Overweight and obesity in India: policy issues from an exploratory multi-level analysis

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1 Health Policy and Planning, 31, 2016, doi: /heapol/czv105 Advance Access Publication Date: 13 November 2015 Original article Overweight and obesity in India: policy issues from an exploratory multi-level analysis Md Zakaria Siddiqui 1 and Ronald Donato 2, * 1 Crawford School of Public Policy, Australian National University, Canberra, Australia; and Institute of Development Studies Kolkata, Kolkata, India, 2 UniSA Business School, University of South Australia, Way Lee Building, City West Campus, North Terrace, Adelaide, 5001, South Australia *Corresponding author. UniSA Business School, University of South Australia, Way Lee Building, City West Campus, North Terrace, Adelaide, 5001, South Australia. ronald.donato@unisa.edu.au Accepted on 2 October 2015 Abstract This article analyses a nationally representative household dataset the National Family Health Survey (NFHS-3) conducted in 2005 to 2006 to examine factors influencing the prevalence of overweight/obesity in India. The dataset was disaggregated into four sub-population groups urban and rural females and males and multi-level logit regression models were used to estimate the impact of particular covariates on the likelihood of overweight/obesity. The multi-level modelling approach aimed to identify individual and macro-level contextual factors influencing this health outcome. In contrast to most studies on low-income developing countries, the findings reveal that education for females beyond a particular level of educational attainment exhibits a negative relationship with the likelihood of overweight/obesity. This relationship was not observed for males. Muslim females and all Sikh sub-populations have a higher likelihood of overweight/obesity suggesting the importance of socio-cultural influences. The results also show that the relationship between wealth and the probability of overweight/obesity is stronger for males than females highlighting the differential impact of increasing socio-economic status on gender. Multi-level analysis reveals that states exerted an independent influence on the likelihood of overweight/obesity beyond individual-level covariates, reflecting the importance of spatially related contextual factors on overweight/obesity. While this study does not disentangle macro-level obesogenic environmental factors from socio-cultural network influences, the results highlight the need to refrain from adopting a one size fits all policy approach in addressing the overweight/obesity epidemic facing India. Instead, policy implementation requires a more nuanced and targeted approach to incorporate the growing recognition of socio-cultural and spatial contextual factors impacting on healthy behaviours. Key words: India; overweight-obesity; multi-level; policy issues Key Messages Increased years of education for women beyond a certain level exhibit a negative relationship with the likelihood of overweight-obesity. This was not observed for men. Gender and rurality matter in how covariates influence the likelihood of overweight-obesity highlighting the need to discern across sub-populations when effecting policy. States exerted an independent influence on the likelihood of overweight-obesity reflecting the significance of sociocultural and spatially-related contextual effects. Formulating more targeted and nuanced health interventions, rather than adopting a one-size-fits-all approach, is of key importance if policy strategies are to be effective. VC The Author Published by Oxford University Press. All rights reserved. For permissions, please journals.permissions@oup.com 582

2 Health Policy and Planning, 2016, Vol. 31, No Introduction The dramatic increase in the prevalence of overweight/obesity over the past two decades and its consequent impact on the burden of non-communicable diseases (NCD) such as diabetes and cardiovascular disease is recognized as a global pandemic (Swinburn et al. 2011; Popkin et al. 2012). India is not immune from this development; recent estimates reveal that >12% of the entire population is overweight or obese and India, after China, has the highest number of people with diabetes in the world. Rapid economic development has seen dietary patterns shift towards energy-dense foods which together with increased urbanization and an associated sedentary lifestyle have contributed to the rise in overweight/obesity (Khandelwal and Reddy 2013; Misra and Shrivastava 2013). Given India s limited resources and the strains on its health system from having the greatest number of malnourished people in the world, rising overweight/obesity with the causal link with NCD represents a major public health challenge (FAO 2014). Much of the health economics research examining overweight/ obesity has focused on individual-level health behaviours associated with choice and changing incentives. The decline in the relative prices of energy-dense foods and an increase in sedentary behaviours in work and leisure have received strong empirical support as important determinants of the increased overweight/obesity over time (Cutler et al. 2003; Chou et al. 2004; Lakdawalla and Philipson 2007). However, recent