Patterns, distribution, and determinants of under- and overnutrition: a population-based study of women in India

BACKGROUND: Little systematic evidence exists for the relation between socioeconomic position and nutritional status in countries experiencing the simultaneous presence of under- and overnutrition. OBJECTIVE: We investigated the socioeconomic distribution of nutritional status in India and whether state-level macroeconomic factors modify the relation between socioeconomic position and nutritional status. DESIGN: Our analysis was based on a nationally representative sample of 77 220 women from India, with multiple categories of body mass index (BMI; in kg/m2) as the outcome, namely, <18.5 (underweight), 23-24.9 (pre-overweight), 25-29.9 (overweight), or > or =30 (obese), with 18.5-22.9 as the reference category. RESULTS: In adjusted models, being underweight was inversely related to socioeconomic position, whereas socioeconomic position was positively related to being pre-overweight, overweight, and obese, and the socioeconomic gradient was most marked for obesity. State-level measures of affluence did not modify the positive association between socioeconomic position and categories of overweight. The risk of underweight was lower in affluent states, but this was seen mainly in women of high socioeconomic position. CONCLUSIONS: Undernutrition and overnutrition are epidemics of the impoverished and the affluent, respectively, in India, and this association is consistent at the individual and ecologic levels. Policies should focus on the complex patterns of social distribution of both under- and overnutrition in the Indian context.     

Measuring wealth-based health inequality among Indian children: the importance of equity vs efficiency

The concentration index is the most commonly used measure of socio-economic-related health inequality. However, a critical constraint has been that it is just a measure of inequality. Equity is an important goal of health policy but the average level of health also matters. In this paper, we explore evidence of both these crucial dimensions-equity (inequality) and efficiency (average health)-in child health indicators by adopting the recently developed measure of the extended concentration index on the National Family Health Survey (NFHS-3) data from India. An increasing degree of inequality aversion is used to measure health inequalities as well as achievement in the following child health indicators: under-2 child mortality, full immunization coverage, and prevalence of underweight, wasting and stunting among children. State-wise adjusted under-2 child mortality scores reveal an increasing trend with increasing values of inequality aversion, implying that under-2 child deaths have been significantly concentrated among the poor households. The level of adjusted under-2 child mortality scores increases significantly with the increasing value of aversion even in states advanced in the health transition, such as Kerala and Goa. The higher values of adjusted scores for lower values of aversion for child immunization coverage are evidence that richer households benefited most from the rise in full immunization coverage. However, the lack of radical changes in the adjusted scores for underweight among children with increasing degrees of aversion implies that household economic status was not the only determinant of poor nutritional status in India.     

中国八省同一家庭内营养不良与营养过剩个体并存的纵向趋势研究

目的描述和解释中国八省1991～2000年营养不良、营养过剩个体在同一家庭内并存的纵向趋势及主要影响因素。方法采用"中国居民健康与营养调查"项目1991、1993、1997、2000年的调查数据。以住户为研究对象,按照0～60岁家庭成员的营养状况将住户分为正常家庭、营养不良家庭、营养过剩家庭、营养不良和营养过剩并存家庭四个类型。并存发生的纵向影响因素分析采用多水平模型。结果中国八省四年的调查住户并存率分别为7.9%、7.7%、6.0%、6.8%,随年代变化呈下降趋势(Z=2.54,P=0.0112)。1991～2000年期间,城市居住、机动车拥有、洗衣机拥有对并存的发生有持续促进作用。结论中国营养不良和营养过剩并存家庭的出现是营养状况变迁的特征之一。营养改善等项目要兼顾营养不良成员与营养过剩成员并存的复杂现实。     

Trends in Diet, Nutritional Status, and Diet-related Noncommunicable Diseases in China and India: The Economic Costs of the Nutrition Transition

Undernutrition is being rapidly reduced in India and China. In both countries the diet is shifting toward higher fat and lower carbohydrate content. Distinct features are high intakes of foods from animal sources and edible oils in China, and high intakes of dairy and added sugar in India. The proportion of overweight is increasing very rapidly in China among all adults; in India the shift is most pronounced among urban residents and high-income rural residents. Hypertension and stroke are relatively higher in China and adult-onset diabetes is relatively higher in India. Established economic techniques were used to measure and project the costs of undernutrition and diet-related noncommunicable diseases in 1995 and 2025. Current WHO mortality projections of diet-related noncommunicable diseases, dietary and body composition survey data, and national data sets of hospital costs for healthcare, are used for the economic analyses. In 1995, China's costs of undernutrition and costs of diet-related noncommunicable diseases were of similar magnitude, but there will be a rapid increase in the costs and prevalence of diet-related noncommunicable diseases by 2025. By contrast with China, India's costs of undernutrition will continue to decline, but undernutrition costs did surpass overnutrition diet-related noncommunicable disease costs in 1995. India's rapid increase in diet-related noncommunicable diseases and their costs projects similar economic costs of undernutrition and overnutrition by 2025.     

