CUNY Institute for Demographic Research On-line Working Paper Series

Unpublished manuscript. Do not copy or cite without author permission.

Urbanization, Women, and Weight Gain: Evidence from India, 1998-2006

By Alka Dev, MHS

Deborah Balk, PhD

Latest Revision: March 4, 2014

PWP-CIDR-2014-001

1

Urbanization, Women, and Weight Gain: Evidence from India, 1998-2006

Alka Dev, MHS*1 Deborah Balk, PhD

CUNY Institute for Demographic Research (CIDR)

One Bernard Baruch Way New York, NY 10010 *adev@gc.cuny.edu

Abstract

While malnutrition remains an important public health concern in poor countries,

particularly in rural areas and among the urban poor, overweight and obesity are

emerging as important public health concerns. The globalization of the fast food industry

and shifts in physical activity patterns related to urbanization can result in different risks

for overweight and obesity among urban and rural residents but also among women and

men. City size may also be positively correlated with obesity and the prevalence of

chronic diseases. We examine Indian National Family Health Survey data on women

ages 15-49, focusing on the effect of urbanization on gains in BMI among women in the

past two survey years. Using multivariate models that assess the role of individual

characteristics, including urban residence, in the likelihood of being overweight or obese,

we find that urban living for women in India is strongly positively associated with being

overweight or obese. Larger city size is also associated with higher proportions of

overweight and obese women, but this relationship needs further evaluation based on

geocoded data. Findings suggest that public health planning in established and emerging

metropolitan areas in India should consider focusing on women’s diet and their

opportunities for exercise.

1ThisstudywasmadepossibleinlargepartbyaCUNYDemographyFellowshiptoMs.Dev.

2

Introduction

The world has seen a doubling of obesity rates in the past three decades, with

more than 10% of the world’s population at risk for deaths related to excessive and

unhealthy weight gain.1 Overweight and obesity, once considered high-income country

problems, are also on the rise in low- and middle-income countries, particularly in urban

settings. While malnutrition remains an important public health problem in poorer

countries, changes in lifestyle and dietary intake as a result of economic growth and

urbanization, can lead to a "double burden" of disease in cities, i.e. the co-existence of

diseases related to under- and over-nutrition.2,3 Obesity is associated with higher

mortality from chronic conditions such as diabetes, hypertension, and cardiovascular

disease4 and a recent study shows that even being overweight increases the risk of

myocardial infarction and ischemic health disease.5 While developing countries, such as

India, have much lower levels of chronic conditions, in large part due to the lower levels

of obesity, an increase in overweight can move a large number of people into higher risk

groups or exacerbate chronic conditions such as diabetes that are on the rise from

population aging alone.6

Urban environments offer greater food choices at lower prices, while urban work

and built environment demands less physical exertion than rural areas.7 An urban

lifestyle may limit time for home cooked meals with replacement from energy‐dense

foodsthatarehighinfat.8 The global rise in per capital income and improved access to

a greater variety of food in urban areas is changing food consumption patterns toward

more meats, sweeteners and fats.9 At the individual level, studies of overweight and

obesity in developing countries have shown a strong positive association between

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socioeconomic status (SES) and body mass index (BMI), especially SES and

overweight.10 However, no study has closely examined the role of urbanization in

increasing rates of overweight and obesity in the developing world. This study aims to

fill that gap. We examine the Indian National Family and Health Survey (NFHS) data on

women (ages 15-49), focusing on the role of individual characteristics, including urban

residence, in the likelihood of becoming overweight or obese. We intend to add to the

existing literature, an assessment of any effect of urban living on BMI gain.

Urbanization has been linked to a global increase in the prevalence of overweight

and obesity among adults and children in all regions of the world. By 2030, the

combined effect of a wider availability of high calorie food products and sedentary

conditions associated with urban living, could contribute to a 75% increase in the

prevalence of overweight and obesity among adults ages 20 years and older worldwide.11

In hub cities, such as Accra, Ghana, urban residents have become more vulnerable to

unhealthy weight gain due to consumption of energy-dense processed foods, late working

hours, and greater alcohol consumption.12 Children are also vulnerable and may be

experiencing faster rates of increases in overweight and obesity compared to adults.13

These increases are especially notable for urbanized and developed countries such as the

US, where one in four urban school-age children is overweight or obese.14 Similar trends

have also been observed in Western Europe, Australia and urban areas in middle-income

countries such as Brazil and China. Furthermore, though city residents are more likely to

walk or cycle, due to urban features such as higher density, greater connectivity, and

more land use mix15, than their sub-urban or rural counterparts, the causal relationship

between this form of ‘active transport’ and health remains unclear.16 Even though active