studies have moved beyond individual-level behaviours and have explored non-market factors, particularly socio-cultural influences in sub-populations, as important determinants of overweight/obesity (Christakis and Fowler 2007; Trogdon et al. 2008; Mora and Gill 2012). The significance of India s socio-cultural diversity and spatial patterning on the spread and social clustering of overweight/obesity represents an important area of inquiry. This study analyses a nationally representative household dataset the National Family Health Survey (NFHS-3) conducted in 2005 to 2006 in examining the nature and extent of individual and macro-level contextual factors associated with the prevalence of overweight/obesity in India. Attention is given to analysing the significance of covariates on particular sub-populations. The study aims to contribute to the broader literature in two major ways. First, separating the population into four sub-groups (urban and rural and females and males) enables a deeper analysis of the factors influencing the likelihood of overweight/obesity and its diverse patterning across the population. Second, we explore the significance of macrolevel contextual factors as an area of enquiry in itself, rather than simply controlling for them when analysing individual-level covariates (Subramanian and Smith 2006; Subramanian et al. 2009). As economic growth continues to transform Indian society, understanding individual-level behaviours and the influence of spatial and socio-cultural contexts are important in developing a more nuanced policy approach for addressing overweight/obesity. Data and methods Data The NFHS-3 dataset analysed in this study is a nationwide household survey, which provides information on health-related matters, including fertility, morbidity and mortality, family planning and nutrition. NFHS-3 covers a representative sample of females aged years and males aged years across 29 states in India. The primary sampling unit (PSU) comprises two main strata urban (wards or municipal localities) and rural (villages) which were drawn separately within each state and are proportional to the size of the respective urban and rural areas. The primary outcome measure used for assessing overweight/obesity was the body mass index (BMI), which is calculated as weight in kilograms divided by height in metres squared (kg/m 2 ). Using World Health Organization classifications, BMI cut-offs were divided into those overweight/obese (BMI 25) and those not overweight/obese (BMI < 25). The study analyses the factors associated with the likelihood of overweight/obesity relative to the population who are not. The dataset was separated into four sub-populations: urban female, rural female, urban male and rural male to enable a detailed analysis of factors associated with high BMI. Covariates Socio-economic status is well recognized as an important determinant of individual-level BMI. To infer economic status, NFHS-3 adopts a wealth index score based on the household ownership of particular assets. The survey uses principal component analysis to weight and standardize the asset scores and each individual within the household is assigned a wealth score. Our analysis treats wealth as a continuous variable. Another important dimension of socio-economic status is education which in this study is treated as a continuous variable specified in terms of years of education. Other covariates include age, town/city, religion/caste, occupation, frequency of TV viewing, access to clean cooking fuel and any form of tobacco use. Age is treated as a continuous variable: years for females and years for males. In the case of the urban sub-populations, large city, small city and town were included as separate categorical variables to discern the degree of urbanization. 1 Religion and caste were combined as a categorical variable, with Hindu divided into two sub-categories: Hindu caste (scheduled castes, scheduled tribe and other backward castes) and Hindu general (those not legally recognized as lower caste). The other religious groupings were Muslims, Sikhs, Christians and others. Occupation was divided into three sub-categories: not working/home, white collar and manual labour. TV viewing was included as a lifestyle categorical variable: the sub-categories were not at all (TV cat-0), less than once a week (TV cat-1), at least once a week (TV cat-2) and almost every day (TV cat-3). Another covariate was cooking fuel availability: the sub-categories were clean fuel (kerosene, gas and electricity) and biofuel (smoke-related solid fuels such as animal dung, wood, coal/charcoal and biomass). 2 Tobacco use (either smoking or chewing) was included as a lifestyle categorical variable. 3 The descriptive statistics for predictor variables considered in this study (in terms of sample size and percentage of overweight/obese), tabulated across the four sub-populations are shown in Appendix 1. Statistical methods A multi-level binomial logit regression model was used to estimate a binary outcome in terms of a log likelihood ratio. The model is written as follows:! 1 ijk ln ¼ a þ X 1 1 m bm X m ijk þ u k þ v jk þ e ijk (1) ijk 1 ijk probability of overweight/obesity for individual i nested in jth PSU nested in kth state X m ijk fixed component covariates where superscript m represents number of covariates u k intercept effect of kth state v jk intercept effect of jth PSU nested in kth state

