International Journal of Epidemiology, 2014, 1328–1335 doi: 10.1093/ije/dyu075 Advance Access Publication Date: 14 March 2014 Original article
Original article
Persistent inequalities in child undernutrition: evidence from 80 countries, from 1990 to today Caryn Bredenkamp,1* Leander R Buisman2 and Ellen Van de Poel2 1
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World Bank, Washington, DC, USA, 2Institute of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands *Corresponding author. World Bank, 1818 H St NW, Washington, DC, USA. E-mail: [email protected] Accepted 6 March 2014
Abstract Background: Global progress in reducing the burden of undernutrition tends to be measured at the population level. It has been hypothesized that population-level improvements may mask widening socioeconomic inequalities, but little attempt has been made to assess whether this is true. Methods: Original data from 131 demographic health surveys and 48 multiple indicator cluster surveys from 1990 to 2011 were used to examine trends in socioeconomic inequalities in stunting and underweight, as well as the relationship between changes in prevalence and changes in inequality, in 80 countries. Socioeconomic inequality is measured using the corrected concentration index. Results: Countries with a higher prevalence of stunting tend to have larger socioeconomic inequalities in stunting (Spearman rank correlation ¼ 0.27 P ¼ 0.014). In most countries, there has been no change in inequality in stunting: in 31 out of 53, the 90% confidence intervals around the changes overlap the zero value. In the remaining 22, there was a reduction in inequality in 11 and an increase in 11. The distributional patterns underlying the summary inequality statistics vary considerably across countries, but in most there have been considerable gains to the poorest quintile. Conclusions: Reductions in the prevalence of undernutrition have generally not been accompanied by widening inequalities. However, inequalities have also not been narrowing. Rather, the picture is one of a strong persistence of existing inequalities. In addition, there are different distributional patterns underlying changes in the summary indices of inequality which will need to be taken into consideration in designing programmes to reach the poor. Key words: Malnutrition, stunting, underweight, anthropometry, inequality, equity, developing countries, trends, MDGs
C The Author 2014; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association V
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Key Messages • Countries with a higher prevalence of undernutrition, whether measured as stunting or as underweight, tend to have
larger socioeconomic inequalities. • There has been a strong persistence of existing inequalities in stunting in 56% of countries, with relatively few coun-
tries exhibiting narrowing or widening inequalities. • Falling prevalence tends not to have been associated with widening inequalities, but only 19% of countries succeeded
in reducing both prevalence and inequalities in stunting. • There are different distributional patterns underlying changes in the summary indices of inequality which will need to
be taken into consideration in designing programmes to reach the poor. • The use of underweight rather than stunting as an indicator of malnutrition leads to more optimistic conclusions
about the changes in inequality in undernutrition over time.
Undernutrition has dire consequences for children’s development. It retards their physical growth, precipitates disease and speeds its progression, and is an important contributor to infant and child mortality. Undernutrition in infancy and childhood is also correlated with poor health outcomes in adulthood, affects cognitive and motor development and limits educational attainment, with adverse consequences for adult work productivity and lifetime earnings potential, ultimately perpetuating poverty.1–3 It is not surprising, then, that the United Nations selected child undernutrition as one of the health-related Millennium Development Goals (MDGs) whose progress would be monitored on a global basis, aiming to halve the prevalence of underweight between 1990 and 2015. Recent global estimates show that the proportion of underweight children under 5 years of age declined from 25% in 1990 to 16% in 2011, and the proportion of stunted children under five declined from 40% to 26% over the same period.4 As Stevens et al. point out, less than half of developing countries are likely to meet the MDG1 target by 2015, and progress is especially slow in Sub-Saharan Africa.5 So far, little attempt has been made at the global level to assess whether aggregate improvements in nutritional status may mask widening socioeconomic inequalities or relatively slower progress among the poor. Most of the literature documenting trends in socioeconomic inequalities in undernutrition in developing countries has focused on individual countries—and even these are few and far between. Recent examples include South Africa,6 Vietnam,7,8 four Andean countries,9 Ghana10,11 and India.12 In terms of multi-country analysis, Wagstaff and Watanabe13 and Van de Poel et al.14 explored socioeconomic inequalities in undernutrition across 20 and 47 developing countries,
respectively, but did not measure trends. Black et al.’s recent study contains estimates of undernutrition for the top and bottom wealth quintiles in a large set of developing countries, but only describes trends in inequalities for three selected countries.15 Trends in inequalities in undernutrition have been explored previously by Paciorek et al., but only stratified by urban-rural location rather than socioeconomic status.16 This paper contributes to the literature in the following ways. First, it provides country-level estimates of current levels and inequalities in undernutrition by including data from the latest available household surveys, both demographic and health surveys (DHS) and multiple indicator cluster surveys (MICS), for a total of 80 countries. Second, it assesses whether socioeconomic inequalities in undernutrition have been widening or narrowing over time, making it the first study to examine trends in socioeconomic inequalities on a global scale; it also looks at how this trend correlates with changes in the prevalence of undernutrition. Third, it attempts to better understand the distributional patterns accompanying the country-specific trends, particularly how the position of the poorest quintile has changed vis-a`-vis wealthier groups.
