

  • S. V. Subramanian ,


  • Emre Özaltin,


  • Jocelyn E. Finlay



  • S. V. Subramanian, 
  • Emre Özaltin, 
  • Jocelyn E. Finlay
  • Published: April 20, 2011
  • DOI: 10.1371/journal.pone.0018962



Adult height is a useful biological measure of long term population health and well being. We examined the cohort differences and socioeconomic patterning in adult height in low- to middle-income countries.


We analyzed cross-sectional, representative samples of 364538 women aged 25-49 years drawn from 54 Demographic and Health Surveys (DHS) conducted between 1994 and 2008. Linear multilevel regression models included year of birth, household wealth, education, and area of residence, and accounted for clustering by primary sampling units and countries. Attained height was measured using an adjustable measuring board. A yearly change in birth cohorts starting with those born in 1945 was associated with a 0.0138 cm (95% CI 0.0107, 0.0169) increase in height. Increases in heights in more recent birth year cohorts were largely concentrated in women from the richer wealth quintiles. 35 of the 54 countries experienced a decline (14) or stagnation (21) in height. The decline in heights was largely concentrated among the poorest wealth quintiles. There was a strong positive association between height and household wealth; those in two richest quintiles of household wealth were 1.988 cm (95% CI 1.886, 2.090) and 1.018 cm (95% CI 0.916, 1.120) taller, compared to those in the poorest wealth quintile. The strength of the association between wealth and height was positive (0.05 to 1.16) in 96% (52/54) countries.




Although height is highly heritable, improvements in attained height over time underscore the importance of environmental factors including nutrition, exposure to infections, and socioeconomic status, especially during childhood [1], [2], [3], [4], [5]. Consequently evaluating the changes in height over time across countries provides critical insights into the variations in childhood living conditions across countries. Further, socioeconomic inequalities in attained height within and across countries reveal the intergenerational nature of the distribution of conditions that influence health and well-being. Height has been shown to predict subsequent socioeconomic status [6], [7], morbidity [8], and mortality [9], [10]. Indeed, a mother's attained height has been shown to also be a strong risk factor of her offspring's mortality and growth failure extending into early childhood [11], [12]. Viewed this way, height is a stable and useful biological measure of standard of living [2], [13], that captures both current and future inequalities in population health. There are few cross-national assessments of adult height, with assessments largely confined to developed countries [14], [15], [16], [17]. Similar cross-national assessments of developing countries have been limited [18], and have not considered the socioeconomic inequalities in the patterning and changes in height over time. Using the largest available, nationally representative, and sample from 54 comparable surveys conducted in low- to middle-income countries with objective measurements of height, we provide an epidemiologic assessment of changes in height over a 40 year period along with its socioeconomic patterning both within and across countries.


Data Sources

Information from Demographic and Health Surveys (DHS) conducted in 54 countries between 1994 and 2008 provided the data for this study (Table 1) [19]. The DHS are nationally representative household sample surveys that measure population, health, socio-economic, and anthropometric indicators, emphasizing maternal and child health [20]. The DHS involves randomly selecting households within a cluster, and then within each household, women eligible for a more detailed individual survey are identified. Typically, these are women between the ages of 15–49. In a limited number of cases, women aged 10–49 are considered eligible, or in some earlier surveys the individual survey was limited to ever-married women. The DHS are important data source for studying population health across low- to middle-income countries due to extensive coverage, comparability, and data quality [21], [22], [23]. To ensure standardization and comparability across diverse sites and time, DHS surveys employ intense interviewer training, standardized measurement tools and techniques, an identical core questionnaire, and instrument pretesting [24]. Country reports detail pretesting and quality assurance measures by survey (see​sults.cfm?Type=5&srchTp=type&newSrch=1). The DHS is modular in structure, comprising a core questionnaire, a set of country-relevant sections, and country-specific variables. The DHS provides data with standardized variables across surveys and imputed dates of key events (see​4DHS.pdf). A multistage stratified cluster design with probabilistic sampling, with each unit of selection having a defined, and non-zero, probability of selection is employed for the DHS [25]. Every survey is stratified by urban and rural status and additionally by country-specific geographic or administrative regions. Detailed sampling plans are available from survey final reports at​sults.cfm?Type=5&srchTp=type&newSrch=1. Table 1 describes each survey by country and year, along with sampling characteristics, response rates and sample sizes.