3 584 Health Policy and Planning, 2016, Vol. 31, No. 5 e ijk residual error of individual i nested in jth PSU nested in kth state The model shown in Equation (1) contains a fixed component which has a separate intercept (a) and a slope parameter (b m ) estimating the effects of a one unit change in the covariates on the log of the odds of overweight/obesity ð1) relative to not being overweight or obese (1 1). The remaining terms in Equation (1) capture the independent effect of geographical space as a separate explanatory variable on the likelihood of overweight/obesity in terms of the impact of the state, and the PSU (neighbourhood) within it, in which the individuals reside. The advantage of the multi-level modelling approach is that it allows intercepts to vary with respect to the state and city/town/village to account for the unobserved macro-level contextual factors of the state and the PSU beyond individual-level factors. Thus, the intercept for any given state and neighbourhood can be expressed as a þ u k and a þ u k þ v jk, respectively. Separate regressions were generated for each sub-population where the maximum likelihood or odds ratio (OR) of overweight/ obesity was estimated relative to the population within that group who were not overweight or obese. Education, wealth and age were treated as continuous variables. The other covariates were treated as categorical variables which involved adopting a reference category as a benchmark from which maximum likelihood or OR was estimated. The multi-level modelling approach attempts to identify the extent to which macro-level contextual factors and individual-level characteristics are associated with overweight/obesity. All econometric analyses were conducted using Stata 12. Results Table 1 presents the binomial logistic regression results for the four sub-populations. The results are divided into a fixed component encompassing the parameter estimates of covariates and an intercept component which incorporates spatially related multi-level effects. The categorical parameter estimates are shown in terms of the OR of overweight/obesity relative to a reference category within the subpopulation. All results reported below are those that are statistically significant at either P < 0.05 or < As expected, the results show that socio-economic status as measured by the wealth variable has a strong positive effect on the likelihood of overweight/obesity across the four sub-populations. Thus for urban females, holding all factors constant, a one unit increase in wealth increases the likelihood of overweight/obesity by a factor of With the exception of rural females, the wealth 2 parameter is not statistically significant, suggesting that there is no diminishing or increasing returns present with respect to incremental changes in wealth. Another way to interpret the wealth variable is to transform the OR into estimated probabilities of overweight/obesity for each subpopulation. Under this transformation, all the covariates of the model presented in Table 1 are held constant at a base level while allowing wealth to vary in order to determine its isolated effect on the estimated probability of overweight/obesity for a particular subpopulation. The base model chosen for each sub-population holds constant the continuous variables of education and age at their respective mean levels and the categorical variables are fixed at the classification which contains the largest population for that subpopulation. The category classifications of covariates held constant for the sub-populations are presented in Table 2. Figure 1 shows the relationship between the estimated probability of overweight/obesity and the increasing wealth levels for the sub-populations. Thus, holding all factors constant at the respective base categories specified in Table 2, urban and rural males have a substantially higher probability of overweight/obesity than urban and rural females for any given wealth point. For example, at the 60th wealth point, 4 urban and rural males have a 48.4 and 45.1% probability of overweight/obesity compared with a probability of 26.1 and 11.4% for urban and rural females, respectively. Interestingly, rural males exhibit a similar probability of overweight/obesity as urban males; this observation is not found with females. With regards to the education variable, all four sub-populations exhibit an increasing likelihood of overweight/obesity with more years of education (Table 1). However, the results show that for urban and rural females the education 2 variable is statistically significant with a parameter estimate of <1 suggesting the likelihood of overweight/obesity increases with respect to education at a diminishing rate. Again, as with the wealth variable, the effect of education on overweight/obesity can be transformed into estimated probabilities for the sub-populations. Figure 2 shows the probability of overweight/obesity within each of the sub-populations for different years of education. (In this case wealth and age are held constant at their mean.) Thus, e.g. using the sub-populations based on their respective categories (Table 2), at 5 years of education, urban females have a 26.4% probability of overweight/obesity while rural females have a 5.6% probability. Interestingly, in the case of females there is an inverted U-shaped relationship in the probability of overweight/ obesity with respect to education. For both urban and rural females, increasing the initial years of education is associated with a rise in the probability of overweight/obesity, but beyond 7 8 years of education the impact of additional years of education begins to lower the probability of overweight/obesity. That is, beyond a certain threshold level of educational attainment, there is a negative association between years of education and the probability of overweight/obesity. No statistically significant negative relationship between education and overweight/obesity was found with males. In relation to the categorical variables in the model, the OR of overweight/obesity is referenced against a benchmark within each category (Table 1). Regarding the degree of urbanization, females living in small towns have a lower likelihood of overweight/obesity (OR ¼ 0.88) relative to those living in major cities. No statistically significant difference was observed for males living in small towns. There was a statistically significant positive association between the frequency of TV viewing and the likelihood of overweight/obesity for urban and rural females and rural males relative to those who did not watch TV. Thus, e.g. urban females who watch TV at least once a week (TV cat-2) or daily (TV cat-3) had 1.25 times and 1.37 times, respectively, the likelihood of overweight/obesity relative to urban females who do not watch TV. Those who did not use tobacco had a lower likelihood of overweight/obesity relative to those who did use tobacco; this was statistically significant for urban and rural females (OR ¼ 0.87 and 0.77, respectively) and urban males (OR ¼ 0.86). With regards to occupation, urban females who work in either white collar or manual occupations have a lower likelihood of overweight/obesity (OR ¼ 0.87 and 0.68, respectively) relative to urban females who do not work. For rural females, only those involved in manual work had a statistically significant lower likelihood of overweight/obesity (OR ¼ 0.67). However, in the case of males, urban and rural white collar workers have a higher likelihood of overweight/obesity (OR ¼ 1.36 and 1.48, respectively) relative to urban and rural males who do not work. For urban and rural females, access to clean cooking fuel (kerosene, gas and electricity)

4 Health Policy and Planning, 2016, Vol. 31, No Table 1. Multi-level analytical model for overweight/obesity in India, Urban female (n ¼ ) Rural female (n ¼ ) Urban male (n ¼ ) Rural male (n ¼ ) Parameters fixed a OR CI b (95%) OR CI b (95%) OR CI b (95%) OR CI b (95%) Wealth 2.10** c ( ) 2.92** c ( ) 2.12** c ( ) 2.76** c ( ) Wealth ( ) 0.92** ( ) 1.03 ( ) 0.98 ( ) Education 1.05** ( ) 1.06** ( ) 1.04* ( ) 1.05* ( ) Education ** ( ) 0.99** ( ) 0.99 ( ) 0.99 ( ) Age 1.38** ( ) 1.35** ( ) 1.33** ( ) 1.37** ( ) Age ** ( ) 0.99** ( ) 0.99** ( ) 0.99** ( ) City (Ref) 1.00 Ref NA NA 1.00 Ref NA NA Small city 0.93 ( ) 1.03 ( ) Town 0.88* ( ) 0.98 ( ) TV cat-0 (Ref) 1.00 Ref 1.00 Ref 1.00 Ref 1.00 Ref TV cat ( ) 1.27** ( ) 0.90 ( ) 1.11 ( ) TV cat ** ( ) 1.37** ( ) 1.02 ( ) 1.18 ( ) TV cat ** ( ) 1.47** ( ) 1.16 ( ) 1.32* ( ) Not working (Ref) 1.00 Ref 1.00 Ref 1.00 Ref 1.00 Ref White collar 0.87** ( ) 1.04 ( ) 1.36** ( ) 1.48* ( ) Manual labour 0.68** ( ) 0.67** ( ) 1.12 ( ) 1.10 ( ) Hindu general (Ref) 1.00 Ref 1.00 Ref 1.00 Ref 1.00 Ref Muslim 1.25** ( ) 1.47** ( ) 1.08 ( ) 0.99 ( ) Christian 0.89 ( ) 1.07 ( ) 0.90 ( ) 0.94 ( ) Sikh 1.38* ( ) 1.43 ( ) 1.59* ( ) 1.99** ( ) Hindu caste 0.84** ( ) 0.93 ( ) 0.84** ( ) 0.85* ( ) Other 0.88 ( ) 1.01 ( ) 0.90 ( ) 1.06 ( ) Biofuel (Ref) 1.00 Ref 1.00 Ref NA NA NA NA Clean cooking fuel 1.08* ( ) 1.16* ( ) No tobacco use (Ref) 1.00 Ref 1.00 Ref 1.00 Ref 1.00 Ref Tobacco use 0.87* ( ) 0.77** ( ) 0.86* ( ) 0.94 ( ) Constant ** ( ) ** ( ) ** ( ) ** ( ) Intercept Effects ICC d CI b (95%) ICC d CI b (95%) ICC d CI b (95%) ICC d CI b (95%) State 0.03** ( ) 0.04** ( ) 0.03** ( ) 0.04* ( ) State/PSU 0.07** ( ) 0.08** ( ) 0.07** ( ) 0.09** ( ) NA ¼ Not applicable a From equation 1, we use exp b1 which is the effect of a one unit increase in the covariate on the odds of being overweight/obese. b CI denotes confidence interval at 95%. c Asterisks denote level of statistical significance: ** P < 0.001; * P < d ICC refers to Intra-class correlation, which measures the proportion of the total variance that is due to the differences between states/psus. has a higher likelihood of overweight/obesity (OR ¼ 1.08 and 1.16, respectively) compared with those who use biofuel. Table 1 shows that urban and rural Muslim females have a much higher likelihood of overweight/obesity relative to the Hindu general group (OR ¼ 1.25 and 1.47, respectively). However, there is no statistical difference in OR for urban and rural Muslim males. Sikhs have a statistically significant higher likelihood of overweight/ obesity across the four sub-populations. Urban and rural Sikh males had a higher likelihood of overweight/obesity (OR ¼ 1.59 and 1.99, respectively) compared with the