rates. 
The  second  group  includes  four  countries  which  collected  data  most  comparable  with  those 
requested. The percentages obtained are similar among these countries. 
The  third  group  (Norway,  Denmark  and  Sweden)  specifically  mentioned  the  terms  ‘injury’  (Denmark 
and  Norway)  or  ‘accident’  (Sweden)  in  the  question  asked  and  the  rates  of  longstanding  illness  or 
health  problems  of  these  countries  are  higher  than  those  of  the  two  first  groups.  It  may  be  that  the 
omission  of  these  terms  would  have  produced  lower  rates,  more  comparable  with  those  of  other 
countries.  On  the  other  hand,  one  would  have  expected  a  lower  rate  for  Norway,  which  specifically 
required the illness or injury to have lasted at least 6 months. 

49

 
 
 
 
 
 
(cid:4) 

2- Age and gender 

Chronic conditions 

2.2.2. Percentage of female population with a longstanding 
illness or health problem, by age

90

80

70

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

DK

E

IRL

NL

A

S

UK

NO

CH

Source: National data 

2.2.3. Percentage of male population with a longstanding 
illness or health problem, by age

90

80

70

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

DK

E

IRL

NL

A

S

UK

NO

CH

Source: National data 

The  rates  found  in  Ireland,  Switzerland  and  Spain,  which  cover  just  longstanding  illness  or  health 
problems limiting daily activities or work, are generally lower than those in other countries. 
Norway, Denmark and Sweden have generally higher rates than other countries in all age groups. As 
indicated  earlier,  this  may  be  due  to  the  specific  inclusion  of  the  consequence  of  injuries  as  health 
problems. 
All countries display similar patterns of increasing rates with age, at least up to the 75-84 age group. 
However, the reversal found in the 85+ age group, especially among women, can be attributed to a 
variety of causes: 

·  

change  in  the  mental  process  among  the  oldest  respondents,  replies  to  the  question  being 
increasingly  made  with  reference  to  persons  of  the  same  age:  many  elderly  persons  may 
consider their health problems as ‘normal’ in view of their age, and not worth mentioning. 

50

 
 
 
 
 
 
 
 
Chronic conditions 

(cid:4) 

·  

the higher mortality among those suffering from poor health, or even among persons  stating 
that  their  health  is  poor  (equivalent to a higher survival of those without longstanding health 
problems or ending not to state them). 

·   an  increasingly  higher  rate  of  institutionalisation  of  the  elderly:  at  a  similar  level  of  objective 
health status, older persons could tend to be more institutionalised than younger ones, either 
because risks are felt higher or because they are more likely to have lost their spouses. They 
would therefore be excluded from the scope of surveys restricted to private households. 

3- Education level 

2.2.4. Percentage of population with a longstanding illness 
or health problem, by educational level

60

50

40

30

20

10

0

DK

B
NL
E
Pre-primary and Primary (ISCED 0-1)

IRL

A

S

UK

NO

CH

Secondary (ISCED 2)

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Austria, Switzerland, Denmark and Spain: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Ireland: Primary, Secondary (Secondary + Upper secondary), Tertiary 
Source: National data 

After adjustment for age and gender, a higher educational level is associated in almost all countries 
with a lower probability of having a longstanding illness or health problem. 

51

 
 
 
 
 
 
Chronic conditions 

2.2.5. Percentage of population with a longstanding illness
or health problem, by activity status

(cid:4) 

4- Activity status 

60

50

40

30

20

10

0

B

DK

E

IRL

NL

A

S

UK

NO

CH

Active

Inactive

Source: National data 

Not  surprisingly,  being  economically  inactive  is  associated  with  a  higher  probability  of  having  a 
longstanding  illness  or  health  problem.  This,  however,  is  not  the  case  for  Spain  and  Iceland.  The 
relationship  between  activity  status  and  chronic  health  conditions  is  indeed  complex.  For  example, 
studies  have  shown  that  some  of  the  economically  inactive  tend  to  blame  their  situation  on  health 
problems, especially among the prematurely inactive4. 

5- Comparative study of countries5 

Level 

Gender 

Age 

Education 

Activity status 

Table 2.2.3. Analysis of gradients 

B 

DK 

E 

IRL  NL 

A 

S 

UK  NO  CH 

0.79 

1.19 

0.72 

0.39 

1.00 

0.87 

1.41 

1.03 

1.07 

0.50 

0.95 

0.98 

0.91 

1.09 

0.83 

0.95 

0.92 

1.06 

0.93 

0.87 

1.17 

0.86 

0.39 

0.82 

1.03 

1.05 

0.44 

1.28 

0.88 

0.53 

0.57 

0.78 

0.52 

0.55 

0.68 

0.94 

0.81 

0.38 

0.83 

0.70 

0.50 

0.59 

1.02 

0.43 

0.64 

0.88 

0.76 

0.56 

0.74 

0.58 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: National data 

The three countries which included only longstanding illnesses or health problems which limited daily 
activities  or  work  (Spain,  Ireland  and  Switzerland)  have  the  lowest  overall  levels  of  longstanding 
illnesses or health problems. Ireland is also, with the UK, the only country, with a higher rate for males 
than for females, but not Spain or Switzerland. The rate is less related with age in Spain than in any 
other country, and a weak relation is also found in Switzerland, but not in Ireland. 
Finally, Ireland displays the strongest association of health problems with economic inactivity, but no 
effect – if not an opposite  effect – is found in Spain. When chronic conditions are restricted to those 
limiting  normal  activities,  the  rates  obtained  are  strongly  reduced  in  comparison  with  less  restricted 
data. But they do not display a higher degree of homogeneity in relation with other variables. 
Apart from the strong age effect found in the Netherlands and the large age effect in the UK, other 
countries display a medium pattern. 

4 It has even been suggested that at least some of those using health problems to justify for their economic 
   inactivity would regain good health once they have past retirement age. The data available do not make it 
   possible to check this hypothesis. 
5 See 1.2.2 above for the definition of gradients. 

52

 
 
 
 
 
 
 
 
 
 
 
                                                             
 
Chronic conditions 

(cid:4) 

For this item, it appears that data are globally not comparable, but there are indications that data are 
more comparable within groups of countries using similar questions. For example, countries restricting 
chronic  conditions  to  those  limiting  normal  activities  display  lower  rates.  However,  they  display 
considerable heterogeneity as regards the relationship with other variables. 

53

 
 
 
 
Temporary cut down 

(cid:4) 

2.3. Temporary cut-down on usual activities 
Temporary  disability  refers  to  temporary  restriction  in  an  individual’s  usual  level  of  functioning.  The 
questions are often about days of restricted activity and bed-days, commonly used in health interview 
surveys. 
Data have been collected on three related indicators: 

·   Persons having cut down on usual activities during the previous two weeks 
·   Average  number  of  days  during  which  activities  have  been  cut  down  (for  those  having  cut-

down on their activities) 

·   Average number of days in bed (for those having cut down on their activities) 

2.3.1. Percentage of population having temporarily cut down on usual 
activities 
Data on the percentage of the population having cut down on their usual activities have been collected 
from national surveys and from the ECHP. The two data sets are analysed in turn. 

2.3.1.1. Percentage of population having temporarily cut down on usual activities 
(national data) 

A. Data requested 
Data were requested on the replies to the question: 
“Think about the two weeks ending yesterday. Have you cut down on any of the things you usually do 
about the house, at work or in your free time because of illness or injury?” 
1.  Number of people who have cut down during these two weeks 
2.  Number of people who have not cut down during these two weeks 
3.  For people who have cut down: average number of days cut down during these two weeks, 

including Saturdays and Sundays 

4.  For  people  who  have  cut  down:  average  number  of  days  in  bed  for  all  or  most  of  the  day 

during these two weeks, including Saturdays and Sundays 

B. Data collected 
Data were obtained from only six countries. 

Table 2.3.1.1.1. Wording of the questions by survey 

Country  Year 

Question 

Response categories 

B 

1997 

DK 

2000 

Think about the two weeks ending yesterday. Have you cut down 
on any of the things you usually do about the house, at work or in 
your free time because of illness or injury?  
Yes / No 

Within the past 2 weeks has illness, injury or ailment made it 
difficult or impossible for you to carry out your usual daily 
activities? (e.g., work outside the home or domestic work, spare 
time activities etc.) 

Temporarily cut down 
Not temporarily cut down 

Temporarily cut down 
Not temporarily cut down 

E 

1997 

During the past 2 weeks, have you had to restrict or cut back your 
usual leisure activities as a result of any pain or symptom? 

Reduced activity 
Not reduced activity 

NL 

2000 

Did you have to take it easy or to refrain from carrying out your 
usual activities due to illness or injury during the past 14 days? 

Temporarily cut down 
Not temporarily cut down 

P 

1999 

In these last two weeks, [have you] [has she/he] quit doing 
something [you/she/he] usually [do/does] at home, at work or in 
[your/her/his] everyday free time, owing to a disease, an accident 
or health related matters (consultations, analyses, X-rays, 
treatments, hospital admissions, etc.)? 

Temporarily cut down 
Not temporarily cut down 

UK 

2000 

Now I'd like you to think about the 2 weeks ending yesterday. 
During those 2 weeks, did you have to cut down on any of the 
things you usually do (about the house/at work or in your free time) 
because of a longstanding illness or some other illness or injury? 

Temporarily cut down 
Not temporarily cut down 

Source: National data 

55 

 
 
 
 
 
 
 
 
(cid:4) 

Temporary cut down 

Table 2.3.1.1.2. Comparative summary and variable averages 

Country 

Activities cut-down 

Causes 

Remarks 

B 

DK 

E 

NL 

P 

UK 

Usual: at home / at 
work 

Usual: at home / at 
work / spare time 

Illness or injury 

Illness, injury or ailment 

Includes activities ‘made 
difficult’ 

Leisure only 

Any pain or symptom 

usual 

Illness or injury 

Includes ‘had to take it 
easy’ 

Usual: at home / at 
work / free time 

Disease, accident or health 
related matters 

Usual: at home / at 
work / free time 

Longstanding illness or other 
illness or injury 

Includes longstanding 
illness 

% cut-
down 

10.4 

14.9 

12.9 

16.5 

13.4 

15.3 

Source: National data 

All  the  countries  used  a  2-week  (14  days)  reference  period,  and  the  overall  percentage  of  the 
population  having  cut  down  on  their  usual  activities  during  the  past  two  weeks  for  health  reasons 
seem relatively similar. 
However, there are large differences in question wording: 

-  Spain  only  requested  reporting  on  pain  or  symptoms  having  restricted  leisure  activities,  not 

usual daily activities at home or at work. 

-  Portugal  explicitly  included  health  related  matters  such  as  consultations,  analyses,  X-rays, 

treatments, etc. 

There  may  also  be  subtle  differences  in  the  meaning  of  ‘cut  down’,  ‘made  difficult’  ‘restrict’,  ‘take  it 
easy’ or ‘refrain’ used in each country. For example, those who had to ‘take it easy’ but continued their 
usual activities were recorded as ‘temporarily cut down’ in the Netherlands, but not in other countries  

C. Analysis 
The  variable  analysed  is  the  percentage  of  the  population  having  temporarily  cut  down  on  usual 
activities during the previous two weeks. 

1- Overall level 

2.3.1.1.1. Percentage of population having temporarily 
cut down on usual activities during the previous two weeks 
(national data)

18

16

14

12

10

8

6

4

2

0

B

DK

E

NL

P

UK

Source: National data 

56 

 
 
 
 
 
 
 
 
 
 
 
 
Temporary cut down 

(cid:4) 

The percentage of the population having cut down on their usual activities during the past two weeks 
range  from  a  minimum  of  10.4%  (Belgium)  to  a  maximum  of  16.5%  (Netherlands).  Although  the 
question in Spain referred to restrictions in leisure activities, the rate is not particularly different from 
other countries. 

2- Age and gender 
The percentage having cut down on usual activities is higher for females in all six countries, even after 
adjustment  for  age  structure,  the  larger  number  of  females  among  elders  having  little  effect.  Within 
each  age  group  of  each  country,  the  percentage  cut  down  is  almost  always  higher  for  women.  The 
major exception being the eldest age group (85+), for which, in four of the six countries, men are more 
likely than women to have cut down on their usual activities because of health reasons. 

2.3.1.1.2. Percentage of female population having temporarily 
cut down on usual activities during the previous two weeks, 
by age (national data)

35

30

25

20

15

10

5

0

15-24

25-34
B

35-44
DK

45-54
E

55-64

65-74

75-84

85+

NL

P

UK

Source: National data 

2.3.1.1.3. Percentage of male population having temporarily cut 
down on usual activities during the previous two weeks,
by age (national data)

35

30

25

20

15

10

5

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

DK

E

NL

P

UK

Source: National data 

57 

 
 
 
 
 
 
 
 
 
 
(cid:4) 

Temporary cut down 

The percentage having cut down on usual activities increases with age in the UK, Portugal and Spain, 
but  much  less  in  Belgium  and  Denmark.  It  is  however  almost  independent  from  age  for  men  in  the 
Netherlands, and even actually declines from the younger age group to the 75-84 group. 
In four countries (Spain, the Netherlands, Portugal and the UK), a large increase appears for men, but 
not for women, between the 75-84 and 85+ age groups. 

3- Educational level 

2.3.1.1.4. Percentage of population having temporarily cut down 
on usual activities during the previous two weeks, by 
educational level (national data)

25

20

15

10

5

0

B

DK

E

NL

P

UK

Pre-primary and Primary (ISCED 0-1)

Secondary (ISCED 2)

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Denmark and Spain: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Source: National data 

In all countries, the percentage having cut down on their activities decreases with higher educational 
level, but the effect is minimal for Spain. In the UK, the percentage declines between the pre-primary / 
primary level and the secondary level, but rises somewhat with higher levels of education. 

58 

 
 
 
 
 
 
Temporary cut down 

4- Activity status 

(cid:4) 

2.3.1.1.5. Percentage of population having temporarily cut down 
on usual activities during the previous two weeks, by activity 
status (national data)

30

25

20

15

10

5

0

B

DK

E

NL

UK

Active

Inactive

Source: National data 

In all the countries, the percentage having cut down on temporary activities for health reasons is more 
prevalent among the inactive.  

5- Comparative study of countries1 

Table 2.3.1.1.3. Analysis of gradients 

Level 

Gender 

Age 

Education 

Activity status 

B 

0.78 

0.81 

0.51 

0.53 

0.76 

DK 

1.11 

0.71 

0.29 

0.77 

0.52 

E 

0.96 

0.76 

1.30 

0.85 

0.82 

NL 

1.23 

0.80 

0.15 

0.70 

0.72 

P 

1.00 

0.77 

1.66 

0.76 

- 

UK 

1.14 

0.89 

1.75 

0.76 

0.44 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: National data 

·   There is no extreme variation among countries in the percentage of the population having cut 

down on their usual activities. 

·   Males are on average, less likely to reduce activities, the effect being largest in Denmark and 

smallest in the UK. 

·   The  age  effect  is  very  large  in  Portugal  and  the  UK,  but  very  small  in  Denmark  and  the 
Netherlands.  The  educational  effect  is  not  very  different  among  countries,  but  is  higher  in 
Belgium and lower in Spain. 
In Denmark, the inactive are twice more likely than the active to cut down on their activities, 
but the opposite appears in Portugal. 

·  

1 See 1.2.2 above for the definition of gradients. 

59 

 
 
 
 
 
 
 
 
 
 
                                                          
(cid:4) 

Temporary cut down 

2.3.1.2. Percentage of population having temporarily cut down on usual activities (ECHP) 
Data on usual activities being cut down during the previous two weeks were also asked in the ECHP. 

B. Data collected 

Year 

1998 

Table 2.3.1.2.1. Wording of the questions by survey 

Question 

Response categories 

During the past two weeks, have you had to cut down on things you 
usually do about the house, at work or in free time because of 
illness or injury?  

Temporarily cut down 
Not temporarily cut down 

Source: European Community Household Panel, 1998 

The question asked is almost identical to the one requested from countries. 

C. Analysis 

1- Overall level 

2.3.1.2.1. Percentage of population having temporarily cut down 
on usual activities during the previous two weeks 
(ECHP, 1998)

25.0

20.0

15.0

10.0

5.0

0.0

B

DK

EL

E

IRL

I

NL

A

P

UK

Source: European Community Household Panel, 1998 

Although the same question was asked in all surveys, there is much more variation among countries 
than with national data. Indeed, the rate in Denmark (20.7%) is nearly five times higher than in Greece 
(4.5%). 

60 

 
 
 
 
 
 
 
 
 
 
 
Temporary cut down 

(cid:4) 

2- Age and gender 
As with national data, females generally report more than men to have cut down on their activities for 
health reasons, but this does not apply to the youngest age group (15-24), and, for several countries, 
the oldest age group (85+).  

2.3.1.2.2. Percentage of female population having temporarily 
cut down on usual activities during the previous two weeks,
 by age (ECHP, 1998)

70

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

DK

EL

E

IRL

I

NL

A

P

UK

 Source: European Community Household Panel, 1998 

2.3.1.2.3. Percentage of male population having temporarily cut 
down on usual activities during the previous two weeks, by age 
(ECHP, 1998)

70

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

DK

EL

E

IRL

I

NL

A

P

UK

Source: European Community Household Panel, 1998 

As  with  national  data,  the  percentage  of  the  population  declaring  to  have  cut  down  on  their  usual 
activities for health reasons in the ECHP also tends to increase with age. However, Denmark presents 
a very different age pattern from other countries, particularly with a decline among females from the 
youngest age group to the 55-64 group. 
Among  other  countries,  the  relationship  with  age  is  very  strong  in  countries  such  as  the  UK  or 
Portugal, but much weaker in Italy. 

61 

 
 
 
 
    
 
 
 
 
 
 
(cid:4) 

3- Educational level 

Temporary cut down 

2.3.1.2.4. Percentage of population having temporarily 
cut down on usual activities during the previous two weeks,
 by educational level (ECHP, 1998)

25

20

15

10

5

0

DK

B
IRL
EL
Pre-primary and Primary (ISCED 0-2)

E

I

NL
Upper secondary (ISCED 3)

A

P

UK

Post-secondary (ISCED 4-6)

Source: European Community Household Panel, 1998 

As with national data, a higher educational level (after adjustment for age and sex) is often associated 
with  a  lower  percentage  having  cut  down  on  their  usual  activities.  The  picture,  however,  is  more 
complex in some countries (Spain, Ireland or the Netherlands). 

4- Activity status 

2.3.1.2.5. Percentage of population having temporarily 
cut down on usual activities during the previous two weeks, 
by activity status (ECHP, 1998)

25

20

15

10

5

0

B

DK

EL

E

IRL

I

NL

A

P

UK

Active

Inactive

Source: European Community Household Panel, 1998 

Inactivity is associated with a higher percentage having cut down on their usual activities. However, a 
small reverse relationship is visible for Italy. 

62 

 
 
 
 
 
 
 
 
 
 
Temporary cut down 

5- Comparative study of countries2 

(cid:4) 

Level 

Gender 

Age 

Education 

Activity status 

Table 2.3.1.2.2. Analysis of gradients 

  B 

1.00 

0.72 

0.63 

0.66 

0.74 

DK 

1.77 

0.83 

0.52 

0.80 

0.86 

EL 

0.38 

0.76 

0.73 

0.72 

0.69 

E 

IRL 

I 

0.84 

0.79 

0.68 

0.73 

0.82 

0.66 

0.84 

0.65 

0.84 

0.66 

0.39 

0.94 

0.32 

0.70 

1.14 

NL 

1.20 

0.71 

0.63 

0.88 

0.82 

A 

1.00 

0.88 

1.16 

0.70 

0.74 

P 

1.05 

0.91 

1.77 

0.54 

0.56 

UK 

1.41 

0.82 

2.12 

0.70 

0.68 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: European Community Household Panel, 1998 

Surprisingly,  there  appears  to  be  more  variability  in  gradients  among  datasets  obtained  from  the 
ECHP.  Overall,  rates  vary  widely  among  countries;  male  rates  are  much  lower  than  female  rates  in 
Belgium and the Netherlands, but much less in Portugal or Austria; the age effect is very high in the 
UK and in Portugal, but much weaker in Italy; education is very important in Portugal, but much less 
so elsewhere; finally, economic activity has opposite effects in Portugal and in Italy. 
Somewhat  surprisingly,  the  harmonisation  of  data  collection  in  the  ECHP  has  not  lead  to  a  higher 
degree of homogeneity in the data collected. 

Comparison of national and ECHP data 
As in earlier topics, national data from the ECHP are shown on the horizontal axis and national data 
on the vertical axis. 

2.3.1.2.6. Percentage of population having 
cut down on usual activities during the 
previous two weeks: National data vs 1998 
ECHP data

2.3.1.2.7. Percentage of population having 
cut down on usual activities during the 
previous two weeks,
 by age: National data vs 1998 ECHP data

22
20
18
16
14
12
10
8

8

10

12

14

16

18

20

22

ECHP data

B
P

DK
UK

E
Diagonal

NL

45

35

25

15

5

-5

0

10

20
ECHP data

30

40

B, Female

P, Female

E, Male

Diagonal

DK, Female

UK, Female

NL, Male

E, Female

B,Male

P; Male

NL, Female

DK, Male

UK, Male

Source: European Community Household Panel (1998) & national data 

For six countries, data are available both from national sources and from the ECHP. The overall levels 
for the total population (15 years old and over) do not match well for Denmark, where the ECHP rate is 
20.7% while the national rate is 14.9%. A somewhat better concordance is found in other countries. 
A comparison by gender and age group is more surprising. At younger ages, national rates are higher 
or much higher than national ECHP rates, except in Denmark. With increasing age, the rates from the 
two sources tend to become equal. At older ages, rates from national sources are lower than those 
from the ECHP, except male rates in Spain and the Netherlands. 
Finally,  all  national  gender  x  age  rates  in  Denmark  are  lower  than  the  corresponding  ECHP  rates, 
except for males aged 55-64. 

2 See 1.2.2 above for the definition of gradients. 

63 

 
 
 
 
 
                                                          
 
 
(cid:4) 

Temporary cut down 

2.3.2. Average number of days cut down (by those who had to cut down 
on their usual activities during the past two weeks for health reasons) 
Data on the percentage of persons having to cut down on their usual activities may be complemented 
with information on the length of time such activities have been cut down. 

A. Data Requested 
Data were requested on average number of days when usual activities had been cut down, for those 
who had cut down on their activities during these two weeks because of illness or injury. This number 
of days includes Saturdays and Sundays. 

B. Data collected 

Table 2.3.2.1. Wording of the questions by survey 

Country  Year 

Question 

B 

DK 

NL 

1997  Number of days with temporary limitations during the last 2 weeks? 

2000  Write total number of days 

2000  How many days did this last during the past 14 days?  

P 

1999 

How many days in these last two weeks [have you] [has she/he] quit doing something 
[you/she/he] usually [do/does] at home, at work or in [your/her/his] everyday free time, 
owing to a disease, an accident or health related matters (consultations, analysis, x-rays, 
treatments, hospital admissions, etc.)? none   … days 

UK 

2000  How many days was this, in all, during these 2 weeks, including Saturdays and Sundays? 

Source: National data 

As noted earlier, there are differences in the determination of persons who had to cut down on their 
usual activities. However, once these persons have been identified, the questions designed to obtain 
the number of days cut down appears, prima facie, rather comparable.  

2.3.2.1. Average number of days usual activities have been
 cut down during the previous two weeks: overall population

C. Analysis 

1- Overall level 

10

9

8

7

6

5

4

3

2

1

0

B

DK

NL

P

UK

Source: National data 

The average number of days cut down by persons having cut down on their usual activities during the 
previous two weeks is very similar among the five countries – from a minimum of 7.4 to a maximum of 
8.8 days.  

64 

 
 
 
 
 
 
 
 
 
 
 
 
 
Temporary cut down 

(cid:4) 

2- Age and gender 
The  number  of  days  cut  down  is  slightly  higher  for  males  at  all  ages  in  the  UK,  but  not  in other 
countries, where there is no significant difference between males and females as regards the number 
of days activities cut down. 

2.3.2.2. Average number of days usual activities have been 
cut-down during the previous two weeks: female population, 
by age

14

12

10

8

6

4

2

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

DK

NL

P

UK

Source: National data 

2.3.2.3. Average number of days usual activities have been cut 
down during the previous two weeks: male population, by age

14

12

10

8

6

4

2

0

15-24

25-34

35-44

45-54

55-64

B

DK

NL

P

75-84

85+

65-74

UK

Source: National data 

On average, young adults having to cut down on their usual activities during the previous two weeks 
do so for 5 to 7 days. The length of activity reduction increases with age: for the older age groups, 
activities become reduced for almost the total length of the period (14 days). 
While  there  are  systematic  –  but  small  –  differences  among  countries  in  the  number  of  days  that 
activities are cut down, the age patterns are very similar. 

65 

 
 
 
 
 
 
 
 
(cid:4) 

3- Educational level 

Temporary cut down 

2.3.2.4. Average number of days usual activities have been 
cut down during the previous two weeks, by educational level

10

9

8

7

6

5

4

3

2

1

0

B

DK

NL

P

UK

Pre-primary and Primary (ISCED 0-1)

Secondary (ISCED 2)

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Denmark: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Source: National data 

When  activities  have  been  cut  down,  the  higher  the  educational  level  (after  adjustment  for  age  and 
sex), the fewer the number of days of activity reduction. 

4- Activity status 

2.3.2.5. Average number of days usual activities have been 
cut down during the previous two weeks, by activity status

12

10

8

6

4

2

0

B

DK

NL

UK

Active

Inactive

Source: National data 

After  adjustment  for  age  and  gender  structure,  the  duration  of  activity  reduction  is  longer  for  the 
economically inactive than for the active, although the difference is small in the Netherlands, and the 
opposite is found in Portugal, with a shorter period of activity cut down among the inactive.  

66 

 
 
 
 
 
 
 
 
 
Temporary cut down 

5- Comparative study of countries3 

Table 2.3.2.2. Analysis of gradients 

(cid:4) 

Level 

Gender 

Age 

Education 

Activity status 

B 

0.95 

1.01 

0.88 

0.70 

0.76 

DK 

1.01 

0.99 

1.31 

0.81 

0.77 

NL 

1.00 

1.01 

0.64 

0.79 

0.95 

P 

0.90 

1.03 

0.75 

0.88 

- 

UK 

1.07 

1.06 

0.87 

0.83 

0.78 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: National data 

The  number  of  countries  for  which  data  are  available  is  too  small  to  draw  definite  conclusions. 
However: 

in addition, male and female levels are very similar; 
the age effect is largest in Denmark and lowest in the Netherlands; 

·   as the table shows, there are little differences in overall levels among countries; 
·  
·  
·   higher education is almost equally related to shorter duration of activity cut down in all countries; 
·  
finally, economic inactivity is associated with a longer duration of activity reduction, but less so 
in the Netherlands. 

Despite differences in the questions asked and in the definition of persons having cut down on their 
usual  activities,  data  on  the  average  number  of  days  such  activities  have  been  cut  down  appear 
relatively comparable among countries. Unfortunately, recent data are available for only five countries.  

2.3.3. Average number of days in bed (for those having cut down on their 
activities during the previous two weeks) 
More detailed information on the number of days activities were cut down can be obtained by asking 
how many of these days had to be spent in bed.  

A. Data requested 
For  people  who  have  cut  down  on  their  usual  activities  during  the  past  two  weeks,  data  were 
requested  on  the  average  number  of  days  in  bed  during  these  two  weeks,  including  Saturdays  and 
Sundays. A ‘day in bed’ is understood if the person has spent all or most of the day in bed. 

B. Data collected 

Table 2.3.3. Wording of the questions by survey 

Country  Year 

Question 

I 

2000  How many days in bed? 

NL 

2000 

Did this include days during which you were completely or largely bedridden? If yes, how 
many days (1-14) have you been ill in bed in that case? 

P 

1999  How many of these days did [you/she/he] have to stay in bed, all day or most of the day? 

NO 

1998  How many days during the past 14 days were you totally bedridden at home? 

IS 

1998 

How many days did you stay in bed more than half of the day due to illness or injury in the 
past 6 months, not counting hospital days? 

Source: National data 

The  reference  period  was  two  weeks  for  all  the  surveys,  except  Iceland  which  used  a  6-month 
reference period. Iceland also included half-days when the person was bed-ridden and covered only 
the population aged 15-74. It would be tempting to divide the data from Iceland by 13 to get figures 

3 See 1.2.2 above for the definition of the gradients. 

67 

 
 
 
 
 
 
 
 
 
 
 
 
 
                                                          
(cid:4) 

Temporary cut down 

that are more comparable with those of other countries, based on a 2-week period, but this would be 
an erroneous procedure. Data from Iceland have not been included in further analyses. 
Portugal’s  survey  includes  days  spent  mostly  in  bed,  and  data  from  the  Netherlands  include  days 
‘largely bedridden’. This is in conformity with the data requested.  The question used in Italy does not 
provide details on the daily duration in bed. Finally, data from Norway have not been included in the 
analysis  because  the  question  made  it  clear  that  only  days  totally  bedridden  at  home  should  be 
counted.   

2.3.3. Average number of days in bed in the overall population 
having cut down on their usual activities 
during the previous two weeks

C. Analysis 

1- Overall level 

4

3.5

3

2.5

2

1.5

1

0.5

0

I

NL

P

Source: National data 

The average number of days spent in bed when usual activities have to be cut down is about 3.7 days 
in Italy and the Netherlands, but somewhat lower in Portugal (2.6 days). 

2- Age and gender 

2.3.4. Average number of days in bed in the female population 
having cut down on their usual activities 
during the previous two weeks, by age

9

8

7

6

5

4

3

2

1

0

15-24

25-34

35-44

45-54

I

NL

Source: National data 

55-64

P

68 

65-74

75-84

85+

 
 
 
 
 
 
 
 
 
 
Temporary cut down 

(cid:4) 

2.3.5. Average number of days in bed in the male population 
having cut down on their usual activities during the previous 
two weeks, by age

9

8

7

6

5

4

3

2

1

0

15-24

25-34

35-44

45-54
NL

I

55-64
P

65-74

75-84

85+

Source: National data 

Women having to cut down on their usual activities tend to spend less time in bed than males, but the 
difference is small and varies by age group. 
With increasing age, persons having had to cut in their usual activities tend to spend more of their time 
in bed. 

3- Educational level 

2.3.6. Average number of days in bed in the population having 
cut down on their usual activities during the previous two 
weeks, by educational level

5.0

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

I

NL

P

Pre-primary and Primary (ISCED 0-1)

Secondary (ISCED 2)

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Source: National data 

In  Italy  and  the  Netherlands,  persons with a higher educational level who have to cut down on their 
usual  activities  tend  to  spend  less  time  in  bed  than  those  with  less  formal  education.  However,  in 
Portugal, there is very little relationship between education and days spent in bed. 

69 

 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

4- Activity status 

Temporary cut down 

2.3.7. Average number of days in bed in the population having 
cut down on their usual activities during the previous two 
weeks, by activity status

4.5

4

3.5

3

2.5

2

1.5

1

0.5

0

I

NL

Active

Inactive

Source: National data 

When  having  to  cut-down  on  their  activities,  the  economically  inactive  in  Italy  and  the  Netherlands 
tend to spend more days in bed. 

5- Comparative study of countries 
Since data are available for only three countries, no meaningful comparative analysis of the gradients 
can be made. 

70 

 
 
 
 
 
 
 
Body Mass Index 

2.4. Body Mass Index (BMI)  

2.4.1. Body Mass Index (BMI) (national data) 

(cid:4) 

A. Data requested 
Data were requested on the Body Mass Index (BMI) calculated by dividing body weight (in kg) by body 
height (in m) squared. It is a crude but simple and popular measure of excessive and of insufficient 
weight for height among adults. 
Since various BMI thresholds have been used and are still being used, data were requested so as to 
accommodate  both  the  ‘old’  and  the  ‘new’  thresholds,  The  distribution  of  persons  by  BMI 
recommended in WHO (1996)1 and requested in the second round of data collection are as follows. 

WHO (1996)1 BMI thresholds 

BMI < 18 
18 £  BMI < 20 
20 £  BMI < 25 
25 £  BMI < 27 
27 £  BMI < 30 
BMI ³  30 

Data requested 

BMI < 18.0 
18.0 £  BMI < 18.5 
18.5 £  BMI < 25.0 
25.0 £  BMI < 27.0 
27.0 £  BMI < 30.0 
BMI ³  30.0 

Source: WHO, 1996 & Eurostat 
Countries  for  which  it  was  impossible  to  use  these  cut-off  points  were  asked  to  indicate  the  cut-off 
points used instead. 

B. Data collected 

Table 2.4.1.1. Wording of the questions by survey 

Country  Year 

Question 

B 

1997 

What is your height without shoes (In cm)? How much do you weigh 
without clothes and shoes (in kg)?  

DK 

2000  How tall are you? How much do you weigh? 

D 

1998 

How many kilograms do you without clothes? 
 And how tall are you without shoes? 

IRL 

1998  What is your weight without clothes? What is your height without shoes? 

I 

2000  Can you please state your current height and weight? 

NL 

2000 

What’s your weight? (without shoes) How much do you weigh? (without 
clothes) 

A 

P 

S 

1999 

How is your height without shoes in cm and how much do you weigh 
without shoes and clothes in kg? 

1999 

What is [your/his/her] height without shoes?  What is [your/her/his] weight 
without clothes nor shoes? 

2000  How tall are you? What is your weight? 

UK 

2000  Weight and height 

IS 

1998  Weight and height 

Response 
categories 

BMI < 18 
18 <= BMI < 20 
20 <= BMI < 27 
27 <= BMI < 30 
BMI >= 30 

BMI < 18 
18 <= BMI < 18.5 
18.5 <= BMI < 25 
25 <= BMI < 27 
27 <= BMI < 30 
BMI >= 30 

BMI < 18 
18 <= BMI < 20 
20 <= BMI < 27 
27 <= BMI < 30 
BMI >= 30 

1 WHO (1996) "Health interview surveys: Towards international harmonization of methods and instruments", De 
Bruin, A.; Picavet, H.S.J. and Nossikov, A., Copenhagen: World Health Organisation. Regional Office for Europe. 
Voorburg: Statistics Netherlands. (WHO Regional Publications. European Series n°58), 1996, xiii + 161 pp.  
http://www.who.dk/document/E7241A.pdf  &  http://www.who.dk/document/E7241B.pdf 

71 

 
 
 
 
 
 
 
 
 
 
                                                          
 
(cid:4) 

Country  Year 

Question 

NO 

1998 

How tall are you (without shoes) How much do you weigh, without clothes 
or shoes? 

CH 

1997 

Can you tell me your height without shoes? (cm) And what is your weight 
(without clothing)? (kg) 

Body Mass Index 

Response 
categories 

BMI < 18 
18 <= BMI < 18.5 
18.5 <= BMI < 25 
25 <= BMI < 27 
27 <= BMI < 30 
BMI >= 30 

BMI < 18 
18 <= BMI < 20 
20 <= BMI < 27 
27 <= BMI < 30 
BMI >= 30 

Source: National data 

In  Germany  and  in  the  UK,  height  and  weight  were  physically  measured.  In  other  countries, 
respondents were requested to provide data on their weight (without clothes) and their height (without 
shoes). All these countries but Norway conducted face-to-face interviews. Several studies have shown 
that statements about weight and height are not particularly reliable. Generally, people tend to over-
estimate  their  height  –  especially  men  and  short  persons  –  and  to  under-estimate  their  weight, 
especially  women  and  the  overweight.  As  a  result,  self-reports  tend  to  lead  to  under-estimates  of 
overweight and obesity rates. However, variations are found among countries and populations. 
All countries provided data with cut-offs for BMI = 18, 27 and 30. Since being overweight (BMI >= 27) 
or  obese  (BMI>=  30)  are  more  important  problems  in  European  countries  than  being  underweight, 
analysis is restricted to excessive weight for height.  

Table 2.4.1.2. Comparative summary and variable averages 

Country 

>= 27 

>= 30 

B 

DK 

D 

F 

IRL 

I 

NL 

A 

P 

S 

UK 

IS 

NO 

CH 

30.0 

24.7 

42.0 

22.3 

24.8 

23.3 

22.8 

20.4 

30.5 

25.2 

42.0 

26.9 

19.4 

18.4 

13.0 

9.5 

20.3 

9.3 

10.3 

8.5 

8.5 

8.6 

12.2 

9.6 

21.2 

9.9 

6.3 

6.7 

Source: National data 

Comparison 
Question 
Without shoes; without 
clothes 

Remark 
Response categories 

18; 20, 27, 30 

- 

18,18.5, 25, 27, 30 

Without shoes; without 
clothes 

18,18.5, 25, 27, 30 

- 

18,18.5, 25, 27, 30 

Without shoes; without 
clothes 

18,18.5, 25, 27, 30 

- 

18,18.5, 25, 27, 30 

Without shoes; without 
clothes 
Without shoes; without 
clothes 
Without shoes; without 
clothes 

- 

- 

- 

Without shoes; without 
clothes 
Without shoes; without 
clothes 

18,18.5, 25, 27, 30 

18,18.5, 25, 27, 30 

18,18.5, 25, 27, 30 

18,18.5, 25, 27, 30 

18,18.5, 25, 27, 30 

18; 20, 27, 30 

18,18.5, 25, 27, 30 

18; 20, 27, 30 

72 

 
 
 
 
 
 
(cid:4) 

2.4.1.2. Percentage of population severely 
overweight (BMI >=30)

Body Mass Index 

C. Analysis 

1- Overall level 

2.4.1.1. Percentage of population 
overweight (BMI >= 27)

45

40

35

30

25

20

15

10

5

0

25

20

15

10

5

0

B DK D F IRL

I NL A

P

S UK IS NO CH

B DK D

F IRL

I NL A

P

S UK IS NO CH

Source: National data 

Two  countries  stand  out  for  their  high  percentage  of  persons  overweight  and  severely  overweight: 
Germany and the UK. Indeed, time series for he UK during the past two decades display a remarkable 
and steady increase in percentage of the population overweight, catching up (together with Australia) 
with the USA, which used to stand as "a class of its own". A similar, but less dramatic increase over 
time is also found in all developed countries. On the other hand, data from Germany and the UK were 
based on physical measurement rather than on self-reports2. The higher figures found for these two 
countries may be due, at least in part, to the use of this more precise measurement procedure. 

The  lowest  percentages  of  population  overweight  are  found  in  Switzerland  and  Norway,  the  latter 
country being the only one to use telephone interviews.  

2 See http://www.ktl.fi/publications/ehrm/product1/section6/.htm 

73 

 
 
 
 
 
 
 
                                                          
(cid:4) 

Body Mass Index 

2- Age and gender 
The percentage of persons overweight, whether or not adjusted for age, is higher for females in only 
four  countries  (Germany,  the  Netherlands,  Portugal,  and  the  UK)  and  lower  in  all  the  other  ten 
countries.  As  regards  persons  severely  overweight,  the  percentage  for  males  is  higher  than  for 
females in all countries. 

2.4.1.3. Percentage of female population overweight
 (BMI >= 27), by age

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

B

S

DK

UK

D

IS

F

NO

IRL

CH

I

NL

A

85+

P

Source: National data 

2.4.1.4. Percentage of male population overweight (BMI >= 27), 
by age

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

B

S

DK

UK

D

IS

F

NO

IRL

CH

I

NL

A

85+

P

Source: National data 

In  almost  all  countries,  the  percentage  overweight  increases  with  age  up  to  the  44-64 or 65-74 age 
groups and declines thereafter. The pattern is similar for all countries, except the remarkably high level 
of 25-34 year old males in the UK and the generally low level found in Norway. 