Table 1. Survey year, sample size, and mean height and year of birth for adult women.


Study population and sample size

The study population comprises women (n = 454272) aged 25–49 years. There were 89577 women (19.72% of the sample) for whom height was intentionally not measured. Among those for whom height should have been measured, 4050 (<1%) did not have a height measure in the data, and a further 136 women (<1%) had implausible or extreme values (less than 100 cm or greater than 200 cm). One hundred and fifty seven observations (<1%) were missing data on covariates. The final analytical sample was 364538 women surveyed and measured between 1994 and 2008 in 54 countries.


Attained height (expressed in centimeters) was specified as a continuous outcome. Trained investigators measured each woman using an adjustable board calibrated in millimeters, and theoretically accurate to 1 millimeter [24].

Independent Variables

Year of birth, household wealth, education, and place of residence (urban or rural) were the key independent variables (Table 2). Education was specified as having no schooling or incomplete primary, complete primary schooling, or having completed secondary or higher schooling. Household wealth was defined in terms of ownership of material possessions [26], with each woman assigned a wealth score based on a combination of different household characteristics that were weighted according to a factor analysis procedure. For this procedure, z-scores were calculated for each indicator variable and a principle components analysis was performed using these z-scores. For each household, the values of the indicator variables were multiplied by the factor loadings and summed to produce a standardized household index value with a mean of 0 and a standard deviation of 1. This standardized score was then divided into quintiles for each country [27], [28].

Table 2. Frequency and percentage distribution of sample by independent variables, and mean height by categories of independent variables for adult women.



Individual country files were created ensuring consistency of variable definitions across countries. We used three types of analytical strategies. First, we conducted a pooled analysis of all the individual data from all countries with height modeled as a function of birth cohort (specified as year of birth), household wealth quintiles, highest educational attainment, urban/rural residence, and country fixed effects in a linear regression model, adjusted for clustering of the individual data by primary sampling units within countries with robust standard errors. The results from these models were used to draw global inference about the association between year of birth, household wealth, and height across all countries. We also specified and tested for interactions between household wealth quintiles and year of birth to assess whether the cohort differences in heights varied by household wealth. Second, we repeated the above strategy separately for every country to estimate the country-specific cohort differences, socioeconomic patterning in addition to assessing the interaction between wealth and year of birth in each country. Finally, since the pooled individual data yields a multilevel data structure of women at level-1 nested within primary sampling units (PSU) at level-2 nested within countries at level-3, we additionally estimated a multilevel linear regression to model the variation in height () for a woman in PSU in country [29], as , where represented the mean height for the reference groups (i.e., rural women born in 1945 with no schooling and in poorest wealth quintile) across all countries; and represents a vector of regression coefficients associated with variables year of birth, schooling categories, household wealth quintiles, and urban residence. The terms inside the brackets represents random effects associated with country (), PSU (), and a residual term for every individual (). Assuming a normal distribution with a 0 mean, the model estimated a variance at level-1 (: between-individual), level-2 (: between-PSU), and level-3 (: the between-country) in height. Results from the multilevel models were used to partition the variation in height attributable to individuals, primary sampling units (that are typically urban neighborhoods or rural villages), and countries [29]. Consequently, the two levels of environmental context in our study were countries, and PSUs within countries. The PSUs are typically smaller scales of geographically delineated administrative units, and as such represents within-country variation that is not attributable to individuals. Regression models were estimated using STATA ver.11.1MP (for the pooled and country specific models) [30], and MLwiN 2.20 (for multilevel models) [31].

Ethical Review

The DHS data collection procedures were approved by the ORC Macro (Calverton, Maryland) Institutional Review Board as well as by the relevant body in each country which approves research studies on human subjects. Oral informed consent for the interview/survey was obtained from respondents by interviewers. The study was reviewed by Harvard School of Public Health Institutional Review Board and was considered exempt from full review because the study was based on an anonymous public use data set with no identifiable information on the survey participants.