State-level income inequality and individual mortality risk: a prospective, multilevel study

OBJECTIVES: Previous studies have linked state-level income inequality to mortality rates. However, it has been questioned whether the relationship is independent of individual-level income. The present study tests whether state-level income inequality is related to individual mortality risk, after adjustment for individual-level characteristics. METHODS: In this prospective, multilevel study design, the vital status of National Health Interview Survey (NHIS) respondents was ascertained by linkage to the National Death Index, with additional linkage of state-level data to individuals in the NHIS. The analysis included data for 546,888 persons, with 19,379 deaths over the 8-year follow-up period. The Gini coefficient was used as the measure of income inequality. RESULTS: Individuals living in high-income-inequality states were at increased risk of mortality (relative risk = 1.12; 95% confidence interval = 1.04, 1.19) compared with individuals living in low-income-inequality states. In stratified analyses, significant effects of state income inequality on mortality risk were found, primarily for near-poor Whites. CONCLUSIONS: State-level income inequality appears to exert a contextual effect on mortality risk, after income is adjusted for, providing further evidence that the distribution of income is important for health.     

Urgent Need to Orient Public Health Response to Rapid Nutrition Transition

India is currently undergoing a rapid transition on economic, demographic, epidemiologic, nutrition, and sociological fronts. There is evidence of a decline in undernutrition with a simultaneous escalation in overnutrition and associated non-communicable diseases (NCDs). However, the current concern and national policy response for tackling malnutrition in India is still primarily restricted to undernutrition diagnosed on the basis of body size (anthropometry). A complex range of interacting factors have been linked to the rising trend of overnutrition and associated NCDs from a global perspective. The burden of overnutrition and associated morbidities is rapidly escalating to alarming proportions, particularly in urban areas and high socio-economic status groups. The poor are not spared from this transition. It is predicted that a more rapid transition may occur amongst poor populations in future with higher economic development. The need of the hour is to launch an integrated public health response to the dual burden beginning from pregnancy and early life. This will obviously require careful deliberation of the strategy and interventions, and a multi-sectoral approach, especially involving the health, women and child development, nutrition, education, agriculture, food processing, trade, architecture, water supply and sanitation, community and non-governmental organizations.     

Characterizing Undernourished Children Under-Five Years Old with Diarrhoea in Mozambique: A Hospital Based Cross-Sectional Study, 2015–2019

Diarrhoea is associated with undernutrition and this association is related to increased morbidity and mortality in children under-five. In this analysis we aimed to assess the frequency and associated factors of undernutrition in children under-five with diarrhoea. A hospital-based cross-sectional study was conducted from January 2015 to December 2019 through a surveillance system in five sentinel hospitals in Mozambique. Sociodemographic and clinical information was collected, including anthropometry. A total of 963 children were analysed. The overall undernutrition frequency was 54.1% (95% CI: 50.9–57.2), with 32.5% (95% CI: 29.6–35.5) stunting, 26.6% (95% CI: 23.9–29.6) wasting and 24.7% (95% CI: 22.1–27.5) underweight. Children from Nampula province had 4.7 (p = 0.016) higher odds for stunting compared with children from Maputo. Children whose caregiver was illiterate had higher odds of being underweight 5.24 (p < 0.001), and the wet season was associated with higher odds 1.70 (p = 0.012) of being wasted. Children born under 2500 g of weight had 2.8 (p = 0.001), 2.7 (p < 0.001) and 2.6 (p = 0.010) higher odds for being underweighted, wasted and stunted, respectively. The HIV positive status of the children was associated with higher odds of being underweight 2.6 (p = 0.006), and stunted 3.4 (p = 0.004). The province, caregiver education level, wet season, child’s birthweight and HIV status were factors associated with undernutrition in children with diarrhoea. These findings emphasise the need for additional caregiver’s education on the child’s nutrition and associated infectious diseases. More studies are needed to better understand the social context in which a child with diarrhoea and undernutrition is inserted.     