Hindu general group. Notwithstanding the significance of particular individual-level covariates, the multi-level modelling results reveal that unobserved factors at state and associated PSU levels significantly explain around 7 9% of the total residual variance. In Figure 3a and b the states are ranked in terms of the impact of state-level unobserved factors on the likelihood of individuals overweight/obesity along with confidence intervals (95%) in the form of caterpillar plots for the urban female and male sub-populations. Due to space limitations only the urban female and male results are presented: they have a substantially higher overweight/obesity prevalence compared with the rural population. From Figure 3a it can be seen that states such as Tamil Nadu, Andhra Pradesh, Kerala, Karnakata and Punjab have a higher likelihood of overweight/obesity relative to the Table 2. Category classifications used in estimating the probability of overweight/obesity Urban female Rural female Urban male Rural male Major city NA Major city NA Watching TV No TV watching Watching TV No TV watching daily daily Not working Manual work White collar Manual work Hindu general Hindu general Hindu general Hindu general Clean cooking Biofuel NA NA fuel No tobacco No tobacco No tobacco Tobacco NA, not applicable. Indian mean. (See Appendix 2 for the acronyms for each of the states.) Conversely, other major states such as Rajasthan, Bihar, Jharkand and Madhya Pradesh have a lower likelihood of overweight/obesity relative to the Indian mean. Ostensibly, the statelevel effects can be considered as intercept shifts of the model estimates. Results also show that some smaller north-eastern states had a lower likelihood of overweight/obesity than the Indian mean: for example, Nagaland, Mizoram and Meghalaya. (Although not shown in this article, our separate analysis reveals that the

5 586 Health Policy and Planning, 2016, Vol. 31, No Urban Female Rural Female Urban Male Rural Male 0.70 Probablity of O-O Wealth Level Figure 1. Probability of overweight/obesity (O-O) by wealth level Probablity of O-O 0.30 Urban Female Rural Female Urban Male Rural Male Years of Educa on Figure 2. Probability of overweight/obesity (O-O) with respect to the level of education (years) North-eastern states also have a lower probability of being underweight relative to the Indian mean.) For urban males the caterpillar plot in Figure 3b shows that some states such as Andhra Pradesh and Tamil Nadu and, to a lesser extent, Karnataka and Punjab have a statistically higher likelihood of overweight/obesity relative to states such as Madhya Pradesh, Bihar and Rajasthan. The results show that even after taking into account individual-level covariates, state-level contextual factors exert a separate influence on the likelihood of overweight/obesity.

6 Health Policy and Planning, 2016, Vol. 31, No (a) State level random effect (b) State level random effect MGH MZR RAJ BIH HMP ASM TRP SKM GOA UTC CHG DEL ORS MHR ARP MAP JHK NGL MNP HAR JAK WBG UTP GUJ KAR PUN ANP KER State (ranked) MAP MGH UTC BIH JHK ARP JAK CHG SKM MHR GUJ TRP DEL ORS MZR WBG GOA ASM NGL HMP RAJ KAR PUN HAR UTP MNP KER TAN ANP TAN State (ranked) Figure 3. (a) State-level intercept effects: urban females. (b) State-level intercept effects: urban males Discussion By disaggregating the dataset into sub-populations and giving focus to macro-level contextual factors our study yields some interesting findings not reported elsewhere. Studies reveal that in developed countries, overweight/obesity is more prevalent among the poorer and less educated sections of the population whereas in low-income developing countries the reverse is the case, where such prevalence is greater among those with higher socio-economic status (Dinsa et al. 2012; Cohen et al. 2013). Consistent with this, a number of studies reveal that Indians from the upper socio-economic strata have a higher calorific and fat content diet and engage in less physical activity in comparison to those from lower socio-economic backgrounds resulting in a higher prevalence of overweight/obesity (Agrawal, 2005; Griffiths and Bentley 2005; Misra et al. 2011; Misra and Shrivastava 2013). Interestingly however, our findings show that for female Indians, in particular urban women, there is initially a positive association between overweight/obesity and education, but once educational attainment exceeds around seven to eight years a

7 588 Health Policy and Planning, 2016, Vol. 31, No. 5 negative association begins to develop and the probability of overweight/obesity declines with additional years of education. One possible interpretation for the inverted U-shaped findings, as argued by Cohen et al. (2013), could be that illiterate and lowly educated females have different physical patterns of activity while highly educated females engage in weight-control behaviours, such as a healthier diet and/or increased physical activity, which becomes dominant at very higher levels of education. In this context, our findings could suggest that highly-educated urban women in India are beginning to exhibit weight-control behavioural traits similar to educated women from developed countries. Interestingly, this inverted-u relationship between education and overweight/obesity, as