The pooled mean height in sample was 155.8 cm (SD 7.2), and mean height varied between 147.3 cm (SD 6.3) in Guatemala and 163.0 cm (SD 6.7) in Senegal (Table 1). 33.4% of the sample did not have any schooling, and 45.2% lived in urban areas (Table 2). In pooled samples, women in richer households, women who were more educated, or women who lived in urban areas were taller (Table 2). There was a strong negative country-level correlation between mean height and the differences in height between the richest and poorest wealth quintile (r = −0.4242, p = 0.0014) such that countries that are shorter on average also tend to have smaller gaps in height between the richest and the poorest wealth quintile (Figure 1).

Figure 1. Country-level scatter plot between mean height (y axis) and gap in height between the richest and poorest wealth quintile (x axis) among adult women.


In mutually adjusted pooled models, on average, height increased by 0.0138 cm (95% CI 0.0107, 0.0169) with one increase in year of birth (Table 3). There was a strong positive association between height and household wealth; those in top two quintile of household wealth were 1.988 cm (95% CI 1.886, 2.090) and 1.018 cm (95% CI 0.916, 1.120) taller, compared to those in the poorest wealth quintile. Women with primary schooling or secondary schooling were 0.159 cm (95% CI 0.0906, 0.227) and 1.328 cm (95% CI 1.252, 1.404) taller, respectively, compared to those with no schooling (Table 3). Urban-rural differentials in height were inconsequential in magnitude once the model was adjusted for women's year birth, education and wealth (Table 3).

Table 3. Unadjusted and mutually adjusted effects of year of birth, wealth quintiles, schooling, and place of residence on height accounting for within and between country variation for adult women.


There was a substantial interaction effect between year of birth and wealth quintile (p = 0.0015), with the annual increase in height being largely restricted to the wealthiest quintile with stagnation in height for the poorest two wealth quintiles (Figure 2).

Figure 2. Predicted association between height and year of birth across wealth quintiles among adult women.


Of the total variation in height, 14.07% was attributable to environments (10.82% for countries, and 3.25% to primary sampling units) (Figure 3). The adjustment for year of birth, years of schooling, household wealth and urban/rural residence did not alter the apportioning of the total variation in height to different levels. Countries varied between a lower and upper bound of 149.3 cm and 162.2 cm respectively, while individuals varied between 144.2 cm and 167.3 cm around the mean of 155.75 cm (Table 4).

Figure 3. Percent of variation in height attributable to the individual and context (primary sampling units (PSU) and country) before and after accounting for the distribution of individual characteristics among adult women.


Table 4. Mean height and 95% Coverage Bounds in heights (in cm) between individuals, between-PSUs, and between-countries after accounting for within-country covariates.


Country-Specific Findings

Country-specific variability around the average was considerable; of the 54 countries, 26 countries were significantly taller than the global (pooled sample) average, and 14 were significantly (α = 0.05) shorter (Table 5). Senegal, Chad, Burkina Faso, Mali, and Niger were the five tallest countries while Guatemala, Bangladesh, Peru, Nepal, and Bolivia were the five countries where women exhibited the most substantially shorter height, compared to the global mean height (Table 5).

Table 5. Country specific differentials in height around the global mean for adult women.


There was considerable variation in the association between year of birth and height across countries (Table 6). Of the 54 countries, 14 experienced a decrease in height in recent birth cohorts, with 7 countries experiencing a decline of over 0.05 cm decrease per year. Further, 39% (21/54) of the countries experienced no significant change, and 33% (19/54) experienced an increase in height ranging between 0.0203 (95% CI 0.0015, 0.0391) in Bangladesh and 0.0926 (95% CI 0.0446, 0.1410) in Kazakhstan. All 14 countries experiencing a decrease in height were in Africa (Table 7). Meanwhile, increases in height were observed in the Europe, Eastern Mediterranean, South-East Asia and Western Pacific regions, with the exception of Cambodia which experienced no significant change. About half (4/9) of the countries in South America had no significant change while the others experienced significant increases in height since 1945.

Table 6. Change in height for a 1-year increase in birth year and a 1 quintile increase in wealth from separate models for adult women.