Female obesity is clearly higher at all ages in Germany and the UK and relatively low in Norway and 
Switzerland. 

74 

 
 
 
 
 
 
 
 
 
Body Mass Index 

(cid:4) 

2.4.1.5. Percentage of female population severely overweight 
(BMI>= 30), by age

35

30

25

20

15

10

5

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

B

S

DK

UK

D

IS

F

NO

IRL

CH

I

NL

A

85+

P

Source: National data 

2.4.1.6. Percentage of male population severely overweight 
(BMI >= 30), by age

35

30

25

20

15

10

5

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

P

DK

S

D

UK

F

IS

IRL

NO

I

CH

NL

A

Source: National data 

The  pattern  of  the  relationship  between  the  percentages  of  males  severely  overweight  and  age  is 
similar  to  that  for  BMI  ³  27, but, of course, at a lower level. Here also, a relatively very high level is 
found for age group 25-34 in the UK. 

75 

 
 
 
 
 
 
 
(cid:4) 

3- Educational level 

Body Mass Index 

2.4.1.7. Percentage of population overweight 
(BMI >= 27), by educational level

50

45

40

35

30

25

20

15

10

5

0

DK

B
NL
F
Pre-primary and Primary (ISCED 0-1)

IRL

D

I

P

A
S
Secondary (ISCED 2)

UK

IS

NO

CH

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Austria, Switzerland, Denmark: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Ireland: Primary, Secondary (Secondary + Upper secondary), Tertiary 
Source: National data 

2.4.1.8. Percentage of population severely overweight 
(BMI>=30), by educational level

30

25

20

15

10

5

0

B

DK

NL
F
Pre-primary and Primary (ISCED 0-1)

IRL

D

I

A

P

UK
Secondary (ISCED 2)

S

IS

NO

CH

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Austria, Switzerland, and Denmark: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Ireland: Primary, Secondary (Secondary + Upper secondary), Tertiary 
Source: National data 

After  adjustment  for  age  and  sex,  there  is  a  very  strong  association  between  educational  level and 
overweight in almost all countries: the higher the educational level, the lower the probability of being 
overweight or obese. The pattern differs only for obesity in Iceland, with the rate decreasing first and 
then increasing slowly with educational level. This may be due to the relatively smaller sample size in 
that country. 

76 

 
 
 
 
 
 
 
 
 
Body Mass Index 

4- Activity status 

2.4.1.9. Percentage of population overweight 
(BMI >= 27), by activity status

(cid:4) 

45

40

35

30

25

20

15

10

5

0

B

DK

D

F

IRL

I

NL

A

S

UK

IS

NO

CH

Source: National data 

Active

Inactive

After  adjustment  for  age  and  gender,  the  probability  of  being  overweight  is  slightly  higher  for  the 
inactive in all countries, with the exceptions of the UK and Iceland, where there is little difference.  

2.4.1.10. Percentage of population severely overweight 
(BMI >=30), by activity status

25

20

15

10

5

0

B

DK

D

F

IRL

I

NL

A

P

S

UK

IS

NO

CH

Source: National data 

Active

Inactive

Obesity is higher for the non-active in all countries, the differential being generally higher than for the 
percentage overweight. 

77 

 
 
 
 
 
 
 
 
(cid:4) 

5- Comparative study of countries1 

Body Mass Index 

Table 2.4.1.3. Analysis of gradients (Overweight)  

B 

1.21 

1.47 

1.59 

0.58 

0.84 

DK 

1.00 

1.37 

0.88 

0.62 

0.94 

D 

1.70 

1.23 

1.75 

0.63 

0.97 

F 

IRL 

I 

0.90 

1.34 

1.10 

0.51 

0.88 

1.00 

1.43 

0.91 

0.63 

0.93 

0.94 

1.42 

1.26 

0.44 

0.90 

NL 

0.92 

1.07 

1.04 

0.49 

0.82 

A 

0.83 

1.28 

1.06 

0.85 

0.94 

P 

1.23 

1.08 

1.33 

0.51 

- 

S 

1.02 

1.28 

0.80 

0.59 

0.83 

UK 

1.70 

1.16 

1.38 

0.78 

1.01 

IS  NO  CH 

1.09 

1.38 

0.96 

0.64 

1.03 

0.79 

1.48 

0.65 

0.58 

0.93 

0.74 

1.37 

1.01 

0.50 

0.95 

Level 

Gender 

Age 

Education 

Activity status 

Source: National data 

Table 2.4.1.4. Analysis of gradients (Severely overweight (obese)) 

B 

1.36 

1.15 

1.77 

0.42 

0.77 

DK 

0.99 

1.07 

0.72 

0.50 

0.84 

D 

2.13 

0.90 

2.04 

0.46 

0.85 

F 

IRL 

I 

0.97 

1.03 

0.89 

0.35 

0.83 

1.08 

1.25 

1.13 

0.45 

0.83 

0.89 

1.09 

1.19 

0.29 

0.83 

NL 

0.89 

0.78 

0.93 

0.34 

0.65 

A 

0.90 

1.02 

1.07 

0.80 

0.79 

P 

1.28 

0.85 

1.36 

0.40 

- 

S 

1.01 

1.03 

0.92 

0.44 

0.64 

UK 

2.22 

0.98 

1.47 

0.69 

0.97 

IS  NO  CH 

1.04 

1.13 

0.97 

0.72 

0.95 

0.66 

1.19 

0.46 

0.52 

0.76 

0.70 

1.02 

0.91 

0.42 

0.81 

Level 

Gender 

Age 

Education 

Activity status 

Source: National data 

It has been suspected, in the previous analysis, that data from Norway could not be comparable with 
those of other countries, possibly because of the use of telephone interviews. Indeed, Norway appears 
to be one of the countries with the lowest overall percentage overweight and obese, with the highest 
male  /  female  ratio  and  the  lowest  age  effect.  Norway  will  therefore  be  omitted  from  the  following 
analysis.  

·   As noted already, the highest percentages overweight and severely overweight are found in 
the UK and Germany, excluding Norway. The lowest levels are found in Austria (overweight) 
and Switzerland (severely overweight).  

·   The  ratio  between  male  and  female  percentages  is  highest  in  Belgium  (overweight)  and 
Ireland (obesity), while the lowest ratios are found in the Netherlands for both overweight and 
severely overweight. 

·   The strongest age effects are found in Germany, and the lowest in the Netherlands. 
·   The differential in percentage overweight between the highest and lowest education groups is 

highest in Italy and lowest in Austria.  

·   The differential between economically active and non-active  persons  is  smaller  than  it  is  for 

education.  

2.4.2 Body Mass Index (BMI) – ECHP 1998 
Data on height and weight were also collected as part of the 1998 round of the ECHP. From these 
data,  the  BMI  of  each  respondent  could  be  calculated.  Height  and  weight  data  were  obtained  from 
self-reports. 

B. Data collected 

Table 2.4.2.1. Wording of the questions by survey 

Year  Question 

Response categories 

1998 

What is your height without shoes? 
How much do you weigh without clothes and shoes? 

BMI < 18 
18 <= BMI < 18.5 
18.5 <= BMI < 25 
25 <= BMI < 27 
27 <= BMI < 30 
BMI >= 30 

Source: European Community Household Panel, 1998 

The data obtained from the ECHP were identical to those requested.  

1 See 1.2.2 above for the definition of gradients. 

78 

 
 
 
 
 
 
                                                          
 
 
 
 
 
 
 
 
Body Mass Index 

(cid:4) 

Table 2.4.2.2. Comparative summary and variable averages 

Country 

>= 27 

>= 30 

Question 

B 

DK 

EL 

E 

IRL 

I 

A 

P 

25.8 

23.5 

27.7 

29.2 

23.5 

21.3 

25.5 

26.2 

10.3 

8.9 

8.9 

12.5 

8.4 

7.7 

10.0 

9.6 

Same 

Same 

Same 

Same 

Same 

Same 

Same 

Same 

Source: European Community Household Panel, 1998 

C. Analysis 

1- Overall level 

2.4.2.1. Percentage of population 
overweight
(BMI >= 27) (ECHP, 1998)

2.4.2.2. Percentage of population severely 
overweight (BMI >= 30) (ECHP, 1998)

30

25

20

15

10

5

0

14

12

10

8

6

4

2

0

B

DK

EL

E

IRL

I

A

P

B

DK

EL

E

IRL

I

A

P

Source: European Community Household Panel, 1998 

The percentages of population overweight are similar among the countries considered. More diversity 
is found for the percentage severely overweight: 12.5% in Spain against 7.7% in Italy. 

79 

 
 
 
 
 
 
 
 
 
(cid:4) 

Body Mass Index 

2- Age and gender 
The  prevalence  of  overweight  is  generally  much  higher  among  males  than  among  females  in  the 
younger age groups. With increasing age, the percentage overweight among females tends to catch 
up with the male rate, and even overtakes it in the oldest age groups. 
Obesity  is  also  much  higher  for  males  in  the  15-24  age  group  in  Austria,  Spain,  Italy,  Portugal,  but 
equal in Denmark and much lower in other countries. While female obesity rates catch up with male 
rates with increasing age in the first four countries, they remain similar to male rates in other countries. 
However, by 54-65, obesity rates are generally higher than male rates. 

2.4.2.3. Percentage of female population 
overweight, by age (BMI >= 27) (ECHP, 1998)

2.4.2.4. Percentage of female population 
severely overweight, by age (BMI >= 30) 
(ECHP, 1998)

15-24 25-34 35-44 45-54 55-64 65-74 75-84 85+

B

I

DK

A

EL

P

E

IRL

2.4.2.5. Percentage of male population 
overweight, by age (BMI >= 27) (ECHP, 1998)

30

25

20

15

10

5

0

30

25

20

15

10

5

0

15-24 25-34 35-44 45-54 55-64 65-74 75-84

85+

B

I

DK

A

EL

P

E

IRL

2.4.2.6. Percentage of male population 
severely overweight, by age (BMI >= 30) 
(ECHP, 1998)

50.0
45.0
40.0
35.0
30.0
25.0
20.0
15.0
10.0
5.0
0.0

50.0

45.0
40.0

35.0
30.0

25.0

20.0
15.0

10.0
5.0

0.0

15-24 25-34 35-44 45-54 55-64 65-74 75-84 85+

B

IRL

DK

I

EL

A

E

P

Source: European Community Household Panel, 1998 

15-24 25-34 35-44 45-54 55-64 65-74 75-84

85+

B

I

DK

A

EL

P

E

IRL

The  relationship  between  age  and  the  percentage  overweight  or  obese  is  similar  to  that  found  with 
national data.  

80 

 
 
 
 
 
 
 
(cid:4) 

2.4.2.8. Percentage of population severely 
overweight (BMI >=30), by educational level 
(ECHP, 1998)

Body Mass Index 

3- Educational level

2.4.2.7. Percentage of population 
overweight
(BMI >= 27), by educational level (ECHP, 
1998)

35

30

25

20

15

10

5

0

16

14

12

10

8

6

4

2

0

B

DK

EL

E

IRL

I

A

P

B

DK

EL

E

IRL

I

A

P

Pre-primary and Primary (ISCED 0-2)

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Pre-primary and Primary (ISCED 0-2)

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Source: European Community Household Panel, 1998 

Higher educational levels are associated with lower rates of overweight and of obesity in all countries, 
with often widely different rates between those with pre-primary level and those with post-secondary 
education. 

4- Activity status 

2.4.2.10. Percentage of population severely 
overweight (BMI >=30), by activity status 
(ECHP, 1998)

2.4.2.9. Percentage of population 
overweight
(BMI >= 27), by activity status (ECHP, 1998)

35

30

25

20

15

10

5

0

14

12

10

8

6

4

2

0

B

DK

EL

E

IRL

I

A

P

B

DK

EL

E

IRL

I

A

P

Active

Inactive

Active

Inactive

Source: European Community Household Panel, 1998 

In  all  countries,  inactive  persons  tend  to  be  more  overweight  or  more  obese  than  the  economically 
active, except for those overweight in Greece. 

81 

 
 
 
 
 
 
 
 
(cid:4) 

5- Comparative study of countries2 

Body Mass Index 

Table 2.4.2.3. Analysis of gradients (Population overweight (BMI (cid:127) 27))  

Level 

Gender 

Age 

Education 

Activity status 

B 

1.01 

1.20 

1.06 

0.56 

0.78 

DK 

0.92 

1.14 

0.76 

0.48 

0.95 

EL 

1.08 

1.17 

1.12 

0.74 

1.06 

E 

1.14 

1.29 

1.31 

0.70 

0.90 

IRL 

0.92 

1.32 

0.84 

0.70 

0.93 

I 

0.83 

1.39 

1.02 

0.58 

0.92 

A 

0.99 

1.29 

1.28 

0.43 

0.88 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: European Community Household Panel, 1998 

Table 2.4.2.4. Analysis of gradients (Population severely overweight (BMI (cid:127) 30))  

Level 

Gender 

Age 

Education 

Activity status 

B 

1.11 

0.91 

0.98 

0.64 

0.77 

DK 

0.96 

0.99 

0.81 

0.38 

0.94 

EL 

0.96 

0.92 

0.97 

0.70 

0.99 

E 

1.35 

1.02 

1.49 

0.60 

0.87 

IRL 

0.91 

0.95 

0.77 

0.56 

0.83 

I 

0.83 

1.07 

1.01 

0.44 

0.86 

A 

1.08 

1.07 

1.37 

0.26 

0.72 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: European Community Household Panel, 1998 

P 

1.02 

1.25 

1.00 

0.58 

0.87 

P 

1.04 

0.97 

0.97 

0.57 

0.69 

·   Differences  among  countries  in  the  percentage  of  population  overweight  are  not  very  large, 
and wider variations are found for severe overweight, with an average obesity rate in Spain 
more than 60% higher than in Italy. 

·   Average  overweight  rates  are  higher  for  males  than  for  females  in  all  countries,  but  female 

and male obesity rates do not differ much.  

·   The age effect is much more pronounced in Spain than in Ireland or Denmark. 
·   Differences by educational level are very high in Austria and Denmark but less so in Spain. 
·   Differences by activity status are much lower. 

2 See 1.2.2 above for the definition of gradients. 

82 

 
 
 
 
 
 
 
 
 
                                                          
Body Mass Index 

(cid:4) 

A comparison of national and ECHP data 
Data on overweight and obesity are available from both national and ECHP sources for six countries 
(Belgium, Denmark, Ireland, Italy, Austria and Portugal). 

2.4.2.11. Percentage of population overweight (BMI >= 27) and 
severely overweight (BMI>=30): national vs 1998 ECHP data

32

28

24

20

16

12

8

4

0

a
t
a
d

l
a
n
o
i
t
a
N

0

4

8

12

B (27)
P (27)
A (30)

DK (27)
B (30)
P (30)

16
ECHP data

IRL (27)
DK (30)
 Diag

20

24

28

32

I (27)
IRL (30)

A (27)
I (30)

Source: European Community Household Panel (1998) & national data 

There  is  a  broad  agreement  between  the  two  sources  as  regards  the  overall  percentage  severely 
overweight, but less for the percentage overweight.  
Gender  – and age  – specific data from the two sources are also in broad agreement, but sometimes 
diverge significantly, without any clear pattern, possibly because of the reduced size of the samples.  
Finally,  the use of identical indicators appears to be insufficient for ensuring data comparability. Data 
collection  methods  matter,  too.  For  example,  it  is  known  that  data  on  height  and  weight  collected 
through telephone interviews lead to lower overweight and obesity rates than self-reported height and 
weight obtained from  face-to-face interviews, while physical measurements lead to still higher  – and 
much more valid  –  rates.  It may be that the rates found in  Germany and the UK are not particularly 
high, but that the reported rates found in other countries are in fact too low.  

83 

 
 
 
 
 
 
 
 
 
 
Smoking 

(cid:4) 

2.5. Smoking 
Tobacco  smoking  is  a  major  factor  of  mortality  and  morbidity  in  all  European  Countries,  and 
considerable efforts have been made to harmonise statistics in this field. 

Data were requested on three aspects of smoking: 

1.  Present smoking 
2.  Number of cigarettes smoked per day by present smokers 
3.  Former smoking 

The three sets of indicators will be examined in turn. 

2.5.1. Present smoking 
Data on the percentage of smokers at the time of interview are available from national surveys as well 
as from the ECHP. 

2.5.1.1. Present smoking (national data) 

A. Data requested 

Data were requested on: 

1.  Number of respondents who do not smoke (non-smokers) 
2.  Number of respondents who smoke occasionally (smokers) 
3.  Number of respondents who smoke daily (smokers) 

B. Data collected 

Table 2.5.1.1.1. Wording of the questions by survey 

Country  Year 

Question 

B 

1997 

Do you smoke? Yes, every day/ Yes, now and then/ No 

DK 

2000 

Do you smoke? Yes, daily/ Yes, but some days I don't smoke / No 

D 

1998 

Did you use to or do you smoke now? Have never smoked (apart 
from trying now and then)/ I smoke now: Yes, daily/  Yes, 
occasionally/ I used to smoke, but have not smoked for at least a 
year/ I have stopped smoking in the last 12 months 

Response categories 
provided 

Do not smoke 
Smoke occasionally 
Smoke daily 

Do not smoke 
Smoke occasionally 
Smoke daily 

Never smoked 
Smoke occasionally 
Smoke daily 
Former smoker 

E 

1997 

Could you please tell me if you smoke?  Yes, smokes every day/ Yes, 
but not every day/ No, but used to smoke / Does not smoke and has 
never smoked habitually 

Do not smoke 
Smoke occasionally 
Smoke daily 

F 

2000  Do you smoke regularly? Yes/ no 

IRL 

1998 

Do you smoke cigarettes now? No/ Yes, regularly/ Yes, occasionally 
(usually less than 1 per day) 

I 

2000 

Do you currently smoke? Yes/ No, but I smoked in the past/ No, I 
never smoked 

Do not smoke 
Smoke (daily or 
occasionally) 

Do not smoke 
Smoke occasionally 
Smoke daily 

Do not smoke 
Smoke occasionally 
Smoke daily 

NL 

2000 

Do you smoke? 
No, but I used to smoke every day/ No, but I used to smoke now and 
then/ No I have never smoked 

Do not smoke 
Smoke occasionally 
Smoke daily 

A 

1999 

Do you smoke? Yes, occasionally/ yes, daily up to 10 cigarettes/ yes, 
daily between 11 and 20 cigarettes/ yes, daily more than 20 
cigarettes / no, I quit smoking/ no, I never smoked. 

Do not smoke 
Smoke occasionally 
Smoke daily 

P 

1999  Do you [does she/he] smoke? Daily/ occasionally/ don’t smoke 

Do not smoke 
Smoke occasionally 
Smoke daily 

85 

 
 
 
 
 
 
 
 
 
 
  
 
(cid:4) 

S 

2000  Do you smoke daily? Do you smoke from time to time? Yes/ no 

Smoking 

Do not smoke 
Smoke occasionally 
Smoke daily 

UK 

2000  Do you smoke cigarettes at all nowadays? Yes/ no 

Yes / no 

IS 

1998 

How often do you usually smoke cigarettes? Do not smoke/less than 
weekly/ once a week/ 2-3 times a week/ 4-6 times a week/ daily 

NO 

1998 

Do you smoke? Yes/ no 
Do you smoke daily or occasionally? Yes/ no 

Do not smoke 
Smoke occasionally 
Smoke daily 

Do not smoke 
Smoke occasionally 
Smoke daily 

CH 

1997 

Do you smoke, if only rarely? Yes/no Have you ever smoked 
regularly for more than 6 months? Yes/no How many years ago did 
you quit smoking? 

Do not smoke 
Smokers 
Former smoker 

Source: National data 

All countries ask a series of questions in order to distinguish different categories of smokers, former 
smokers  and  those  who  have  never  smoked.  The  way  the  questions  are  asked  differs  among 
countries,  as  well  as  their  sequence  and  the  response  categories,  which  is  likely  to  affect  the 
comparability of the results. 
Some countries simply ask respondents the simple question “Do you smoke?”. Others attempt first to 
determine those who have ever smoked and then to determine which of them are present smokers. In 
the same way, some of the occasional present smokers may well reply ‘No’ to the question “Do you 
smoke?”, while they would have replied ‘Yes’ to the question (used in Switzerland) “Do you smoke, if 
only rarely?”.  
Most  surveys  collect  separate  data  on  occasional  smokers  and  on  daily  smokers.  Exceptions  are 
France and Switzerland, where those who smoke daily are not distinguished from those who smoke 
occasionally. As a result, analysis will focus on the percentage of present smokers (daily or occasional 
smokers). Analysis of the percentage of persons smoking 20 cigarettes or more per day and of former 
smokers will be presented further. 

Table 2.5.1.1.2. Comparative summary and variable averages 

Country 

Present Smokers 
(daily or occasionally) 

Remarks 

More detailed initial question 

B 

DK 

D 

E 

F 

IRL 

I 

NL 

A 

P 

S 

UK 

IS 

NO 

CH 

Source: National data 

30.3 

37.0 

32.0 

35.7 

26.1 

30.8 

25.2 

24.8 

45.1 

20.3 

29.9 

26.9 

35.3 

41.6 

33.2 

86 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

2.5.1.1.1. Percentage of present smokers in the population

Smoking 

C. Analysis 

1- Overall level 

50

45

40

35

30

25

20

15

10

5

0

B

DK

D

E

F

IRL

I

NL

A

P

S

UK

IS

NO

CH

Source: National data 

The percentage of present smokers in the population is highest in Austria (45%), followed by Norway 
and Iceland.  The  lowest  percentage  of  present  smokers  is  found  in  Portugal  (20%)  but  possibly  the 
use  of  proxy  responses  lead  to  an  underestimation  of  the  number  of  smokers.  Generally,  countries 
asking a series of detailed questions (Austria, Sweden, Norway and Switzerland) or determining first 
those persons who have ever smoked (Germany) present higher rates of smoker. 

2- Age and gender 
Women  tend  to  smoke  less  than  men  in  all  countries  except  in  Sweden  and  Iceland  where  the 
average smoking rates are very similar. 

2.5.1.1.2. Percentage of present smokers
 in the female population, by age

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

B

A

DK

P

D

S

E

UK

F

IS

IRL

NO

75-84

I

CH

85+

NL

Source: National data 

87 

 
 
 
 
 
 
 
(cid:4) 

Smoking 

2.5.1.1.3. Percentage of present smokers 
in the male population, by age

60

50

40

30

20

10

0

15-24

25-34

B

A

DK

P

35-44
D

S

45-54
E

UK

55-64
F

IS

65-74
IRL

NO

75-84
I

CH

85+
NL

Source: National data 

There is a wide variation in smoking rates among the youngest age group (age 15-24), from 12.5% for 
females  in  Portugal  to  57%  for  males  in  Austria.  In  most  countries,  the  percentage  of  smokers  first 
increases with age to reach a maximum at 25-34, 35-44 or, less often, 45-54. The percentages then 
decline to much lower levels, reaching a minimum for the 85+ age group. 
The strength of the age effect varies among countries: for example, there is much less variation by 
age of smoking rates among the Danish population, especially males, than in other countries. 
Care must be taken not to interpret these age data as life profiles: for example, the smoking rate ten 
years earlier of individuals aged 45-54 years at the time of the survey certainly differ from the smoking 
rate of individuals aged 35-44 at the time of the survey. 
In  most  countries,  the  percentage  of  male  and  female  smokers  vary  more  or  less  in  parallel  over 
successive age groups, up to the 75-84 age group, the female percentages being almost always lower 
than  the  male  ones.  In  other  countries  (especially  Portugal,  Spain  or  Belgium),  the  percentages  of 
female  smokers  decline  considerably  faster  with  age  than  the  corresponding  percentages  among 
males.

88 

 
 
 
 
 
Smoking 

3- Educational level 

2.5.1.1.4. Percentage of present smokers, by educational level

(cid:4) 

60

50

40

30

20

10

0

B

D

DK
Pre-primary and Primary (ISCED 0-1)

IRL

E

F

I

NL

A

P

S

UK

IS

NO

CH

Secondary (ISCED 2)

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Austria, Switzerland, Denmark and Spain: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Ireland: Primary, Secondary (Secondary + Upper secondary), Tertiary 
Source: National data 

After adjustment for age and gender, the percentage of smokers generally declines with educational 
level. However, Portugal and Austria appear as exceptions. It may be noted that in several countries, 
the  percentage  of  smokers  among  those  with  secondary  education  is  higher  than  or  similar  to  the 
percentage of those with primary education, the decline appearing only for higher levels of education. 

4- Activity status 

2.5.1.1.5. Percentage of present smokers, by activity status

50

45

40

35

30

25

20

15

10

5

0

B

DK

D

E

F

IRL

I

NL

A

S

UK

IS

NO

CH

Source: National data 

Active

Inactive

There is no clear relationship between smoking and economic activity. In nine countries, the smoking 
rate is higher among the active population, but the reverse can be observed in the other six countries. 

89 

 
 
 
 
 
 
 
 
 
(cid:4) 

5- Comparative study of countries1 

Smoking 

Table 2.5.1.1.3. Analysis of gradients 

B  DK  D 

E 

F 

IRL 

I 

NL  A 

P 

S  UK 

IS  NO  CH 

Level 

Gender 

Age 

0.98  1.20  1.04  1.16  0.85  1.00  0.82  0.81  1.46  0.66  0.97  0.87  1.15  1.35  1.08 

1.54  1.13  1.29  1.58  1.46  1.04  1.69  1.12  1.18  3.27  0.97  1.09  0.99  1.06  1.31 

0.78  0.26  1.46  1.07  1.64  0.93  0.77  0.59  1.08  1.03  0.82  0.76  0.79  1.21  1.14 

Education 

0.62  0.65  0.60  0.91  0.74  0.56  0.88  0.76  1.04  1.42  0.69  0.48  0.49  0.56  0.84 

Activity status 

0.71  0.91  1.03  0.96  1.50  0.78  1.40  1.10  1.14 

- 

1.04  0.86  1.03  0.93  1.10 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: National data 

·   As  noted  earlier,  average  smoking  rates  vary  widely  among  countries.  Portugal,  with  the 
lowest overall level, is also the country where the gender and education effects are strongest.  
·   A  strong  gender  effect  (women  smoking  less  than  men)  is  also  found  in  other  Southern 
European countries (Spain and Italy), but also in Belgium. The opposite (women smoking as 
much or slightly more than men) is found in Sweden and Iceland.  
·   The age effect is strongest in France but much lower in Denmark.  
·  

In  the  same  way,  education  has  much  less  influence  on  the  smoking  status  in  the  UK  and 
Iceland than in Portugal.  

·   Finally,  the  influence  of  activity  status  is  highest  in  Portugal  where  the  economically  active 
have a 89% higher probability to smoke than the inactive. At the opposite end of the spectrum, 
the Belgians who are economically active have a 29% lower probability of smoking than the 
inactive peers. 

2.5.1.2. Present smoking (ECHP) 

B. Data collected 

Table 2.5.1.2.1. Wording of the questions by survey 

Topic 

Year 

Question 

Response categories 

Present 
smoking 

1998  Do you smoke?  

Do not smoke 
Smoke occasionally 
Smoke daily 

Source: European Community Household Panel, 1998 

Table 2.5.1.2.2. Comparative summary and variable averages 

Country 

Present smokers 
(daily or occasionally) 

B 

DK 

EL 

E 

IRL 

I 

A 

P 

29.3 

39.5 

45.9 

34.1 

32.8 

30.0 

31.4 

21.3 

Source: European Community Household Panel, 1998 

1 See 1.2.2 above for the definition of gradients. 

90 

 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
                                                          
(cid:4) 

2.5.1.2.1. Percentage of present smokers in the population 
(ECHP, 1998)

Smoking 

C. Analysis 

1- Overall level 

50

45

40

35

30

25

20

15

10

5

0

B

DK

EL

E

IRL

I

A

P

Source: European Community Household Panel, 1998 

As with national data, the percentage of present smokers in the ECHP 1998 ranges from 20% to 45%. 

2- Age and gender 

2.5.1.2.2. Percentage of female population who smoke,
 by age (ECHP, 1998)

80

70

60

50

40

30

20

10

0

15-24

25-34

B

DK

35-44

EL

45-54

E

55-64

65-74

IRL

I

A

75-84

P

85+

Source: European Community Household Panel, 1998 

91 

 
 
 
 
 
 
 
 
 
 
(cid:4) 

Smoking 

2.5.1.2.3. Percentage of male population who smoke, 
by age (ECHP, 1998)

80

70

60

50

40

30

20

10

0

15-24

B

25-34
DK

35-44
EL

45-54
E

55-64

IRL

I

65-74
A

75-84
P

85+

Source: European Community Household Panel, 1998 

The age pattern of the percentage of persons smoking is similar in all countries, and does not differ 
from the pattern found from national data. 

3- Educational level 

2.5.1.2.4. Percentage of population who smoke, 
by educational level (ECHP, 1998)

50

45

40

35

30

25

20

15

10

5

0

B

DK

E

IRL

I

A

P

Pre-primary and Primary (ISCED 0-2)

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Austria, Denmark and Spain: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Ireland: Primary, Secondary (Secondary + Upper secondary), Tertiary 
Source: European Community Household Panel, 1998 

The  pattern  of  decline  in  smoking  with  increasing  educational  level  is  similar  to  that  found  from 
national data.  

92 

 
 
 
 
 
 
 
 
 
 
Smoking 

4- Activity status 

2.5.1.2.5. Percentage of population who smoke,
by activity status (ECHP, 1998)

(cid:4) 

60

50

40

30

20

10

0

B

DK

EL

E

IRL

I

A

P

Source: European Community Household Panel, 1998 

Active

Inactive

As  with  national  data,  there  is  no  clear  correlation  between  economic  activity  status  and  present 
smoking.  

5- Comparative study of countries2 

Table 2.5.1.2.3. Analysis of gradients 

Level 

Gender 

Age 

Education 

Activity status 

B 

0.91 

1.55 

0.77 

0.58 

1.02 

DK 

1.23 

1.23 

0.38 

0.64 

0.94 

EL 

1.43 

1.65 

1.21 

0.85 

1.29 

E 

1.06 

1.81 

1.39 

0.77 

1.30 

IRL 

1.02 

1.05 

0.59 

0.59 

1.07 

I 

0.93 

1.94 

0.92 

0.81 

1.47 

A 

0.98 

1.43 

1.19 

0.59 

1.34 

P 

0.66 

3.48 

0.83 

0.87 

1.28 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: European Community Household Panel, 1998 

·   The percentage of current smokers varies widely among the countries studied, Greece having 

the highest rate and Portugal the lowest. 

·   Portugal also has the strongest gender effect, with considerably higher rates for men than for 
women.  However,  a  very  different  picture  emerges  in  Ireland,  where  the  male  and  female 
rates are almost equal. 

·   The age effect is strongest in Greece and Austria, but extremely weak in Denmark. 
·   After adjustment for age and gender, higher education is associated with lower smoking rates, 

particularly in Belgium and Ireland. 

·   Economic activity is more associated with smoking than inactivity, particularly in Italy. But little 

difference between the economically active and inactive is found in Denmark. 

2 See 1.2.2 above for the definition of gradients. 

93 

 
 
 
 
 
 
 
 
 
 
                                                          
(cid:4) 

Smoking 

A comparison of national and ECHP data by gender and age groups 
Data on the percentage of smokers are available from both national surveys and the 1998 ECHP for 
seven countries. 

2.5.1.2.6. Comparison of percentage of smokers: 
national data vs 1998 ECHP data (all population)

a
t
a
d

l
a
n
o
i
t
a
N

50
45

40

35
30

25

20
15

10

5
0

0

5

10

15

20

25

30

35

40

45

50

1998 ECHP data

B

DK

E

IRL

I

A

P

Diagonal

Source: European Community Household Panel (1998) & national data 

For all countries except Austria, the overall percentages of present smokers are rather similar in both 
sources, although fewer smokers are found in the national survey in Italy (25.2%) than in the ECHP 
survey  (30.0%).  The  population  aged  85  years  and  above  was  not  covered  in  the  1998  ECHP  in 
Austria, so that the national survey, including an age group with few smokers, could be expected to 
display a slightly lower overall rate. But what is, in fact, found is quite the opposite.  

2.5.1.2.7. Comparison of percentage of smokers: 
national data vs 1998 ECHP data (by age adjusted on gender)

a
t
a
d

l

a
n
o
i
t
a
N

60
55
50
45
40
35
30
25
20
15
10
5
0

0

5

10

15

20

35
30
25
1998 ECHP data

40

45

50

55

60

B

DK

E

IRL

I

A

P

Diagonal

Source: European Community Household Panel, 1998 

There  is  in  most  countries  a  reasonably  good  correspondence  between  national  and  ECHP  1998 
percentages  of  smokers  by  gender  and  age,  and  many  of  the  discrepancies  can  be  attributed  to 
problems of sample size.  

94 

 
 
 
 
 
 
 
 
 
 
 
Smoking 

(cid:4) 

Differences between national and ECHP 1998 prevalence rates in Austria appear systematic: for all 
age groups, national figures by age and gender are considerably higher than ECHP data. 

2.5.2. Number of cigarettes smoked per day 
Even if they have the same percentages of smokers, two countries will face very different public health 
consequences  if  one  includes  only  occasional  or  light  smokers,  while  tobacco  users  in  the  other 
country  are  heavy  smokers.  Hence  the  importance  of  knowing  the  percentage  of  heavy  smokers  –  
defined as smoking 20 cigarettes or more per day – among current smokers. 

2.5.2.1. Number of cigarettes smoked per day (national data) 

A. Data requested 

Number of cigarettes smoked per day 

1.  For smokers: number of respondents who smoke less than 20 cigarettes per day 
2.  For smokers: number of respondents who smoke 20 cigarettes per day or more 

B. Data collected 

Table 2.5.2.1.1. Wording of the questions by survey 

Country  Year 

Question 

Response categories 
constructed 

B 

DK 

D 

E 

F 

IRL 

I 

NL 

A 

P 

UK 

IS 

CH 

How many cigarettes do you smoke on average 
per day?  

Less than 20 cigarettes per day 
20 cigarettes and more per day 

1997 

2000  How much do you smoke a day on average?  

Less than 20 cigarettes per day 
20 cigarettes and more per day 

How much do you smoke on average each 
day? 

1998 

Less than 20 cigarettes per day 
20 cigarettes and more per day 

How many cigarettes do you smoke on an 
average day?  

1997 

Less than 20 cigarettes per day 
20 cigarettes and more per day 

How many of the following do you smoke per 
day? 

2000 

Less than 20 cigarettes per day 
20 cigarettes and more per day 

In a day how many cigarettes do you usually 
smoke? 

1998 

Less than 20 cigarettes per day 
20 cigarettes and more per day 

2000  How much do you currently smoke? 

Less than 20 cigarettes per day 
20 cigarettes and more per day 

How many cigarettes do you smoke on an 
average day? 

2000 

Less than 20 cigarettes per day 
20 cigarettes and more per day 

Do you smoke? Yes, occasionally/ yes, daily up 
to 10 cigarettes/ yes, daily between 11 and 20 
cigarettes/ yes, daily more than 20 cigarettes / 
no, I quit smoking/ no, I never smoked. 

1999 

Less than 20 cigarettes per day 
20 cigarettes and more per day 

How many cigarettes do you [does she/he] 
smoke per day, on average? 

1999 

Less than 20 cigarettes per day 
20 cigarettes and more per day 

About how many cigarettes a day do you 
usually smoke on weekdays and about how 
many cigarettes a day do you usually smoke on 
weekends?  

2000 

Less than 20 cigarettes per day 
20 cigarettes and more per day 

How many cigarettes do you usually smoke 
each day? 

1998 

Less than 20 cigarettes per day 
20 cigarettes and more per day 

How many cigarettes do you smoke on average 
every day? 

Less than 20 cigarettes per day 
20 cigarettes and more per day 

1997 

Source: European Community Household Panel, 1998 

For  all  countries  except  Austria,  the  variable  studied  is  the  percentage  of  smokers,  including 
occasional  smokers,  who  smoke  20  cigarettes  or  more  per  day.  In  Austria,  the  percentage  is  given 
only over daily smokers. 

95 

 
 
 
 
 
 
 
 
 
 
Smoking 

2.5.2.1.1. Percentage of present smokers who smoke 
more than 20 cigarettes per day

(cid:4) 

C. Analysis 

1- Overall level 

70

60

50

40

30

20

10

0

B

DK

D

E

F

IRL

I

NL

A

P

UK

IS

CH

Source: European Community Household Panel, 1998 

In most countries, about 20 to 40% of smokers smoke more than 20 cigarettes a day. The percentage 
is highest in Portugal, and lowest in Austria. It was noted earlier that among countries for which data 
are  available,  in  Portugal  and  Austria,  smoking  prevalence  is  lowest  and  highest,  respectively.  The 
result for Austria is somewhat surprising, since the percentage relates only to daily smokers.  
The following graph displays the relationship between the two variables for countries for which both 
data are available. 

2.5.2.1.2. Relationship between smoking prevalence in the 
population and heavy smoking among smokers

0
2
n
a
h
t

e
r
o
m
g
n
i
k
o
m
s
s
r
e
k
o
m
s

f
o

%

y
a
d
r
e
p
s
e
t
t
e
r
a
g
i
c

70.0

60.0

50.0

40.0

30.0

20.0

10.0

0.0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

% of smokers in the population

Source: European Community Household Panel (1998) & national data 

From the graph, it appears that in countries with a low percentage of smokers, these tend to be heavy 
smokers.  Conversely,  countries  with  high  smoking  rates  tend  to  have  lighter  smokers.  As  a 
consequence, a decrease in the prevalence of smoking in a country cannot be equated with an equal 
decrease in health risks. Since smoking prevalence and smoking intensity appear to move in opposite 

96 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Smoking 

(cid:4) 

directions,  it  is  of  interest  to  examine  the  percentage  of  heavy  smokers  (more  than  20  cigarettes  a 
day) in the total population. 

2.5.2.1.3. Percentage of population who smoke more than 20 
cigarettes per day

14

12

10

8

6

4

2

0

B

DK

D

E

F

IRL

I

NL

A

P

UK

IS

CH

Source: European Community Household Panel, 1998 

A  rather  different  picture  appears  if  the  analysis  concentrates  on  heavy  smokers,  rather  than  all 
smokers.  In  six  countries,  the  percentage  of  heavy  smokers  ranges  from  11  to  14%,  while  in  Spain 
and Austria heavy smokers account for fewer than 7%.  

2- Age and gender 

2.5.2.1.4. Percentage of female smokers who smoke  
more than 20 cigarettes per day, by age

90

80

70

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

A

DK

P

D

UK

E

IS

F

CH

IRL

I

NL

Source: European Community Household Panel, 1998 

Heavy smoking among females tends to increase with age, reaches a maximum between 45-54 and 
declines  afterwards.  Spain  and  Austria  present,  however,  a  very  different  pattern:  in  Spain,  the 
percentage  of  heavy  smokers  increases  up  to  86%  in  the  65-74  age  group  (no  data  for  older  age 
groups),  very  probably  because  of  the  restricted  size  of  the  sample.  In  Austria,  the  percentage  of 
heavy  smokers  among  female  smokers  is  relatively  much  lower  than  in  other  countries,  especially 

97 

 
 
 
 
 
 
 
 
(cid:4) 

Smoking 

above 55. Smoking prevalence at 85 and above is generally small, so that erratic movements of the 
curves after 75-84 are due to small sample sizes.  

