MEASURE DHS assists countries worldwide in the collection and use of data to monitor and 

evaluate population, health, and nutrition programs. Additional information about the 

MEASURE DHS project can be obtained by contacting ICF Macro, 11785 Beltsville Drive, 

Suite 300, Calverton, MD 20705 (telephone: 301-572-0200; fax: 301-572-0999; e-mail: 

reports@measuredhs.com; internet: www.measuredhs.com). 

The main objectives of the MEASURE DHS project are: 

• to provide decisionmakers in survey countries with information useful for informed policy 

choices; 

• to expand the international population and health database; 

• to advance survey methodology; and 

• to develop in participating countries the skills and resources necessary to conduct high-quality 

demographic and health surveys. 
DHS Comparative Reports No. 27 

Trends in Nutritional Status of Adult Women 

in Sub-Saharan Africa 

Michel Garenne 

ICF Macro 

Calverton, Maryland, USA 

July 2011 

Corresponding author: Michel Garenne, Institut Pasteur, Unité d’Epidémiologie des Maladies Emergentes, 25 

rue du Docteur Roux, 75724 Paris, France; Phone: +33-1-40-61-39-58; Email: Michel.Garenne@pasteur.fr
Editor: Bryant Robey 

Document Production: Christopher Gramer and Yuan Cheng 

This study was carried out with support provided by the United States Agency for International 

Development (USAID) through the MEASURE DHS project (#GPO-C-00-08-00008-00). The views 

expressed are those of the authors and do not necessarily reflect the views of USAID or the United States 

Government. 

Recommended citation: 

Garenne, Michel. 2011. Trends in Nutritional Status of Adult Women in Sub-Saharan Africa. DHS 

Comparative Reports No. 27. Calverton, Maryland, USA: ICF Macro.
 iii 

Table of Contents 

Tables and Figures .................................................................................................................................... vii 

Preface ......................................................................................................................................................... xi 

Acknowledgements .................................................................................................................................. xiii 

Executive Summary .................................................................................................................................. xv

Introduction ................................................................................................................................................. 1 

Background ...................................................................................................................................... 1 

Study objectives ............................................................................................................................... 2 

Data and Methods ....................................................................................................................................... 3 

Data .................................................................................................................................................. 3 

DHS surveys .............................................................................................................................. 3 

Sampling biases ......................................................................................................................... 3 

Precision of estimates ................................................................................................................ 3 

Digit preference ......................................................................................................................... 4 

Reference sets ............................................................................................................................ 5 

Methods ........................................................................................................................................... 5 

Country analysis ........................................................................................................................ 5 

Trends ........................................................................................................................................ 6 

Coverage .................................................................................................................................... 6 

Body Mass Index of adult women and body shape ................................................................... 6 

Merging countries ...................................................................................................................... 6 

Differential analysis .................................................................................................................. 6 

Other sources of data ................................................................................................................. 7 

Results .......................................................................................................................................................... 9 

Part I: Overview on Anthropometric Indicators .............................................................................. 9 

1.1 Typology of body shapes ................................................................................................. 9

1.2 Age pattern of height of adult women ........................................................................... 11 

1.3 Age pattern of BMI by group of body shape ................................................................. 11 

1.4 Average anthropometric indicators of adult women (raw data) .................................... 13 

1.5 Distribution of heights in African populations .............................................................. 14 

1.6 Distribution of BMI in African populations .................................................................. 15 

1.7 Changes from survey to survey in the same country ..................................................... 15 

Height ............................................................................................................................ 16 

Weight and BMI ............................................................................................................ 16 

1.8 Relationship of anthropometric indicators with wealth ................................................. 17 
 iv 

Part II: Trends and Differentials in Height .................................................................................... 18 

2.1 Trends in height of adult women ................................................................................... 18 

2.2 Trends in height: differential analysis ........................................................................... 18 

2.3 Multivariate analysis on height of adult women ............................................................ 20 

2.4 Height and income per capita ........................................................................................ 21 

2.5 Height and income, by body shape ................................................................................ 21 

2.6 Height and income at country level ............................................................................... 22 

2.7 Recent trends (cohorts 1982-1992) ................................................................................ 23 

Part III: Trends in Weight and Body Mass Index .......................................................................... 24 

3.1 Underlying age patterns of BMI (net effect) ................................................................. 24 

3.2 Interpretation of BMI trend figures ............................................................................... 25 

A. Madagascar: no age effect, no cohort effect ............................................................. 25 

B. Rwanda: no age effect, increasing cohort effect ....................................................... 25 

C. Senegal: changing age effect, no cohort effect ......................................................... 26 

3.3 Trends in standardized BMI .......................................................................................... 26 

3.4 Trends in standardized BMI by population group ......................................................... 27 

3.5 Multivariate analysis of BMI (raw data) ....................................................................... 28 

Trends in BMI (net effect) ............................................................................................. 28 

3.6 Correlation with economic development ....................................................................... 29 

3.7 Average BMI and prevalence of thinness and obesity .................................................. 29 

Discussion .................................................................................................................................................. 31 

References .................................................................................................................................................. 33 

Appendix A: Annex Tables ...................................................................................................................... 37 

Appendix B: Country Studies .................................................................................................................. 45 

Introduction to country studies ...................................................................................................... 45 

1. Benin .................................................................................................................................... 46 

2. Burkina Faso ........................................................................................................................ 48 

3. Cameroon ............................................................................................................................. 49 

4. Central African Republic ..................................................................................................... 51 

5. Chad ..................................................................................................................................... 52 

6. Comoro Islands .................................................................................................................... 54 

7. Congo-Kinshasa (Democratic Republic of Congo) ............................................................. 55 

8. Congo-Brazzaville (People’s Republic of Congo) ............................................................... 57 

9. Côte d’Ivoire ........................................................................................................................ 58 

10. Ethiopia ................................................................................................................................ 60 

11. Gabon ................................................................................................................................... 61 

12. Ghana ................................................................................................................................... 63 

13. Guinea .................................................................................................................................. 64 
 v 

14. Kenya ................................................................................................................................... 66 

15. Lesotho ................................................................................................................................. 67 

16. Liberia .................................................................................................................................. 69 

17. Madagascar .......................................................................................................................... 70 

18. Malawi ................................................................................................................................. 72 

19. Mali ...................................................................................................................................... 74 

20. Mozambique ......................................................................................................................... 75 

21. Namibia ................................................................................................................................ 77 

22. Niger ..................................................................................................................................... 78 

23. Nigeria .................................................................................................................................. 80 

24. Rwanda ................................................................................................................................. 81 

25. Senegal ................................................................................................................................. 83 

26. Sierra Leone ......................................................................................................................... 84 

27. South Africa ......................................................................................................................... 86 

28. Swaziland ............................................................................................................................. 87 

29. Tanzania ............................................................................................................................... 89 

30. Togo ..................................................................................................................................... 90 

31. Uganda ................................................................................................................................. 92 

32. Zambia ................................................................................................................................. 93 

33. Zimbabwe ............................................................................................................................. 95 
 
 vii 

Tables and Figures 

Table 1 Large groups of body shapes in sub-Saharan countries ....................................................... 10 

Table 2 Average values of anthropometric indicators, by category, women age 20-49 ................... 13 

Table 3 Characteristics of the distribution of heights, by body shape, women 20-49, raw data ....... 14 

Table 4 Characteristics of the distribution of BMI, by body shape, women 20-49, raw data ........... 15 

Table 5 Average gains in weight and BMI, over five-year period, (women age 20-49, not

pregnant, not in post-partum period) .................................................................................... 16 

Table 6 Reconstructed trends in adult height (cm) by socio-economic categories ........................... 20 

Table 7 Results of multivariate regression on adult female height (raw data) .................................. 20 

Table 8 Positive slopes of height versus cohort since 1982 .............................................................. 23 

Table 9 Underlying age pattern of BMI (net effects in multivariate analysis after controlling for 

cohort trend and wealth) ....................................................................................................... 24 

Table 10 Underlying cohort trends in standardized BMI (net effects in multivariate analysis after 

controlling for age, urban residence, and wealth). ............................................................... 28 

Table 11 Net effects in multivariate analysis of age, cohort, urban residence, and wealth on BMI ... 28 

Table A-1 List of surveys included in the study of trends in women’s nutritional status ..................... 37 

Table A-2 Trends in height, total .......................................................................................................... 38 

Table A-3 Trends in height, urban areas ............................................................................................... 39 

Table A-4 Trends in height, rural areas ................................................................................................. 40 

Table A-5 Trends in standardized BMI, total ........................................................................................ 41 

Table A-6 Trends in standardized BMI, urban areas............................................................................. 42 

Table A-7 Trends in standardized BMI, rural areas .............................................................................. 43 

Table B-1 Sample size and basic anthropometric characteristics, adult women, Benin ....................... 46 

Table B-2 Sample size and basic anthropometric characteristics, adult women, Burkina Faso ........... 48 

Table B-3 Sample size and basic anthropometric characteristics, adult women, Cameroon ................ 49 

Table B-4 Sample size and basic anthropometric characteristics, adult women, Central African 

Republic ............................................................................................................................... 51 

Table B-5 Sample size and basic anthropometric characteristics, adult women, Chad ........................ 52 

Table B-6 Sample size and basic anthropometric characteristics, adult women, Comoro Islands ....... 54 

Table B-7 Sample size and basic anthropometric characteristics, adult women, Congo-Kinshasa ...... 55 

Table B-8 Sample size and basic anthropometric characteristics, adult women, Congo-Brazzaville ... 57 

Table B-9 Sample size and basic anthropometric characteristics, adult women, Côte d’Ivoire ........... 58 

Table B-10 Sample size and basic anthropometric characteristics, adult women, Ethiopia ................... 60 

Table B-11 Sample size and basic anthropometric characteristics, adult women, Gabon ...................... 61 

Table B-12 Sample size and basic anthropometric characteristics, adult women, Ghana ...................... 63 

Table B-13 Sample size and basic anthropometric characteristics, adult women, Guinea ..................... 64 

Table B-14 Sample size and basic anthropometric characteristics, adult women, Kenya ...................... 66 
 viii 

Table B-15 Sample size and basic anthropometric characteristics, adult women, Lesotho .................... 67 

Table B-16 Sample size and basic anthropometric characteristics, adult women, Liberia ..................... 69 

Table B-17 Sample size and basic anthropometric characteristics, adult women, Madagascar ............. 70 

Table B-18 Sample size and basic anthropometric characteristics, adult women, Malawi ..................... 72 

Table B-19 Sample size and basic anthropometric characteristics, adult women, Mali ......................... 74 

Table B-20 Sample size and basic anthropometric characteristics, adult women, Mozambique ............ 75 

Table B-21 Sample size and basic anthropometric characteristics, adult women, Namibia ................... 77 

Table B-22 Sample size and basic anthropometric characteristics, adult women, Niger ........................ 78 

Table B-23 Sample size and basic anthropometric characteristics, adult women, Nigeria ..................... 80 

Table B-24 Sample size and basic anthropometric characteristics, adult women, Rwanda .................... 81 

Table B-25 Sample size and basic anthropometric characteristics, adult women, Senegal .................... 83 

Table B-26 Sample size and basic anthropometric characteristics, adult women, Sierra Leone ............ 84 

Table B-27 Sample size and basic anthropometric characteristics, adult women, South Africa ............ 86 

Table B-28 Sample size and basic anthropometric characteristics, adult women, Swaziland ................ 87 

Table B-29 Sample size and basic anthropometric characteristics, adult women, Tanzania .................. 89 

Table B-30 Sample size and basic anthropometric characteristics, adult women, Togo ........................ 90 

Table B-31 Sample size and basic anthropometric characteristics, adult women, Uganda .................... 92 

Table B-32 Sample size and basic anthropometric characteristics, adult women, Zambia ..................... 93 

Table B-33 Sample size and basic anthropometric characteristics, adult women, Zimbabwe ................ 95 

Figure 1 Digit preference for age, weight and height in DHS surveys of sub-Saharan Africa ............ 4 

Figure 2 Correspondence between height and BMI in DHS surveys of sub-Saharan Africa ............... 9 

Figure 3 Groups of body shape in DHS surveys of sub-Saharan Africa ............................................ 10 

Figure 4 Comparison of growth in height among African adolescent women with international 

reference sets ........................................................................................................................ 11 

Figure 5 Comparison of age-specific BMI of African women with international reference sets ....... 12 

Figure 6 Age patterns of BMI in the four groups of body shape (raw data) ...................................... 13 

Figure 7 Distribution of women’s height, by group of body shape (raw data) ................................... 14 

Figure 8 Distribution of BMI among women age 20-49, by group of body shape (raw data) ........... 15 

Figure 9 Average change in weight and BMI over a five-year period, women age 20-44 at first 

survey, by group of body size .............................................................................................. 17

Figure 10 Average height and BMI by wealth, women age 20-49 (raw data) ..................................... 17 

Figure 11 Trends in height of adult women, sub-Saharan Africa ......................................................... 18 

Figure 12 Trends in height of adult women, by body shape and residence (urban and rural) .............. 19 

Figure 13 Reconstructed trends in height of adult women, by wealth ................................................. 19 

Figure 14 Correlation between trends in income per capita and trends in women’s height, 33 

African countries .................................................................................................................. 21 

Figure 15 Correlation between trends in income per capita and trends in women’s height, by 

groups of body shape ........................................................................................................... 22 
 ix 

Figure 16 Underlying age pattern of BMI, by group of body size (net effect after controlling for 

cohort and wealth) ................................................................................................................ 24 

Figure 17 Age and cohort effects in Madagascar ................................................................................. 25 

Figure 18 Age and cohort effects in Rwanda ....................................................................................... 25 

Figure 19 Age and cohort effects in Senegal ........................................................................................ 26 

Figure 20 Reconstructed trends in standardized BMI of adult women ................................................ 27 

Figure 21 Reconstructed trends in standardized BMI of adult women, by population group .............. 27 

Figure 22 Relationship between average BMI and income per capita at country level ....................... 29 

Figure 23 Relationship between average BMI and prevalence of thinness, underweight, 

overweight and obesity, 70 DHS surveys in sub-Saharan Africa, women age 20-49 .......... 30 
 
xi 

Preface 

One of the most significant contributions of the MEASURE DHS program is the creation of an 

internationally comparable body of data on the demographic and health characteristics of populations in 

developing countries. The DHS Comparative Reports series examines these data across countries in a 

comparative framework. The DHS Analytical Studies series focuses on specific topics. The principal 

objectives of both series are to provide information for policy formulation at the international level and to 

examine individual country results in an international context. 

Whereas Comparative Reports are primarily descriptive, Analytical Studies have a more 

analytical approach. The Comparative Reports series covers a variable number of countries, depending on 

the availability of data sets. Where possible, data from previous DHS surveys are used to evaluate trends 

over time. Each report provides detailed tables and graphs organized by region. Survey-related issues 

such as questionnaire comparability, survey procedures, data quality, and methodological approaches are 

addressed as needed. 

The topics covered in Comparative Reports are selected by MEASURE DHS staff in conjunction 

with the U.S. Agency for International Development. Some reports are updates of previously published 

reports. 

It is anticipated that the availability of comparable information for a large number of developing 

countries will enhance the understanding of important issues in the fields of international population and 

health by analysts and policymakers. 

Ann Way 

Project Director 

xiii 

Acknowledgements 

The author thanks the MEASURE DHS project and USAID for their support and for free access to the 

original data; and thanks in particular Sunita Kishor, Shea Rutstein, and the editors of the Comparative 

Reports series. 

xv 

Executive Summary 

The study investigates long-term trends in nutritional status of adult African women, using data 

from 70 Demographic and Health Surveys (DHS) conducted in 33 sub-Saharan countries. The study 

reports recent data on women’s height and Body Mass Index (BMI). Overall, the average height of adult 

women increased from 157.3 cm for cohorts born in 1940 to 158.7 cm for cohorts born in 1965, then 

decreased to 157.5 cm for cohorts born in 1990, almost back to its original level. Trends in adult female 

height were highly correlated with the rise and fall in income per capita at the time of adolescence for the 

same cohorts. 

This pattern of a rise in height followed by a fall was found in both urban and rural areas, and for 

all categories of wealth, for the 33 countries overall. However, it was not universal among the countries. 

In affluent countries of Southern Africa, trends in height were only positive. In Sahelian countries, where 

the average height was much higher than elsewhere, there was no negative trend in urban areas, and only 

the rural areas underwent a minor decline. The largest declines were in Coastal West Africa, Central 

Africa, and most of Eastern Africa. Trends were therefore contrasted depending on the level of income, 

trends in income, and regional peculiarities (genetic endowment or diet). Only a few countries indicated a 

reversal to a positive trend in recent years for the youngest generations, a trend that remains to be 

confirmed by further studies. 

Trends in BMI were assessed using an age pattern for women between age 15 and 49 years, 

calculated separately by large region and by area of residence. In countries of Southern Africa, the BMI 

was much higher than elsewhere, was increasing fast with age, and the age effect tended to increase with 

time, in both urban and rural areas. In Sahelian countries, the BMI was much lower but tended to catch up 

with time. In urban areas of the Sahelian countries the age pattern was marked, but was less pronounced 

in rural areas. In countries of Coastal West Africa, Central Africa, and Eastern Africa, cohort trends were 

not linear: the age standardized BMI tended to first increase, then to decrease with time, and the negative 

trends were larger in urban areas. The age effect was moderate in Coastal West Africa and in Central 

Africa, and very small in Eastern Africa. Weight gains with age were very small in most of the countries 

ranging from Ethiopia to Madagascar, which are the poorest countries in the continent. 

The study also displays differences in height and BMI by socio-economic status defined by an 

absolute wealth index (AWI). The findings were confirmed by multivariate analysis. 

Key words: Health transition; Adult height; Secular trend in height; Body Mass Index (BMI); Body 

shape; Women health; Economic development; Income per capita; Genetic factors; Sub-Saharan Africa. 

 1 

Introduction 

Background 

The Demographic and Health surveys (DHS) offer a unique opportunity to study long-term trends 

in nutritional status in developing countries. DHS surveys have included child anthropometry since 

Round I (1984-1989), adult female anthropometry from the next round (1989-1993), and have more 

recently included biomarkers, such as anemia/iron deficiency, vitamin A deficiency, and iodine 

deficiency. These data have been analyzed in a number of DHS publications. Loaiza [1997] compared the 

nutritional status of mothers in the first two rounds of DHS surveys and found major differences in 

chronic energy deficiency and obesity by continent. Sommerfelt and Stewart [1994] compared the 

nutritional status of children in the DHS surveys conducted in Round I and Round II. They studied the 

prevalence of malnutrition, the age patterns, the distribution of nutritional status, and the differentials by 

various socio-economic, demographic and health variables. Mukuria et al. [2005] repeated a similar 

analysis of nutritional status of children for surveys of Round III and Round IV. They added a section on 

overweight and obesity among children. They also reported the relationship between child anthropometry 

and mother’s Body Mass Index (BMI), but without a full-scale analysis of BMI for adult women. Other 

reports have focused on micronutrients and related health problems among adult women, such as anemia, 

iron supplementation, iodized salt and vitamin A [Mukuria and Kothari, 2007]. Bradley and Mishra 

[2008] studied the complex relationship of BMI with HIV infection among adult women in 12 African 

countries, based on cross-sectional analysis. 

In developing countries, trends in height and weight have been largely favorable since 1950, as 

was the case in developed countries much earlier, since about 1850. These trends are assumed to be 

closely related with the health transition, in particular with the control of infectious diseases, as well as 

with improving nutrition associated with rising income per capita. However, Tobias [1985] showed 

declining height in a number of African countries. Ganguly [1979] showed that the average height of 40 

out of 60 groups from India also declined. On average, the height of Indians seems to have declined by 

about 1cm between 1880 and 1960. In a series of papers, Moradi [2002, 2006, 2010] analyzed the height 

of African women, using DHS data. This author showed that African countries had different dynamics, 

and that cases of increasing or declining height were often associated with periods of economic growth or 

recession during the first years of life or at puberty. Akachi and Canning [2007, 2010] also found a 

declining trend in height of adult women in many African countries, and showed an unexpected 

correlation between decreasing child mortality and declining height in recent decades. 

