Unveiling the Alcohol Harm Paradox: Analysis of Socioeconomic Differences in Alcohol Harm in Finland in a Nationwide Cohort Study
Conditions: Alcohol-Related Disorders
Sponsor: Finnish Institute for Health and Welfare

STUDY PROTOCOL
Unveiling the alcohol harm paradox: analysis of socioeconomic differences in alcohol
harm in Finland in a nationwide cohort study
Sponsored by:
Finnish Instituted for Health and Welfare
Collaborators:
The Social Insurance Institution of Finland (KELA)
Funded by:
The study is funded by the Finnish Sustainable Growth Programme (RRP) from the
Ministry of Social Affairs and Health and the National Health Index project funded by the
Social Insurance Institution (grant 72/26/2021).
Authors
Sebastián Peña,1 Elsi Lindell,1 Sonja Lumme,1 Laura Kestilä,1 Kati Sarnola,2 Marke
Jääskeläinen,1 Päivikki Koponen1
1. Department of Health and Welfare, Finnish Institute for Health and Welfare,
Mannerheimintie 166, Helsinki, Finland
2. The Social Insurance Institution of Finland (KELA), Nordenskiöldinkatu 12,
Helsinki, Finland
FINAL version 1.0
This protocol has not been revised
Date of document: April 25, 2023

Background
Alcohol consumption is a leading risk factor for death and disability,1 resulting in
substantial societal costs2 and social and community harm.3 Alcohol-related harm has been
consistently shown to be higher among people of lower socioeconomic status, despite the
fact they often report similar or lower levels of alcohol use.4 This apparent contrast has
been called the alcohol harm paradox.
Understanding what explains the alcohol harm paradox is crucial for the design and
implementation of population health interventions to reduce socioeconomic differences in
alcohol harm. Explanations for the paradox can be broadly categorised into three groups: (i)
differential exposure to alcohol,5 drinking patterns6,7 and trajectories and to other
behavioural risk factors and joint effects among them,6,8,9 (ii) differential vulnerability
resulting from individual factors, such as biological characteristics, psychological traits or
stress, cumulative disadvantage10 and broader community and societal upstream factors,11
and (iii) differential biases in the measurement of alcohol exposure.12
A crucial weakness of existing empirical studies comes from the operationalization of
alcohol harm. With few exceptions,13 a vast majority of studies used a composite endpoint
combining several causes of alcohol-attributable deaths or hospitalizations or even merging
alcohol-attributable hospital admissions with deaths in a single outcome.6,9 While this
strategy increases the statistical power to analyse rare events, composite endpoints are
prone to misclassification bias by masking divergent underlying patterns and associations.14
Cause-specific analyses might shed light on different mechanisms driving the
socioeconomic differences in overall alcohol-attributable harm, as well as opening potential
avenues for policy interventions to reduce them. We will take advantage of a recently
formed dataset covering the total Finnish population to explore associations between
socioeconomic status and cause-specific alcohol-attributable events. An additional
contribution to the literature will be reporting both relative and absolute inequalities in
alcohol-attributable harm. Absolute differences have been rarely reported8,15 even though
might be as relevant as relative differences because, from a pragmatic standpoint, they are
more feasible to reduce.16
The study will aim to (1) examine the relative and absolute socioeconomic differences in
cause-specific alcohol-attributable hospital admissions and deaths; (2) describe the
geographical differences in cause-specific alcohol-attributable hospital admissions and
deaths; and (3) quantify the relative contribution of each specific alcohol-attributable cause
to the overall alcohol-attributable harm.