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city residents may be in better health, the duration and intensity of physical activity

required to prevent transitioning to overweight and obesity is much higher than

previously recommended, and higher than the average city dweller may walk or cycle in

a day.17,18

India experienced significant economic growth starting in the early 1990s, which

has led to widespread changes in the urban environment, as well as the continued growth

or emergence of new metropolitan areas. Since 1990, the country’s urban population has

increased from 26% to 31% and the pace of annual urban growth far surpasses annual

rural growth.19 It is estimated that by 2025, over 37% of the Indian population will be

urban.20 While these may be seemingly low urban fractions, India already has more large

cities than any other country except China: in 2010, India had 43 cities of more than 1

million persons, three of which had close to 15 million or more persons.21 Although less

than a third of India’s people live in cities and towns, they generate over two-thirds of the

country’s gross domestic product and account for 90% of government revenue; most

future economic growth is projected to result from moving the labor force from farming

to non-farm activities.22 The population and economic growth has come at the cost of

increasing income inequality23; urban sprawl resulting in long work commutes, lack of

quality outdoor spaces, and poor safety for pedestrians and cyclists7; growth of large

informal settlements and slums24; and greater consumption of sugars and fat.25

Rising trends in overweight and obesity have also been observed in India in the

recent past, mostly in urban areas and among adults.19 The prevalence of overweight and

obesity has been documented to be 28% in urban Delhi, and was higher among females

than males.26 Higher income adults in Indian urban areas also have higher rates of

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overweight and obesity (32.2% among males, 50% among females) than the middle

classes (16.2% males, 30.3% females), lower socio-economic groups (7.0% males, 27.8%

females), followed by those living in urban slums (1.0% males, 4.0% females).27

Subramanian and colleagues28 found similar associations of pre-overweight, overweight,

and obese categories with a more than 2-fold socioeconomic gradient across standard-of-

living index quintiles for pre-overweight women and 7-fold gradient for obese women in

India. At least one study from India also found 4 times higher rates of obesity among

urban school children in private versus public schools, and significantly higher rates of

overweight in metropolitan versus non-metropolitan areas.29 Longitudinal data also

suggests that there is a trend toward faster overweight prevalence growth rates for the

lowest wealth and education groups in urban areas as well.30 A study of rural to urban

migrants in India found that urban male and female migrants reported an increase in fat

intake and reduced physical activity compared to their rural siblings, with corresponding

higher levels of obesity and diabetes.31 Combined with food consumption patterns, slum

dwellers could be vulnerable to unhealthy weight gain as they age.

The Indian NFHS data have been used to estimate prevalence of overweight and

obesity among women by education and socioeconomic status.28 However, the effect of

urban residence itself on the rates of overweight and obesity prevalences over the

reproductive life span of women in India is not known. This paper provides a “thick”

description of the proportions of overweight and obese women in India, a fuller

understanding of who is more likely to become overweight, and a discussion of the role

of urbanization in these trends. In the sections that follow, we describe the data and

sampling frame, including the greater attention to Indian cities that allows for this

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analysis, the basic description of BMI among Indian women, and the results of a

multivariate analysis on BMI gains.

Data and Sample

The data set included the last two rounds of the Indian NFHS, a representative

cross-sectional survey of women aged 15-49 years. NFHS-2 (1998-1999), covered all 26

states and interviewed 90,303 women while NFHS-3 (2005-2006) covered all 29 states

(including 3 new states that were created) and interviewed 124,385 women. In each

state, the rural sample was typically selected in two stages involving the selection of

Primary Sampling Units (PSUs) or villages with probability proportional to population

size (PPS) in the first stage and random selection of households within each selected PSU

in the second stage. In urban areas, the sample was selected in three stages: the first stage

was PPS selection of wards arranged by geographic region and female literacy, followed

by random selection of one census enumeration block (CEB) from each selected ward,

and the random selection of households from each CEB. In NFHS-3, CEB-wise data

were also acquired for eight selected cities of which four are India’s well-known mega

cities (see map Figure 7) and four are emerging urban areas. The third round also

oversampled urban households in states with very small urban populations as well as

slum and non-slum households in the eight selected cities, to yield a sample large enough

to calculate stable estimates.32 District code names for NFHS-2 districts were supplied

by the data provider, ICF Macro International, and were used to identify the eight

selected cities that are included by name in NFHS-3. We make the assumption that

districts with the same city name in the earlier round only include the urban population.

We restricted our sample to women who were not pregnant and who were not missing

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data for the outcome variable. As opposed to prior studies, we did not exclude women

who were smoking or breastfeeding as we wanted to adjust for these variables during

analysis. This yielded a final sample of 77,613 women in NFHS-2 and 113,075 women

in NFHS-3. Ancillary data from the Indian Census (2011) is used to map the proportion

of each districts level of urbanization and correlations with district-level proportions

overweight, though this information is not used in the individual-level analysis because

the more specific NFHS data on the individual’s residence (city-size or rural) is used

instead.

Dependent variable

Our outcome of interest is BMI, calculated as weight in kilograms divided by

height in meters squared (kg/m2). Both weight and height were measured by the

interviewer. The following cut-offs were used for bivariate analysis with BMI as a

categorical outcome: <16 (severely thin), 16–18.49 (moderately to mildly thin), 18.5–

22.9 (normal), 23-24.9 (Indian overweight), 25-29.9 (global overweight), and > 30

(obese). A BMI cutoff of 23 kg/m2 was used in the context of recommendations to use

lower cut-offs for Asian Indians who might be at higher risk of type 2 diabetes and

cardiovascular disease at lower BMIs than the existing WHO cut-off point of 25 kg/m2

for overweight, hence the Indian overweight classification.33 For the multivariate

analysis, BMI was used as a continuous variable and represents the full range.