Table 7. Summary table showing frequency and percentage of countries across different WHO regions that experienced a statistically significant decline, or no change or increase in height among adult women.


In 51 of 54 countries, there was a statistically significant (p<0.05) difference between the poorest two and richest two wealth quintiles in the association between year of birth and height (Table 8). In general, countries that experienced average decline in height across birth cohorts, the decline was substantially greater among the poorest two wealth quintiles. Conversely, countries that experienced a positive average increase in height, such increases were largely concentrated among the wealthier quintiles. For instance, in Brazil the heights of the wealthier two quintiles increased by 0.16 cm while those for the poorest two quintiles increased by 0.01 cm. In Mozambique, which experienced an overall decline in height, the annual decrease was −0.08 cm for the poorest two quintiles, and −0.02 cm for the richest two quintiles.

Table 8. Country-specific predicted effects of change in height for a 1-year increase in birth year in the two poorest and two richest wealth quintiles among adult women.


The strength of the positive association between height and wealth also varied across countries; in 41 out of 54 countries there was a positive and statistically significant (á = 0.05) association for the effect of a change in wealth quartile on height, varying between 0.121 cm in Malawi to 1.132 cm in Honduras. The association between wealth and adult height, while positive, was not statistically significant in 9 countries. In Ethiopia and Uganda there was a statistically significant negative association between height and wealth such that women in wealthier households were shorter (Table 6).


The study has three salient findings. First, the birth cohort differences in attained heights among women living in low- to middle-income countries suggest a decline or stagnation in height in a majority of the countries. While decline in height was particularly concentrated in Africa, about half of the countries surveyed in South America showed stagnation. Second, cohort differences in height varied substantially by individuals' socioeconomic status both in pooled analysis as well as country-specific analysis, such that decline and stagnation are largely observed for socioeconomically disadvantaged groups, while the increases are concentrated among the socioeconomically advantaged groups. Finally, the positive association between socioeconomic status and attained height appears to be a consistent and universal pattern in low- to middle-income countries, with some heterogeneity in the strength of such an association.

Before we interpret these findings, we outline the limitations of our data. First, our assessment of increases or decreases in attained height in birth cohort differences is based upon self-reports of current age. Age reporting in low- to middle-income countries has been raised as an important concern because reporting of ages in multiples of five or ten is relatively common and leads to age clustering. However, an analysis of the quality of age reporting in the DHS for all surveys between 1985–2003 found that the overall quality of age-reporting was found to be high with most surveys exhibiting few problems [32]. The DHS also employs extensive imputation procedures and consistency checks to ensure that birth years and ages are as accurate as possible [33]. Our modeling of year of birth as a continuous variable, we believe, attenuates some of these concerns since we are not making inferences to birth cohorts born during specific periods, which will be more sensitive to misclassification of reported age. Second, the typical method of assessing changes in height using birth cohorts relies on the assumption that heights are not changing by age. We ensured this by considering only women between 25 and 49 years of age where we do not expect heights to change. Even though prior research suggests that girls attain height by age 20, we chose 25 as the lower conservative cut-point for age since the empirical data suggested that heights among 15–24 year olds was shorter than the other age groups suggesting that potentially height was yet to be attained (see Figure S1, and Table S1). At the other extreme it is known that humans also shrink with age [34], even though it is unlikely to significantly influence the age range considered in this study. Third, our assessment of women's socioeconomic status, through household wealth and education was measured at the same time when height was measured. We therefore make the assumption that the level of socioeconomic status (as captured through contemporary household wealth or education) is reflective of the socioeconomic status during the women's childhood and growing years. In addition, wealth can be a consequence of height through a variety of mechanisms [35]. Therefore, no causal interpretation of the effects associated with socioeconomic markers should be made based on this study. These important limitations are however offset by having measured height on representative samples of women from a large cross-section of low- to middle-income countries allowing a rich description of the global and country-specific changes and patterning of adult height, thus allowing insights into the level of inequalities in population health across and within countries.

