ORIGINAL ARTICLE

Praween Agrawal & Vinod Mishra & Sutapa Agrawal

Received: 27 July 2011 /Accepted: 1 December 2011 /Published online: 27 December 2011# Springer-Verlag 2011

AbstractBackground/Objectives India is currently witnessing a rapidgrowth in obesity epidemic. We examined the covariates ofoverweight and obesity and the association between over-weight and obesity and adverse pregnancy outcomes amongadult married women in northern India.Subjects/Methods The analysis is based on 5,273 ever-married women of reproductive age (15–49) in the statesof Delhi and Punjab, included in India’s second NationalFamily Health Survey (NFHS-2), conducted in 1998–1999.Body mass index (BMI) was used to define the termsoverweight (25≤BMI<30 kg/m2) and obesity (BMI≥30.0 kg/m2). Adverse pregnancy outcome was defined as a

miscarriage or stillbirth. Binary and multinomial logisticregression methods were used to estimate the effects of thecovariates ofn overweight and obesity and effects of over-weight and obesity on adverse pregnancy outcomes.Results Age, urban residence, media habits, anemia status,and economic living standard are most important covariates ofoverweight and obesity among women in India. A separateanalysis by women’s age finds that media habits and livingstandard are more important covariates for older women (30–49), whereas place of residence and education are more im-portant covariates for younger women (15–29). The study alsofinds that overweight and obese women age 15–29 are signif-icantly more likely to have experienced adverse pregnancyoutcome (miscarriage or a stillbirth) in their lifetime. Thisrelationship is not observed among older women.Conclusions The results are useful in identifying populationgroups for public health campaigns to promote appropriatediets and effective lifestyles to prevent the obesity epidemicand associated adverse pregnancy outcomes.

Keywords BMI . Overweight . Obesity . Adverse pregnancyoutcomes .Miscarriage . Stillbirth .Women . NFHS-2 . India

Introduction

Obesity is a complex condition, with serious social andpsychological dimensions (WHO 2003). Once considereda problem related to affluence, obesity has reached epidemicproportions globally, with more than 1 billion adults areoverweight; at least 300 million of them clinically obese,which is a major contributor to the global burden of chronicdisease and disability (Prentice 2006; WHO 2003; WHO2000). Often coexisting with undernutrition, obesity is now

P. AgrawalOrganizational Learning and Evaluation,International Planned Parenthood Federation South Asia RegionalOffice, IPPF House,66 Sunder Nagar,New Delhi 110003, Indiae-mail: praween_agrawal@rediffmail.come-mail: PKAgrawal@ippfsar.orgURL: http://www.ippfsar.org

V. MishraPopulation Policy Section, United Nations,Manhattan, NY 10017, USAe-mail: mishrav@un.org

S. Agrawal (*)South Asia Network for Chronic Disease,Public Health Foundation of India,C1/52, First floor, SDA,New Delhi 110016, Indiae-mail: sutapaiips@rediffmail.com

S. Agrawale-mail: sutapa.agrawal@phfi.orgURL: www.sancd.org

J Public Health (2012) 20:387–397DOI 10.1007/s10389-011-0477-4

Covariates of maternal overweight and obesity and the riskof adverse pregnancy outcomes: findings from a nationwidecross sectional survey

quickly growing in many developing countries. Owing toincreasing urbanization, mechanization of jobs and trans-portation, availability of processed and fast foods, and de-pendence on television for leisure, people are quicklyadopting less physically active lifestyles and consumingthe more “energy-dense, nutrient-poor” diets which are fuel-ling this global epidemic (WHO 2003; Bell et al. 2002;Popkin 2002; Popkin et al. 2001; Drewnowski and Popkin1997; Saw and Rajan 1997).

Research on obesity has found the prevalence of obesity tobe higher among women, among economically better off per-sons (Griffiths and Bentley 2001; Misra et al. 2001; Zargar etal. 2000; Gopinath et al. 1994; Dhurandhar and Kulkarni1992). Urban residences, childbearing, and sedentary lifestyleshave also been linked to obesity (Bhasin et al. 2001; Gopalan1998; Kliegman and Gross 1985). Several other studies havealso related overweight/obese conditions with various chronichealth problems including diabetes, hypertension, and heartdisease (Shah et al. 2010; Hossain et al. 2007; Raymond et al.2006; Venkatramana and Reddy 2002; Ishikawa-Takata et al.2002; Misra et al. 2001; Ko et al. 1999; Willett et al. 1995;Gopinath et al. 1994; McKeigue et al. 1991) and severalreproductive disorders including menstrual dysfunction, infer-tility, pregnancy complications and adverse pregnancy out-comes such as misacarriage and still births (Owens et al.2010; Rosenberg et al. 2005; Lashen et al. 2004; Cedergren2004; Douchi et al. 2002; Sebire et al. 2001; Norman and Clark1998; Pettigrew and Hamilton-Fairley 1997; Lake et al. 1997;Hartz et al. 1979).