Results

Sample Characteristics

Not surprisingly, urban women in both survey years were more likely to have

completed secondary school and much more likely to have even higher education levels

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than rural women. Both rural and urban women were also better educated in the third

than the second survey; 49.6% of urban and 31.9% of rural women had completed

secondary education in NFHS-3 compared to 34.7% of urban and only 17.7% of rural

women in NFHS-2. The proportion of rural women with a secondary education also

doubled between the two surveys and a similar, though smaller, increase was seen among

urban women too. Age distributions across the two surveys were similar except for

teenagers, who formed a higher proportion of sample in NFHS-3. A majority of the

women were non-smokers with rural women having somewhat higher proportions of

smoking. Urban women also had fewer children on average and were less likely to be

breastfeeding at the time of the survey. Parity decreased for both urban and rural women

from the second to the third survey. A larger proportion of urban women in each survey

year also had a higher standard of living than rural women.

Spatial Patterns

We also explored spatial, urban patterns of weight distribution. Urban areas,

owing to their comparably small geographic land area, are hard to visualize on a map,

however it is important to note that the proportions of overweight and obesity are

spatially clustered among women. The map in Figure 1 shows the proportion of women

whose BMI is greater than 23 kg/m2 by district in NFHS-3. The global Moran’s I using

first-order polygon contiguity was 0.40 (p= 0.000), indicating a high degree of spatial

autocorrelation or “hot spots” of neighboring districts with higher proportions of

overweight and obese women (25% or more). A district’s level of urbanization (shown

in Figure 1 as circles of varying sizes) was also positively correlated with the proportion

of overweight and obese women in that district (shown as red hues in Figure 1), with a

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Pearson’s correlation coefficient of 0.465 (p=0.000) between percent urban and

overweight/obese. However, with the exception of the eight oversampled cities, the

districts of which are fully urban, we cannot spatially distinguish between the urban and

rural populations within districts. Survey data that identifies specific urban and rural

locations within districts would be necessary to further explore spatial patterns and

identify potential contributors in the urban environment, including the size (and form and

expansiveness) of the city. While a map at this scale is not a precise tool for policy, it

allows for preliminary identification of regions with disproportionately high risk.

Bivariate Analysis

We looked at the proportion of women in each BMI category by simple residence

(i.e. urban vs. rural), size of the city (town, small city, large/capital city), and for the eight

selected cities (Figure 2). Rural women were much more likely to be severely or

moderately thin than urban women in NFHS-2 (38% vs. 21%) and NFHS-3 (36% vs.

23%). In contrast, urban women were nearly three times more likely to be overweight at

BMI > 23 kg/m3 in NFHS-2 (25% vs. 12%) and twice as likely in NFHS-3 (30% vs.

15%) than rural women. Obesity was much higher among urban than rural women: 6%

versus 1% in NFHS-2 and 6% vs. 2% in NFHS-3. In selected cities, which include some

of India’s largest cities, the proportions of obese women were even higher: 8% in both

rounds. However, these cities also had their share of severely and moderately thin

women: 16% in NFHS-2 and 21% in NFHS-3. Comparing the two rounds, it was notable

that there were higher proportions of malnourished women in urban areas and obese

women in rural women in the more recent survey, although the increases were small.

Proportions of overweight women increased in both urban and rural areas.

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There is an unambiguous urban gradient in the share of overweight and obese

women by city size. Figure 3 shows that the largest proportion of women in these

categories are to be found in large cities, followed by small cities and towns, and finally

rural areas. In NFHS-3, 32% of the women in capital or large cities were overweight at

BMI > 23 kg/m3, compared to 30% in small cities, 28% in towns, and 15% in rural areas.

Obesity was measured in 8% of women in large cities, followed by 6% in small cities,

4% in towns, and 2% in rural areas. These comparisons are even more stark if the Indian

Overweight benchmark is used instead of the global cut-off, 40% of women in large

cities are overweight or obese, and while smaller shares are found in the medium sized

cities and town, more than a third of women in these localities are also overweight; the

proportions of rural women in this category is about half as much. Data from NFHS-2 is

not presented here due to the absence of city size classifications.

Of the slum dwellers sampled in the eight selected cities in NFHS-3, those

meeting the interviewer team-designated or census-based slum definition criteria were

included in the analysis. There were 10,982 slum dwelling women and 8,741 non-slum

dwelling women in this sample. With regard to the slum sample from eight cities in

NFHS-3, women in slums were not as obese as women in non-slum urban areas but they

did show some evidence of the ‘double burden’ of underweight and overweight (Figure

4). In slums, 22% of the women were severely or moderately thin while 32% were

overweight and 8% obese. Among non-slum women, there were only slightly greater

proportions of overweight (36%) and obese (9%) women but fewer thin women (18%).

In other words, and noteworthy, slum dwellers are more likely than other women to be

underweight (and presumably malnourished) than other urban women, but living in a

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slum does not prevent women from becoming overweight.