Growth and nutritional status in a probabilistic sample of schoolchildren from Rio de Janeiro, 1999

Growth and nutritional status were assessed in a probabilistic sample of public schoolchildren (1,705 girls and 1,682 boys) in the city of Rio de Janeiro in 1999. Nutritional status of the under-10-year group was assessed by z < -2 (undernutrition) for body mass for height and height for age and by z > +2 for body mass for height (overweight). For children over 10, the 5th and 85th percentiles of body mass index for age were used to assess underweight and overweight, respectively. Prevalence rates of overweight and obesity according to the International Obesity Task Force criteria were also calculated. Prevalence rates for undernutrition were below expected for the reference population. Prevalence of overweight was approximately 18% and 14% for girls and boys, respectively, and obesity was 5% for both sexes. The results of the present study are similar to findings from population-based surveys in Brazil: low prevalence of undernutrition and high prevalence of overnutrition.     

Husband's preference for a son and women's nutrition: examining the role of actual and desired family composition on women's anaemia and body mass index in India

This study examines the relationship between the husband's preference for a son, sex composition of children and risk of anaemia and underweight among married Indian women. Information was collected regarding 29,517 couples having at least one child in the nationally representative 2005-06 National Family Health Survey of India. The exposures were husband's preference for a son and sex composition of children: sons only, daughters only and mixed. Outcome included maternal underweight and moderate/severe anaemia. Husband's preference for a son was not found to be associated with his wife's risk of anaemia or underweight. Sex composition of the children was modestly associated with increased odds of anaemia among women from households with daughters only as compared with those with sons only [AOR: 1.19; 95% CI 1.04, 1.35]. The findings from this population-based study of socio-cultural norms around preference for a son and married Indian women's nutritional status do not support the hypothesis that husband's preference for sons influences the nutritional status of their wives. However, having daughters only is associated with maternal anaemia for reasons that remain to be established.     

Whose health is affected by income inequality? A multilevel interaction analysis of contemporaneous and lagged effects of state income inequality on individual self-rated health in the United States

The empirical relationship between income inequality and health has been much debated and discussed. Recent reviews suggest that the current evidence is mixed, with the relationship between state income inequality and health in the United States (US) being perhaps the most robust. In this paper, we examine the multilevel interactions between state income inequality, individual poor self-rated health, and a range of individual demographic and socioeconomic markers in the US. We use the pooled data from the 1995 and 1997 Current Population Surveys, and the data on state income inequality (represented using Gini coefficient) from the 1990, 1980, and 1970 US Censuses. Utilizing a cross-sectional multilevel design of 201,221 adults nested within 50 US states we calibrated two-level binomial hierarchical mixed models (with states specified as a random effect). Our analyses suggest that for a 0.05 change in the state income inequality, the odds ratio (OR) of reporting poor health was 1.30 (95% CI: 1.17-1.45) in a conditional model that included individual age, sex, race, marital status, education, income, and health insurance coverage as well as state median income. With few exceptions, we did not find strong statistical support for differential effects of state income inequality across different population groups. For instance, the relationship between state income inequality and poor health was steeper for whites compared to blacks (OR=1.34; 95% CI: 1.20-1.48) and for individuals with incomes greater than $75,000 compared to less affluent individuals (OR=1.65; 95% CI: 1.26-2.15). Our findings, however, primarily suggests an overall (as opposed to differential) contextual effect of state income inequality on individual self-rated poor health. To the extent that contemporaneous state income inequality differentially affects population sub-groups, our analyses suggest that the adverse impact of inequality is somewhat stronger for the relatively advantaged socioeconomic groups. This pattern was found to be consistent regardless of whether we consider contemporaneous or lagged effects of state income inequality on health. At the same time, the contemporaneous main effect of state income inequality remained statistically significant even when conditioned for past levels of income inequality and median income of states.     