Methods Data We analysed 179 household survey datasets (131 DHS and 48 MICS) for which data on child growth (either height or weight) were available between 1990 and 2011; 80 (80) countries have information on height (weight) for at least one period, and 53 (52) countries have information on height (weight) for at least two periods. For all estimations, analysis of the original datasets was undertaken. Although there is more than sufficient similarity across the MICS and DHS surveys to generate comparable results,17 there
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Introduction
International Journal of Epidemiology, 2014, Vol. 43, No. 4
Statistical analysis Child undernutrition is measured using stunting and underweight among children up to 5 years of age. These anthropometric indicators were selected because they are the most common measures used in international monitoring and reporting of child undernutrition (such as for the purposes for the MDGs); we recognize, though, that they do not capture all dimensions of the undernutrition problem, including acute undernutrition (i.e. wasting, or low weight-for-height) and micronutrient deficiencies. Children are considered stunted (underweight) if their height-for-age (weight-for-age) is more than two standard deviations below the median of the 2006 World Health Organization Child Growth Standards.19,20 Stunting reflects the slowing of skeletal growth. Compared with the underweight indicator, it also more accurately reflects the nutritional deficiencies and illnesses that occur during the early-life period, that will hamper growth and development and, therefore, is preferred as a reliable indicator of long-term undernutrition.15 The analyses in this paper focus primarily on stunting, but we also report findings for underweight since, although not able to discriminate between long-term and temporary undernutrition, it is the main indicator for MDG 1c. Measuring socioeconomic inequalities requires a variable with which to rank households from poorest to richest. As the DHS and MICS lack consumption measures—the preferred measure of household economic status in developing countries21—we use a wealth index constructed by conducting principal components analysis on a set of variables related to asset ownership and dwelling characteristics.22 All DHS and most MICS datasets are released with a wealth index, but for three MICS we had
to construct a similar wealth index from the assets and housing characteristics included in the dataset. Socioeconomic inequality is measured using a concentration index, which allows inequalities across the entire socioeconomic distribution to be summarized in one index, and therefore facilitates comparison across countries and over time. Applied to binary variables, such as stunting and underweight, the bounds of the standard concentration index are heavily dependent on the mean, which is especially problematic in cross-country comparisons.23–25 We therefore use the corrected version of the concentration index23 which deals with these shortcomings but shares the characteristics and interpretation of the standard concentration index namely: (i) negative values imply that undernutrition is more concentrated among poorer children; (ii) if all children, irrespective of their socioeconomic status, suffer equally from undernutrition, the index would equal zero; and (iii) transferring undernutrition from a richer to a poorer individual reduces the value of the index. For binary outcomes, the index (Cy) is calculated as follows: Cy ¼8covðyi ,Ri Þ
ð1Þ
where yi is a measure of child i’s nutritional status and Ri is the child’s fractional rank in the distribution of the variable used to proxy socioeconomic status. The interpretation of the corrected concentration index is one of absolute, rather than relative, inequality.26 The bounds of the index tend to zero if the average malnutrition prevalence tends to zero, increase if the mean rises and reach a maximum when exactly half of the sample is malnourished (at which point the bounds are equal to 1 and þ1). Standard errors of the indices are obtained using the convenient regression approach.27 Statistical inference of changes in prevalence and inequality is calculated using a t-test. Since all surveys use multistage sampling procedures, sample weights are used.
Results Current socioeconomic inequalities in undernutrition Unsurprisingly, in all countries, undernutrition is concentrated among the poor. Peru shows the highest socioeconomic inequality in stunting (with a concentration index of 0.4), followed by Guatemala, Honduras, Bolivia and Nicaragua (all with concentration indices between 0.3 and 0.4). Indeed, most Latin American countries have very high inequality. Inequality is smallest in Egypt, followed by Madagascar, Comoros, Vanuatu and Jordan, where concentration indices range between 0 and 0.05. Detailed estimates for each country are displayed in Table S1 (available as Supplementary data at IJE online).
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are some measurement differences in particular countries. For example: the assets included in the DHS wealth index sometimes differ slightly across countries to take into account cultural differences, over time to allow for changes in technology and, more recently, include additional assets that better distinguish between urban-rural areas;18 whereas most surveys contain measures of both height and weight, some collect only one or the other; and, finally, whereas in almost all surveys the reference age for anthropometrics is up to 5 years, in 15 early DHS the reference age is only up to the age of 3 or 4 years. If growth retardation typically worsens beyond age 3 or 4 years, then this may bias the extent of undernutrition downwards in these few surveys. Consequently, we also check the robustness of our main results by restricting the sample to children aged less than 3 or 4 years, as the case may be, for all surveys from these countries when calculating trends.
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Socioeconomic inequalities in undernutrition exhibit a high degree of persistence. Figure 2 shows the change in socioeconomic inequality in stunting (using 90% confidence intervals) between each country’s oldest survey and its most recent. In most countries, 31 to be precise, the confidence intervals around the changes do not overlap the zero value and in 22 they do. Of the latter, 11 exhibit narrowing inequalities and 11 exhibit widening inequalities. The results do not change when robustness checks are conducted for possible bias due to differences in the age groups of children in earlier surveys. Figure 3 shows that there is no obvious relationship between trends in stunting prevalence and trends in socioeconomic inequality. Although the majority of countries have experienced some reduction in the prevalence of stunting, this decline is as likely to be accompanied by an increase in inequalities as a decrease in inequalities. There are some interesting regional patterns. Declining prevalence has been associated with narrowing inequalities in the Europe and Central Asia region as well as in the Middle East and North Africa region, whereas in South Asia it has been associated with widening inequalities. Generally, though, and including in the Sub-Saharan Africa region which makes up most of the sample, the data do not support a generalization that in those countries where undernutrition
Albania Armenia Azerbaijan Bangladesh Benin Bolivia Burkina Faso Cambodia Cameroon Central African Republic Chad Colombia Comoros Cote d'Ivoire DRC Congo Dominican Republic Egypt Ethiopia Ghana Guinea Guinea-Bissau Guyana Haiti India Jordan Kazakhstan Kenya Kyrgyz Republic Lao PDR Lesotho Madagascar Malawi Mongolia Morocco Mozambique Namibia Nepal Nicaragua Niger Nigeria Peru Rwanda Senegal Sierra Leone Suriname Tanzania The Gambia Togo Turkey Uganda Uzbekistan Zambia Zimbabwe
−.2
Trend in inequality in stunting −.1 0 .1
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Figure 1. Relationship between the prevalence of stunting and socioeconomic inequality. Source: DHS and MICS surveys, most recent data available. Notes: Socioeconomic inequality is measured by the corrected concentration index; a negative value indicates that stunting is more concentrated among the poor, with larger absolute values indicating a larger degree of concentration.