The last bivariate description we consider before a multivariate analysis is age

structure. Stratifying on place of residence for BMI outcome by age shows that BMI

increases with age much more so in urban areas and especially in the four mega-cities of

Chennai, Delhi, Kolkota, and Mumbai (Figure 5). In each survey, younger urban women

had lower BMI than their rural counterparts but by age 20, urban women gain BMI much

more than rural women. By age 30, women living in the four mega cities had an average

BMI of 23 kg/m3 or above while women in all urban areas (including the four megacities)

reached overweight by age 38, on average, in NFHS-2 and by age 35 in NFHS-3. At all

post-adolescence ages and in all settings, BMI is higher among women in the more recent

survey round.

These two pieces of evidence suggest that this issue may be a fast evolving one

for women in urban India. We specifically refer to women here because a bivariate

analysis comparing men and women suggests that increase in weight gain is much more

pronounced among women than men (Figure 6). This finding will require further

analysis of the male sample, controlling for socioeconomic status, lifestyle and gender-

specific risk factors and cultural norms. Smoking, for example, is much more prevalent

among both urban and rural men than women, and may be associated with lower rates of

overweight in men.

Multivariate Analysis

To further explore our bivariate findings for BMI and place, we modeled several

known confounders of weight gain among women, including: age, education, parity,

breastfeeding, smoking, occupation, standard of living, and wealth score. Age,

12

education, wealth, and parity were included as continuous variables while breastfeeding,

smoking, occupation, and standard of living were categorical. Occupation was recoded

to three categories to reflect potential physical strain: agricultural, laborer, and

clerical/professional. Standard of living was included in both surveys and a more refined

measure of socioeconomic status, the wealth score, was also available and used for

NFHS-3. Both measures were based on a weighted combination of household living

conditions and assets. Standard of living was measured as being low, medium or high,

while wealth was included as a raw score.

We estimated weighted, multivariate, linear regression models to assess the

independent effect of place of residence on BMI and how the magnitude of this effect

changed after controlling for the covariates. We added in an effect for the interaction

between urban residence and age as well as urban residence and wealth. We also

estimated multivariate, logistic regression models for Indian overweight or obese

(BMI>23) and underweight (BMI<18.5) outcomes as well as multinomial logistic

regression models to compare the effect of residence and urban-age interaction on BMI

for Indian overweight or obese and underweight compared to normal weight. All models

accounted for clustering at the PSU level (rural) or CEB level (urban).

In both surveys, predicted BMI was positively correlated with age, education,

being a non-smoker, high standard of living/wealth (Table 2). The interaction between

wealth and urban residence was not statistically significant suggesting that while wealth

increases BMI, its affect is equal in urban and rural areas (result not shown). The

interaction between urban residence and age was positively associated with BMI—

importantly, urban residence doubles the effect of age on BMI. All else being equal,

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urban women had about 0.10 point higher BMI per year of age than rural women in each

survey year. This was in addition to a similar gain just due to age alone. In contrast,

BMI was negatively associated with higher parity, breastfeeding, low standard of living,

and working in agriculture or manual labor, all else being equal and conforming to a

priori expectations. Having a high standard of living or greater wealth as well as the

effect of urban residence on age had the largest effect on BMI but as noted, the

magnitude of the urban interaction with wealth was quite small (results not shown).

Multinomial predicted probabilities for weight class support the interaction

between age and urban residence for increased BMI (Figure 7). Other things being equal,

women in urban areas have a higher probability of being overweight rather than

underweight at age 26 while women in rural areas only experience this crossover at age

45, which points to the vast difference in risk factors among urban and rural dwellers in a

poor country like India. In urban areas however, place clearly accelerates the effect of

aging by putting women at risk of overweight at a very young age. Multinomial logistic

regression results confirm the above finding (results not shown). Additionally, women in

slums were more likely to be overweight and not likely to be underweight, highlighting

the complexity of slum life and its relationship to urban exposures. Women in slums, for

example, could be poor but still have access to cheap, high fat foods and engage in much

less physical activity than agricultural women in rural areas.

Discussion

We looked at the effect of urban residence on overweight at the WHO-

recommended cut-off of 23km/m2 for Indian women ages 15-49 years across two rounds

of the Indian demographic health surveys spanning eight years. The effect of urban

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residence on BMI is consistent across both surveys: women who live in urban areas are

more likely to be overweight and obese than women who live in rural areas, holding

equal individual level factors such as education, parity, age, and socioeconomic status.

Spatial clustering at the district level is also present in the proportion of overweight and

obese women. This pattern is strongly associated with the level of urbanization. On a

linear scale, the effect of urban residence doubles the effect of aging on BMI, while the

effect of rural residence has no additional impact on raising BMI beyond the expected

effect of aging, i.e. women become overweight in later reproductive years. Women in

urban areas also have a higher probability of being overweight than underweight nearly

20 years earlier than women in rural areas, suggesting that urban residence is an

important modifier of the association between aging and BMI gain.