Methods
Study design and setting
The study is a nationwide population cohort study of the entire Finnish population from
January 1, 2016, to 28 February 2020. Individuals in Finland are assigned a unique
identifier at birth or immigration and used in all public and private administrative records.
We will use the unique identifier to link socioeconomic information with hospital
admissions and deaths.
Participants
Participants will be all permanent residents (see below for a definition) aged 25 and over in
Finland by the last day of 2015 and without an alcohol-attributable hospital admission
during the past three years (January 2013-December 2015). The age limit of 25 was
considered because younger adults might have their education ongoing and their income
and occupational class may not reflect their true socioeconomic standing. We will exclude
residents with prior hospital admissions during the past three years to reduce the risk of
reverse causality.
Data sources
We will use data from the FOLK database (basic module, module for Household Dwelling
Unit and module of Degree/Qualifications) maintained by Statistic Finland.17 The FOLK
database contains individual-level data on income, education, family, degrees and
qualifications and cohabitation. Educational level data comes from the Register of
Completed Education and Degrees. Income data derive from Statistics Finland’s income
distribution statistics and personal taxation data. Occupation data derives from a
combination of sources from registers of employment relationships, statistics on wages and
salaries and data on enterprises.18 We will use data on hospital admissions from the Finnish
Care Register for Health Care (HILMO) which covers all hospital admissions in public and
private inpatient hospitals in Finland. We will use mortality data from the Causes of Death
register administered by Statistics Finland.
Outcomes
The outcome will be cause-specific alcohol-attributable hospital admissions and deaths. We
will include any wholly-attributable cause (i.e. an attributable fraction of 1) included in the
main and secondary causes of hospitalization as well as the underlying and contributory
causes of death. Drawing on national reports and previous studies,19 we will use the
following International Classification of Disease (ICD) codes:

ICD-10 code
Diagnosis
F10.0-1
Alcohol intoxication
F10.2
Alcohol dependence
F10.3-9
Psycho-organic syndrome caused by alcohol
K70
Alcoholic liver disease
K85.2, K86.0
Pancreatic diseases caused by alcohol
K29.2
Gastritis caused by alcohol
I42.6
Alcoholic cardiomyopathy
G312, G4051,
G621, G721
Degeneration of the nervous system due to
alcohol, Epileptic seizures related to alcohol,
Alcoholic polyneuropathy, Alcoholic myopathy
T51, X45
Alcohol poisoning
We will exclude perinatal causes (O354, P043, Q860) as they are impossible in this study
population.
Hospital admissions. We will define hospital admission as any event including at least one
overnight stay in inpatient care. Patients having two hospital admissions in two hospitals
with overlapping dates will be considered as a single hospital admission. We will include
admissions where the main or secondary causes are attributable to alcohol. Given we are
examining cause-specific alcohol-attributable events, we defined criteria to attribute the
cause in case of overlapping or multiple alcohol-attributable ICD-10 codes. For hospital
admissions with more than one alcohol-attributable cause, we will consider the main cause
as the hospitalization cause. In hospital admissions with more than one alcohol-attributable
ICD-10 code, we will consider the main cause as the hospitalization cause. If there are
several alcohol-attributable main causes, we will consider first the complexity of the
inpatient provider (provider code in the HILMO dataset). In the case of ties by complexity,
we will consider the longest stay. We will not include outpatient visits, day-hospital
treatments or emergency care visits.
Deaths.
We
will
define
alcohol-attributable
deaths
as
any
death
where
an
alcohol-attributable cause is either the underlying, immediate, intermediate or contributory
cause of death.
We will follow participants until the first hospital admission, death or end of follow-up. We
will restrict follow-up until February 28, 2020, to avoid confounding due to the COVID-19
pandemic.