Trends in socioeconomic inequalities in undernutrition
Figure 2. Changes in socioeconomic inequality in stunting. Source: DHS and MICS surveys, 1990-2011. Notes: Absolute difference between the corrected concentration indices for each country’s earliest and most recent survey is shown, with 90% confidence intervals. Positive values indicate increasing inequality.
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Prevalence of stunting (%) 20 30 40 50
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There is also a clear correlation between socioeconomic inequalities and prevalence, with countries with a higher prevalence of stunting exhibiting larger socioeconomic inequalities in stunting, i.e. a more negative concentration index (Spearman rank correlation ¼ 0.27, P ¼ 0.014) (see Figure 1).
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has been declining, there has been a widening of inequalities. It also seems that achieving both a reduction in the prevalence of stunting as well as a reduction in the inequality of stunting is a tough ambition. In only 19 countries in our sample do we find this combination, and in only 9 of these (namely Colombia, Dominican Republic, Egypt, Kyrgyz Republic, Mongolia, Morocco, Nigeria, Turkey and Zambia) did the confidence intervals on both the change in prevalence and the change in inequalities not overlap the zero value.
Beyond summary indicators: shifts in distributional patterns by quintile Changes in inequality can be due to gains or losses among different socioeconomic groups. Narrowing inequalities may reflect a large improvement in the health status of the very poor or it may reflect that the middle classes are catching up to the rich with the ultra-poor still being left behind. Similarly, widening inequalities in undernutrition may reflect reductions in undernutrition among the richest quintile or among all except the poor. Since different distributional patterns may imply different policies, or different implementation modalities to reach those in need, policy makers and programme staff may be interested to explore on a country-by-country basis what distributional patterns underlie the aggregate change. In particular, they should pay attention to what is happening to undernutrition among the poorest of the poor.
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Figure 3. Relationship between the trend in stunting and trend in inequalities. Source: DHS and MICS surveys, 1990-2011. Note: X-axis shows the absolute difference between the stunting prevalence for each country’s earliest and most recent survey; y-axis shows the absolute difference between the corrected concentration indices of the surveys. Positive values indicate increasing prevalence or inequality.
To investigate these patterns, we plot for selected countries the distribution of stunting across five wealth quintiles by survey year, for countries with narrowing (see Figure 4) and widening (see Figure 5) inequalities. Each horizontal line represents a different survey year, with the more recent surveys at the top of the graphs. Black dots designate the wealthiest quintile and red dots the poorest quintile, with others dots (yellow, green and blue) showing quintiles four through two. The prevalence of undernutrition increases from left to right such that a migration of the lines from right to left over time indicates falling undernutrition. Longer lines indicate greater dispersion in the prevalence of undernutrition across quintiles and, therefore, a higher degree of inequality. For brevity’s sake, we present the results only for those countries where the estimates are among the most precise in the sample, specifically where the P-values of the reduction in stunting as well as the P-values of the change inequality are less than 0.10. Plots for the remaining countries can be found in Figure S1 (available as Supplementary data at IJE online). Our analysis shows that narrowing inequalities over time can be the result of many different patterns of change (Figure 4). In the Dominican Republic, where mean prevalence fell over time, inequalities narrowed because the poorer quintiles exhibited rapid catch-up (with the prevalence of stunting falling from 27% to 17% between 1996 and 2007). Colombia and Zambia display a similar pattern, although there is also some catch-up among the second and third quintiles. In Mongolia, where the mean prevalence of undernutrition also fell over time, narrowing inequalities exhibited a different pattern: they were the result of a combination of large improvements in the poorest quintile (with stunting falling from 40% to 32% between 2000 and 2005) and deteriorating nutritional status among the wealthiest quintile (with the prevalence of stunting increasing from 14% to 19%). A similar trend is observed in Morocco and Egypt. There are also other patterns: in Turkey, between the two most recent surveys, there was not much change in the nutritional status of the wealthiest two quintiles, sharp gains for the next two quintiles and almost no change among the poorest; in the Kyrgyz Republic, stunting fell drastically in all quintiles, disproportionally more so among the poorest. The most common pattern underlying widening inequalities is a pattern where nutritional status improves for all quintiles, but the gains are considerably larger for the wealthier (fourth and fifth) quintiles (Figure 5). This is the case in Nepal, Nicaragua and India, for example. An extreme example is that of Ethiopia, where widening inequalities are driven almost entirely by the gains to the wealthiest quintile, with much smaller improvements in
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Figure 5. Changes in the distribution of the prevalence of stunting, by quintile, for selected countries with reductions in stunting prevalence and widening socioeconomic inequality. Source: DHS and MICS surveys, 1990-2011. Notes: Mean prevalence of stunting (in %) on the x-axis, survey year on the y-axis.
the poorer quintiles. The prevalence of stunting in the top quintile fell by almost 20 percentage points (from 48% in 2000 to 40% in 2005 to 29% in 2011) compared with 10 percentage points in the poorest quintile and second poorest quintiles (from 59% to 49%, and from 58% to 48%, respectively). It is also possible, although less common,
that widening inequalities could be indicative of a deterioration of nutritional status in the poorest quintiles. The very different distributional patterns that underlie a summary conclusion of ‘widening’ or ‘narrowing’ inequality point to the importance of examining not only aggregate changes in inequality, but also which particular
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Figure 4. Changes in the distribution of the prevalence of stunting, by quintile, for selected countries with reductions in stunting prevalence and narrowing socioeconomic inequality. Source: DHS and MICS surveys, 1990-2011. Notes: Mean prevalence of stunting (in %) on the x-axis, survey year on the y-axis.
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socioeconomic groups are driving those changes, especially if one is interested in implementing policies that effectively address widening inequalities in undernutrition and target the worst-off groups. Nevertheless, it is encouraging that in almost all countries where undernutrition has fallen, there have also been improvements for the poorest quintile.