Our findings related specifically to estimating overall birth cohort differences in height for countries are in agreement with an ecological study that examined changes in heights over time in low- to middle-income countries using the same data [18], and reported that while heights in Africa have been declining, they have been increasing in the rest of the developing world. Our study differed on one important methodological aspect. While we use the disaggregated individual data to estimate changes in height using year of birth as an explanatory variable in a regression model, the comparison study was ecological with average heights being computed for each birth year-country combination, and using this as the outcome and countries as the explanatory variable, weighted by the size of each group. Our study with the use of individual data overcomes the cross-level bias that characterizes aggregate analysis [36], [37]. Importantly, as our findings reveal, average descriptions of changes in height seem to also mask the important social inequalities in how heights have changed over the last forty years.

Twenty six percent (14/53) of the low- and middle-income countries included in this study experienced a significant (p = 0. 05) decline in attained height since 1945 while another 43% experienced no change in height over birth cohorts. This is driven entirely by declining heights in Africa; with half of countries in South America showing stagnation in height since 1945 and heights modestly increasing in the rest of the world. Importantly, in most cases, the increases within countries were restricted to the socioeconomically advantaged groups. This differs from evidence on changes in height in developed countries. For instance, the evidence from European countries showed a mostly positive increase in height over the last century, including during the same time period as this study [14], [15], [17], [38], [39], [40]. Likewise, even though there was stagnation in height growth between the years 1965–1974 for white adults in the United States, in subsequent years a positive trend in height has been reported [41]. At the same time, however, black women in the US experienced a decline in height [41]. Since black women in the US, on average, are of lower socioeconomic status within the US, it suggests a pattern similar to what we observe for lower socioeconomic groups within and among low- to middle-income countries. Other country-specific studies on height trends from developing countries from Turkey [42], Iran [43], India [44], and the United Arab Emirates [45], have reported a positive trend or increase in stature over time. Our results are consistent with the study from Turkey, and India, which are the two countries that overlap with our cross-national sample.

The association between socioeconomic status and height has consistently been shown across developed countries [14], [38], [39], [46]. A review on 10 European countries showed significant persistent education-related differences in height for both men and women: the range of differences for higher educated men was 1.6–3.0 cm, and women was 1.2–2.2 cm [14]. The results from our study indicated that a one quintile increase in wealth for 41 out of 54 countries was associated with an increase in height ranging from 0.3–1.5cm depending on the country. Focusing on the importance of the childhood environment to attained height, it seems that childhood conditions have not improved concurrent with improvements in infant mortality for those countries experiencing relatively few changes in height and may have actually worsened (or inequalities increased) for those exhibiting negative cohort differences given the results of year of birth estimates. Similarly, childhood conditions may be more unequal for countries that exhibited a steeper gradient in the association between socioeconomic status and height. At the same time, in 2 countries (Uganda and Ethiopia) the correlation between height and wealth was negative, which is contrary to the near-universal pattern of a positive association. Whether this finding represents a genuine exception or if it is artifact of a systematically biased measurement of height or wealth index remains a subject for further investigation.

In summary, a salient finding of our study is the decline or stagnation in attained heights over the last 40 years, particularly in Africa and for the poorer populations across the world. Increases in height are largely restricted to non-African better off populations. Thus, even though infant mortality and other acute childhood morbidities have decreased substantially over time in these countries, the stagnation and decline across cohorts in attained height suggest little improvements, and perhaps deterioration, in early childhood living conditions including nutritional and environmental circumstances. While our study focuses on attained height for women from birth years that preceded the concerted global efforts on improved childhood health and nutrition spurred by the Millennium Development Goals, it will be important to monitor the height patterns for subsequent birth cohorts in these populations. The persistent country differences in adult height even among recent birth cohorts indicate the intergenerational continuity in differences in childhood living conditions. The marked socioeconomic differentials observed consistently within every country suggest that health inequalities in these countries will be persistent in future. Further research is needed to understand the environmental determinants that enable realization of the potential height of a child, and especially a girl child, which in turn have substantial consequences for their own health and social well being as well as the health of their offspring.

Supporting Information

Figure S1.

Mean Height (cm) By Age Pooled Across Countries.



Table S1.

Mean Height (cm) By Age Groups, By Country.