Indian women’s increase in overweight and obesity has been associated with

higher socioeconomic status at every age.34 According to the socioeconomic indicators

available in the Indian NFHS data, urban women are wealthier than rural women and

controlling for place of residence may mask the independent effect of place on BMI. Our

finding that living in urban areas leads to more weight gain at much earlier ages than

living in rural areas goes beyond the expected effect due to education or socioeconomic

status alone, both of which are also higher in urban areas. Unmeasured factors regarding

changes in lifestyle due to the changing urban environment clearly increase BMI among

urban women when compared to rural women. Rural women, even those who are

secluded, may be involved in physical agricultural activities, but secluded urban women

may have disproportionately few opportunities for physical activity. Future studies

15

should aim to identify behavioral mechanisms as well as features of urban place that may

contribute to this trend.

Furthermore, it is notable that there were still considerable proportions of

underweight women in urban areas in both surveys, with an increase between NFHS-2

and NFHS-3. This is alarming because it suggests that cities in India may experience a

double burden of malnutrition (both under and over-nutrition). Without a direct

indication of physical activity, it becomes more difficult to interpret the effect of the

urban environment, as income or socioeconomic status can be related to food

consumption (both in quantity and quality) or it may reflect physical activity. We

attempted to proxy this through occupation and found results in the expected direction –

that more laborious work led to decreases in BMI at every age. However, given that a

majority of the sample reported being a homemaker, it is not possible to generalize these

findings. We assume that wealthier women are not physically active but there may be an

income threshold beyond which women choose to and can afford to engage in regular

exercise.

There could be additional neighborhood level inequities if poor people live

alongside the wealthier classes in mixed neighborhoods as has been suggested in a review

of over 80 DHS surveys.35 Richer women in mixed neighborhoods may be at risk from

the availability of higher-fat, western style fast foods combined with lower rates of

exercise while poor women could be at greater risk of malnutrition from low wages and

poor availability of affordable food. The motivation for this study was to disentangle the

effects of the degrees of urbanization underway in India, on weight status, especially on

going over normal weight status. While socioeconomic status has a large effect on gain

16

in BMI, this is not the complete picture as clearly, unmeasured factors in the urban

environment also contribute to BMI gain as women get older. This was certainly

suggested in our finding of the likelihood of being overweight and obese in urban slum as

well. Therefore, future studies of nutrition in India’s urban population must consider

inclusion of higher level characteristics of city life.

The strengths of our study are that we look at large datasets across two national

surveys with reliable and spatially-specific (district-level) measures of BMI during a

period of extensive economic growth and urban change in India. Therefore, we are able

to capture an urban effect that had not yet extended into rural areas of the country at the

time of the surveys. Our study identifies a strong association of BMI increase with a

simple risk factor, age, among urban women. This finding proves useful to help focus

public health policy in India. However, the persistent problem of undernutrition in urban

areas needs to be addressed by municipal public health systems.

A limitation of this study is that we are not able to consider any specific urban

factors that may affect BMI outcome due to de-identification of data beyond the urban-

rural distinction. Although DHS datasets include a variable for city size, these general

definitions are based on population size and do not convey information about the specific

features of their urban environments, including those relating to access to physical

activity, exercise and diet. A lack of comparable definitions of urban and rural as well as

the differences in urban environment has been noted as a common limitation across

similar studies.36,37 Further, the spatial process of urbanization, especially outward

spatial growth (or sprawl) of cities, could be an impediment to adequate coverage of

urban public health services.38 This study does not present information on the spatial

17

aspect of urban growth and its potential impact on BMI outcome and is limited by

holding the physical and social environment as a static condition. However, we do find

spatial clustering of overweight and obesity as has been suggested in other settings as

well.39,40 Further research is warranted to better understand any spatial effect of the

economic, environmental, and social drivers of obesity that restrict healthy behavior

among individuals.41

Research is also needed to determine the replicability of these results among men

in the same setting, as well as across the urban-rural gradient in other developing

countries to inform the implications for urban health planning in India and elsewhere.

Datasets with geocoded urban locations and anthropometry measures would be much

more ideal for identifying intra-urban differences in BMI by specific features of the local

environment. Research into the framing of obesity as a public health concern by

municipal public health officials is also needed in light of the profusion of obesity-related

myths and presumptions.42 Finally, policy environments tailored to the urban setting,

with particular attention to city size differences and city growth can shape preventive

individual and environmental factors that reduce obesity.43

18

19

0 20 40 60 80 100

SelectedCities

Urban

Rural

SelectedCities

Urban

Rural

Figure2:ProportioninWeightCategorybyResidence:NFHS‐2(1998‐99)&NFHS‐3(2005‐06)

SevereThinness Mild/ModThinness Normal

AsianOverweight GlobalOverweight Obese

NFHS3

NFHS2

0 20 40 60 80 100

LargeCity/Capital

SmallCity

Town

Countryside

Figure3:ProportioninWeightCategorybyCitySize:NFHS‐3(2005‐06)

Severethinness Mild/Modthinness Normal

AsianOverweight GlobalOverweight Obese

20

0 20 40 60 80 100

Slum

Notaslum

Figure4:ProportioninWeightCategorybySlumResidence:

NFHS‐3(2005‐06)

Severethinness Mild/Modthinness Normal

AsianOverweight GlobalOverweight Obese

21

In the legend: (3) refers to NFHS-3 and (2) refers to NFHS-2. Four cities include: Chennai, Delhi, Kolkota, and Mumbai