Exposure
The exposure of interest is socioeconomic status. Our main proxy of socioeconomic status
will be disposable household income. In addition, we will use educational level and
occupational class. All exposures are measured by the last day of the calendar year (2015).
We will calculate equivalised disposable income by adjusting the combined disposable
income of a household to the household size unit using the OECD-modified equivalence
scale. The income measure in this study is the sum of wage and salary earnings,
self-employment income, property income and current State transfers after subtracting
direct taxes and social security contributions. For persons in non-dwelling units (e.g. living
in institutions, homeless, abroad or registered as unknown) we will consider their personal
net income without equivalization. We will also run sensitivity analyses excluding those
participants.20
We will categorise educational level according to the highest qualification completed
degree into lower secondary education or less (missing data on education), upper secondary
education or post-secondary (levels 3 and 4) and or short-cycle tertiary education,
Bachelor’s or equivalent level and higher (levels 5, 6, 7 and 8). Due to restrictions in
available data, we do not have information on individuals’ educational attainment if it is
less than lower secondary education. Because of this, we do not know the proportion of the
study population the information is truly missing.
We will categorise occupational class into manual workers (codes 51, 52, 53, 54, 59),
self-employed persons (codes 10 and 20), lower-level employees (codes 41, 42, 43 and 44)
and upper-level employees (31, 32, 33, 34), students, pensioners and those unemployed.
This classification is created by Statistics Finland and derives from the person’s main type
of activity, occupation, occupational status and industry. Persons outside the labour force
are assigned the same class as the reference person in the household. For persons in
non-dwelling units, given there is no reference person, we will treat this group as a separate
category of occupational class.
The Supplementary Appendix includes a detailed
description of how these variables are created.
Confounders
We will control for sex, age, marital status, and mother tongue. We will categorise sex as
male or female. We will categorise marital status as unmarried, married, in a registered
partnership or separated, divorced or divorced from a registered partnership or widowed or
widowed from a registered partnership. We will categorise mother tongue as Finnish,
Swedish and other.
Bias
Confounding bias. We will reduce the risk of confounding bias by adjusting for potential
confounders that are associated with the exposure and the outcome. Our data derives from
national registries and provides excellent coverage of observed sociodemographic
characteristics. However, we do not have data on behavioural factors, such as tobacco or

alcohol use, or personality traits. The risk of residual confounding can only be attenuated
but not be fully removed in our study design.
Information bias. Information from national registries is not subject to recall or social
desirability bias. Education data can be subject to measurement error due to missing
information for lower levels of education. This can be more severe for immigrants because
the Register of Education and Completed Degrees might not register degrees obtained
abroad. Statistics Finland has been able to obtain information on more than 13.000 degrees
obtained in Nordic countries and 26.000 degrees in other countries.21 Nonetheless, the
educational attainment of foreigners who studied abroad might be underestimated. In
sensitivity analyses, we will re-run the analyses excluding foreigners. We anticipate,
however, that this will have a negligible effect on our estimates, as foreigners represent a
small proportion of the population. The Finnish economy is highly formalized and informal
employment is estimated at 1-3% of those employed. These include freelancers,
own-account workers in the informal economy, contributing family workers and grant
recipients. Therefore, income and employment statistics include 97-99% of the employed
population.22 Statistics Finland also has routine procedures to assess the quality of registry
data, starting from the studies carried out to compare census with survey data in the 1980s
and from employment surveys conducted regularly.18 The quality of Finnish registry data is,
hence, subject to minimal information bias.
Selection bias. Our data comes from the total Finnish population and, therefore, is not
subject to selection due to participation. The data includes permanent residents in Finland,
defined as all Finnish nationals and foreign nationals who have a legal domicile in Finland
and intend to stay (or have stayed) for at least one year. This excludes foreign nationals
living in Finland for less than a year, asylum seekers who have not been granted a legal
domicile and temporary migrant workers (e.g. cherry pickers and construction workers),
and Finnish nationals living temporarily abroad.23
Selection bias can also arise due to loss of follow-up, although in Finland deaths missing a
death certificate are extremely rare (about 0.2%).24 We consider the risk of selection bias
due to loss of follow-up relatively small.
Quantitative variables
All variables included in the analyses are categorical.
Ethical approval
The use of administrative records does not require ethical approval in Finland. All data
used was pseudonymised and linked by Statistics Finland and will be analysed in a
protected remote access system FIONA. We will comply with Finnish regulations
established under the National Statistics Act and the supplementary National Data
Protection Act as well as with the General Data Protection Regulation from the European
Union.25 Permission for data usage and linkage was given to the Finnish Institute for Health