2.5.2.1.5. Percentage of male smokers who smoke 
more than 20 cigarettes per day, by age

90

80

70

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

A

DK

P

D

UK

E

IS

F

CH

IRL

I

NL

Source: European Community Household Panel, 1998 

Male  smokers  are  generally  more  likely  to  be  heavy  smokers  than  female  smokers,  and  the 
relationship  with  age  is  less  homogeneous  than  among  females.  Here  also,  the  patterns  of  heavy 
smoking among males in Spain and Austria appear to be different from those of other countries.  

3- Educational level 

2.5.2.1.6. Percentage of smokers who smoke more
 than 20 cigarettes per day, by educational level

70

60

50

40

30

20

10

0

B

DK

D

E

F

IRL

I

NL

A

P

UK

IS

CH

Pre-primary and Primary (ISCED 0-1)

Secondary (ISCED 2)

Upper secondary (ISCED 3)

Post-secondary (ISECD 4-6)

Austria, Switzerland, Denmark and Spain: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Ireland: Primary, Secondary (Secondary + Upper secondary), Tertiary 
Source: European Community Household Panel, 1998 

98 

 
 
 
 
 
 
 
 
 
Smoking 

(cid:4) 

After adjustment for gender and age, smokers with a relatively higher educational level are less likely 
to  be  heavy  smokers  than  those  with  a  lower  educational  level.  As  pointed  out  earlier,  the  opposite 
pattern in Spain may be due to the size of the sample. 

4- Activity status 

2.5.2.1.7. Percentage of smokers who smoke more than 20 
cigarettes per day, by activity status

70

60

50

40

30

20

10

0

B

DK

D

E

F

IRL

I

NL

A

P

UK

IS

CH

Source: European Community Household Panel, 1998 

Active

Inactive

Among smokers, there is no clear relationship between economic activity status and the probability of 
being a heavy smoker.  

5- Comparative study of countries3 

Table 2.5.2.1.2. Analysis of gradients 

B 

DK 

D 

E 

F 

IRL 

I 

NL 

A 

P 

UK 

IS 

Level 

Gender 

Age 

1.11  0.98  1.23  0.54  1.00  1.21  1.09  0.94  0.41  1.77  0.90  0.99 

1.24  1.53  1.54  0.54  1.39  1.19  1.80  1.00  1.88  1.69  1.41  1.47 

1.01  1.08  1.14  1.22  1.08  1.05  1.21  0.65  0.54  1.03  1.23  1.33 

Education 

0.55  0.68  0.73  1.48  0.67  0.65  0.72  0.50  1.03  0.88  0.69  0.48 

Activity status 

0.78  1.01  0.96  0.79  1.11  0.96  1.09  0.96  1.20 

- 

0.79  1.02 

CH 

1.19 

1.38 

1.01 

0.71 

1.08 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: National data 

·   Among current smokers, the percentage of those who smoke 20 or more cigarettes per day 

varies widely among countries 

·   Except in Spain, male smokers are systematically more likely to be heavy smokers 
·   The percentage of heavy smokers among all smokers tends to increase then decrease with 

age, the age pattern being much more pronounced in Iceland than in Austria 

·   More educated smokers are less likely to be heavy smokers, but this is not the case in Spain. 
·   There is no clear relationship with the activity status of smokers. 

2.5.2.2. Number of cigarettes smoked per day (ECHP) 

In the 1998 ECHP, current and past smokers were asked how many cigarettes they were smoking or 
had been smoking daily. From these data, the percentage of persons smoking 20 cigarettes or more 
per  day  among  present  or  past  smokers  could  be  derived.  Unfortunately,  the  percentage  among 
present smokers could not be obtained. 

3 See 1.2.2 above for the definition of gradients. 

99 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                          
(cid:4) 

B. Data collected 

Smoking 

Table 2.5.2.2.1. Wording of the questions by survey 

Topic 

Year 

Question 

Response categories 
constructed 

Cigarettes 
smoked 

1998 

Number of cigarettes smoked per day (currently or in the 
past)  

Less than 20 cigarettes  
20 cigarettes or more 

Source: European Community Household Panel, 1998 

Table 2.5.2.2.2. Comparative summary and variable averages 

Country 

Cig>=20 (currently or in the past ) 

B 

DK 

EL 

E 

IRL 

I 

A 

P 

UK 

52.7 

43.5 

77.3 

57.9 

56.0 

46.0 

61.3 

66 

40 

Source: European Community Household Panel, 1998 

C. Analysis 

Analysis refers to the percentage of the population who smoke at present or smoked in the past 20 or 
more cigarettes a day. 

1- Overall level 

2.5.2.2.1. Percentage of population who smoke or smoked 20 or 
more cigarettes per day (ECHP, 1998)

80

70

60

50

40

30

20

10

0

B

DK

EL

E

IRL

I

A

P

UK

Source: European Community Household Panel, 1998 

The percentage of heavy smokers among all smokers varies less among countries than is apparent 
from national data. 

100 

 
 
 
 
 
 
 
 
 
 
 
 
Smoking 

2- Age and gender 

2.5.2.2.2. Percentage of female population who smoke or 
smoked 20 or more cigarettes per day, by age (ECHP, 1998)

(cid:4) 

100

90

80

70

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

DK

EL

E

IRL

I

A

P

UK

Source: European Community Household Panel, 1998 

For females, data from the ECHP 1998 appear less homogenous than national data, with more variety 
among countries. 

2.5.2.2.3. Percentage of male population who smoke or smoked 
20 or more cigarettes per day, by age (ECHP, 1998)

100

90

80

70

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

DK

EL

E

IRL

I

A

P

UK

Source: European Community Household Panel, 1998 

The age pattern for men is similar to that found in national surveys, but 1998 ECHP data are more 
homogeneous than national data (the opposite was found with data for women). 

101 

 
 
 
 
 
 
 
 
(cid:4) 

3- Educational level 

Smoking 

2.5.2.2.4. Percentage of population who smoke or smoked 20 or 
more cigarettes per day, by educational level (ECHP, 1998)

80

70

60

50

40

30

20

10

0

B

DK

EL

E

IRL

I

A

P

UK

Pre-primary and Primary

Upper secondary

Post-secondary

Source: European Community Household Panel, 1998 

As in the national surveys, more educated smokers are less likely to be heavy smokers, independent 
of age and gender.  

4- Activity status 

2.5.2.2.5. Percentage of population who smoke or smoked  20 
or more cigarettes per day, by activity status (ECHP, 1998)

80

70

60

50

40

30

20

10

0

B

DK

EL

E

IRL

I

A

P

UK

Active

Inactive

Source: European Community Household Panel, 1998 

No systematic relationship with activity status can be observed. 

102 

 
 
 
 
 
 
 
 
 
Smoking 

5- Comparative study of countries4 

(cid:4) 

Table 2.5.2.2.3. Analysis of gradients 

Level 

Gender 

Age 

Education 

Activity status 

B 

0.94 

1.15 

1.00 

0.84 

0.98 

DK 

0.78 

1.57 

1.08 

0.54 

0.72 

EL 

1.38 

1.40 

0.30 

0.96 

1.02 

E 

1.03 

1.62 

0.94 

0.94 

1.15 

IRL 

1.00 

1.32 

1.07 

1.03 

1.06 

I 

0.82 

2.03 

0.78 

0.81 

1.09 

A 

1.09 

1.52 

0.96 

1.02 

1.11 

P 

1.18 

1.63 

0.64 

0.92 

1.03 

UK 

0.71 

1.19 

1.09 

0.84 

0.93 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: European Community Household Panel, 1998 

The conclusions from the 1998 ECHP data are similar to those obtained from national surveys. 

A comparison of national and 1998 ECHP data 
As  already  indicated,  national  data  relate  to  the  percentage  of  present  smokers  who  smoke  20 
cigarettes or more per day, while 1998 ECHP data relate to the percentage of present or past smokers 
who smoke of 20 cigarettes or more per day. Furthermore, past smokers include people who just tried 
once or twice to smoke. The indicators from the two data sources are therefore not comparable. 

2.5.2.2.6. Comparaison of percentage of persons who smoke 
more than 20 cigarettes a day: national data (% of current 
smokers) and 1998 ECHP data (% of current and former 
smokers)

a
t
a
d

l

a
n
o
i
t
a
N

70

60

50

40

30

20

10

0

0

B

10

20

30

40

ECHP 1998 data

DK

E

IRL

I

A

50

P

60

UK

70

Diag

Source: European Community Household Panel, 1998 

Indeed, national data, which include former smokers, shows a lower percentage of persons smoking 
20 cigarettes or more per day than does the ECHP, with very large differences in Spain and Austria. 
The graph tends to show that there is a higher percentage of heavy smokers among present smokers 
than among former smokers. 

4 See 1.2.2 above for the definition of gradients. 

103 

 
 
 
 
 
 
 
 
 
 
 
                                                          
(cid:4) 

2.5.3. Former smokers 

2.5.3.1. Former smokers (national data) 

A. Data requested 

Former smoking: 

Smoking 

1.  For non-smokers: number of respondents who never smoked 
2.  For non-smokers: number of respondents who used to smoke occasionally 
3.  For non-smokers: number of respondents who used to smoke daily 

B. Data collected 

Table 2.5.3.1.1. Wording of the questions by survey 

Country  Year 

Question 

B 

1997  Did you ever smoke? Yes, every day / yes, now and then / no 

Response categories 
constructed 

Never smoked 
Smoked daily 
Smoked occasionally 

DK 

2000 

Have you ever been a smoker? 
Yes, I quit within the past 6 months / Yes, I quit more than 6 
months ago  / No   

Never smoked 
Smoked (daily or occasionally) 

D 

1998 

Did you use to or do you smoke now?       
Have never smoked (apart from trying now and then)/ 
smoke now: Yes, daily; Yes, occasionally /I used to smoke, 
but have not smoked for at least a year / I have stopped 
smoking in the last 12 months 

Never smoked 
Have not smoked for at least a 
year 
Have stopped smoking in the 
last 12 months 

1997 

Could you please tell me if you smoke?  Yes, smokes every 
day/ 
Yes, but not every day/ No, but used to smoke / Does not 
smoke and has never smoked habitually 

2000  Did you used to smoke? No, never/Yes 

E 

F 

Never smoked on daily basis 
Smoked (daily or occasionally) 

Never smoked 
Smoked (daily or occasionally) 

IRL 

1998 

Did you ever smoke cigarettes in the past? No, never / 
Current smoker/ Occasionally (usually less than one cigarette 
per day)/ Yes, regularly 

I 

2000 

NL 

2000 

A 

1999 

Do you currently smoke? 
Yes/ No, but I used to…/No, I’ve never smoked… 
How much do you currently smoke or did you used to 
smoke? 
Every day/ Occasionally 
Do you smoke? 
No, but I used to smoke every day/ No, but I used to smoke 
now and then/ No, I have never smoked. 
Do you smoke? Yes, occasionally/ yes, daily up to 10 
cigarettes/ yes, daily between 11 and 20 cigarettes/ yes, daily 
more than 20 cigarette/ no, I quit smoking/ no, I never 
smoked. 

P 

1999 

Have you [has she/he] ever smoked? 
Daily/ occasionally/ never smoked 

S 

2000 

Have you smoked daily at any previous period in your life? 
For approximately how many years have you smoked daily? 
Deduct any fairly long periods when this was not the case. 
Never smoked daily/ used to smoke daily 

UK 

2000  Have you ever smoked cigarettes regularly? Yes/ no 

IS 

1998  Have you ever smoked cigarettes daily? Yes/ no 

Never smoked 
Smoked daily 
Smoked occasionally 

Never smoked 
Smoked daily 
Smoked occasionally 

Never smoked 
Smoked daily 
Smoked occasionally 

Never smoked 
Smoked (daily or occasionally) 

Never smoked 
Smoked daily 
Smoked occasionally 

Never smoked on daily basis 
Smoked daily 

Never smoked 
Smoked 
Never smoked  
Smoked 

Source: National data 

104 

 
 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

2.5.3.1.1. Percentage of former smokers in the population

Smoking 

C. Analysis 

1- Overall level 

50

45

40

35

30

25

20

15

10

5

0

B

DK

D

E

F

IRL

I

NL

A

P

S

UK

IS

Source: National data 

The  percentage  of  former  smokers  in  the  total  population  varies  widely  among  countries,  from  a 
maximum of 47% in the Netherlands to 17% in Portugal. 

2- Age and gender 

2.5.3.1.2. Percentage of former smokers among females,
 by age

70

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

B

NL

DK

A

D

P

E

S

F

UK

IRL

IS

85+

I

Source: National data 

The  age  pattern  of  former  smokers  in  the  female  population  also  varies  among  countries.  In  many 
cases, the percentage increases with age, reaches a maximum and declines at older ages. But the 
pattern of the relationship differs considerably among countries.  

105 

 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

Smoking 

2.5.3.1.3. Percentage of former smokers among males, 
by age

100

90

80

70

60

50

40

30

20

10

0

15-24
B

NL

25-34

35-44

45-54

55-64

65-74

75-84

DK

A

D

P

E

S

F

UK

IRL

IS

85+

I

Source: National data 

There is much more homogeneity among countries in the age pattern of former smokers in the male 
population.  

3- Educational level 

2.5.3.1.4. Percentage of former smokers in the population,
 by educational level

50

45

40

35

30

25

20

15

10

5

0

B

D

DK
Pre-primary and Primary (ISCED 0-1)

IRL

E

F

I

NL

P
Secondary (ISCED 2)

A

S

UK

IS

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Austria,  Denmark and Spain: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Ireland: Primary, Secondary (Secondary + Upper secondary), Tertiary 
Source: National data 

After adjustment for gender and age, no clear pattern appears in the relationship between educational 
status and the percentage of former smokers.  

106 

 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

2.5.3.1.5. Percentage of former smokers in the population,
 by activity status

Smoking 

4- Activity status 

50

45

40

35

30

25

20

15

10

5

0

B

DK

D

E

F

IRL

I

NL

A

S

UK

IS

Active

Inactive

Source: National data 

Similarly, no clear pattern emerges in relation with activity status. 

5- Comparative study of countries5 

Table 2.5.3.2.1. Analysis of gradients 

Level 

Gender 

Age 

Education 

Activity status 

B 

1.00 

1.81 

0.84 

1.26 

1.05 

DK 

1.13 

1.25 

1.19 

1.07 

0.99 

D 

0.98 

2.05 

0.96 

1.01 

0.89 

E 

0.79 

3.09 

0.71 

1.35 

1.64 

F 

1.00 

1.92 

0.96 

1.63 

1.11 

IRL 

1.17 

1.24 

0.63 

1.10 

1.01 

I 

0.78 

2.65 

0.87 

1.38 

1.10 

NL 

1.42 

1.26 

1.48 

1.11 

1.03 

A 

0.71 

2.33 

0.67 

1.19 

0.98 

P 

0.51 

7.80 

0.58 

1.94 

1.41 

S 

1.00 

1.44 

1.33 

0.90 

0.96 

UK 

0.70 

1.40 

0.81 

1.23 

0.99 

IS 

1.16 

1.08 

1.20 

0.64 

0.90 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: National data 

The  percentage  of  former  smokers  in  the  population  results  from  the  interaction  of  two  different 
phenomena over time. Indeed, a former smoker must, by definition, have been at some time a smoker 
in the past and have ceased smoking before the time of the survey. With differences among countries 
in  the  evolution  over  time  of  smoking  rates  and  cessation  rates,  various  percentages  of  former 
smokers  are  found,  each  reflecting  national histories of smoking attitudes and behaviours, and anti-
smoking policies. 

2.5.3.2. Former smokers (ECHP data) 

B. Data collected 

Table 2.5.3.2.1. Wording of the questions by survey 

Topic 

Year 

Question 

Former 
smoking 

1998  Did you ever smoke? 

Source: European Community Household Panel, 1998 

5 See 1.2.2 above for the definition of gradients. 

107 

Response categories 
constructed 

Never smoked 
Smoked occasionally 
Smoked daily 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                          
(cid:4) 

Country 
B 
DK 
EL 
E 
IRL 
I 
A 
P 

Table 2.5.3.2.2. Comparative summary and variable averages 

Smoking 

Former Smokers 
31.2 
37.7 
17.1 
23.5 
24.1 
18.1 
24.6 
16.5 

Source: European Community Household Panel, 1998 

C. Analysis 

Analysis refers to the percentage of former smokers in the population. 

1- Overall level 

2.5.3.2.1. Percentage of former smokers in the population
 (ECHP, 1998)

40

35

30

25

20

15

10

5

0

B

DK

EL

E

IRL

I

A

P

Source: European Community Household Panel, 1998 

Many  of the percentages of former smokers in the ECHP appear lower than those found in national 
data. 

108 

 
 
 
 
 
 
 
 
 
Smoking 

2- Age and gender 

2.5.3.2.2. Percentage of former smokers among females, 
by age (ECHP, 1998)

(cid:4) 

50

45

40

35

30

25

20

15

10

5

0

15-24

25-34

B

DK

35-44
EL

45-54
E

55-64

IRL

65-74

I

A

75-84
P

85+

Source: European Community Household Panel, 1998 

2.5.3.2.3. Percentage of former smokers among males, 
by age (ECHP, 1998)

90

80

70

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

DK

EL

E

IRL

I

A

P

Source: European Community Household Panel, 1998 

The overall age pattern – or series of patterns, for females – found in national surveys also appear in 
ECHP data. 

109 

 
 
 
 
 
 
 
 
(cid:4) 

3- Educational level 

Smoking 

2.5.3.2.4. Percentage of former smokers in the population,
by educational level (ECHP, 1998)

40

35

30

25

20

15

10

5

0

B

DK

EL

E

IRL

I

A

P

Pre-primary and Primary (ISCED 0-2)

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Austria, Denmark and Spain: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Ireland: Primary, Secondary (Secondary + Upper secondary), Tertiary 
Source: European Community Household Panel, 1998 

As  with  national  surveys,  the  relationship  between  educational  status  and  the  percentage  of  former 
smokers varies among countries. 

4- Activity status 

2.5.3.2.5. Percentage of former smokers in the population,
by activity status (ECHP, 1998)

40

35

30

25

20

15

10

5

0

B

DK

EL

E

IRL

I

A

P

Source: European Community Household Panel, 1998 

Active

Inactive

Here also, there is no clear relationship with the economic activity status of the population. 

110 

 
 
 
 
 
 
 
 
 
 
 
Smoking 

5- Comparative study of countries6 

(cid:4) 

Level 

Gender 

Age 

Education 

Activity status 

B 

1.31 

2.07 

1.07 

0.82 

1.00 

Table 2.5.3.2.3. Analysis of gradients 

DK 

1.58 

1.26 

1.35 

1.04 

1.00 

EL 

0.72 

6.47 

0.63 

1.31 

1.20 

E 

0.99 

3.84 

1.39 

1.20 

1.08 

IRL 

1.01 

1.55 

1.16 

0.97 

1.19 

I 

0.76 

3.66 

0.86 

1.23 

1.04 

A 

1.03 

2.38 

0.95 

0.87 

0.96 

P 

0.69 

7.59 

0.59 

1.46 

1.18 

Source: European Community Household Panel, 1998 

The  gradients  form  the  1998  ECHP  data  and  those  from  national  surveys  carry  very  similar 
information. 

A comparison of national and ECHP data by gender and age groups 

2.5.3.2.6. Comparison of percentage of former smokers in the 
population: national vs 1998 ECHP data

a
t
a
d

l

a
n
o
i
t
a
N

40

35

30

25

20

15

10

5

0

0

5

10

15

20

25

30

35

40

B

DK

E

IRL

I

A

P

Diagonal

ECHP 1998 data

Source: European Community Household Panel (1998) & national data 

The  percentages  of  former  smokers  in  the  total  population  as  obtained  from  national  surveys  are 
generally similar to those obtained from the 1998 ECHP. However, a rather large difference is found in 
Ireland, where the national rate (38.8%) is much higher than the ECHP rate (24.0%). 

6 See 1.2.2 above for the definition of gradients.  

111 

 
 
 
 
 
 
 
 
 
 
 
 
                                                          
(cid:4) 

Smoking 

2.5.3.2.7. Comparison of percentage of former smokers in the 
population: national vs 1998 ECHP data 
(by country and age group)

a
t
a
d

l
a
n
o
i
t
a
N

60

50

40

30

20

10

0

0

B

10

20

30
ECHP 1998 data

40

50

60

DK

E

IRL

I

A

P

Diagonal

Source: European Community Household Panel (1998) & national data 

Many of the national data by age group are similar to the corresponding 1998 ECHP figures, but in 
general slightly higher (points above the diagonal in the graph). However, in Ireland, all national age-
specific data are much larger than those of the ECHP, especially at younger ages.  

112 

 
 
 
 
 
 
Alcohol Drinking 

(cid:4) 

2.6. Alcohol drinking 
Two major public health issues are involved with alcohol consumption: 

·   daily or regular alcohol consumption, when exceeding small amounts of alcohol, is a cause or 
an  aggravating  factor  of  a  number  of  health  problems,  even  when  it  does  not  involve 
drunkenness; 

·   occasional  excessive  (binge)  drinking  also  presents  health  risks  but  involves  much  higher 

risks of accident. 

It has proved difficult to get internationally comparable data on occasional excessive drinking, mainly 
because of translation and cultural differences: what is felt ‘excessive’ drinking varies among countries 
and population groups. In the same way, drunkenness is interpreted differently between countries. 

A. Data requested 
Data were therefore requested only on ‘regular’ drinking: 

Drinkers of alcohol in the past 12 months 

1.  Number of respondents who drank any alcohol  (beer, wine, spirits, other local beverages) in 

the past 12 months 

2.  Number  of  respondents  who  did  not  drink  any  alcohol  (beer,  wine,  spirits,  other  local 

beverages) in the past 12 months 

Drinkers of alcohol in the past 4 weeks 

1.  Number of respondents who drank any alcohol (beer, wine, spirits, other local beverages) in 

the past 4 weeks  

2.  Number  of  respondents  who  did  not  drink  any  alcohol  (beer,  wine,  spirits,  other local 

beverages) in the past 4 weeks 

3.  Average  number  of  days  (only  for  drinkers)  in  the  past  4  weeks,  where  people  drank  any 

alcohol 

Countries  with  different  response  categories  were  asked  to  provide  the  corresponding  data  and 
indicate the wording of the question and/or the response categories used. 

B. Data collected 
The  four  countries  providing  data  on  alcohol  drinking  during  the  previous  four  weeks  used  four 
different time periods: Denmark (last week day); Spain (the last 2 weeks); Ireland (the last month); and 
UK  (the  last  7  days).  As  a  result,  analysis  is  restricted  to  alcohol  consumption  during  the 
previous 12 months. 

113 

 
 
 
 
 
 
(cid:4) 

Alcohol Drinking 

Table 2.6.1. Wording of the questions by survey 

Country  Year 

Question  (alcohol drinking during the last 12 months) 

B 

D 

1997 

Indicate on the following list which alcoholic beverages you drank during the 
last twelve months (even if you drank it once? Beer, wine, liqueur, gin, long-
drinks ... Yes/No 

1998 

How often do you drink the following: Diet beer and low-calorie beer/ beer with 
normal or high alcohol content/Wine, champagne, fruit wine/  high-proof 
alcoholic drinks (e.g. rum, brandy, liqueur, schnapps)?  Please consider the last 
12 months.  

IRL 

1998  How long ago did you last have an alcoholic drink? Is it more than 12 months? 

Response 
categories 
constructed 

Drinker 
Non-drinker 

Drinker 
Non-drinker 

Drinker 
Non-drinker 

I 

2000 

In which quantity do you usually have the following drinks? wine, beer, aperitifs, 
bitters, spirits, liqueurs 

Drinker 
Non-drinker 

NL 

2000 

Please indicate on the following list which alcoholic beverages do you ever 
drink 

Drinker 
Non-drinker 

1999 

Have you [has she/he] taken any of the following drinks in the last 12 months?  
Wine / beer / brandy / port / Martini / liqueurs / whiskey / gin?  

Drinker 
Non-drinker 

P 

S 

2000  Drinking in the past 12 months? 

UK 

2000 

Thinking now about all kinds of drinks, how often have you had an alcoholic 
drink of any kind during the last 12 months? 

IS 

1998  How often do you usually drink alcohol? Drinks, liqueur, wine or beer?  

Drinker 
Non-drinker 

Drinker 
Non-drinker 

Drinker 
Non-drinker 

NO 

1998 

Approximately how often have you drunk any form of alcohol over the past 12 
months? 

Drinker 
Non-drinker 

CH 

1997 

How frequently do you drink alcohol, i.e. beer, wine, liqueurs/aperitifs or spirits 
such as shorts: do you generally consume these drinks several times a day or 
approximately how frequently? 

Drinker 
Non-drinker 

Source: National data 

There is a wide variety in the questions asked and in the reply categories: 

·   Some  surveys  specifically  refer  to  a  12-month  period,  others  ask  about  ‘usual’  drinking 

patterns 

·   Some countries provide lists of alcoholic beverages; others do not 
·  

In Iceland, the question was asked to persons up to 74 years old; in Germany and Sweden, up 
to 84 years old. In other countries, all respondents aged 15 years old and over were asked the 
question. 

114 

 
 
 
 
 
Alcohol Drinking 

(cid:4) 

C. Analysis 
Analysis is restricted to the percentage of alcohol drinkers (as against non-drinkers). 

Table 2.6.2. Comparative summary and variable averages 

Country 

B 

D 

IRL 

I 

NL 

P 

S 

UK 

IS 

NO 

CH 

Drank any alcohol in 
the past 12 m (%) 
84.9 

84.7 

83.6 

79.1 

85.8 

57.9 

87.6 

99.2 

88.0 

84.0 

81.3 

Source: National data 

1- Overall level 

Comparison 
Question 
List of drinks 

List of drinks 

No list of drinks 

List of drinks 

List of drinks 

List of drinks 

No list of drinks 

No list of drinks 

List of drinks 

No list of drinks 

List of drinks 

Period 

Population 

12M 

12M 

12M 

Usually 

Usually 

12M 

12M 

12M 

Usually 

12M 

Usually 

15+ 

15-84 

15+ 

15+ 

15+ 

15+ 

15-84 

15+ 

15-74 

15+ 

15+ 

2.6.1. Percentage of population having drunk alcohol 
in the past 12 months

100

90

80

70

60

50

40

30

20

10

0

B

D

IRL

I

NL

P

S

UK

IS

NO

CH

Source: National data 

In  all  countries  except  Portugal,  between  79%  and  88%  of  the  population  aged  15  to  64  declare  
having  drunk  alcohol  during  the  past  12  months.  The  percentage  is  higher  in  the  UK  (99%)  and 
remarkably lower in Portugal (58%). It is not clear whether that the low rate found in Portugal is due to 
the  way  the  question  was  asked,  but  it  could  be  due  to  the  high  level  of  proxy  respondents  in  the 
Portuguese survey. 

115 

 
 
 
 
 
 
 
 
(cid:4) 

2- Age and gender 

Alcohol Drinking 

2.6.2. Percentage of female population having drunk alcohol in 
the past 12 months, by age

100

90

80

70

60

50

40

30

20

10

0

15-24
B
UK

25-34

35-44

45-54

55-64

65-74

75-84

D
IS

IRL
NO

I
CH

NL

P

85+
S

Source: National data 

2.6.3. Percentage of male population having drunk any alcohol 
in the past 12 months, by age

100

90

80

70

60

50

40

30

20

10

0

15-24
B
UK

25-34

35-44

45-54

55-64

65-74

75-84

D
IS

IRL
NO

I
CH

NL

P

85+

S

Source: National data 

The  percentage  of  persons  having  drunk  alcohol  in  the  past  12  months  is  higher  for  men  than  for 
women for all age groups in all countries, except in the UK, where the men and women rates are very 
similar  for  all  age  groups.  Indeed,  the  percentages  in  the  UK  are  almost  100%  for  both  men  and 
women in all except the oldest age groups. 
In addition, the predominance of men alcohol drinkers tends to increase with age. Overall, the median 
drinking rate for the whole population is 90% for men and 80% for women.  

116 

 
 
 
 
 
 
 
 
Alcohol Drinking 

(cid:4) 

Two countries stand apart in the distribution of men drinkers by age: 

·  

the UK, where almost 100% of the men declared to have had an alcoholic drink of any kind 
during the last 12 months at all ages, including the 15-24 years old, and 

·   Portugal, with a much lower average percentage of drinkers, and the lowest percentage in the 

15-44 and 85+ age groups. 

For other countries, there tends to be some increase between the 15-24 and 25-34 age groups, but 
not in all countries. This is followed by a progressive decline in alcohol consumption with age up to the 
75-84 age group. The 85+ age group, however, displays a reversal in some countries, but not in all. 
Again,  the  UK  and  Portugal  display  an  age  pattern  that  differs  from  other  countries.  The  mixed 
evolution found between the 15-24 and 25-34 age groups among men is also found among women, 
and the decline with age is somewhat more pronounced. Several countries display a reversal after 84 
years of age among men, but only one for women. 

3- Educational level 

2.6.4. Percentage of population having drunk alcohol in the 
past 12 months, by educational level

100

90

80

70

60

50

40

30

20

10

0

B

IRL
Pre-primary and Primary (ISCED 0-1)

NL

D

I

S

P
Secondary (ISCED 2)

UK

IS

NO

CH

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Switzerland: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Ireland: Primary, Secondary (Secondary + Upper secondary), Tertiary 
Source: National data 

After adjustment for gender and age, the percentage of persons having drunk alcohol during the past 
12  months  tends  to  be  slightly  higher  in  all  countries  among  those  with  a  higher  formal  educational 
level. The effect is not significant in the UK, where rates are almost 100% whatever the educational 
level.  

117 

 
 
 
 
 
 
Alcohol Drinking 

2.6.5. Percentage of population having drunk alcohol in the 
past 12 months, by activity status

(cid:4) 

4- Activity status 

100

90

80

70

60

50

40

30

20

10

0

B

D

IRL

I

NL

S

UK

IS

NO

CH

Source: National data 

Active

Inactive

After  adjustment  for  age  and  sex,  the  percentage  of  alcohol  drinkers  is  lower  for  the  inactive,  the 
difference being modest in most countries. 

5- Comparative study of countries1 

Table 2.6.3. Analysis of gradients 

Level 

Gender 

Age 

Education 

Activity status 

  B 

D 

IRL 

I 

1.00 

1.13 

0.74 

1.32 

1.13 

1.00 

1.15 

0.53 

1.35 

1.10 

0.99 

1.07 

1.45 

1.15 

1.06 

0.94 

1.28 

0.75 

1.14 

1.09 

NL 

1.01 

1.15 

0.58 

1.22 

1.11 

P 

0.69 

1.86 

0.50 

1.13 

- 

S 

1.03 

1.07 

0.99 

1.06 

1.16 

UK 

1.17 

1.00 

0.07 

1.01 

1.02 

IS 

1.04 

1.07 

1.15 

1.19 

1.04 

NO 

0.99 

1.11 

1.33 

1.15 

1.07 

CH 

0.96 

1.19 

0.51 

1.19 

1.11 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: National data 

In the gradient analysis, the UK and Portugal stand apart. The UK, with rates approaching 100%, has 
the highest level and the lowest gender, age, education and activity gradients. Portugal has the lowest 
level and the second lowest age gradient, but the highest gender gradient. 
Among other countries, 

·  
·  

·  

·  

there is little difference in the percentage of alcohol drinkers during the previous years 
the highest gender effect is found in Italy (28% more men than women drinkers) and lowest in 
Ireland, Sweden and Iceland (7% more men drinkers),  
the  education  effect  (more  drinkers  among  persons  with  higher  education)  is  sizeable  in 
Germany and Belgium, but relatively small in Sweden, and 
in the same way, modest differences are found between the economically active and inactive. 

1 See 1.2.2 above for the definition of gradients. 

118 

 
 
 
 
 
 
 
 
 
 
                                                          
In-patient Hospitalisation 

(cid:4) 

2.7. Hospitalisation in the past 12 months 
Data  on  hospitalisation  are  relatively  common  in  household  surveys  and  have  been  collected  for  a 
long  time.  It  may  be  surprising  that  so  few  data  are  available  in  European  countries.  However, 
hospitalisation  indicators  suffer  from  a  host  of  international  comparability  issues,  from  the  various 
definitions of ’hospital’ in different countries to the scope and definition and days in hospitals. 

2.7.1. Hospitalisation in the past 12 months (national data) 

A. Data requested 
Data had been requested on: 

a. Day-patient hospitalisation in the past 12 months 

1.  Number of respondents with day-patient hospitalisation in the past 12 months 
2.  Number of respondents with no day-patient hospitalisation in the past 12 months 

b. In-patient hospitalisation in the past 12 months 

1.  Number of respondents with in-patient hospitalisation in the past 12 months 
2.  Number of respondents with no in-patient hospitalisation in the past 12 months 

As  for  other  items,  countries  for  which  response  categories  differ  were  asked  to  provide  the 
corresponding data and indicate the wording of the question and/or the response categories used. 
No  attempt  was  made  to  collect  data  on  the  number  of  days  of  hospitalisation,  because  of  known 
international comparability issues. 

B. Data collected 

a. Day-patient hospitalisation in the past 12 months 

Table 2.7.1.1a.  Wording of the questions by survey 

Country 

Year 

Questions 

Response categories 
constructed 

1998 

When was the last time (in the 12 months) you 
received a medical assistance (Out-patients / health 
centre / clinic)? 

Day-patient hospitalisation 
No day-patient hospitalisation 

1999 

Have you attended any consultation in a health 
centre in the past three months? 

During the last year, have you been in hospital for 
treatment as a day-patient, i.e., admitted to a 
hospital bed or day ward, but not required to remain 
overnight? 

UK 

2000 

Source: National data 

Yes 
No 

Yes 
No 

D 

P 

Data were received from Germany, Portugal and United Kingdom. Only questions from Germany and 
UK used the reference period of 12 months, while Portugal refers to a 3-month period. Data do not 
allow valid international comparisons. 

119 

 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

In-patient Hospitalisation 

b. In-patient hospitalisation in the past 12 months 

Table 2.7.1.1b.  Wording of the questions by survey 

Country  Year 

Question 

Response categories 
constructed 

B 

1997 

Have you been admitted to the hospital in the course of 
last year that is since __/__/19__? Yes, No, don't know. 

In-patient hospitalisation 
No in-patient hospitalisation 

DK 

2000 

Within the past 3 months have you consulted a doctor 
because of complaints, illness or injury? (Include only 
consultations on account of your own complaints – not your 
children’s) 

In-patient hospitalisation 
No in-patient hospitalisation 

D 

E 

F 

I 

1998 

In the last twelve months how many nights did you spend 
in hospital for treatment? 

In-patient hospitalisation 
No in-patient hospitalisation 

1997 

During the past 12 months, i.e. between the end of August 
1994 and yesterday have you been hospitalised as a 
patient for one night or more? 

2000  Have you been hospitalised in the past 3 months? 

In-patient hospitalisation 
No in-patient hospitalisation 

In-patient hospitalisation 
No in-patient hospitalisation 

2000 

During the last 3 months were you admitted to a public or 
private hospital for at least one night? 

In-patient hospitalisation 
No in-patient hospitalisation 

NL 

2000 

Did you stay in a hospital or clinic for one night or more 
during the past year, i.e. since '12 months ago'? If so, how 
often? 

In-patient hospitalisation 
No in-patient hospitalisation 

A 

1999 

How often during the last 12 months did you stay for one 
night or longer in a hospital? 

In-patient hospitalisation 
No in-patient hospitalisation 

UK 

2000 

During the last year, have you been in hospital as an 
inpatient, overnight or longer? 

In-patient hospitalisation 
No in-patient hospitalisation 

IS 

1998  Have you been hospitalised in the past 12 months? 

In-patient hospitalisation 
No in-patient hospitalisation 

NO 

1998 

Have you been admitted to hospital during the last 12 
months? 

In-patient hospitalisation 
No in-patient hospitalisation 

CH 

1997 

For how many days have you been in a hospital or in a 
special clinic, not including visits to health resorts, in the 
last 12 months? 

In-patient hospitalisation 
No in-patient hospitalisation 

Source: National data 

The most visible comparability problem relates to the reference period used. Nine countries use a 12-
month  (one  year)  reference  period,  while  the  reference  period  for  three  other  countries  is  three 
months. It is not possible to estimate 12-month hospitalisation prevalence on the basis of convert 3-
month hospitalisation rates without additional data on the distribution of duration of stay in hospitals. 
The corresponding countries therefore had to be excluded from the analysis. 

120 

 
 
 
 
 
In-patient Hospitalisation 

(cid:4) 

Table 2.7.1.2. Comparative summary and variable averages 

Country 

Inpatient 

Period 

Remark 
Response 
categories 

B 

DK 

D 

E 

F 

I 

NL 

A 

UK 

IS 

NO 

CH 

12.7 

7.4 

12.8 

8.5 

3.9 

4.0 

6.4 

13.3 

8.9 

10.7 

10.3 

12.3 

12 months 

3 months 

12 months 

<= 84 years 

12 months 

3 months 

3 months 

Past year 

12 months 

Last year 

12 months 

<= 74 years 

Last 12 months 

12 months 

Conclusion 

Excluded 

Excluded 

Excluded 

Source: National data 

A second difference relates to the concept on hospitalisation and the question asked. Some countries 
ask about: 
·  
·   whether the person has been admitted to a hospital 
·   whether the person has spent one night or more in a hospital 
·   whether the person has been hospitalised 

the number of nights spent in hospital 

Other  less  visible  differences  reside  in  the  concept  of  ’hospital’  and  ’clinic’.  In  some  countries, 
institutions called ’clinic’ in the vernacular language would be called ’hospital’ elsewhere.  

C. Analysis 
Analysis relates to the percentage of persons hospitalised in the past 12 months (as against those not 
hospitalised). 

1- Overall level 

2.7.1.1. Percentage of population having been hospitalised as 
an in-patient in the past 12 months

14

12

10

8

6

4

2

0

B

D

E

NL

A

UK

IS

NO

CH

Source: National data 

121 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

In-patient Hospitalisation 

In four countries, about one in eight persons aged 15-64 had been hospitalised for at least one night 
during the previous year. The hospitalisation rate is lowest in  the Netherlands (6.4%) and highest in 
Austria (13.3%).  

2- Age and gender 
Among  the  nine  countries  except  Austria,  the  overall  hospitalisation  rate  in  the  15-64  age  group  is 
higher,  or  much  higher  among  females  (median  for  all  countries:13.2%)  than  for  males  (median: 
8.3%). A very small part of the differential is due to the difference in age structure; after adjustment for 
age, the female and male medians become 8.4% and 13.1% 
The age pattern of hospitalisation rates differs strongly by gender. 

2.7.1.2. Percentage of female population having been 
hospitalised as an in-patient during the previous 12 months, 
by age

30

25

20

15

10

5

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

D

E

NL

A

UK

IS

NO

CH

Source: National data 

Among women, hospitalisation rates are generally higher in the 25-34 age group than in the adjacent 
age  intervals  of  15-24  and  35-44,  reflecting  hospitalisations  related  to  child  birth.  The  subsequent 
increase with age and the decline in the oldest age group is still more visible. 