With respect to body composition, many developing countries, especially the most economically 

advanced, have experienced an increase in weight and in weight-for-height since 1960, leading to an 

obesity epidemic [Prentice, 2006; Villamor et al. 2006; Amuna and Zotor, 2008; Himes, 1979]. Little 

attention has been devoted so far in Africa to prevailing conditions before the rise of obesity, to cases of 

declining weight-for-height, and in our search we did not find any published study providing a synthesis 

of trends in BMI in Africa. 

Beyond national trends, many analysts have noted marked differentials in height and in weightfor-height by socio-economic status [Boix and Rosenbluth, 2006; Deaton, 2003, 2007, 2008; Moradi and 

Baten, 2005; Singh-Manoux et al. 2010; Stecker, 1995]. Usually the higher socio-economic strata have 

greater height, and often obesity is more prevalent among the poorer strata, although not always. 
 2 

Study objectives 

The aim of this study is to investigate trends in height and BMI of sub-Saharan African women, 

at the national level, and for selected socio-demographic categories, in particular urban and rural areas, 

and selected categories of wealth. Africa went through various phases of development over the past 

century, with ups and downs, and in some cases with major economic downturns, and major periods of 

political instability or civil wars. The good times are expected to have a positive impact on 

anthropometric indicators, whereas the difficult times are expected to have a negative impact, in particular 

on the height of adult women. Obesity requires enough wealth to provide a surplus diet, so the poorest 

strata of African societies are expected to be free of this condition. This study focuses on periods with 

positive trends in height (improvements in nutritional status), periods with negative trends (deterioration) 

and periods with no change. Trends in BMI are more difficult to analyze: an increase in weight-for-height 

from very low values indicates an improvement, but an increase from average value to very high values 

indicates obesity, a deterioration in health status. This study is restricted to height and weight of adult 

women, since very few African DHS surveys included anthropometric data on men. 
 3 

Data and Methods 

Data 

DHS surveys 

All DHS surveys from sub-Saharan Africa available with anthropometric measurements for adult 

women were considered for this study. The list of these surveys is provided in Table A-1. This set 

accounts for 33 countries, 70 surveys, and 438,220 women with weight and height measurements. 

Outstanding cases of abnormally low weight or height were excluded: women age 15-49 with weight 

<30.0 Kg or ≥200 kg, with height <130 cm or ≥205 cm, of with a BMI <13.0 or ≥60 kg/m2. These might 

be cases of outstanding anthropometry (dwarfs, giants), or simply data errors. 

Sampling biases 

Most surveys were based on representative samples of women age 15-49. Some focused on 

women who gave birth in the past three or five years. Earlier studies showed that this introduced only a 

negligible bias compared with proper samples of women age 15-49 years [Moradi, 2006]. Of the 70 

surveys in our study, 32 were based on a complete sample of women, 32 were based on samples of about 

one woman in every two (birth in the past five years), and 6 were based on smaller samples (Cameroon 

1998; Comoros 1996; Nigeria 1999; Senegal 2005; Uganda 2006; Zimbabwe 1994). On average, selected 

women tended to be from somewhat poorer households and had a somewhat lower level of education, 

most likely because they had higher fertility and therefore were more likely to be selected. All biases in 

the second and third groups were statistically significant, but were of small magnitude. In the second 

group, with an average sampling fraction of 51%, the average absolute wealth index was 2.3 compared 

with 2.4 in the total population, and the average level of women’s education was 3.3 years compared with 

3.8 years in the total population. (see below ‘differential analysis’ for the definition of the absolute wealth 

index). In the third group, with an average sampling fraction of 31%, the average wealth index was 3.1 

compared with 3.5 in the total population, and the average level of education was 4.2 years compared 

with 5.7 years in the total population. Two surveys are questionable because of small sample size and 

large bias: Comoros 1996 and Nigeria 1999. They should be considered with caution. 

Precision of estimates 

In DHS surveys, anthropometric measures are taken by two well trained persons. Height is 

measured to the nearest millimeter (mm) using measuring boards with a headpiece made in the USA for 

DHS. Weight is taken to the nearest 100 grams (0.1 kg) using Seca digital scales. Measurements 

procedures are standardized during the training of the field workers [Macro International, 2008]. 

Due to the very large data set for this study, the sampling errors are very low, and the precision is 

very high for average country estimates, if one ignores measurement errors. To give an idea, in a national 

sample of 4,000 women, an average height of 160.0 cm will be given with a precision of ±0.2 cm, an 

average weight of 60.0 kg will be given with a precision of ±0.3 kg, and an average BMI of 23.0 kg/m2 

will be given with a precision of ±0.1 kg/m2. For trend analysis, a linear regression model was used, and 

proper estimates of standard errors are provided in Appendix A for computing confidence intervals. 

Except in the case of erratic data, in the case of very small samples, or in the case of trends computed on 

short periods, the precision of the estimates provided by trend analysis is also very high. The analysis of 

consistency between surveys conducted in the same country showed that measurement errors appeared 

small, and of about the same order of magnitude as the sampling errors (see below § 1.7 for details). 
 4 

Digit preference 

Another way at looking at precision of measurements is to consider digit preference for age, 

weight and height. The analysis of the 70 surveys revealed significant preferences for ages ending in 0 

and 5, for weights ending in .0 and .5 kg and for heights ending in .0 and .5 cm (Figure 1). The Myers 

index for age was 10.5 and the Whipple index was 147, which are relatively high values. Large variations 

could be seen by survey. The Myers index for age varied from 3.6 to 24.6 and the Whipple index from 

100 to 223. Low values were found in Southern Africa (South Africa, Zambia, Zimbabwe, Swaziland, 

Namibia) and in two countries with good vital registration (Congo-Brazzaville and Madagascar). High 

values were found in West Africa (Sierra Leone, Chad, Niger, Guinea, Benin, Mali), the worst case of all 

countries being Nigeria. 

Figure 1 Digit preference for age, weight and height in DHS surveys of sub-Saharan Africa 

The Myers index for weight was 5.9 and the Whipple index was 127, which are moderate values 

indicating relatively good precision of the scales and the readings. Here again, variations between 

countries and surveys were very large. Three surveys had no precision, since most weights were rounded 

to integer values of kilograms: Nigeria 1999 (89%), Madagascar 2008 (71%), and South Africa 1998 

(78%). For the others, the Myers index varied from 0.9 to 18.4, and the Whipple index from below 100 to 

192. There was no obvious geographical pattern, and the quality could vary greatly in the same country 

from survey to survey. For instance, in Burkina Faso the Myers index was low in 1993 and 1999 (4.4 and 

2.8 respectively), but high in 2003 (18.4). Similar variations were found in Madagascar. Some countries 

were consistently good, such as Malawi (Whipple index ≤105 in 3 surveys), or Uganda (Whipple index 

≤100 in 3 surveys). 

The Myers index for height was 18.4 and the Whipple index was 187, which are high values 

indicating poor precision of the equipment or of the readings. Here again, variations by survey were large. 

Two surveys had no precision, since most heights were rounded to integer values of centimeters: Nigeria 

1999 (88%), and Congo-Brazzaville 2005 (67%). For the others, the Myers index varied from 3.7 to 39.6. 

Most surveys (60/68) had a Whipple index classified as ‘bad’ or ‘very bad’ (≥125 or ≥175), and only two 

surveys were in the category ‘good’ or ‘very good’ (<110 or <105): Uganda 1995 and Rwanda 2000. 

0%

5%

10%

15%

20%

25%

90123456789

Percent population

Digit ending

Baseline

Age

Weight

Height
 5 

How the lack of precision affects the averages computed for weight, height and BMI remains 

unclear. Digit preference suggests that many of the values rounded to 0 or 5 come from the nearby 

categories ending in 8, 9, 1, 2 or 3, 4, 6, 7. If so, the precision is again ±2 decimals, which is consistent 

with the inaccuracies found between surveys. Assuming that there was no systematic bias, this would not 

affect the means or the trends. If anything, these biases would tend to increase the variance between 

yearly estimates, and therefore would decrease the power of the statistical testing for trends. So, when 

trends are significant despite this limitation, they are in fact probably even more significant in the real 

world. 

Reference sets 

There are no international anthropometric standards for adults, as there are for under-five children 

[WHO, 1995; WHO, 2007]. For the sake of international comparisons, two reference sets were used: one 

American (USA), and one European (France). Height for adolescents (age 15-19) was compared with the 

CDC-2000 set from USA [Kuczmarski et al., 2002], and with the INSERM sets from France [Sempe et al. 

1979]. Both sets were equivalent, leading to a median adult height= 163 cm, and similar growth between 

age 15 and age 20. The BMI was compared with the NHANES-III set from USA [McDowell et al., 2009] 

and with the IRSA set from France published by Rolland-Cachera et al. [1991]. Here the two reference 

sets were very different, the American set having much higher values, although they had a similar slope 

with respect to age (see below § 3.1). 

The BMI was calculated as the ratio of weight by height-squared (W/H2). We did not find any 

justification to use another formula, such as that suggested by Nestel and Rutstein [2002]. First, as noted 

by these authors and by others, “the convenience of using 2 as an exponent in the Quetelet index 

outweighs the advantage of other de facto indexes.” Second, we checked the empirical relationship 

between weight and height in our samples. The average power in the African samples was 1.93, and it 

varied by age from 1.82 at age 20-29 to 2.04 at age 40-49, as well as by group of body shape, from 1.64 in 

the Southern group to 2.06 in the Sahelian group (see below § 1.1 for the definition of groups). So the 

choice of 2 for the power of the Quetelet index seemed obvious in this case. 

Methods 

Country analysis 

Countries were first analyzed one by one, displaying the relationship between height, BMI, and 

year of birth. Results of the country analysis are displayed in Appendix B. When several DHS surveys 

were available for the same country, they were merged together, cohort by cohort, after checking the data 

consistency on height and BMI between surveys. Note that since the study deals with cohorts, there is no 

need to adjust for population increase between surveys. So, each dataset includes weight and height data 

from the oldest cohort in the first survey to the youngest cohort in the latest survey. Weighted datasets 

were used in each case for calculating the mean height, weight, and BMI. 

This procedure ignores the minor bias associated with selection for mortality in the same cohort 

between two surveys. Note that selection biases might also affect the estimation of cohort trends: if 

shorter women are more likely to die earlier, the mean height of a cohort will be higher for survivors at 

age 50 than for survivors at age 20, which may lead to a false impression of decreasing height over time. 

Likewise, if fatter women are more likely to die earlier, the mean BMI of a cohort will be lower for 

survivors at age 50 than for survivors at age 20, and one may get a false impression of increasing obesity. 

These biases cannot be estimated from DHS surveys since they would require prospective studies. We 

tried to estimate the magnitude of these biases from our own prospective studies conducted in 1983-1984 

in Niakhar, a rural area in Senegal [Garenne et al. 2000]. Some 6,800 women were measured for weight 
 6 

and height and were followed for mortality for an average of five years. Mortality was higher for women 

of short stature, and for women with higher BMI (unpublished results). Selection for mortality between 

age 20 and age 50 induced very minor biases: +0.13 cm for an average height of 160.8 cm, and -0.03 

kg/m2 for an average BMI of 21.3 kg/m2, which are negligible quantities given the sampling and 

measurement errors in DHS surveys. 

Trends 

The trend analysis is based on a straightforward study of height and BMI by cohort, defined as 

the year of birth. DHS surveys provide anthropometric measures for women age 15-49 at time of survey, 

and provide directly the year of birth of respondents. For height, the analysis by cohort is straightforward 

with only a minor correction for adolescents, since height is approximately constant between age 20 and 

49 (see below § 1.2). No attempt was made to correct for the very minor changes in height that occur after 

age 40. For BMI, a method was developed to make a similar analysis by using an age pattern stratified by 

large groups of body shape (see below § 3.1). Trends were then tested by ordinary least square (OLS) 

regression. 

Coverage 

Anthropometric measures among women age 15-49 were included in DHS surveys in the early 

1990s, and the latest surveys available when this study was done were conducted in the mid-2000s. So, 

this study has the potential to investigate trends for about 50 years of birth cohorts, ranging from cohort 

1940 to cohort 1990. Trends were therefore estimated over this time frame, for all the countries involved, 

with of course some gaps. In order to have full scale trends for the continent as a whole and for groups of 

countries, some of the trends were extrapolated before the oldest cohort available, or after the youngest 

cohorts. These have no value for each country individually, and should be considered as imputed values 

for missing years for the whole set. This procedure should provide reliable estimates for the continent as a 

whole and for large groups of countries. 

Body Mass Index of adult women and body shape 

BMI was calculated directly from the surveys as weight (in kg) divided by height-squared (in m2). 

Women who were known to be pregnant at time of the measurement, and women in the post-partum 

period (two months after delivery) were excluded from the computations. The BMIs have a variety of age 

patterns among adults age 15-49, and there is no accepted standard for the BMI [WHO, 1995]. So, no 

standard can be readily used for computing cohort estimates. The conversion of age-specific BMI data 

into cohort estimates is explained below (see § 3.1). In brief, four age profiles were identified in Africa, 

based on country of residence, and all calculations were made for urban and rural areas separately. 

Merging countries 

As mentioned, the analysis was first conducted country by country, and trends were estimated for 

all cohorts available (see Appendix B for details by country). An average for sub-Saharan Africa, or for 

subgroups, was computed by weighting each country by its population in year 2000. The 2000 population 

estimates were taken from the United Nations Population Division, 2006 revision [United Nations, 2007]. 

Differential analysis 

Differential analysis focused on urban versus rural residence, and on wealth status. Urban 

residence was taken directly from the DHS surveys. In some cases, the proportion of urban women age 

15-49 in DHS surveys might differ from the proportion urban in population censuses, although the gap 
 7 

between the two is usually small. Of course, urban residence is fixed at time of survey, and does not 

include previous urban/rural migration. 

A wealth index was computed for each survey, as a sum of dummy variables indicating the 

presence of modern goods or amenities in the household. This Absolute Wealth Index (AWI) has been 

described in detail elsewhere [Garenne and Hohmann, 2003; Hohmann and Garenne, 2010]. In brief, the 

wealth index score varies from 0 to 14, 0 indicating no modern goods, and large numbers (12+) indicating 

all modern comforts (running water, flushing toilet, electricity, radio, television, refrigerator, means of 

transportation, telephone, etc.). This Absolute Wealth Index has been shown to correlate well with 

mortality (under-five children and young adults), with fertility, with age at marriage, and with nutritional 

status [Hohmann and Garenne, 2009]. Here again, the wealth index is computed at time of survey and 

ignores previous changes in wealth for the households. 

For the sake of presenting results in tables, a typology of wealth was computed from the Absolute 

Wealth Index, in five categories: poorest (0-1), poorer (2-3), medium (4-5), wealthier (6-7), wealthiest 

(8+). Another typology was also used, using five categories and mixing urban residence and wealth: rural 

poor (0-2), urban poor (0-2), rural medium (3-7), urban medium (3-7), and wealthy (AWI ≥8), whatever 

the area of residence. Some tables refer to ‘poor’, ‘medium’, and ‘wealthy’ based on the same thresholds, 

and mixing urban and rural areas together. The proportion poor, or in poverty, also refers to the 

proportion of households whose AWI is lower than 3. Note that all the differential analysis is based on 

current status at time of survey (urban residence, country, wealth), which is a limitation to this study. 

Other sources of data 

Beyond statistical analysis at micro-level (individual), we also conducted analysis at macro-level 

(country). In particular, we correlated aggregate values of anthropometry with national income. For 

income per capita at the country level, we used the database developed by Angus Maddison and 

colleagues, in its latest 2010 update [Maddison, 2010]. This set provides estimates of Gross Domestic 

Product per capita in Parity Purchasing Power (GDP-PPP), expressed in constant US dollars. It is 

complete for all countries in this study, and covers the period from 1950-2008 in its latest edition. It is a 

perfect match for cohorts 1940-1990, who became adolescent over the study period. 
 
 9 

Results 

Part I: Overview on Anthropometric Indicators 

This part is devoted to general considerations on anthropometric indicators in sub-Saharan Africa. 

The first section is devoted to heterogeneity, and the identification of groups of body shapes; the second 

section on average values and comparison with international standards; the third section to differential 

analysis. 

1.1 Typology of body shapes 

The distributions of height, weight and BMI vary widely throughout Africa, and there is a great 

variety of body shapes in Africa, striking in the data and also visible in photographs of people, or from 

direct observation when visiting African countries. For this analysis, four large groups of body shapes 

were identified, based on the cross-classification of BMI by height (Figure 2). 

Figure 2 Correspondence between height and BMI in DHS surveys of sub-Saharan Africa 

Note: Country codes are those used by DHS surveys (see Table A-1). 

Thresholds were arbitrarily selected based on geographical patterns: 157 cm and 160 cm for 

height, and 24 kg/m2 for BMI. Countries were classified according to these thresholds. Countries on the 

borderline were grouped with the nearest group geographically. Four large groups of body shapes were 

identified empirically from DHS survey data, by plotting height against BMI (Figure 3). 

BJ

BF

CM

CF

TD

KM

CD

CG

CI

ET

GA

GH

GN

KE

LS

LB

MD

MZ MW ML

NM

NI

NG

RW

SN

SL

ZA

SZ

TZ

TG

UG

ZM

ZW

19

20

21

22

23

24

25

26

27

28

29

152 154 156 158 160 162 164

BMI (Kg/m2)

Mean height (cm)
 10 

Figure 3 Groups of body shape in DHS surveys of sub-Saharan Africa 

A first group of countries, with average height and average BMI, was found in countries from 

Central Africa, in countries from Coastal West Africa, and in Uganda and Kenya. The second group had 

higher height and lower BMI (tall and slim), and was found in Sahelian countries, from Senegal to Chad. 

The third group had lower height and lower BMI (short and slim), and was found in Eastern Africa from 

Ethiopia to Madagascar. The last group had average height and higher BMI, and was found in the five 

countries of Southern Africa. 

Due to their geographical pattern, the first group was labeled ‘Central,’ the second ‘Sahelian,’ the 

third ‘Eastern’ and the fourth ‘Southern.’ Note that this classification is empirical and was necessary for 

our study. It is based on a combination of genetic, nutritional and socio-economic factors: the Sahelian 

pattern seems primarily due to genetic factors, or possibly to a different diet, since women are taller 

despite being poorer, whereas the Eastern pattern is associated primarily with very low income and major 

stunting, and the Southern pattern with higher income and prevalent obesity (Table 1). 

Table 1 Large groups of body shapes in sub-Saharan countries 

Location 

Group 1 

Average 

Group 2 

Tall and slim 

Group 3 

Short and slim 

Group 4 

High BMI 

Coastal West and 

Central Africa Sahelian Africa Eastern Africa Southern Africa 

Countries Others SN, ML, BF, NI, TD MD, KM, MZ, MW, TZ, ET ZA, LS, SZ, NM, ZW 

Anthropometry 

Height range 157-159 ≥160 <157 

BMI range <24 <24 <24 ≥24 

Average height 158.6 161.6 155.7 159.8 

Average BMI 22.7 21.7 21.6 24.8 

Correlates 

GDP per capita 936 815 690 3,261 

Wealth (AWI) 3.0 2.3 1.5 5.6 

Poverty (%) 49% 69% 82% 24% 

Note: Country codes are DHS codes (see Table A-1); GDP in constant USD (source: Maddison, 2010); Wealth: AWI computed from 

DHS data; Percent in poverty= AWI < 3 (see text for details) 
 11 

1.2 Age pattern of height of adult women 

The height of adult women is considered stable after age 20, so it can be readily used for cohort 

estimates of adult height. The height of women age 15-19 was also used after correcting for increase in 

height between age (x) in months and age 20. The main reason for doing so was to investigate whether 

some kind of reversal trend could be seen among the most recent cohorts. The age pattern used for the 

correction was derived from the average pattern of linear growth found in Africa. In fact, maturation and 

the adolescent growth spurt occur somewhat later in Africa than in Europe or in the United States, so we 

used the African pattern for making the correction (see Figure 4). 

Figure 4 Comparison of growth in height among African adolescent women 

with international reference sets 

Note: USA: CDC-2000 standard; France: Sempe standard; Africa: author’s calculations from DHS surveys. 