In the past, the Government of India has paid little attentionto the problems of overweight and obesity. Now with a rapidlygrowing obesity epidemic and associated chronic diseases, thesituation is beginning to change. However, partly due to lack ofdata, understanding of causes and consequences of this rapidlygrowing public health threat remains poor. This study analysesdata from a population-based survey in India, which collectedanthropometric data from a nationally representative sample ofmore than 90,000 ever-married women of reproductive age.This study aimed to describe the geographic variation in prev-alence of overweight and obesity and examined the covariatesof overweight and obesity and its association with adversepregnancy outcomes in India, which is undergoing rapidchanges in lifestyles, physical activity patterns, and diets.

Data and methods

Data Data are from India’s second National Family HealthSurvey (NFHS-2) conducted in 1998–1999. NFHS-2 col-lected demographic, socioeconomic, and health informationas well as data on food habits, media habits, anemia status,and pregnancy outcomes from a cross-sectional nationallyrepresentative probability sample of 90,303 ever-married

women age 15−49 years residing in 92,486 households.All states of India are represented in the sample (exceptthe small union territories), covering more than 99% ofcountry’s population. The sample is a multi-stage clustersample with an overall response rate of 98%. The surveywas conducted using an interviewer-administered question-naire in the native language of the respondent. A total of 18languages were used in the survey with back translation toEnglish to ensure accuracy and comparability.

Details of sample design, including sampling frame andsample implementation, are provided in the basic surveyreport for all India (IIPS and ORC Macro 2000). The anal-ysis here focuses on ever-married women age 15−49 innorthern Indian states of Delhi (n02477) and Punjab (n02,796), with highest prevalence of overweight and obesity inthe country.

Response variables In NFHS-2, each ever-married womanwas weighed using a solar-powered scale with an accuracyof ± 100 gms. Their height was measured using an adjust-able wooden measuring board, specifically designed to pro-vide accurate measurements (to the nearest 0.1 cm) in adeveloping-country field situation. The weight and heightdata were used to calculate the body mass index (BMI).Based on BMI cut-offs, we created a four-category variableof nutritional status of women, indicating underweight(<18.5 kg/m2), normal BMI (18.5–24.9 kg/m2), overweight(25.0–29.9 kg/m2), and obese (>30.0 kg/m2). The surveyalso asked each woman whether she ever experienced amiscarriage or stillbirth. We examined whether women’snutritional status (as defined by BMI) is correlated withthe risk of an adverse pregnancy outcome. When examiningcovariates of overweight and obesity, women who werepregnant at the time of the survey or women who had givenbirth during the 2 months preceding the survey were ex-cluded from the analysis.

Covariates The variables included in the analysis are: age(15−19, 20−24, 25−29, 30−34, 35−39, 40−44, 45−49); res-idence (urban, rural); education (illiterate, literate but lessthan middle school complete, middle school complete butless than high school complete, high school complete orhigher); husband’s education (same category as wife’s);media habits—watches television every week (yes, no),reads newspaper or magazine every week (yes, no); foodhabits—milk or curd daily (yes, no), fruits daily (yes, no),green, leafy vegetables daily (yes, no), eggs at least weekly(yes, no), chicken, meat, or fish at least weekly (yes, no);anemia status (not anemic, mild anemia, moderate or severeanemia); work status (works at home, works outside, doesnot work); type of work (professional/technical, clerical/sales/service, laborer including agricultural work, householdworker); religion (Hindu, Muslim, Sikh, Other); caste/tribe

388 J Public Health (2012) 20:387–397

(scheduled caste/scheduled tribe, other backward class, oth-er); and standard of living (low, medium, high).