Income inequality and weight status in US metropolitan areas

Prior empirical studies have demonstrated an association between income inequality and general health endpoints such as mortality and self-rated health, and findings have been taken as support for the hypothesis that inequality is detrimental to individual health. Unhealthy weight statuses may function as an intermediary link between inequality and more general heath endpoints. Using individual-level data from the 1996-98 Behavioral Risk Factor Surveillance System, we examine the relationship between individual weight status and income inequality in US metropolitan areas. Income inequality is calculated with data from the 1990 US Census 5% Public Use Microsample. In analyses stratified by race-sex groups, we do not find a positive association between income inequality and weight outcomes such as body mass index, the odds of being overweight, and the odds of being obese. Among white women, however, we do find a statistically significant inverse association between inequality and each of these weight outcomes, despite adjustments for individual-level covariates, metropolitan-level covariates, and census region. We also find that greater inequality is associated with higher odds for trying to lose weight among white women, even adjusting for current weight status. Although our findings are suggestive of a contextual effect of metropolitan area income inequality, we do not find an increased risk for unhealthy weight outcomes, adding to recent debates surrounding this topic.     

Income inequality and alcohol use: a multilevel analysis of drinking and drunkenness in adolescents in 34 countries

BACKGROUND: Economic inequality has been hypothesized to be a health determinant, independent of poverty and household income. The goal of this study was to explore the contextual influences of income inequality on alcohol use and frequency of drunkenness in adolescents. METHODS: The Health Behaviour in School-aged Children study surveyed 162 305 adolescents (ages 11, 13 and 15 years) in 34 countries, providing self-report data on family affluence, alcohol consumption and episodes of drunkenness. Country-level data on income inequality and overall wealth were retrieved from the United Nations Development Program. RESULTS: Multilevel logistic regression revealed that 11- and 13-year-olds in countries of high income inequality consumed more alcohol than their counterparts in countries of low income inequality (after adjustment for sex, family affluence and country wealth). No such effect on alcohol consumption was found in 15-year-olds. Eleven-year-olds in countries of high income inequality reported more episodes of drunkenness than their counterparts in countries of low income inequality. No such effect of income inequality on drunkenness was found in 13- or 15-year-olds. CONCLUSIONS: Income inequality may have a contextual influence on the use of alcohol among younger adolescents. Findings suggest that economic policies that affect the distribution of wealth within societies may indirectly influence the use of alcohol during early and mid-adolescence.     

Socioeconomic inequalities in depression: a meta-analysis

Low socioeconomic status (SES) is generally associated with high psychiatric morbidity, more disability, and poorer access to health care. Among psychiatric disorders, depression exhibits a more controversial association with SES. The authors carried out a meta-analysis to evaluate the magnitude, shape, and modifiers of such an association. The search found 51 prevalence studies, five incidence studies, and four persistence studies meeting the criteria. A random effects model was applied to the odds ratio of the lowest SES group compared with the highest, and meta-regression was used to assess the dose-response relation and the influence of covariates. Results indicated that low-SES individuals had higher odds of being depressed (odds ratio = 1.81, p < 0.001), but the odds of a new episode (odds ratio = 1.24, p = 0.004) were lower than the odds of persisting depression (odds ratio = 2.06, p < 0.001). A dose-response relation was observed for education and income. Socioeconomic inequality in depression is heterogeneous and varies according to the way psychiatric disorder is measured, to the definition and measurement of SES, and to contextual features such as region and time. Nonetheless, the authors found compelling evidence for socioeconomic inequality in depression. Strategies for tackling inequality in depression are needed, especially in relation to the course of the disorder.     

The burden of disease from undernutrition and overnutrition in countries undergoing rapid nutrition transition: a view from Brazil

On the basis of 3 comparable population-based surveys conducted in Brazil, we identified clear changes in the relative magnitude of women's undernutrition and overnutrition. In 1975, there were almost 2 cases of underweight to 1 case of obesity, whereas in 1997, there were more than 2 cases of obesity to 1 case of underweight. In 1997, Brazilian low-income women were significantly more susceptible than high-income women to both underweight and obesity.     

Poverty and childhood undernutrition in developing countries: A multi-national cohort study