and Welfare as part of the Compensation Criteria and Evaluation of Provinces project
(permission K/3184/07.03.00/2022, U1098_al9)
Statistical methods
We will use the relative index of inequality (RII) and the slope index of inequality (SII) to
compare and quantify the socioeconomic gradient in cause-specific alcohol-attributable
hospital admissions and deaths. We will use regression models for estimation with
individual time-to-event data: Cox proportional hazards models for relative inequalities and
Aalen additive hazard models for absolute inequalities.
For the RII, we will fit the following Cox proportional hazards model,
(1)
λ(𝑡 |𝐺= 𝑘) =  β0(𝑡)𝑒𝑥𝑝{β𝑥(𝑘) + β(𝑡)𝐿𝑖}  
Where
represents the socioeconomic group and we approximate the rank of each person
𝐺
in the
th is socioeconomic group by the rank
defined as the percentage of the
𝑘
𝑥(𝑘)
population in strictly higher groups plus half of the percentage in group
, and
denotes a
𝑘
𝐿𝑖 
vector of covariates (sex, marital status, etc.).26 This method allows us to incorporate the
whole socioeconomic gradient, instead of comparing the lowest and highest socioeconomic
groups. The estimate of
is equivalent to
.26
𝑅𝐼𝐼
𝑒𝑥𝑝(β)
We will use age as the timescale, as age is strongly correlated with the risk of
hospitalization and mortality.27 We will report regression estimates as hazard ratios (HRs)
with 95% confidence intervals (CIs). Given we studied cause-specific events, deaths due to
other causes represent competing risks. To account for this, we will use cause-specific
hazards by censoring other-cause events to the time of the death by other causes or the end
of follow-up on February 28, 2020. We will examine the proportional hazards assumption
globally and for each covariate using scaled Schoenfeld residuals.28
For the SII, we will fit age-standardized Aalen additive hazard models,
(2)
λ(𝑡 |𝐺= 𝑘) =  α0𝑠(𝑡) + α𝑠𝑥(𝑘) + α(𝑡)𝐿𝑖   
where the age standardization is the weighted sum of estimated age-group-specific indices.
For this we will fit separate additive hazard models within each age-group
.
𝑠 =  1,...., 𝑆,
The estimate of the
is equal to
.
𝑆𝐼𝐼
α
Same as before, we will use age as the timescale and report regression estimates as events
per 100,000 person-years with 95% confidence intervals. We will report cause-specific
hazards to account for competing risks.

In practice, we will run a model for each cause-specific condition (nine models in total)
separately for alcohol-attributable hospital admissions and alcohol-attributable deaths.
For objective 2, we will run stratified analyses by hospital districts and Åland (n =21). We
will report the RII and SII by region in plots or maps in order to obtain a visual
representation of both the degree of between-region inequalities and geographical patterns
in the distribution of alcohol-attributable harm. Considering we might not have enough
statistical precision to obtain informative estimates for regional differences for all
cause-specific outcomes, we will report overall alcohol-attributable admissions and deaths
(separately) and, in the Supplementary Appendix, the most common specific causes.
For objective 3, we will quantify the relative contribution of each specific cause by dividing
the SII for the specific cause by the overall SII for either hospital admission or death and
multiplying by 100.
We will run the same Cox proportional hazards and additive hazards models for educational
level and occupational class. However, given these are categorical variables with an unclear
ordinality and the relationship with the outcome might not be linear, we are not able to
estimate the RII or SII. Instead, we will use the standard approach of comparing the hazard
ratio and additive hazard comparing the category to a reference group (highest education
and upper-level employees, respectively).
Missing data for covariates in the FOLK datasets is minimal. Sex, age, marital status and
mother tongue do not have missing data. We described above how we will handle missing
data for education. For models using income and occupation, we will use multiple
imputation using chained equations to address missing data.
We will carry out additional analyses separating hospital admissions and deaths where
alcohol is the main cause of hospitalization and the underlying cause of death versus a
secondary or contributory cause of death. Alcohol use is more clearly a sufficient
component cause in the main and underlying causes of admissions and death, and we would
expect stronger socioeconomic differences.
We will use SAS version 9.4 and R version 3.6.2 for all analyses.