Analysis We first examined inter-state differentials in theprevalence of overweight and obesity. Analysis of covari-ates of overweight and obesity is limited to the states ofDelhi and Punjab. Covariates of overweight and obesity areanalyzed using binary and multinomial logistic regressionmethods. Because of large age differentials in the prevalenceof overweight and obesity and because the effects of cova-riates are likely to vary by age, the analysis is also carriedout separately for women age 15−29 and 30−49. Effects ofBMI (underweight, normal, overweight, obese) on the riskof adverse pregnancy outcomes (miscarriage or stillbirth) iscarried out using logistic regression method separately forwomen age 15−29 and 30−49. In multivariate models, thehusband’s education was not included because it is highlycorrelated with woman’s education. Also, the type of workvariable was not included due to small numbers of cases(85% of women in our sample do not work).

The logistic regression models are estimated using theStata statistical software package (Stata 2001). In thesurvey, certain states and certain categories of respond-ents were oversampled. In all our analysis, weights areused to restore the representativeness of the sample. Theresults are presented in the form of relative risks (RR)and odds ratios (OR), with 95% confidence intervals(95% CI).

Human subjects informed consent

The analysis presented in this study is based on second-ary analysis of existing survey data, with all identifyinginformation removed. The survey personnel obtained in-formed consent from each respondent before askingquestions.

Results

Prevalence of overweight and obesity According to NFHS-2, about 11% of ever-married women (15−49) in India areoverweight or obese—8.4% overweight and 2.3% obese(see Table 1). The prevalence of overweight and obesity ishighest in the northern states of Delhi and Punjab, whereabout one in three women are overweight or obese; followedby Goa and Kerala, where about one in five are overweightor obese. The prevalence of overweight and obesity is muchlower in the economically less developed states of UttarPradesh, Madhya Pradesh, Rajasthan, Orissa, Assam, andBihar (4−8%). Further analysis in this study has been re-stricted to the highest prevalence states of Delhi and Punjab.

In both Delhi and Punjab, prevalence of overweight andobesity increases rapidly with age (see Table 2). In Delhi,for example, the proportion for overweight rises from lessthan 10% at age 15−24 to about 40% at age 45−49. Corre-spondingly, the proportion for obesity rises from less than1% at age 15−24 to about 19% at age 45−49 (Table 2).Women living in urban areas are much more likely to beoverweight and obese than those living in rural areas. Wom-an’s education is strongly positively correlated with thelevels of overweight and obese in both states. For examplein Punjab, the proportion for obese increases from less than5% among illiterate women to more than 15% among thosewith high school or more education. A similar pattern isobserved in view of the husband’s education.

Women who regularly watch television or read news-papers or magazines are much more likely to be overweight

Table 1 Percentage of overweight and obese women (age 15–49) inIndia by state, 1998-99

State Overweight Obese Overweight orobese

(25.0≤BMI<30.0)

(BMI≥30.0)

(BMI≥25.0)

Delhi 24.7 9.3 34.0

Punjab 21.3 9.2 30.6

Goa 16.8 4.5 21.3

Kerala 17.0 3.9 20.9

Haryana 12.8 3.9 16.7

Gujarat 11.4 4.5 15.9

Sikkim 13.1 2.5 15.6

Tamil Nadu 12.1 2.7 14.7

Jammu 10.7 3.0 13.7

Karnataka 10.8 2.9 13.7

HimachalPradesh

10.9 2.4 13.3

Andhra Pradesh 9.8 2.3 12.1

Maharashtra 9.0 2.9 11.9

Manipur 9.7 1.2 10.8

West Bengal 7.3 1.4 8.6

Nagaland 7.8 0.7 8.5

Tripura 6.7 1.7 8.4

Uttar Pradesh 5.9 1.6 7.5

Rajasthan 5.5 1.7 7.1

Madhya Pradesh 4.9 1.2 6.1

Meghalaya 4.7 1.3 5.9

Mizoram 4.8 0.5 5.3

ArunachalPradesh

4.6 0.6 5.2

Orissa 3.8 0.6 4.4

Assam 3.6 0.7 4.3

Bihar 3.2 0.5 3.7

India 8.4 2.3 10.6

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Table 2 Percentage of overweight and obese women (age 15–49) in Delhi and Punjab by selected background characteristics, 1998–1999

Delhi Punjab

Overweight Obese Overweight ObeseCharacteristic (25.0≤BMI<30.0) (BMI≥30.0) (25.0≤BMI<30.0) (BMI≥30.0)