The importance of reducing childhood undernutrition has been enshrined in the United Nations’ Millennium Development Goals. This study explores the relationship between alternative indicators of poverty and childhood undernutrition in developing countries within the context of a multi-national cohort study (Young Lives). Approximately 2000 children in each of four countries – Ethiopia, India (Andhra Pradesh), Peru and Vietnam – had their heights measured and were weighed when they were aged between 6 and 17 months (survey one) and again between 4.5 and 5.5 years (survey two). The anthropometric outcomes of stunted, underweight and wasted were calculated using World Health Organization 2006 reference standards. Maximum-likelihood probit estimation was employed to model the relationship within each country and survey between alternative measures of living standards (principally a wealth index developed using principal components analysis) and each anthropometric outcome. An extensive set of covariates was incorporated into the models to remove as much individual heterogeneity as possible. The fully adjusted models revealed a negative and statistically significant coefficient on wealth for all outcomes in all countries, with the exception of the outcome of wasted in India (Andhra Pradesh) and Vietnam (survey one) and the outcome of underweight in Vietnam (surveys one and two). In survey one, the partial effects of wealth on the probabilities of stunting, being underweight and wasting was to reduce them by between 1.4 and 5.1 percentage points, 1.0 and 6.4 percentage points, and 0.3 and 4.5 percentage points, respectively, with each unit (10%) increase in wealth. The partial effects of wealth on the probabilities of anthropometric outcomes were larger in the survey two models. In both surveys, children residing in the lowest wealth quintile households had significantly increased probabilities of being stunted in all four study countries and of being underweight in Ethiopia, India (Andhra Pradesh) and Peru in comparison to children residing in the highest wealth quintile households. Random effects probit models confirmed the statistical significance of increased wealth in reducing the probability of being stunted and underweight across all four study countries. We conclude that, although multi-faceted, childhood undernutrition in developing countries is strongly rooted in poverty.     

Socioeconomic inequality in voting participation and self-rated health

OBJECTIVES: This study tested the hypothesis that disparities in political participation across socioeconomic status affect health. Specifically, the association of voting inequality at the state level with individual self-rated health was examined. METHODS: A multilevel study of 279,066 respondents to the Current Population Survey (CPS) was conducted. State-level inequality in voting turnout by socioeconomic status (family income and educational attainment) was derived from November CPS data for 1990, 1992, 1994, and 1996. RESULTS: Individuals living in the states with the highest voting inequality had an odds ratio of fair/poor self-rated health of 1.43 (95% confidence interval [CI] = 1.22, 1.68) compared with individuals living in the states with the lowest voting inequality. This odds ratio decreased to 1.34 (95% CI = 1.14, 1.56) when state income inequality was added and to 1.27 (95% CI = 1.10, 1.45) when state median income was included. The deleterious effect of low individual household income on self-rated health was most pronounced among states with the greatest voting and income inequality. CONCLUSIONS: Socioeconomic inequality in political participation (as measured by voter turnout) is associated with poor self-rated health, independently of both income inequality and state median household income.     

Social justice and the demographic transition: lessons from India's Kerala State

Kerala is a small, densely crowded state in South India. It is a poor state, even by Indian standards. Its per capita income of US$80 lies well below the all-India average of US$120, and it suffers from the lowest per capita caloric intake in India. Nevertheless, Kerala has managed to achieve the demographic transition from high (premodern) to low (modern) birth and death rates-something no other Indian state has been able to attain. Indeed, the magnitude of Kerala's fertility decline-the birth rate fell from 39 in 1961 to 26.5 in 1974-has never before been observed in a nation with comparable levels of income and undernutrition. Other indices of Kerala's soical development are equally surprising: levels of literacy, life expectancy, female education, and age at marriage are the highest in India, while mortality rates, including infant and child mortality, are the lowest among Indian states. But Kerala's anomalous and unexpected demographic trends and levels are not the result of the direct interventions designed to influence health and fertility levels elsewhere in India-conventional strategies of population control and health services delivery that thus far are notable for their failure to generate such positive results. Instead, Kerala's demographic levels evidently reflect a broad social response to structural reforms in its political economy.     

The nutrition transition is underway in India

Nutrition research in India has previously focused on the serious problem of undernutrition related to nutrient deficit and high rates of infection. Recent data from the National Family Health Survey 1998/99 (NFHS 2), however, identified a significant proportion of Indian women as overweight, coexisting with high rates of malnutrition. This paper examines the emerging nutrition transition for women living in rural and urban communities of Andhra Pradesh, India. NFHS 2 provides nationally representative data on women's weight and height. In this paper, we examine representative data from the state of Andhra Pradesh (n = 4032 women). Logistic regression analyses are applied to the data to identify socioeconomic, regional and demographic determinants of overweight and thinness. The major nutrition problem facing women continues to be undernutrition, with 37% having a low body mass index [(BMI) < 18.5 kg/m(2)]; 8% of these women are severely malnourished (BMI < 16 kg/m(2)). However, 12% of the women can be classified as overweight (BMI > 25 kg/m(2)) and 2% are obese (BMI > 30 kg/m(2)). Furthermore, in the large cities of the state in which 4% of the sample live, 37% of women are overweight or obese, whereas in the rural areas in which 74% reside, 43% have a low BMI. Women from lower socioeconomic groups are also significantly more likely to have a low BMI. Findings from the logistic regression models reveal socioeconomic status to be a more important predictor of both over- and underweight than location of residence.     