122 

 
 
 
 
 
 
 
 
In-patient Hospitalisation 

(cid:4) 

2.7.1.3. Percentage of male population having been 
hospitalised as an in-patient during the previous 12 months,
 by age

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

D

E

NL

A

UK

IS

NO

CH

Source: National data 

Among men, hospitalisation rates remain stable in the 15-44 age range, then increase with age.The 
subsequent increase with age is similar to the one found among females. From 15 to 84 years of age, 
the  rates  are  generally  lowest  for  the  Netherlands  and  highest  for  Austria.  But  even  if  the 
hospitalisation  rate  in  Austria  is  three  times  the  Netherlands’  rate  in  the  15-24  age  group,  it  is only 
60% higher in the 75-84 group. The large increase in the hospitalisation rate in Spain for the oldest 
age group (85+) may be due to the small sample size. In other countries, the rate tends to stabilise, 
taper  off  or  even  decline,  possibly  reflecting  differences  in  institutionalisation  rates  for  the  eldest 
population group. 

3- Educational level 

2.7.1.4. Percentage of population having been hospitalised as 
an in-patient, by educational level

18

16

14

12

10

8

6

4

2

0

B

D

E

NL

A

UK

IS

NO

CH

Pre-primary and Primary (ISCED 0-1)
Upper secondary (ISCED 3)

Secondary (ISCED 2)
Post-secondary (ISCED 4-6)

Austria, Switzerland, and Spain: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Source: National data 

123 

 
 
 
 
 
 
 
 
(cid:4) 

In-patient Hospitalisation 

In  almost  all  countries,  hospitalisation  rates  tend  to  decline  with  a  higher  formal  educational  level, 
particularly  in  Belgium.  There  is,  however,  little  effect  of  education  in  countries  such  as  Germany, 
Iceland or Switzerland. 

4- Activity status 

2.7.1.5. Percentage of population having been hospitalised as 
an in-patient, by activity status

18

16

14

12

10

8

6

4

2

0

B

D

E

NL

A

UK

IS

NO

CH

Source: National data 

Active

Inactive

Even after adjustment for age and sex, hospitalisation among the economically inactive tends to be 
higher than for active persons. This reflects the complex relationships between health and economic 
activity. However, a small opposite effect is found in Spain. 

5- Comparative study of countries1 

Level 

Gender 

Age 

Education 

Activity status 

Table 2.7.1.3. Analysis of gradients 

B 

1.19 

0.82 

1.71 

0.53 

0.69 

D 

1.17 

0.75 

1.04 

0.92 

0.55 

E 

0.79 

0.98 

0.75 

0.76 

1.14 

NL 

0.60 

0.84 

0.99 

0.68 

0.60 

A 

1.24 

1.03 

1.27 

0.86 

0.95 

UK 

0.83 

0.75 

1.13 

0.69 

0.39 

IS 

1.00 

0.63 

0.94 

0.91 

0.64 

NO 

0.96 

0.65 

0.94 

0.76 

0.51 

CH 

1.15 

0.87 

0.89 

0.88 

0.59 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: National data 

As the table shows, 

·   hospitalisation rates are about twice as high in Austria as they are in the Netherlands. 
·  

they are generally lower for males than for females–up to a third lower in Iceland or Norway, 
but slightly higher only in Austria,  
the age effect is lowest in Spain, and highest in Belgium, 
the  negative  effect  of  education  on  hospitalisation  rates  is  strongest  in  Germany,  lowest  in 
Belgium, and 
the relationship between economic inactivity and hospitalisation is strongest in the UK but the 
reverse effect is found in Spain. 

·  
·  

·  

1 See 1.2.2 above for the definition of  gradients. 

124 

 
 
 
 
 
 
 
 
 
 
 
                                                          
(cid:4) 

In-patient Hospitalisation 

2.7.2. Hospitalisation in the past 12 months (ECHP) 

B. Data collected 

The question asked in the ECHP was: 

Table 2.7.2.1. Wording of the question 

Question 

Response categories 

'During the past 12 months, have you been admitted to a 
hospital as an in-patient?  

In-patient hospitalisation 
No in-patient hospitalisation 

Year 

1998 

Source: European Community Household Panel, 1998 

Table 2.7.2.2. Comparative summary and variable averages 

Country 

% of hospitalised persons 

Age 

B 

DK 

EL 

E 

F 

IRL 

I 

NL 

A 

P 

S 

UK 

11.8 

10.5 

5.9 

8.4 

10.7 

10.5 

8.6 

7.7 

14.3 

5.9 

4.1 

10.6 

15 + 

15 + 

15 + 

15 + 

15 + 

15 + 

15 + 

15 + 

15 + 

15 + 

15-84 

15 + 

Source: European Community Household Panel, 1998 

The only differences to be found among countries result  from the translation of the question in each 
country, and in the omission of the population aged 85 years and over in Sweden. 

125 

 
 
 
 
 
 
 
 
 
 
In-patient Hospitalisation 

2.7.2.1. Percentage of population having been hospitalised as 
an in-patient in the past 12 months (ECHP, 1998)

(cid:4) 

C. Analysis 

1- Overall level 

14

12

10

8

6

4

2

0

B

DK

EL

E

F

IRL

I

NL

A

P

S

UK

Source: European Community Household Panel, 1998 

As with national data, the highest hospitalisation rate is found in Austria. Much lower rates are found in 
Sweden (4.1%), Greece and Portugal. 

2- Age and gender 

2.7.2.2. Percentage of female population having been 
hospitalised as an in-patient in the past 12 months (ECHP, 
1998), by age

40

35

30

25

20

15

10

5

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

NL

DK

A

EL

P

E

S

F

UK

IRL

I

Source: European Community Household Panel, 1998 

126 

 
 
 
 
 
 
 
 
 
 
In-patient Hospitalisation 

(cid:4) 

2.7.2.3. Percentage of male population having been 
hospitalised as an in-patient in the past 12 months (ECHP, 
1998), by age

45

40

35

30

25

20

15

10

5

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B
NL

DK
A

EL
P

E
S

F
UK

IRL

I

Source: European Community Household Panel, 1998 

The age patterns of hospitalisation rates for men and for women are very similar to those derived from 
national surveys: local peak for women in the 25-34 age group, overall increase with age and mixed 
variations in the oldest age group. The increase in hospitalisation rates with age appears much more 
moderate in Sweden and Portugal than in other countries.  

3- Educational level 

2.7.2.4. Percentage of population having been hospitalised as 
an in-patient, by educational level (ECHP, 1998)

16

14

12

10

8

6

4

2

0

B

DK

EL

E

F

IRL

I

NL

A

P

S

UK

Pre-primary and Primary (ISCED 0-2)
Post-secondary (ISCED 4-6)

Upper secondary (ISCED 3)

Source: European Community Household Panel, 1998 

Here also, hospitalisation tends to decline with higher educational level, although little relationship is 
found in Austria, and no clear relationship in Spain. 

127 

 
 
 
 
 
 
 
 
 
(cid:4) 

4- Economic status 

In-patient Hospitalisation 

2.7.2.5. Percentage of population having been hospitalised as 
an in-patient, by activity status (ECHP, 1998)

18

16

14

12

10

8

6

4

2

0

B

DK

EL

E

F

IRL

I

NL

A

P

UK

Source: European Community Household Panel, 1998 

Active

Inactive

As  with  national  data,  economic  inactivity  is  associated  with  higher  hospitalisation  rates,  with  no 
exception. However, the relationship is not significant in Greece and Portugal. 

5- Comparative study of countries 

Level 

Gender 

Age 

Table 2.7.2.3.  Analysis of gradients 

  B 

DK 

EL 

E 

F 

IRL 

I 

NL 

A 

P 

S 

UK 

1.24 

1.10 

0.62 

0.88 

1.12 

1.10 

0.90 

0.81 

1.50 

0.62 

0.43 

1.11 

0.93 

0.73 

0.98 

1.10 

0.89 

0.92 

1.04 

0.81 

0.90 

1.13 

0.91 

0.71 

1.05 

1.02 

0.97 

1.04 

1.22 

1.51 

0.99 

0.86 

1.82 

0.63 

0.49 

0.75 

Education 

0.72 

0.88 

0.81 

0.81 

0.65 

0.87 

0.65 

0.65 

1.05 

0.69 

0.73 

0.82 

Economic status 

0.65 

0.57 

0.92 

0.81 

0.67 

0.78 

0.78 

0.75 

0.80 

0.95 

n.a. 

0.67 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: European Community Household Panel, 1998 

·   While the overall hospitalisation rate in Austria is about twice as high than in the Netherlands, 

as found with national surveys, it is more than three times the Swedish rate. 

·   The gender effect (higher hospitalisation rate among females) is strongest in Denmark, and he 
opposite  is  found  in  Portugal,  Spain,  (no  significant  effect  in  national  data)  and  Italy.  The 
opposite effect found in national Austrian data is not found in the ECHP. 

·   ECHP data also display a much stronger age effect in Austria than national data. A very small 

effect is found in Sweden. 

·   As with national data, higher education is associated with a lower hospitalisation rate, the only 

·  

exception being Austria. 
In  all  countries,  hospitalisation  is  positively  associated  with  economic  inactivity,  the 
relationship being particularly visible in Denmark. 

Overall, national and ECHP data are remarkably consistent.  

128 

 
 
 
 
 
 
 
 
 
In-patient Hospitalisation 

(cid:4) 

A comparison of national and ECHP data by gender and age 
Data are available from the 1998 ECHP and from national surveys for five countries. In all cases, the 
national survey was conducted within a year, which differs from the ECHP (1998). But a comparison of 
the two sets of data may be of interest.  

2.7.2.6. Comparison of percentage of population having been 
hospitalised as an in-patient in the past 12 months: national vs. 
1998 ECHP data

a
t
a
d

l
a
n
o
i
t
a
N

15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0

0

1

2

B

3

E

4

5

6

7

9
1998 ECHP data

8

10

11

12

13

14

15

NL

A

UK

Diag

Source: European Community Household Panel (1998) & national data 

The  overall  hospitalisation  rates  are  somewhat  similar.  Austria  has  the  highest  average  rate  in  both 
sources. 

2.7.2.7. Comparison of percentage of population having been 
hospitalised as an in-patient in the past 12 months: national vs. 
1998 ECHP data, by age

a
t
a
d

l
a
n
o
i
t
a
N

35

30

25

20

15

10

5

0

0

5

10

15
20
1998 ECHP data

25

30

35

B

E

NL

A

UK

Diag

Source: European Community Household Panel (1998) & national data 

129 

 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

In-patient Hospitalisation 

Since hospitalisation rates tend to increase with age, the comparative curves tend to move from the 
bottom left to the top right of the graph. For ages up to 74 years (the first six points on the curves), the 
two  sources  somewhat  agree.  Differences  at  older  ages  may  be  due  to  reduced  sample  sizes.  A 
systematic  difference  between  national  and  ECHP  data  is  found  in  Austria  for  ages  above  35,  the 
increase with age in hospitalisation rates in the national survey being less than half the corresponding 
increase found in the 1998 ECHP. This applies to male as well as to female rates (not shown). 

130 

 
 
(cid:4) 

Medical doctor 

2.8. Consultation with a medical doctor 

2.8.1. Consultation with a medical doctor in the past 12 months  

A. Data requested 
The following data were requested: 

Consulting a medical doctor (including GP, Specialist) during the past 12 months 
1. Number of respondents who consulted a medical doctor during the past 12 months 
2. Number of respondents who did not consult a medical doctor during the past 12 months 

B. Data collected 

Table 2.8.1.1. Wording of the questions by survey 

Country  Year 

Question 

D 

E 

1998 

When was the last time you sought the services of a doctor (excluding 
dentist) or a member of his staff? 12 months ago: yes or no? 

1997 

What was the speciality of the doctor whom you consulted in the past 12 
months? (Interviewer: ask about a list of specialities) 

NL 

2000 

Do you remember when you visited your family physician / Specialist for the 
last time; was it this year, last year or longer ago? 

A 

1999 

How often during the last 12 months did you consult a GP – gynaecologist – 
internist – podiatrist – other specialist – outpatient clinic – dentist (dental 
outpatient clinic) because of complaints or for checking purposes? 

IS 

1998  Did you go to the doctor in the past 12 months? 

NO 

1998 

Approximately how many contacts with general practitioners (GP's), medical 
centre, municipal medical officer, emergency medical service or a company 
doctor in the past 12 months due to own illness? 

Response 
categories 

Yes 
Never 

Yes 
Never 

Yes 
Never 

Yes 
Never 

Yes 
Never 

Yes 
Never 

Source: National data 

Different  questions  can  be  asked  to  elicit  the  same  or  similar  results.  The  simplest  way  is  to  ask 
whether the respondent consulted a doctor, as in Iceland. Another way is to ask when the respondent 
consulted a doctor for the last time (Germany). In the Netherlands, Austria and Norway, respondents 
were asked how many times they consulted a doctor. Those responding once or more had consulted 
(at  least)  a  doctor  during  the  past  12  months.  Austria  provided  a  list  of  specialities,  reducing  the 
probability  of  respondents  forgetting  consultations.  Spain  used  an  indirect  way,  asking  about  the 
speciality of the doctor consulted in the past 12 months. 
The  term  ’doctor’  or,  more  precisely,  its  national  translation  in  different  countries  carries  different 
meanings which are likely to affect the comparability of the results. 
In Norway, respondents are asked only about consultations ’due to own illness’. The omission of those 
who  had  only  consultations  for  other  purposes  may  have  caused  an  underestimation  of  figures  for 
Norway. Austria indicates that consultations are to be ’for complaints or for checking purposes’. Other 
countries do not set clear restrictions to the purpose of consultations. Finally, consultations of the staff 
of  a  doctor  were  included  in  Germany:  the  inclusion  of  nurses  and  other  medical  staff  other  than 
doctors may therefore have overstated German figures in comparison with those of other countries. 
All countries listed used a 12-month reference period. 
Data from Germany, Austria and Iceland do not include the 85+ population. 

131 

 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

C. Analysis 

Medical doctor 

Analysis  relates  to  the  percentage  of  respondents  who  had  consulted  once  a  doctor  at  least  in  the 
previous 12 months. 

Table 2.8.1.2. Comparative summary and variable averages 

Country 

% of consultants in one 
year 
89.5 

Specific Details 

Doctor and staff 

Remark 
response categories 
Yes/no-never 

82.8 

80.4 

87.2 

74.3 

71.6 

Specialities 

Specialist 

List of specialities 

Doctor 

GP et al. 

Yes/no-never 

Yes/no-never 

Yes/never 

Yes/never 

Yes/no-never 

D 

E 

NL 

A 

IS 

NO 

Source: National data 

1- Overall level 

2.8.1.1. Percentage of population who had consulted a medical 
doctor in the past 12 months

100

95

90

85

80

75

70

65

60

D

E

NL

A

IS

NO

Source: National data 

Germany,  which  includes  visits  to  medical  staff  other  than  doctors,  had  the  highest  percentage  of 
population having had at least one consultation during the previous 12 months. The lowest percentage 
is found in Norway, which restricted consultations to those for own illness. 

132 

 
 
 
 
 
 
 
 
 
 
Medical doctor 

(cid:4) 

2- Age and gender 
With the exception of Spain, the probability of having consulted a doctor in the past year tends to be 
slightly higher for women, except in Spain.  

2.8.1.2. Percentage of female population who had consulted a 
medical doctor in the past 12 months, by age

100

95

90

85

80

75

70

65

60

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

D

E

NL

A

IS

NO

Source: National data 

The  probability  of  women  having  consulted  a  doctor  increases  somewhat  with  age  in  Germany,  the 
Netherlands, Austia and Norway. However, it tends to decrease in Spain and Iceland. 

2.8.1.3. Percentage of male population who had consulted a 
medical doctor in the past 12 months, by age

100

95

90

85

80

75

70

65

60

15-24

25-34

D

35-44

45-54

55-64

E

NL

A

IS

65-74

NO

75-84

85+

Source: National data 

For men, the likelihood of visiting a doctor tends to increase with age, except in Spain. 

133 

 
 
 
 
 
 
 
 
 
(cid:4) 

3- Educational level 

Medical doctor 

2.8.1.4. Percentage of population who had consulted a medical 
doctor in the past 12 months, by educational level

100

95

90

85

80

75

70

65

60

D

E

NL

A

IS

NO

Pre-primary and Primary (ISCED 0-1)

Secondary (ISCED 2)

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Austria, Spain: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Source: National data 

In three countries (Spain, Austria and Iceland), respondents with a higher educational level are more 
likely to have visited a doctor in the past 12 months. But the opposite is found in the Netherlands and, 
to a lesser degree, in Norway.  

4- Activity status 

2.8.1.5. Percentage of population who had consulted a medical 
doctor in the past 12 months, by activity status

100

95

90

85

80

75

70

65

60

D

E

NL

A

IS

NO

Source: National data 

Active

Inactive

After  adjustment  for  age  and  gender,  the  economically  inactive  have  a  slightly  higher  probability  of 
having visited a doctor in the past 12 months, except in Spain. 

134 

 
 
 
 
 
 
 
 
 
 
Medical doctor 

5- Comparative study of countries1 

(cid:4) 

Table 2.8.1.3. Analysis of gradients 

Level 

Gender 

Age 

Education 

Activity status 

D 

1.10 

0.90 

0.55 

0.94 

0.97 

E 

1.01 

1.06 

-1.97 

1.04 

1.03 

NL 

0.99 

0.86 

1.93 

0.93 

0.96 

A 

1.07 

0.98 

0.15 

1.13 

0.91 

IS 

0.91 

0.92 

-1.10 

1.16 

0.98 

NO 

0.88 

0.91 

1.91 

0.96 

0.91 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: National data 

The  highest  (Germany)  and  the  lowest  (Norway)  levels  may  be  due  to  comparability  problems.  The 
age gradients are not meaningful, since the probability of having consulted a doctor does not follow a 
consistent pattern among countries (it decreases strongly with age in Spain and in Iceland, for women, 
while  it  increases  in  other  countries).  The  education  effect  is  moderate  and  the  employment  status 
gradient is not significant.  

2.8.2. Number of visits to medical doctors in the past year 

A. Data requested 
Data were requested on: 
Consulting a medical doctor (including GP, Specialist) during the past 12 months 
Estimate for average number of consultations in one year per person in the population 

B. Data collected 

Table 2.8.2.1. Wording of the questions by survey 

Country  Year 

Question 

F 

I 

2000  Average number of consultations in one year per person in the population 

2000 

For each kind of consultation, indicate the total number of consultations, the number of 
fully paid consultations, the number of house-calls/ GP in one year 

NL 

2000 

How many times did you have contact with your family physician / Specialist during the 
past 2 months, i.e. since '2 months ago'? (This gives information whether on the visit 
was in the past 4 weeks/ past 28 days) 

A 

1999 

Overall, how often did you go for treatment or because of symptoms or for a check-up or 
screening, over the last twelve months, i.e. since September 1998, to a GP? 

S 

2000 

During the last three months, i.e. during ... (specify which months), have you been to a 
doctor’s surgery or seen a doctor in a hospital because of own illness? Number of 
occasions 

IS 

1998  Number of visits to the doctor (GP and specialist) in past 12 months? 

NO 

1998 

Approximately how many contacts with general practitioners (GP's), medical centre, 
municipal medical officer, emergency medical service or a company doctor in the past 
12 months due to own illness? 

Source: National data 

The reference period used was 2 months in the Netherlands, three months in Sweden and one year 
(12 months) in other countries. 

1 See 1.2.2 above for the definition of gradients. 

135 

 
 
 
 
 
 
 
 
 
 
 
 
                                                          
(cid:4) 

Medical doctor 

Table 2.8.2.2. Comparative summary and variable averages 

Country 

Average number of 
consultations 

F 

I 

NL 

A 

S 

IS 

NO 

Source: National data 

6.8 

6.6 

6.5 

6.6 

3.4 

3.0 

2.6 

Period 

12 Months 

12 Months 

Adjustment of the reference period to one year 

12 Months 

Adjustment of the reference period to one year 

12 Months 

12 Months 

For the Netherlands and Sweden, data collected for a different reference period were adjusted to fit 
the reference period (one year) requested in the guidelines. The sample from Iceland does not include 
the population aged 75 years and above, and the Swedish survey covers the population aged 15 to 
84. 

C. Analysis 

1- Overall level 

2.8.2.1. Average number of consultations with a medical doctor
 in one year

8

7

6

5

4

3

2

1

0

F

I

NL

A

S

IS

NO

Source: National data 

France has the highest average number of doctor consultations per year, followed closely by Austria, 
Italy  and  the  Netherlands.  Much  lower  numbers  are  found  in  the  three  Nordic  countries,  Sweden, 
Iceland and Norway. 

136 

 
 
 
 
 
 
 
 
 
 
 
 
Medical doctor 

(cid:4) 

2- Age and gender 
Except for the oldest age groups, women tend to have more doctor consultations per year than men, 
especially in the 25-34 age group. However, the differential tends to narrow with age. 

2.8.2.2. Average number of consultations with a medical doctor 
in one year by age, female population

16

14

12

10

8

6

4

2

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

F

I

NL

A

S

IS

NO

Source: National data 

2.8.2.3. Average number of consultations with a medical doctor 
in one year by age, male population

16

14

12

10

8

6

4

2

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

F

I

NL

A

S

IS

NO

Source: National data 

The number of consultations hardly increases with age in Sweden, Iceland and Norway. Outside the 
Nordic  countries,  the  number  of  consultations  increases  with  age,  with,  in  several  cases,  a  reversal 
between the 75-84 and 85+ age groups. 

137 

 
 
 
 
 
 
 
 
 
 
(cid:4) 

3- Educational level 

Medical doctor 

2.8.2.4. Average number of consultations with a medical doctor
 in one year, by educational level

9

8

7

6

5

4

3

2

1

0

F

I

NL

A

S

IS

NO

Pre-primary and Primary (ISCED 0-1)

Secondary (ISCED 2)

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Austria: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Source: National data 

There is no clear link between the number of visits to doctors and educational level. 

4- Activity status 

2.8.2.5. Average number of consultations with a medical doctor
 in one year, by activity status

9

8

7

6

5

4

3

2

1

0

F

I

NL

A

S

IS

NO

Source: National data 

Active

Inactive

The  economically  inactive  tend  to  have  more  doctor  consultations  than  the  economically  active,  but 
the difference is insignificant in France. 

138 

 
 
 
 
 
 
 
 
 
Medical doctor 

5- Comparative study of countries2 

(cid:4) 

Table 2.8.2.3. Analysis of gradients 

Level 

Gender 

Age 

Education 

Activity status 

F 

1.05 

0.61 

2.03 

1.56 

0.97 

I 

1.02 

0.96 

1.31 

0.85 

0.71 

NL 

1.00 

0.75 

1.17 

0.81 

0.74 

A 

1.02 

0.80 

1.16 

1.25 

0.81 

S 

0.52 

0.76 

0.62 

0.81 

0.69 

IS 

0.46 

0.59 

0.20 

0.90 

0.60 

NO 

0.40 

0.76 

0.27 

0.63 

0.67 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: National data 

From the analysis of the gradients, it appears that France is a special case as regards the number of 
consultations to medical doctors: 

·  

·  

·  

·  

·  

It has the highest average number of consultations (although closely followed by Italy, Austria 
and the Netherlands). 
It  has  the  lowest  difference  in  number  of  consultations  between  men  and  women,  once  the 
effect of the difference in age structure is removed. 
It  has  by  far  the  largest  age  effect,  with  a  considerable  increase  with  age  in  the  number  of 
consultations, especially among men. 
It  has  the  highest  education  effect  –  after  adjustment  for  gender  and  age,  the  number  of 
medical consultations among those with at least upper secondary education is more than 50% 
higher than for those with at most primary education. 
It also has the lowest differential between the economically active and inactive.  

Here,  the  French  specificity  probably  does  not  reflect  an  issue  of  data  comparability,  but  an  actual 
difference with the other six countries. 

2 See 1.2.2 above for the definition of gradients. 

139 

 
 
 
 
 
 
 
 
                                                          
 
Consultations with the dentist/orthodontist 

2.9.  Consultation with dentists or orthodontists 

(cid:4) 

2.9.1. Consultation with dentists or orthodontists in the past 12 months  

2.9.1.1. Consultation with dentists or orthodontists in the past 12 months  (national 
data) 

A. Data requested 
Data were requested on: 

Consultations with the dentist/orthodontist (past 4 weeks) 

1.  Number of respondents who consulted a dentist/orthodontist during the past 4 weeks 
2.  Number of respondents who did not consult a dentist/orthodontist during the past 4 weeks 

Consultations with the dentist/orthodontist (past 12 months) 

1.  Number of respondents who consulted a dentist during the past 12 months 
2.  Number of respondents who did not consult a dentist during the past 12 months 
3.  Estimate for average number of visits in one year per person in the population 

B. Data collected 
Very  few  comparable  data  were  obtained  on  consultations  with  dentists  or  orthodontists  during  the 
past 4 weeks due to the use of different time periods (2 months for Belgium and the Netherlands; 3 
months for Denmark and Spain; 1 month for France and Italy). For this reason, analysis is restricted to 
consultations during the past 12 months. 

Table 2.9.1.1.1. Wording of the questions by survey 

Country  Year 

Question 

Response 
categories 
constructed 

Age 
groups 

D 

1998 

Which of the following doctors, including dentists, 
have you seen in the last 12 months and how often 
(including home visits)? 

NL 

2000 

Do you remember when you visited your dentist for 
the last time; was it this year, last year or longer ago? 

A 

1999 

How often during the last 12 months did you consult a 
GP – gynaecologist – internist – pediatrist – other 
specialist – outpatient clinic – dentist (dental 
outpatient clinic) because of complaints or for 
checking purposes? 

P 

1999 

Have you [has she/he] seen a stomatologist, a dentist 
or an orthodontic technician in the past twelve 
months? 

Yes 
Never 

Yes 
Never 

Yes 
Never 

Yes 
No 

S 

2000  When did you last visit a dentist or dental hygienist? 

IS 

1998  Did you go to the dentist in the past 12 months? 

CH 

1997 

Have you been to one of the following specialists due 
to health problems or for a check-up in the last 12 
months? Dentist / physiotherapist / chiropractor / 
optician / psychologist /......... 

Less than one 
year 
More than one 
year 

Yes 
Never 

Yes 
Never 

15-84 

15 + 

15 + 

15 + 

15-84 

15-84 

15 + 

For the questions in terms of frequencies in Austria, the Netherlands, Sweden:  If people reply a number of days greater than 0 
or  having  visited  the  dentist  this  year,  they  belong  to  the  ‘yes’  category,  otherwise  they  are  as sumed not to have consulted a 
dentist or orthodontist. 
Source: National data 

141 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

Consultations with the dentist/orthodontist 

Table 2.9.1.1.2. Comparative summary and variable averages 

Country 

% of consultants in one year 

Period 

Response 
categories 

A 

CH 

D 

IS 

NL 

P 

S 

61.7 

65.5 

74.9 

61.0 

76.8 

36.1 

69.4 

12 Months/List of specialities 

Yes/never 

12M 

12 Months/list including 
dentists 

12 Months 

This year 

12 Months 

Less than one year 

Yes/no 

Yes/never 

Yes/never 

Yes/no-never 

Yes/no 

Less or more 
than 1 year 

Source: National data 

Analysis: Yes (one or more consultations in the year) versus no/never (no consultation) 

C. Analysis 

1- Overall level 

2.9.1.1.1. Percentage of population having consulted a dentist 
or orthodontist in the past 12 months

90

80

70

60

50

40

30

20

10

0

D

NL

A

P

S

IS

CH

Source: National data 

In Portugal, about 36% of the population had consulted a dentist / orthodontist during the previous 12 
months.  In  other  countries,  the  percentage  ranged  from  61%  to  a  maximum  of  77%  in  the 
Netherlands. Data for Germany, Sweden and Iceland do not cover the population aged 85 years and 
above and are therefore slightly overestimated in comparison with those of other countries.  

142 

 
 
 
 
 
 
 
 
 
 
Consultations with the dentist/orthodontist 

(cid:4) 

2- Age and gender 
In all countries, women under 55 years of age tended to consult dentists slightly more than men. After 
age  55,  either  the  difference  between  men  and  women  remains  small  or  is  even  reversed,  with 
relatively more consultations among men.  

2.9.1.1.2. Percentage of female population having consulted a 
dentist or orthodontist in the past 12 months, by age

100

90

80

70

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

D

NL

A

P

S

IS

CH

Source: National data 

In  Portugal,  the  dentist  consultation  rates  for  women  are  lower  for  all  age  groups  than  in  the  other 
countries studied. The rate declines regularly with age, to a very low level for those aged 85 years and 
above.  In  Sweden,  after  a  small  decline  between  the  15-24 and 25-34  age  groups,  the  percentage 
rises to a maximum in the 55-64 age group and declines thereafter, but remains the highest among 
the countries considered.  
In  other  countries,  the  rate  remains  more  or  less  stationary  for  the  first  age  groups  and  declines 
progressively for older age groups. The decline appears much steeper in the Netherlands than in other 
countries, e.g., Austria. 

2.9.1.1.3. Percentage of male population having consulted a 
dentist or orthodontist in the past 12 months, by age

100

90

80

70

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

D

NL

A

P

S

IS

CH

Source: National data 

143 

 
 
 
 
 
(cid:4) 

Consultations with the dentist/orthodontist 

The  relationship  between  dentist  consultations  and  age  for  men  is  very  similar  to  that  found  for 
women, but the decline with age is not as steep as it is for women.  

3- Educational level 

2.9.1.1.4. Percentage of population having consulted a dentist 
or orthodontist in the past 12 months, by educational level

90

80

70

60

50

40

30

20

10

0

D
Pre-primary and Primary (ISCED 0-1)

NL

A

P

S

IS
Secondary (ISCED 2)

CH

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Austria, Switzerland: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Source: National data 

In  all  countries,  consultations  with  dentists  increase  with  formal  educational  level,  particularly  in 
Portugal. 

4- Activity status 

2.9.1.1.5. Percentage of population having consulted a dentist 
or orthodontist in the past 12 months, by activity status

90

80

70

60

50

40

30

20

10

0

D

NL

A

S

IS

CH

Active

Inactive

Source: National data 

There  is  very  little  difference  in  consultation  rates  between  the  economically  active  and  the 
economically inactive. 

144 

 
 
 
 
 
 
 
 
 
 
Consultations with the dentist/orthodontist 

5- Comparative study of countries1 

(cid:4) 

Table 2.9.1.1.3. Analysis of gradients 

Level 

Gender 

Age 

Education 

Activity status 

D 

1.14 

0.93 

1.16 

1.22 

1.03 

NL 

1.17 

0.94 

1.76 

1.36 

1.10 

A 

0.94 

0.94 

0.61 

1.20 

1.00 

P 

0.55 

0.88 

0.85 

1.84 

0.97 

S 

1.06 

0.93 

0.10 

1.24 

1.03 

IS 

0.93 

0.83 

1.51 

1.62 

1.07 

CH 

1.00 

0.92 

1.06 

1.35 

1.04 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: National data 

·   As noted earlier, the overall dentist consultation rate does not differ greatly among countries, 

·  

except for the low level found in Portugal.  
In the same way, the gender effect is moderate, and the larger tendency of women to consult 
dentists disappears or is reversed for older age groups.  

·   The age effect differs, in Sweden, from other countries, and is strongest in the Netherlands.  
·   Persons  with  higher  formal  educational  levels  tend  to  consult  dentists  more,  especially  in 

Portugal. 

·   The apparent effect of activity status is not significant. 

Data on consultations with dentists / orthodontists appears reasonably comparable among countries, 
but are available only for seven countries. 

2.9.1.2. Consultation with dentists or orthodontists in the past 12 months (ECHP) 

A. Data requested 

Table 2.9.1.2.1. Wording of the questions by survey 

Question 

Response categories 

During the past 12 months, about how many times have 
you consulted a dentist? 

Yes 
No 

Year 

1998 

Source: European Community Household Panel, 1998 

B. Data collected 

Table 2.9.1.2.2. Comparative summary and variable averages 

Country 

% having consulted a dentist during 
the past year 

B 

DK 

EL 

E 

IRL 

I 

NL 

A 

P 

S 

55.0 

77.2 

23.3 

31.3 

36.7 

36.0 

74.8 

65.2 

28.4 

70.0 

The persons who had consulted a dentist at least once during the previous year are those who give a 
reply of once or more to the question.  

1 See 1.2.2 above for the definition of gradients. 

145 

 
 
 
 
 
 
 
 
 
 
 
 
 
                                                          
Consultations with the dentist/orthodontist 

2.9.1.2.1. Percentage of population having consulted a dentist 
in the past 12 months (ECHP, 1998)

(cid:4) 

C. Analysis 

1- Overall level 

90

80

70

60

50

40

30

20

10

0

B

DK

EL

E

IRL

I

NL

A

P

S

Source: European Community Household Panel, 1998 

The percentage of persons having consulted a dentist during the last 12 months varies widely among 
countries, being much lower in Mediterranean countries (Greece, Spain, Italy and Portugal), but also 
in Ireland, and higher in northern European countries (Denmark, the Netherlands and Sweden), and 
also in Austria. 

2- Age and gender 

2.9.1.2.2. Percentage of female population having consulted a 
dentist in the past 12 months, by age (ECHP, 1998)

100

90

80

70

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

DK

EL

E

IRL

I

NL

A

P

S

Source: European Community Household Panel, 1998 

146 

 
 
 
 
 
 
 
 
 
 
Consultations with the dentist/orthodontist 

(cid:4) 

2.9.1.2.3. Percentage of male population having consulted a 
dentist in the past 12 months, by age (ECHP, 1998)

100

90

80

70

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

B

DK

EL

E

IRL

I

65-74

NL

75-84

85+

A

P

S

Source: European Community Household Panel, 1998 

The general decline with age in dentist consultations found in national data also appears here, but with 
a  varying  intensity;  the  age  effect  is  very  large  in  the  Netherlands,  but  much  weaker  in  the 
Mediterranean countries.  

3- Educational level 

2.9.1.2.4. Percentage of population having consulted in the past 
12 months, by educational level (ECHP, 1998)

90

80

70

60

50

40

30

20

10

0

DK

B
IRL
EL
Pre-primary and Primary (ISCED 0-2)
Post-secondary (ISCED 4-6)

E

I

NL

A

P

S

Upper secondary (ISCED 3)

Austria, Denmark: ISCED 0-2 (Pre-primary and Primary + Secondary ) 
Ireland: Primary, Secondary (Secondary + Upper secondary), Tertiary 
Source: European Community Household Panel, 1998 

As was found with national data, visits to dentists were more common among persons with a higher 
educational level. However, the effect varies among countries, being much stronger in Portugal than in 
the Netherlands, where the level of education has little impact on the probability of visiting a dentist. 

147 

 
 
 
 
 
 
 
 
 
Consultations with the dentist/orthodontist 

2.9.1.2.5. Percentage of population having consulted a dentist 
in the past 12 months, by activity status (ECHP, 1998)

(cid:4) 

4- Activity status 

90

80

70

60

50

40

30

20

10

0

B

DK

EL

E

IRL

I

NL

A

P

Source: European Community Household Panel, 1998 

Active

Inactive

The very slight positive association of economic activity with visits to dentists found in national surveys 
is  also  visible  here.  As  in  national  data,  Portugal  is  the  only  country  where  there  is  a  very  low 
correlation of economic inactivity with visits to dentists.  

5- Comparative study of countries2 

Table 2.9.1.2.3. Analysis of gradients 

B 

DK 

EL 

E 

IRL 

I 

NL 

A 

P 

S 

1.20 

1.68 

0.57 

0.68 

0.80 

0.79 

1.63 

1.42 

0.62 

1.52 

0.92 

0.90 

0.85 

0.78 

0.81 

0.90 

0.95 

0.92 

0.82 

0.93 

1.32 

1.27 

0.95 

0.77 

1.25 

0.90 

2.12 

1.57 

0.85 

0.25 

Level 

Gender 

Age 

Education 

1.41 

1.26 

1.96 

1.85 

2.07 

1.42 

1.07 

1.28 

2.37 

1.06 

Activity status 

1.11 

1.09 

1.04 

1.04 

1.15 

1.18 

1.06 

1.02 

0.98 

n.a. 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: European Community Household Panel, 1998 

Using  national  data  from  seven  countries,  the  Netherlands  had  the  highest  overall  percentage  of 
persons  having  visited  a  dentist  in  the  past  12  months,  with  the  highest  gender  and  age  gradients. 
Using 1998 ECHP data for 10 countries, the Netherlands had the second highest level and, again, the 
highest gender and age gradients.  

2 See 1.2.2 above for the definition of gradients. 

148 

 
 
 
 
 
 
 
 
 
 
                                                          
Consultations with the dentist/orthodontist 

(cid:4) 

A comparison of national and ECHP 1998 data by gender and age 
Data  on  the  percentage  of  the  population  having  visited  a  dentist  or  orthodontist  are  available  from 
both national surveys and the 1998 ECHP for only four countries. 

2.9.1.2.6. Comparison of percentage of population having 
consulted a dentist or orthodontist in the past 12 months: 
national vs 1998 ECHP data

a
t
a
d

l
a
n
o
i
t
a
N

80

70

60

50

40

30

20

10

0

0

10

20

30

40
1998 ECHP data

50

60

70

80

NL

A

P

S

Diagonal

Source: European Community Household Panel (1998) & national data 

2.9.1.2.7. Comparison of percentage of population having 
consulted a dentist or orthodontist in the past 12 months: 
national vs 1998 ECHP data (by country and age groups)

a
t
a
d

l

a
n
o
i
t
a
N

100

90

80

70

60

50

40

30

20

10

0

0

10

20

30

40

50
1998 ECHP data

60

70

80

90

100

NL

A

P

S

Diagonal

Source: European Community Household Panel (1998) & national data 

The average levels for the four countries are remarkably close, as well as the data by age (adjusted 
for  gender  composition)  in  the  Netherlands,  Portugal  and  Sweden.  In  Austria,  the  percentages  are 
lower in the national survey than in the ECHP for the younger age groups (higher percentages, at the 
right of the curve) but lower for older age groups (lower percentages, at the left of the curve). Indeed, 
the ECHP data displayed a very large age effect for Austria, while a rather small effect was found from 
national data. 

149 

 
 
 
 
 
 
 
 
 
 
(cid:4) 

Consultations with the dentist/orthodontist 

2.9.2. Average number of consultations with a dentist in one year 

2.9.2.1. Average number of consultations with a dentist in one year (national data) 

A. Data collected 

Table 2.9.2.1.1. Wording of the questions by survey 

Country  Year 

Question 

D 

F 

I 

1998 

Which of the following doctors, including dentists, have you seen in the last 12 months and 
how often (including home visits)? 

2000  Average number of consultations in one year per person in the population? 

2000 

For each kind of consultation, indicate the total number of consultations, the number of 
fully paid consultations, the number of house-calls./ Dentist in one year 

NL 

2000 

How many times did you visit the dentist during the past 2 months i.e. since '2 months 
ago'? 

A 

1999 

Overall, how often did you go for treatment or because of symptoms or for a check-up or 
screening, over the last twelve months, i.e. since September 1998 to a dentist (dental out-
patients’ department)? 

IS 

1998  Number of visits to the dentist in past 12 months? 

Source: National data 

Although the questions asked differ, they appear to be generally comparable.  

Table 2.9.2.1.2. Comparative summary and variable averages 

Country 

Average number of consultations 

D 

F 

I 

NL 

A 

IS 

Source: National data 

2.6 

1.0 

0.8 

2.4 

1.3 

1.5 

Period 

12 Months 

12 Months 

12 Months 

Adjustment of the period time to one year 

12 Months 

12 Months 

150 

 
 
 
 
 
 
 
 
Consultations with the dentist/orthodontist 

C. Analysis 

1- Overall level 

2.9.2.1.1. Average number of consultations with a dentist or 
orthodontist in one year

(cid:4) 

3.0

2.5

2.0

1.5

1.0

0.5

0.0

D

F

I

NL

A

IS

Source: National data 

Data on the percentage of population having had a dentist consultation and on the average number of 
consultations are available for only 4 countries.  
In  two  countries  (Germany  and  the  Netherlands)  the  percentage  of  population  having had a dentist 
consultation  is  relatively  large  (75%  and  77%)  and, moreover,  those  users  of  dentist’s  services  had 
had more consultations (3.5 and 3.1).  
In the other two countries (Austria and Iceland) a lower percentage of the population had had a dentist 
consultation (62 and 61%), and those having had a consultation had a lower average number (2.1 and 
2.5). 