The African pattern was derived from the same DHS surveys, by plotting height at age (x) against 

age in month, from 180 months (age 15) to 240 months (age 20). Data were smoothed using a second 

degree polynomial. A coefficient K(x) was calculated as height(240) / Height(x). This coefficient was 

applied to the observed value of height at survey for all women age 15-19. It was not considered 

necessary to stratify by body shape, since these coefficients were almost identical for each of the four 

population groups considered, decreasing from about 1.020 at age 15 (range 1.016 to 1.024) to 1.0 at age 

19.5. 

1.3 Age pattern of BMI by group of body shape 

In order to convert age specific BMI data into cohort data, one needs an age pattern. This was 

done by plotting the BMI from age 15 to age 49. In affluent populations, the BMI tends to increase from 

age 25 or 30 until age 50 or 60, in a log-linear pattern. The pattern of BMI change between age 15 and 

152

154

156

158

160

162

164

14 15 16 17 18 19 20 21 22

Height (cm)

Age (year)

CDC-2000

France

Africa

Smoothed
 12 

age 25 is complex, usually with a fast increase followed by some kind of plateau. In affluent populations 

with prevalent obesity, as in the USA, the average BMI can reach high values (26 kg/m2 at age 50). In 

affluent populations with low obesity, as in France, it is lower at age 50, but still with a steady increase 

since age 25. The average BMI observed in African DHS surveys lies somewhere in between (Figure 5). 

Average BMI in Africa was similar to that in France or in the USA at age 15, then increased faster than in 

France but not as fast as in the USA between age 15 and age 20, then increased at a slower pace up to age 

35, and then stabilized. This pattern suggests a mixture of age and cohort effects, since the nutritional 

status of younger cohorts could be much better than that of older cohorts (see below § 3.2 for a full 

analysis of age and cohort effects). 

For this study, the BMI at age 30 was taken as the reference for standardizing BMI by age. For 

the final analysis, the age pattern of the BMI was taken after controlling for cohort effects, as well as for 

wealth and urban residence (see below § 3.1 for details). This allowed one to compute a ‘standardized 

BMI,’ corresponding to the BMI that the same cohort was expected to have at age 30. This was the only 

way to produce cohort estimates from period data, and to investigate trends, as was done for height. 

Figure 5 Comparison of age-specific BMI of African women with international reference sets 

This procedure assumes that the age pattern of BMI remains the same for all cohorts, and that it is 

the same for all countries in the same group (Figure 6). There are no data so far with which to investigate 

whether this hypothesis is realistic in situations of changing malnutrition or increasing obesity, and most 

likely it is not. This analysis should therefore be considered with caution, and be later checked in specific 

situations. One could only hope that it provides a realistic view for aggregate data. Whenever possible—

that is, when several surveys were available in the same country—the study provides the comparison with 

the variations between surveys. It will be seen below (see § 1.7) that the average weight gain between 

surveys in the same countries matches the average weight gain expected from the age pattern. 

18

20

22

24

26

28

10 15 20 25 30 35 40 45 50 55

BMI (kg/m2)

Age

Africa, observed

Africa, fitted

US standard

France
 13 

Figure 6 Age patterns of BMI in the four groups of body shape (raw data) 

1.4 Average anthropometric indicators of adult women (raw data) 

The average height of adult women age 20-49 was 158.6 cm, average weight was 56.5 kg, and 

average BMI was 22.4 kg/m2. All these parameters had large variations by age, area of residence, wealth, 

and group of body shape (Table 2). 

Table 2 Average values of anthropometric indicators, by category, women age 20-49 

 Number of 

women Weight (kg) Height (cm) BMI (kg/m2) 

 Total 298,706 56.5 158.6 22.4 

 

Group Central 143,472 57.1 158.6 22.7 

 Sahelian 55,692 56.6 161.6 21.6 

 Eastern 68,946 52.1 155.7 21.5 

 Southern 30,596 63.3 159.8 24.8 

 

Area Urban 101,810 61.2 159.5 24.0 

 Rural 196,896 54.4 158.2 21.7 

 

Wealth Poorest 110,107 52.8 157.7 21.2 

 Poorer 87,448 55.4 158.7 22.0 

 Medium 44,022 59.0 159.1 23.3 

 Wealthier 29,034 61.9 159.5 24.3 

 Wealthiest 28,095 65.9 160.5 25.6 

 

Category Rural-Poor 140,903 53.1 157.9 21.3 

 Urban-Poor 21,401 56.2 158.4 22.4 

 Rural-Medium 51,381 57.3 159.0 22.7 

 Urban-Medium 56,926 60.7 159.4 23.9 

 Wealthy 28,095 65.9 160.5 25.6 

Note: About 10% of women were excluded from computations because they were pregnant or in 

the postpartum period at time of survey. 

18

20

22

24

26

28

30

10 15 20 25 30 35 40 45 50 55

BMI, Kg/m2

Age

Central

Sahelian

Eastern

Southern
 14 

1.5 Distribution of heights in African populations 

The distributions of heights of adult women were close to log-normal distributions, with a mild 

skewness (>0), and a mild coefficient of concentration (kurtosis >3) (Table 3). Of course, they differed by 

category of body shape, but in each category they could be fitted by log-normal curves (Figure 7). 

Table 3 Characteristics of the distribution of heights, by body shape, women 20-49, raw 

data 

Group 

Number of 

women Mean 

Standard 

Deviation Skewness Kurtosis 

Central 172,335 158.6 6.66 0.114 3.953 

Sahelian 68,469 161.6 6.17 0.090 3.417 

Eastern 79,439 155.7 6.17 0.160 3.584 

Southern 33,852 159.8 6.49 0.051 3.525 

Figure 7 Distribution of women’s height, by group of body shape (raw data) 

0%

2%

4%

6%

8%

10%

12%

14%

125 130 135 140 145 150 155 160 165 170 175 180 185 190 195 200 205

Population

Height (cm)

Central

Sahelian

Eastern

Southern
 15 

1.6 Distribution of BMI in African populations 

The distribution of BMI in African populations was skewed towards the right (high values), as in 

all populations. The distribution of BMI varied considerably by type of body shape (Table 4, Figure 8). 

The Central group had the most regular distribution, with a mean of 22.8 and a standard deviation of 4.36. 

The distributions of BMI in the Sahelian group and in the Eastern group were quite similar, despite the 

major difference in height, with somewhat higher mean, higher dispersion, and more skewness in the 

Sahelian group. The Southern group was very different, with much higher values, much wider dispersion, 

and less skewness, producing a flatter distribution. 

Table 4 Characteristics of the distribution of BMI, by body shape, women 20-49, raw data 

Group 

Number of 

women Mean 

Standard 

Deviation Skewness Kurtosis 

Central 143,245 22.8 4.36 1.64 8.61 

Sahelian 56,342 21.7 3.67 1.81 9.80 

Eastern 68,578 21.0 3.19 1.63 9.04 

Southern 30,801 25.6 5.87 1.09 4.26 

Note: Pregnant and post-partum women excluded. 

Figure 8 Distribution of BMI among women age 20-49, by group of body shape (raw data) 

1.7 Changes from survey to survey in the same country 

Among the 33 countries considered, 22 had several surveys that could be used for studying 

changes over time and data consistency. Among these, 10 countries had two surveys, 9 countries had 

three surveys, and 3 countries had four surveys, so that 37 intervals between successive surveys could be 

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50

Population

BMI (kg/m2)

Central

Sahelian

Eastern

Southern
 16 

studied. Any time there were successive surveys, the mean height, weight, and BMI were computed for 

the same cohorts seen at both surveys. 

Height

Height was expected to remain constant for the same cohorts. Using all possible pairs of 

successive surveys with the same cohorts, there was a small average systematic bias of +0.32 cm in height 

from the index survey to the next survey, which is probably a fair indication of measurement errors. 

Among the 37 intervals considered, 18 of these differences were not statistically significant, and could be 

simply attributed to random fluctuations due to sample size. In 17 cases the difference was significantly 

positive, and in two cases the difference was significantly negative. These last two cases were both from 

Kenya: cohorts born from 1953 to 1978 measured 160.1 cm in 1998 and 159.8 cm in 2003; cohorts born 

from 1958 to 1983 measured 159.9 cm in 1998 and 159.6 cm in 2003. Such biases could come from 

variations in the sampling scheme, from mortality selection and possibly from HIV/AIDS, as well as from 

minor variations in measurement tools or systematic reading errors. Even if these differences are 

statistically significant, they remain very small in absolute value, and fall with the ±0.3 cm range of 

possible measurement errors. 

Weight and BMI

Contrary to height, weight and BMI were expected to increase from survey to survey in the same 

country for the same cohorts, simply because women tend to gain weight between age 20 and age 50. The 

interval between surveys was on the average 6.0 years, with a range from 4.0 to 14.4 years, so the mean 

change over time was standardized for a five-year period. On average, women of the same cohorts gained 

2.2 kg per five years between surveys, which corresponds to an increase of BMI of 0.77 kg/m2. These 

values are consistent with expected values from age patterns of BMI (Table 5). Of the 37 cases 

considered, the average change in weight and BMI was significant in 33 cases, not significant in 3 cases, 

and significantly negative in only one case: in Madagascar, weight of women born between 1959 and 

1984 declined from 49.6 kg to 49.1 kg between the 2003 and the 2008 surveys. This may be a real 

negative change, which requires further investigation. 

Table 5 Average gains in weight and BMI, over five-year period, (women age 20-49, not pregnant, not in 

post-partum period) 

Number of 

cases 

Average change 

over 5 years From age 

pattern of BMI 

Weight (kg) BMI (kg/m2) (kg/m2) 

Total sample 

Number of survey intervals 37 2.20 0.77 0.41 

Number with significant increase 33 2.50 0.87 

Number with no change 3 -0.14 -0.02 

Number with decline 1 -0.57 -0.34 

Group of body shape 

Central 19 2.09 0.74 0.42 

Sahelian 8 1.48 0.53 0.50 

Eastern 7 1.07 0.33 0.17 

Southern 3 2.45 0.80 0.65 

Note: The changes obtained from the age pattern were calculated after controlling for wealth. 

Differences in weight change by group of body size were very marked, and matched the age 

pattern of BMI noted above. In the Eastern group, weight gains averaged 1.07 kg (BMI: +0.33 kg/m2), in 

the Sahelian group 1.48 kg (BMI: +0.53 kg/m2), in the Central group 2.09 kg (BMI: +0.74 kg/m2), and in 

the Southern group 2.45 kg (BMI 0.80 kg/m2) (Table 5, Figure 9). These values and their hierarchy also 

matched the values in weight gain and the hierarchy by body shape obtained from age patterns of BMI. 
 17 

Figure 9 Average change in weight and BMI over a five-year period, 

women age 20-44 at first survey, by group of body size

1.8 Relationship of anthropometric indicators with wealth 

The relationship of weight, height and BMI with wealth was almost linear, despite the 

heterogeneity in body shape in sub-Saharan Africa. When the Absolute Wealth Index (AWI) increased 

from 0 (poorest) to 12+ (wealthiest), weight increased from 53.2 to 68.1 kg, height from 158.0 to 161.0 

cm, and BMI from 21.3 to 26.3 kg/m2 (Figure 10). 

Figure 10 Average height and BMI by wealth, women age 20-49 (raw data) 

In conclusion, the study found that height, weight and BMI were very heterogeneous in subSaharan Africa. Average height was below Western standards, with large differences by country. Average 

BMI was somewhere between American and European reference sets, also with large variations between 

countries. The relationship of height with socio-economic status was as expected from the experience of 

developed countries. In contrast, high values of BMI were concentrated among the highest socioeconomic groups, and the lowest values among the poorest groups, a pattern different from that of 

developed countries. 

1.07

1.48

2.09

2.45

0

1

2

3

Eastern Sahelian Central Southern

Weight gain (Kg)

Group of body shape

0.33

0.53

0.74

0.80

0.0

0.2

0.4

0.6

0.8

1.0

Eastern Sahelian Central Southern

Change in BMI (Kg/m2)

Group of body shape

20

22

24

26

28

30

156

158

160

162

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

Height (cm)

BMI (kg/m2)

Wealth (AWI)

Height

BMI 
 18 

Part II: Trends and Differentials in Height 

2.1 Trends in height of adult women 

For sub-Saharan Africa as a whole, there was a marked pattern of increase then decrease in the 

height of adult women. Height averaged 157.3 cm for cohort 1940, 158.7 for cohort 1967, and was almost 

back to its original level (157.5 cm) for the 1990 cohort. Trends were basically the same for urban and 

rural areas, although the increase was somewhat faster in urban areas, and the decline faster in rural areas. 

In urban areas height was only slightly greater for cohort 1990 than for cohort 1940, and in rural areas it 

was even lower (Figure 11). The positive trend (0.6 cm per decade) was consistent with trends found 

earlier in Europe or the United States [Chamla, 1983; Chamla and Gloor, 1986; Cole, 2003]. The 

declining trend, however, was very marked, its duration was longer than in most downward trends 

identified elsewhere in the 20th century, and its magnitude was larger than the long downward trend in the 

USA in the first part of the 19th century [Carson, 2010]. 

Figure 11 Trends in height of adult women, sub-Saharan Africa 

2.2 Trends in height: differential analysis 

Trends varied for the four large groups of body shape. Women in Southern Africa experienced a 

steady increase in height on the average, more pronounced in urban areas. Urban women in the Sahelian 

band, who were much taller originally, had no change over time, whereas rural women in the same group 

experienced a decline in average height. Women in the two other groups experienced a rise followed by a 

decline, and changes were more pronounced in the Central group than in the Eastern group (Figure 12). 

154

156

158

160

162

164

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Height (cm)

Year of birth

Urban Rural Reference
 19 

Figure 12 Trends in height of adult women, by body shape and residence (urban and rural) 

Increasing and decreasing trends, although they varied by country, affected all socio-economic 

groups (Figure 13; Table 6). The two lowest wealth categories (poorest and poorer) had a net loss in 

height, after some minor increase; the two medium categories (medium and wealthier) behaved the same 

way: they had some increase over the whole period, resulting from a major increase followed by a 

decline. The highest socio-economic group (wealthiest) followed the same path, at a higher level. 

Figure 13 Reconstructed trends in height of adult women, by wealth 

154

156

158

160

162

164

Height (cm)

Year of birth

Central Sahelian Eastern Southern

Urban areas

154

156

158

160

162

164

Height (cm)

Year of birth

Central Sahelian Eastern Southern

Rural areas

155

156

157

158

159

160

161

162

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Height (cm)

Year of birth

Wealthiest Wealthier Medium Poorer Poorest
 20 

Table 6 Reconstructed trends in adult height (cm) by socio-economic categories 

Socio-economic 

category (wealth) 

Baseline 

1940 

Peak value 

1965-1970 

Endpoint 

1990 

Net change 

1940-1990 

1 Poorest 157.1 157.6 156.2 -0.9 

2 Poorer 157.9 158.7 157.6 -0.3 

3 Medium 157.4 159.3 158.1 +0.7 

4 Wealthier 157.4 159.2 158.0 +0.6 

5 Wealthiest 159.4 160.7 159.9 +0.5 

Cohort trends were tested using a regression line of height on cohort, for women age 20-49 and 

using raw data by wealth category. All trends were significant (all positive before 1967 and all negative 

after 1967), and all changes in trends were highly significant (all with P <10-10). The average year of 

change in trends was 1968, without any difference between the wealth categories. 

2.3 Multivariate analysis on height of adult women 

Linear multivariate regressions were run on the original sample of DHS data, that is without 

weighting by country. The results confirmed the major changes in the slope of the relationship between 

height and year of birth, positive before 1965 and negative after 1965 (Table 7). They also confirmed the 

net effect of each variable: greater height in urban areas, increasing height with increasing household 

wealth, greater height of Sahelians (+3.0 cm), lower height in Eastern Africa (-2.3 cm), and somewhat 

greater height of Southern Africans (+0.4 cm). Because the sample size was so large, all coefficients were 

highly significant (P <10-10). When comparing cohorts 1940-1965 with cohorts 1965-1990, the magnitude 

of the effects changed somewhat over time, although the magnitude of changes remained small: the effect 

of urban residence became slightly higher (+0.2 cm), the net effect of wealth increased also (+0.3 cm for 

8 points increase in wealth), the natural advantage of Sahelians became somewhat more important 

(+0.2 cm), as well as the advantage of Southern Africans (+0.6 cm), and the coefficient for Eastern Africa 

did not change significantly. In summary, even after controlling for socio-economic status, urban 

residence, and major groups of body shape, heights of women increased by some 2.0 cm from 1940 to 

1965 (157.4 to 159.4 cm), then decreased at about the same speed from 1965 to 1990 (159.4 to 157.5 cm). 

This phenomenon seems largely independent from urban residence, racial group and socio-economic 

status. 

Table 7 Results of multivariate regression on adult female height (raw data) 

Cohorts 1940-1965 Cohorts 1965-1990 Change 

Coefficient Standard Error Coefficient Standard Error P-value 

Cohort (year of birth) +0.07708 0.00361 -0.07332 0.00153 * 

Urban residence +0.15768 0.05210 +0.33317 0.02755 * 

Wealth (AWI) +0.18995 0.00918 +0.23014 0.00474 * 

Sahelian +3.01959 0.05207 +3.25479 0.03073 * 

Eastern -2.34818 0.05217 -2.29317 0.02842 NS 

Southern +0.42581 0.06823 +1.03090 0.03914 * 

Central (Ref) 

Constant 7.16576 7.07497 302.42343 3.01901 

* P <0.05; NS = not significant 
 21 

2.4 Height and income per capita 

The rise and fall of average height of adult women for cohorts 1940-1990 was strikingly similar 

to the rise and fall of income per capita between 1950 and 2000 (Figure 14). For the 33 countries in this 

study, the average income per capita in parity purchasing power (GDP-PPP) increased from $823 in 1950 

to $1,303 in 1974, then fell to $1,038 in 1994, stabilized until 2000, and then increased rapidly thereafter. 

The critical years of reversal in trends in income corresponded to the years when cohort 1965 became 

adolescent. Cohorts of women born in year (t) were matched with period year (t+10). The correlation 

between average height of the cohort (t) and average income in year (t+10) was high (ρ= 0.97). One could 

therefore argue that trends in height were primarily determined by the adolescent growth spurt: over 

periods with economic growth, the nutrition of adolescent girls improved and adult height of women 

increased; over periods with economic downturn, the nutrition of adolescent girls deteriorated, and their 

ultimate height was lower. 

Figure 14 Correlation between trends in income per capita and trends in women’s height, 

33 African countries 

2.5 Height and income, by body shape 

The relationship between income per capita and average height varied by groups of body shape 

(Figure 15). The pattern in the Central group and in the Eastern group was similar to the average pattern 

(correlation=0.93 and 0.90 respectively). In the Sahelian group, the economic downturn was less 

pronounced, as was the decline in height, and overall there was no correlation between both variables 

(r=0.07). In the Southern group, income was much higher, economic fluctuations were much smaller in 

relative terms, and they did not have any negative effect on height. 

156

157

158

159

1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

500

600

700

800

900

1000

1100

1200

1300

1400

1500

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Height (cm)

Cohort (t)

GDP-PPP ($)

Year (t + 10)

Income per capita Height
 22 

Figure 15 Correlation between trends in income per capita and trends in women’s height, 

by groups of body shape 

2.6 Height and income at country level 

At the country level, the relationship between changes in height and income growth was complex 

and could not be studied by simple correlations. For instance, in Swaziland the correlation between height 

and GDP was strongly positive (r=+0.91), whereas in Lesotho it was strongly negative (r=-0.98). Both 

countries had steady economic growth, but one had a slightly positive trend in height while the other had 

a slightly negative trend in height, which confuses the comparison despite the similarities between the two 

economic situations. 

Based on the GDP-PPP data gathered by Maddison [2010], periods of positive and negative 

economic growth were identified. Among the 33 countries under study, 78 periods were considered. 

During the periods with declines in GDP-PPP, the average rate of change of height was -0.42 cm per 

decade, whereas it was +0.02 cm per decade during periods with positive economic growth. During large 

economic downturns, defined as periods when the rate of economic growth was below -2% per year, there 

was no case of increasing height. These observations underline at the country level the relationship 

between income growth (or decline) and changes in adult height found at aggregate levels. 