Socio-economic differences in height and body mass index of children and adults living in urban areas of Karachi, Pakistan

OBJECTIVE: To study the socio-economic differences in height and body mass index (BMI) in urban areas of Karachi. DESIGN: A comparative study was undertaken to compare the heights and BMIs of adults and children belonging to three distinctively different income groups living in urban areas of Karachi. SETTING: Data was collected from families living in small, medium and large houses located in the authorised urban residential areas of Karachi. SUBJECTS: A total of 600 families, 200 from each income group, were included in the study. Anthropometric measurements of 1296 females and 1197 males of different ages were taken. METHODS: All the housewives were interviewed to collect socio-demographic information. Height and weight of all the available family members were measured. In order to determine the socio-economic difference in height status, the mean height in cm of adults was compared. For children (2-17 y) means of height-for-age Z-scores determined on the basis of NCHS reference values were compared. For studying the weight status the BMI of all the respondents was calculated and they were grouped into categories of under-, normal or overweight according to the NCHS recommended cut-off points. For adult men and women BMI values <18.5 kg/m(2) indicated underweight and >25 kg/m(2) indicated overweight. Among children, those having BMI values below the 5th percentile of the NHANES III reference values were categorised as underweight and those above the 95th percentile were termed overweight. RESULTS: Height status improved with income level among adults and children of both sexes. Among males the difference in weight status was significant only among 2 to 18-y-olds (P<0.05 in each case). The rate of overweight among 2 to 18-y-old males was significantly higher (P=0.004) at the middle-income level (15%) as compared to low or high income. The rate of underweight was significantly higher (P=0.025) at the low-income level among 2 to 18-y-old males (31%, 21% and 22% at low-, middle- and high-income levels, respectively). Among females, rates of underweight were not significantly different at any age. Rates of overweight increased significantly (P=0.048) with income level among 41 to 60-y-old women (38%, 53% and 60% at low-, middle- and high-income levels, respectively). CONCLUSION: Chronic undernutrition as indicated by deficit in height decreased with increasing income level. Socio-economic differences in weight status were not uniform among various age-sex groups. The influence of increasing affluence is likely to be seen both in the form of increased obesity among older females and underweight among children. Differing patterns of association between income and weight status among male and female children need to studied further with more accurate birth records, so as to further clarify the situation. In terms of prevention of nutrition-related disorders both problems of under- and over-nutrition need to be addressed.     