yet, does not extend to males. Nor, as yet, does a similar relationship apply between wealth status and overweight/obesity. One can speculate on the reasons for this but clearly further research is required to understand the differing dynamics governing the relationship between education and gender, the differing perceptions and attitudes to overweight/obesity and the changing behavioural patterns overtime. It is also worth noting, as argued by Cohen et al. (2013), the importance of functional form and the need to consider non-linear models when modelling the relationship between education and overweight/obesity as we have done in this study. Also our study has given particular consideration to isolating the independent effects of education on the probability of overweight/obesity (i.e. Figure 2). Attention to these aspects may explain in part why other studies have not discerned the positive effect that education can play in lowering the likelihood of overweight/obesity for females in India. Education is particularly important because it enhances knowledge of good health maintenance and offers the potential to increase the effectiveness of public health policies, including dietary information and healthy lifestyle campaigns, and thereby deal with the deleterious effects of both under and over nutrition (Lecerof et al. 2011). Our study findings reveal Muslim females and Sikh males and females have a greater likelihood of overweight/obesity relative to the Hindu general group after allowing for socio-economic status and other individual-level covariates. In the case of female sub-populations, some studies have raised the spectre of cultural practises and social restrictions on physical activity (and on workforce participation) and on dietary behaviour as possible reasons for higher obesity prevalence rates but data limitations, as is the case in our study, prevented further analysis (Griffiths and Bentley 2001; Chopra et al. 2013; Misra and Shrivastava 2013; Misra et al. 2013). Regarding the Sikh sub-population, our separate analyses (not reported here) of the 61st round ( ) of the National Sample Survey on consumption expenditure, reveal that notwithstanding differences in dietary patterns, the total calorific and fat intake of Sikhs across the income groups differs little from the equivalent income groups of the general Indian population. Data limitations in this study prevent us from identifying salient socio-cultural characteristics particular to the Sikhs which may contribute to their high BMI. Although we are not able to determine the underlying causal mechanisms accounting for the differences reported in this study there is a growing recognition of the need to better understand the significance of socio-cultural determinants of dietary choices and physical activity which impact on overweight/obesity (Swinburn et al. 2011). In this regard, policy is likely to be more effective if it is targeted in a way which recognizes socio-cultural diversity rather than being applied uniformly across all socio-cultural groups. Another important observation in this study is that the relationship between wealth and overweight/obesity is more intense for males than for females. Thus, it needs to be recognized that as the socio-economic status of the Indian population increases over time overweight/obesity is likely to rise more markedly for men than women. The multi-level analysis reveals that, in addition to individuallevel covariates such as socio-economics status, state-level macro environment exerts an independent influence over the likelihood of overweight/obesity. One can speculate as to whether some states may have a more obesogenic environment e.g. a built environment limiting physical activity, a high concentration of fast-food outlets or particular regionally based socio-cultural-attributes which may commonly expose individuals to overweight/obesity types of behaviour (Cohen and Fletcher 2008; Swinburn et al. 2011). The nature of the underlying causal linkages of state-level factors beyond the individual-level characteristics on overweight/ obesity needs to be more fully understood. Related to macro-level contextual factors, a growing body of empirical literature analysing the role of social influences affecting overweight/obesity has accumulated over the past two decades (Christakis and Fowler 2007; Auld 2011; Fletcher 2011; Renna et al. 2008; Trogdon et al. 2008; Mora and Gil 2012). Cultural norms and values, habituation and imitative behaviour are embedded in the social network which influences the behaviour of individuals in an interdependent way. The implication is that policy interventions which target the social network rather than the individual can create a virtuous circle of behavioural change as people respond interdependently to changes in their social network (Smith and Christakis 2008; Corrado and Distante 2012). Associated with social transmission mechanisms, is the need to understand socio-cultural perceptions regarding body size and related weight-control activity. A recent multi-city study in India found there was a prevalent misconception that an overweight child was considered a healthy child leading to unhealthy dietary habits flowing from mother to child (Gulati et al. 2013). Similarly, a positive