156

157

158

159

160

700

800

900

1000

1100

1200

1300

1400

1500

Height (cm)

Cohort (t)

GDP-PPP ($)

Year (t+10)

Central

Income per capita Height

154

155

156

157

500

600

700

800

900

1000

Height (cm)

Cohort (t)

GDP-PPP ($)

Year (t+10)

Eastern

Income per capita Height

160

161

162

163

500

600

700

800

900

1000

Height (cm)

Cohort (t)

GDP-PPP ($)

Year (t+10)

Sahelian

Income per capita Height

157

158

159

160

161

2000

2500

3000

3500

4000

Height (cm)

Cohort (t)

GDP-PPP ($)

Year (t+10)

Southern

Income per capita Height
 23 

2.7 Recent trends (cohorts 1982-1992) 

Income per capita has been rising since 1994 for this set of 33 African countries. The last sample 

of anthropometric data was gathered in 2004-2008 at best, and often before, so that one could not 

properly investigate trends in height associated with recent economic growth. However, one could check 

on individual data trends for the cohorts born after 1982. Six countries showed significant positive trends 

in height: Cameroon, Guinea, Mozambique, Namibia, Swaziland and Zimbabwe (Table 8). In Cameroon, 

trends were positive for both urban and rural areas, and for the ‘poorest’ category as well as ‘wealthier’ 

category. The same was true in Mozambique and Namibia. In Guinea and Swaziland, trends in urban 

areas were not significant. In Zimbabwe, trends were not significant in rural areas. Trends were not 

significant for other wealth categories. Even though these data are very limited, and are mostly based on 

adjusted adolescent heights, they tend to suggest that the downward trend in height of adult women may 

be coming to an end, at least in some countries. With the exception of Zimbabwe, all these countries had 

increasing GDP-PPP after 1992. 

Table 8 Positive slopes of height versus cohort since 1982 

Country Last cohort Total 

Area of residence Wealth 

Urban Rural Poorest Wealthier 

Cameroon 1989 0.204 0.192 0.188 0.251 0.204 

Guinea 1990 0.122 0.188 0.243 0.122 

Mozambique 1988 0.173 0.169 0.172 0.174 0.173 

Namibia 1992 0.096 0.137 0.088 0.153 0.096 

Swaziland 1992 0.098 0.111 0.423 0.098 

Zimbabwe 1991 0.081 0.119 0.145 0.081 

Note: All slopes displayed were significantly higher than 0, with p <0.05. 
 24 

Part III: Trends in Weight and Body Mass Index 

3.1 Underlying age patterns of BMI (net effect) 

The underlying age patterns of BMI were estimated using multivariate analysis after controlling 

for trends, for each category of body shape, and separating urban and rural areas. The model was a simple 

linear regression, the age pattern was a logarithm function, and controls were cohort trends and wealth 

(Figure 16, Table 9). 

BMI = A + B×Log(Age) + C×(Cohort) + D×(Wealth) 

Figure 16 Underlying age pattern of BMI, by group of body size 

(net effect after controlling for cohort and wealth) 

In urban areas, the BMI increased rapidly with age in three out of the four groups, and slowly in 

the Eastern group. In urban areas of the Southern group, the BMI increased from 23.6 kg/m2 at age 20 to 

28.1 kg/m2 at age 50; corresponding values were 22.1 to 26.2 kg/m2 in the Central group, 21.2 to 27.0 

kg/m2 in the Sahelian group and 21.6 to 23.2 kg/m2 in the Eastern group. The increase in BMI with age 

was much less pronounced in rural areas, except in the Southern group where it was also very marked. In 

rural areas of the Southern group, the BMI increased from 22.2 at age 20 to 25.7 kg/m2 at age 50; 

corresponding values were 21.1 to 23.0 kg/m2 in the Central group, 20.1 to 22.3 kg/m2 in the Sahelian 

group and 20.8 to 21.6 kg/m2 in the Eastern group. 

Table 9 Underlying age pattern of BMI (net effects in multivariate analysis after controlling for 

cohort trend and wealth) 

Estimates at age Central Sahelian Eastern Southern Total 

Urban areas 

20 22.1 21.2 21.6 23.6 22.1 

30 23.9 23.8 22.3 25.6 23.8 

40 25.2 25.6 22.8 27.0 25.1 

50 26.2 27.0 23.2 28.1 26.0 

Rural areas 

20 21.1 20.1 20.8 22.2 20.9 

30 21.9 21.1 21.2 23.8 21.8 

40 22.6 21.7 21.4 24.9 22.4 

50 23.0 22.3 21.6 25.7 22.9 

These underlying age patterns, by group of body shape and area of residence, were used to 

compute the standardized BMI, in order to study cohort trends. The standardized BMI can be interpreted 

as the BMI of the cohort at age 30, given the pattern of increase by age in the same category of body 

shape and urban residence. For instance, if weight gain between age 30 and 40 corresponds to an increase 

in BMI of +1 kg/m2, then a BMI of 23 at age 40 is equivalent to a BMI of 22 at age 30. 

18

20

22

24

26

28

10 15 20 25 30 35 40 45 50 55

BMI (kg/m2)

Age

Urban areas Central

Sahelian

Eastern

Southern

18

20

22

24

26

28

10 15 20 25 30 35 40 45 50 55

BMI (kg/m2)

Age

Rural areas Central

Sahelian

Eastern

Southern
 25 

3.2 Interpretation of BMI trend figures 

The interpretation of the figures displaying trends in standardized BMI is delicate, since they may 

combine several effects: a cohort effect (weight-for-height may be increasing with cohort, and in some 

cases may be decreasing), and interactions with age, which are a period effect (weight may be increasing, 

or decreasing, after a given period, and this may affect certain cohorts more than others). Here are some 

typical cases that occurred in this sample. 

A. Madagascar: no age effect, no cohort effect

In Madagascar, the three surveys indicate about the same level of BMI, without any change by 

age, therefore without any age effect or cohort effect over a long period (Figure 17). The standardized 

BMI therefore has no slope with respect to cohort. Note that BMI values for Madagascar are among the 

lowest in the whole sample of African countries and much lower than European standards, indicating a 

very marked deficit in weight. 

Figure 17 Age and cohort effects in Madagascar 

B. Rwanda: no age effect, increasing cohort effect

In Rwanda, BMI changed little between 2000 and 2005, but was decreasing with age in both 

surveys, which indicates a marked cohort effect. This cohort effect appears clearly in the standardized 

BMI trends (Figure 18). In Rwanda, the picture is further complicated with the late maturation of 

adolescents, and the strong period effect due to the civil war, which appears as a new negative cohort 

effect for the very recent cohorts in the standardized BMI figure. 

Figure 18 Age and cohort effects in Rwanda 

18

20

22

24

26

28

30

32

10 15 20 25 30 35 40 45 50 55

BMI, kg/m2

Age at survey

Average BMI, by age and survey, Madagascar

1997

2003

2008

18

20

22

24

26

28

30

32

Standardized BMI

Year of birth

Standardized BMI, by cohort, Madagascar

Madagascar

Trend

18

20

22

24

26

28

30

32

Standardized BMI

Year of birth

Standardized BMI, by cohort, Rwanda

Rwanda

Trend

18

20

22

24

26

28

30

32

10 15 20 25 30 35 40 45 50 55

BMI, kg/m2

Age at survey

Average BMI, by age and survey, Rwanda

2000

2005
 26 

C. Senegal: changing age effect, no cohort effect

In Senegal, the age effect was already marked in 1993, but was much more pronounced in 2005, 

while the BMI below age 25 changed very little. This is typical of age and period interactions, the weight 

of older adult women increasing more rapidly with age in the recent period. As a result, the slope of the 

standardized BMI was negative (Figure 19). The fitting of the curve is not perfect on the figure since the 

standardized BMI is not equal to the BMI at age (x). The figure is simply illustrative of the trend after the 

conversion to standardized BMI. 

Figure 19 Age and cohort effects in Senegal 

3.3 Trends in standardized BMI 

For sub-Saharan Africa as a whole, reconstructed trends in standardized BMI showed some minor 

changes: first a small increase from 21.0 kg/m2 for cohort 1940 to 22.6 kg/m2 for cohort 1967, then a 

steady state (22.3 kg/m2 for cohort 1990). This can be interpreted as a mild catch-up of weight for 

younger cohorts (about +4 kg for a height of 1.60 m), assuming constant age patterns used for the 

conversion. The reconstructed data showed a change in trends over time for the surveyed countries as a 

whole, the peak being for cohorts 1965-1969, as was the case for the changing trends for height. 

As noted in many country studies, trends for urban and rural areas diverged. In urban areas trends 

were flat, around 23.6 kg/m2, indicating steady increase of weight with age, whereas rural trends were 

upwards from low values (20.5 kg/m2 for cohort 1940), indicating catch-up of weight for recent cohorts. 

As a result, the gap between urban and rural areas was narrowing for the most recent cohorts (Figure 20). 

The narrowing gap seems to be due primarily to the catch-up of most recent cohorts in rural areas, who 

reached values of BMI closer to international standards at young ages, as was the case already for urban 

areas. However, in urban areas cohorts continued to gain weight later with age, and much faster than in 

rural areas, as noted above. 

18

20

22

24

26

28

30

32

10 15 20 25 30 35 40 45 50 55

BMI, kg/m2

Age at survey

BMI of women, by age and survey, Senegal

1993

2005

18

20

22

24

26

28

30

32

Standardized BMI

Year of birth

Standardized BMI, by cohort, Senegal

Senegal

Trend
 27 

Figure 20 Reconstructed trends in standardized BMI of adult women 

3.4 Trends in standardized BMI by population group 

Trends in standardized BMI also diverged for the four groups defined by body shape. In urban 

areas of the Southern group, trends were downward, indicating increasing obesity. In the Sahelian group, 

trends were upwards from low values, indicating catch-up weight. In the two other groups, trends were 

not steady: catch-up weight was visible for the earlier cohorts, but regressed for the younger cohorts. In 

both cases, long-term weight gains were minimal, as if earlier improvements were later cancelled, as was 

the case for height (Figure 21). 

Figure 21 Reconstructed trends in standardized BMI of adult women, by population group 

18

20

22

24

26

28

30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Standardized BMI (kg/m2) 

Year of birth

Urban Rural

18

20

22

24

26

28

30

32

Standardized BMI (kg/m2) 

Year of birth

Urban areas

Central Sahelian Eastern Southern

18

20

22

24

26

28

30

32

Standardized BMI (kg/m2) 

Year of birth

Rural areas

Central Sahelian Eastern Southern
 28 

In rural areas, trends were in the same direction, but with different slopes. In the Southern group, 

the increase in obesity was smaller, and catch-up weight was lower in the Sahelian group. Changing 

trends were also visible in the Central group and in the Eastern group, but the reversals were less 

pronounced. All these differences were highly significant. 

3.5 Multivariate analysis of BMI (raw data) 

A multivariate analysis of BMI was conducted similar to the analysis of height, on raw data. 

Here, results are presented for the 1940-1990 cohorts together, except for the two groups with trend 

reversal (Central and Eastern). Standardization by age was done by fitting a Log-linear relationship with 

age. Results show a strong interaction with body shape and urban residence, as noted before, so that 

results are presented for each group separately. 

Trends in BMI (net effect)

Compared with the major trends in height identified above, trends in BMI were small after 

controlling for other factors (age, urban residence, and wealth) (Table 10). BMI tended to increase 

somewhat in the Sahelian group, and to decrease in the Eastern and Southern groups, with virtually no 

change in the Central group and for the countries as a whole. All trends were highly significant, and 

differences compared with the average pattern were all significant (P <10-8) except for the Central pattern. 

These findings confirm the analysis conducted using the standardized BMI. 

Table 10 Underlying cohort trends in standardized BMI (net effects in multivariate analysis after controlling for age, urban 

residence, and wealth). 

 Central Sahelian Eastern Southern Total 

P value (trends) 7.0E-13 <E-99 1.6E-04 5.2E-06 1.4E-07 

Estimated standardized BMI 

1940 22.2 20.5 21.9 25.3 21.9 

1965 22.3 21.3 21.8 24.3 22.1 

1990 22.2 22.1 20.8 23.3 22.3 

The effects of the other factors (age, cohort, wealth, urban residence) were as expected (Table 

11). As seen above, the age effect was strong in the Southern pattern, and weak in the Eastern pattern. 

The effect of wealth was consistent in all four groups, and with a major gradient. The effect of urban 

residence was small in the Southern pattern, because rural areas also have a strong propensity to obesity 

in this group, and very small in the Eastern pattern where both urban and rural areas have very low BMIs. 

All coefficients were highly significant. 

Table 11 Net effects in multivariate analysis of age, cohort, urban residence, and wealth on BMI 

 Central Sahelian Eastern Southern Total 

Constant -11.1790 -56.7237 46.2567 87.8677 -3.0400 

Log(age) 3.0198 3.4972 1.1663 4.1409 2.8886 

Cohort 0.0113 0.0332 -0.0150 -0.0401 0.0073 

Wealth 0.2990 0.2869 0.3661 0.3142 0.3640 

Urban 0.8196 1.3914 0.0629 0.1655 0.6208 
 29 

3.6 Correlation with economic development 

The correlation between average BMI and the Logarithm of income per capita was significant, 

although rather weak. All wealthier countries (GDP-PPP >1500 USD) had a BMI above average (≥23.5), 

whereas all countries with a low BMI (<22 kg/m2) were among the poorest (GDP-PPP <1000 USD) 

(Figure 22). 

Figure 22 Relationship between average BMI and income per capita at country level 

3.7 Average BMI and prevalence of thinness and obesity 

Investigating the prevalence of thinness and obesity was beyond the scope of this study, and 

prevalence is well documented in most final reports of DHS surveys. Furthermore, the average BMI was a 

more sensitive indicator for studying trends than any measure of prevalence. At survey level, the 

relationship between average BMI and the prevalence of thinness (BMI <17 kg/m2), under-weight (BMI 

<18.5 kg/m2), over-weight (BMI ≥25 kg/m2), and obesity (BMI ≥30 kg/m2) can be used for the conversion 

(Figure 23). Roughly speaking, an average BMI of 28 kg/m2 corresponds to 30% obese and 60% 

overweight, and to virtually no very thin women. An average BMI of 20 kg/m2 corresponds to 6% very 

thin women and 24% underweight, and virtually no obesity. 

18

19

20

21

22

23

24

25

26

27

28

0 500 1000 1500 2000 2500 3000 3500 4000 4500

BMI (kg/m2)

GDP-PPP ($)

Correlation between income per capita and BMI
 30 

Figure 23 Relationship between average BMI and prevalence of thinness, underweight, 

overweight and obesity, 70 DHS surveys in sub-Saharan Africa, women age 20-49 

Much remains to be studied on trends in prevalence of thinness and obesity by age, cohort and 

period, for the same countries and same categories. We chose to present the mean BMI for the sake of 

simplicity and for providing a synthetic view, since with changing nutritional status the whole distribution 

shifts, with consequences for both ends (underweight and overweight). 

0%

10%

20%

30%

40%

50%

60%

70%

18 20 22 24 26 28 30

Percent population

Average BMI (kg/m2)

BMI >= 25

BMI >= 30

BMI < 18.5

BMI < 17
 31 

Discussion 

Trends in nutritional status of African women appeared as irregular and contrasted. Compared 

with the regular trend towards lower under-five mortality observed since 1950 [United Nations, 2007; 

Ahmad et al., 2000; Garenne and Gakusi; 2005], one could have expected regular improvements in 

nutritional status, and regular improvements in adult height. This was not the case for cohorts born 

between 1940 and 1990, and in particular, trends in height reversed for those born after 1965, as has been 

already noted by other authors [Moradi, 2006; Akachi and Canning, 2010]. The main reason seems to be 

the overwhelming economic downturn around 1975, which affected most African countries, lasted about 

25 years, and seems to have had a major effect on the adolescent growth spurt for cohorts born after 1965. 

Compared with trends in height in other developing countries, as well as in developed countries, 

an increase of about 2.0 cm over 25 years (1940-1965) was a significant achievement for sub-Saharan 

Africa. However, the decline in height that followed is a major downturn, with possibly long-term health 

consequences. Hopefully, this downturn is coming to an end with recent increases in income per capita. 

Variations in women’s heights by country, by urban or rural residence, and by wealth were large. 

In some situations (as in urban Senegal or urban Chad), the average height of adult women was almost 

identical to that of American or French women. In contrast, in the poorest situations (as in rural 

Madagascar or rural Comoro Islands) women were almost 10 cm below the international standards. Urban 

women were always doing better than rural women, and wealthier women than poorer women, as could 

have been anticipated. Gradients by wealth were particularly important (±5 cm), and were rather larger 

than elsewhere. 

Overall, the average BMI of young African women was rather favorable, and consistent with 

international standards. This average hides very large disparities. In the poor countries and in the poor 

strata of most countries, the BMI was rather low, indicating a deficit in weight and nutrition. However, 

trends were indicating some catch-up for the poorer groups, and a moderate increase of weight with age, 

which is considered healthy. On the contrary, for wealthier countries, for urban areas and for the 

wealthiest strata the increase of weight with age was very marked and was increasing with time, 

indicating the fast emergence of obesity. Some countries and some socio-economic groups had a higher 

average BMI and more obesity than the USA, a country considered to have abnormally high prevalence 

of excess weight. 

The study has a number of limitations. We considered only four large groups of body size and 

body shape, and classified each country into one of the four groups. This is obviously a serious limitation 

of the study. Of course, numerous other patterns are likely to exist in Africa, even within the same 

country. Some countries, such as Cameroon, Nigeria, Kenya or Uganda, are a mix of very different body 

shapes. More analysis could be conducted at the country and regional levels. The origin of these 

differences remains also to be analyzed. In our analysis they persisted after controlling for wealth, 

whereas for under-five children little differences persisted for higher socio-economic groups [Hohmann 

and Garenne, 2010]. Genetic factors probably play a role, since they are known to affect the ratio of leg 

length to trunk length and to originate primarily during the adolescence growth spurt. However, the 

quality of diet might also play a role, in particular the quantity of milk and micronutrients ingested. The 

role of diet could also be further analyzed. 

Another limitation of this study is the use of current status of the household at time of survey, in 

particular for urban residence and household wealth. This however is likely to only minimize the 

differences in height and BMI in the real world. 
 32 

We did not discuss the relationship of anthropometry with HIV/AIDS, which is complex. As 

noted by Bradley and Mishra [2002], the relationship of average BMI with prevalence of HIV is contrary 

to what could be anticipated (higher BMI among HIV positive women). This was true within countries as 

well as among countries. However, we checked the change in standardized BMI for cohorts 1955 to 1975 

(age 30 in 1985 to 2005) in comparison with HIV prevalence in 2005. In countries with low (<5%) or 

moderate (5-14%) HIV prevalence in 2005, the increase in BMI was moderate (+0.50 and +0.89 kg/m2 

respectively), whereas in countries with high HIV prevalence (≥15%) there was a decline in standardized 

BMI (-0.34 kg/m2), as expected. These relationships could be studied in more detail. 