Slums and Malnourishment: Evidence From Women in India

Objectives. We examined the association between slum residence and nutritional status in women in India by using competing classifications of slum type.Methods. We used nationally representative data from the 2005–2006 National Family Health Survey (NFHS-3) to create our citywide analysis sample. The data provided us with individual, household, and community information. We used the body mass index data to identify nutritional status, whereas the residential status variable provided slum details. We used a multinomial regression framework to model the 3 nutrition states—undernutrition, normal, and overnutrition.Results. After we controlled for a range of attributes, we found that living in a census slum did not affect nutritional status. By contrast, living in NFHS slums decreased the odds of being overweight by 14% (95% confidence interval [CI] = 0.79, 0.95) and increased the odds of being underweight by 10% (95% CI = 1.00, 1.22).Conclusions. The association between slum residence and nutritional outcomes is nuanced and depends on how one defines a slum. This suggests that interventions targeted at slums should look beyond official definitions and include current living conditions to effectively reach the most vulnerable.More than 50% of the world population was classified as urban for the first time in 2009 and is expected to reach around 69% in 2050.1 The proportion of the urban population in the developing world is expected to increase from 45% to 66% during the same period. One of the immediate consequences of population pressure in urban spaces is the growth of slums or urban communities that are characterized by poor access to civic services, inadequate housing, and overcrowding.2 It has been estimated that slum populations would double before 2035 in the low- and middle-income countries.3One of the main concerns regarding the growth of slum populations is that the living conditions of the slum dwellers could become a public health issue. The attention gained by the relation between poor health outcomes and living conditions is neither new nor restricted to the developing world. As early as the 19th century, the Public Health Acts of Britain aimed to improve water systems and sanitation facilities in slums.2 This was also true of other developed countries—notably, France and the United States—which attempted to regulate residential dwellings to contain the spread of disease among other things.Although the pace of urbanization in India historically has been slow, it is increasing rapidly. India’s urban population grew by about 230 million between 1971 and 2008, and it is estimated that 250 million more will swell the urban population within the next 2 decades.4 This urban growth has led to a population explosion in cities, and India boasts of 2 cities with a population of at least 10 million (Delhi and Mumbai).Literature from the developing world suggests that both communicable and noncommunicable diseases are a major concern for urban populations, particularly the slum populations. Already malnourished slum dwellers may experience additional stress because of overcrowding and poor living conditions and are more likely to have poor health outcomes. However, India-specific research findings paint a mixed picture. A study on urban slums in Maharashtra in 1999 indicated that women living in slums were more disadvantaged with respect to antenatal care than were women not living in slums.5 This was reaffirmed by another study that compared the health status of poor populations in slums and in resettlement colonies in Delhi and Chennai and found that slum dwellers had worse health outcomes than those in resettlement colonies.6 Recent research in Chandigarh that used primary data collected in 2006 showed that immunization status of children younger than 5 years was poorer in slum areas than in the rural and urban areas.7 In contrast, a 2005–2006 National Family Health Survey (NFHS-3) report suggested that slum residents were not necessarily worse off than nonslum residents on several deprivation dimensions including poor health.8 These studies have used prevalence rates of all illnesses, morbidity rates, incidence of hospitalization, and other health indicators as various proxies of health status.Our study examined the distribution of women’s malnutrition in 8 cities across slum and nonslum populations. Malnutrition is a significant problem among Indian women. According to several studies that used the NFHS-3, only 52% of the women were within the normal weight range for a given height.8,9 Following the World Health Organization, we defined malnutrition to include the dual burden of undernutrition and overnutrition. Until recently, attention has been exclusively focused on undernutrition. However, recent trends indicate that Indian women are facing a double burden of malnutrition because of the increasing prevalence of overnutrition largely caused by changing lifestyle and diet patterns.10Being underweight could affect productivity and pose health risks, particularly for women, by increasing the likelihood of negative maternal health outcomes, including low-birth-weight infants.11 However, being overweight also could lead to poor health outcomes because of the increased risk of diabetes, cardiovascular diseases, hypertension, and respiratory-related mortality.12Figures 1 and ​and22 show the prevalence of underweight and overweight women, respectively, in 8 cities in India by slum residence status. Women residing in nonslum areas were more likely to be overweight, whereas those residing in slum areas were more likely to be underweight. In cities such as Delhi, these gaps appear to be large, with 36% being overweight in nonslum areas as opposed to 26% in slum areas; in Indore, 38% were undernourished in slums, but only 28% appear to be undernourished in nonslum areas. These numbers suggest that undernutrition is a larger problem in slums, and overnutrition is mainly a nonslum problem. Therefore, slums could be used as a valid unit to study undernutrition-related policies, and nonslum areas could be used to study overweight-related problems.Open in a separate windowFIGURE 1—Distribution of underweight women in 8 cities in India by slum status: 2005–2006 National Family Health Survey (NFHS-3).Note. Prevalence was calculated with 2005–2006 NFHS-3 data that were weighted with the provided weights. The prevalence ratios were calculated as follows: the numerator is the number of people who have body mass index (BMI) < 18.5 kg/m², and the denominator is those with normal weight (BMI = 18.5–24.99 kg/m²). The slum variable includes both census-defined slums and those identified as slums by NFHS field staff.Open in a separate windowFIGURE 2—Distribution of overweight women in 8 cities in India by slum status: 2005–2006 National Family Health Survey (NFHS-3).Note. Prevalence was calculated with 2005–2006 NFHS-3 data that were weighted with the provided weights. The prevalence ratios were calculated as follows: the numerator is the number of people who have body mass index (BMI) ≥ 25 kg/m², and the denominator is those with normal weight (BMI = 18.5–24.99 kg/m²). The slum variable includes both census-defined slums and those identified as slums by NFHS field staff.However, such differences in prevalence may be attributed to differences in the configuration of infrastructure, socioeconomic and other amenities that distinguish a slum from a nonslum area, or individual characteristics between those who live in slums and those who live in nonslum areas. Individual differences tend to matter more for malnutrition outcomes than do slum characteristics.