Age in years

15–19 7.2 NC 1.3 1.7

20–24 9.9 0.7 11.6 0.9

25–29 17.7 5.1 16.8 5.5

30–34 26.3 9.5 18.1 10.2

35–39 29.7 12.5 27.9 13.3

40–44 30.5 13.0 28.7 12.4

45–49 39.5 18.9 30.9 14.5

Residence

Urban 25.0 9.7 28.2 16.3

Rural 21.3 4.6 18.2 6.0

Educationa

Illiterate 19.4 4.8 16.2 4.4

Literate, <middlecomplete

20.9 8.0 20.8 9.4

Middle complete 22.9 11.1 20.6 9.8

High school or more 29.9 12.3 29.0 15.3

Husband’s education

Illiterate 13.9 4.5 13.0 4.6

Literate, <middlecomplete

19.9 5.5 20.6 5.9

Middle complete 18.3 9.1 17.7 9.5

High school or more 28.4 10.8 27.3 13.0

Media habits

Watches TV every week

Yes 25.8 9.7 24.3 11.0

No 13.5 5.7 11.1 3.3

Reads newspaper/magazine every week

Yes 28.6 12.7 28.6 16.4

No 20.7 5.9 18.0 6.0

Food habits

Milk or curd daily

Yes 27.5 10.5 23.0 9.9

No 20.5 7.5 14.7 6.5

Fruits daily

Yes 28.3 15.9 31.3 17.6

No 23.3 6.7 19.2 7.4

Green, leafy vegetables daily

Yes 26.6 9.3 22.4 10.0

No 22.1 9.4 18.4 7.0

Eggs at least weekly

Yes 26.6 7.2 27.7 11.4

No 24.2 9.9 20.6 9.0

Chicken, meat, or fish at least weekly

Yes 27.0 9.1 27.6 14.4

No 24.3 9.4 21.1 9.0

Anemia statusb

Not anemic 27.0 10.7 23.6 10.9

Mild anemia 23.5 7.8 20.1 7.4

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Table 2 (continued)

Delhi Punjab

Overweight Obese Overweight ObeseCharacteristic (25.0≤BMI<30.0) (BMI≥30.0) (25.0≤BMI<30.0) (BMI≥30.0)