Table 2.9.2.1.3. Comparative summary on the average number of consultations among 
countries with data on dentist consultations  

Country 

Percentage of population having had at least one consultation 
in the year 

Average number of consultations (all population) 

Average number of consultations (persons having had at least 
one consultation) 

Source: National data 

D 

74.9 

2.60 

3.47 

NL 

76.8 

2.40 

3.13 

A 

61.7 

1.30 

2.11 

IS 

61.0 

1.50 

2.46 

This  may  explain  why  there  is  considerable  difference  among  countries  in  the  number  of  dentist 
consultations per year, the dispersion being larger than for the percentage having had at least a visit.  

151 

 
 
 
 
 
 
 
 
 
 
(cid:4) 

2- Age and gender 

Consultations with the dentist/orthodontist 

2.9.2.1.2. Average number of consultations with a dentist or 
orthondontist in one year in the female population, by age

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

D

F

I

NL

A

IS

Source: National data 

2.9.2.1.3. Average number of consultations with a dentist or 
orthodontist in one year in the male population, by age

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

D

F

I

NL

A

IS

Source: National data 

There are also more variations among countries in the relationship of average number of consultations 
with age.  

152 

 
 
 
 
 
 
 
 
Consultations with the dentist/orthodontist 

(cid:4) 

2.9.2.1.4. Average number of consultations with a dentist or 
orthondontist in one year among those having had at least one 
consultation, by age

6.0

5.0

4.0

3.0

2.0

1.0

0.0

15-24

25-34

D, Female

A, Female

35-44

D, Male

A, Male

45-54

55-64

65-74

75-84

NL, Female

IS, Female

NL, Male

IS, Male

Source: National data 

It  is  of  interest  to  note  that  the  percentage  of  the  population  having  had  at  least  one  consultation 
during the year decreases with age, the average number of visits of those who had had a consultation 
tends either to rise with age or to remain remarkably stable. 

3- Educational level 

2.9.2.1.5. Average number of consultations with a dentist or 
orthodontist in one year, by educational level

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

D

F

I

NL

A

IS

Pre-primary and Primary (ISCED 0-1)

Secondary (ISCED 2)

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Austria: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Source: National data 

The  average  number  of  dentist  consultations  tends  to  increase  with  educational  level,  particularly  in 
France, but the relationship is weak in Austria and Germany. 

153 

 
 
 
 
 
 
 
 
 
 
Consultations with the dentist/orthodontist 

2.9.2.1.6. Average number of consultations with a dentist or 
orthodontist in one year, by activity status

(cid:4) 

4- Activity status 

3.0

2.5

2.0

1.5

1.0

0.5

0.0

D

F

I

NL

A

IS

Source: National data 

Active

Inactive

There is little relationship between activity status and average number of consultation with a dentist.  

5- Comparative study of countries3 

Table 2.9.2.1.4. Analysis of gradients 

Level 

Gender 

Age 

Education 

Activity status 

D 

1.86 

0.93 

2.15 

1.26 

0.96 

F 

0.71 

0.58 

0.78 

2.60 

1.00 

I 

0.57 

0.88 

1.03 

1.33 

1.14 

NL 

1.71 

0.85 

2.27 

1.47 

1.09 

A 

0.93 

0.93 

0.73 

1.25 

1.00 

IS 

1.07 

0.63 

2.23 

1.42 

1.14 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: National data 

The table displays large variations in levels, a modest gender effect, except in France, a strong age 
effect in the Netherlands, Iceland and Germany, but much less in the other three countries, a positive 
education effect, twice as large in France than in Germany and little relationship with activity status. 

2.9.2.2. Average number of consultations with a dentist in one year (ECHP) 

A. Data requested 

Year 

1998 

Table 2.9.2.2.1. Wording of the questions by survey 

Question 

Estimate for average number of consultations with a dentist in one year 

Source: European Community Household Panel, 1998 

3 See 1.2.2 above for the definition of the gradients. 

154 

 
 
 
 
 
 
 
 
 
 
 
 
 
                                                          
Consultations with the dentist/orthodontist 

B. Data collected 

Table 2.9.2.2.2. Comparative summary and variable averages 

Country 

Average number of consultations 

Period 

(cid:4) 

B 

DK 

EL 

E 

IRL 

I 

NL 

A 

P 

S 

UK 

1.3 

1.8 

0.7 

0.8 

0.7 

1.1 

1.6 

1.1 

0.8 

1.0 

1.4 

12 Months 

12 Months 

12 Months 

12 Months 

12 Months 

12 Months 

12 Months 

12 Months 

12 Months 

12 Months 

12 Months 

Source: European Community Household Panel, 1998 

C. Analysis 

1- Overall level 

2.9.2.2.1. Average number of consultations with a dentist
 in one year (ECHP, 1998)

2.0

1.8

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

B

DK

EL

E

IRL

I

NL

A

P

S

UK

Source: European Community Household Panel, 1998 

The  average  annual  number  of  dentist  consultations  varies  considerably  among  countries,  being 
nearly three times higher in Denmark than in Greece. 

155 

 
 
 
 
 
 
 
 
(cid:4) 

2- Age and gender 

Consultations with the dentist/orthodontist 

2.9.2.2.2. Average number of consultations with a dentist in one 
year in the female population, by age (ECHP, 1998)

2.5

2.0

1.5

1.0

0.5

0.0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

A

DK

P

EL

S

E

UK

IRL

I

NL

Source: European Community Household Panel, 1998 

2.9.2.2.3. Average number of consultations with a dentist in one 
year in the male population, by age (ECHP, 1998)

2.5

2.0

1.5

1.0

0.5

0.0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

A

DK

P

EL

S

E

UK

IRL

I

NL

Source: European Community Household Panel, 1998 

The relationship between the number of consultations with a dentist and age is similar to the pattern 
found from national surveys. There is some decline with age in the number of dentist consultations, 
except in the UK, where the consultation rate actually tends to increase with age, and in Spain, where 
no clear pattern emerges. 

156 

 
 
 
 
 
 
 
 
 
Consultations with the dentist/orthodontist 

3- Educational level 

(cid:4) 

2.9.2.2.4. Average number of consultations with a dentist 
in one year, by educational level (ECHP, 1998)

2.5

2.0

1.5

1.0

0.5

0.0

B

DK

EL

E

IRL

I

NL

A

P

S

UK

Pre-primary and Primary (ISCED 0-2)

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Austria, Denmark: ISCED 0-2 (Pre-primary and Primary + Secondary ) 
Ireland: Primary, Secondary (Secondary + Upper secondary), Tertiary 
Source: European Community Household Panel, 1998 

Generally,  the  number  of  dentist  consultations  increases  with  the  educational  level  of  respondents. 
There is, however, no clear relationship in the Netherlands and in Sweden, and an inverse relationship 
in the UK. 

4- Activity status 

2.9.2.2.5. Average number of consultations with a dentist
 in one year, by activity status (ECHP, 1998)

2.0

1.8

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

B

DK

EL

E

IRL

I

NL

A

P

UK

Source: European Community Household Panel, 1998 

Active

Inactive

As  was  the  case  for  national  data,  there  is  no  clear  relationship  between  economic  activity  and 
number of consultations with dentists. 

157 

 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

5- Comparative study of countries4 

Consultations with the dentist/orthodontist 

Table 2.9.2.2.3. Analysis of gradients 

B 

DK 

EL 

E 

IRL 

I 

NL 

A 

P 

S 

UK 

1.18 

1.64 

0.64 

0.73 

0.64 

1.00 

1.45 

1.36 

0.73 

0.91 

1.27 

0.92 

0.89 

0.75 

0.78 

0.75 

0.77 

0.88 

0.88 

0.88 

0.91 

1.08 

0.93 

0.97 

0.97 

0.60 

0.90 

1.24 

1.45 

1.53 

0.86 

0.48 

-0.44 

Level 

Gender 

Age 

Education 

1.27 

1.20 

2.00 

1.43 

2.40 

1.30 

1.00 

1.23 

2.00 

1.10 

0.87 

Activity status 

1.00 

1.27 

0.86 

0.89 

1.17 

1.09 

1.00 

0.94 

1.14 

n.a. 

1.08 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: European Community Household Panel, 1998 

·   Similarly  to  national  data,  there  is  a  wide  range  of  average  annual  number  of  consultations 

with dentists. 

·   After adjustment for age structure, men tend to visit the dentist less often than women, except 

in Greece. 

·   Age gradients are not significant since the median pattern of relationship of the indicator with 

age is unclear, with a diverging pattern for the UK (negative gradient). 

·   A higher educational level is generally associated with more visits  to  a  dentist,  especially  in 

·  

Ireland and Portugal, but no difference appears in the Netherlands. 
In some countries, the economically active tend to visit the dentist more often than the inactive 
(Denmark, Ireland and Portugal), but the opposite is found in Greece, Spain and Austria. 

A comparison of national and 1998 ECHP data by gender and age 
Data  on  the  average  annual  number  of  consultations  with  a  dentist  are  available  from  national  and 
1998 ECHP for only three countries. 

2.9.2.2.6. Comparison of average annual   
number of consultations with a dentist or   
orthodontist: national data vs 1998 ECHP  

2.5 

2.9.2.2.7. Comparison of average annual 
number of consultations with a dentist or 
orthodontist: national data vs 1998 ECHP 
(by age)

2.0 

1.5 

1.0 

0.5 

0.0 

3.0

2.5

2.0

1.5

1.0

0.5

0.0

0.0 

0.5 

1.0 

1.5 

2.0 

2.5 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

I 

1998 ECHP data 
A 
NL 

Diagonal 

I

NL

A

Diagonal

1998 ECHP data

Source: European Community Household Panel (1998) & national data 

The average numbers of consultations from the two sources are similar in Italy and Austria, but the 
national figure in the Netherlands is much higher than the figure from the ECHP. 
In  the  same  way,  age-specific data (after adjustment for gender) from the two sources do not differ 
much in Italy, but much more in Austria and the Netherlands. 

4 See 1.2.2 above for the definition of gradients. 

158 

 
 
 
 
 
 
 
                                                          
 
 
 
 
 
Preventive care 

(cid:4) 

2.10. Preventive care 
Data were requested on three aspects of preventive care most likely to provide comparable data: 

·   Vaccination against influenza 
·   Screening for breast cancer 
·   Screening for cervical cancer 

These will be reviewed in turn. 

2.10.1. Vaccination against influenza 
Vaccination against influenza is a major priority of preventive health policies in Europe, especially for 
elderly persons and persons with underlying health problems. 

A. Data requested 
The following data on immunisation/vaccination against influenza were requested: 
·   Number of respondents who had ever been vaccinated against influenza 
·   Number of respondents who had never been vaccinated against influenza 

B. Data collected 

Table 2.10.1.1. Wording of the questions by survey 

Country 

Year 

Question 

D 

I 

NL 

A 

1998 

Have you been vaccinated against influenza during the last 10 
years? 

2000 

Have you had a flu vaccination during the last 12 months? 

2000 

1999 

Have you ever been vaccinated against influenza? (to prevent 
your catching influenza) 

Do you have a valid protection provided by vaccination against 
polio – tetanus – influenza – FSME? 

Source: National data 

Response 
categories 
(influenza) 

Yes 
No 

Yes 
No 

Yes 
No 

Yes 
No 

All four countries requested information on vaccination against influenza, but used different reference 
periods: ten years in Germany, 12 months in Italy, ‘ever’ in the Netherlands and ‘a valid protection’ in 
Austria. 

Table 2.10.1.2. Comparative summary and variable averages 

Country 

% of vaccinated 
persons 

D 

I 

NL 

A 

Source: National data 

25.2 

15.2 

18.8 

14.1 

Illnesses 

Influenza 

Influenza 

Influenza 

Period considered 

10 years 

12 months 

Ever been 
vaccinated 

Polio, tetanus, influenza, FSME (in the 
same question but asked separately) 

Not specified 

159 

 
 
 
 
 
 
 
 
 
 
Preventive care 

2.10.1.1. Percentage of population vaccinated against influenza

(cid:4) 

C. Analysis 

1- Overall level 

30

25

20

15

10

5

0

D

I

NL

A

Source: National data 

The highest vaccination rates are found in Germany, which used a 10-year reference period (but did 
not include persons aged 85 and over) and the Netherlands, which asked for persons ever vaccinated. 
However, the rate in the Netherlands does not differ much from those of Italy or Austria. 

2- Age and gender 

2.10.1.2. Percentage of female population vaccinated 
against influenza, by age

70

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54
I

D

55-64
NL

A

65-74

75-84

85+

Source: National data 

160 

 
 
 
 
 
 
 
 
 
 
 
Preventive care 

(cid:4) 

2.10.1.3. Percentage of male population vaccinated 
against influenza, by age

70

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54
I

D

55-64
NL

65-74
A

75-84

85+

Source: National data 

Vaccination rates among the young are relatively low; they increase with age in all countries, but much 
less  so  in  Austria  than  in  the  other  three  countries.  There  is  also  a  reversal  at  older  ages  in  the 
Netherlands and Austria, where vaccination rates appear to be lower in the oldest (85 and over) age 
groups than among the 75-84 year-olds.  

3- Educational level 

2.10.1.4. Percentage of population vaccinated against influenza, 
by educational level

30

25

20

15

10

5

0

D

I

NL

A

Pre-primary and Primary (ISCED 0-1)

Secondary (ISCED 2)

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Austria: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Source: National data 

After adjustment for gender and age, vaccination rates do not differ much by educational level, with no 
clear relationship among the four countries.  

161 

 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

4- Activity status 

Preventive care 

2.10.1.5. Percentage of population vaccinated against influenza, 
by activity status

30

25

20

15

10

5

0

D

I

NL

A

Active

Inactive

Source: National data 

After  adjustment  for  gender  and  age,  vaccination  rates  are  somewhat  higher  among  the  inactive  in 
three of the countries, but not in Austria. 

5- Comparative study of countries1 

Level 

Gender 

Age 

Education 

Activity status 

Table 2.10.1.3. Analysis of gradients 

D 

1.48 

0.95 

1.29 

0.95 

0.87 

I 

0.89 

1.04 

1.21 

1.09 

0.88 

NL 

1.11 

1.12 

1.34 

1.11 

0.77 

A 

0.83 

1.02 

0.37 

1.31 

1.01 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: National data 

The small number of countries prevents any meaningful comparative analysis of the data. 
A  better  harmonisation  of  the  questions  asked  about  influenza  vaccination  would  certainly  provide 
much more comparable data. 

1 See 1.2.2 above for the definition of gradients. 

162 

 
 
 
 
 
 
 
 
 
 
 
 
                                                          
Preventive care 

(cid:4) 

2.10.2. Screening for breast cancer 
Screening for breast cancer is one of the major priorities for preventive health. Data were requested 
on: 

·   The percentage of women ever having had a mammography 
·   For women ever having had a mammography, the time since the last mammography. 

The two sets of indicators will be studied in turn. 

2.10.2.1. Percentage of women who ever had a mammography 

A. Data requested 
Screening for breast cancer 

1.  Number of women who ever had a mammography 
2.  Number of women who never had a mammography 

B. Data collected 

Table 2.10.2.1.1. Wording of the questions by survey 

Country  Year 

Question 

Response categories 
constructed 

DK 

2000  When was the last time you had a mammogram? 

F 

I 

2000  Have you had a mammography? 

2000 

Have you ever had a mammography done in absence of disorders or 
symptoms?  

NL 

2000  Did you ever have an X-ray photograph of one or both breasts? 

P 

IS 

1999 

In the last three months how often [have you] [has she/he] had 
mammography? 

1998  Ever had breast cancer screening? 

Source: National data 

Table 2.10.2.1.2. Comparative summary and variable averages 

Country 

% of females having had a 
breast cancer test 

Reference 
period 

Restrictions 

DK 

F 

I 

NL 

P 

IS 

Source: National data 

30.4 

48.9 

36.6 

46.8 

5.3 

70.6 

No restriction 

No restriction 

No restriction 

No restriction 

3 months 

No restriction 

None 

None 

In absence of disorders or 
symptoms 

None 

None 

None 

Yes 
Never 

Yes 
Never 

Yes 
Never 

Yes 
Never 

Yes 
None 

Yes 
Never 

Response 
categories 
constructed 

Yes/no 

Yes/no 

Yes/no 

Yes/no 

Yes/no 

Yes/no 

Four  of  the  six  countries  directly  asked  women  whether  they  had  ever  had  a  mammography.  The 
same information could be derived for the more detailed question asked in Denmark. 
Portugal  used  a  three-month  reference  period,  which  means  that  their  much  lower  rates  are  not 
comparable with those from other countries and could not be included in the subsequent analysis.  

163 

 
 
 
 
 
 
 
 
 
 
(cid:4) 

C. Analysis 

Preventive care 

The indicator analysed is the percentage of women ever having had a breast cancer test (as against 
those who had never had one). 

1- Overall level 

2.10.2.1.1. Percentage of female population 
ever having had a mammography

80

70

60

50

40

30

20

10

0

DK

F

I

NL

IS

Source: National data 

There is considerable variation among countries in the percentage of women ever having had a breast 
cancer screening test, from 30% in Denmark to 71% in Iceland (which did not include persons aged 85 
and  over  in  its  sample).  The  percentage  for  Italy  would  probably  have  been  larger  if  breast  cancer 
screening following health disorders or symptoms had been included. 

2- Age 

2.10.2.1.2. Percentage of female population ever having had a 
mammography, by age

120

100

80

60

40

20

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

DK

F

I

NL

IS

Source: National data 

164 

 
 
 
 
 
 
 
 
 
 
 
Preventive care 

(cid:4) 

The evolutions with age in the percentage of women ever having had a breast screening test at the 
time  of  the  survey  reflects  preventive  activities  taken  during  the  past  decades  and  are  not  easy  to 
interpret.  Not  surprisingly,  very  few  young  women  (aged  15  to  24)  had  ever  had  a  breast  cancer 
screening test. The rate increases with age, but declines after age 55-64, most probably because of 
the lower screening rates in the past.  

3- Educational level 

2.10.2.1.3. Percentage of female population ever having had a 
mammography, by educational level

80

70

60

50

40

30

20

10

0

DK

F
Pre-primary and Primary (ISCED 0-1)

I

NL

IS

Secondary (ISCED 2)

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Denmark: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Source: National data 

After adjustment for age, women with a higher educational level are more likely to have ever had a 
mammography,  particularly  in  Italy  and France. But the effect of education is very small in Denmark 
and Iceland, and even opposite in the Netherlands. 

4- Activity status 

2.10.2.1.4. Percentage of female population ever having had a 
mammography, by activity status

80

70

60

50

40

30

20

10

0

DK

F

I

NL

IS

Active

Inactive

Source: National data 

After adjustment for age, little difference is found between the economically active and inactive. 

165 

 
 
 
 
 
 
 
 
 
 
(cid:4) 

5- Comparative study of countries2 

Preventive care 

Level 

Age 

Education 

Activity status 

Table 2.10.2.1.3. Analysis of gradients 

DK 

0.73 

0.56 

1.11 

0.98 

F 

1.17 

1.02 

1.29 

1.12 

I 

0.88 

0.59 

1.49 

1.06 

NL 

1.12 

1.04 

0.94 

0.97 

IS 

1.69 

0.80 

1.04 

0.96 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: National data 

Although  the  sample  is  too  small  for  any  meaningful  comparative  analysis,  however,  internationally 
comparable data can nevertheless be obtained by harmonising the questions asked. 

2.10.2.2. Time since last mammography 

A. Data requested 
Data were requested on the distribution of women ever having had a mammography by the time since 
they last had one: 

1.  Number of women who had their last mammography less than one year ago 
2.  Number of women who had their last mammography between 1 and 2 years ago 
3.  Number of women who had their last mammography 2 years ago or more 

B. Data collected 

Table 2.10.2.2.1. Wording of the questions by survey 

Country  Year 

Question 

DK 

2000  When was the last time you had a mammogram? 

F 

I 

2000  What is the year of last mammography? 

2000 

At what age was your first mammography done? 
How often have you had subsequent controls done 
after the first mammography? 

NL 

2000  If yes, Year  : ......... Month 

IS 

1998 

How long is it since your last breast cancer 
screening? 

Source: National data 

Response categories 

Within the past year 
Between 1-2 years ago 
More than 2 years ago 

In the past 3 years (1998 to 2000) 
More than 3 years ago 

At least once a year 
Every 2 years 
Every 3 years or 
Less often 

Within the past year 
Between 1-2 years ago 
More than 2 years ago 

Within the past year 
Between 1-2 years ago 
More than 2 years ago 

France  did  not  use  the  response  categories  requested,  so  that  their  data  are  not  comparable  and 
could not be included in the analysis. Similarly, the required data could not de derived from the replies 
to the questions asked in Italy. 
The responses were given in other countries either in intervals of time or in years. The responses in 
years were allocated according to the three time intervals requested. For Italy, information on the year 
on last mammography had to be converted into a duration. 

2 See 1.2.2 above for the definition of gradients. 

166 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                          
Preventive care 

(cid:4) 

Table 2.10.2.2.2. Comparative summary and variable averages 

Country 

% of females having had a breast cancer test 
within the past year 

DK 

NL 

IS 

Source: National data 

18.9 

38.3 

45.5 

Age group 

15 and over 

15-84 

15-74 

Analysis will refer to the percentage of women who had had a breast cancer screening test less than 
one year before the survey among all women who had ever had a breast cancer screening test. 

2.10.2.2.1. Women having had a mammography within the past 
year as a percentage of all women ever having had a 
mammography 

C. Analysis 

1- Overall level 

50

45

40

35

30

25

20

15

10

5

0

DK

NL

IS

Source: National data 

Among  women  ever  having  had  a  mammography,  those  who  had  their  last  breast  screening test 
within the preceding 12 months accounted for less than a year earlier is less than 19% in Denmark, 
but  this  average  includes  older  women  who  tend  to  have  fewer  tests  than  middle-aged  ones.  More 
comparable  figures  would  have  been  obtained  if  all  averages  had  been  restricted  to  the  25-74 age 
group, common to all surveys. 

167 

 
 
 
 
 
 
 
 
 
 
Preventive care 

2.10.2.2.2. Women having had a mammography within the past 
year as a percentage of all women ever having had a 
mammography, by age

(cid:4) 

2- Age  

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

DK

NL

IS

Source: National data 

When data are examined by age, the ranking of countries differs according to age group. Denmark, 
which  has  the  lowest  rate  of  women  ever  having  had  a  breast  screening  test,  also  has  the  lowest 
percentage of tests during the past year, except for the youngest age group. Iceland, with the highest 
rate of women screened for breast cancer also has the highest test rate within the past year in the 15-
64  age  group.  No  clear  international  pattern  appears  for  the  relationship  between  the  indicator  and 
age. 

3- Educational level 

2.10.2.2.3. Women having had a mammography within the past 
year as a percentage of all women ever having had a 
mammography, by educational level

50

45

40

35

30

25

20

15

10

5

0

DK

NL

IS

Pre-primary and Primary (ISCED 0-1)

Secondary (ISCED 2)

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Denmark: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Source: National data 

There is no clear pattern among countries in the relationship of the indicator with educational status. 

168 

 
 
 
 
 
 
 
 
 
Preventive care 

4- Activity status 

(cid:4) 

2.10.2.2.4. Women having had a mammography within the past 
year as a percentage of all women ever having had a 
mammography, by activity status

50
45
40
35
30
25
20
15
10
5
0

DK

NL

IS

Active

Inactive

Source: National data 

After adjustment for age, economically inactive women ever having a breast cancer screening test are 
slightly  more  likely  than  active  women  to  have  had  the  last  one  during  the  past  year.  However,  the 
difference is not significant, and even opposite in Denmark.  

5- Comparative study of countries 

Table 2.10.2.2.3. Analysis of gradients 

Level 

Age 

Education 

Activity status 

DK 

0.52 

0.47 

1.25 

1.06 

NL 

1.06 

0.91 

0.99 

0.94 

IS 

1.26 

1.00 

1.27 

0.99 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: National data 

Data  are  available  for  only  three  countries  and  no  clear  link  with  age,  education  or  activity  status 
emerges, not making a comparative analysis possible. 
It is difficult to conclude on the comparability of data on breast cancer screening, in view of the small 
number of countries for which data could be collected. 

2.10.3. Screening for cervical cancer 
Screening  for  cervical  cancer  is  also  a  major  preventive  health  concern.  Here  also,  data  were 
requested on the percentage of women ever having had a cervical cancer test and, for those women, 
on the time since the last one. 

2.10.3.1. Percentage of women ever having had a cervical cancer test 

A. Data requested 
Screening for cervical cancer 

1.  Number of respondents who ever had a cervical cancer test  
2.  Number of respondents who never had a cervical cancer test 

169 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

B. Data collected 

Preventive care 

Table 2.10.3.1.1. Wording of the questions by survey 

Country  Year 

Question 

Response categories 
constructed 

DK 

2000  When was the last time you had a vaginal smear? 

I 

2000 

Have you ever had a pap-test (vaginal smear) in absence of 
disorders or symptoms? 

NL 

2000 

When has a preventive screening for cervical cancer (by means 
of smears) been made for the last time? 

IS 

1998  Ever had cervical cancer screening? 

Source: National data 

Yes 
No 

Yes 
No 

Yes 
No 

Yes 
No 

Table 2.10.3.1.2. Comparative summary and variable averages 

Country 

% of females ever having had 
a cervical cancer test 

Restrictions 

Age group 

DK 

I 

NL 

IS 

Source: National data 

83.1 

60.8 

73.8 

91.1 

No restriction 

15 and over 

Absence of disorders or symptoms 

25 and over 

No restriction 

No restriction 

15 and over 

15-74 

In Denmark and the Netherlands, women who had never had a cervical cancer screening test could 
be identified from the question asked. 
In  Italy,  women  who  had  had  a  pap-test  (vaginal  smear)  in  the  presence  of  disorders  or  symptoms 
could not reply ‘yes’ to the question, which could be a possible cause of under-estimation, relative to 
other countries. 
As in data on breast cancer screening, age groups differ among countries. Women aged 15-24 were 
not covered by Italian data and data from Iceland are restricted to women aged less than 75 years. 

170 

 
 
 
 
 
 
 
 
Preventive care 

(cid:4) 

C. Analysis 
Percentage  of  women  ever  having  had  a  cervical  cancer  test  versus  women  never  having  had  a 
cervical cancer test. 

1- Overall level 

2.10.3.1.1. Percentage of female population ever having had a 
cervical cancer screening test

100

90

80

70

60

50

40

30

20

10

0

DK

I

NL

IS

Source: National data 

Between 61 and 91% of women had had a cervical cancer screening test. The lowest level is found in 
Italy, where only pap tests (vaginal smears), in the absence of disorders or symptoms, were reported. 

2- Age  

2.10.3.1.2. Percentage of female population ever having had a 
cervical cancer screening test, by age

100

90

80

70

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

DK

I

NL

IS

Source: National data 

The evolutions with age in the percentage of women ever having had a cervical cancer screening test 
at the time of the survey reflects preventive activities taken during the past decades and are not easy 
to interpret. Not surprisingly, very few young women (aged 15 to 24) had ever had a cervical cancer 
screening test. The rate increases with age, but declines after age 55-64, most probably because of 

171 

 
 
 
 
 
 
 
 
 
(cid:4) 

Preventive care 

the lower screening rates in the past. Under age 75, age-specific rates are lower in Italy than in other 
countries. This may not have been the case if women who had a cervical cancer screening test in the 
presence of disorders or symptoms had been included.  

3- Educational level 

2.10.3.1.3. Percentage of female population ever having had a 
cervical cancer screening test, by educational level

100

90

80

70

60

50

40

30

20

10

0

DK

I

Pre-primary and Primary (ISCED 0-1)
Upper secondary (ISCED 3)

NL
Secondary (ISCED 2)
Post-secondary (ISCED 4-6)

IS

Denmark: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Source: National data 

After adjustment for age, women with a higher educational level are more likely to have ever had a 
cervical  cancer  screening  test,  particularly  in  Italy.  But  the  effect  of  education  is  very  small  in  the 
Netherlands and Iceland. 

4- Activity status 

2.10.3.1.4 Percentage of female population ever having had a 
cervical cancer screening test, by activity status

100

90

80

70

60

50

40

30

20

10

0

DK

I

NL

IS

Source: National data 

Active

Inactive

172 

 
 
 
 
 
 
 
 
 
 
Preventive care 

(cid:4) 

After adjustment for age, there is little difference between economically active and inactive women in 
the probability of ever having had a cervical cancer screening test, except in Italy, possibly because of 
the different indicator definition used. 

5- Comparative study of countries3 

Table 2.10.3.1.3. Analysis of gradients 

Level 

Age 

Education 

Economic status 

DK 

1.06 

0.77 

1.10 

1.05 

I 

0.78 

0.68 

1.32 

1.13 

NL 

0.94 

1.16 

1.06 

0.99 

IS 

1.16 

0.54 

1.02 

1.00 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: National data 
Data are available for only four countries, which prevents meaningful international comparisons. 

2.10.3.2. Time since last screening test for cervical cancer 

A. Data requested 

Time: for respondents who ever had a cervical cancer test 

1.  Number of respondents who had their last cervical cancer test less than one year ago 
2.  Number of respondents who had their last cervical cancer test between 1 and 2 years ago 
3.  Number of respondents who had their last cervical cancer test 2 years ago or more 

B. Data collected 

Table 2.10.3.2.1. Wording of the questions by survey 

Country  Year 

Question 

Response categories 

DK 

2000  When was the last time you had a vaginal smear? 

I 

2000 

How often have you had subsequent controls done after 
the first pap test? 

NL 

2000  Year: ......... Month:......... 

IS 

1998  How long is it since your last cervical cancer screening? 

Source: National data 

Within the past year 
Between 1-3 years ago 
More than 3 years ago 

At least once a year 
Every 2 years 
Every 3 years or less often 

Within the past year 
Between 1-2 years ago 
More than 2 years ago 

Within the past year 
Between 1-3 years ago 
More than 3 years ago 

Similarly to data on times since the last mammography, the requested indicators could not be derived 
from Italian data. 
In the other three countries, responses were given either in intervals of time or in years. 
The responses in years were divided according to the 3 required intervals of the guideline above. 

3 See 1.2.2 above for the definition of gradients. 

173 

 
 
 
 
 
 
 
 
 
 
 
 
 
                                                          
(cid:4) 

Preventive care 

Table 2.10.3.2.2. Comparative summary and variable averages 

Country 

% of females having had a cervical cancer 
test within the past year 

DK 

NL 

IS 

Source: National data 

32.7 

25.4 

57.2 

Reference period 

No restriction 

No restriction 

No restriction 

Analysis criterion: screening on cervical cancer within the past year versus more than one year. 

2.10.3.2.1. Women having had a cervical cancer screening test 
within the past year as a percentage of all women ever having 
had a cervical cancer screening test

C. Analysis 

1- Overall level 

60

50

40

30

20

10

0

DK

NL

IS

Source: National data 

Between  25%  and  57%  of  women  ever  having  had  a  cervical  cancer  screening  test  had  had  one 
within the preceding 12 months. The average is highest in Iceland, partly because data do not cover 
women above age 75, who tend to have lower rates. 

174 

 
 
 
 
 
 
 
 
 
 
(cid:4) 

2.10.3.2.2. Women having had a cervical cancer screening test 
within the past year as a percentage of all women ever having 
had a cervical cancer screening test, by age

Preventive care 

2- Age  

80

70

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

DK

NL

IS

Source: National data 

With increasing age, women ever having had a cervical cancer screening test are progressively less 
likely to have had the last one within the preceding 12 months. 

3- Educational level 

2.10.3.2.3. Women having had a cervical cancer screening test 
within the past year as a percentage of all women ever having 
had a cervical cancer screening test, by educational level

70

60

50

40

30

20

10

0

DK

NL

IS

Pre-primary and Primary (ISCED 0-1)

Secondary (ISCED 2)

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Denmark: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Source: National data 

Among women ever having had a cervical cancer screening test, those with a higher educational level 
are slightly more likely to have had one less than one year ago. 

175 

 
 
 
 
 
 
 
 
(cid:4) 

4- Activity status 

Preventive care 

2.10.3.2.4. Women having had a cervical cancer screening test 
within the past year as a percentage of all women ever having 
had a cervical cancer screening test, by activity status

70

60

50

40

30

20

10

0

DK

NL

IS

Active

Inactive

Source: National data 

Based on these data, there is no clear relationship with activity status. 

5- Comparative study of countries4 

Table 2.10.3.2.3. Analysis of gradients 

Level 

Age 

Education 

Economic status 

DK 

0.87 

1.05 

1.11 

1.01 

NL 

0.68 

0.84 

1.07 

1.20 

IS 

1.53 

1.12 

1.23 

0.90 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: National data 

No meaningful international comparative analysis can be performed since data are available for only 
three countries. 
Similarly  to  indicators  on  breast  cancer  screening,  it  is  difficult  to  conclude  on  the  international 
comparability of cervical cancer screening data. Here also, the issue is one of data availability. 

4 See 1.2.2 above for the definition of gradients. 

176 

 
 
 
 
 
 
 
 
 
 
                                                          
Use of medicines 

(cid:4) 

2.11. Use of medicines 
With  the  continuous  increase  in  the  consumption  of  medicines,  prescribed  or  not  prescribed,  it  has 
become  increasingly  important  to  get  information  on  the  pattern  of  medicine-taking  within  the 
population and on the characteristics of medicine consumers. Data on medicines not prescribed by a 
physician  tend  to  have  more  comparability  problems,  particularly  because  ’medicines’  and  similar 
terms may be understood very differently by individual respondents. 

A. Data requested 
Since  data  on  the  use  of  prescribed  medicines  is  more  common  and  likely  to  be  more  comparable 
across countries, data were requested on: 

Medicines prescribed by a physician 

1.  Number of respondents who had used medicines prescribed by a physician during the 

past two weeks 

2.  Number of respondents who had not used medicines prescribed by a physician during 

the past two weeks 

B. Data collected 

Table 2.11.1. Wording of the questions by survey 

Country  Year 

Question 

Response categories 

B 

1997 

Neither the use of medicines during hospitalisation nor the 
pill must be taken into account here. Did you use any 
medicines during the last two weeks? 

DK 

2000 

Within the past 2 weeks have you taken any prescribed 
medicine? 

F 

2000 

Do you use medicines prescribed by a physician during the 
30 days of observation? 

IRL 

1998 

Are you regularly taking any prescribed pills or medication? 
Yes/no 

I 

2000 

During the past 2 days, did you use drugs/medication? By 
prescription or advice of a physician 

NL 

2000 

Did you / the child use the prescribed medication during 
the past 14 days? 

A 

1999 

Have you used during the past four weeks medicines 
prescribed by a physician? 

IS 

1998 

Have you taken prescription medication for illness or injury 
in the past month? 

Yes 
No 

Yes 
No 

Yes 
No 

Yes 
No 

Yes 
No 

Yes 
No 

Yes 
No 

Yes 
No 

Source: National data 

Table 2.11.2. Comparative summary and variable averages 

Country 

% of medicines users 

Period considered 

B 

DK 

F 

IRL 

I 

NL 

A 

IS 

Source: National data 

43.8 

34.9 

36.9 

32.4 

33.1 

38.1 

38.2 

8.7 

2 Weeks 

2 Weeks 

30 Days 

Regularly 

2 Days 

14 Days 

4 Weeks 

Past month 

Only  three  countries  used  the  requested  reference  period  of  two  weeks.  Three  countries  used  a  4-
week reference period, while France used the month nearly equal to 4 months. The Irish survey asked

177 

 
 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

Use of medicines 

about  ’regularly  taking  pills  or  medication   ’  and  their  data  have  been  excluded  from  further 
analysis.  Finally,  data  from  the  Italian  survey  have  not  been  included  in  the  analysis  as  it  used  a
two days reference period.

There are also variations in the way questions are asked and in their wording.  

C. Analysis 
The indicator analysed is the percentage of population who used prescribed medicines within the two 
or four weeks preceding the survey.  

1. Overall level

2.11.1. Percentage of population having used prescribed 
medicines in the past weeks

50

45

40

35

30

25

20

15

10

5

0

B

DK

NL

F

A

IS

2 weeks                                                         4 weeks

Source: National data 

Among the six countries, the percentage of population having taken prescribed drugs  within the past 
four weeks is not higher than that observed when the shorter two-week period is used.  
The  remarkably  low  percentage  in  Iceland  cannot  be  explained  by  the  fact  that  the  sample  did  not 
cover the population aged 75 years and above, or by the relatively small sample size of the survey. 

178 

 
 
 
 
 
 
 
 
 
Use of medicines 

2- Age and gender 

(cid:4) 

2.11.2. Percentage of female population having used prescribed 
medicines in the past 2 or 4 weeks, by age

100

90

80

70

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

DK

NL

F

A

IS

Source: National data 

2.11.3. Percentage of male population having used prescribed 
medicines in the past 2 or 4 weeks, by age

100

90

80

70

60

50

40

30

20

10

0

15-24

25-34

35-44

B

DK

45-54

NL

55-64

65-74

75-84

85+

F

A

IS

Source: National data 

Apart from the very low percentages found for Iceland, all other countries share the same pattern of 
use  of  prescribed  medicine  by  age.  At  younger  ages,  some  20-30%  of  women  and  15-20% of men 
take  prescribed  medicines,  whether  during  the  two  or  the  four  weeks  before  the  interview.  The 
percentage then rises with increasing age, rising to over 70% in the older age group.  
With  the  exception  of  Iceland,  women  at  younger  ages  are  generally  much  more  likely  to  take 
prescribed  drugs  than  men  (maybe  gynecologically  related).  With  increasing  age,  the  differential 
between the female and male percentages tends to decrease.  

179 

 
 
 
 
 
 
 
 
(cid:4) 

3- Educational level 

Use of medicines 

2.11.4. Percentage of population having used prescribed 
medicines in the past 2 or 4 weeks, by educational level

60

50

40

30

20

10

0

B

DK

NL

F

A

IS

Pre-primary and Primary (ISCED 0-1)
Upper secondary (ISCED 3)

Secondary (ISCED 2)
Post-secondary (ISCED 4-6)

Denmark, Austria: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Source: National data 

After  adjustment  for  gender  and  age,  the  percentage  of  population  taking  prescribed  medicine 
decreases  with  the  level  of  formal  education  in  all  counties  except  in  France,  where  the  opposite 
patterns appears and, to a lesser degree, in Iceland.  

4- Activity status 

2.11.5. Percentage of population having used prescribed 
medicines in the past 2 or 4 weeks, by activity status

60

50

40

30

20

10

0

B

DK

NL

F

A

IS

Active

Inactive

Source: National data 

After  adjustment  for  gender  and  age,  the  economically  inactive  are  more  likely  to  take  prescribed 
drugs than the inactive peers. 