pattern of attitudes towards obesity found in a study of parents of obese teenagers in the Punjab raised concerns over parents not taking measures to combat overweight/obesity (Rashmi and Jaswal 2011). Interestingly, a recent study of female adults in India reported a discrepancy between self-perceived and actual body weight (Agrawal et al. 2014). The study reported that 25% of overweight females and 10% of obese females perceived themselves as being a normal weight and a majority of these women did not engage in physical activity to reduce their weight. A similar misconception of understating body weight was found by Ramesh (2012) in a study of adolescents in the Indian state of Kerala. Given the considerable diversity of the Indian sub-continent, policy strategies need to be more nuanced, recognizing the socio-cultural and macro-level contextual factors which influence decisions regarding dietary behaviour, physical activity and perceptions about body size. This study has a number of caveats. A key limitation is that lifestyle factors cannot be fully explored using NFHS-3 data as it does not collect information on energy intake or expenditure. Thus, watching TV was included as a proxy for physical inactivity, but it is a crude measure and does not necessarily capture differences in sedentary behaviours across sub-populations or differences in lifestyle behaviours affecting diet. 5 In addition, whilst our study reveals that sub-populations from different socio-religious/socio-cultural backgrounds have differing probabilities of overweight/obesity, we are unable to identify underlying casual mechanisms by which the imbalance between calorie intake and expenditure occurs across such groups. Accordingly, further research (especially of a qualitative nature) is required. Similarly, without further research we are unable to determine the nature of the transmission mechanism by

8 Health Policy and Planning, 2016, Vol. 31, No which education for females, but not males, exhibits an inverted U-shaped relationship in its association with the probability of overweight/obesity. Clearly, more needs to be understood in terms of how gender interacts with education in this respect. Finally, although the use of multi-level analysis reveals the significance of state/psu-related contextual factors influencing overweight/ obesity, we are not able to disentangle macro-level obesogenic environmental factors from social influences in explaining such variations. In this regard, our analysis can be considered exploratory in revealing the importance of spatially and sociocultural related contextual factors in addition to individual-level factors in explaining the variations in the prevalence of overweight/ obesity. Conclusion Study findings reveal considerable differences in the underlying relationships influencing the likelihood of overweight/obesity across sub-populations in India. For females beyond a particular education level there is a negative association with overweight/obesity. Findings show that Muslim females and all of the Sikh sub-populations have higher overweight/obesity likelihoods relative to the Hindu general sub-population. This suggests the importance of socio-cultural influences. In addition, there is a stronger proclivity in the likelihood of overweight/obesity with regards to wealth for males raising the spectre of the differential impact of economic development on particular sub-populations. Also, multi-level analysis reveals that spatially related factors exert an independent influence on overweight/obesity. Although this study is limited in identifying the precise causal mechanisms underpinning particular macro-level contextual factors, the results highlight the need to refrain from adopting a one size fits all approach to policy. Instead, policy implementation requires a more nuanced and targeted approach to incorporate the growing recognition of spatial and socio-cultural related factors on overweight/obesity and to exploit such influences in spreading healthy behaviours. India is undergoing considerable economic growth, which is seeing a massive transformation in nutrition patterns, increasing urbanization and a growing sedentary lifestyle which all contribute to the overweight/obesity epidemic. Moreover, for any given BMI, Indians are much more susceptible than Caucasians to obesity-related diseases and metabolic-related abnormalities (Khandelwal and Reddy 2013). Such diseases accounted for more than 50% of deaths in 2008 and NCD represents a major public health challenge for the Indian government (Upadhyay 2012). Understanding the determinants driving the overweight/obesity pattern, both at the individual and the macro levels, is critical to the implementation of appropriate policy strategies. This article makes an initial contribution towards this understanding. Conflict of interest statement. None declared. Notes Large city, small city and town populations were >1 million, to 1 million and < , respectively. The notion of distinguishing the type of cooking fuel between clean and biofuel was adopted from Jose and Navaneethan (2010). Our analysis reveals that alcohol consumption was not statistically significant across any of the sub-populations and it was therefore excluded from the base model. 