Sensitivity analysis: using underweight rather than stunting Figure 6 illustrates that, as in the case of stunting, countries with a higher prevalence of underweight also tend to have larger socioeconomic inequalities (i.e. a more negative concentration index), and the association is even stronger (Spearman rank correlation ¼ 0.78, P < 0.0001). Countryspecific estimates are shown in Table S1 (available as Supplementary data at IJE online). Results on changes in inequalities are similarly sensitive to the use of underweight rather than stunting. Among those countries where the confidence intervals around the changes do not overlap the zero value, we find 20 countries with narrowing socioeconomic inequalities and 14 countries with widening socioeconomic inequalities, compared with 11 and 11, respectively, in the case of stunting. Persistent inequalities are thus observed in only 18 countries. In 15 countries both the prevalence of underweight and socioeconomic inequality in underweight fell, whereas this happened for stunting in only 9 countries.
inequalities. At the same time, it cannot be concluded that the general trend is one of narrowing inequalities. Rather, the overall pattern is one of persistence of existing inequalities. There is a lot of variation across countries and regions, though. Indeed, inequalities have risen in a number of countries, both those with a declining and those with a rising prevalence of undernutrition, making it important to monitor not only progress in the mean prevalence of undernutrition but also changes in inequalities over time. The measure of nutritional status matters. The use of underweight rather than stunting leads to more optimistic conclusions about the changes in inequality in undernutrition over time. This may be because weight deficits are more responsive to changes in living standards than deficits in height,1 but further speculation as to the reasons is beyond the scope of this paper. What is important is that, notwithstanding the fact that stunting and underweight capture different dimensions of nutritional status, the selection of one over the other has implications for the conclusions that national policy makers and the global community reach about progress in inequalities in nutritional status, especially since underweight among children under 5 years of age is (not uncontroversially4) the official indicator of MDG target 1c. Finally, especially for the purposes of programme development, it is useful to go beyond summary statistics of inequality and also look at the different patterns underlying the widening/narrowing of inequalities, examining especially closely what is happening to the poorest. The most appropriate policy interventions are likely to be different if the widening inequalities arise from the poor being left behind (which should imply highly targeted interventions), or from the rich galloping ahead (leaving the vast mass of the population still vulnerable) or the second quintile catching up to the rich and leaving the rest behind. This paper is not without its limitations. The geographical distribution of DHS and MICS makes it difficult to draw firm conclusions about regional trends beyond the Africa region where most of the surveys are carried out. Future research could include combining these data with other national surveys, especially in middle-income countries, to obtain a more global picture. In addition, it would be important to start to explore why inequalities in undernutrition appear to be so persistent, as well as to attempt to understand the factors that contribute to the success of the few positive deviants who succeed in reducing both prevalence and inequalities in undernutrition.
Discussion In countries where undernutrition has fallen significantly, it has not necessarily been accompanied by widening
Supplementary Data Supplementary data are available at IJE online.
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Figure 6. Relationship between the prevalence of underweight and socioeconomic inequality. Source: DHS and MICS surveys, most recent data available. Notes: Socioeconomic inequality is measured by the corrected concentration index; a negative value indicates that underweight is more concentrated among the poor, with larger absolute values indicating a larger degree of concentration.
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Funding This work was supported by funding from Netherlands Organization for Scientific Research (Veni project 451-11-031) to E.v.d.P. and from the World Bank, including from the Rapid Social Response Multi-Donor Trust Fund, to C.B. and L.B. The findings, interpretations and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors or the governments of the countries they represent. Conflict of interest: None declared.
References
13. Wagstaff A, Watanabe N. Socioeconomic Inequalities in Child Malnutrition in the Developing World. Policy Research Working Paper 2434. Washington, DC: World Bank, 2000. 14. Van de Poel E, Hosseinpoor A, Speybroeck N et al. Socioeconomic inequality in malnutrition in developing countries. Bull World Health Organ 2008;84:282–91. 15. Black R, Victora CG, Walker SP et al. Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet 2013;382:427–51. 16. Paciorek CJ, Stevens GA, Finucane MM et al. Children’s height and weight in rural and urban populations in low-income and middle-income countries: a systematic analysis of population-representative data. Lancet Global Health 2013; 5:e300–09. 17. Hancioglu A, Arnold F. Measuring coverage in MNCH: Tracking progress in health for women and children using DHS and MICS household surveys. PLoS Med 2013;10:e1001391. 18. Rutstein S. The DHS Wealth Index: Approaches for Rural and Urban Areas. Demographic Health Survey working paper 60. Calverton, MD: ORC Macro International, 2008. 19. De Onis M, Onyango A, Borghi E et al. Comparison of the World Health Organization child growth standards and the National Center for Health Statistics/WHO international growth reference: implications for child health programmes. Public Health Nutr 2006;9:942–47. 20. World Health Organization. Child Growth Standards. Length/ height-for-age, Weight-for-age, Weight-for-length, Weight-forheight and Body mass index-for-age. Geneva:World Health Organization, 2006. 21. Deaton A. The Analysis of Household Surveys: A Microeconomic Approach to Development Policy. Washington, DC: World Bank, 1997. 22. Filmer D, Pritchett L. Estimating wealth effects without expenditure data – or tears: An application to educational enrollments in states of India. Demography 2001;38:115–32. 23. Erreygers G. Correcting the concentration index. J Health Econ 2009;28:504–15. 24. Wagstaff A. The bounds of the concentration index when the variable of interest is binary, with an application to immunization inequality. Health Econ 2005;14:429–32. 25. Erreygers G. Correcting the concentration index: A reply to Wagstaff. J Health Econ 2009;28:521–24. 26. Wagstaff A. Reply to Guido Erreygers and TomVan Ourti’s comment on ‘The concentration index of a binary outcome revisited’. Health Econ 2011;20:1166–68. 27. O’Donnell O, van Doorslaer E, Wagstaff A, Lindelow M. Analyzing Health Equity Using Household Survey Data: A Guide to Techniques and Their Implementation. Washington, DC: World Bank, 2008.