Moderate or severe anemia 13.4 5.0 13.7 5.3

Work status

Works at home 21.4 6.8 27.3 18.7

Works outside 25.4 7.1 28.6 12.3

Does not work 24.8 9.9 20.6 8.7

Type of work

Profession/technical 36.1 11.8 38.8 20.6

Clerical/sales/service 28.7 3.8 31.6 23.5

Laborer, including agricultural work 17.8 6.8 21.7 10.2

Household worker 11.4 3.8 13.7 4.2

Religionc

Hindu 24.6 9.0 21.3 11.3

Muslim 18.7 10.8 24.6 5.3

Sikh 37.8 13.7 21.0 8.0

Other 16.9 5.3 31.1 4.7

Caste/tribed

SC/ST 18.3 3.3 14.9 3.5

OBC 15.6 4.9 20.3 7.9

Other 28.6 12.0 24.8 12.4

Standard of livinge

Low 2.0 NC 5.9 1.1

Medium 14.8 4.8 14.2 4.2

High 29.6 11.5 26.4 12.7

Number of women 2,071 2,496

Note: NC not enough casesaWomen’s educational status was measured as years of schooling. The cutoffs for years of schooling that were based on typical educationbenchmarks: 0 years (illiterate), 1–5 years (literate but less than middle school complete), 6–8 years (middle school complete), 9+ years (highschool complete or more)b Anemia in women is defined as a hemoglobin level of less than 12.0 g/dl for non-pregnant women. Mild anemia means 10.0–11.9 g/dl for nonpregnant women, moderate anemia (7.0–9.9 g/dl), and severe anemia (less than 7.0 g/dl)c Other religions include Christian, Buddhist, Jain, Jewish, Zoroastrian, and othersd Caste was based on the women’s self-identification as belonging to scheduled caste, scheduled tribe, other backward class, other caste, or no castegroup. Scheduled castes and scheduled tribes are castes and tribes that the Government of India identifies as socially and economically backwardand in need of special protection from social injustice and exploitation. Scheduled tribe and scheduled caste are the most socially disadvantagedgroups. Scheduled caste consists of castes that are lowest in the traditional Hindu caste hierarchy (Chitnis 1997) and as a consequence thatexperience intense social and economic segregation and disadvantage. Scheduled tribes comprise 700 tribes who tend to be geographically isolatedwith limited economic and social interaction with the rest of the population. “Other backward class” is a diverse collection of intermediate castesthat were considered low in the traditional caste hierarchy but are clearly above scheduled castes. “Other caste” is thus a default residual group thatenjoys higher status in the caste hierarchye Standard of living was defined in terms of household assets and material possessions and these have been shown to be reliable and have valid measuresof household material well-being (Filmer and Pritchett 2001). It is an index which is based on ownership of a number of different consumer durables andother household items. It is calculated by adding the following scores: house type: 4 for pucca, 2 for semi-pucca, 0 for kachha; toilet facility: 4 for ownflush toilet, 2 for public or shared flush toilet or own pit toilet, 1 for shared or public pit toilet, 0 for no facility; source of lighting: 2 for electricity, 1 forkerosene, gas or oil, 0 for other source of lighting; main fuel for cooking: 2 for electricity, liquefied natural gas, or biogas, 1 for coal, charcoal, orkerosene, 0 for other fuel; source of drinking water: 2 for pipe, hand pump, or well in residence/yard/plot, 1 for public tap, hand pump, or well, 0 for otherwater source; separate room for cooking: 1 for yes, 0 for no; ownership of house: 2 for yes, 0 for no; ownership of agricultural land: 4 for 5 acres or more,3 for 2.0−4.9 acres, 2 for less than 2 acres or acreage not known, 0 for no agricultural land; ownership of irrigated land: 2 if household owns at least someirrigated land, 0 for no irrigated land; ownership of livestock: 2 if own livestock, 0 if not own livestock; durable goods ownership: 4 for a car or tractor, 3each for a moped/scooter/motorcycle, telephone, refrigerator, or color television, 2 each for a bicycle, electric fan, radio/transistor, sewingmachine, blackand white television, water pump, bullock cart, or thresher, 1 each for a mattress, pressure cooker, chair, cot/bed, table, or clock/watch. Index scores rangefrom 0−14 for low SLI to 15−24 for medium SLI to 25−67 for high SLI

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and obese in both Delhi and Punjab. In Punjab, women whowatch television at least once a week are more than three-times (11% vs. 3%) as likely to be obese, and women whoread newspapers or magazines at least once a week are morethan two-times likely (16% vs. 6%) to be obese as otherwomen. Women in Punjab who consume milk/curd, fruits,green, leafy vegetables daily and eggs and chicken, mean orfish at least weekly, tend to be more overweight and obesethan women who do not consume them. However, obesityamong women in Delhi does not seem to be correlated withdaily or weekly consumption of green, leafy vegetables,eggs, and chicken, meat, or fish.

The likelihood of being overweight and obese is stronglynegatively correlated with the anemia status of women, asexpected. Women who are not anemic are about twice aslikely to be overweight and obese as women with moderateto severe anemia in both Delhi and Punjab. Work status ofwomen does not show any clear relationship with overweightor obesity, except in Punjab where women who work at homeare more likely to be obese than those who do not work.However, the type of work is strongly correlated with over-weight and obesity. Among working women, those in profes-sional or technical jobs are much more likely to be overweightor obese than other types of workers. Household workers areleast likely to be overweight or obese. In Punjab, more thanone-half of all women in professional, technical, clerical,sales, and service jobs are overweight or obese.

Prevalence of overweight and obesity among women isalso correlated with religion and caste/tribe status. One outof every two Sikh women in Delhi is overweight or obese.By caste/tribe, women belonging to scheduled castes, sched-uled tribes, and other backward class are considerably lesslikely to be overweight or obese than other women.

Household living standard, measured by household own-ership of consumer durables, is strongly positively correlat-ed with the prevalence of overweight and obesity. Forexample in Punjab, the proportion for overweight rises fromjust 6% for women in low standard of living households to26% for those in high standard of living households, and theproportion for obese rises from just 1% in low standard ofliving households to 13% in high standard of living house-holds. A similar pattern is observed in Delhi also.