We acknowledge the support of Macro International Inc. (Washington, DC) for providing access to the various Demographic and Health Surveys.

Author Contributions

Conceived and designed the experiments: SVS. Performed the experiments: JEF SVS EÖ. Analyzed the data: JEF SVS EÖ. Contributed reagents/materials/analysis tools: SVS JEF EÖ. Wrote the paper: SVS EÖ JEF.


  1. 1. Silventoinen K (2003) Determinants of variation in adult body height. J Biosoc Sci 35: 263–285.
  2. 2. Steckel RH (2009) Heights and human welfare: Recent developments and new directions. Explorations in Economic History 46: 1–23.
  3. 3. Crimmins EM, Finch CE (2006) Infection, inflammation, height, and longevity. Proc Natl Acad Sci U S A 103: 498–503.
  4. 4. Cole TJ (2003) The secular trend in human physical growth: a biological view. Econ Hum Biol 1: 161–168.
  5. 5. Tucker-Seeley RD, Subramanian SV (2011) Childhood circumstances and height among older adults in the United States. Econ Hum Biol 9: 194–202.
  6. 6. Deaton A, Arora R (2009) Life at the top: the benefits of height. Econ Hum Biol 7: 133–136.
  7. 7. Case A, Paxson C (2008) Stature and status: height, ability, and labor market outcomes. Journal of Political Economy 116: 499–532.
  8. 8. Gunnell D, Okasha M, Smith GD, Oliver SE, Sandhu J, et al. (2001) Height, leg length, and cancer risk: a systematic review. Epidemiol Rev 23: 313–342.
  9. 9. Davey Smith G (2002) The conundrum of height and mortality. West J Med 176: 209.
  10. 10. Davey Smith G, Hart C, Upton M, Hole D, Gillis C, et al. (2000) Height and risk of death among men and women: aetiological implications of associations with cardiorespiratory disease and cancer mortality. J Epidemiol Community Health 54: 97–103.
  11. 11. Subramanian SV, Ackerson LK, Davey Smith G, John NA (2009) Association of maternal height with child mortality, anthropometric failure, and anemia in India. Jama 301: 1691–1701.
  12. 12. Ozaltin E, Hill K, Subramanian SV (2010) Association of maternal stature with offspring mortality, underweight, and stunting in low- to middle-income countries. Jama 303: 1507–1516.
  13. 13. Komlos J, Baten J (2004) Looking backward and looking forward: Anthropometric research and the development of Social Science history. Social Science History 28: 191–210.
  14. 14. Cavelaars AE, Kunst AE, Geurts JJ, Crialesi R, Grotvedt L, et al. (2000) Persistent variations in average height between countries and between socio-economic groups: an overview of 10 European countries. Ann Hum Biol 27: 407–421.
  15. 15. Garcia J, Quintana-Domeque C (2007) The evolution of adult height in Europe: a brief note. Econ Hum Biol 5: 340–349.
  16. 16. Bozzoli C, Deaton A, Quintana-Domeque C (2009) Adult height and childhood disease. Demography 46: 647–669.
  17. 17. Hatton TJ, Bray BE (2010) Long run trends in the heights of European men, 19th-20th centuries. Econ Hum Biol 8: 405–413.
  18. 18. Akachi Y, Canning D (2010) Health trends in Sub-Saharan Africa: conflicting evidence from infant mortality rates and adult heights. Econ Hum Biol 8: 273–288.
  19. 19. DHS (2009) Demographic and Health Surveys. Calverton, MD: MEASURE DHS.
  20. 20. Rutstein SO, Rojas G (2003) Guide to DHS Statistics. Calverton, Maryland: ORC Macro, MEASURE DHS+.
  21. 21. Wirth ME, Wirth E, Delamonica E, Sacks D, Balk A, et al. (2006) Monitoring health equity in the MDGs: A practical guide. New York: CIESIN/UNICEF.
  22. 22. Vaessen M (1996) The potential of the demographic and health surveys (DHS) for the evaluation and monitoring of maternal and child health indicators. In: Khlat M, editor. Demographic evaluation of health programmes (Proceedings). Paris: CICRED/UNFPA.