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1. Black R, Allen LH, Bhutta ZA et al. Maternal and child undernutrition: global and regional exposure and health consequences. Lancet 2008;371:243–60. 2. Victora C, Adair L, Fall C et al. Maternal and child undernutrition: consequences for adult health and human capital. Lancet 2008;371:340–57. 3. Walker SP, Wachs TD, Grantham-McGregor S et al. Inequality in early childhood: risk and protective factors for early child development. Lancet 2011;378:1325–38. 4. UNICEF, WHO, World Bank. Levels and Trends in Child Malnutrition. Joint Child Malnutrition Estimates. New York: United Nations International Children’s Fund; Geneva: World Health Organization; Washington, DC: World Bank, 2012. 5. Stevens GA, Finucane MM, Paciorek CJ et al. Trends in mild, moderate, and severe stunting and underweight, and progress towards MDG 1 in 141 developing countries: a systematic analysis of population representative data. Lancet 2012;380:824–34. 6. Zere E, McIntyre D. Inequities in under-five child malnutrition in South Africa. Int J Equity Health 2003;2:7. 7. Wagstaff A, van Doorslaer E, Watanabe N. On decomposing the causes of health sector inequalities with an application to malnutrition inequalities in Vietnam. J Econometrics 2003;112: 207–23. 8. Thang NM, Popkin BM. In an era of economic growth, is inequity holding back reductions in child malnutrition in Vietnam? Asia Pac J Clin Nutr 2003;12:405–10. 9. Larrea C, Freire W. Social inequality and child malnutrition in four Andean countries. Am J Public Health 2002;11:356–64. 10. Hong R. Effect of economic inequality on chronic childhood undernutrition in Ghana. Public Health Nutr 2007;10:371–78. 11. Van de Poel E, Hosseinpoor A, Jehu-Appiah C, Vega J, Speybroeck N. Malnutrition and the disproportional burden on the poor: the case of Ghana. Int J Equity Health 2007,6:21. 12. Pathak PK, Singh A. Trends in malnutrition among children in India: growing inequalities across different economic groups. Soc Sci Med 2007;73:576–85.
International Journal of Epidemiology, 2014, Vol. 43, No. 4
Original article
Persistent inequalities in child undernutrition: evidence from 80 countries, from 1990 to today Caryn Bredenkamp,1* Leander R Buisman2 and Ellen Van de Poel2 1
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World Bank, Washington, DC, USA, 2Institute of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands *Corresponding author. World Bank, 1818 H St NW, Washington, DC, USA. E-mail: [email protected] Accepted 6 March 2014
Abstract Background: Global progress in reducing the burden of undernutrition tends to be measured at the population level. It has been hypothesized that population-level improvements may mask widening socioeconomic inequalities, but little attempt has been made to assess whether this is true. Methods: Original data from 131 demographic health surveys and 48 multiple indicator cluster surveys from 1990 to 2011 were used to examine trends in socioeconomic inequalities in stunting and underweight, as well as the relationship between changes in prevalence and changes in inequality, in 80 countries. Socioeconomic inequality is measured using the corrected concentration index. Results: Countries with a higher prevalence of stunting tend to have larger socioeconomic inequalities in stunting (Spearman rank correlation ¼ 0.27 P ¼ 0.014). In most countries, there has been no change in inequality in stunting: in 31 out of 53, the 90% confidence intervals around the changes overlap the zero value. In the remaining 22, there was a reduction in inequality in 11 and an increase in 11. The distributional patterns underlying the summary inequality statistics vary considerably across countries, but in most there have been considerable gains to the poorest quintile. Conclusions: Reductions in the prevalence of undernutrition have generally not been accompanied by widening inequalities. However, inequalities have also not been narrowing. Rather, the picture is one of a strong persistence of existing inequalities. In addition, there are different distributional patterns underlying changes in the summary indices of inequality which will need to be taken into consideration in designing programmes to reach the poor. Key words: Malnutrition, stunting, underweight, anthropometry, inequality, equity, developing countries, trends, MDGs
C The Author 2014; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association V
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Key Messages • Countries with a higher prevalence of undernutrition, whether measured as stunting or as underweight, tend to have
larger socioeconomic inequalities. • There has been a strong persistence of existing inequalities in stunting in 56% of countries, with relatively few coun-
tries exhibiting narrowing or widening inequalities. • Falling prevalence tends not to have been associated with widening inequalities, but only 19% of countries succeeded
in reducing both prevalence and inequalities in stunting. • There are different distributional patterns underlying changes in the summary indices of inequality which will need to
be taken into consideration in designing programmes to reach the poor. • The use of underweight rather than stunting as an indicator of malnutrition leads to more optimistic conclusions
about the changes in inequality in undernutrition over time.