This study was primarily general and exploratory, and aimed at providing a broad overview of 

trends in height and BMI. Much remains to be studied on trends and patterns of nutritional status in 

Africa and their relationships with economic and public health variables. Further research could be 

conducted on genetic factors and nutritional factors at ethnic group level. More research could be 

conducted on interactions with economic trends at local level, in particular with economic crises in urban 

areas, and with food shortages in rural areas. Many countries with very low height and with apparent 

underweight (as in Ethiopia and Madagascar) are also prone to recurrent famines [Devereux, 2007; 

Garenne, 2007]. The role of urban residence could also be further explored, since urban areas are 

associated with major differences not only in levels but also in trends in nutritional status of adult African 

women. In this respect, the emergence of extreme poverty in the slums of large cities deserves further 

attention with respect to the nutritional status of adult women. 
 33 

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37 

Appendix A: Annex Tables 

Table A-1 List of surveys included in the study of trends in women’s nutritional status

Code Country DHS-II DHS-III DHS-IV DHS-V

BJ Benin 1996 2001 2006

BF Burkina Faso 1993 1999 2003

CM Cameroon 1998 2004

CF Central African Rep. 1994

TD Chad 1997 2004

KM Comoros 1996

CD Congo-Kinshasa (RDC) 2007

CG Congo-Brazzaville (RPC) 2005

CI Côte d’Ivoire 1994 1999

ET Ethiopia 2000 2005

GA Gabon 2000

GH Ghana 1993 1999 2003 2008

GN Guinea 1999 2005

KE Kenya 1993 1998 2003 2008

LS Lesotho 2004

LB Liberia 2007

MD Madagascar 1997 2003 2008

MW Malawi 1992 2000 2004

ML Mali 1995 2001 2006

MZ Mozambique 1997 2003

NM Namibia 1992 2007

NI Niger 1992 1998 2006

NG Nigeria 1999 2003 2008

RW Rwanda 2000 2005

SN Senegal 1993 2005

SL Sierra Leone 2008

ZA South Africa 1998

SZ Swaziland 2007

TZ Tanzania 1996 2004

TG Togo 1998

UG Uganda 1995 2001 2006

ZM Zambia 1992 1997 2001 2007

ZW Zimbabwe 1994 1999 2005
38 

Table A-2 Trends in height, total

Country Period Begin End Slope Intercept

Standard error

Slope Intercept

Benin 1 1946 1970 0.04877 63.528 0.01431 28.104

Benin 2 1970 1993 -0.09259 341.825 0.00768 15.206

Burkina Faso 1 1943 1965 0.04196 79.388 0.01629 31.913

Burkina Faso 2 1965 1993 -0.03456 229.850 0.00724 14.308

Cameroon 1 1948 1989 -0.01164 183.235 0.00814 16.077

Central African Rep 1 1944 1965 0.13572 -107.218 0.04894 95.958

Central African Rep 2 1965 1993 -0.07705 310.524 0.04563 89.963

Chad 1 1947 1965 0.01682 130.098 0.03985 78.145

Chad 2 1965 1993 -0.08542 331.099 0.01330 26.276

Comoro Islands 1 1946 1981 -0.02878 211.230 0.02731 53.726

Congo Kinshasa 1 1940 1975 0.06101 38.069 0.03276 64.467

Congo-Kinshasa 2 1975 1993 -0.13868 431.830 0.02653 52.643

Congo-Brazzaville 1 1955 1965 0.11187 -61.174 0.08971 176.007

Congo-Brazzaville 2 1965 1993 -0.05424 265.446 0.01477 29.233

Côte d’Ivoire 1 1944 1984 0.02019 119.478 0.00889 17.506

Ethiopia 1 1950 1990 -0.00217 160.744 0.00413 8.147

Gabon 1 1951 1985 0.00413 150.807 0.01720 33.935

Ghana 1 1944 1993 -0.00675 172.255 0.00504 9.955

Guinea 1 1949 1990 -0.00115 161.126 0.00774 15.277

Kenya 1 1943 1968 0.08977 -16.659 0.01471 28.870

Kenya 2 1968 1993 -0.05189 262.311 0.00691 13.681

Lesotho 1 1955 1989 -0.02706 210.798 0.01094 21.610

Liberia 1 1957 1992 -0.04897 254.007 0.00762 15.066

Madagascar 1 1948 1965 0.05881 39.051 0.02914 57.143

Madagascar 2 1965 1993 -0.07378 299.209 0.00589 11.659

Malawi 1 1942 1965 0.03741 83.009 0.01528 29.937

Malawi 2 1965 1993 -0.04311 241.161 0.00657 12.982

Mali 1 1946 1991 -0.03674 233.783 0.00350 6.912

Mozambique 1 1947 1960 0.15571 -148.789 0.05610 109.810

Mozambique 2 1960 1993 -0.03327 221.100 0.00691 13.640

Namibia 1 1942 1992 -0.01419 188.855 0.00533 10.521

Niger 1 1942 1991 -0.03413 227.415 0.00544 10.719

Nigeria 1 1949 1965 0.11746 -71.600 0.03408 66.849

Nigeria 2 1965 1993 -0.11053 377.001 0.00474 9.385

Rwanda 1 1950 1970 0.01881 121.273 0.01591 31.225

Rwanda 2 1970 1993 -0.22081 593.487 0.01193 23.616

Senegal 1 1943 1990 0.03488 94.126 0.00638 12.573

Sierra Leone 1 1958 1993 -0.06933 293.416 0.01954 38.668

South Africa 1 1942 1983 0.03310 93.919 0.00764 15.003

Swaziland 1 1956 1992 0.00074 157.536 0.00881 17.426

Tanzania 1 1947 1967 0.07765 4.264 0.02276 44.636

Tanzania 2 1967 1993 -0.04165 238.968 0.00928 18.352

Togo 1 1948 1983 -0.03587 229.309 0.01492 29.366

Uganda 1 1945 1965 0.05067 59.595 0.02646 51.867

Uganda 2 1965 1993 -0.07235 301.142 0.00953 18.822

Zambia 1 1942 1965 0.08608 -10.124 0.01678 32.878

Zambia 2 1965 1993 -0.07921 314.420 0.00650 12.843

Zimbabwe 1 1945 1991 -0.01553 190.523 0.00489 9.659
39 

Table A-3 Trends in height, urban areas

Country Period Begin End Slope Intercept

Standard error

Slope Intercept

Benin 1 1946 1970 0.06033 41.380 0.02330 45.768

Benin 2 1970 1993 -0.09507 347.369 0.01169 23.152

Burkina Faso 1 1943 1988 0.00882 144.914 0.00996 19.656

Cameroon 1 1948 1989 -0.01969 199.400 0.01112 21.969

Central African Rep 1 1944 1965 0.18827 -209.224 0.07879 154.652

Central African Rep 2 1965 1993 -0.06985 297.556 0.06415 126.545

Chad 1 1947 1965 -0.04808 257.679 0.05870 115.117

Chad 2 1965 1993 -0.04895 259.435 0.02338 46.153

Comoro Islands 1 1946 1981 -0.02587 206.144 0.05525 108.882

Congo-Kinshasa 1 1940 1975 0.06704 27.787 0.04961 97.684

Congo-Kinshasa 2 1975 1993 -0.08438 326.320 0.03566 70.766

Congo-Brazzaville 1 1955 1990 -0.03400 226.298 0.01478 29.233

Côte d’Ivoire 1 1944 1984 0.03528 90.340 0.01391 27.400

Ethiopia 1 1950 1990 -0.00430 165.643 0.00971 19.164

Gabon 1 1951 1985 0.00814 143.449 0.01985 39.167

Ghana 1 1944 1993 -0.01326 185.642 0.00755 14.900

Guinea 1 1949 1990 0.00019 159.284 0.01469 29.016

Kenya 1 1943 1968 0.04817 65.382 0.04456 87.503

Kenya 2 1968 1993 -0.08260 323.164 0.01401 27.734

Lesotho 1 1955 1989 -0.04841 253.172 0.02297 45.389

Liberia 1 1957 1992 -0.06883 293.973 0.01243 24.586

Madagascar 1 1948 1965 0.10432 -49.759 0.06342 124.411

Madagascar 2 1965 1993 -0.06617 284.932 0.01353 26.775

Malawi 1 1942 1965 0.14047 -117.724 0.04693 92.021

Malawi 2 1965 1993 -0.06510 285.711 0.01624 32.122

Mali 1 1946 1991 -0.02533 212.096 0.00644 12.727

Mozambique 1 1947 1988 -0.01118 178.927 0.00905 17.874

Namibia 1 1942 1992 -0.00134 163.734 0.00847 16.725

Niger 1 1942 1991 -0.01374 188.545 0.01291 25.453

Nigeria 1 1949 1965 0.12438 -84.118 0.05553 108.932

Nigeria 2 1965 1993 -0.11013 377.376 0.00793 15.711

Rwanda 1 1950 1970 -0.01166 182.717 0.04163 81.767

Rwanda 2 1970 1993 -0.23476 622.245 0.02255 44.652

Senegal 1 1943 1990 0.04648 71.587 0.00936 18.457

Sierra Leone 1 1958 1993 -0.12595 406.796 0.03280 64.965

South Africa 1 1942 1983 0.06322 34.954 0.00706 13.839

Swaziland 1 1956 1992 0.02677 106.299 0.01944 38.465

Tanzania 1 1947 1967 0.06199 35.544 0.02651 51.994

Tanzania 2 1967 1993 -0.05131 258.520 0.01046 20.699

Togo 1 1948 1983 -0.11544 386.862 0.03212 63.267

Uganda 1 1945 1965 0.16313 -160.187 0.08311 163.152

Uganda 2 1965 1993 -0.10611 368.204 0.02396 47.381

Zambia 1 1942 1965 0.12187 -79.470 0.02690 52.725

Zambia 2 1965 1993 -0.06119 279.874 0.00945 18.687

Zimbabwe 1 1945 1991 -0.02028 200.511 0.00853 16.840
40 

Table A-4 Trends in height, rural areas

Country Period Begin End Slope Intercept

Standard error

Slope Intercept

Benin 1 1946 1970 0.03650 87.259 0.01809 35.511

Benin 2 1970 1993 -0.10009 356.224 0.01015 20.087

Burkina Faso 1 1943 1965 0.03729 88.431 0.01745 34.194

Burkina Faso 2 1965 1993 -0.05319 266.473 0.00806 15.932

Cameroon 1 1948 1989 -0.01311 185.863 0.01038 20.498

Central African Rep 1 1944 1965 0.10159 -40.880 0.06181 121.226

Central African Rep 2 1965 1993 -0.09211 339.302 0.06257 123.385

Chad 1 1947 1965 0.01545 132.756 0.04166 81.695

Chad 2 1965 1993 -0.10167 363.116 0.01440 28.435

Comoro Islands 1 1946 1981 -0.02897 211.406 0.03137 61.745

Congo-Kinshasa 1 1940 1975 0.00407 148.906 0.04298 84.565

Congo-Kinshasa 2 1975 1993 -0.19760 546.778 0.03619 71.807

Congo-Brazzaville 1 1955 1990 -0.03007 216.291 0.01516 29.974

Côte d’Ivoire 1 1944 1984 0.01169 135.832 0.01124 22.118

Ethiopia 1 1950 1990 -0.00356 163.356 0.00452 8.909

Gabon 1 1951 1985 -0.01892 194.656 0.03300 65.139

Ghana 1 1944 1993 -0.01466 187.535 0.00663 13.070

Guinea 1 1949 1990 -0.00944 177.174 0.00914 18.019

Kenya 1 1943 1968 0.09087 -18.890 0.01569 30.778

Kenya 2 1968 1993 -0.04324 245.178 0.00795 15.733

Lesotho 1 1955 1989 -0.01570 188.184 0.01175 23.226

Liberia 1 1957 1992 -0.05000 255.536 0.00972 19.196

Madagascar 1 1948 1965 0.04961 56.978 0.03281 64.349

Madagascar 2 1965 1993 -0.07502 301.459 0.00652 12.910

Malawi 1 1942 1965 0.01969 117.561 0.01613 31.596

Malawi 2 1965 1993 -0.04237 239.484 0.00716 14.149

Mali 1 1946 1991 -0.05077 261.079 0.00418 8.250

Mozambique 1 1947 1960 0.17411 -184.728 0.06026 117.963

Mozambique 2 1960 1993 -0.02944 213.436 0.00779 15.376

Namibia 1 1942 1992 -0.02377 207.552 0.00687 13.569

Niger 1 1942 1991 -0.04014 238.985 0.00596 11.751

Nigeria 1 1949 1965 0.10767 -52.892 0.04262 83.604

Nigeria 2 1965 1993 -0.11036 376.006 0.00584 11.559

Rwanda 1 1950 1970 -0.00104 159.863 0.01664 32.641

Rwanda 2 1970 1993 -0.21078 573.290 0.01287 25.476

Senegal 1 1943 1990 0.02718 109.043 0.00806 15.892

Sierra Leone 1 1958 1993 -0.05373 261.864 0.02430 48.084

South Africa 1 1928 1983 0.03902 81.721 0.00863 16.914

Swaziland 1 1956 1992 -0.00598 170.760 0.00986 19.501

Tanzania 1 1947 1967 0.07746 4.749 0.02390 46.865

Tanzania 2 1967 1993 -0.04088 237.588 0.01010 19.972

Togo 1 1948 1965 0.03081 98.051 0.04928 96.642

Togo 2 1965 1993 -0.08691 329.807 0.03265 64.400

Uganda 1 1945 1965 0.03635 87.587 0.02794 54.771

Uganda 2 1965 1993 -0.06776 292.020 0.01039 20.538

Zambia 1 1942 1965 0.05517 49.890 0.02131 41.754

Zambia 2 1965 1993 -0.09632 347.463 0.00873 17.263

Zimbabwe 1 1945 1991 -0.01910 197.251 0.00595 11.751
41 

Table A-5 Trends in standardized BMI, total

Country Period Begin End Slope Intercept

Standard error

Slope Intercept

Benin 1 1946 1963 0.07551 -125.515 0.02716 53.213

Benin 2 1963 1993 0.00100 20.845 0.00451 8.921

Burkina Faso 1 1943 1988 0.06813 -113.023 0.00240 4.723

Cameroon 1 1948 1989 0.00049 22.751 0.00554 10.933

Central African Rep 1 1945 1979 0.08935 -154.322 0.00893 17.576

Chad 1 1947 1989 0.03568 -49.522 0.00439 8.658

Comoro Islands 1 1946 1981 0.00370 15.100 0.01975 38.860

Congo-Kinshasa 1 1957 1992 0.05814 -93.395 0.00539 10.659

Congo-Brazzaville 1 1955 1962 0.18541 -339.820 0.09506 186.385

Congo-Brazzaville 2 1962 1993 -0.03199 86.589 0.00690 13.662

Côte d’Ivoire 1 1944 1984 0.04542 -66.665 0.00573 11.283

Ethiopia 1 1950 1964 0.07416 -125.663 0.00933 18.268

Ethiopia 2 1964 1993 0.00160 17.066 0.00291 5.762

Gabon 1 1951 1985 -0.01144 46.629 0.01178 23.248

Ghana 1 1944 1965 0.13607 -243.946 0.02084 40.859

Ghana 2 1965 1993 0.00346 16.446 0.00524 10.357

Guinea 1 1949 1990 0.04654 -69.849 0.00433 8.543

Kenya 1 1943 1966 0.08955 -152.974 0.01273 24.955

Kenya 2 1966 1993 -0.01098 44.593 0.00394 7.785

Lesotho 1 1955 1989 -0.05023 124.681 0.00872 17.232

Liberia 1 1957 1992 0.00180 19.218 0.00503 9.947

Madagascar 1 1948 1994 -0.00034 21.282 0.00212 4.184

Malawi 1 1942 1990 -0.01056 42.851 0.00199 3.935

Mali 1 1946 1991 0.05109 -78.445 0.00220 4.347

Mozambique 1 1947 1960 0.10166 -176.757 0.03974 77.803

Mozambique 2 1960 1993 -0.03045 82.184 0.00389 7.678

Namibia 1 1942 1963 0.16298 -295.831 0.02732 53.518

Namibia 2 1963 1993 -0.06487 151.736 0.00562 11.126

Niger 1 1942 1991 0.03802 -53.646 0.00329 6.487

Nigeria 1 1949 1965 0.08445 -142.846 0.02353 46.156

Nigeria 2 1965 1993 -0.03382 89.799 0.00290 5.739

Rwanda 1 1950 1983 0.09447 -163.808 0.00286 5.639

Rwanda 2 1983 1993 -0.17827 376.526 0.02425 48.145

Senegal 1 1943 1965 0.03627 -48.509 0.01856 36.350

Senegal 2 1965 1993 -0.03244 86.566 0.00728 14.398

Sierra Leone 1 1958 1993 0.03330 -42.269 0.00995 19.682

South Africa 1 1902 1960 0.03495 -40.740 0.01216 23.665

South Africa 2 1960 1993 -0.15147 325.040 0.01285 25.333

Swaziland 1 1956 1992 -0.11569 255.687 0.00756 14.958

Tanzania 1 1947 1967 0.03879 -53.547 0.01504 29.500

Tanzania 2 1967 1993 -0.07942 179.250 0.00535 10.577

Togo 1 1948 1965 0.08269 -140.598 0.02757 54.065

Togo 2 1965 1993 0.00007 21.778 0.01586 31.272

Uganda 1 1945 1991 0.05673 -89.693 0.00330 6.510

Zambia 1 1942 1992 0.03138 -39.538 0.00242 4.768

Zimbabwe 1 1945 1991 0.00689 10.147 0.00323 6.378
42 

Table A-6 Trends in standardized BMI, urban areas

Country Period Begin End Slope Intercept

Standard error

Slope Intercept

Benin 1 1946 1965 0.13508 -240.964 0.03983 78.097

Benin 2 1965 1993 -0.02060 64.735 0.00818 16.184

Burkina Faso 1 1943 1988 0.10928 -191.498 0.00712 14.057

Cameroon 1 1948 1989 -0.04462 112.929 0.00825 16.306

Central African Rep 1 1945 1979 0.10616 -186.752 0.01576 31.022

Chad 1 1947 1975 0.00449 12.390 0.01201 23.625

Chad 2 1975 1993 0.25141 -475.701 0.03569 70.654

Comoro Islands 1 1946 1981 0.01638 -8.561 0.04850 95.611

Congo-Kinshasa 1 1940 1975 -0.00804 37.932 0.03114 61.311

Congo-Kinshasa 2 1975 1993 0.06428 -104.902 0.01906 37.836

Congo-Brazzaville 1 1955 1990 -0.04565 114.305 0.00802 15.858

Côte d’Ivoire 1 1944 1984 0.03250 -40.118 0.01027 20.218

Ethiopia 1 1950 1964 0.09954 -173.897 0.03900 76.439

Ethiopia 2 1964 1993 -0.00085 23.048 0.00841 16.629

Gabon 1 1951 1985 -0.02727 78.330 0.01406 27.742

Ghana 1 1944 1965 0.16937 -307.682 0.04085 80.113

Ghana 2 1965 1993 -0.01526 54.701 0.00819 16.196

Guinea 1 1949 1990 0.00409 15.395 0.00952 18.804

Kenya 1 1943 1994 -0.02105 66.323 0.00656 12.960

Lesotho 1 1955 1989 -0.07932 182.635 0.01757 34.710

Liberia 1 1957 1992 -0.03652 96.179 0.00889 17.584

Madagascar 1 1948 1994 -0.01428 49.668 0.00541 10.681

Malawi 1 1942 1990 -0.04773 117.614 0.00626 12.356

Mali 1 1946 1991 0.06291 -100.005 0.00465 9.189

Mozambique 1 1947 1960 0.20026 -367.808 0.10726 210.117

Mozambique 2 1960 1993 -0.10728 235.173 0.00758 14.973

Namibia 1 1942 1960 0.15304 -273.597 0.07480 146.522

Namibia 2 1960 1993 -0.09467 211.800 0.00833 16.459

Niger 1 1942 1970 0.10460 -181.826 0.02732 53.651

Niger 2 1970 1993 -0.00487 33.779 0.02094 41.443

Nigeria 1 1949 1969 0.05574 -85.424 0.02436 47.851

Nigeria 2 1969 1993 -0.06874 160.114 0.00619 12.275

Rwanda 1 1950 1983 0.09824 -170.491 0.00809 15.968

Rwanda 2 1983 1993 -0.39047 798.872 0.04558 90.556

Senegal 1 1943 1990 -0.05086 123.772 0.00691 13.631

Sierra Leone 1 1958 1993 0.00578 13.624 0.01826 36.151

South Africa 1 1928 1983 -0.05067 126.519 0.00627 12.301

Swaziland 1 1956 1992 -0.10185 228.668 0.01649 32.618

Tanzania 1 1947 1969 0.03593 -47.580 0.01458 28.623

Tanzania 2 1969 1987 -0.05654 134.297 0.00839 16.592

Togo 1 1948 1983 -0.04292 107.733 0.02553 50.290

Uganda 1 1945 1991 0.05169 -77.683 0.01083 21.379

Zambia 1 1942 1971 -0.01192 46.626 0.01189 23.348

Zambia 2 1971 1993 0.05292 -81.084 0.00897 17.767

Zimbabwe 1 1945 1991 -0.04091 105.725 0.00614 12.134
43 

Table A-7 Trends in standardized BMI, rural areas

Country Period Begin End Slope Intercept

Standard error

Slope Intercept

Benin 1 1946 1991 0.01043 1.267 0.00357 7.046

Burkina Faso 1 1943 1988 0.04187 -61.922 0.00214 4.210

Cameroon 1 1948 1989 -0.00929 41.337 0.00631 12.454

Central African Rep 1 1945 1979 0.07078 -118.227 0.01023 20.133

Chad 1 1947 1989 0.01992 -18.670 0.00429 8.461

Comoro Islands 1 1946 1981 0.00043 21.097 0.02016 39.679

Congo-Kinshasa 1 1940 1978 0.08842 -153.762 0.01384 27.255

Congo-Kinshasa 2 1978 1993 0.01955 -17.487 0.02044 40.598

Congo-Brazzaville 1 1955 1990 0.00100 20.305 0.00733 14.495

Côte d’Ivoire 1 1944 1984 0.04172 -60.220 0.00600 11.799

Ethiopia 1 1950 1964 0.07048 -118.656 0.00886 17.360

Ethiopia 2 1964 1993 -0.00419 28.256 0.00291 5.744

Gabon 1 1951 1985 0.02128 -19.357 0.01845 36.413

Ghana 1 1944 1966 0.08634 -147.534 0.01809 35.468

Ghana 2 1966 1993 -0.00025 22.616 0.00619 12.234

Guinea 1 1949 1990 0.04768 -72.708 0.00439 8.649

Kenya 1 1943 1966 0.05785 -91.310 0.01258 24.655

Kenya 2 1966 1993 -0.01014 42.316 0.00410 8.116

Lesotho 1 1955 1989 -0.04394 111.929 0.00957 18.913

Liberia 1 1957 1992 0.00120 19.539 0.00554 10.953

Madagascar 1 1948 1994 0.00355 13.368 0.00222 4.393

Malawi 1 1942 1965 0.05822 -92.238 0.00896 17.542

Malawi 2 1965 1993 -0.03641 93.709 0.00354 7.006

Mali 1 1946 1991 0.02422 -26.301 0.00216 4.264

Mozambique 1 1947 1960 0.09313 -160.076 0.04153 81.293

Mozambique 2 1960 1993 -0.03002 81.154 0.00424 8.363

Namibia 1 1942 1970 0.09757 -169.254 0.01610 31.601

Namibia 2 1970 1993 -0.05556 132.445 0.00919 18.223

Niger 1 1942 1991 0.03222 -42.778 0.00296 5.832

Nigeria 1 1949 1965 0.07875 -132.216 0.02700 52.950

Nigeria 2 1965 1993 -0.02477 71.251 0.00336 6.649

Rwanda 1 1950 1985 0.06857 -113.115 0.00259 5.105

Rwanda 2 1985 1993 -0.24552 509.777 0.04018 79.869

Senegal 1 1943 1965 0.04535 -66.883 0.02186 42.827

Senegal 2 1965 1993 -0.05462 129.621 0.00831 16.426

Sierra Leone 1 1958 1993 0.03000 -36.545 0.01119 22.122

South Africa 1 1928 1983 -0.01790 61.098 0.00691 13.537

Swaziland 1 1956 1992 -0.11857 261.268 0.00850 16.816

Tanzania 1 1947 1969 0.02698 -30.725 0.01152 22.615

Tanzania 2 1969 1987 -0.02893 79.173 0.00703 13.903

Togo 1 1948 1983 0.04520 -67.654 0.00779 15.336

Uganda 1 1945 1991 0.04869 -74.188 0.00327 6.456

Zambia 1 1942 1966 0.06074 -97.915 0.01065 20.877

Zambia 2 1966 1993 0.01778 -13.470 0.00432 8.538

Zimbabwe 1 1945 1991 0.01750 -11.470 0.00354 6.991

45 

Appendix B: Country Studies 

Introduction to country studies 

This section provides a detailed analysis of trends in height and in standardized Body Mass Index 

(BMI) country by country. Data from different surveys in the same country were aggregated as described 

in the “Methods” section of the report. Trends were tested using standard linear regression models (OLS). 