180 

 
 
 
 
 
 
 
 
 
 
 
Use of medicines 

5- Comparative study of countries1 

(cid:4) 

Table 2.11.3. Analysis of gradients 

Level 

Gender 

Age 

Education 

Activity status 

  B 

1.15 

0.75 

1.06 

0.85 

0.78 

DK 

0.92 

0.80 

0.85 

0.82 

0.60 

NL 

1.00 

0.80 

1.00 

0.78 

0.74 

F 

1.00 

0.68 

0.90 

1.21 

0.89 

A 

1.04 

0.87 

1.05 

0.87 

0.86 

IS 

0.24 

0.75 

0.12 

0.75 

0.66 

Note: for each gradient, the highest values are in bold and the lowest values are in italic. 
Source: National data 

Iceland  clearly  appears  as  an  outsider  among  the  six  countries  considered:  little  difference  is  found 
among  the  other  countries  as  regards  the  average  percentage  of  persons  taking  prescription 
medicines, with similar gender, age, education and activity status patterns.  
More in-depth analysis is therefore needed to explain the very different figures obtained in Iceland.  
The  study  of  data  from  the  other  four countrie s  tends  to  show  that  they  are  probably  rather 
comparable, although it would be preferable for all countries to use the same reference period.  

1 See 1.2.2 above for the definition of gradients. 

181 

 
 
 
 
 
 
                                                          
 
 Physical and sensory functional limitations 

(cid:4) 

3.  Analysis of health indicators with relatively weak level of 

comparability 

Indicators for which international comparability is relatively weak are reviewed in this chapter. These 
include the following areas: 

1. 
2. 
3. 
4. 
5. 
6. 
7. 

Physical and sensory functional limitations 
Personal care activities 
Mental health 
Physical activities 
Use of drugs 
Diet 
Quality of life 

Since  they  differ  in  terms  of  comparability  problems,  data  relating  to  these  indicators  will  not  be 
presented in detail as was done in the previous part. Moreover, the item "Activity restriction" was not 
included at all in the analysis due to the same problems and the fact it is a too much general question, 
so it was preferred to analyse only the specific limitations.  
However, data on limitations as regards walking will be analysed, as was done for more comparable 
indicators,  to  show  how  that  it  may  be  possible  to  bring  out  general  tendencies  in  terms  of  age, 
education  or  activity  status  even  when  the  indicators  cannot  be  directly  compared  among  countries 
because instruments differ too much. 

3.1. Physical and sensory functional limitations 
Information  on  disabilities  has  become  an  increasingly  important  aspect  of  public  health.  A  wide 
variety of instruments have been developed during the past decades through sets of questions on the 
ability  to  perform  activities  directly  associated  with  specific  physical  or  sensory  capabilities.  Many  of 
these  instruments  are  based  on  extensive  questionnaires,  which  assess  different  severity  levels  of 
limitation. 
Physical or sensory imitations for which data are most currently available among European countries 
are  those  relating  to  walking,  seeing  (near  or  far),  hearing  and  carrying  an  object.  But  differences  in 
questions and in response categories lead to a multiplicity of levels of country-specific severity of each 
limitation, which make any comparison of the results problematic. 
Data were requested on the following physical and sensory limitations: 

·   Limitation as regards walking (preferably 500 metres) 
·   Limitation in seeing newspaper print clearly 
·   Limitation in seeing clearly the face of someone from 4 meters (across a road)  
·   Limitation in hearing what is said in a conversation with one person 
·   Limitation in lifting and carrying a shopping bag of 5 kgs 

For  each  category  of  limitation,  the  difficulties  of analys ing  data  from  questions  that  are  often  not 
comparable in their wording and response categories which differ in their implied level of disability will 
be presented.  

3.1.1. Limitations as regards walking 

A. Data requested 
Previous studies by Euro-REVES  and  the  OECD  have  recommended  the  use  of  questions  such  as: 
“Can  you  walk  400  metres  without  resting?“,  with  the  following  levels  of  severity; ‘without  difficulty’, 
‘with minor difficulty’, ‘with major difficulty’, ‘not able to’. Euro-REVES 2 recently recommended a 500 
metres distance for data collection. 
Data were requested on limitations as regards walking (preferably, 500 metres). 

183 

 
 
 
 
 
 
 
 
 
(cid:4) 

B. Data collected 

Physical and sensory functional limitations 

Table 3.1.1.1. Wording of the questions by survey 

Country  Year 

Question 

B 

1997 

What is the furthest can you walk on your own without 
stopping and without severe discomfort? 

DK 

2000 

Are you normally able to walk 400 meters without resting 
with no difficulty, with minor difficulty, with major difficulty 
or not at all? 

Response categories 
provided 

Walk only a few meters 
Walk less than 200 meters 
Walk 200 meters or more 

Walk 400 meters without difficulty 
Walk 400 meters with minor difficulty 
Walk 400 meters with major difficulty 
Walk 400 meters: not at all 

D 

1998 

Does your health now limit you in these activities? If so, 
how much? Walking more than a mile 

IRL 

1998 

Please indicate which statement best describes your 
own health state today: Mobility (I have no problems in 
walking about/ I have some problems in walking about / 
I am confined to bed) 

Yes, limits me a lot 
Yes, limits me a little 
No, not limited at all 

Can’t walk 
Yes, without any problem 

I 

2000 

What is the longest distance he/she can walk by 
him/herself without stopping and without getting too 
tired? 

Walk only a few meters 
Walk less than 200 meters 
Walk 200 meters or more 

NL 

2000 

Can you walk a distance of 400 metres without any 
stops? 

P 

1999 

How far can you [/she/he] walk on a flat ground without a 
pause nor feeling discomfort? 

Can’t walk 
Walk with great difficulty 
Walk with some difficulty 
Walk without any problem 

Can’t walk 
Walk in a wheelchair alone 
Walk only a few meters 
Walk less than 200 meters 
Walk 200 meters or more 

S 

2000 

Can you take a short walk, say five minutes, at a fairly 
brisk pace? 

Can’t take a short walk 
Can take a short walk 

UK 

2000 

Cannot walk 200 yards or more on own without stopping 
or discomfort (with walking aid if normally used)? 

Can’t walk 
Can walk 

NO 

1998 

Does your health now limit you in these activities? If so, 
how much? Walking several blocks? [in Norwegian this 
is translated into 2-300 meters] 

Yes, limits me a lot 
Yes, limits me a little 
No, not limited at all 

CH 

1997 

For what distance are you able to walk alone (unaided) 
without stopping and without too much difficulty? 

Can’t get around/walk 
Walk only a few meters 
Walk less than 200 meters 
Walk 200 meters or more 

Source: National data 

The  response  categories  in  italic  are  those  regrouped  to  form  the  category  of  people  who  have 
walking problems to walk versus those without problems. 

184 

 
 
 
 
 
 
 Physical and sensory functional limitations 

(cid:4) 

Table 3.1.1.2. Comparative summary and variable averages 

Country 

Distance 

Limited (%) 

Specific details 

B 

DK 

D 

IRL 

I 

NL 

P 

S 

UK 

NO 

CH 

200 m 

400 m 

1 mile 

No 

200 m 

400 m 

200 m 

5 min 

200 yards 

300 m 

200 m 

15.4 

8.0 

18.8 

11.4 

6.5 

8.6 

9.5 

5.1 

9.6 

13.5 

2.6 

Without stopping 

Without resting 

- 

- 

Without stopping 

Without any stops 

Without a pause 

- 

Without stopping 

Walking several blocks 

Without stopping 

Source: National data 

1. Wording of the question 
None of the countries used the reference distance requested in the guidelines (500 m). 

·   Belgium, Italy, Portugal and Switzerland used 200 meters (with the same response categories, 
the  level  of  difficulty  being  included  in  the  question,  i.e.  precision  on  ‘without  stopping’  & 
‘without severe discomfort’). 

·   The UK used a distance of 200 yards. 
·   Denmark and the Netherlands used 400 meters. 
·   Germany used 1 mile. 
·  
·   Sweden used a time measure (5 minutes) . 
·  

In Norway, the reference distance was ‘several blocks’. 

Ireland and Iceland asked a general question without referring to distance or time. 

The  questions  are  phrased  differently,  so  that  respondents  may  have  understood  them  differently. 
Some questions are centred on the distance to ‘go without stopping’ while others are on the ‘level of 
difficulty to walk a certain distance’. In addition, response categories vary widely. 

2. Comparability 
It is not possible to find a mutual unit of distance between the different questions and to determine a 
priori comparable response categories. The following analysis will therefore make use of data which 
all measure, with different metric systems , the same broad concept of walking limitation, but are not 
mutually comparable. 

185 

 
 
 
 
 
 
Physical and sensory functional limitations 

3.1.1.1. Percentage of population limited in terms of walking

(cid:4) 

C. Analysis 

1. Overall 

20

18

16

14

12

10

8

6

4

2

0

B

DK

D

IRL

I

NL

P

S

UK

NO

CH

Source: National data 

The countries with the highest recorded levels of walking limitations (with different degrees of difficulty) 
are Germany, Belgium and Norway with more than 13% of persons affected. The high figure found in 
Germany may be explained by the use of a distance of one mile. However, the same argument cannot 
apply to Belgium, which uses a 200-metre distance.  
The lowest percentages of persons with walking limitations are found in Switzerland and Sweden. It 
may  be  that  a  question  formulated  in  terms  of  time,  as  in  Sweden,  leads  to  a  low  percentage  of 
persons with walking difficulties, but there is no evidence to support such a statement and so it cannot 
be used to explain the very low level found in Switzerland. 
It appears, indeed, that beyond differences in questions and response categories, apparently similar 
ones lead to a variety of perceptions, understanding and interpretation by respondents. 

186 

 
 
 
 
 
 
 
 
 Physical and sensory functional limitations 

2. Age and gender 

3.1.1.2. Percentage of female population 
limited in terms of walking, by age

(cid:4) 

90

80

70

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B

S

DK

UK

D

NO

IRL

CH

I

NL

P

Source: National data 

3.1.1.3. Percentage of male population
 limited in terms of walking, by age

70

60

50

40

30

20

10

0

15-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

B
S

DK
UK

D
NO

IRL
CH

I

NL

P

Source: National data 

Whatever the distance to walk, the percentage of males and women with walking limitations increases 
with  age.  However,  the  effect  of  age  on  walking  limitations  varies  widely  among  countries  – for 
example, age has much less impact in Switzerland than in other countries. 

187 

 
 
 
 
 
 
 
 
 
(cid:4) 

3. Educational level 

Physical and sensory functional limitations 

3.1.1.4. Percentage of population limited in terms of walking, by 
educational level

25

20

15

10

5

0

B

DK

D

IRL

I

NL

P

S

UK

NO

CH

Pre-primary and Primary (ISCED 0-1)

Secondary (ISCED 2)

Upper secondary (ISCED 3)

Post-secondary (ISCED 4-6)

Denmark and Switzerland: ISCED 0-2 (Pre-primary and Primary + Secondary) 
Ireland: Primary, Secondary (Secondary + Upper secondary), Tertiary 
Source: National data 

After adjustment for sex and age, the higher the education, the lower the probability of suffering from 
walking limitations. 

4. Activity status 

3.1.1.5. Percentage of population limited in terms of walking, 
by activity status

25

20

15

10

5

0

B

DK

D

IRL

I

NL

S

UK

NO

CH

Active

Inactive

Source: National data 

Limitations as regards walking are generally much more prevalent among economically inactive 
persons than among active ones, but there is little difference between the two groups in Italy, 
especially when compared with the very large differential found in the UK.  

188 

 
 
 
 
 
 
 
 
 
 
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(cid:4) 

3.1.2. Limitations in seeing clearly near 
Vision limitations are one of the priorities of the recommendations given by WHO and Euro-REVES. 
These  include  the  ability  to  see  near  and  the  ability  to  see  far,  which  are  each  recorded  through 
different instruments. Limitations in seeing far will be examined in the next section. 

A. Data requested 
Data were requested on limitation in seeing newspaper print clearly. 

B. Data collected 

Table 3.1.2.1. Wording of the questions by survey 

Country  Year 

Question 

DK 

2000 

Are you normally able to read ordinary newspaper print (with 
glasses if normally worn) with no difficulty, with minor difficulty, 
with major difficulty or not at all? 

NL 

2000 

Are your eyes still good enough to read the small print in the 
newspapers (with glasses or contact lenses, if necessary)? 

Response categories 
provided 

Not at all 
With major difficulty 
With minor difficulty 
With no difficulty 

Can’t 
With great difficulty 
With some difficulty 
Without any problem 

S 

2000 

Can you read ordinary text in a newspaper without difficulty 
(with or without spectacles)? 

NO 

1998 

Can you without difficulty or wearing glasses if need be, see 
ordinary sized newsprint? 

No 
Yes 

No 
Yes 

CH 

1997 

Do you see well enough to read a book or a newspaper? (With 
glasses or contact lenses if you usually wear them) 

Can’t 
With major difficulty 
Without too much difficulty 
With no difficulty 

Source: National data 

Response categories refer to different degree of difficulty to read, but they allow a distinction between 
those who can read without any problem and those who have difficulty or are unable to do so. 

Table 3.1.2.2. Comparative summary and variable averages 

Country 

Limited (%) 

Specific details 

DK 

NL 

S 

NO 

CH 

4.2 

14.3 

1.5 

3.5 

5.5 

Ordinary newspaper print 

Small print 

Ordinary text 

Ordinary sized newsprint 

Book or newspaper 

Source: National data 

1. Wording of the question 
The  common  denominator  of  the  questions  is  the  faculty  to  read  newspaper  or  book  print,  with  or 
without glasses or contact lenses. 
Data were requested on limitations in reading ‘newspaper print’. The Netherlands inquired about the 
ability to read ‘small print’, and this may explain the much higher percentage of persons with reading 
difficulties  in  that  country.  Other  countries  indicate, to  various  extents,  that  the  question  is  about 
‘ordinary  print’.    Since  the  questions  are  phrased  differently,  they  may  affect  the  way  respondents 
understand them.  
More  importantly,  the  levels  of  difficulty  in read ing  implied  in  the  response  categories  differ  among 
countries. 

189 

 
 
 
 
 
 
 
 
 
 
(cid:4) 

Physical and sensory functional limitations 

2. Comparability 
It  is  not  possible  to  find  a  common  type  of  print  characters  and  a  common  set  of  limitation  levels 
among the different questions to get sufficiently comparable data. 

3.1.3. Limitations in seeing clearly far  
Another sub-domain of visual health often asked in surveys is the ability to see at a long distance. 

A. Data requested 
The  recommended  instrument  of  Euro-REVES  dealt  with  ‘Limitation  in  seeing  clearly  the  face  of 
someone from 4 metres (across a road)’. 

B. Data collected  

Table 3.1.3.1. Wording of the questions by survey 

Country  Year  Question 

Response categories 
provided 

B 

1997 

Can you see well enough to recognise a friend at a 
distance of one metre (at arms length) / four metres (across 
a road)? 

With great difficulty 
With some difficulty 
Without any problem 

I 

2000 

Does he/she see enough to recognise a friend 4 metres 
away (on the other side of the street), using eyeglasses or 
contact lenses if necessary? 

Limitations 
Without limitations 

NL 

2000 

Can you recognise someone's face at a distance of 4 
metres (with glasses or contact lenses, if necessary)? 

P 

1999 

Are you [is she/he] able to see in order to recognize a 
friend? (with or without glasses or contact lenses)? 

Can’t 
With great difficulty 
With some difficulty 
Without any problem 

Not able even within 1 meter 
Within 1 meter 
Within 4 meters, for instance, 
from the other side of the street 

UK 

2000 

Cannot see well enough to recognise a friend across the 
road (four yards away) (with glasses or contact lenses if 
normally worn)? 

Can’t see 
Can see 

Source: National data 

The common denominator of the question is the faculty to recognise a known person without difficulty 
at a short distance (4 metres, across a road) versus having difficulty or not being able to do so. 

Table 3.1.3.2. Comparative summary and variable averages 

Country 

Distance 

Limited (%) 

Precision in the question 

B 

I 

NL 

P 

UK 

1 to 4 metres 

4 metres 

4metres 

4 metres 

4 yards 

4.2 

2.6 

5.1 

5.1 

2.8 

Recognise a friend 

Recognise a friend 

Recognise someone’s face 

Recognise a friend 

Recognise a friend 

Source: National data 

Countries give the same definition than the one proposed in the guidelines to describe what  be can 
see at less than 4 metres.  

1. Wording of the question 
The level of difficulty developed by the countries in the response categories is different. The term ‘see 
well enough’ is largely employed to describe the action as recommended. 
The Netherlands has a small difference with the use of ‘someone’s face’ in order to ‘a friend’, which 
could perhaps influence the response rate. 
All the questions indicate the possibility for the respondents to use glasses or contact lenses. 

190 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 Physical and sensory functional limitations 

(cid:4) 

2. Comparability 
The common reference distance and wording for 5/6 of the countries favours the comparability of the 
questions. But the different level of limitation disturbs their interpretation. 

3.1.4. Limitations in hearing 
Another sub-item of the functional limitations dealt with hearing. 

A. Data requested 
The instrument recommended by Euro-REVES is a question on ’limitation in hearing what is said in a 
conversation with one person’. 

B. Data collected 

Table 3.1.4.1. Wording of the questions by survey 

Country  Year 

Question 

B 

1997 

Is your hearing good enough to follow a TV programme at a 
volume others find acceptable? 

Response categories 

Hearing: higher severity 
level 
Hearing: low severity 
level 
Hearing without disability 

DK 

2000 

Are you normally able to hear what is said in a normal 
conversation between 3 persons or more (with hearing aid if 
normally worn) with no difficulty, with minor difficulty, with major 
difficulty or not at all? 

Not at all 
With major difficulty 
With minor difficulty 
Without difficulty 

IRL 

1998 

Do you find it very difficult to follow a conversation if there is 
background noise for example from a TV, radio or children 
playing? 

Yes 
No 

I 

2000 

Does he/she manage to hear a television program by raising the 
volume? 

NL 

2000 

Can you follow a conversation in a group of 3 or more persons 
(with a hearing aid, if necessary)? 

P 

1999 

Are you [is she/he] able to listen to a TV or radio programme? 
(With or without a hearing device)? 

Limitations in hearing TV 
even with loud volume 
Limitations in hearing TV 
without loud volume 
Without difficulties 

No 
With great difficulty 
With some difficulty 
Without difficulty 

Not able to hear at all 
even at high volume 
Only hearing at high 
volume 
Not as loud as to bother 
other people 

UK 

2000 

Cannot follow a TV programme at a volume others find 
acceptable (with hearing aid if normally worn)? 

NO 

1998 

Can you without difficulty, or wearing a hearing aid if need be, 
hear what is said during the course of a normal conversation with 
at least two other people 

Yes 
No 

No 
Yes 

CH 

1997  Can you follow a conversation with at least to other persons? 

No 
With major difficulty 
With too much difficulty 
Without difficulty 

Source: National data 

Analyses: The common denominator is the faculty to hear the TV or people speaking versus hearing 
nothing. 
‘Limited’ versus ‘Not limited’ (‘at all’) ó ‘Can’t hear’ (whatever the limitation, i.e. response categories in 
grey) versus ‘without effort/difficulty’. 

191 

 
 
 
 
 
 
 
 
 
 
(cid:4) 

Physical and sensory functional limitations 

Table 3.1.4.2. Comparative summary and variable averages 

Country 

Type 

Limited 

Precision in the question 

B 

DK 

IRL 

I 

NL 

P 

UK 

NO 

CH 

TV 

Conversation 

Conversation 

TV 

Conversation 

TV or radio 

TV 

Conversation 

Conversation 

Source: National data 

6.7 

11.6 

29.8 

5.0 

2.9 

9.5 

5.0 

4.8 

5.3 

At a volume others find 
acceptable 

3 persons or more 

With noise around 

By raising the volume 

3 or more 

In general 

At a volume others find 
acceptable 

2 other people 

2 other persons 

The  type  of  listening  does  not  always  correspond  to  those  requested  in  the  guidelines  used  by  the 
different countries. 
1. Wording of the question 
The  collection  of  information  was  requested  on  a  person’s  capacity  to  hear  what  is  said  in  a 
conversation with another person. However, all the countries have either a different thing to hear or a 
different  precision  in  the  question.  The  level  of  difficulty  developed  by the countries in the response 
categories is different too. 
The questions are phrased differently with different wording so it could change the understanding of 
the respondents. However, most  of the questions  specify the possibility for the respondents to use a 
hearing aid. 

2. Comparability 
It is not possible to find a common type of listening and a common set of limitation levels among the 
different questions to analyse the comparability of the questions. 
The  questions  are  comparable  two  by  two  as  much  as  possible  which  is  why  an  eventual  analysis 
would just show the tendency according to age and gender. 

3.1.5. Limitations in carrying a shopping bag  
A. Data requested 
Data were requested on the percentage of persons with a ‘Limitation in lifting and carrying a shopping 
bag of 5 kgs’. 

B. Data collected 

Table 3.1.5.1. Wording of the questions by survey 

Country  Year 

Question 

DK 

2000 

Are you normally able to carry 5 kg (e.g. a shopping bag) with 
no difficulty, with minor difficulty, with major difficulty or not at 
all? 

D 

1998 

Does your health now limit you in these activities? If so, how 
much? Lifting or carrying groceries 

NL 

2000 

Can you carry an object of 5 kilograms, for example a full 
shopping bag, over a distance of 10 metres? 

NO 

1998 

Does your health now limit you in these activities? 
If so, how much? Lifting or carrying groceries? 

Source: National data 

192 

Response categories 
provided 

Not at all 
With major difficulty 
With minor difficulty 
With no difficulty 

Yes, limits me a lot 
Yes, limits me a little 
No, not limited at all 
Can’t 
With great difficulty 
With some difficulty 
Without any problem 

Yes, limits me a lot 
Yes, limits me a little 
No, not limited at all 

 
 
 
 
 
 
 
 Physical and sensory functional limitations 

(cid:4) 

Analyses: The common denominator is the faculty to carry something versus not carrying something. 
‘Limited’ versus ‘Not limited’ (‘at all’) ó Can’t carry (whatever the limitation i.e. response categories in 
grey) versus ‘without efforts/difficulty’. 
Reference weight requested: 5kg. 

Table 3.1.5.2. Comparative summary and variable averages 

Country 

Weight 

Limited (%) 

Specific details 

DK 

D 

NL 

NO 

5 kg 

- 

5 kg 

- 

9.5 

21.3 

15.7 

19.7 

Source: National data 

Carry a shopping bag 

Lifting or carrying groceries 

Carry a full shopping bag 
Over a distance of 10 metres 

Lifting or carrying groceries 

1. Wording of the question 
Two of the four countries (Germany and Norway) use the expression ‘lifting and carrying groceries’, 
but do not indicate the weight of the object to be lifted and carried. The other two countries (Denmark 
and the Netherlands) use the verb ‘carry’, do not specify the type of object to be carried, but indicate 
that the object should weigh 5 kgs.  
The levels of difficulty implicit in the response categories also differ among countries. 
The questions are phrased differently with different wording so it could change the understanding of 
the respondents. In the meaning, three countries have the same idea (D, MT) and the two others are 
different because the Netherlands introduces a notion of distance. 

2. Comparability 
It  is  difficult  to  find  a  mutual  measure  of  weight  and  a  common  set  of  limitation  levels  between  the 
different questions so as to be able to analyse the comparability of the questions. Here also, the use of  
common  question  and  response  categories  would  go  a  long  way  in  improving  data  comparability 
between countries. 

193 

 
 
 
 
 
 
 
Personal care activities 

(cid:4) 

3.2. Personal care activities 
Data were requested from the Euro-REVES 2000 recommendations: 
Data requested are replies to the questions: 
“In  everyday  life,  ignoring  temporary  problems,  do  you  usually  without  any  difficulty,  without 
(human/technical) help,  feed yourself / transfer in and out of bed / dress and undress yourself / use 
toilets/ bath and shower yourself?” 
Self-care disabilities include:  

·   Difficulty in feeding oneself 
·   Difficulty in transferring oneself in and out of bed 
·   Difficulty in dressing and undressing oneself 
·   Difficulty in using toilets 
·   Difficulty in bathing and showering oneself 

3.2.1. Limitations in feeding oneself 

A. Data requested 
Being able to feed oneself is one of the priorities of the recommendations given by WHO and Euro-
REVES. 
Data were therefore requested on 'Difficulty in feeding oneself’. 
Reference period requested: None 

B. Data collected 

Table 3.2.1.1. Wording of the questions by survey 

Country  Year 

Question 

B 

1997 

Can you, without the help of someone else, feed yourself and 
cutting up food yourself? 

I 

2000 

Can he/she eat without help, even cutting the food without 
help? 

NL 

2000 

Could you indicate whether you can eat and drink without 
difficulty, with difficulty or only with the help of others? 

P 

1999 

Can [you/she/he] eat (prepare the food to eat on the dish and 
take it to the mouth, drink)? 

UK 

2000  Cannot feed, including cutting up food without difficulty? 

IS 

1998  How difficult is it generally for you to eat? 

CH 

1997 

Can you eat without help, for example are you able to cut food 
such as meat, fruits, etc? 

Response categories 
provided 

Only with aid 
With some effort 
Without efforts/difficulty 

Only with aid 
With some effort 
Without efforts/difficulty 

Only with aid 
With great difficulty 
With some effort 
Without efforts/difficulty 

Only with aid 
With some effort/difficulty 
Easily 

Yes 
No 

Not difficult at all 
Slightly difficult 
Rather difficult 
Very difficult 

No 
With major difficulty 
Without too much difficulty 
Without efforts/difficulty 

Source: National data 

Analyses: The common denominator is the faculty to feed oneself in general (eat, drink or cut food) 
‘Limited’ versus  ‘Not  limited’ ( ‘at  all’)  ó  ‘can’t  feed  oneself’  (whatever  the  limitation  i.e.  response 
categories in grey) versus ‘without efforts/difficulty’ 

195 

 
 
 
 
 
 
 
 
 
(cid:4) 

Personal care activities 

Table 3.2.1.2. Comparative summary and variable averages 

Country 

Limited (%) 

Specific details 

B 

I 

NL 

P 

UK 

IS 

CH 

4.1 

2.2 

1.8 

2.8 

0.9 

2.8 

0.3 

Source: National data 

Feed and cut up food 

Yourself 

Feed and cut up food 

Without requiring anyone’s/ without help 

Feed and drink 

With the help of others 

Feed and prepare food 

Feed and cut up food 

Yourself 

Feed 

Feed and cut up food 

Without help 

Countries  have  the  same  meaning  even  if  they  do  not  always  formulate  the  questions  in  the  same 
way. 

1. Wording of the question 
The level of severity in the response categories is often different between countries. The questions are 
phrased  differently  with  different  wording  so  it  could  change  the  understanding  of  the  respondents. 
Although  the  questions  ask  if  people  are  able  to  feed  themselves,  some  countries  focus  on  the 
possibility to feed only and others on the capacity to feed and cut food or even on the difficulty to eat 
and drink. The action to accomplish is therefore often different. 
Some countries specify what they mean by eating or give examples of the term ’food’ (Switzerland). 
The phrasing to introduce the capacity to feed depends on the verb used. It can be ‘Can’, ‘How difficult 
is it’, ‘Are you able’, ‘Do you usually manage’…. These verbs do not lead to the same perception of the 
question.  
The degree of difficulty could be including directly in the question such as for UK or Netherlands. 

2. Comparability 
It is not possible to find a common action and a common set of disability levels between the different 
questions to analyse the comparability of the questions. 

3.2.2. Limitations in getting in and out of bed 

A. Data requested 
Being able to get in and out of bed is one of the priorities of the recommendations given by WHO and 
Euro-REVES. 
Data were requested on 'Difficulty in transferring oneself in and out of bed’. 
Reference period requested: None 

196 

 
 
 
 
 
 
 
 
 
 
 
Personal care activities 

B. Data collected 

Table 3.2.2.1. Wording of the questions by survey 

(cid:4) 

Country  Year  Question 

B 

1997  Can you get in and out of bed on your own? 

I 

2000  Can he/she go to bed and get out of bed without help? 

NL 

2000 

Could you indicate whether you can get in and out of bed 
without difficulty, with difficulty or only with the help of others? 

Response categories 
provided 

Only with aid 
With some effort 
Without efforts/difficulty 

Only with aid 
With some effort 
Without efforts/difficulty 

Only with aid 
With great difficulty 
With some effort 
Without efforts/difficulty 

Only with aid 
With some effort 
Easily 

1999  Can you [she/he] get into and out of bed? 

P 

S 

2000 

Do you need help (need manage / help oneself) to get up or 
go to bed? 

Can’t get their own 
Bed transfer on own 

UK 

2000  Cannot get in and out of bed on own without difficulty? 

CH 

1997  Are you able to get into and out of bed by yourself? 

Yes 
No 

No 
With major difficulty 
Without too much difficulty 
Without efforts/difficulty 

Source: National data 

Analyses:  The  common  denominator  is  the  faculty  to  transfer  oneself  versus  not  being  able  to 
transfer oneself. 
‘Limited’ versus  ‘Not  limited’ ( ‘at  all’)  ó  ‘can’t  transfer  oneself  in  and  out  of  bed’  (whatever  the 
limitation i.e. response categories in grey) versus ‘without efforts/difficulty’ 

Table 3.2.2.2. Comparative summary and variable averages 

Country 

Specific details 

Limited (%) 

Response categories 
with different degree of difficulty 

B 

I 

NL 

P 

S 

UK 

CH 

Your own 

Without help 

Only with the help of 
others 

Question based only on 
the need of someone 

On own 

By yourself 

10.8 

4.8 

14.1 

9.2 

0.8 

4.5 

0.4 

Yes 

Yes 

Yes 

Yes 

No 

Yes  
(in the question) 

Yes 

Source: National data 

Questions  between  countries  have  the  same  meaning  even  if  they  do  not  always  formulate  the 
questions (UK) in the same way. 

197 

 
 
 
 
 
 
 
 
 
 
(cid:4) 

Personal care activities 

1. Wording of the question 
The level of severity in the response categories often differs between the countries. 
Most questions ask if people are able to transfer themselves. 
The entire question speaks of ’going in and out of bed’. The UK and the Netherlands introduce into the 
question a reference level for the disability.  
The phrasing to introduce the capacity to transfer oneself depends on the verb used: ’Can’, ’Do you 
need help’, ‘Are you able’, ‘Do you usually manage’…. These verbs give rise to different perceptions of 
the  question.  These  nuances  in  the  formulation  could  well  influence  positively  or  negatively  the 
perception of theses question. 

2. Comparability 
It  is  not  possible  to  find  a  common set of disability level s  between  the  different  questions,  so  as to 
analyse the comparability of the questions. 

3.2.3. Limitations in dressing and undressing oneself 

A. Data requested 
Being  able  to  dress  and  undress  oneself  is  one  of  the  priorities  of  the  recommendations  given  by 
WHO and Euro-REVES. 
Data were requested on 'Difficulty in dressing and undressing oneself’. 
Reference period requested: None 

B. Data collected 

Table 3.2.1.3. Wording of the questions by survey 

Country  Year 

Question 

B 

1997  Can you dress and undress yourself on your own? 

IRL 

1998 

Please indicate which statement best describes your own 
health state today: Self-care (I have no problems with self-
care / I have some problems with washing and dressing 
myself  / I am unable to wash and dress myself) 

I 

2000  Can he/she get dressed and undressed without help? 

NL 

2000 

Could you indicate whether you can dress and undress 
without difficulty, with difficulty or only with the help of 
others? 

P 

1999  Can you [she/he] get dressed and undressed? 

UK 

2000  Cannot dress and undress without difficulty? 

NO 

1998  Can you dress/undress yourself? 

CH 

1997  Can you dress and undress yourself without help? 

Response categories 
provided 

Only with aid 
With some effort 
Without effort/difficulty 

Very difficult 
Without effort/difficulty 

Only with aid 
With some effort 
Without effort/difficulty 

Only with aid 
With great difficulty 
With some effort 
Without effort/difficulty 

Only with aid 
With some effort 
Easily 

Yes 
No 

Only with aid 
With some effort 
Without effort/difficulty 

No 
With major difficulty 
Without too much difficulty 
Without effort/difficulty 

Source: National data 

Analyses: The common denominator is the faculty to dress and undress oneself versus no faculty at 
all. 
‘Limited’ versus  ‘Not  limited’ ( ‘at  all’)  ó  ‘can’t  dress  oneself’  (whatever  the  limitation  i.e.  response 
categories in grey) versus ‘without effort/difficulty’ 

198 

 
 
 
 
 
 
 
 
 
Personal care activities 

(cid:4) 

Table 3.2.4.2. Comparative summary and variable averages 

Country 

Specific details 

Limited (%) 

Response categories 

with different degree of difficulty 

B 

IRL 

I 

NL 

P 

UK 

NO 

CH 

Your own 

Myself 

Without help 

- 

- 

- 

Yourself 

By yourself 

10.2 

2.9 

3.9 

10.2 

9.1 

2.9 

14.7 

0.6 

Source: National data 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 
(In the question) 
Yes 

Yes 

Countries retain the same meaning even if they do not always formulate the questions  (UK) in the same 
way. 

1. Wording of the question 
The level of severity in the response categories often differs between countries. 
All questions ask if people are able to dress and undress themselves. 
The UK and the Netherlands introduce into the question a reference level for the disability  
The  question  posed  by  Ireland  belongs  to  a  list  of  personal  care  activities.  More  particularly,  the 
question on dressing is combined with the one on washing.  
The  phrasing  to  introduce  the  capacity  to  transfer  oneself  depends  on  the  verb  used:  ’Can’,  ’How 
difficult’, ’Are you able’, ’Do you usually manage’…. The use of these different verbs could well lead 
respondents  to  perceiving  the  subject  in  different lights ,  influencing  positively  or  negatively  the 
perception of the question. 

2. Comparability 
It is not possible to find a common set of disability level s between the different questions which could 
make it possible to analyse the comparability of the questions. 

3.2.4. Limitations in using toilets 

A. Data requested 
Guideline: 'Difficulty in using toilets’ 
Reference period requested: No 

B. Data collected 

Table 3.2.4.1. Wording of the questions by survey 

Country  Year 

Question 

Response categories 

B 

1997  Can you get to and use the toilet on your own?  

P 

1999  Can you [she/he] go to the toilet/wc and use it? 

UK 

2000  Cannot get to and use toilet on own without difficulty? 

Source: National data 

Only with aid 
With some effort 
Without effort 

Only with aid 
With some effort 
Easily 

Yes 
No 

Analyses: The common denominator is the faculty to go to the toilets versus no faculty at all. 
‘Limited’ versus 
categories in grey) versus ‘without effort/easily’. 

‘Not  limited’ ( ‘at  all’)  ó’can’t  use  toilets’  (whatever  the  limitation,  i.e.  response 

199 

 
 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

Personal care activities 

Table 3.2.4.2. Comparative summary and variable averages 

Country 

Limited (%) 

Response categories 

with degree of difficulty 

Specific details 

B 

P 

UK 

4.3 

6.4 

1.2 

Source: National data 

Yes 

Yes 

Your own 

Toilet 

WC/Toilet-WC 

Yes 
(In the question) 

On own 

Toilet 

Countries have the same meaning even if they do not always formulate the questions (P, UK). 

1. Wording of the question 
The level of severity in the response categories often differs between countries. 
The entire questions ask if people are able ‘to go to the WC and use it’ themselves. 
UK introduces into the question a reference level for the disability. 
The phrasing to introduce the capacity to go to the WC depends on the verb used. It can be ’Can’, ‘Are 
you able’,  ’Do you usually manage’…. This different wording may give rise to varying perceptions of 
the question and they could well influence positively or negatively the perception of the question. 

2. Comparability 
It is not possible to find a common set of disability levels between the different questions in order to be 
able to analyse the comparability of the questions. 

3.2.5. Limitations in washing oneself 

A. Data requested 
Data were requested on replies to the question: “In everyday life, ignoring temporary problems, do you 
usually without any difficulty, without (human/technical) help, use bath and shower yourself?”. 

B. Data collected 
Data on limitations in bathing and/or showering were obtained from Germany, Italy, the Netherlands 
and Sweden. Norway’s question "Can you manage to attend to your own personal hygiene?", may be 
considered as somewhat equivalent, if one assumes that bathing and showering are the most difficult 
personal hygiene activities. 

200 

 
 
 
 
 
 
 
 
 
 
 
 
Personal care activities 

(cid:4) 

Table 3.2.5.1. Wording of the questions by survey 

Country  Year 

Question 

Response categories 
provided 

B 

1997  Can you wash your hands and face on your own? 

D 

1998 

Does your health now limit you in these activities? 
If so, how much? Bathing yourself 

I 

2000  Can he/she bath or shower without help? 

NL 

2000 

Could you indicate whether you can wash yourself completely 
without difficulty, with difficulty or only with the help of others? 

P 

1999  Can [you/she/he] wash [your/her/his] hands and face? 

S 

2000 

Do you need help (need manage / help oneself) to take a bath 
or shower? 

UK 

2000  Cannot wash hands and face without difficulty? 

NO 

1998  Can you manage to attend to your own personal hygiene? 

Only with aid 
With some effort 
Without efforts/difficulty 

Yes, limited a lot 
Yes, limited a little 
Not limited at all 
Only with aid 
With some effort 
Without efforts/difficulty 
Only with aid 
With great difficulty 
With some effort 
Without efforts/difficulty 
Only with aid 
With some effort 
Easily 
Can’t on their own 
Can on their own 

Yes 
No 

Only with aid 
With some effort 
Without efforts/difficulty 

Source: National data 

Analyses: The common denominator is the faculty to wash all or the part of the body versus not at all 
washing (whichever part of the body to wash) oneself at all. 
‘Limited’ versus  ‘Not limited’ (‘at all’) ó ‘can’t wash’ (whatever the limitation, i.e. response categories 
in grey) versus ‘without effort/difficulty’. 

Table 3.2.5.2. Comparative summary and variable averages 

Country 

Specific details 

B 

D 

I 

NL* 

P 

S 

NO 

On your own 

Yourself 

Without help 

Yourself 

- 

On your own 

Yourself 

Part of the body 

Wash hands and face 

Bath 

Wash hands and face / Bath or shower 

Washing face and hands / Wash yourself completely 

Wash hands and face 

Need help to bath or shower 

Own personal hygiene 

* The Netherlands: only for persons aged 55 years and above 
Source: National data 

1. Wording of the question 
It seems that only Italy’s question is the closest to the recommended one. 
The level of severity in the response categories often differs between countries. Norway, Italy and the 
Netherlands have questions completely different with required to the part of the body to wash. 
The Netherlands and the UK introduce into the question a reference level for the disability. 
The phrasing to introduce the capacity to wash and shower oneself depends on the verb used: ’Can’, 
’Can you manage’, ’Do you need help…’, ‘Are you able’, ’Do you usually manage’…. These verbs may 
well  lead  the  respondents  to  interpret  the  subject  in  different  ways.  There  are  some  nuances  in  the 
beginning  of  the  questions,  which  could  influence  positively  or  negatively  the  perception  of  the 
question. 
Belgium, Portugal and the United Kingdom provided data on limitations in washing hands and/or face, 
a much more severe limitation. 