Undernutrition has dire consequences for children’s development. It retards their physical growth, precipitates disease and speeds its progression, and is an important contributor to infant and child mortality. Undernutrition in infancy and childhood is also correlated with poor health outcomes in adulthood, affects cognitive and motor development and limits educational attainment, with adverse consequences for adult work productivity and lifetime earnings potential, ultimately perpetuating poverty.1–3 It is not surprising, then, that the United Nations selected child undernutrition as one of the health-related Millennium Development Goals (MDGs) whose progress would be monitored on a global basis, aiming to halve the prevalence of underweight between 1990 and 2015. Recent global estimates show that the proportion of underweight children under 5 years of age declined from 25% in 1990 to 16% in 2011, and the proportion of stunted children under five declined from 40% to 26% over the same period.4 As Stevens et al. point out, less than half of developing countries are likely to meet the MDG1 target by 2015, and progress is especially slow in Sub-Saharan Africa.5 So far, little attempt has been made at the global level to assess whether aggregate improvements in nutritional status may mask widening socioeconomic inequalities or relatively slower progress among the poor. Most of the literature documenting trends in socioeconomic inequalities in undernutrition in developing countries has focused on individual countries—and even these are few and far between. Recent examples include South Africa,6 Vietnam,7,8 four Andean countries,9 Ghana10,11 and India.12 In terms of multi-country analysis, Wagstaff and Watanabe13 and Van de Poel et al.14 explored socioeconomic inequalities in undernutrition across 20 and 47 developing countries,
respectively, but did not measure trends. Black et al.’s recent study contains estimates of undernutrition for the top and bottom wealth quintiles in a large set of developing countries, but only describes trends in inequalities for three selected countries.15 Trends in inequalities in undernutrition have been explored previously by Paciorek et al., but only stratified by urban-rural location rather than socioeconomic status.16 This paper contributes to the literature in the following ways. First, it provides country-level estimates of current levels and inequalities in undernutrition by including data from the latest available household surveys, both demographic and health surveys (DHS) and multiple indicator cluster surveys (MICS), for a total of 80 countries. Second, it assesses whether socioeconomic inequalities in undernutrition have been widening or narrowing over time, making it the first study to examine trends in socioeconomic inequalities on a global scale; it also looks at how this trend correlates with changes in the prevalence of undernutrition. Third, it attempts to better understand the distributional patterns accompanying the country-specific trends, particularly how the position of the poorest quintile has changed vis-a`-vis wealthier groups.
Methods Data We analysed 179 household survey datasets (131 DHS and 48 MICS) for which data on child growth (either height or weight) were available between 1990 and 2011; 80 (80) countries have information on height (weight) for at least one period, and 53 (52) countries have information on height (weight) for at least two periods. For all estimations, analysis of the original datasets was undertaken. Although there is more than sufficient similarity across the MICS and DHS surveys to generate comparable results,17 there
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Introduction
International Journal of Epidemiology, 2014, Vol. 43, No. 4
Statistical analysis Child undernutrition is measured using stunting and underweight among children up to 5 years of age. These anthropometric indicators were selected because they are the most common measures used in international monitoring and reporting of child undernutrition (such as for the purposes for the MDGs); we recognize, though, that they do not capture all dimensions of the undernutrition problem, including acute undernutrition (i.e. wasting, or low weight-for-height) and micronutrient deficiencies. Children are considered stunted (underweight) if their height-for-age (weight-for-age) is more than two standard deviations below the median of the 2006 World Health Organization Child Growth Standards.19,20 Stunting reflects the slowing of skeletal growth. Compared with the underweight indicator, it also more accurately reflects the nutritional deficiencies and illnesses that occur during the early-life period, that will hamper growth and development and, therefore, is preferred as a reliable indicator of long-term undernutrition.15 The analyses in this paper focus primarily on stunting, but we also report findings for underweight since, although not able to discriminate between long-term and temporary undernutrition, it is the main indicator for MDG 1c. Measuring socioeconomic inequalities requires a variable with which to rank households from poorest to richest. As the DHS and MICS lack consumption measures—the preferred measure of household economic status in developing countries21—we use a wealth index constructed by conducting principal components analysis on a set of variables related to asset ownership and dwelling characteristics.22 All DHS and most MICS datasets are released with a wealth index, but for three MICS we had
to construct a similar wealth index from the assets and housing characteristics included in the dataset. Socioeconomic inequality is measured using a concentration index, which allows inequalities across the entire socioeconomic distribution to be summarized in one index, and therefore facilitates comparison across countries and over time. Applied to binary variables, such as stunting and underweight, the bounds of the standard concentration index are heavily dependent on the mean, which is especially problematic in cross-country comparisons.23–25 We therefore use the corrected version of the concentration index23 which deals with these shortcomings but shares the characteristics and interpretation of the standard concentration index namely: (i) negative values imply that undernutrition is more concentrated among poorer children; (ii) if all children, irrespective of their socioeconomic status, suffer equally from undernutrition, the index would equal zero; and (iii) transferring undernutrition from a richer to a poorer individual reduces the value of the index. For binary outcomes, the index (Cy) is calculated as follows: Cy ¼8covðyi ,Ri Þ
ð1Þ
where yi is a measure of child i’s nutritional status and Ri is the child’s fractional rank in the distribution of the variable used to proxy socioeconomic status. The interpretation of the corrected concentration index is one of absolute, rather than relative, inequality.26 The bounds of the index tend to zero if the average malnutrition prevalence tends to zero, increase if the mean rises and reach a maximum when exactly half of the sample is malnourished (at which point the bounds are equal to 1 and þ1). Standard errors of the indices are obtained using the convenient regression approach.27 Statistical inference of changes in prevalence and inequality is calculated using a t-test. Since all surveys use multistage sampling procedures, sample weights are used.
Results Current socioeconomic inequalities in undernutrition Unsurprisingly, in all countries, undernutrition is concentrated among the poor. Peru shows the highest socioeconomic inequality in stunting (with a concentration index of 0.4), followed by Guatemala, Honduras, Bolivia and Nicaragua (all with concentration indices between 0.3 and 0.4). Indeed, most Latin American countries have very high inequality. Inequality is smallest in Egypt, followed by Madagascar, Comoros, Vanuatu and Jordan, where concentration indices range between 0 and 0.05. Detailed estimates for each country are displayed in Table S1 (available as Supplementary data at IJE online).
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are some measurement differences in particular countries. For example: the assets included in the DHS wealth index sometimes differ slightly across countries to take into account cultural differences, over time to allow for changes in technology and, more recently, include additional assets that better distinguish between urban-rural areas;18 whereas most surveys contain measures of both height and weight, some collect only one or the other; and, finally, whereas in almost all surveys the reference age for anthropometrics is up to 5 years, in 15 early DHS the reference age is only up to the age of 3 or 4 years. If growth retardation typically worsens beyond age 3 or 4 years, then this may bias the extent of undernutrition downwards in these few surveys. Consequently, we also check the robustness of our main results by restricting the sample to children aged less than 3 or 4 years, as the case may be, for all surveys from these countries when calculating trends.