15

17

19

21

23

25

27

29

15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

BMI(kg/m

2)

Age(years)

Figure5:MeanBMIbyAgeamongIndianWomen:NFHS‐2(1998‐99)&NFHS‐3(2005‐06)

Rural‐3 Urban‐3 FourCities‐3

Rural‐2 Urban‐2 FourCities‐2

IndianOverweight

GlobalOverweight

22

16

18

20

22

24

26

28

15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53

MeanBMI(kg/m

2)

Age(years)

Figure6:MeanBMIbyAgeamongIndianWomenandMen:NFHS‐3(2005‐06)

Urban‐F Urban‐M Rural‐F

Rural‐M FourCities‐F FourCities‐M

IndianOverweight

23

U: urban, R: rural NB: Circles indicate the age at which the probability of being overweight or obese exceeds the probability of being underweight, on average, among urban (solid) and rural (dashed) women.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

Probability

Age(years)

Figure7:PredictedProbabilityforWeightClassbyAgeamongWomen15‐49years(NFHS‐3)

Under‐U Over‐U Under‐R Over‐R

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Table 1: Sample characteristics by place of residence

NFHS-2 (1998-1999) NFHS-3 (2005-2006)

All (n=77613)

Urban (n=24,521

)

Rural (n=53,092)

All (n=113,075)

Urban (n=51,038)

Rural (n=62,037)

Education level

None 52.6 28.5 61.3 40.3 21.9 49.1 Primary 17.4 16.8 17.6 14.9 12.5 16.1 Secondary 22.3 34.7 17.7 37.7 49.6 31.9 Higher 7.8 20.0 3.4 7.1 16.0 2.9

Age (years)

15-19 7.8 4.2 9.1 19.8 18.6 20.5 20-24 16.7 14.3 17.5 16.9 17.1 16.8 25-29 19.8 19.5 19.9 16.0 16.2 15.9 30-34 17.6 19.1 17.1 14.5 14.4 14.6 35-39 15.6 17.5 14.9 13.3 13.9 13.1 40-44 12.7 14.2 12.1 11.1 11.3 10.9 45-49 9.9 11.1 9.4 8.3 8.7 8.2

BMI Status

Severely thin 6.8 4.8 7.7 6.8 5.4 7.5 Mild/moderately thin

29.3 18.3 33.3 28.7 19.6 33.0

Normal 45.3 40.4 47.0 43.3 39.6 45.1 Indian Overweight 8.0 13.3 6.0 8.6 12.0 7.0 Global 8.4 17.7 5.0 9.8 17.4 6.1 Obese 2.2 5.9 0.9 2.8 6.1 1.2

Standard of Living

Low 32.8 12.8 40.1 24.0 8.2 31.6 Medium 46.5 45.3 47.9 33.0 22.9 37.9 High 20.7 41.9 13.0 43.0 68.9 30.5

Occupation

Professional/ Clerical

2.5 5.8 1.3 3.5 7.0 2.0

Agricultural 26.4 4.0 34.5 25.7 3.3 36.4 Laborer 8.3 10.5 7.6 9.7 10.9 9.2

Non-smoker 98.0 99.4 97.5 88.9 93.1 86.9 Breastfeeding 25.5 19.0 27.8 20.0 13.2 23.2 Parity (mean) 3.1 2.8 3.2 2.3 1.9 2.5

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Table 2: OLS Regression Coefficients for BMI on Selected Covariates (Unless noted, all coefficients are significant at p = 0.000) NFHS-2 (1998-1999)

N = 77,536 NFHS-3 (2005-2006)

N = 113,052 Variable Unit Mean (SD) Model 1 Model 2 Model 3 Model 4 Mean (SD) Model 1 Model 2 Model 3 Model 4 Model 5 Age Years 31.6 (8.8) .10 .07 .06 .05 29.4 (9.6) .15 .11 .08 .09 .10 Urban -1.58 -1.78 -1.81 -1.96 -1.46 -1.70 -2.75 -2.67 -1.99 Education Yes 3.6 (4.5) .19 .19 .19 .10 5.1 (5.0) .15 .15 .02 .01 .07 Parity Number 3.1 (2.1) -.10 -.08 -.06 -.05 2.3 (2.1) -.15 -.12 -.06 -.06 -.09 Urban*Age -- .10 .10 .10 -- .11 .11 .11 .10 Breastfeeding Yes 26% -- -- -.37 -.38 20% -- -- -- -.02 -.09** Non-Smoker Yes 98% -- -- .81 .62 89% -- -- -- .76ns .89 Standard of Living

Low 33% -- -- -- -.59 24% -- -- -- -- -.50 Medium Y/N 46% -- -- -- ref 33% ref -- -- -- ref High 21% -- -- -- 1.46 43% -- -- -- -- 1.02

Wealth Score 1.34 1.30 Occupation

Agricultural Yes 3% -- -- -- -.65 4% -- ref -- -- -.70 Laborer Yes 26% -- -- -- -.38 26% -- -- -- -- -.47 Clerical/Prof Yes 8% -- -- -- ref 10% -- -- -- -- ref

nsp > 0.05 **p ≤ 0.01

26

References 1. WHO.Obesityandoverweight.WorldHealthOrganization.2013.Availableat:

http://www.who.int/mediacentre/factsheets/fs311/en/index.html.AccessedJune4,2013.