4 5 Wealth is divided into equally discrete units on a scale ranging from 1 to 102 units. We are unable to determine whether the lower likelihood of overweight/obesity from tobacco use is associated with lower health status (from the deleterious effects of smoking) or the possible appetite suppressant nature of tobacco. Also, it is outside the scope of this study to determine whether the positive association between the use of cleaner cooking fuel and the probability of overweight/obesity is associated with higher health status (by avoiding exposure to toxic pollutants) or the possible changes in meal preparation and dietary patterns brought about by the use of more time-convenient cooking fuel. References Agrawal P Role of lifestyle and diet in emerging obesity among Indian women and its impact upon their health status. 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9 590 Health Policy and Planning, 2016, Vol. 31, No. 5 Misra A, Singhal N, Sivakumar B et al Nutrition transition in India: secular trends in dietary intake and their relationship to diet-related noncommunicable diseases. Journal of Diabetes 3: Misra A, Shrivastava U Obesity and dyslipidemia in South Asians. Nutrients 5: Mora T, Gil J Peer effects in adolescent BMI: evidence from Spain. Health Economics 22: Popkin B, Adair L, Ng S Global nutrition transition and the pandemic of obesity in developing countries. Nutrition Reviews 70: Ramesh K. 2012b. Body/weight perceptions and prevalence of obesity among adolescents Kerala, India. International Journal of Health and Allied Sciences 1: Rashmi, Jaswal S. (2011). Attitudes of parents, peers and teachers towards obese teenagers. Journal of Psychology 2: Renna F, Grafova I, Thakur N The effect of friends on adolescent body weight. Economics and Human Biology 6: Appendix Smith K, Christakis N Social networks and health. Annual Review of Sociology 34: Subramanian S, Smith G Patterns, distribution, and determinants of under- and overnutrition: a population-based study of women in India. The American Journal of Clinical Nutrition 84: Subramanian S, Perkins J, Khan K Do burdens of underweight and overweight coexist among lower socioeconomic groups in India? The American Journal of Clinical Nutrition 90: Swinburn B, Sacks G, Hall K et al The global obesity pandemic: shaped by global drivers and local environment. The Lancet 378: Trogdon J, Nonnemaker J, Pais J Peer effects in adolescent overweight. Journal of Health Economics 27: Upadhyay R An overview of the burden of non-communicable diseases in India. Iranian Journal Public Health 41: 1 8. Appendix 1. Descriptive statistics for NFHS-3 dataset showing sample size of different predictor variables and respective overweight/obese percentage. Urban Female Rural Female Urban Male Rural Male N (% overweight) N (% overweight) N (% overweight) N (% overweight) Total sub-population (23.2) (7.3) (16.7) (5.9) Wealth Poorest 866 (4.2) (1.7) 497 (3.9) 6199 (1.3) Poorer 2361 (6.8) (3.5) 1511 (3.5) 8217 (2.1) Middle 6330 (11.2) (6.3) 4555 (6.9) 9497 (4.6) Richer (17.7) (13.3) (11.1) 7088 (10.2) Richest (32.4) 8015 (24.7) (24.9) 3571 (23.7) Education No education (17.2) (4.9) 3016 (7.7) 6910 (3.1) Primary 6201 (21.0) (8.4) 4144 (10.9) 6624 (4.2) Secondary (24.0) (9.7) (15.2) (6.6) Higher 9402 (30.8) 2516 (15.3) 7575 (28.3) 2824 (14.9) Age (years) (4.9) (1.4) 6037 (3.5) 6152 (0.7) (10.9) (3.6) 6242 (7.7) 5309 (2.9) (20.3) (6.2) 5159 (14.5) 5080 (5.5) (29.2) 9412 (8.7) 4498 (20.1) 4548 (7.6) (35.0) 8362 (11.6) 4155 (24.9) 4408 (8.5) (41.8) 6784 (13.6) 3565 (26.0) 3713 (8.7) (42.2) 5101 (15.7) 2933 (27.6) 3185 (9.5) NA NA 2067 (28.8) 2177 (9.9) Type of residence Capital city (26.8) NA (18.4) NA Small city 8763 (22.4) NA 4567 (16.3) NA Town (20.8) NA (15.5) NA TV watching Never 4730 (12.7) (3.6) 1670 (8.6) 6962 (2.9) Less than once a week 3662 (16.4) 8647 (5.9) 3196 (10.2) 7936 (3.5) At least once a week 5748 (19.4) 8704 (8.2) 4590 (12.5) 6618 (4.7) Everyday (26.1) (13.1) (19.1) (10.6) Occupation Not working (24.4) (9.2) 5904 (7.2) 4271 (2.3) White collar 9471 (25.4) 3914 (16.1) (24.1) 6010 (12.2) Manual labour 6817 (14.9) (4.2) (13.0) (5.1) Socio-religious group Hindu general (28.6) (10.9) 9283 (21.1) 6450 (9.6) Muslim 8435 (22.7) 7151 (8.2) 5472 (13.8) 3079 (5.1) Christian 4706 (26.1) 5925 (12.3) 2673 (20.7) 3699 (9.9) Sikh 763 (41.7) 1927 (27.7) 336 (35.8) 629 (21.9) Hindu caste (19.3) (5.3) (13.8) (4.4) (continued)

10 Health Policy and Planning, 2016, Vol. 31, No Appendix 1. (continued) Urban Female Rural Female Urban Male Rural Male N (% overweight) N (% overweight) N (% overweight) N (% overweight) Other 1799 (20.9) 1972 (4.4) 1239 (22.4) 1011 (2.9) Cooking Biofuel (12.7) (5.8) NA NA Clean fuel (28.9) 8210 (22.5) NA NA Use of tobacco No (23.4) (7.5) (17.5) (6.5) Yes 2557 (18.4) 5374 (3.6) 6106 (12.9) 8886 (4.4) NA ¼ Not applicable Appendix 2. Glossary of acronyms for Indian states. ANP Andhra Pradesh ARP Arunachal Pradesh ASM Assam BIH Bihar CHG Chhattisgarh DEL Delhi GOA Goa GUJ Gujarat HAR Haryana HMP Himachal Pradesh JAK Jammu and Kashmir JHK Jharkhand KAR Karnataka KER Kerala MAP Madhya Pradesh MGH Meghalaya MHR Maharashtra MNP Manipur MZR Mizoram NGL Nagaland ORS Orissa PUN Punjab RAJ Rajasthan SKM Sikkim TAM Tamil Nadu TRP Tripura UTC Uttaranchal UTP Uttar Pradesh WBG West Bengal