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Socioeconomic inequalities in undernutrition exhibit a high degree of persistence. Figure 2 shows the change in socioeconomic inequality in stunting (using 90% confidence intervals) between each country’s oldest survey and its most recent. In most countries, 31 to be precise, the confidence intervals around the changes do not overlap the zero value and in 22 they do. Of the latter, 11 exhibit narrowing inequalities and 11 exhibit widening inequalities. The results do not change when robustness checks are conducted for possible bias due to differences in the age groups of children in earlier surveys. Figure 3 shows that there is no obvious relationship between trends in stunting prevalence and trends in socioeconomic inequality. Although the majority of countries have experienced some reduction in the prevalence of stunting, this decline is as likely to be accompanied by an increase in inequalities as a decrease in inequalities. There are some interesting regional patterns. Declining prevalence has been associated with narrowing inequalities in the Europe and Central Asia region as well as in the Middle East and North Africa region, whereas in South Asia it has been associated with widening inequalities. Generally, though, and including in the Sub-Saharan Africa region which makes up most of the sample, the data do not support a generalization that in those countries where undernutrition
Albania Armenia Azerbaijan Bangladesh Benin Bolivia Burkina Faso Cambodia Cameroon Central African Republic Chad Colombia Comoros Cote d'Ivoire DRC Congo Dominican Republic Egypt Ethiopia Ghana Guinea Guinea-Bissau Guyana Haiti India Jordan Kazakhstan Kenya Kyrgyz Republic Lao PDR Lesotho Madagascar Malawi Mongolia Morocco Mozambique Namibia Nepal Nicaragua Niger Nigeria Peru Rwanda Senegal Sierra Leone Suriname Tanzania The Gambia Togo Turkey Uganda Uzbekistan Zambia Zimbabwe
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Trend in inequality in stunting −.1 0 .1
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Figure 1. Relationship between the prevalence of stunting and socioeconomic inequality. Source: DHS and MICS surveys, most recent data available. Notes: Socioeconomic inequality is measured by the corrected concentration index; a negative value indicates that stunting is more concentrated among the poor, with larger absolute values indicating a larger degree of concentration.
Trends in socioeconomic inequalities in undernutrition
Figure 2. Changes in socioeconomic inequality in stunting. Source: DHS and MICS surveys, 1990-2011. Notes: Absolute difference between the corrected concentration indices for each country’s earliest and most recent survey is shown, with 90% confidence intervals. Positive values indicate increasing inequality.
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Prevalence of stunting (%) 20 30 40 50
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There is also a clear correlation between socioeconomic inequalities and prevalence, with countries with a higher prevalence of stunting exhibiting larger socioeconomic inequalities in stunting, i.e. a more negative concentration index (Spearman rank correlation ¼ 0.27, P ¼ 0.014) (see Figure 1).
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has been declining, there has been a widening of inequalities. It also seems that achieving both a reduction in the prevalence of stunting as well as a reduction in the inequality of stunting is a tough ambition. In only 19 countries in our sample do we find this combination, and in only 9 of these (namely Colombia, Dominican Republic, Egypt, Kyrgyz Republic, Mongolia, Morocco, Nigeria, Turkey and Zambia) did the confidence intervals on both the change in prevalence and the change in inequalities not overlap the zero value.
Beyond summary indicators: shifts in distributional patterns by quintile Changes in inequality can be due to gains or losses among different socioeconomic groups. Narrowing inequalities may reflect a large improvement in the health status of the very poor or it may reflect that the middle classes are catching up to the rich with the ultra-poor still being left behind. Similarly, widening inequalities in undernutrition may reflect reductions in undernutrition among the richest quintile or among all except the poor. Since different distributional patterns may imply different policies, or different implementation modalities to reach those in need, policy makers and programme staff may be interested to explore on a country-by-country basis what distributional patterns underlie the aggregate change. In particular, they should pay attention to what is happening to undernutrition among the poorest of the poor.
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Figure 3. Relationship between the trend in stunting and trend in inequalities. Source: DHS and MICS surveys, 1990-2011. Note: X-axis shows the absolute difference between the stunting prevalence for each country’s earliest and most recent survey; y-axis shows the absolute difference between the corrected concentration indices of the surveys. Positive values indicate increasing prevalence or inequality.
To investigate these patterns, we plot for selected countries the distribution of stunting across five wealth quintiles by survey year, for countries with narrowing (see Figure 4) and widening (see Figure 5) inequalities. Each horizontal line represents a different survey year, with the more recent surveys at the top of the graphs. Black dots designate the wealthiest quintile and red dots the poorest quintile, with others dots (yellow, green and blue) showing quintiles four through two. The prevalence of undernutrition increases from left to right such that a migration of the lines from right to left over time indicates falling undernutrition. Longer lines indicate greater dispersion in the prevalence of undernutrition across quintiles and, therefore, a higher degree of inequality. For brevity’s sake, we present the results only for those countries where the estimates are among the most precise in the sample, specifically where the P-values of the reduction in stunting as well as the P-values of the change inequality are less than 0.10. Plots for the remaining countries can be found in Figure S1 (available as Supplementary data at IJE online). Our analysis shows that narrowing inequalities over time can be the result of many different patterns of change (Figure 4). In the Dominican Republic, where mean prevalence fell over time, inequalities narrowed because the poorer quintiles exhibited rapid catch-up (with the prevalence of stunting falling from 27% to 17% between 1996 and 2007). Colombia and Zambia display a similar pattern, although there is also some catch-up among the second and third quintiles. In Mongolia, where the mean prevalence of undernutrition also fell over time, narrowing inequalities exhibited a different pattern: they were the result of a combination of large improvements in the poorest quintile (with stunting falling from 40% to 32% between 2000 and 2005) and deteriorating nutritional status among the wealthiest quintile (with the prevalence of stunting increasing from 14% to 19%). A similar trend is observed in Morocco and Egypt. There are also other patterns: in Turkey, between the two most recent surveys, there was not much change in the nutritional status of the wealthiest two quintiles, sharp gains for the next two quintiles and almost no change among the poorest; in the Kyrgyz Republic, stunting fell drastically in all quintiles, disproportionally more so among the poorest. The most common pattern underlying widening inequalities is a pattern where nutritional status improves for all quintiles, but the gains are considerably larger for the wealthier (fourth and fifth) quintiles (Figure 5). This is the case in Nepal, Nicaragua and India, for example. An extreme example is that of Ethiopia, where widening inequalities are driven almost entirely by the gains to the wealthiest quintile, with much smaller improvements in
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Figure 5. Changes in the distribution of the prevalence of stunting, by quintile, for selected countries with reductions in stunting prevalence and widening socioeconomic inequality. Source: DHS and MICS surveys, 1990-2011. Notes: Mean prevalence of stunting (in %) on the x-axis, survey year on the y-axis.