Covariates of overweight and obesity Table 3 shows theestimated effects of selected demographic and socioeconomiccovariates on the risk of being overweight and obese amongever-married women in northern India (Delhi and Punjabcombined).With other factors in the model controlled, relativerisks of being overweight and obese increase rapidly with age.Women age 40−49 are more than 12 times as likely to beoverweight or obese as women age 15−19. Urban residencealso significantly increases the risk of obesity (RR01.57; 95%CI: 1.06, 2.32), but not the risk of overweight (RR01.16;

95% CI: 0.92, 1.47). Watching television every week hassignificant positive effect on the risk of overweight (RR01.45; 95% CI: 1.09, 1.93) and reading newspaper or magazineevery week has a significant positive effect on the risk ofobesity (RR01.50; 95 %CI: 1.07, 2.10).

Effects of food habits on the risks of overweight andobesity are generally small and not significant statistically,except that eating fruits daily is associated with significantlyincreased risk of being obese. As expected, anemic womenare significantly less likely to be overweight (RR00.59;95% CI: 0.43, 0.81) or obese (RR00.53; 95% CI: 0.32,0.86). Effects of religion and caste/tribe status on the riskof being overweight are small and not significant, but wom-en belonging to a scheduled caste or scheduled tribe andthose belonging to a religion other than Hindu, Muslim, orSikh are significantly less likely to be obese than otherwomen. As in the unadjusted case discussed in Table 2,household standard of living is strongly correlated with risksof being overweight and obese. Women in high standard ofliving households have a 3.8 times greater risk of beingoverweight (95% CI: 1.42, 10.00), and 3.6 times greaterrisk of being obese (95% CI: 0.46, 28.43). Large confidenceintervals mainly reflect small numbers of cases in the sam-ple. With various demographic and socioeconomic factorsin the model controlled, women living in Punjab are signif-icantly more likely to be obese than those living in Delhi(RR01.78; 95% CI: 1.22, 2.60).

Table 4 presents the estimated effects of selected cova-riates on the combined risk of being overweight or obese inthe pooled sample for Delhi and Punjab. Separate modelsare estimated for 15−29, 30−49, and 15−49 age groups. Forwomen aged 15–49 (last three columns in Table 4), consis-tent with the results in Table 3, woman’s age (ORs rangefrom 4.14 to 13.49), urban residence (OR: 1.27; 95% CI:1.01–1.60), media habits (such as TV watching every week:OR: 1.43; 95%CI: 1.10–1.86 and reading newspaper/maga-zine every week OR: 1.24; 95% CI: 1.01–1.52) , and highliving standard (OR: 3.70; 95% CI: 1.52–9.02) have signif-icant effects on the risk of being overweight or obese. Inaddition, scheduled caste and scheduled tribe women (OR:0.76; 95% CI: 0.61–0.95) have significantly lower risk ofoverweight and obesity, and women living in Punjab (OR:1.28; 95% CI: 1.02–1.60) have significantly higher risk ofoverweight and obesity.

Separate analysis by age shows that the covariates ofoverweight and obesity are quite different among youngerwomen (15−29) than among older women (30−49). Amongyounger women, urban residence, education, and living inPunjab are more important covariates of overweight andobesity, whereas among older women, media habits, caste/tribe status, and economic standard of living are more im-portant covariates. Anemia status is a significant covariatefor both groups of women.

392 J Public Health (2012) 20:387–397

Effects of BMI on adverse pregnancy outcomes Table 5presents the adjusted effects of women’s nutritional statuson the risk of adverse pregnancy outcomes in Delhi andPunjab. Results suggest that overweight and obesity havestrong effects on the likelihood of having experienced anadverse pregnancy outcome among younger women, but noeffect is seen among older women. After controlling for thesocioeconomic factors, obese women age 15–29 are morethan twice as likely to have experienced a miscarriage orstillbirth as those with a normal BMI (OR02.10; 95% CI:1.02, 4.29). On the contrary, among the older women age30–49, underweight women are significantly more likely tohave experienced an adverse pregnancy outcome (OR0

1.37; 95% CI: 1.01, 1.87). Among the control variables,higher living standard is associated with lower likelihoodof adverse pregnancy outcomes among younger women andhigher education is associated with higher likelihood ofadverse pregnancy outcomes among older women.