Each country study follows the same plan: presentation of available data in the country, with a brief 

discussion of consistency between surveys if more than one survey is available; trends in height, for urban 

and rural areas separately; trends in standardized BMI, for urban and rural areas separately; and when 

applicable a brief comment on the peculiarity of the country. 

The first table presents the raw data for women age 20-49 at time of survey. All the other 

information in text and figures refer to age standardized values—that is, values of height corrected for 

women age 15-19, and age standardized BMI, as defined in the “Methods” section. The standardized BMI 

can be interpreted as the BMI at age 30 assuming the pattern of weight increase prevalent in the same 

group of body shape, and same area of residence (urban or rural). Numeric values of trends, with standard 

errors, are presented in Appendix A, for each country as a whole, and for urban and rural areas separately. 

Trends are presented for all cohorts available born between 1942 and 1993, depending on the survey 

dates. 

The interpretation of trends in standardized BMI is delicate. A flat trend usually indicates no 

change over time in level and age pattern. An increasing trend from low values usually indicates catch-up 

weight: it implies that older cohorts were rather underweight for their height, and that the weight of 

younger cohorts is greater for the same height. A decreasing trend at high values usually indicates 

increasing obesity: it implies that the age pattern is changing over time, with more obesity among older 

persons than expected from the underlying age pattern (Figure B.0). Other interactions between age, 

period and cohort may also happen in certain countries. A declining trend from low values may indicate 

worsening nutritional status, defined as lower than expected weight for height. An increasing trend from 

high values may indicate increasing obesity among very young adults. 
46 

Figure B-0 Typical patterns of changes in standardized BMI 

1. Benin 

Benin conducted three surveys with anthropometric data on adult women, in 1996, 2001 and 

2006. All three were consistent in terms of mean and standard deviation, with some minor differences. 

Differences in period estimates of height and BMI were significant (P <0.001 in both cases). Some of the 

differences in BMI are due to low values of weight in the first survey, possibly due to a systematic bias. 

Table B-1 Sample size and basic anthropometric characteristics, adult women, Benin

Year of survey

1996 2001 2006

Number of women 2337 5113 14351

Height, women 20-49

Mean (cm) 158.5 158.5 159.1

Standard deviation 6.07 6.17 6.27

BMI, women 20-49

Mean (kg/m2) 21.4 22.8 22.9

Standard deviation 3.28 4.27 4.27

Trends in cohort height were not steady: average height increased from 158.1 cm (cohort 1940) to 

159.5 cm (cohort 1969), and declined as fast afterwards, reaching 157.6 cm for cohort 1990. The change 

occurred for women who were adolescent in the mid-1980s, and seemed to have continued ever since. 

Both slopes were highly significant (P=0.001 for 1940-1969, and P<0.001 for 1969-1990), and the change 

in slopes even more so (P<E-10). Trends were similar for urban and rural areas (Figure B-1.1). There 

were some significant differences in average height by survey, but all three surveys showed a significant 

declining trend since cohort 1970 (slopes= -0.107, -0.096, -0.120 respectively), the differences between 

the slopes being not significant. 

18

20

22

24

26

28

30

32

1930 1940 1950 1960 1970 1980 1990 2000

Year of birth Standardized BMI (kg/m2)

Period effect: increasing obesity

Steady state

Cohort effect: catch-up weight
47 

At the national level, the standardized BMI tended to increase slowly over time, with a significant 

slope, from 21.5 for cohort 1947 to 22.8 for cohort 1990. In urban areas, the standardized BMI increased 

first up to cohort 1964, then declined slowly, the change in slope being significant (P=0.0001). In rural 

areas, it remained almost steady, with only a very mild significant increase (P=0.003) (Figure B-1.2). 

Figure B-1.1 Trends in average height, adult women, Benin 

Figure B-1.2 Trends in standardized BMI, adult women, Benin

150

152

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156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural

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32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
48 

2. Burkina Faso 

Burkina-Faso conducted three surveys with anthropometric data on adult women, in 1993, 1999 

and 2003. All three were consistent in terms of mean and standard deviation, with no differences between 

surveys. 

Table B-2 Sample size and basic anthropometric characteristics, adult women, 

Burkina Faso

Year of survey

1993 1999 2003

Number of women 3873 3693 9723

Height, women 20-49

Mean (cm) 161.6 161.6 161.6

Standard deviation 5.92 5.87 5.99

BMI, women 20-49

Mean (kg/m2) 21.2 21.0 21.2

Standard deviation 2.86 2.55 3.53

Trends in cohort height were not steady: average height increased from 160.9 cm (cohort 1940) to 

161.8 cm (cohort 1966), and declined afterwards, reaching 161.0 cm for cohort 1990. The change 

occurred for women who were adolescent in the mid-1980s, but seemed to have continued ever since. 

Both slopes were highly significant (P=0.001 for 1940-1965, and P<0.001 for 1965-1990), and the change 

even more so (P<2E-05). Trends were changing only in rural areas, but not in urban areas (Figure B-2.1). 

There were no differences in levels and trends by survey. 

At the national level, the standardized BMI tended to increase steadily over time, from 19.3 

cohort 1943 to 22.4 for cohort 1988. The standardized BMI increased more rapidly in urban than in rural 

areas, in both cases from low values for cohort 1943, increasing the gap between both areas, the 

difference in slopes being highly significant (P<E-10) (Figure B-2.2). 

Figure B-2.1 Trends in average height, adult women, Burkina Faso 

150

152

154

156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural
49 

Figure B-2.2 Trends in standardized BMI, adult women, Burkina Faso 

3. Cameroon 

Cameroon conducted two surveys with anthropometric data on adult women, in 1998 and 2004. 

Both were consistent in terms of mean and standard deviation, with no difference between surveys for 

height, but a higher BMI in the second survey (P <E-10). 

Table B-3 Sample size and basic anthropometric 

characteristics, adult women, Cameroon

Year of survey 1998 2004

Number of women 1722 3857

Height, women 20-49

Mean (cm) 160.1 160.4

Standard deviation 6.31 6.18

BMI, women 20-49

Mean (kg/m2) 22.9 24.1

Standard deviation 3.71 4.44

There was no trend in cohort height at the national level (P=0.145), and average height remained 

basically constant at 160.4 cm. Both surveys showed the same level and trend. Urban and rural trends 

were basically identical, with only a small gap between them (Figure B-3.1). 

At the national level, the standardized BMI remained overall steady around 23.7. In urban areas, 

the standardized BMI was decreasing slowly and steadily (P <E-07), whereas it was steady in rural areas 

(P=0.141) (Figure B-3.2). 

18

20

22

24

26

28

30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
50 

Figure B-3.1 Trends in average height, adult women, Cameroon 

Figure B-3.2 Trends in standardized BMI, adult women, Cameroon 

150

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158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural

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32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
51 

4. Central African Republic 

The Central African Republic conducted one survey with anthropometric data on adult women, in 

1994. 

Table B-4 Sample size and basic 

anthropometric characteristics, adult 

women, Central African Republic

Year of survey

1994

Number of women 2058

Height, women 20-49

Mean (cm) 158.8

Standard deviation 6.70

BMI, women 20-49

Mean (kg/m2) 21.2

Standard deviation 2.72

The trend in height was not steady at the national level. Women’s height increased from cohort 

1945 (156.8 cm) to cohort 1964 (159.3 cm), then decreased to reach 158.0 for the last cohort available 

(1980). The first increasing slope was significant (P=0.006), but the second was not (P=0.091). However 

the change in slope was highly significant (P<0.001). Trends were the same in urban and rural areas, with 

no significant differences between the two areas (Figure B-4.1). 

At the national level, the standardized BMI increased over time from low values for cohort 1945 

(19.5 kg/m2). Trends were similar in both areas of residence, perhaps somewhat faster in urban areas, 

although the difference between slopes was borderline (P=0.060) (Figure B-4.2). 

Figure B-4.1 Trends in average height, adult women, Central African Republic

150

152

154

156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural
52 

Figure B-4.2 Trends in standardized BMI, adult women, Central African Rep. 

5. Chad 

Chad conducted two surveys with anthropometric data on adult women, in 1997 and 2004. Both 

led to very similar estimates for height and BMI. 

Table B-5 Sample size and basic anthropometric 

characteristics, adult women, Chad

Year of survey

1997 2004

Number of women 4019 3396

Height, women 20-49

Mean (cm) 162.8 162.4

Standard deviation 6.33 6.26

BMI, women 20-49

Mean (kg/m2) 20.8 20.9

Standard deviation 2.74 3.04

The trend in height was not steady at the national level. Women’s height was high (163.0 cm) 

from cohort 1945 to cohort 1965, then decreased to reach 161.2 for cohort 1990. The decreasing slope 

was significant (P<E-10), as was the change in slopes (P=0.015). Trends were the same in urban and rural 

areas, with no significant differences between the two areas (Figure B-5.1). 

At the national level, the standardized BMI increased slowly over time from low values, with a 

significant slope (P<0.001), from 19.9 for cohort 1947 to 21.4 for cohort 1989. Trends were similar in 

both areas of residence up to cohort 1977, then the standardized BMI increased rapidly (P<E-10) in urban 

areas for the recent cohorts, to reach 24.4 kg/m2 in 1989 (Figure B-5.2). 

18

20

22

24

26

28

30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
53 

Figure B-5.1 Trends in average height, adult women, Chad

Figure B-5.2 Trends in standardized BMI, adult women, Chad

150

152

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156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural

18

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22

24

26

28

30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
54 

6. Comoro Islands 

Comoro Islands conducted one survey with anthropometric data on adult women, in 1996, on a 

tiny sample. 

Table B-6 Sample size and basic 

anthropometric characteristics, adult 

women, Comoro Islands

Year of survey

1996

Number of women 832

Height, women 20-49

Mean (cm) 154.7

Standard deviation 5.57

BMI, women 20-49

Mean (kg/m2) 22.7

Standard deviation 3.82

Due to the small sample size, no trend was statistically significant. Height averaged 154.5 cm, 

and standardized BMI 22.4 kg/m2. The apparent profile was the same in urban and rural areas, with only a 

small gap between both areas (Figures B-6.1 and B-6.2). 

Figure B-6.1 Trends in average height, adult women, Comoro Islands

150

152

154

156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural
55 

Figure B-6.2 Trends in standardized BMI, adult women, Comoro Islands

7. Congo-Kinshasa (Democratic Republic of Congo) 

The Democratic Republic of Congo conducted one survey with anthropometric data on adult 

women, in 2007. 

Table B-7 Sample size and basic 

anthropometric characteristics, adult 

women, Congo-Kinshasa

Year of survey

2007

Number of women 4034

Height, women 20-49

Mean (cm) 157.4

Standard deviation 7.56

BMI, women 20-49

Mean (kg/m2) 21.5

Standard deviation 3.45

At the national level, height was first increasing from 157.5 (cohort 1957) to 158.4 (cohort 1972), 

then it declined to reach 155.9 in 1990. The first slope was not significant from zero, the second slope was 

highly significantly negative (P=2E-07), as well as the change in slopes (P=2E-06). Trends were similar 

in urban and rural areas, although the decline in the second period was more pronounced in rural areas 

than in urban areas (P=0.026), reaching a low 153.4 for cohort 1990 (Figure B-7.1). 

Contrary to height, trends in standardized BMI were positive for the country as a whole, with a 

highly significant slope (P<E-10). Trends seemed somewhat different in urban and rural areas. In urban 

areas, trends were steady at first, then increased for the recent cohorts, whereas the opposite was true in

rural areas. The first difference in slopes between urban and rural was statistically significant (P=0.005), 

but not the second (P=0.110) (Figure B-7.2). 

18

20

22

24

26

28

30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
56 

Figure B-7.1 Trends in average height, adult women, Congo-Kinshasa 

Figure B-7.2 Trends in standardized BMI, adult women, Congo-Kinshasa 

150

152

154

156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural

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22

24

26

28

30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
57 

8. Congo-Brazzaville (People’s Republic of Congo) 

The People’s Republic of Congo conducted one survey with anthropometric data on adult 

women, in 2005. 

Table B-8 Sample size and basic 

anthropometric characteristics, adult 

women, Congo-Brazzaville

Year of survey

2005

Number of women 5344

Height, women 20-49

Mean (cm) 158.2

Standard deviation 8.16

BMI, women 20-49

Mean (kg/m2) 23.4

Standard deviation 4.47

At the national level, height was steady from cohort 1955 to cohort 1967 at 158.2 cm, then it 

declined to reach 157.5 cm in 1990, which are trends similar to those of Congo-Kinshasa, although much 

less pronounced. The first slope was not significant from zero (P=0.212), but the second slope was highly 

significantly negative (P=0.0002), and the change in slopes was borderline (0.068). Trends were similar 

in urban and rural areas (Figure B-8.1). 

Unlike Congo-Kinshasa, trends in standardized BMI were negative for the country as a whole, 

with a highly significant slope (P<2E-09). However, trends diverged between the urban and rural areas. In 

rural areas, there was no change over time (P=0.892), whereas the standardized BMI tended to decrease 

steadily in urban areas, with a highly significant slope (P<E-07). The difference in slopes between urban 

and rural areas was also highly significant (P<E-10) (Figure B-8.2). 

Figure B-8.1 Trends in average height, adult women, Congo-Brazzaville 

150

152

154

156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural
58 

Figure B-8.2 Trends in standardized BMI, adult women, Congo-Brazzaville 

9. Côte d’Ivoire 

Côte d’Ivoire conducted two surveys with anthropometric data on adult women, in 1994 and 

1999. Both surveys were compatible in levels and trends, although both height and BMI were somewhat 

higher in the second survey (P=0.009 and P<E-10 respectively). 

Table B-9 Sample size and basic anthropometric 

characteristics, adult women, Côte d’Ivoire

Year of survey

1994 1999

Number of women 3004 2248

Height, women 20-49

Mean (cm) 159.0 159.5

Standard deviation 5.89 6.20

BMI, women 20-49

Mean (kg/m2) 22.2 22.9

Standard deviation 3.41 4.09

At the national level, height was increasing slowly from 158.8 (cohort 1945) to 159.5 (cohort 

1984), the slope being significant (P=0.023). Trends were significant in urban areas, but not in rural areas 

(Figure B-9.1). 

The standardized BMI tended to increase for the country as a whole, with a highly significant 

slope (P<E-10). Trends were similar in urban and rural areas, with no difference between the two 

(P=0.44) (Figure B-9.2). 

18

20

22

24

26

28

30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
59 

Figure B-9.1 Trends in average height, adult women, Côte d’Ivoire

Figure B-9.2 Trends in standardized BMI, adult women, Côte d’Ivoire

150

152

154

156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural

18

20

22

24

26

28

30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
60 

10. Ethiopia 

Ethiopia conducted two surveys with anthropometric data on adult women, in 2000 and 2005. 

Both surveys were compatible in levels and trends, although both height and BMI were somewhat higher 

in the second survey (P=2E-06 and P<E-10 respectively). 

Table B-10 Sample size and basic anthropometric characteristics, adult women, Ethiopia

Year of survey

2000 2005

Number of women 11851 5018

Height, women 20-49

Mean (cm) 156.4 156.9

Standard deviation 6.00 6.35

BMI, women 20-49

Mean (kg/m2) 20.1 20.4

Standard deviation 2.46 2.68

At the national level, height was very steady, contrasting with most other countries, and this was 

the same in both urban and rural areas (Figure B-10.1). 

The standardized BMI was very low, and tended to increase for the country as a whole, with a 

highly significant slope up to cohort 1965, but not after, the change in slopes being highly significant 

(P<E-10). Trends were similar in urban and rural areas, although the cut-off point of changing slopes was 

possibly earlier in urban areas (1962) than in rural areas (1967) (Figure B-10.2). 

Figure B-10.1 Trends in average height, adult women, Ethiopia 

150

152

154

156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural
61 

Figure B-10.2 Trends in standardized BMI, adult women, Ethiopia

11. Gabon 

Gabon conducted one survey with anthropometric data on adult women, in 2000. 