the poorer quintiles. The prevalence of stunting in the top quintile fell by almost 20 percentage points (from 48% in 2000 to 40% in 2005 to 29% in 2011) compared with 10 percentage points in the poorest quintile and second poorest quintiles (from 59% to 49%, and from 58% to 48%, respectively). It is also possible, although less common,
that widening inequalities could be indicative of a deterioration of nutritional status in the poorest quintiles. The very different distributional patterns that underlie a summary conclusion of ‘widening’ or ‘narrowing’ inequality point to the importance of examining not only aggregate changes in inequality, but also which particular
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Figure 4. Changes in the distribution of the prevalence of stunting, by quintile, for selected countries with reductions in stunting prevalence and narrowing socioeconomic inequality. Source: DHS and MICS surveys, 1990-2011. Notes: Mean prevalence of stunting (in %) on the x-axis, survey year on the y-axis.
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socioeconomic groups are driving those changes, especially if one is interested in implementing policies that effectively address widening inequalities in undernutrition and target the worst-off groups. Nevertheless, it is encouraging that in almost all countries where undernutrition has fallen, there have also been improvements for the poorest quintile.
Sensitivity analysis: using underweight rather than stunting Figure 6 illustrates that, as in the case of stunting, countries with a higher prevalence of underweight also tend to have larger socioeconomic inequalities (i.e. a more negative concentration index), and the association is even stronger (Spearman rank correlation ¼ 0.78, P < 0.0001). Countryspecific estimates are shown in Table S1 (available as Supplementary data at IJE online). Results on changes in inequalities are similarly sensitive to the use of underweight rather than stunting. Among those countries where the confidence intervals around the changes do not overlap the zero value, we find 20 countries with narrowing socioeconomic inequalities and 14 countries with widening socioeconomic inequalities, compared with 11 and 11, respectively, in the case of stunting. Persistent inequalities are thus observed in only 18 countries. In 15 countries both the prevalence of underweight and socioeconomic inequality in underweight fell, whereas this happened for stunting in only 9 countries.
inequalities. At the same time, it cannot be concluded that the general trend is one of narrowing inequalities. Rather, the overall pattern is one of persistence of existing inequalities. There is a lot of variation across countries and regions, though. Indeed, inequalities have risen in a number of countries, both those with a declining and those with a rising prevalence of undernutrition, making it important to monitor not only progress in the mean prevalence of undernutrition but also changes in inequalities over time. The measure of nutritional status matters. The use of underweight rather than stunting leads to more optimistic conclusions about the changes in inequality in undernutrition over time. This may be because weight deficits are more responsive to changes in living standards than deficits in height,1 but further speculation as to the reasons is beyond the scope of this paper. What is important is that, notwithstanding the fact that stunting and underweight capture different dimensions of nutritional status, the selection of one over the other has implications for the conclusions that national policy makers and the global community reach about progress in inequalities in nutritional status, especially since underweight among children under 5 years of age is (not uncontroversially4) the official indicator of MDG target 1c. Finally, especially for the purposes of programme development, it is useful to go beyond summary statistics of inequality and also look at the different patterns underlying the widening/narrowing of inequalities, examining especially closely what is happening to the poorest. The most appropriate policy interventions are likely to be different if the widening inequalities arise from the poor being left behind (which should imply highly targeted interventions), or from the rich galloping ahead (leaving the vast mass of the population still vulnerable) or the second quintile catching up to the rich and leaving the rest behind. This paper is not without its limitations. The geographical distribution of DHS and MICS makes it difficult to draw firm conclusions about regional trends beyond the Africa region where most of the surveys are carried out. Future research could include combining these data with other national surveys, especially in middle-income countries, to obtain a more global picture. In addition, it would be important to start to explore why inequalities in undernutrition appear to be so persistent, as well as to attempt to understand the factors that contribute to the success of the few positive deviants who succeed in reducing both prevalence and inequalities in undernutrition.
Discussion In countries where undernutrition has fallen significantly, it has not necessarily been accompanied by widening
Supplementary Data Supplementary data are available at IJE online.
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Figure 6. Relationship between the prevalence of underweight and socioeconomic inequality. Source: DHS and MICS surveys, most recent data available. Notes: Socioeconomic inequality is measured by the corrected concentration index; a negative value indicates that underweight is more concentrated among the poor, with larger absolute values indicating a larger degree of concentration.
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Funding This work was supported by funding from Netherlands Organization for Scientific Research (Veni project 451-11-031) to E.v.d.P. and from the World Bank, including from the Rapid Social Response Multi-Donor Trust Fund, to C.B. and L.B. The findings, interpretations and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors or the governments of the countries they represent. Conflict of interest: None declared.
References
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