201 

 
 
 
 
 
 
 
(cid:4) 

2. Comparability 

Limitations in washing the body 

Personal care activities 

Table 3.2.5.3. Comparative summary and variable averages (1) 

Country  Specific details 

Limited (%) 

Part of the body 

Response categories: 
degree of difficulty 

Level of 
comparability 

D 

I 

By yourself 

Without help 

NL* 

Yourself 

9.6 

5.5 

8.0 

S 

On your own 

(1.1) 

NO 

Yourself 

9.0 

Bath 

Bath or shower 

Wash yourself 
completely 

Need help to bath 
or shower 

Own personal 
hygiene 

Yes 

Yes 

Yes 

No 

Yes 

Acceptable 

Acceptable 

Acceptable 

No 

Acceptable 

The Netherlands: only for persons  aged 55 years and above 
Source: National data 

The responses categories differ among countries: 

Table 3.2.5.4. Comparison of the specific details (1) 

Country 

Limitation 

No limitation 

D 

I 

NL 

S 

NO 

Limited 

a lot 

Limited a little 

Not limited at all 

Only with aid 

With some effort 

Only with aid 

With great 
difficulty 

With some effort 

Without effort / 
difficulty 

Without effort / 
difficulty 

Can't on their own 

Can on their 

Own 

Only with aid 

With some effort 

Without effort / 
difficulty 

Source: National data 

To obtain comparable data, rough equivalences have to be set between the response categories, One 
option  is  to  determine  the  percentage  unable  to  bathe  or  shower  themselves  on  their  own.  For 
Germany,  the  highest  degree  of  limitation  is  ‘limited  a  lot’  as  regards  bathing,  including  therefore 
persons  who  can  bathe  themselves,  albeit  with  great  difficulty.  A  second  option  would  then  be  to 
include  those  who  can  bathe  or  shower  themselves  with  great  difficulty,  but  this  is  not  feasible  with 
data  from  Italy  and  Sweden.  The  last  option  is  to  determine  those  able  to bathe/shower themselves 
without difficulty, but data are not available for Sweden. 

Limitations in hand washing 

Table 3.2.5.5. Comparative summary and variable averages (2) 

Country  Specific details  Limited (%)  Part of the body 

Response categories: 
degree of difficulty 

Level of 
comparability 

B 

P 

UK 

Your own 

- 

- 

4.1 

2.7 

0.8 

Hands and face 

Hands and face 

Yes 

Yes 

Hands and face 

Yes/no 

Difficult 

- 

- 

Source: National data 

202 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Personal care activities 

(cid:4) 

Table 3.2.5.6. Comparison of the specific details (2) 

Country 

Limitation 

No limitation 

B 

P 

Only with aid  With some effort  Without effort / difficulty 

Only with aid  With some effort 

Easily 

UK 

‘Cannot 

without difficulty’ 

Without difficulty 

Source: National data 

It is not possible to find a common procedure of washing a  common part of the body and a common 
set  of  disability level s  between  all  the  different  questions  in  order  to  be  able  to  analyse  the 
comparability of the questions. 

203 

 
 
 
 
 
 
 
 
Mental health 

3.3. Mental Health 

(cid:4) 

The  concept  of  mental  health  encompasses  a  number  of  dimensions.  For  example,  the  EuroHIS 
Mental Health Indicator Network (2002)1 proposes the following concepts to be included in a common 
instrument: 

·   Positive mental health 
·   Psychological distress 
·   Role limitation 
·   Social support and social isolation 
·   Assessment of the substantial common psychiatric disorders 
·   Sleeping problem 
·   Alcohol and drug dependency2 
·   Suicide3 

A number of instruments have been developed to measure issues of mental health, while others are 
more general but include questions related to mental health. These will not be developed here. 
The Consensus Group on Mental Health4 recommended using the following popular instruments: 

-  

-  

the  MHI-5  (if  also  using  CIDI-SF,  otherwise  the GHQ -12)  to  collect  data  on  psychological 
distress, 
the SF-36 energy/vitality plus Andrews' item on happiness to collect data on positive mental 
health / psychological well-being, 

-   Kessler's CIDI-SF to collect data on anxiety and depression, 
-   CAGE  to  collect  data  on  alcohol  and  drug  dependency,  one  item  to  collect  data  on  suicide 

attempts and 
IQCODE to collect data on cognitive functioning5

. 

-  

A. Data requested 
Data  requested  on  mental  health  referred  to  three  summary  scores  derived  from  the  most  used 
instruments: 
The General Health Questionnaire GHQ-12, using the (0,0,1,1) scoring system: 

1.  Number of people who have a summary score = 0 
2.  Number of people who have a summary score = 1 
3.  Number of people who have a summary score = 2 
4.  Number of people who have a summary score = 3 
5.  Number of people who have a summary score >= 4 
6.  Average summary score 

Psychological distress (MHI-5 from the SF-36) 
How much, during the past 4 weeks…. 
1.  did you feel very nervous? 
2.  have you felt so down in the dumps, nothing could cheer you up? 
3.  have you felt calm and peaceful? 
4.  have you felt down-hearted and depressed? 
5.  have you been happy? 

The response categories are: All of the time / Most of the time / Some of the time / A little of the time / 
None of the time.  A score for each individual  is then derived for each individual from the responses 
provided. Data were requested on: 

1.  Number of people who have a score <= 56 
2.  Number of people who have a score between 60 and 76 
3.  Number of people who have a score >= 80 
4.  Average summary score 

1  Meltzer, Howard (2002) "Chapter 4. Development of a Common Instrument for Mental Health". Draft chapter of the EuroHIS Mental 

Health Indicator Network's book, Sept. 2002, 25 pp. 

2  Alcohol and drug dependency are covered in this document as separate topics. 
3 

In  addition,  the  group  discussed  the  concept  of  stressful  life  events  and  also  considered  whether  it  should  recommend  instruments  to 
measure cognitive functioning and the mental health of children. 

4  

Jagger, Carol and Robine, Jean -Marie “Setting up a Coherent Set of Health Indicators for the EU (Euro -REVES) – Report on Consensus 
Workshop on Mental Health", Montpellier, 16 March 2001, 8 pp. 

5  An instrument to collect data on dementia /  Alzheimer’s dylease was still to be discussed. 

205 

 
 
 
 
 
                                                          
(cid:4) 

Mental health 

Positive mental health (5 questions in the SF-36 on energy and vitality) with: 

1.  Number of people who have a score <= 60 
2.  Number of people who have a score between 65 or 70 
3.  Number of people who have a scoring between 75 and 100 
4.  Average summary score 

If it was impossible for countries to use the thresholds requested, countries were asked to indicate the 
thresholds they used instead. 
Data requested are the summary scores calculated from the GHQ-12, MHI-5 and Vitality as explained 
in EuroHIS Mental Health Indicator Network (2002). 
For example, the instrument (MHI-5) on psychological distress, as found in EuroHIS (2000). 

B. Data collected and results 

1.  General Health Questionnaire GHQ-12 

The GHQ12 is a checklist of mental and emotional symptoms (standard for survey measurement of 
mental health). The frequency of each symptom in the past few weeks allows  the measurement of a 
score going from 0 to more than 4. 

General Health Questionnaire GHQ-12, using (0,0,1,1) scoring system: 

·   Number of people who have a summary score = 0 
·   Number of people who have a summary score = 1 
·   Number of people who have a summary score = 2 
·   Number of people who have a summary score = 3 
·   Number of people who have a summary score >= 4 
·   Average summary score 

The United Kingdom provided the following data from the GHQ-12 from their survey conducted in  
2000:  

Table 3.3.1. Variable averages 

Sub items 

Number of 
respondents 

Percent 

Score=0 

  Score=1 

Score=2 

Score=3 

4 662 

905 

499 

331 

62.3% 

12.1% 

6.7% 

4.4% 

Score>=4 

1 084 

14.5 % 

Source: National data 

The average summary score was 1. 
Comparability: since only the UK provided the data required, no comparability analysis is feasible.  

2. Psychological distress 
Mental Health Indicator 5 (MHI-5) is derived from the SF-36 and is shorter and has similar reliability to 
the  GHQ-12.  Nevertheless,  the  mixture  of  positive  and  negative  statements  to  be  rated  could  be 
sometimes  confusing  for  the  respondents.  However,  such  alternation  of  positive  and  negative 
statements to be rated is a key part in the construction of an instrument collecting attitudinal data as it 
mitigates  against a   ‘response  set’,  i.e.  respondents  giving  the  same  answers  to  all  the  questions. 
Essentially,  this  technique  makes  the  respondent  think  about  each  question  and  the  response 
categories. 

Psychological distress (MHI-5 from the SF-36) with item: 

·   Number of people who have a score <= 56 
·   Number of people who have a score between 60 and 76 
·   Number of people who have a score ³  80 
·   Average summary score 

206 

 
 
 
 
 
 
 
 
 
 
 
 
 
Mental health 

(DK) Denmark 2000 

(cid:4) 

“How much of the time within the past 4 weeks: Have you been a very nervous person? Have you felt 
so down in the dumps that nothing could cheer you up? Have you felt calm and peaceful? Have you 
felt downhearted and blue? Have you been a happy person?” 

Table 3.3.2. Variable averages 

Sub Items 

Number of respondents 

Percent 

Score <= 56 

Score 60-76 

Score >= 80 

Source: National data 

7 62 

2 150 

7 258 

7.5% 

21.1% 

71.4% 

The average summary score was 83. 

(NO) Norway 1998 

“How much of the time during the past 4 weeks: Have you been a very nervous person? Have you felt 
so down in the dumps that nothing could cheer you up? Have you felt calm and peaceful? Have you 
felt downhearted and blue? Have you been a happy person?” 

Table 3.3.3. Variable averages 

Sub Items 

Number of respondents 

Percent 

Score <= 58 

Score 58-78 

Score >= 78 

Source: National data 

1 053 

2 683 

6 113 

10.7 % 

27.2 % 

62.1 % 

The average summary score was 79. 
Comparability: only two countries possess the data and the score thresholds are different. 
We just obtained 2 countries with such an indicator, which was not enough to analyse the scores. 
Nevertheless, we note that they use the recommended instrument, but not the proposed thresholds, 
which increases the impossibility of comparison. 
On the other hand, the study of the average summary score could be made without difficulty and could 
enable  comparisons  between  same  age  groups,  same  gender  or  equivalent  economic  activities  or 
education level. 

3. Positive mental health 
Positive mental health (5 questions in the SF36 on energy and vitality) with: 

·   Number of people who have a score <= 60 
·   Number of people who have a score between 65 or 70 
·   Number of people who have a scoring between 75 and 100 
·   Average summary score 

(DK) Denmark 2000 

“How  much  of  the  time  within  the  past  4  weeks:  Did  you  feel  full  of  pep?  -  Did  you  have  a  lot  of 
energy? - Did you felt worn out? - Did you feel tired?” 

Table 3.3.4. Variable averages 

Sub items 

Number of respondents 

Percent 

Score <= 60 

Score 65-70 

Score >= 71 

Source: National data 

The average summary score was 70. 

29.4% 

15.7% 

55.0% 

3 000 

1 598 

5 611 

207 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

(NO) Norway 1998 

Mental health 

“How much of the time during the past 4 weeks: Did you feel full of pep? Did you have a lot of energy? 
Did you feel worn out? Did you feel tired?” 

Table 3.3.5. Variable averages 

Sub items 

Number of respondents 

Percent 

Score <= 62.5 

Score 62.5-72.5 

Score >= 72.5 

Source: National data 

5 250 

1 945 

2 678 

53.2 % 

19.7 % 

27.1 % 

The average summary score was 59. 
Comparability: only two countries have these data and the score thresholds are different. 

208 

 
 
 
 
 
 
Physical activities 

(cid:4) 

3.4. Physical activity 
Insufficient physical activity is known to increase the risk of chronic diseases such as coronary heart 
disease,  hypertension,  or  diabetes...  Various  indicators  of  measure  of  physical  activity  have  been 
experimented, with respondents having to indicate the number of hours spent in various leisure and 
physical activities, or to indicate the type of activities practised, or both. 

A. Data requested 

Data were requested on: 

1.  Number of respondents who practise hard training and competitive sports more than once a 

week in their leisure time activities. 

2.  Number of respondents who practise jogging and other recreational sports or heavy gardening 

at least 4 hours a week in their leisure time activities. 

3.  Number of respondents who practise walking, bicycling or other light activities at least 4 hours 

a week in their leisure time activities. 

4.  Number  of  respondents  who  practise  reading,  watching  TV  or  other  sedentary  activities  in 

their leisure time activities. 

If more than one code was applicable for a particular respondent, the lowest code (‘the most active’) 
was to be chosen. 

B. Data collected 

Table 3.4.1. Wording of the questions by survey 

Country  Year 

Question 

Response categories 
provided or constructed 

B 

1997 

What describes best your leisure time activities 
during the last year? 

§  Physically active  
§  Not physically active 

DK 

2000 

If we look back on the past year, what would you 
say best describes your spare time activities? 

D 

1998 

What is the average time per day (24 hours) you 
spend doing the following: Only physical activities 
are meant. Please try to divide up the whole of the 
24 hours into the 5 categories. 

E 

1997 

What type of physical exercises your work or usual 
activity involves. Of the possibilities on this card, 
which would you say best describe your main 
activity? 

§  Hard training and competitive sports 
§  Jogging and other recreational sports or 

heavy gardening 

§  Walking, bicycling or other light activities at 

least 4 hours a week 

§  Reading, watching TV or other sedentary 

activity 

§  Strenuous activity (competitive sport...) 
§  Moderate activities (jogging, cleaning, 

cycling, swimming...) 

§  Light activities (cooking, walking, 

shopping...) 
§  Sitting down 
§  Sleeping, relaxing 

§  Physically active  
§  Not physically active 

I 

2000 

Over the last 12 months, in your free time, have you 
done one or more continuous physical activities or 
sports that required INTENSIVE TRAINING 
(competitive and non-competitive sports, gym, etc..) 
/ any physical activity regularly, that made you a 
little sweaty? Do you habitually carry out a LIGHT 
physical, exercise activity (taking at least a one-
kilometre walk, climbing stairs, etc..)? 

§  Hard training and competitive sports 4 hours 

per week 

§  Jogging and other recreational sports or 

heavy gardening 4 hours per week 

§  Walking, bicycling or other light activities 4 

hours per week 

§  Did not practices any of the other physical 

activity 

A 

1999 

At least once a week, do you engage in physical 
activity like jogging, fast or long biking or aerobic, to 
work up sweat? If yes, many days per week? 

§  Physical activity about 2 times a week 
§  Physical activity once a week 
§  3 times a week 
§  4 times a week 
§  5 times a week 
§  6 times a week 
§  7 times a week 
§  Not physically active 

209 

 
 
 
 
 
 
 
 
(cid:4) 

Country  Year 

Question 

P 

1999 

Which of these situations describes best 
[your/her/his] free-time activities during the last 
year (last 12 months)? 

IS 

1998 

How often do you exert yourself physically so that 
you sweat or get out of breath? 

CH 

1997  Physical activity? 

Source: National data 

Physical activities 

Response categories 
provided or constructed 

§  Hard training and competitive sports 

more than once a week. 

§  Jogging or practising other leisure 

sports and gardening at least 4 hours 
/ week. 

§  Taking a walk, riding a bicycle or 

practicing other light activities at least 
4 hours per week 

§  Reading, watching TV or other 

sedentary activities 

§  Less than once a month 
§  Once a month 
§  2-3 times a month 
§  1-2 times a week 
§  Never 

§  3 times or more  week 
§  1 to 2 times a week 
§  Not physically active 

Only Belgium, Denmark, Italy and Portugal could provide the data requested, i.e. half of the countries 
with data on sport. 
The two main tendencies are either to provide ‘frequencies’ on the sport practised (number of times by 
week or by month) or to distinguish physical activities according to their intensity (intensive, moderate 
or light activities). In this way, Austria, Germany and Iceland give data on frequencies. 
Belgium  and  Spain  provided  data  just  in  the  first  round  and  it  was  asked  to  qualify  respondents  as 
either ‘physically active’ or ’physically inactive’. 
In the first round, questions were more general without defining the term ’physical activity’. It was just 
asked to give the percentage of athletic people and non-athletic people. So, countries provided data 
grouped into the 2 requested categories. 
Austria, Iceland and Switzerland asked about frequencies of physical activity. 
It  appears  that  the  requested  questions  may  be  too  complex  for  some  respondents,  who  have 
difficulties understanding them.  

C. Analysis  
Due to the variety of questions and response categories, the only common indicator is the percentage 
of ’physically active’ (in italic in the table) as against ’not physically active’.  
Surveys can be distributed into four groups, depending on the type of question asked: 

Country 

Not physically 
Active (%) 

Period in the question 

Remark 
response categories 

Level of 
comparability 

Table 3.4.2.  As requested 

DK 

I 

P 

Source: National data 

76.8 

62.2 

90.9 

Past year 

As requested 

Comparable 

Last 12 months 

As requested 

Comparable 

Last 12 months 

As requested 

Comparable 

Table 3.4.3. ’Physically active’ or ’not physically active’ 

Country 

Not physically 
Active (%) 

Period in the question 

Remarks 
response categories 

Level of 
comparability 

B 

E 

82.1 

84.3 

Source: National data 

Last year 

Usual activity 

210 

Grouping in physical and 
not physical 

Grouping in physical and 
not physical 

- 

- 

 
 
 
 
 
 
 
 
 
Physical activities 

(cid:4) 

Table 3.4.4. Frequencies of physical activity 

Country 

Not physically 
Active (%) 

Period in the question 

Remarks 
response categories 

Level of 
comparability 

A 

IS 

CH 

Source: National data 

44.5 

4.1 

41.2 

Per week 

Frequency in the week 

- 

- 

Frequency in the week or 
month 

Frequency in the month 

- 

- 

- 

Since data for Iceland cover only the population aged 15-74 years, it was expected to find a relatively 
low level of physical inactivity. However, an examination of data by age shows that for all age groups, 
physical inactivity is extremely low for all age groups.  

Country 

Not physically 
Active (%) 

Period in the question 

Remarks 
response categories 

Level of 
comparability 

Table 3.4.5. Other 

 D 

16.0 

Per day 

Source: National data 

Sharing out the 24 hours 
between the different 
activities 

Not comparable 

Three countries (Denmark, Italy and Portugal) provide data on the percentage of respondents, which 
consider themselves as ’physically active’. However, it is not clear whether the large differences found 
among  the  three  countries  are  real  or  are  due  to  different  interpretations  of  the  question  in  each 
language and to cultural differences.  

In no case are questions and response categories comparable for more than three countries, so that 
no meaningful comparative analysis can be performed. 

211 

 
 
 
 
 
 
 
 
 
 
Use of drugs 

(cid:4) 

3.5. Drugs used in the past 12 months 
The European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) showed that cannabis is 
the  most  used  drug  in  the  EU  before  hard  drugs  such  as  cocaine,  ecstasy…  On  the  other  hand, 
illegality  and  social  disapproval  make  populations  less  honest  in  their  replies  and  may  considerably 
distort  figures.  For  this  reason,  data  collection  on  so  called  ‘soft  drugs’  such  as  cannabis  tend  to 
provide more reliable results. 

A. Data requested 
Data  requested  followed  the  EMCDDA  recommendations  on  questions  about  the  drugs  used  in  the 
past 12 months: 

1.  Number of respondents who have taken cannabis in the past 12 months 
2.  Number of respondents who have never taken cannabis in the past 12 months 

B. Data collected 

Table 3.5.1. Wording of the questions by survey 

Country  Year 

Question 

Response categories 
provided 

 DK 

2000  Have you ever tried cannabis within the past year? 

 D 

1998 

Have you ever taken any of the following drugs in your life 
(cannabis, marijuana or hashish)? 

 IRL 

1998 

Have you used marijuana (grass, pot) or cannabis (hash, 
hash oil) during the last 12 months? 

Source: National data 

Yes 
No 

Yes 
No 

Yes 
No 

Table 3.5.2. Comparative summary and variable averages 

Country 

% of drug users 

Period 

Past year 

Kind of drug 

Cannabis 

Life 

Cannabis, marihuana, hashish 

Last 12 months 

Marijuana, cannabis 

DK 

D 

IRL 

Source: National data 

5.3 

5.8 

7.5 

The  type  of  drug  and  the  reference  period  are  all  different,  so  that  no  comparison  can  be  made 
between the data, even if the overall rates are relatively similar. 
We can just observe that, as expected, the longer the list of drugs, the higher the percentage of drug 
users. 

C. Analysis 
It is not possible to analyse the percentage of drug users because of the total lack of comparability 
between data. 

213 

 
 
 
 
 
 
 
  
 
 
 
Diet/food consumption habits 

(cid:4) 

3.6. Diet/food consumption habits 
Special diets and dietary regimes are an important determinant of an individual’s nutrient intake, and 
constitute  a  valuable  variable  in  data  analysis,  especially  in  relation  to  overweight  and  obesity.  In 
addition, major chronic diseases such as cardiovascular disease and several cancers are associated 
with dietary factors. 

A. Data requested 
Data were requested from the WHO 1996 recommendations on: 

1.  Number of respondents who have a special diet or follow a particular dietary regime 
2.  Number  of  respondents  who  do  not  have  a  special  diet  or  do  not  follow  a  particular  dietary 

regime 

Countries  with  different  response  categories  were  asked  to  provide  the  corresponding  data  and  to 
indicate the wording of the question and/or the response categories used. 

B. Data collected 

Table 3.6.1. Wording of the questions by survey 

Country  Year 

Question 

Response categories 
constructed 

F 

1998 

Have you a special diet or do you follow a particular dietary 
regime prescribed by a physician for your health? 

IRL 

1998 

Do you follow any of the following diets? 
Vegetarian 
Vegan 
Diabetic 
Gluten free 
Weight reducing 
Low cholesterol 
Other, please specify: … 
Do not follow a special diet 

I 

2000 

Do you follow a special diet or regular, particular diet plan? (low-
salt, reducing, vegetarian,…) 

A 

1999 

I show you now a list of activities concerning health behaviour. 
What are you doing to keep yourself healthy or to prevent 
illness? 
Health-conscious diet 
Physical activities… 

Source: National data 

There is a wide variety in the questions asked: 

Yes 
No 

Yes 
No 

Yes 
No 

Yes 
No 

·  

·  

·  

two  surveys  provide  a  list  of  specific  diets.  Ireland’s  list  is  relatively  detailed  whereas  the 
Italian one is less exhaustive, 
in  France,  no  question  is  asked  about  the  type  of  diet  followed,  as  long  as  it  has  been 
prescribed by a physician for health reasons, and 
the Austrian survey question is the most general. 

There are also differences in time frame: four countries ask questions about present behaviour. 

Table 3.6.2. Comparative summary and variable averages 

Country 

Persons who follow a special diet (%) 

Specific details 

F 

IRL 

I 

A 

Source: National data 

11.4 

74.5 

11.3 

48.8 

Prescribed by a physician 

List of diets 

List of special diet 

List of activities 

Not  surprisingly,  the  percentage  of  the  population  who  follow  (or  have  ever  followed,  in  the  case  of 
Germany)  a  diet  varies  widely  among  countries.  This  lack  of  comparability  between  the  questions 
does not allow a meaningful comparative analysis of the data collected. 

215 

 
 
 
 
 
 
 
 
 
 
Quality of life 

(cid:4) 

3.7. Quality of life 
Quality of life is a topic that has attracted a great deal of interest over the past decade, particularly in 
the areas of health and social services, but increasingly in medicine, education, employment and other 
fields. 
The study of quality of life and its subsequent application helps make it possible for people, within their 
environments,  to  live  in  ways  that  are  best  for  them,  i.e.  quality  lives  that  are  both  meaningful  and 
enjoyed. 

A. Data requested 
Data were requested on replies to the question: ”How would you rate your quality of life?”: 

1.  Number of respondents replying ‘very poor’ 
2.  Number of respondents replying ‘poor’ 
3.  Number of respondents replying ‘neither poor nor good’ 
4.  Number of respondents replying ‘good’ 
5.  Number of respondents replying ‘very good’ 

B. Data collected 
Data have been collected from three countries. However, there is so much variation in the quality of 
life data collected that they will be presented country by country 

Germany (1998) 

Question: “How content / satisfied are you with the following areas of your life? Very dissatisfied…very 
satisfied” 

Table 3.7.1. Percentages by response categories 

Sub items 

Number 

Percent 

Score=1 

Score=2 

Score=3 

Score=4 

Score=5 

Score=6 

Score=7 

44 

79 

170 

642 

1 313 

2 669 

1 305 

0.7 % 

1.3 % 

2.7 % 

10.3 % 

21.1 % 

42.9 % 

21.0 % 

Source: National data 

Ireland  (1998) 

Question: “How would you rate your quality of life?” 

Table 3.7.2. Percentages by response categories 

Sub items 

Number 

Percent 

Very poor 

Poor 

Neither poor nor 
good 
Good 

Source: National data 

Sweden  (2000) 

112 

223 

1 050 

4 826 

1.8 % 

3.6 % 

16.9 % 

77.7 % 

Question:  “At  last  we  would  like  to  ask  you  about  your  living  conditions  in  general.  If  you  rate  your 
living  condition  on  a  scale  from  zero  (0)  representing  the  worst  living  conditions  to  ten  (10) 
representing the best possible living conditions. How do you estimate your own situation?” 

217 

 
 
 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

Quality of life 

Table 3.7.3. Percentages by response categories 

Sub items 

Number 

Percent 

Score=0 

Score=1 

Score=2 

Score=3 

Score=4 

Score=5 

Score=6 

Score=7 

Score=8 

Score=9 

Score=10 

Source: National data 

24 

25 

52 

174 

243 

974 

881 

2 100 

3 385 

1 616 

1 782 

0.2 % 

0.2 % 

0.5 % 

1.5 % 

2.2 % 

8.7 % 

7.8 % 

18.7 % 

30.1 % 

14.4 % 

15.8 % 

C. Analysis 
Data  are  available  for  only  three  countries.  The  questions  and  the  response  categories  are  not 
comparable and therefore no meaningful comparative analysis can be performed. 
It  appears  that  the  data  that  are  needed  to  compare  the  questions  and  response  categories  are 
inadequate to evaluate countries’ use of the proposed instrument. 
Ireland  provides  data  from  the  recommended  question  with  some  of  the  proposed  response 
categories. 
Germany  uses  the  word  ’content/satisfied’  in  the  question  which  could  influence  the  reply,  while 
Sweden uses the word ’own situation’ in order to ‘quality of life’. 
Both propose replies in the form of scores with different levels in their scale. 
Given these points, the national surveys do not allow a comparative study among countries. 

218 

 
 
 
 
Conclusion 

(cid:4) 

4. Conclusion: towards better data comparability 

Besides problems arising from the use of different reference periods, the main difficulty is evaluating 
whether respondents’ perceptions of questions diverge. 

4.1. International comparability of health interview survey data 

Firstly, the questions vary in their meaning and in the definition of the terms used. For example, when 
a  question  refers  to  ‘doctors’  in  general,  this  word  may  be  interpreted  in  many  ways  by  different 
respondents. Some may equate ‘doctors’ with generalists, others will also include specialists, but will 
differ on the specifics to which the term may apply. For others still, a doctor is any health professional 
who “acts like a doctor”… 
The first step is clearly to continue encouraging Member States and other countries to apply existing 
recommended  questions  and  response  categories  in  their  surveys,  i.e.  questions  which  have  been 
largely tested beforehand and include definitions and special instructions for ambiguous words. 
This  second  round  of  health  interview  data  collection  has  clearly  shown  a  gain  in  international 
comparability  for  the  most  popular  topics  (self-perceived  health,  smoking,  BMI,  inter  alia).  Surveys 
used  for  the  second  round  include  more  internationally  recommended  questions  than  the  previous 
surveys, such as those used in the first round.  
It might be necessary for improving the comparability of European health data to encourage national 
institutes to adopt those questions that are already used in a large number of other national surveys. 
In particular, the inventory of current HIS/HES conducted in European countries could be a very useful 
tool for this purpose.   
Moreover,  there  is  a  need  to  harmonise  future  surveys  and  to  develop  and  apply  recommended 
common survey instruments in new areas such as mental health or quality of life, where considerable 
progress can be made. The use of the new standard disability instruments developed recently should 
also be promoted, including the Minimum European Health Module.  
This preliminary comparative analysis of the data already collected by countries highlights both some 
of their common points and differences, which could be reduced in the future through the development 
and  implementation  of  relatively  simple  common  instruments.  With  a  view  to  the  work  so  far 
completed,  there  is  a  huge  potential  for  improving  the  international  comparability  of  health  interview 
survey data. 
The two procedures used to improve the comparability of the 18 health selected items – pre-and post-
harmonisation – must be pursued in the future. On the one hand, it is often possible to improve the 
comparability  of  survey  data  from  different  countries  by  taking  into  account  the  differences  between 
the  questions  and  the  surveys,  or  between  the  populations  covered.  On  the  other  hand,  post-
harmonisation  techniques  are  often  feasible  in  only  a  few  very  specific  cases,  which  makes  pre-
harmonisation the preferable option. 
For the next data collection of the 18 HIS items foreseen for 2004, it would be also very useful that 
Member  States,  as  well  as  Acceding  and  Candidate  Countries  that  will  also  be  involved  for  the  first 
time,  investigate  other  national  sources  available  and,  when  necessary,  use  more  than  one  or  two 
surveys  for  preparing  the  data  sets.  This  will  allow  a  broader coverage of the items, as it is unlikely 
that  so  many  of  them  –  not  covered  by  national  data  from  various  countries  in  the  2002  data   
collection – are not covered by any national survey.  

4.2. 

 Recommendations for the future 

The  one  activity  where  energies  would  be  most  efficiently  spent  would  be  to  continue  developing 
guidelines with recommended questions, response categories and methodologies and to test them in 
various settings. The definition of the key words must be clear and unambiguous so that results can be 
compared, and all the terms used should be clarified by including detailed definitions with, if needed, 
country-specific  instructions.  Without  further  harmonisation  of  questions,  response  categories  and 
survey  methodologies,  it  will  be  difficult  to  obtain  comparable  data,  even  if  post-harmonisation 
techniques move forward.  
For some topics, the issue is to apply existing robust standards; for others, new instruments have to 
be developed and agreed upon. As a last resort, the development of output harmonisation techniques 
should be furthered, particularly for topics where comparability cannot otherwise be obtained. 
Several  working  groups  on  health  and  health  related  data  such  as  the  WHO  project,  EUROHIS,  
EUROREVES or the EMCDDA, among others, have developed or are developing instruments for the  
most  commonly  collected  items,  to  test  their  validity  and  translate  questions  into  various  European 
219 

 
 
 
 
 
 
 
 
 
(cid:4) 

Conclusion 

languages. Such work should be pursued and encouraged, and more efforts should be channelled into 
disseminating their recommendations and their implementation, e.g., modules to be developed for the 
future European Health Interview Survey. 
In parallel, there is a need for more co-ordinated work in the area of semiological studies, of a more 
qualitative  nature  than  usual  surveys,  designed  to  identify  cultural  differences  in  the  perception  of 
questions and in the mental construction of replies within Europe.  
Finally, analysing the international comparability of health data also raises the often neglected issue of 
data comparability within countries. It is commonly known that different population groups understand 
and interpret many questions differently, and do not construct their replies in the same way, whether 
men  or  women,  the  young  or  the  old,  national  or  foreign  residents.  There  is  even  wide  variation 
according to the origin of foreign residents. Progress in the international comparability of health data 
will certainly also improve the comparability of data within European countries.  
The  road  towards  achieving  a  high  level  of  comparability  will  certainly  be  a  long  one.  However,  the 
progress  already  made  shows  that  it  is  possible  to  work  towards  this  aim.  Comparability  problems 
should  be  tackled  step  by  step,  with  realistic  objectives  and  using  quality  assessment procedures to 
measure the progress made at each stage.  

220 

 
 
Annexes 

Annex 1: Items with relatively high level of comparability 

221 

 
 
 
 
 
 
 
 
Annexes                                                                  

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(cid:4) 

Annexes  

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Annexes                                                                                                                               

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(cid:4) 

Annexes  

L
R

I

F

E

L
E

K
D

B

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A

1
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1
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j

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F

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A

1
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F

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1
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j

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A

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F

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1
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j

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M

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m
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F

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l

A

1
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j

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A

s
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a
M

s
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a
m
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F

'

d
o
o
g
y
r
e
v

'

r
o

'

d
o
o
g

'

s
a
h
t
l
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h
r
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p
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:
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c
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S

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annexes                                                                                                                

(cid:4) 

K
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L
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1
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(cid:4) 

Annexes  

-

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Annexes                                                                                                   

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3

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(cid:4) 

Annexes  

K
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P

L
N

E

K
D

B

l
l

A

1
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s
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j

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1
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1
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1
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j

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1
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l
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1
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j

l
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s
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s
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1
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1
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3
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2

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2
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1

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8
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2
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3
3
1

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6
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:

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3
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1

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5
6
1

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1
4
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1
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2
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3
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3
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Annexes                                                                                                                              

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(cid:4) 

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(cid:4) 

Annexes  

-

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2
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Annexes                                                                  

(cid:4) 

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(cid:4) 

Annexes

-

2
9

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2
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9
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3
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3
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Annexes                                                                  

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(cid:4) 

Annexes

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4
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242

7
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Annexes                                                                  

(cid:4) 

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(cid:4) 

Annexes

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Annexes                                                                  

(cid:4) 

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(cid:4) 

Annexes

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Annexes                                                                  

(cid:4) 

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(cid:4) 

Annexes

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1
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Annexes                                                                  

(cid:4) 

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(cid:4) 

Annexes

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(cid:4) 

Annexes

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262

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annexes                                                                  

(cid:4) 

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(cid:4) 

Annexes

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5
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266

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Annexes                                                                  

(cid:4) 

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(cid:4) 

Annexes

-

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270

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annexes                                                                  

(cid:4) 

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(cid:4) 

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272

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annexes                                                                  

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273

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(cid:4) 

Annexes

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(cid:4) 

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(cid:4) 

Annexes

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(cid:4) 

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(cid:4) 

Annexes

-

3
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Annexes                                                                  

(cid:4) 

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(cid:4) 

Annexes

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280

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annexes                                                                  

(cid:4) 

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(cid:4) 

Annexes

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282

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annexes                                                                  

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283

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

Annexes

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284

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annexes                                                                  

(cid:4) 

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285

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annexes 

Annex 2: Items with relatively weak level of comparability 

287

 
 
 
 
 
Annexes                                                                  

(cid:4) 

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(cid:4) 

Annexes

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Annexes                                                                  

(cid:4) 

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(cid:4) 

Annexes

-

3
3

.

2
2

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5
1

.

3
1

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9
0

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0
2

.

2
3

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1
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3
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(cid:4) 

Annexes

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(cid:4) 

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Annexes                                                                  

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Annexes                                                                  

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(cid:4) 

Annexes

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Annexes                                                                  

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(cid:4) 

Annexes

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Annexes                                                                  

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(cid:4) 

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Annexes                                                                  

(cid:4) 

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2

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annexes 

Annex 3: Guidelines for the collection of data on 18 HIS items 

313 

 
 
 
 
 
 
 
Guidelines for the collection of data on 18 HIS items 

(cid:4) 

Guidelines for the collection of data on 18 HIS items 

Contents 

3 

1.4. 

1.1. 
1.2. 
1.3. 

2.1. 
2.2. 
2.3. 
2.4. 

1.  Explanatory notes ............................................................................................................................... 316 
Health topics................................................................................................................................. 316 
Background variables.................................................................................................................... 316 
Tables........................................................................................................................................... 317 
Macro data ............................................................................................................................................ 317 
Micro data............................................................................................................................................. 317 
Transmission of files ............................................................................................................................. 317 
Additional information ................................................................................................................. 318 
2.  Specifications of the background variables ........................................................................................ 319 
Sex [SEX]..................................................................................................................................... 319 
Age [AGEGROUP]....................................................................................................................... 319 
Educational level [EDUC]............................................................................................................. 319 
Economic activity [ECON] ........................................................................................................... 319 
Specifications of the variables on the 18 topics .................................................................................. 320 
Chronic conditions........................................................................................................................ 320 
3.1. 
Self-perceived health..................................................................................................................... 320 
3.2. 
Activity restriction (general question) ........................................................................................... 320 
3.3. 
Physical and sensory functional limitations ................................................................................... 321 
3.4. 
3.5. 
Personal care activities.................................................................................................................. 321 
3.6.  Mental health................................................................................................................................ 321 
Temporary cut down of usual activities ......................................................................................... 322 
3.7. 
Height and Weight........................................................................................................................ 322 
3.8. 
3.9. 
Present and former smoking.......................................................................................................... 323 
3.10.  Consumption of alcohol ................................................................................................................ 323 
3.11.  Physical activity............................................................................................................................ 323 
3.12. 
In patient care............................................................................................................................... 324 
3.13.  Out patient care ............................................................................................................................ 324 
3.14.  Preventive care ............................................................................................................................. 325 
3.15.  Use of medicines........................................................................................................................... 325 
3.16.  Use of drugs.................................................................................................................................. 326 
3.17.  Diet/food consumption habits........................................................................................................ 326 
3.18.  Quality of life................................................................................................................................ 326 
References ................................................................................................................................................... 328 
Appendix 1 - Examples of SAS and SPSS set-ups to produce the macro data files requested by Eurostat ... 330 
1. Example of SAS set up to produce the files requested ........................................................................ 330 
2. Example of SPSS set up to produce the files requested....................................................................... 332 
Appendix 2 - Country-specific details........................................................................................................ 334 

315 

 
 
 
 
 
(cid:4) 

1.  Explanatory notes 

  Guidelines for the collection of data on 18 HIS items 

In follow up to the discussions in the Task Force ‘Health and health related survey data’ (3-4 October 
2001  and  18-19  April  2002),  and  in  the  Working  Group  Public  Health  Statistics  (22-23  November 
2001)  Eurostat  is  collecting  aggregated  survey  data  on  18  HIS  topics  according  to  4  background 
variables. In a first round of data collection in 1999/2000, 12 topics were initially selected, mainly on 
the  basis  of  an  inventory  of  health  surveys  in  the  EU  (Hupkens,  1997).  The  list  was  subsequently 
modified and enlarged, taking into account the work performed within the framework of the European 
Community  Health  Indicators  project,  the  EuroHIS  (Harmonization  of  Health  Interview  Surveys  in 
Europe) project of the WHO, the Euro-REVES1 network and others (see References).  

This  document  specifies  the  variables  related  to  these  topics,  proposed  variable  names,  with  the 
recommended  instruments  and  the  background  variables.  Only  the  most  recent  available  data  are 
requested. 

1.1. 

Health topics 

18 HIS items 

temporary cut down of usual activities 
height and weight (BMI) 
present and former smoking 

chronic conditions  
self perceived health 
activity restriction (general question) 
physical and sensory functional limitations 
personal care activities 

1 
2 
3 
4 
5 
6  mental health 
7 
8 
9 
10  consumption of alcohol 
11  physical activity 
12 
13  out patient care (medical doctor, dentist) 
14  preventive care (check ups) 
15  use of medicines (prescribed/non prescribed) 
16  use of drugs (specific items) 
17  diet/food consumption habits 
18  quality of life 

in patient care (hospitalisations) 

1.2. 

Background variables 

Variable 
Name 
SEX 
sex 
AGEGROUP  age 
EDUC 
ECON 

educational level 
economic activity 

1   Euro-REVES is a European Concerted Action on Harmonization of Health Expectancy Calculations in Europe 

REVES is a French acronym of Réseau sur l'espérance de vie en santé (Network on Health Expectancy) 

316 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                 
Guidelines for the collection of data on 18 HIS items 

1.3. 

Tables 

(cid:4) 

Data should preferably be sent under the  Excel format or as ‘export files’ from  SAS (or SPSS if you do 
not use SAS). 

Data may be sent either as macro data or as micro-data files (one file per survey used). 

Macro data 

Two  data  tables  (e.g.,  two  distinct  Excel  files)  including  each  the  whole  set  of  variables  (see  below, 
section 2) are requested: 

·   one table according to sex and age and educational level, and 
·   one table according to sex and age and economic activity. 