2. TanumihardjoSA,AndersonC,Kaufer‐HorwitzM,etal.Poverty,Obesity,andMalnutrition:AnInternationalPerspectiveRecognizingtheParadox.JournaloftheAmericanDieteticAssociation.2007;107(11):1966–1972.doi:10.1016/j.jada.2007.08.007.

3. WHO.Doubleburdenofmalnutritionindevelopingcountries.Rome,Italy:FoodandAgriculturalAssociationoftheUnitedNations;2006.Availableat:ftp://ftp.fao.org/docrep/fao/009/a0442e/a0442e01.pdf.

4. FlegalKM.AssociationofAll‐CauseMortalityWithOverweightandObesityUsingStandardBodyMassIndexCategories&lt;subtitle&gt;ASystematicReviewandMeta‐analysis&lt;/subtitle&gt;&lt;alt‐title&gt;All‐CauseMortalityUsingBMICategories&lt;/alt‐title&gt;JAMA.2013;309(1):71.doi:10.1001/jama.2012.113905.

5. ThomsenM,NordestgaardBG.Myocardialinfarctionandischemicheartdiseaseinoverweightandobesitywithandwithoutmetabolicsyndrome.JAMAInternMed.2014;174(1):15–22.doi:10.1001/jamainternmed.2013.10522.

6. DanaeiG,FinucaneMM,LuY,etal.National,regional,andglobaltrendsinfastingplasmaglucoseanddiabetesprevalencesince1980:systematicanalysisofhealthexaminationsurveysandepidemiologicalstudieswith370country‐yearsand2·7millionparticipants.TheLancet.2011;378(9785):31–40.doi:10.1016/S0140‐6736(11)60679‐X.

7. PopkinBM.TheNutritionTransitionintheDevelopingWorld.DevelopmentPolicyReview.2003;21(5/6):581.

8. RegmiA.Changingstructureofglobalfoodconsumptionandtrade:anintroduction.USDA;2001:1–3.

9. RegmiA,DeepakM,SealeJ,BernsteinJ.Cross‐countryanalysisoffoodconsumptionpatterns.USDA;2001:1–3.

10. SubramanianSV,PerkinsJM,ÖzaltinE,SmithG.Weightofnations:asocioeconomicanalysisofwomeninlow‐tomiddle‐incomecountries.AmJClinNutr.2011;93(2):413–421.doi:10.3945/ajcn.110.004820.

11. KellySA,MelnykBM.SystematicReviewofMulticomponentInterventionswithOverweightMiddleAdolescents:ImplicationsforClinicalPracticeand

27

Research.WorldviewsonEvidence‐BasedNursing.2008;5(3):113–135.doi:10.1111/j.1741‐6787.2008.00131.x.

12. Agyei‐MensahS,AikinsAde‐G.EpidemiologicalTransitionandtheDoubleBurdenofDiseaseinAccra,Ghana.JUrbanHealth.2010;87(5):879–897.doi:10.1007/s11524‐010‐9492‐y.

13. PopkinBM,CondeW,HouN,MonteiroC.IsThereaLagGloballyinOverweightTrendsforChildrenComparedwithAdults?Obesity.2006;14(10):1846–1853.doi:10.1038/oby.2006.213.

14. WangY,LobsteinT.Worldwidetrendsinchildhoodoverweightandobesity.IntJPediatrObes.2006;1(1):11–25.

15. SaelensBE,SallisJF,FrankLD.Environmentalcorrelatesofwalkingandcycling:findingsfromthetransportation,urbandesign,andplanningliteratures.AnnBehavMed.2003;25(2):80–91.

16. PucherJ,BuehlerR,BassettDR,DannenbergAL.WalkingandCyclingtoHealth:AComparativeAnalysisofCity,State,andInternationalData.AmJPublicHealth.2010;100(10):1986–1992.doi:10.2105/AJPH.2009.189324.

17. SarisWHM,BlairSN,VanBaakMA,etal.Howmuchphysicalactivityisenoughtopreventunhealthyweightgain?OutcomeoftheIASO1stStockConferenceandconsensusstatement.ObesityReviews.2003;4(2):101–114.doi:10.1046/j.1467‐789X.2003.00101.x.

18. DonnellyJE,BlairSN,JakicicJM,ManoreMM,RankinJW,SmithBK.AppropriatePhysicalActivityInterventionStrategiesforWeightLossandPreventionofWeightRegainforAdults:Medicine&ScienceinSports&Exercise.2009;41(2):459–471.doi:10.1249/MSS.0b013e3181949333.

19. WorldBank.Urbanpopulation(%oftotal).WorldBank.Availableat:http://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS.AccessedJune4,2013.

20. UNDESA.India:DemographicProfile(1950‐2025).UnitedNationsDepartmentofEconomicandSocialAffairs.Availableat:http://esa.un.org/unup/unup/p2k0data.asp.AccessedJune4,2013.