Table B-11 Sample size and basic 

anthropometric characteristics, adult 

women, Gabon

Year of survey

2000

Number of women 2170

Height, women 20-49

Mean (cm) 159.0

Standard deviation 6.28

BMI, women 20-49

Mean (kg/m2) 23.9

Standard deviation 4.26

At the national level, height was very steady, and this was the same in both urban and rural areas 

(Figure B-11.1). 

The standardized BMI was steady over time, around a mean of 24.1 kg/m2, hiding divergent 

trends between areas of residence. In urban areas, the standardized BMI tended to decline, whereas the 

opposite trend occurred in rural areas. Both slopes were borderline significant, one negative, the other 

positive, the difference between urban and rural areas being significant (P=0.036) (Figure B-11.2). 

18

20

22

24

26

28

30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
62 

Figure B-11.1 Trends in average height, adult women, Gabon 

Figure B-11.2 Trends in standardized BMI, adult women, Gabon 

150

152

154

156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural

18

20

22

24

26

28

30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
63 

12. Ghana 

Ghana conducted four surveys with anthropometric data on adult women, in 1993, 1999, 2003 

and 2008. All four surveys were compatible in levels and trends. There were some minor differences 

between successive surveys: height was higher in 2003 than in 1999 (P=0.018), and the BMI was always 

higher than at previous survey (P=0.007, P<E-10, P=7E-0.9 in 1999, 2003 and 2008 respectively). 

Table B-12 Sample size and basic anthropometric characteristics, adult women, Ghana

Year of survey

1993 1999 2003 2008

Number of women 1789 2146 4253 3818

Height, women 20-49

Mean (cm) 158.7 158.7 159.1 159.1

Standard deviation 6.02 6.26 6.12 6.42

BMI, women 20-49

Mean (kg/m2) 21.9 22.2 23.6 24.2

Standard deviation 3.59 3.80 4.62 4.79

At the national level, height was very steady, with a very mild declining slope which was not 

significant, and this was the same in both urban and rural areas, although the slope was significantly 

lower than zero in rural areas (P=0.027), and borderline in urban areas (P=0.079) (Figure B-12.1). 

The standardized BMI tended to increase rapidly for cohorts 1945-1962, then became steady for 

the country as a whole, the change in slope being highly significant (P<E-09). Dynamics were similar in 

urban and rural areas. In rural areas, the standardized BMI tended to increase steadily, from 20.4 to 22.1 

(cohorts 1945-1966), then remained steady. In urban areas, the standardized BMI increased from 21.7 to 

24.8 (cohorts 1945-1963), then remained steady. Differences in slopes between urban and rural areas 

were not significant (Figure B-12.2). 

Figure B-12.1 Trends in average height, adult women, Ghana

150

152

154

156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural
64 

Figure B-12.2 Trends in standardized BMI, adult women, Ghana

13. Guinea 

Guinea conducted two surveys with anthropometric data on adult women, in 1999 and 2005. Both 

surveys were compatible in levels and trends. There was a minor difference between successive surveys: 

the BMI was higher in the second survey (P=0.005). 

Table B-13 Sample size and basic anthropometric 

characteristics, adult women, Guinea

Year of survey

1999 2005

Number of women 3405 3134

Height, women 20-49

Mean (cm) 158.8 158.8

Standard deviation 6.19 6.34

BMI, women 20-49

Mean (kg/m2) 21.8 22.1

Standard deviation 3.25 3.50

At the national level, height was very steady, with a very mild declining slope, which was not 

significant, and this was the same in both urban and rural areas (Figure B-13.1). 

The standardized BMI tended to increase steadily for the country as a whole from 20.9 kg/m2 

(cohort 1950) to 22.8 (cohort 1990). However, dynamics were different in urban and rural areas. In urban 

areas, the standardized BMI remained at the same level (about 23.5 kg/m2), whereas it increased in rural 

areas, from 20.3 to 22.2 kg/m2 (Figure B-13.2). 

18

20

22

24

26

28

30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
65 

Figure B-13.1 Trends in average height, adult women, Guinea 

Figure B-13.2 Trends in standardized BMI, adult women, Guinea 

150

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158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural

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22

24

26

28

30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
66 

14. Kenya 

Kenya conducted four surveys with anthropometric data on adult women, in 1993, 1998, 2003 

and 2008. The four surveys were compatible in levels and trends. There were some minor differences 

between successive surveys: differences in height in successive surveys in 1998, 2003 and 2008 

(P=1E-08, P=0.050 and P=0.003 respectively); and BMI was higher in the third survey than in the second 

(P<E-10). 

Table B-14 Sample size and basic anthropometric characteristics, adult women, Kenya

Year of survey

1993 1998 2003 2008

Number of women 3521 3333 5973 6708

Height, women 20-49

Mean (cm) 159.3 160.1 159.8 159.5

Standard deviation 6.14 6.41 6.36 6.37

BMI, women 20-49

Mean (kg/m2) 22.1 22.1 23.2 23.3

Standard deviation 3.22 3.55 4.31 4.36

At the national level, height was first increasing, from 157.8 (cohort 1943) to 160.1 (cohort 

1969), then decreasing to reach 159.1 (cohort 1990). Trends were similar in urban and rural areas, with 

only a minor difference between the two (Figure B-14.1). 

The standardized BMI tended to increase slowly for the country as a whole between cohort 1943 

(21.0 kg/m2) and cohort 165 (23.0 kg/m2), then it remained stable. BMI dynamics were in the opposite 

direction in urban and rural areas. In rural areas, the standardized BMI followed the national pattern, and 

increased then stabilized after cohort 1965. In contrast, in urban areas, the standardized BMI decreased 

steadily from 25.4 to 24.4 (cohorts 1943-1990) (Figure B-14.2). 

Figure B-14.1 Trends in average height, adult women, Kenya

150

152

154

156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural
67 

Figure B-14.2 Trends in standardized BMI, adult women, Kenya 

15. Lesotho 

Lesotho conducted one survey with anthropometric data on adult women, in 2004. 

Table B-15 Sample size and basic 

anthropometric characteristics, adult 

women, Lesotho

Year of survey

2004

Number of women 2574

Height, women 20-49

Mean (cm) 157.5

Standard deviation 6.54

BMI, women 20-49

Mean (kg/m2) 26.0

Standard deviation 5.59

At the national level, height was slowly decreasing, from 157.9 (cohort 1955) to 157.0 (cohort 

1990), the trend being significant (P=0.013). Trends were similar in urban and rural areas, but 

significantly negative only in urban areas (P=0.035) and not in rural areas (P=0.182) (Figure B-15.1). 

The standardized BMI tended to decrease slowly for the country as a whole, from 26.5 (cohort 

1955) to 24.8 (cohort 1989) (P<E-08). Trends were similar in urban and rural areas, with a stronger effect 

in urban areas (Figure B-15.2). 

18

20

22

24

26

28

30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
68 

Figure B-15.1 Trends in average height, adult women, Lesotho 

Figure B-15.2 Trends in standardized BMI, adult women, Lesotho 

150

152

154

156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural

18

20

22

24

26

28

30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
69 

16. Liberia 

Liberia conducted one survey with anthropometric data on adult women, in 2007. 

Table B-16 Sample size and basic anthropometric characteristics, adult 

women, Liberia

Year of survey

2007

Number of women 5982

Height, women 20-49

Mean (cm) 157.4

Standard deviation 6.31

BMI, women 20-49

Mean (kg/m2) 22.9

Standard deviation 4.20

At the national level, height was slowly decreasing, from 158.0 (cohort 1960) to 156.6 (cohort 

1990), the trend being significant (P=1E-10). Trends were similar in urban and rural areas, and highly 

significant in both cases (P=3E-08 and P=3E-07 respectively) (Figure B-16.1). 

The standardized BMI was steady for the country as a whole, hiding divergent trends by area of 

residence. In urban areas the standardized BMI tended to decrease from 24.7 (cohort 1957) to 23.4 

(cohort 1992) (P<E-10), whereas no trend was visible in rural areas, with an average of 21.9 kg/m2 

(Figure B-16.2). 

Figure B-16.1 Trends in average height, adult women, Liberia 

150

152

154

156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural
70 

Figure B-16.2 Trends in standardized BMI, adult women, Liberia 

17. Madagascar 

Madagascar conducted three surveys with anthropometric data on adult women, in 1997, 2003 

and 2008. All three were compatible in terms of levels and trends in height and BMI. However, heights 

were significantly lower at first survey (P=1E-07), and BMI higher at second survey (P<E-10). 

Table B-17 Sample size and basic anthropometric characteristics, adult women, 

Madagascar

Year of survey

1997 2003 2008

Number of women 2767 6308 6514

Height, women 20-49

Mean (cm) 153.1 153.8 153.7

Standard deviation 5.79 5.83 5.87

BMI, women 20-49

Mean (kg/m2) 20.6 21.0 20.6

Standard deviation 2.46 2.95 3.10

At the national level, height was low, and at first slowly increasing, from 153.7 (cohort 1950) to 

154.5 (cohort 1964), then decreasing to reach 152.4 for cohort 1991. Both trends were significant, as was 

the change in slopes (P=8E-06). Trends were similar in urban and rural areas, with only a small gap in 

between, and the change in trends was also significant in both cases (P=0.009 for urban and P=0.0004 for 

rural areas) (Figure B-17.1). 

The standardized BMI was also low, and steady for the country as a whole (at about 20.6 kg/m2), 

hiding somewhat divergent trends by area of residence. In urban areas, the trend in standardized BMI was 

somewhat negative (P=0.008), whereas the trend was positive in rural areas, although not different from 

zero (P=0.111). The difference between the two slopes was statistically significant (P=0.003) (Figure B17.2). 

18

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26

28

30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
71 

Note that Madagascar had the lowest height, and the next-to-lowest BMI in the sample 

considered for this study. 

Figure B-17.1 Trends in average height, adult women, Madagascar 

Figure B-17.2 Trends in standardized BMI, adult women, Madagascar 

150

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1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural

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1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
72 

18. Malawi 

Malawi conducted three surveys with anthropometric data on adult women, in 1992, 2000 and 

2004. All three were compatible in terms of levels and trends in height and BMI. However, BMI was 

somewhat lower at the first survey (P<E-10). 

Table B-18 Sample size and basic anthropometric characteristics, adult women, 

Malawi

Year of survey

1992 2000 2004

Number of women 2482 10296 8687

Height, women 20-49

Mean (cm) 156.2 156.1 156.2

Standard deviation 5.77 5.90 6.12

BMI, women 20-49

Mean (kg/m2) 21.9 22.3 22.3

Standard deviation 2.82 3.10 3.25

At the national level, height was almost constant, with only a very mild increase from 1945 to 

1965 and then a decline, the change in trends being statistically significant (P=1E-06). The mild increase 

then decrease seemed to be due almost entirely to the rising then falling trends in urban areas, since rural 

areas exhibited basically no change. The change in slope in urban areas was highly significant (P=3E-05) 

(Figure B-18.1). 

The standardized BMI was steady for the country as a whole at about 22.0, hiding divergent 

trends by area of residence for cohorts 1945-1990. In urban areas the standardized BMI tended to 

decrease from 24.8 (cohort 1944) 22.6 (cohort 1990) (P<E-10). In rural areas, the standardized BMI 

tended first to increase from 20.8 (cohort 1942) to 22.2 (cohort 1965), then to decline down to 21.3 

(cohort 1989); both slopes were significant (P<E-10 in both cases). The decline in the BMI in the rural 

areas is surprising, and seems to indicate a worsening of the nutritional status of the recent cohorts (Figure 

B-18.2). 
73 

Figure B-18.1 Trends in average height, adult women, Malawi 

Figure B-18.2 Trends in standardized BMI, adult women, Malawi 

150

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156

158

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162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural

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30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
74 

19. Mali 

Mali conducted three surveys with anthropometric data on adult women, in 1995, 2001 and 2006. 

All three were compatible in terms of levels and trends in height and BMI. However, height was 

somewhat lower in the last survey (P=0.009), and BMI tended to increase from survey to survey (P<E-07 

in both cases). 

Table B-19 Sample size and basic anthropometric characteristics, adult women, 

Mali

Year of survey

1995 2001 2006

Number of women 4458 9896 11818

Height, women 20-49

Mean (cm) 161.5 161.6 161.4

Standard deviation 6.12 6.10 6.30

BMI, women 20-49

Mean (kg/m2) 21.3 22.3 22.6

Standard deviation 3.03 3.65 4.04

At the national level, height was decreasing steadily from 162.3 to 160.7 (1945 to 1990), the 

slope being highly significant (P<E-10). The declining trend was significant in both urban and rural areas 

(P=8E-05 and P<E-10 respectively), and the decline was more pronounced in rural areas (P=0.001) 

(Figure B-19.1). 

The standardized BMI was steadily increasing for the country as a whole, from 21.0 (cohort 

1946) to 23.2 (cohort 1990). Trends were in the same direction in both areas, but more pronounced in 

urban areas. In urban areas the standardized BMI tended to decrease from 22.7 (cohort 1950) to 25.2 

(cohort 1990) (P<E-10); in rural areas it increased only from 20.9 to 21.9 for the same cohorts (P<E-10) 

(Figure B-19.2). 

Figure B-19.1 Trends in average height, adult women, Mali 

150

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156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural
75 

Figure B-19.2 Trends in standardized BMI, adult women, Mali

20. Mozambique 

Mozambique conducted two surveys with anthropometric data on adult women, in 1997 and 

2003. Both were compatible in terms of levels and trends in height and BMI. However, BMI was higher 

in the second survey (P<E-10). 

Table B-20 Sample size and basic anthropometric 

characteristics, adult women, Mozambique

Year of survey

1997 2003

Number of women 3226 9451

Height, women 20-49

Mean (cm) 155.5 155.4

Standard deviation 6.25 6.19

BMI, women 20-49

Mean (kg/m2) 21.8 22.4

Standard deviation 2.93 3.60

At the national level, height was first increasing from 154.4 to 156.1 (cohort 1947 to 1958), the 

slope being significant (P=0.006), then it declined to reach 155.0 (cohort 1988), with again a significant 

negative slope (P=1E-06), and a significant change (P=0.001). In urban areas, the trend was steady and 

mildly declining, from 157.2 to 156.7, although the slope was not significantly different from zero 

(cohorts 1947-1988; P=0.217). Rural areas showed an increase (P=0.004) followed by a decrease 

(P=0.0002), with a significant change in slope (P=0.001) (Figure B-20.1). 

The standardized BMI for the country as a whole was first increasing, then decreasing, and the 

trends were the same in urban and rural areas. In urban areas, the standardized BMI increased from 22.3 

(cohort 1948) to 24.8 (cohort 1961), then decreased to 21.9 (cohort 1988). Corresponding values in rural 

areas were 21.3, 22.3 and 21.5 respectively. Changes in slopes were significant in both cases (P=0.004 in 

urban areas, and P=0.003 in rural areas). The gap between both areas was particularly small for the recent 

cohorts (Figure B-20.2). 

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30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
76 

Figure B-20.1 Trends in average height, adult women, Mozambique

Figure B-20.2 Trends in standardized BMI, adult women, Mozambique

150

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1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural

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1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
77 

21. Namibia 

Namibia conducted two surveys with anthropometric data on adult women, in 1992 and 2007. 

Both were compatible in terms of levels and trends in height and BMI. However, BMI was higher in the 

second survey (P<E-10). 

Table B-21 Sample size and basic anthropometric 

characteristics, adult women, Namibia

Year of survey

1992 2007

Number of women 2288 7757

Height, women 20-49

Mean (cm) 160.7 160.9

Standard deviation 6.44 6.57

BMI, women 20-49

Mean (kg/m2) 22.8 24.0

Standard deviation 4.42 5.53

At the national level, height was slowly declining, from 161.3 to 160.6 (cohort 1942 to 1990), the 

slope being significant (P=0.008). There was hardly any significant difference in slope and in trends 

between urban and rural areas, although the declining trend was slightly more pronounced in rural areas 

(P=0.040) (Figure B-21.1). 

The standardized BMI increased rapidly for the first cohorts (1942-1964), from 20.2 to 24.3 

kg/m2, then declined steadily, to reach 22.6 in 1990, the change being highly significant (P<E-10). The 

BMI dynamics were similar in urban and rural areas, but with a time gap. In urban areas, the standardized 

BMI increased from 23.8 (cohort 1943) to 26.2 (cohort 1960), then decreased to reach 23.2 (cohort 1992), 

with a significant change in slopes (P=0.001). In rural areas, the increase lasted longer, from 20.3 (cohort 

1943) to 23.0 (cohort 1970), then also declined to reach 21.8 (cohort 1992), again with a significant 

change in slopes (P<E-10) (Figure B-21.2). 

Figure B-21.1 Trends in average height, adult women, Namibia 

150

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158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural
78 

Figure B-21.2 Trends in standardized BMI, adult women, Namibia

22. Niger 

Niger conducted three surveys with anthropometric data on adult women, in 1992, 1998 and 

2006. All three were compatible in terms of levels and trends in height and BMI. However, BMI was 

higher in the last survey (P<E-10). 

Table B-22 Sample size and basic anthropometric characteristics, adult women, 

Niger

Year of survey

1992 1998 2006

Number of women 3674 3583 3709

Height, women 20-49

Mean (cm) 160.3 160.4 160.2

Standard deviation 6.06 5.97 5.90

BMI, women 20-49

Mean (kg/m2) 21.0 20.9 21.8

Standard deviation 2.98 2.99 3.60

At the national level, height was slowly declining, from 161.1 to 159.5 (cohort 1942 to 1990), the 

slope being significant (P=4E-10). There was no change in height in urban areas (P=0.287), and the 

decline in height was concentrated in rural areas (P<E-10) (Figure B-22.1). 

The standardized BMI increased slowly over time for the country as a whole, from low values: 

20.2 for cohort 1942 to 22.0 for cohort 1990. Trends were somewhat different in urban and rural areas. In 

urban areas the standardized BMI tended to decrease first, from 21.3 to 24.2 (cohorts 1942-1970), then 

stabilized, with a significant change in slopes (P=0.001). In rural areas on the contrary, the BMI remained 

steadily increasing over time, from 19.8 (cohort 1942) to 21.4 (cohort 1991), the slope being highly 

significant (P<E-10) (Figure B-22.2). 

18

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26

28

30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
79 

Figure B-22.1 Trends in average height, adult women, Niger 

Figure B-22.2 Trends in standardized BMI, adult women, Niger 

150

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160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural

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30

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1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
80 

23. Nigeria 

Nigeria conducted three surveys with anthropometric data on adult women, in 1999, 2003 and 

2008. All three were compatible in terms of levels and trends in height and BMI. However, height was 

somewhat lower (P=0.006) and BMI was somewhat higher in the last survey (P=0.005). 

Table B-23 Sample size and basic anthropometric characteristics, adult women, 

Nigeria

Year of survey

1999 2003 2008

Number of women 2195 5749 25861

Height, women 20-49

Mean (cm) 158.9 158.7 158.4

Standard deviation 10.46 6.23 6.82

BMI, women 20-49

Mean (kg/m2) 22.7 22.9 23.1

Standard deviation 4.77 4.41 4.48

At the national level, height was first increasing, from 157.5 to 159.6 (cohort 1950 to 1967), then 

decreasing to reach 157.0 for cohort 1991, both slopes being significant (P=0.006 and P<E-10 

respectively). Trends were identical in urban and rural areas, and there were no differences in slopes for 

either time period (P=0.811, and P=0.981 respectively) (Figure B-23.1). 

The standardized BMI was slowly decreasing from 21.8 (cohort 1950) to 23.3 (cohort 1967), to 

decline thereafter and reach 22.4 (cohort 1993) for the country as a whole. Trends were similar in urban 

and rural areas, with an increasing phase and a decreasing phase, and a gap between both. In both urban 

and rural areas, positive and negative slopes were significantly different from zero, and the changes in 

slope were highly significant (P<E-06 in urban and P<E-04 in rural) (Figure B-23.2). 

Figure B-23.1 Trends in average height, adult women, Nigeria 

150

152

154

156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural
81 

Figure B-23.2 Trends in standardized BMI, adult women, Nigeria

24. Rwanda 

Rwanda conducted two surveys with anthropometric data on adult women, in 2000 and 2005. 