Please  find  in  Appendix  1  dummy  tables included as examples. This appendix indicates, both for SPSS 
and  SAS,  the  requested  data  format  and  the  programme  statements  needed  to  obtain  it.  Please  note 
that  these  examples,  to  fit  on  one  page,  use  a  very  condensed  variable  for  age  (3  classes)  and only 
contain a limited number of variables on the 18 topics (but sufficient to show examples on the ‘sum’ and 
‘mean’ type of outcomes requested). In general,  weighted  (using the usual national weights)  absolute 
sample numbers are requested. The numbers of missing values should be added in separate rows and 
columns. With the help of these figures, Eurostat should be able to calculate percentages. 
It  should  be  noted  that  these  detailed  tables  are  needed  for  standardisation,  for  example  outcomes  by 
education, standardised for age/sex differences between educational levels. It is not intended to publish 
these detailed tables as such or make them available in the original form. 

Percentages  and  indicators  can  only  be  computed  from  the  macro  data  provided.  For example, if the 
number  of  replies  "yes"  and  "no"  are  provided,  the  percentage  of  "yes"  will  be  computed  as  number  of 
"yes"/(number  of  "yes"  +  number  of  "no"),  omitting  the  "missing  values"  category.  Figures  for  the 
"missing values" category are needed to  check the consistency between the numbers provided and the 
sample totals. Explanations should be provided for any discrepancy.  

Micro data 

If you prefer to send a micro-data file instead of the macro (aggregated) data, this would also be possible; 
the  tables  will  then  be  produced  by  us.  Please  contact  us on the preferred format of these files (e.g., 
SAS or SPSS export/portable files). The micro-data file (or several micro-data files, if different surveys are 
used),  each  line  corresponding  to  a  respondent,  should  be  clearly  documented  as  regards  variables, 
response  categories,  codes  for  missing  data,  etc.  The  weights  to  be  used  should  be provided as the 
individual weight assigned to each respondent.  

Transmission of files 

The macro or micro data files should be sent by e-mail or on diskette or CD-R to Eurostat and OECD. 

317 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

  Guidelines for the collection of data on 18 HIS items 

1.4. 

Additional information 

To  interpret  these  data  Eurostat  would  like  to  receive,  for  each  health  survey  used  for  the  data 
delivery,  background  information  on  the  methodology  of  the  survey:  check  our  information  on  the 
survey  (resulting  from  the  HIS/HES  database  inventory)  as  attached  as  Appendix  2  to  our  letter 
requesting the data. If this needs to be corrected or updated, please do so (hand-written annotations 
will  do).  If  we  do  not  attach  information  on  a  survey  that  you  use  for  data  delivery:  give  the 
information on this survey in a table attached to your reply. 
Names  are  proposed  below  for  each  variable.  For  classification  variables  (sex,  age,  education  and 
economic  activity,  it  is  suggested  to  use  numbers  for  each  response  category  (e.g.,  male  =  1,       
female = 2). For other variables, it is suggested to code each possible modality by adding a digit to 
the variable name.  
For  simplifying  data  entry,  it  would  be  desirable  to  provide  a  list  of  the  names  and  labels  of  each 
response  categories.  For  example,  the  possible  responses  to  variable  CHRON  (chronic  conditions) 
should be coded: 

-   CHRON1  have a long-standing illness or health problem 
-   CHRON2  do not have a long-standing illness or health problem 
-   CHRON3  missing value  

For  each  survey,  Eurostat  would  like  to  receive  the  questionnaire(s)  –  or  relevant  parts  -  in  the 
original language and in English (if available), clearly indicating the relevant questions and response 
categories  by  means  of  the  variable  names.  For  example,  the  following  question  could  include  the 
following   (hand-written) indications: 

‘Do you have any long-standing illness or health problem?’ (CHRON) 

Yes  (CHRON1) 
No  (CHRON2)) 
Missing values (CHRON3) 

318 

 
 
 
 
 
Guidelines for the collection of data on 18 HIS items 

2.  Specifications of the background variables 

(cid:4) 

2.1. 

Sex [SEX] 

1.  male 
2. 

female 

2.2. 

Age [AGEGROUP] 

1.  15-24 
2.  25-34 
3.  35-44 
4.  45-54 
5.  55-64 
6.  65-74 
7.  75-84 
8.  85+ 

If  in  your  survey  the  age-range  for  a  certain  HIS  topic  is  limited,  please  deliver  the  available  age 
groups only 

2.3. 

Educational level [EDUC] 

It is proposed to use the same four categories as in the previous data collection exercise: 

- classification according to highest level completed 
- respondents still in full time education: classification according to the level they are attaining 
- specification of the four ISCED 972 levels: 

lower secondary or second stage of basic education (ISCED 2) 

1.  pre-primary and primary (ISCED 0-1) 
2. 
3.  upper secondary (ISCED 3) 
4.  post secondary (ISCED (4-6) 

If the specifications you used are different, please give details on your specifications in an attachment 
to your reply. 

2.4. 

Economic activity [ECON] 

It is proposed to use the same two categories as in the previous data collection exercise: 

1.  active 
2.  non-active 

based on a detailed classification as follows: 

1.  active: 

employees, at least 1 hour per week 
self-employed persons, at least 1 hour per week (incl. family workers) 
unemployed persons, taking active steps to find work 

2.  non-active: 

students and other persons in training  
conscripts on compulsory military or community service 
housewives, housemen  
permanently disabled persons  
retired persons 

If  the  specifications  you  used  are  different,  please  give  the  details  on  your  specification  in  an 
attachment to your reply. 

2   See UNESCO (1997) and UNESCO Institute for Statistics (1999). 

319 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                 
(cid:4) 

  Guidelines for the collection of data on 18 HIS items 

3 

Specifications of the variables on the 18 topics 

In order to enhance the international comparability of the data, Eurostat prefers to receive data that 
conform  to  the  specifications  as  far  as  possible.  The  items  below  correspond  to  the  specifications 
proposed by WHO(1996), EUROHIS (2001 & 2002)3, Euro-REVES (2000), and others.  

Please note that Eurostat welcomes any similar data on these topics if the proposed data is 
not available.  

3.1. 

Chronic conditions  

The overall prevalence of chronic conditions is requested, preferably measured by means of the 
open-ended question: ‘Do you have any long-standing illness or health problem?’ Yes / No: 

[CHRON] 

Prevalence of chronic conditions  

1.  Number of respondents having a long-standing illness or health problem 
2.  Number of respondents not having a long-standing illness or health problem 

If you have used a similar question or if your response categories differ, please provide us with the 
corresponding data and indicate the wording of the question and/or the response categories used. 

3.2. 

Self-perceived health  

Data are requested on the replies to the question: 'How is your health in general?’ 

[HEALTH] 

Assessment of the self perceived health 

1.  Number of respondents replying ‘very good’ 
2.  Number of respondents replying ‘good’ 
3.  Number of respondents replying ‘fair’ 
4.  Number of respondents replying ‘bad’ 
5.  Number of respondents replying ‘very bad’ 

If you have used a similar question or if your response categories differ, please provide us with the 
corresponding data and indicate the wording of the question and/or the response categories used. 

3.3. 

Activity restriction (general question) 

Data requested are replies to the question: 
'For  the  past  6  months  or  more  have  you  been  limited  in  activities  people  usually  do because of a 
health problem?' : 

[LIMACT] 
activities 

Assessment of the limitation because of a health problem in usual 

1.  Number of respondents replying “yes, strongly limited” 
2.  Number of respondents replying “yes, limited” 
3.  Number of respondents replying “no, not limited” 

3   Note that at this stage, EuroHIS and Euro-REVES proposals are still provisional (projects in progress) 

320 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                 
Guidelines for the collection of data on 18 HIS items 

(cid:4) 

If you have used a similar question or if your response categories differ, please provide us with the 
corresponding data and indicate the wording of the question and/or the response categories used. 

3.4. 

Physical and sensory functional limitations 

Data requested on the Functional limitations refers to: 

[WALK] 
[SEENEAR] 
[SEEFAR] 

[HEAR] 

[CARRY] 

Limitation as regards walking (preferably 500 meters) 
Limitation in seeing clearly newspaper print 
Limitation in seeing clearly the face of someone from 4 meters 
(across a road)  
Limitation in hearing what is said in a conversation with one 
person 
Limitation in lifting and carrying a shopping bag of 5 kgs 

For each item, please provide the number of replies according to the response categories (severity of 
the disability) of your national survey. Do not forget to provide the exact wording of the questions and 
the response categories used. 

3.5. 

Personal care activities 

Data requested are replies to the questions: 
‘In  everyday  life,  ignoring  temporary  problems,  do  you  usually  without  any  difficulty,  without 
(human/technical) help, feed yourself / transfer in and out of bed / dress and undress yourself / use 
toilets/ bath and shower yourself’?'. 

[FEED] 
[BED] 
[DRESS] 
[TOILET] 
[BATH] 

Difficulty in feeding oneself 
Difficulty in transferring oneself in and out of bed 
Difficulty in dressing and undressing oneself 
Difficulty in using toilets 
Difficulty in bathing and showering oneself 

For  each  item,  please  provide  the  number  of  replies  according  to  the  response  categories  of  your 
national survey. Do not forget to provide the exact wording of the questions. 

If your data is not available in this but in a similar format, such as ADL items (items according to the 
WHO-Euro or OECD indicators) please provide it. 

3.6. 

Mental health 

Data requested on Mental Health refers to three summary scores: 

[GHQ] 

General Health Questionnaire GHQ-12, using (0,0,1,1) scoring 
system: 

1.  Number of people who have a summary score =  0 
2.  Number of people who have a summary score =  1 
3.  Number of people who have a summary score =  2 
4.  Number of people who have a summary score =  3 
5.  Number of people who have a summary score >=  4 
6.  Average summary score 

321 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

  Guidelines for the collection of data on 18 HIS items 

[MHI] 

Psychological distress (MHI-5 from the SF-36) which items 

1.  Number of people who have a score <= 56 
2.  Number of people who have a score between 60 and 76 
3.  Number of people who have a score ³  80 
4.  Average summary score  

[EVI] 

Positive mental health (5 questions in the SF36 on energy and 
vitality) which  

1.  Number of people who have a score <= 60 
2.  Number of people who have a score between 65 or 70 
3.  Number of people who have a scoring between 75 and 100 
4.  Average summary score 

If it is impossible for you to use these thresholds, please indicate the thresholds you used instead. 
Data requested are the summary scores calculated from the GHQ-12, MHI5 and Vitality as explained 
in EuroHIS Mental Health Indicator Network (2002) (See Reference 5 p14)4.  

3.7. 

Temporary cut down of usual activities 

Data are requested on the replies to the question: 
‘Think about the two weeks ending yesterday. Have you cut down on any of the things you usually do 
about the house, at work or in your free time because of illness or injury?’ 

[CUTDOWN]  Assessment of the temporary cut down in usual activities 

1.  Number of people who have cut down during these two weeks 
2.  Number of people who have not cut down during these two weeks 
3.  For  people  who  have  cut  down:  average  number  of  days  cut  down  during  these  two 

weeks, including Saturdays and Sundays 

4.  For  people  who  have  cut  down:  average  number  of days in bed for all or most of the 

day during these two weeks, including Saturdays and Sundays 

If you have used a similar question or if your response categories differ, please provide us with the 
corresponding data and indicate the wording of the question and/or the response categories used. 

3.8. 

Height and Weight 

[BMI] 

Body Mass Index 

BMI  is  calculated  by  dividing  body  weight  in  kg  by  body  height  (in  m)  squared.  In  order  to 
accommodate both the "old" and the "new" BMI thresholds, the distribution of persons by BMI should 
be as follows: 

1.  Number of respondents who have a BMI < 18.0 
2.  Number of respondents who have: 18.0 £  BMI < 18.5 
3.  Number of respondents who have: 18.5 £  BMI < 25.0 
4.  Number of respondents who have: 25.0 £  BMI < 27.0 
5.  Number of respondents who have: 27.0 £  BMI < 30.0 
6.  Number of respondents who have: BMI ³  30.0 

If  it  is  impossible  for  you  to  use  these  cut-off  points,  please  indicate  the  cut-off  points  you  used 
instead. 

4 For further information, contact Ms Aurélie Jacquin (aurelie.jacquin@planistat.lu) 

322 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                 
Guidelines for the collection of data on 18 HIS items 

3.9. 

Present and former smoking 

Data are requested on:  

[SMOKE] 

Present smoking 

(cid:4) 

1.  Number of respondents who do not smoke (non-smokers) 
2.  Number of respondents who smoke occasionally (smokers) 
3.  Number of respondents who smoke daily (smokers) 

[CIGSMOKE] 

 Number of cigarettes smoked per day 

1.  For smokers: number of respondents who smoke less than 20 cigarettes per day 
2.  For smokers: number of respondents who smoke 20 cigarettes per day or more 

[PASTSMOKE] Former smoking 

1.  For non-smokers: number of respondents who never smoked 
2.  For non-smokers: number of respondents who used to smoke occasionally 
3.  For non-smokers: number of respondents who used to smoke daily 

If  occasional  smokers  cannot  be  separated  form  daily  smokers,  then  use  the  category  ‘smokers’ 
including both daily and occasionally. 

3.10.  Consumption of alcohol  

Data are requested on: 

[DRINK_12M]  Drinkers of alcohol in the past 12 months 

1.  Number  of  respondents  who  drank  any  alcohol    (beer,  wine,  spirits,  other  local 

beverages) in the past 12 months 

2.  Number  of  respondents  who  did  not  drink  any  alcohol  (beer,  wine,  spirits,  other  local 

beverages) in the past 12 months 

[DRINK_4W]  Drinkers of alcohol in the past 4 weeks 

1.  Number  of  respondents  who  drank  any  alcohol  (beer,  wine,  spirits,  other  local 

beverages) in the past 4 weeks  

2.  Number  of  respondents  who  did  not  drink  any  alcohol  (beer,  wine,  spirits,  other  local 

beverages) in the past 4 weeks 

3.  For drinkers in the past 4 weeks: average number of days in the past 4 weeks, where 

people drank any alcohol 

If  your  response  categories  differ,  please  provide  us  with  the  corresponding  data  and  indicate  the 
wording of the question and/or the response categories used. 

3.11.  Physical activity  

Data are requested on: 

323 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

  Guidelines for the collection of data on 18 HIS items 

[PHYSACT] 

Physical activity practices 

1.  Number  of  respondents  who  practise  hard  training  and  competitive  sports  more  than 

once a week in their leisure time activities 

2.  Number  of  respondents  who  practise  jogging  and  other  recreational  sports  or  heavy 

gardening at least 4 hours a week in their leisure time activities 

3.  Number of respondents who practise walking, bicycling or other light activities at least 4 

hours a week in their leisure time activities 

4.  Number of respondents who practise reading, watching TV or other sedentary activities 

in their leisure time activities 

If  more  than  one  code  applicable  for  a  particular  respondent:  choose  the  lowest  code  (‘the  most 
active’). 
If  your  response  categories  differ,  please  provide  us  with  the  corresponding  data  and  indicate  the 
wording of the question and/or the response categories used. 

3.12. 

In patient care  

Data are requested on: 

[INPAT] 

Inpatient hospitalisation in the past 12 months 

1.  Number of respondents with inpatient hospitalisation in the past 12 months 
2.  Number of respondents with no inpatient hospitalisation in the past 12 months 

[DAYPAT] 

Daypatient hospitalisation in the past 12 months  

1.  Number of respondents with daypatient hospitalisation in the past 12 months 
2.  Number of respondents with no daypatient hospitalisation in the past 12 months 

If  your  response  categories  differ,  please  provide  us  with  the  corresponding  data  and  indicate  the 
wording of the question and/or the response categories used. 

3.13.  Out patient care 

Data are requested on: 

[DOCTOR_4W] 

Consulting a medical doctor (including GP, Specialist) 

during the past 4 weeks 

1.  Number of respondents who consulted a medical doctor during the past 4 weeks 
2.  Number of respondents who did not consult a medical doctor during the past 4 weeks 

[DOCTOR_1Y] Consulting a medical doctor (including GP, Specialist) during the 

past 12 months 

1.  Number of respondents who consulted a medical doctor during the past 12 months 
2.  Number of respondents who did not consult a medical doctor during the past 12 months 

3.  Estimate for average number of consultations in one year per person in the population 

[DENTIST_4W] Consultations to the dentist/orthodontist  (past 4 weeks) 

1.  Number of respondents who consulted a dentist/orthodontist during the past 4 weeks 
2.  Number of respondents who did not consult a dentist/orthodontist during the past 4 

weeks 

324 

 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
Guidelines for the collection of data on 18 HIS items 

(cid:4) 

[DENTIST_1Y] Consultations to the dentist/orthodontist (past 12 months) 

1.  Number of respondents who consulted a dentist during the past 12 months 
2.  Number  of  respondents  who  did  not  consult  a  dentist  during  the  past  12  months 

3.  Estimate for average number of consultations in one year per person in the population 

If you use in your national questionnaire different reference periods, please specify the periods used. 
Note  with  respect  to  ‘estimation  for  the  average  number  of  consultations  etc:  include  persons  with 
zero consultations in the denominator. If the reference period in your question is less than one year, 
multiply with the appropriate factor.  

3.14.  Preventive care 

Data are requested on: 

[VACCIN] 

Immunisation/vaccination against influenza: 

1.  number of respondents who have ever been vaccinated against influenza 
2.  number of respondents who have never been vaccinated against influenza  

[DELAYVACCIN] 

Delay: for respondents who have been vaccinated against 

influenza 

1.  number of respondents who have had their last vaccination less than one year ago 
2.  number of respondents who have had their last vaccination between 1 and 2 years ago 
3.  number of people who have had their last vaccination 2 years ago or more 

[BREAST] 

Screening on breast cancer 

1.  number of women who ever had a mammography 
2.  number of women who never had a mammography 

[DELAYBREAST]  Delay: for women who ever had a mammography 

1.  number of women who had their last mammography less than one year ago 
2.  number of women who had their last mammography between 1 and 2 years ago 
3.  number of women who had their last mammography 2 years ago or more 

[CERVICAL] 

Screening on cervical cancer 

1.  number of respondents who ever had a cervical cancer test  
2.  number of respondents who never had a cervical cancer test 

[DELAYCERVICAL]  Delay: for respondents who ever had a cervical cancer test 

1.  number of respondents who had their last cervical cancer test less than one year ago 
2.  number of respondents who had their last cervical cancer test between 1 and 2 years 

ago 

3.  number of respondents who had their last cervical cancer test 2 years ago or more 

If  your  response  categories  differ,  please  provide  us  with  the  corresponding  data  and  indicate  the 
wording of the question and/or the response categories used. 

3.15.  Use of medicines  

Data are requested on: 

325 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

  Guidelines for the collection of data on 18 HIS items 

[PRESCMED]  Medicines prescribed by a physician 

1.  Number of respondents who used medicines prescribed by a physician during the past 

two weeks 

2.  Number of respondents who did not use medicines prescribed by a physician during the 

past two weeks 

[NPRESCMED] Medicines not prescribed by a physician 

1.  Number of respondents who used medicines not prescribed by a physician during the 

past two weeks 

2.  Number of respondents who did not use medicines not prescribed by a physician during 

the past two weeks 

If  your  response  categories  differ,  please  provide  us  with  the  corresponding  data  and  indicate  the 
wording of the question and/or the response categories used. 

3.16.  Use of drugs 

Data are requested on: 

[DRUG_30D]  Drugs used in the past 30 days 

1.  Number of respondents who have taken cannabis in the past 30 days 
2.  Number of respondents who have never taken cannabis in the past 30 days 

[DRUG_12M]  Drugs used in the past 12 months 

1.  Number of respondents who have taken cannabis in the past 12 months 
2.  Number of respondents who have never taken cannabis in the past 12 months 

If  your  response  categories  differ,  please  provide  us  with  the  corresponding  data  and  indicate  the 
wording of the question and/or the response categories used. 

3.17.  Diet/food consumption habits  

Data are requested on: 

[DIET] 

Diet followed 

1.  Number of respondents who have a special diet or follow a particular dietary regime 
2.  Number  of  respondents  who  do  not  have  a  special  diet  or  do  not  follow  a  particular 

dietary regime 

[CHANGEAT]  Change in eating habits 

1.  Number  of  respondents  who  have  changed  their  eating  habits  in  the  past  3  years  to 

lose weight 

2.  Number of respondents who have not changed their eating habits in the past 3 years to 

lose weight 

If  your  response  categories  differ,  please  provide  us  with  the  corresponding  data  and  indicate  the 
wording of the question and/or the response categories used. 

3.18.  Quality of life   

Data are requested on replies to the question: ‘How would you rate your quality of life?’ 

326 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Guidelines for the collection of data on 18 HIS items 

(cid:4) 

[QOL] 

Assessment of the quality of life 

1.  Number of respondents replying “very poor” 
2.  Number of respondents replying “poor” 
3.  Number of respondents replying “neither poor nor good” 
4.  Number of respondents replying “good” 
5.  Number of respondents replying “very good” 

If you have used a similar question or if your response categories differ, please provide us with the 
corresponding data and indicate the wording of the question and/or the response categories used. 

327 

 
 
 
 
(cid:4) 

References 

  Guidelines for the collection of data on 18 HIS items 

1.  ECHI Working Group (2001).  "Design for a Set of European Community Health Indicators - 

Final Report by the ECHI Project" 15 Feb. 2001, 93 pp. 

2.  EMCDDA (2000)  "Co-ordination of an Expert Working Group to develop instruments and 

guidelines to improve quality and comparability of general population surveys on drugs in the 
EU. Follow up of EMCDDA project CT.96.EP.08"  EMCDDA project CT.97.EP.09, European 
Monitoring Centre for Drugs and Drug Addiction, Aug.2000, Final Report, 71 pp. Technical 
Annexes, 141 pp. 
http://www.emcdda.org/multimedia/project_reports/situation/expert_group_comp_report.pdf 
and 
http://www.emcdda.org/multimedia/project_reports/situation/expert_group_comp_annex.pdf  

3.  EuroHIS (2001).  "A WHO Coordinated International Health Survey – Field-Test 

Questionnaire", EuroHIS, draft, 23 March 2001 

4.  EuroHIS Chronic Physical Conditions Network (2002)  "Draft Common Instruments for 

Measuring Chronic Physical Conditions in Health Interview Surveys" Draft, 2002 

5.  EuroHIS Mental Health Indicator Network (2002)  "Chapter 4. Development of Common 

Instruments for Mental Health"  Draft, 2002  

6.  EuroHIS Use of Curative Medical Services’ Indicator Network (2002)  "Chapter 7. 

Development of a Common Instrument for Use of Curative Medical Services" Draft, 2002 

7.  Euro-REVES (2000a). "Selection of a Coherent Set of Health Indicators, Final draft - A First 

Step Towards a User's Guide to Health Expectancies for the European Union” Institut 
National de la Santé et de la Recherche Médicale. (I.N.S.E.R.M.). Démographie et Santé. 
Réseau Espérance de Vie en Santé. (REVES), Euro-REVES, Montpellier (France), 2000, 
191pp  

8.  Euro-REVES (2000b)  "Selection of a Coherent Set of Health Indicators, Final Draft - A First 
Step Towards a User's Guide to Health Expectancies for the European Union – Executive 
Summary" 6 pp. 

9.  Hupkens C. (1998).  "Coverage of Health Topics by Surveys in the European Union" 

OS/E3/97/HIS/2 rev.1(EN), Working Group "Public Health Statistics", 20-21 Nov.1997; 
Eurostat Working Paper 3/1998/E/n.°10, Population and Social Conditions, European 
Commission, Luxembourg, 1998;  Paper presented at the UNSD/UNICEF/EUROSTAT/CDC 
International Seminar on Measurement of Disability, United Nations Statistics Division, New 
York, 4-6 June 2001, 62 + 174 pp.  

10.  Hupkens, Christianne and Swinkels, Henk (2001)  "Health Interview Surveys in The 

European Union: Overview of Methods and Contents" Project ‘Health surveys in the EU: HIS 
and HIS/HES evaluations and models’, Statistics Netherlands, 2001, 31 pp. + Annexes 

11.  Jagger, Carol (2001)*  "Mental Health Indicators in Europe"  ESA/STAT/AC.81/7-5, 22 May 
2001, Paper presented at the UNSD/UNICEF/EUROSTAT/CDC International Seminar on 
Measurement of Disability, United Nations Statistics Division, New York, 4-6 June 2001, i + 
18 pp.  

12.  Jagger, Carol and Robine, Jean-Marie (2001). “Setting up a Coherent Set of Health 

Indicators for the EU (Euro-REVES) – Report on Consensus Workshop on Mental Health, 
Montpellier, 16/3/2001, 8 pp.. 

13.  Robine, J.M. ; Jagger, C. and Egidi, V., eds. (2000a & 2000b).  See Euro-REVES (2000a & 

2000b) 

14.  Tolonen, Hanna; Kuulasmaa, Kari; Laatikainen, Tiina; Wolf, Hermann and the EHRM 

(2001)  "Recommendation for indicators, international collaboration, protocol and manual of 
operations for chronic disease risk factor surveys – Draft" European Health Risk Monitoring 
(EHRM) Project, 1 Feb. 2001  Accessible from 
http://www.ktl.fi/ehrm/documents/product2/title.htm 

328 

 
 
 
Guidelines for the collection of data on 18 HIS items 

(cid:4) 

15.  Vicente, Julian and Hartnoll, Richard (2001). “Key Indicator “Prevalence and Patterns of 
Drug Use Among the General Population –Population Surveys”, Task Force on “Health and 
Health related Survey Data” EMCDDA, Statistics Netherlands,, Lisbon, 2001, 7pp. 

16.  UNESCO (1997)  "International Standard Classification of Education (ISCED) 1997, Unesco, 

Nov. 1997 v + 42 pp. http://unescostat.unesco.org/Documents/ISCED_A.doc, 
http://www.uis.unesco.org/en/pub/isced/ISCED_A.doc  or 
http://www.uis.unesco.org/en/pub/isced/ISCED_A.pdf  
"Classification internationale type de l'éducation 1997 (CITE), UNESCO, Nov. 1997  iv + 32 
pp., http://unescostat.unesco.org/Documents/ISCED_F.doc , 
http://www.uis.unesco.org/fr/pub/isced/ISCED_F.doc or 
http://www.uis.unesco.org/fr/pub/isced/ISCED_F.pdf 

17.  UNESCO Institute for Statistics (1999) "Operational Manual for ISCED-1997 (International 

Standard Classification of Education)", 1st ed., June 1999, i + 38 pp.  
http://www.uis.unesco.org/en/pub/isced/ISCEDMan146.doc or 
http://www.uis.unesco.org/en/pub/isced/ISCEDMan146.pdf 

18.  WHO (1996). [de Bruin, A.; Picavet, H.S.J. and Nossikov, A.]  "Health Interview Surveys: 
towards International Harmonisation of Methods and Instruments. WHO Regional Office for 
Europe & Statistics Netherlands, WHO Regional Publications, European Series No. 58, 
Copenhagen, 1996, xiii + 161 pp.  

19.  WHO (1998)  “Guidelines for Controlling and Monitoring the Tobacco Epidemic”  WHO, 

Geneva, 1998, x + 190 pp. 

329 

 
 
 
 
(cid:4) 

  Guidelines for the collection of data on 18 HIS items 

Appendix 1  -  Examples  of  SAS  and  SPSS  set-ups to produce the macro data files 
requested by Eurostat 

1. Example of SAS set up to produce the files requested  

Notes:  
(1) 

(2) 

In this example the recode for age is shortened. As noted in the guidelines, the requested 
recode is 15-24, 25-34, .. , 75-84, 85+ 
The example is only on 'sex by age by education'. For making the table 'sex by age by 
economic activity' the variable 'education' is replaced by 'economic activity'  

(3)  Only a limited number of topics/variables are shown in this example; refer to the guidelines for 

the full list of requested variables 
The variable names in the file on which this example was based, were: 

(4) 

wgtfac = the usual weight factor for this file 
gslop  = sex (2 categories) 
lftop  = age (in years) 
oplop  = level of education completed (4 categories) 
gezond = perceived health (6 categories with missing value) 
langda = chronic condition(s) yes/no (3 categories with missing value) 
var052 = cutdown in 2 weeks yes/no (3 categories with missing value) 
var053 = number of days cutdown in 2 weeks (1-14) 

data EURO (keep= wgtfac gslop agegroup health1-health6 chron1-chron3 
cutdown1-cutdown3 var053); set XXXX.XXX; 

if (15 le lftop le 34) then agegroup=1;if (35 le lftop le 49) then 
agegroup=2; 
if (lftop ge 50)  then agegroup=3; NOTE: these agegroups only for the 
example; 8 agegroups are requested 

if (oplop ge 4)   then educ=4; 

else educ=oplop;  

if ( gezond = 1 ) then health1 = 1 ; 
if ( gezond = 2 ) then health2 = 1 ; 
if ( gezond = 3 ) then health3 = 1 ; 
if ( gezond = 4 ) then health4 = 1 ; 
if ( gezond = 5 ) then health5 = 1 ; 
if ( gezond = . ) then health6 = 1 ; 
if ( langda = 1 ) then chron1 = 1 ; 
If ( langda = 2 ) then chron2 = 1 ; 
If ( langda = . ) then chron3 = 1 ; 
if ( var052 = 1 ) then cutdown1 = 1 ; 
if ( var052 = 2 ) then cutdown2 = 1 ; 
if ( var052 = . ) then cutdown3 = 1 ; 

if (1 le var053 le 14) then var053=var053; 
          else var053='.';  
run; 

proc sort data = EURO out=EURO; by sex agegroup educ; run; 

 proc means data=EURO noprint; 
 by sex agegroup educ; 
 var health1-health6 chron1-chron3 cutdown1-cutdown3 var053; 
  weight wgtfac; 
 output out=EUROSTAT (drop=  _TYPE_  _FREQ_ var053) sum= 
mean(var053)=ncutdow; 
run; 

330 

 
 
 
 
 
 
 
               
 
 
 
 
 
 
Guidelines for the collection of data on 18 HIS items 

(cid:4) 

Convert the resulting SAS-file (EUROSTAT) to a SAS Transport file with the 
Cport procedure. Send this SAS Transport file to Eurostat. 

Note that it would be preferable to send a copy of this file in format 
Excel to Eurostat with the Export procedure explained below: 

Proc Export data = EUROSTAT outfile ='H:/…/…/EUROSTAT.xls' 

Dbms=excel replace; 

run; 

To show how the SAS-output file (and the Excel-output file) should look 
like see below: 

SE
X 

AGE 
GROUP 

ED
UC 

HEALTH
1 

HEALT
H2 

HEALT
H3 

HEALT
H4 

HEALT
H5 

HEALT
H6 

CHRO
N1 

CHRO
N2 

CHRO
N3 

CUTDO
WN1 

CUTDO
WN2 

CUTDO
WN3 

NCUTD
OW  

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

1 

1 

1 

1 

2 

2 

2 

2 

3 

3 

3 

3 

1 

1 

1 

1 

2 

2 

2 

2 

3 

3 

3 

3 

1 

2 

3 

4 

1 

2 

3 

4 

1 

2 

3 

4 

1 

2 

3 

4 

1 

2 

3 

4 

1 

2 

3 

4 

42 

457 

596 

132 

150 

400 

138 

60 

68 

33 

195 

429 

174 

84 

291 

139 

99 

46 

199 

274 

53 

43 

55 

453 

468 

96 

139 

337 

125 

30 

50 

167 

76 

19 

97 

64 

36 

222 

448 

143 

101 

239 

132 

62 

46 

215 

117 

53 

7 

22 

64 

42 

16 

66 

43 

27 

10 

55 

27 

18 

1 

38 

91 

31 

9 

51 

45 

20 

13 

58 

55 

26 

. 

. 

7 

9 

3 

6 

5 

8 

2 

14 

11 

5 

1 

6 

7 

5 

1 

10 

10 

5 

. 

7 

4 

16 

. 

1 

2 

. 

2 

1 

5 

1 

3 

5 

3 

. 

. 

5 

2 

. 

2 

5 

1 

. 

3 

2 

4 

23 

2 

6 

14 

4 

2 

8 

11 

7 

. 

4 

6 

. 

9 

12 

. 

11 

6 

9 

3 

13 

. 

4 

7 

5 

34 

56 

22 

16 

44 

24 

17 

7 

37 

18 

10 

6 

44 

52 

19 

19 

43 

30 

11 

10 

36 

20 

19 

55 

631 

941 

330 

216 

712 

283 

173 

116 

504 

172 

115 

78 

660 

841 

254 

221 

593 

282 

101 

98 

413 

233 

95 

12 

23 

. 

4 

19 

23 

6 

9 

7 

. 

4 

7 

6 

18 

23 

. 

6 

11 

14 

19 

2 

3 

4 

6 

. 

5 

9 

7 

3 

5 

7 

7 

1 

2 

5 

5 

. 

8 

15 

18 

6 

11 

17 

9 

4 

13 

3 

7 

60 

659 

986 

345 

228 

748 

300 

183 

121 

537 

185 

120 

84 

696 

878 

255 

234 

625 

294 

103 

104 

435 

249 

107 

12 

14 

8 

9 

31 

6 

25 

. 

14 

4 

7 

12 

3 

. 

5 

9 

3 

17 

2 

. 

6 

8 

5 

9,03 

6,75 

5,38 

3,5 

12,89 

5,86 

14 

9,8 

8,96 

10,53 

24 

4,8 

8,9 

6,09 

8,43 

5,67 

8,49 

10,13 

9 

14,33 

17,52 

23 

12,16 

. 

8,21 

331 

 
 
 
 
 
 
 
 
 
 
 
 
(cid:4) 

  Guidelines for the collection of data on 18 HIS items 

2. Example of SPSS set up to produce the files requested 

Notes:  
(1) 

(2) 

In this example the recode for age is shortened. As noted in the guidelines, the requested 
recode is 15-24, 25-34, .. , 75-84, 85+ 
The example is only on 'sex by age by education'. For making the table 'sex by age by 
economic activity' the variable 'education' is replaced by 'economic activity'  

(3)  Only a limited number of topics/variables are shown in this example; refer to the guidelines for 

the full list of requested variables 
The variable names in the file ‘health96.sav’ on which this example was based, were: 

(4) 

wgtfac = the usual weight factor for this file 
gslop  = sex (2 categories) 
lftop  = age (in years) 
oplop  = level of education completed (4 categories) 
gezond = perceived health (6 categories with missing value) 
langda = chronic condition(s) yes/no (3 categories with missing value) 
var052 = cutdown in 2 weeks yes/no (3 categories with missing value) 
var053 = number of days cutdown in 2 weeks (1-14) 

GET FILE health96.sav. 

WEIGHT BY wgtfac. 

RECODE lftop ( 15 THRU 34 = 1 )( 35 THRU 49 = 2 )( 50 THRU HI = 3 )… 
  INTO agegroup. NOTE: these agegroups only for the example; 8 agegroups 

RECODE oplop ( 4 THRU HI = 4 ) INTO educ. 

are requested 

IF ( gezond = 1 ) health1 = 1. 
IF ( gezond = 2 ) health2 = 1. 
IF ( gezond = 3 ) health3 = 1. 
IF ( gezond = 4 ) health4 = 1. 
IF ( gezond = 5 ) health5 = 1. 
IF ( gezond = . ) health6 = 1. 
IF ( langda = 1 ) chron1  = 1. 
IF ( langda = 2 ) chron2  = 1. 
IF ( langda = . ) chron3  = 1. 
IF ( var052 = 1 ) cutdown1 = 1. 
IF ( var052 = 2 ) cutdown2 = 1. 
IF ( var052 = . ) cutdown3 = 1. 

RECODE var053 ( 1 THRU 14 = COPY )( ELSE = SYSMIS ) 
  INTO ncutdow.  

AGGREGATE OUTFILE = * 
        / BREAK   = sex agegroup educ 
        /        health1 health2 health3 health4 health5 health6 
          = SUM( health1 health2 health3 health4 health5 health6) 
        /        chron1 chron2 chron3 cutdown1 cutdown2 cutdown3 
          = SUM( chron1 chron2 chron3 cutdown1 cutdown2 cutdown3) 
        /         ncutdown 
          = MEAN(ncutdown ). 

EXPORT OUTFILE =  'eurostat.por'.   /* send this SPSS export file to 
Eurostat .  

Note that it would be preferable to send a copy of this file in format 
Excel to Eurostat from SPSS with the command explained below: 

SAVE OUTFILE =‘H:\……\…\eurostat.xls’ /* send this Excel file to Eurostat . 

332 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Guidelines for the collection of data on 18 HIS items 

(cid:4) 

To show how the output (SPSS and Excel Files) looks like, the following 
table was produced (by means of the 'LIST' command in SPSS (see below)):   

    A                                                      C   C     C  
    G           H    H    H    H    H   H                  U   U     U     N  
    E           E    E    E    E    E   E    C    C   C    T   T     T     C  
    G           A    A     A    A    A   A    H    H   H    D   D     D     U  
    R    E      L    L    L    L    L   L    R    R   R    O   O     O     T  
S   O    D      T    T    T    T    T   T    O    O   O    W   W     W     D  
E   U    U      H    H    H    H    H   H    N    N    N    N   N     N     O  
X   P    C      1    2    3    4    5   6    1    2   3    1   2     3     W  

1   1    1     81  196   14    7    1   5   50   249  2   40  259   22   6,17 
1   1    2     26   55    5    1    2  14   14    76  .   12   78    5   5,57 
1   1    3     61   83    5    3    .  24   27   126  3   22  130    7   6,47 
1   1    4     73  121   10    5    1   .   31   179  8   25  185    .   5,85 
1   2    1     54  128   35   23   15   3   87   167 11   50  205    4   9,19 
1   2    2     97  204   28   10    5   5   94   251  5   34  311    6   8,64 
1   2    3     36   72    8    4    4   9   36    87  4    9  114   12   9,48  
1   2    4    296  536   58   21    7  11  203   714  .  114  802   13   7,77 
1   3    1     53  153  118   49   29   1  231   172  8   63  340    9  10,27 
1   3    2     44  129   62   18   12   4  123   141  9   35  229    .  10,34 
1   3    3     18   46   17    7    4   2   45    47 11   13   79    2  10,67 
1   3    4     90  276   83   32   13   5  215   280 23   57   439    9  10,79 
2   1    1     69  175   22    9    1   8   56   220  8   42  233    7   5,38 
2   1    2     11   46    4    2    1   .    9    55  2    4   60    5   3,95 
2   1    3     43  110   10    6    .   7   38   132  .   24  145    3   7,90 
2   1    4     72  117   14    7    1  12   50   160  3   32  178    6   5,31 
2   2    1     43  145   53   35   15   8  115   176  8   67  225    .   9,11 
2   2    2     70  199   47   23    5   7  118   225  4   59  285   14   7,97 
2   2    3     62  150   27    7    8   6   77   178  7   41  214   12   8,91 
2   2    4    198  505   75   29   11   .  220   597  9  141  677    9   7,43 
2   3    1     65  271  209  101   43  11  410   278  1  112  576    3   9,82 
2   3    2     43  186   79   46   13   6  181   186  6   68  298    7   9,84 
2   3    3     45  121   44   17    6   7  117   115  .   34  198    1   9,67 
2   3    4     40  152   58   30   12   .  170   122  7   57  235    .  10,79 

333 

 
 
                                                     
     
 
(cid:4) 

  Guidelines for the collection of data on 18 HIS items 

Appendix 2 - Country-specific details 

In this part, Eurostat present the background information on the methodology of the national survey 
available from the HIS/HES database inventory.

334