21. UnitedNationsDepartmentofEconomicandSocialAffairs.File12:PopulationofUrbanAgglomerationswith750,000InhabitantsorMorein2011,byCountry,1950‐2025(thousands).WorldUrbanizationProspects:The2011Revision.2012.Availableat:http://esa.un.org/unup/CD‐ROM/Urban‐Agglomerations.htm.AccessedOctober30,2013.

28

22. SanyalS,NagrathS,SinglaG.Urbanization&sustainabilityinIndia:anindependentagenda.India:WorldWidlifeFund;2009.

23. AzamM.ChangesinWageStructureinUrbanIndia,1983–2004:AQuantileRegressionDecomposition.WorldDevelopment.2012;40(6):1135–1150.doi:10.1016/j.worlddev.2012.02.002.

24. NilekaniN.ImaginingIndia:ideasforthenewcentury.India:Penguin;2008.

25. VepaS.ImpactofglobalizationonfoodconsumptionofurbanIndia.Rome,Italy:FAO;2004.

26. GopinathN,ChadhaSL,JainP,ShekhawatS,TandonR.AnepidemiologicalstudyofobesityinadultsintheurbanpopulationofDelhi.JAssocPhysiciansIndia.1994;42(3):212–215.

27. Gopalan,C.ObesityintheIndianMiddleClass.NFIBulletin.19(1):1–4.

28. SubramanianSV,KawachiI,SmithGD.Incomeinequalityandthedoubleburdenofunder‐andovernutritioninIndia.JEpidemiolCommunityHealth.2007;61(9):802–809.doi:10.1136/jech.2006.053801.

29. MisraA,ShahP,GoelK,etal.TheHighBurdenofObesityandAbdominalObesityinUrbanIndianSchoolchildren:AMulticentricStudyof38,296Children.AnnalsofNutritionandMetabolism.2011;58(3):203–211.doi:10.1159/000329431.

30. Jones‐SmithJC,Gordon‐LarsenP,SiddiqiA,PopkinBM.Istheburdenofoverweightshiftingtothepooracrosstheglobe?Timetrendsamongwomenin39low‐andmiddle‐incomecountries(1991‐2008).IntJObes(Lond).2012;36(8):1114–1120.doi:10.1038/ijo.2011.179.

31. EbrahimS,KinraS,BowenL,etal.TheEffectofRural‐to‐UrbanMigrationonObesityandDiabetesinIndia:ACross‐SectionalStudy.PLoSMed.2010;7(4):e1000268.doi:10.1371/journal.pmed.1000268.

32. InternationalInstituteforPopulationSciences(IIPS),MacroInternational.NationalFamilyHealthSurvey(NFHS‐3),2005–06:India.Mumbai:IIPS;2007.

33. WHOExpertConsultation.Appropriatebody‐massindexforAsianpopulationsanditsimplicationsforpolicyandinterventionstrategies.TheLancet.2004;363(9403):157–163.doi:10.1016/S0140‐6736(03)15268‐3.

34. SubramanianS,PerkinsJM,KhanKT.DoburdensofunderweightandoverweightcoexistamonglowersocioeconomicgroupsinIndia?AmericanJournalofClinicalNutrition.2009;90(2):369–376.doi:10.3945/ajcn.2009.27487.

29

35. MarkM,PaulCH.UrbanPovertyandHealthinDevelopingCountries:HouseholdandNeighborhoodEffects.Demography.2005;42:397–425.

36. AllenderS,WickramasingheK,GoldacreM,MatthewsD,KatulandaP.Quantifyingurbanizationasariskfactorfornoncommunicabledisease.JUrbanHealth.2011;88:906–18.doi:10.1007/s11524‐011‐9586‐1.

37. ChandrasekharS,SharmaA.UrbanizationandspatialpatternsofinternalmigrationinIndia.SpatialDemography.Forthcoming2014.

38. CampbellT,CampbellA.EmergingDiseaseBurdensandthePoorinCitiesoftheDevelopingWorld.JUrbanHealth.2007;84(1):54–64.doi:10.1007/s11524‐007‐9181‐7.

39. MichimiA,WimberlyMC.SpatialpatternsofobesityandassociatedriskfactorsintheconterminousU.S.AmJPrevMed.2010;39(2):e1–12.doi:10.1016/j.amepre.2010.04.008.

40. GallosLK,BarttfeldP,HavlinS,SigmanM,MakseHA.Collectivebehaviorinthespatialspreadingofobesity.SciRep.2012;2.doi:10.1038/srep00454.

41. SturmR.Stemmingtheglobalobesityepidemic:Whatcanwelearnfromdataaboutsocialandeconomictrends?PublicHealth.2008;122(8):739–746.doi:10.1016/j.puhe.2008.01.004.

42. CasazzaK,FontaineKR,AstrupA,etal.Myths,Presumptions,andFactsaboutObesity.NewEnglandJournalofMedicine.2013;368(5):446–454.doi:10.1056/NEJMsa1208051.

43. DeanJA,ElliottSJ.PrioritizingObesityintheCity.JUrbanHealth.2012;89(1):196–213.doi:10.1007/s11524‐011‐9620‐3.