Discussion

The study finds that considerable proportions of women inmany parts of India are already overweight and obese. Theproblem is particularly severe in the northern Indian statesof Delhi and Punjab, where about one in three women areoverweight or obese. Older age, urban residence, mediahabits, anemia, and economic living standard emerged asmost important covariates of overweight and obesity amongwomen in these states. A separate analysis by woman’s agesuggests that media habits and living standard are moreimportant covariates for older women, whereas place ofresidence and education are more important covariates foryounger women. Results also suggest that overweight andobesity may increase the risk of adverse pregnancy out-comes among younger women, whereas this relationship isnot observed among older women. Several other studies

Table 3 Adjusted effects (relative risks) of selected factors on the riskof being overweight (25.0≤BMI<30.0) or obese (BMI≥30.0) amongever-married women age 15–49 in Delhi and Punjab, 1998–1999

Overweight Obese

95% CI 95% CI

Characteristic RR LL UL RR LL UL

Age

15–19a 1.00 - - 1.00 - -

20–29 5.06 1.58 16.15 2.31 0.32 16.73

30–39 10.00 3.08 32.47 8.48 1.15 62.72

40–49 14.04 4.39 44.87 12.35 1.68 90.81

Residence

Urban 1.16 0.92 1.47 1.57 1.06 2.32

Rurala 1.00 - - 1.00 - -

Education

Illiteratea 1.00 - - 1.00 - -

Literate, <middle complete 0.96 0.75 1.23 1.28 0.81 2.03

Middle complete 0.98 0.72 1.34 1.46 0.88 2.41

High school or more 1.17 0.89 1.54 1.43 0.89 2.29

Media habits

Watches TV every week 1.45 1.09 1.93 1.37 0.88 2.15

Reads newspaper/magazineevery week

1.15 0.92 1.43 1.50 1.07 2.10

Food habits

Milk or curd daily 1.06 0.88 1.27 0.85 0.64 1.13

Fruits daily 1.06 0.87 1.29 1.54 1.19 2.00

Green, leafy vegetablesdaily

1.16 0.98 1.37 1.01 0.79 1.28

Eggs at least weekly 1.21 0.96 1.54 0.75 0.50 1.14

Chicken, meat, or fish atleast weekly

1.05 0.74 1.50 1.53 0.93 2.54

Anemia status

Not anemica 1.00 - - 1.00 - -

Mild anemia 0.90 0.74 1.09 0.75 0.58 0.96

Moderate or severe anemia 0.59 0.43 0.81 0.53 0.32 0.86

Work status

Works at home 1.06 0.74 1.51 1.27 0.77 2.12

Works outside 0.99 0.77 1.28 0.70 0.46 1.07

Does not worka 1.00 - - 1.00 - -

Religion

Hindua 1.00 - - 1.00 - -

Muslim 1.02 0.73 1.44 1.09 0.52 2.30

Sikh 0.96 0.74 1.24 0.87 0.60 1.26

Other 0.83 0.44 1.60 0.42 0.18 0.99

Caste/tribe

SC/ST 0.87 0.68 1.10 0.49 0.35 0.69

OBC 0.88 0.69 1.12 0.77 0.51 1.17

Othera 1.00 - - 1.00 - -

Standard of living

Lowa 1.00 - - 1.00 - -

Medium 2.49 0.92 6.75 2.47 0.31 19.46

Table 3 (continued)

Overweight Obese

95% CI 95% CI

Characteristic RR LL UL RR LL UL

High 3.77 1.42 10.00 3.61 0.46 28.43

State

Delhia 1.00 - - 1.00 - -

Punjab 1.12 0.88 1.41 1.78 1.22 2.60

Number of women 3,716

a Reference category: LL lower limit; UL upper limit

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Table 4 Adjusted effects (odds ratios) of selected factors on the risk of being overweight or obese (BMI≥25.0) among ever-married women age15–49 in Delhi and Punjab, 1998–1999