  23. 23. Pullum TW (2008) An Assessment of the Quality of Data on Health and Nutrition in the DHS Surveys, 1993-2003. Calverton, Maryland, USA: Macro International Inc.
  24. 24. Macro O (2006) Demographic and Health Survey Interviewer's Manual. In: Calverton , Maryland , editors. U.S.A.: ORC Macro.
  25. 25. Survey DaH (1996) Sampling Manual. DHS-III. Calverton, MD: Macro International.
  26. 26. Filmer D, Pritchett LH (2001) Estimating wealth effects without expenditure data—or tears: an application to educational enrollments in states of India. Demography 38: 115–132.
  27. 27. Gwatkin DR, Rustein S, Johnson K, Pande RP, Wagstaff A (2000) Socioeconomic differences in health, nutrition, and population in India. Washington DC: World Bank.
  28. 28. Rutstein SO, Johnson , K (2004) The DHS wealth asset index. Calverton, Maryland: ORC Macro. no. 6 no. 6 p.
  29. 29. Goldstein H (2003) Multilevel Statistical Models. London: Arnold.
  30. 30. StataCorp (2008) Statistical Software: Release 10SE. College Station, TX: Stata Corporation.
  31. 31. Rasbash J, Steele F, Browne WJ, Goldstein H (2009) A User's Guide to MLwiN, v2.10. Bristol, UK: Centre for Multilevel Modelling, University of Bristol.
  32. 32. Pullum TW (2006) An Assessment of Age and Date Reporting in the DHS Surveys, 1985-2003. Calverton, Maryland: Macro International Inc.
  33. 33. Croft T (1991) DHS Data Editing and Imputation. Columbia, Maryland: IRD/ORC Macro. pp. 1337–1356.
  34. 34. Sorkin JD, Muller DC, Andres R (1999) Longitudinal change in the heights of men and women: consequential effects on body mass index. Epidemiol Rev 21: 247–260.
  35. 35. Strauss J, Thomas D (2008) Health over the life course. In: Schultz TP, Strauss J, editors. Handbook of Development Economics vol 4, Amsterdam: Elsevier/North-Holland. pp. 3375–3474.
  36. 36. Susser M (1994) The logic in the ecological. American Journal of Public Health 84: 825–835.
  37. 37. Subramanian SV, Jones K, Kaddour A, Krieger N (2009) Revisiting Robinson: the perils of individualistic and ecologic fallacy. Int J Epidemiol 38: 342–360; author reply 370-343.
  38. 38. Kues AB (2010) Taller - Healthier - more equal? The biological standard of living in Switzerland in the second half of the 20th century. Econ Hum Biol 8: 67–79.
  39. 39. Webb EA, Kuh D, Pajak A, Kubinova R, Malyutina S, et al. (2008) Estimation of secular trends in adult height, and childhood socioeconomic circumstances in three Eastern European populations. Econ Hum Biol 6: 228–236.
  40. 40. Singh-Manoux A, Gourmelen J, Ferrie J, Silventoinen K, Gueguen A, et al. (2010) Trends in the association between height and socioeconomic indicators in France, 1970-2003. Econ Hum Biol 8: 396–404.
  41. 41. Komlos J (2010) The recent decline in the height of African-American women. Econ Hum Biol 8: 58–66.
  42. 42. Ozer BK (2008) Secular trend in body height and weight of Turkish adults. Anthropological Science 116: 191–199.
  43. 43. Haghdoost AA, Mirzazadeh A, Alikhani S (2008) Secular trend of height variations in Iranian population born between 1940 and 1984. Iranian J Publ Health 36: 1–7.
  44. 44. Perkins JM, Khan KT, Smith GD, Subramanian SV (2011) Patterns and trends of adult height in India in 2005-2006. Econ Hum Biol 9: 184–193.
  45. 45. Baynouna LM, Revel AD, Nagelkerke NJD, Jaber TM, Omar AO, et al. (2009) Secular trend in height in Al Ain-United Arab Emirates. Economics & Human Biology 7: 405–406.
  46. 46. Komlos J (2009) Recent Trends in Height by Gender and Ethnicity in the US in Relation to Levels of Income. NBER Working Paper.