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Supplementary Appendix to the protocol
Unveiling the alcohol harm paradox: analysis of socioeconomic differences in alcohol
harm in Finland in a nationwide cohort study
Sebastián Peña, Elsi Lindell, Sonja Lumme, Laura Kestilä, Kati Sarnola, Marke
Jääskeläinen, Päivikki Koponen

1. Description of exposures definition
3
Each of the three exposures are defined using the study population’s status at the baseline.
Educational level and occupational status are defined as of 31.12.2015.
Income
Database source: FOLK module for household dwelling unit
Original variable names: Disposable money income (household-dwelling unit),
Consumption units according to modified OECD-scale, Disposable income
Technical names: kturaha_ak, modeocd, kturaha_k
Statistics Finland maintains individual-level information on income in its Income
distribution Statistics. As a measure of income level in this study, we use equivalised
disposable income calculated for each individual belonging to a household dwelling unit. It
is obtained by calculating the combined disposable income of a household and adjusting it
by the number of consumption units in the household. We use the modified OECD scale of
consumption units. Negative income is set to zero. The income describing variables
(kturaha_ak and kturaha_k) are defined and composed the same way by Statistics Finland
at the personal and household level.
For the analysis models, we categorise the individuals’ income into deciles. The income
decile limits are computed taking into account the total population aged 25 or older at the
end of 2015. The equivalised disposable income is not available for those that do not
belong to a household dwelling unit. These include e.g. institutionalised people, homeless
and people registered as unknown. We will use the personal net income of these people
without equivalisation.
Educational level
Database source: FOLK module for degree/qualification
Original variable name: Educational level of highest qualification/degree
Technical name: kaste
Information on individuals’ highest completed degree or qualification is maintained by the
National Register of Completed Education and Degrees. We use the definitions of Statistics
Finland and recategorize the individuals’ educational background into three categories as
follows.

Category
Codes
Lower than upper secondary
education
91 Level of education unknown
Missing: no information on kaste can be linked
Upper secondary education
31 General upper secondary education
32 Vocational upper secondary education and training
33 Further qualification
41 Specialist qualification
Tertiary education or higher
51 Post-secondary non-higher vocational education
52 Lowest level tertiary education
61 Professional tertiary education
62 Polytechnic bachelor's degree
63 University bachelor degree
71 Higher polytechnic degree (Master)
72 Higher university degree (Master)
73 Professional specialisation in medicine, veterinary and dentistry
81 Licentiate's degree
82 Doctoral or equivalent level
Occupational status
Database source: FOLK module for basic data
Original variable name: Socioeconomic group
Technical name: sose
Statistics Finland formes occupational class using information on individual’s economic
activity, occupation and industry. We categorize occupational class according to Statistics
Finland’s definitions as follows.
Category
Codes
Self-employed persons
10 Farmers and forestry entrepreneurs
20 Self-employed persons, not in agriculture and forestry
Upper-level employees
31 Senior officials and upper management
32 Senior officials and employees in research and planning
33 Senior officials and employees in education and training
34 Other senior officials and employees
Lower-level employees
41 Supervisors
42 Clerical and sales workers, independent work
43 Clerical and sales workers, routine work
44 Other lower-level employees
Manual workers
51 Workers in agriculture, forestry and commercial fishing
52 Manufacturing workers
53 Other production workers
54 Distribution and service workers
59 Other workers
Students
60 Students
Pensioners
70 Pensioners
Unemployed
81 Unemployed

Others or unknown
82 Others (conscripts)
99 Unknown