Both were compatible in terms of levels and trends in height and BMI, although the second survey 

showed a systematic gap in BMI by cohort, which may indicate a fast change in the age pattern associated 

with the political turmoil of the 1990s. Both differences in height (P<E-10) and BMI (P=3E-05) were 

significant. 

Table B-24 Sample size and basic anthropometric 

characteristics, adult women, Rwanda

Year of survey

2000 2005

Number of women 7451 4342

Height, women 20-49

Mean (cm) 157.9 157.3

Standard deviation 6.38 6.37

BMI, women 20-49

Mean (kg/m2) 22.4 22.2

Standard deviation 2.90 2.83

At the national level, height was increasing very slowly, from 158.0 to 158.3 (cohort 1950 to 

1971), then decreased rapidly to reach 154.1 for cohort 1990, the second slope being highly significant 

(P<E-10), and the change as well (P<E-10). Trends were identical in urban and rural areas, and there were 

no differences in slopes for either time period (P=0.813, and P=0.356 respectively). The decline in height 

started long before the civil war (Figure B-24.1). 

The standardized BMI was increasing steadily from 20.4 to 23.4 for the country as a whole for a 

long time (cohorts 1950-1981), then declined very rapidly to reach 21.8 for cohort 1990, the change in 

slopes being highly significant (P<E-10). Trends were similar in urban and rural areas, although the 

increasing slope between cohort 1965 and 1983 was somewhat higher in urban areas (P=0.0005). The 

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1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
82 

cohorts the most affected by the decline in standardized BMI were also the ones who were adolescent or 

older children during the civil war years (1994-1997). It seems that this crisis had lasting effects for 

cohorts 1983-1990. This case in unique among the countries studied (Figure B-24.2). 

Figure B-24.1 Trends in average height, adult women, Rwanda 

Figure B-24.2 Trends in standardized BMI, adult women, Rwanda

150

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158

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162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural

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1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
83 

25. Senegal 

Senegal conducted two surveys with anthropometric data on adult women, in 1993 and 2005. 

Both were compatible in terms of levels and trends in height and trends for BMI. Both the average height 

and the average BMI were higher in the second survey (P=7E-06 and P<E-10 respectively). 

Table B-25 Sample size and basic anthropometric 

characteristics, adult women, Senegal

Year of survey

1993 2005

Number of women 3163 3456

Height, women 20-49

Mean (cm) 162.4 163.1

Standard deviation 6.08 6.52

BMI, women 20-49

Mean (kg/m2) 22.0 23.1

Standard deviation 3.63 4.72

At the national level, height was increasing slowly, from 162.0 to 163.5 (cohort 1945 to 1990), 

reaching the level of the USA or Western Europe, the slope being highly significant (P<5E-08). Levels 

and trends were similar in urban and rural areas, and the difference between urban and rural slopes was 

not significant (P=0.118) (Figure B-25.1). 

The standardized BMI was increasing slowly from 22.0 (cohort 1943) to 22.8 (cohort 1966), then 

declined also slowly to come back to its original value of 22.0 for cohort 1990. Trends were different in 

urban and rural areas. In urban areas, the trend was declining steadily from 25.0 to 22.6, with a highly 

significant slope (P<E-10). In rural areas, the trend followed the national pattern, with a mild increase 

followed by a mild decline, the change in slope being highly significant (P<2E-05) (Figure B-25.2). 

Figure B-25.1 Trends in average height, adult women, Senegal 

150

152

154

156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural
84 

Figure B-25.2 Trends in standardized BMI, adult women, Senegal

26. Sierra Leone 

Sierra Leone conducted one survey with anthropometric data on adult women, in 2008. The 

anthropometric data were very heterogeneous in Sierra Leone: data plotted by year of birth were very 

erratic, outstanding values of height and weight were found, and the standard deviation of height and BMI 

was much higher than in the other surveys, suggesting poor quality of anthropometric data. 

Table B-26 Sample size and basic 

anthropometric characteristics, adult 

women, Sierra Leone

Year of survey

2008

Number of women 3000

Height, women 20-49

Mean (cm) 156.4

Standard deviation 10.30

BMI, women 20-49

Mean (kg/m2) 23.5

Standard deviation 4.77

At the national level, height seemed to be declining slowly for available cohorts, from 157.5 to 

155.4 (cohort 1960 to 1990). Trends were similar in urban and rural areas, the difference between urban 

and rural slopes being not significant (P=0.077) (Figure B-26.1). 

The standardized BMI was increasing steadily from 22.9 (cohort 1958) to 24.1 (cohort 1993) for 

the country as a whole. However, trends were divergent in urban and rural areas. In urban areas the 

standardized BMI remained steady, at about 25.0 kg/m2, whereas it tended to increase in rural areas, the 

difference between the two slopes being significant (P=0.0003) (Figure B-26.2). 

18

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24

26

28

30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
85 

Figure B-26.1 Trends in average height, adult women, Sierra Leone 

Figure B-26.2 Trends in standardized BMI, adult women, Sierra Leone 

150

152

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156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural

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32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
86 

27. South Africa 

South Africa conducted one survey with anthropometric data on adult women, in 1998. The 

peculiarity of this survey is that adult women of all ages were taken weight and height, even above age 

50, which provides more stability for trends of the earlier cohorts. 

Table B-27 Sample size and basic 

anthropometric characteristics, adult 

women, South Africa

Year of survey

1998

Number of women 4568

Height, women 20-49

Mean (cm) 158.9

Standard deviation 7.02

BMI, women 20-49

Mean (kg/m2) 27.3

Standard deviation 6.40

At the national level, height was increasing slowly for the earlier cohorts (1940-1953), then 

became almost stable afterwards. The change in slopes was significant (P=3E-05). Levels and trends were 

similar in urban and rural areas, with more increase in the urban areas for the most recent cohorts, the 

difference between urban and rural slopes being significant (P=0.030) (Figure B-27.1). 

The standardized BMI was very high, and for a long time, with a mild decline from 27.2 (cohort 

1948) to 25.9 (cohort 1983). Trends were similar in urban and rural areas, although more pronounced in 

urban areas. In urban areas the standardized BMI tended to decline, from 27.8 to 26.0, but in rural areas 

from 26.2 to 25.6. The high values of BMI in rural areas are outstanding for Africa, except in the 

Southern area (South Africa, Swaziland, Lesotho). For the most recent cohort, the difference between 

urban and rural areas had vanished, although the age-period interaction (the increasing obesity) remains 

stronger in urban areas (Figure B-27.2). 

Figure B-27.1 Trends in average height, adult women, South Africa 

150

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154

156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural
87 

Figure B-27.2 Trends in standardized BMI, adult women, South Africa 

28. Swaziland 

Swaziland conducted one survey with anthropometric data on adult women, in 2007. Swaziland 

bears many similarities with South Africa, for geographical and historical reasons. 

Table B-28 Sample size and basic 

anthropometric characteristics, adult 

women, Swaziland

Year of survey

2007

Number of women 3833

Height, women 20-49

Mean (cm) 159.0

Standard deviation 6.14

BMI, women 20-49

Mean (kg/m2) 27.5

Standard deviation 5.94

At the national level, height was constant for the cohorts considered (1955-1990). The same was 

true in urban and rural areas, and the apparent very small difference between urban and rural slopes was 

not significant (P=0.133) (Figure B-28.1). 

The standardized BMI was record high in both urban and rural areas. It tended to decline at the 

national level, from 29.4 to 25.5 for the time frame investigated (cohorts 1956-1990), and similarly in 

urban and rural areas, all slopes being highly significant (P<E-10 in both cases). The lack of difference 

between urban and rural areas is remarkable for sub-Saharan Africa (Figure B-28.2). 

18

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24

26

28

30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
88 

Figure B-28.1 Trends in average height, adult women, Swaziland

Figure B-28.2 Trends in standardized BMI, adult women, Swaziland 

150

152

154

156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural

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26

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30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
89 

29. Tanzania 

Tanzania conducted two surveys with anthropometric data on adult women, in 1996 and 2004. 

Both surveys were compatible for levels and trends in height and BMI, with some mild differences for 

height of cohorts 1970-1980. In the second survey, average height was somewhat higher (P=0.007), as 

well as BMI (P<E-10). 

Table B-29 Sample size and basic anthropometric 

characteristics, adult women, Tanzania

Year of survey

1996 2004

Number of women 3992 8010

Height, women 20-49

Mean (cm) 156.4 156.8

Standard deviation 6.22 6.39

BMI, women 20-49

Mean (kg/m2) 22.1 22.8

Standard deviation 3.23 3.83

At the national level, height increased first, from 155.4 to 157.0 (1947-1967), then declined 

slowly to reach 156.1 for cohort 1991, the change in slope being significant (P=1E-06). The same was 

true in urban and rural areas, and there was no difference in slopes, and only a tiny difference in levels 

between urban and rural areas (Figure B-29.1). 

The standardized BMI increased slowly from 22.0 (cohort 1947) to 22.9 (cohort 1969) to decline 

thereafter (21.2 for cohort 1990), reaching a value lower than at baseline. Trends in both areas were 

similar, and differences between urban and rural areas were very small. The change in slopes was 

significant in urban areas (P<E-07) and in rural areas (P<E-04) (Figure B-29.2). 

Figure B-29.1 Trends in average height, adult women, Tanzania 

150

152

154

156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural
90 

Figure B-29.2 Trends in standardized BMI, adult women, Tanzania 

30. Togo 

Togo conducted one survey with anthropometric data on adult women, in 1998. 

Table B-30 Sample size and basic 

anthropometric characteristics, adult 

women, Togo

Year of survey

1998

Number of women 3301

Height, women 20-49

Mean (cm) 158.8

Standard deviation 6.11

BMI, women 20-49

Mean (kg/m2) 21.8

Standard deviation 3.44

At the national level, height decreased slightly over the time frame considered, from 159.4 to 

158.2 (1948-1983), the slope being significant (P=0.016). The same was true in urban areas, but in rural 

areas the pattern was different, with first a mild increase, and then a fast decline, the change in slopes 

being significant (P=0.046) (Figure B-30.1). 

The standardized BMI increased first at the national level, from 20.5 to 21.9 (cohorts 1948-1965), 

then became steady. Trends diverged in urban and rural areas. In urban areas, the standardized BMI 

tended to decline steadily, from 24.1 (cohort 1948) to 22.8 (cohort 1982), whereas in rural areas it 

increased steadily from 20.4 to 21.9 for the same cohorts (1948-1982). The rural slope was highly 

significant (P<E-08); the urban slope was only borderline (P=0.09), and the difference between both was 

significant (P=0.001) (Figure B-30.2). 

18

20

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24

26

28

30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
91 

Figure B-30.1 Trends in average height, adult women, Togo 

Figure B-30.2 Trends in standardized BMI, adult women, Togo

150

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166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural

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32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
92 

31. Uganda 

Uganda conducted three surveys with anthropometric data on adult women, in 1995, 2001 and 

2006. All three surveys were compatible in levels and trends of height and BMI. Heights were the same in 

the three surveys, and BMI tended to increase from survey to survey, the differences being significant 

each time (P<E-10, and P=0.002 respectively). 

Table B-31 Sample size and basic anthropometric characteristics, adult women, 

Uganda

Year of survey

1995 2001 2006

Number of women 3553 5248 2303

Height, women 20-49

Mean (cm) 158.5 158.6 158.8

Standard deviation 6.39 6.50 6.48

BMI, women 20-49

Mean (kg/m2) 21.7 22.1 22.4

Standard deviation 2.94 3.50 3.72

At the national level, height tended to firstly increase, from 158.2 to 159.1 (1943-1964), then to 

decrease to reach 157.2 for cohort 1990, the change in slope being significant (P=1E-05). The same was 

true in urban and in rural areas, although the pattern was more pronounced in urban areas, both changes in 

slopes being significant (P=0.0018 and P=0.0005 respectively). Differences between urban and rural 

slopes were also significant (P=0.050 for the first period, and P=1E-05 for the second period) (Figure 

B-31.1). 

The standardized BMI increased steadily at the national level, from 20.7 (cohort 1946) to 23.2 

(cohort 1990). Trends were in the same direction in urban and rural areas, and very parallel, without any 

difference between slopes (P=0.79). In urban areas, the standardized BMI increased from 22.9 to 25.2 

kg/m2 (cohort 1946-1990), and from 20.7 to 22.7 in rural areas. Of course, both slopes were highly 

significant (P<E-05 and P<E-10 respectively) (Figure B-31.2). 

Figure B-31.1 Trends in average height, adult women, Uganda 

150

152

154

156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural
93 

Figure B-31.2 Trends in standardized BMI, adult women, Uganda

32. Zambia 

Zambia conducted four surveys with anthropometric data on adult women, in 1992, 1997, 2001 

and 2007. All four surveys were compatibles in levels and trends of height and BMI. Heights were the 

same in the four surveys, and BMI was somewhat lower in 2001 compared with 1997 and 2007 

(P<4E-05, and P<E-10 respectively). 

Table B-32 Sample size and basic anthropometric characteristics, adult women, Zambia

Year of survey

1992 1997 2001 2007

Number of women 3362 4028 5998 5607

Height, women 20-49

Mean (cm) 158.3 158.2 158.2 158.1

Standard deviation 6.03 6.02 6.24 6.44

BMI, women 20-49

Mean (kg/m2) 22.1 22.2 21.9 23.0

Standard deviation 3.32 3.25 3.66 4.06

At the national level, height tended first to increase from 156.4 to 158.9 (1942-1964), then to 

decrease to reach 156.8 for cohort 1990, the change in slope being significant (P<E-10). The same was 

true in urban areas and in rural areas, both changes in slopes being significant (P<E-10 in both cases). 

Differences between urban and rural slopes were also significant (P=6E-06 for the first period, and P<E10 for the second period), but of minor importance (Figure B-32.1). 

The standardized BMI increased steadily at the national level from 21.4 to 22.9 (cohorts 1942-

1990). Trends were divergent in urban and rural areas. In urban areas, the trend was first steady (cohort 

1942-1970), then increasing, from 23.2 to 24.3 (cohorts 1970-1992), the change in slope being significant 

(P<E-05). In rural areas, the trend was first positive, from 20.0 (cohort 1942) to 21.5 (cohort 1966), then 

mildly positive, reaching 22.0 for cohort 1992, the change in slope being significant (P=0.0002) (Figure 

B-32.2). 

18

20

22

24

26

28

30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
94 

Figure B-32.1 Trends in average height, adult women, Zambia 

Figure B-32.2 Trends in standardized BMI, adult women, Zambia 

150

152

154

156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural

18

20

22

24

26

28

30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
95

33. Zimbabwe 

Zimbabwe conducted three surveys with anthropometric data on adult women, in 1994, 1999 and 

2005. All three surveys were compatibles in levels and trends of height and BMI. Height was somewhat 

lower in the first survey (P=0.001), and BMI was somewhat higher in the second survey (P<E-10, and 

P<4E-08 compared with the first and third survey). 

Table B-33 Sample size and basic anthropometric characteristics, adult women, 

Zimbabwe

Year of survey

1994 1999 2005

Number of women 1893 4234 6699

Height, women 20-49

Mean (cm) 159.4 160.0 160.1

Standard deviation 5.99 6.39 6.02

BMI, women 20-49

Mean (kg/m2) 23.3 24.0 23.6

Standard deviation 3.72 4.25 4.28

At the national level, height was almost steady and declined very slowly, from 160.3 to 159.6 

(1945-1990), the slope being significant (P=0.002). The same was true in urban areas and in rural areas, 

with basically the same slope, and only a minor gap of less than a centimeter between both areas (Figure 

B-33.1). 

The standardized BMI was steady at the national level, with a mean of 23.7 (cohorts 1945-1990). 

Trends were divergent in urban and rural areas. In urban areas, the trend was negative (26.2 to 24.3 for 

the same cohorts), whereas it was positive in rural areas (22.6 to 23.4 for the same cohorts). Both trends 

were significantly different from zero (P<E-10 in urban, and P<E-06 in rural areas) (Figure B-33.2). 

Figure B-33.1 Trends in average height, adult women, Zimbabwe 

150

152

154

156

158

160

162

164

166

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth Height (cm)

Urban Trend urban Rural Trend rural
96 

Figure B-33.2 Trends in standardized BMI, adult women, Zimbabwe 

18

20

22

24

26

28

30

32

1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year of birth BMI (kg/m2)

Urban Trend urban Rural Trend rural
DHS Comparative Reports Series 

1. Westoff, Charles F. 2001. Unmet Need at the End of the Century. 

2. Westoff, Charles F., and Akinrinola Bankole. 2002. Reproductive Preferences in Developing 

Countries at the Turn of the Century.

3. Rutstein, Shea O. 2002. Fertility Levels, Trends, and Differentials 1995-1999.

4. Mahy, Mary. 2003. Childhood Mortality in the Developing World: A Review of Evidence from the 

Demographic and Health Surveys.

5. Westoff, Charles F. 2003. Trends in Marriage and Early Childbearing in Developing Countries.

6. Rutstein, Shea O., and Kiersten Johnson. 2004. The DHS Wealth Index.

7. Yoder, P. Stanley, Noureddine Abderrahim, and Arlinda Zhuzhuni. 2004. Female Genital Cutting in 

the Demographic and Health Surveys: A Critical and Comparative Analysis. 

8. Stallings, Rebecca. 2004. Child Morbidity and Treatment Patterns.

9. Rutstein, Shea O., and Iqbal H. Shah. 2004. Infecundity, Infertility, and Childlessness in 

Developing Countries. 

10. Mukuria, Altrena, Jeanne Cushing, and Jasbir Sangha. 2005. Nutritional Status of Children: Results 

from the Demographic and Health Surveys, 1994–2001. 

11. Mukuria, Altrena, Casey Aboulafia, and Albert Themme. 2005. The Context of Women’s Health: 

Results from the Demographic and Health Surveys, 1994-2001. 

12. Yoder, P. Stanley, Noureddine Abderrahim, and Arlinda Zhuzhini. 2005. L’excision dans les Enquêtes 

Démographiques et de Santé: Une Analyse Comparative.

13. Garenne, Michel, and Julien Zwang. 2006. Premarital Fertility and Ethnicity in Africa.

14. Westoff, Charles F. 2006. New Estimates of Unmet Need and the Demand for Family Planning. 

15. Fort, Alfredo L., Monica T. Kothari, and Noureddine Abderrahim. 2006. Postpartum Care: Levels and 

Determinants in Developing Countries. 

16. Khan, Shane, Vinod Mishra, Fred Arnold, and Noureddine Abderrahim. 2007. Contraceptive Trends 

in Developing Countries. 

17. Johnson, Kiersten, and Yuan Gu. 2009. Men’s Reproductive Health: Findings from Demographic and 

Health Surveys, 1995-2005. 

18. Garenne, M. Michel. 2008. Fertility Changes in Sub-Saharan Africa.

19. Khan, Shane, and Vinod Mishra. 2008. Youth Reproductive and Sexual Health.

20. Kishor, Sunita, and Lekha Subaiya. 2008. Understanding Women’s Empowerment: A Comparative 

Analysis of Demographic and Health Surveys (DHS) Data. 

21. Mishra, Vinod, Shane Khan, Li Liu, and Benny Kottiri. 2008. Medical Injection Use and HIV in SubSaharan Africa. 

22. Mishra, Vinod, Amy Medley, Rathavuth Hong, Yuan Gu, and Bryant Robey. 2009. Levels and Spread 

of HIV Seroprevalence and Associated Factors: Evidence from National Household Surveys. 

23. Sneeringer, Stacy E. 2009. Fertility Transition in Sub-Saharan Africa: A Comparative Analysis of 

Cohort Trends in 30 Countries. 

24. Mishra, Vinod, Praween Agrawal, Soumya Alva, Yuan Gu, and Shanxiao Wang. 2009. Changes in 

HIV-Related Knowledge and Behaviors in Sub-Saharan Africa.

25. Westoff, Charles F. 2010. Desired Number of Children: 2000-2008.

26. Wang, Wenjuan, Soumya Alva, Shanxiao Wang, and Alfredo Fort. 2011. Levels and Trends in the 

Use of Maternal Health Services in Developing Countries.

27. Garenne, Michel. 2011. Trends in Nutritional Status of Adult Women in Sub-Saharan Africa DHS 

Surveys.