15–29 30–49 15–49

95% CI 95% CI 95% CI

Characteristics OR LL UL OR LL UL OR LL UL

Age

15–19a NA NA NA NA NA NA 1.00 - -

20–29 NA NA NA NA NA NA 4.14 1.49 11.45

30–39 NA NA NA NA NA NA 9.51 3.40 26.59

40–49 NA NA NA NA NA NA 13.49 4.95 36.77

Residence

Urban 1.52 0.99 2.34 1.22 0.91 1.62 1.27 1.01 1.60

Rurala 1.00 - - 1.00 - - 1.00 - -

Education

Illiteratea 1.00 - - 1.00 - - 1.00 - -

Literate, <middle complete 1.11 0.65 1.92 0.94 0.71 1.25 1.03 0.80 1.32

Middle complete 1.36 0.76 2.45 0.91 0.63 1.33 1.08 0.80 1.46

High school or more 1.77 1.05 3.00 0.97 0.71 1.33 1.22 0.95 1.58

Media habits

Watches TV every week 1.00 0.58 1.71 1.54 1.17 2.03 1.43 1.10 1.86

Reads newspaper/magazine every week 1.10 0.74 1.63 1.36 1.05 1.76 1.24 1.01 1.52

Food habits

Milk or curd daily 0.83 0.57 1.20 1.06 0.87 1.28 1.00 0.84 1.19

Fruits daily 1.03 0.76 1.40 1.28 1.02 1.61 1.18 0.98 1.42

Green, leafy vegetables daily 1.09 0.81 1.47 1.13 0.94 1.36 1.12 0.95 1.31

Eggs at least weekly 1.02 0.67 1.56 1.08 0.83 1.41 1.08 0.86 1.35

Chicken, meat, or fish at least weekly 1.07 0.58 1.97 1.18 0.81 1.72 1.15 0.83 1.59

Anemia status

Not anemica 1.00 - - 1.00 - - 1.00 - -

Mild anemia 0.71 0.51 0.98 0.92 0.75 1.13 0.86 0.73 1.02

Moderate or severe anemia 0.56 0.34 0.92 0.59 0.41 0.84 0.57 0.43 0.77

Work status

Works at home 0.62 0.24 1.56 1.26 0.85 1.86 1.12 0.80 1.56

Works outside 1.33 0.73 2.42 0.87 0.67 1.13 0.91 0.71 1.16

Does not worka 1.00 - - 1.00 - - 1.00 - -

Religion

Hindua 1.00 - - 1.00 - - 1.00 - -

Muslim 0.92 0.41 2.10 1.12 0.70 1.79 1.04 0.73 1.48

Sikh 1.11 0.80 1.55 0.84 0.64 1.11 0.93 0.73 1.18

Other 0.44 0.12 1.59 0.85 0.41 1.79 0.72 0.39 1.33

Caste/tribe

SC/ST 0.86 0.57 1.28 0.71 0.55 0.92 0.76 0.61 0.95

OBC 0.90 0.56 1.43 0.83 0.64 1.08 0.85 0.67 1.08

Othera 1.00 - - 1.00 - - 1.00 - -

Standard of living

Lowa 1.00 - - 1.00 - - 1.00 - -

Medium 1.19 0.40 3.53 6.03 1.34 27.16 2.48 0.99 6.21

High 1.94 0.65 5.78 9.10 2.09 39.67 3.70 1.52 9.02

State

Delhia 1.00 - - 1.00 - - 1.00 - -

Punjab 1.55 1.02 2.34 1.22 0.94 1.59 1.28 1.02 1.60

Number of women 1,190 2,526 3,716

a Reference category: LL lower limit; UL upper limit; NA not applicable

394 J Public Health (2012) 20:387–397

(Metwally et al. 2008; Chu et al. 2007) in developedcountries also found maternal obesity to be associated with

an increased risk of miscarriage and stillbirth, although themechanisms to explain this association are not clear.

Table 5 Adjusted effects (odds ratios) of body mass index (BMI) on the risk of adverse pregnancy outcomes (miscarriage or stillbirth) amongwomen age 15–49 in Delhi and Punjab, 1998–1999

15–29 30–49

95% CI 95% CI

Characteristics OR LL UL OR LL UL

Body mass index

Underweight (BMI<18.5) 1.07 0.75 1.54 1.37 1.01 1.87

Normal (18.5≤BMI<25.0)a 1.00 - - 1.00 - -

Overweight (25.0≤BMI<30.0) 1.57 1.06 2.33 1.15 0.90 1.46

Obese (BMI≥30.0) 2.10 1.02 4.29 0.84 0.58 1.22

Residence

Urban 0.87 0.54 1.39 1.11 0.84 1.47

Rurala 1.00 - - 1.00 - -

Education

Illiteratea 1.00 - - 1.00 - -

Literate, <middle complete 1.28 0.83 1.98 1.29 0.95 1.76

Middle complete 0.78 0.45 1.34 1.74 1.19 2.55

High school or more 0.85 0.49 1.48 1.40 0.98 1.99

Media habits

Watches TV every week 1.49 0.92 2.41 1.03 0.76 1.38

Reads newspaper/magazine every week 0.85 0.56 1.27 0.83 0.61 1.12

Food habits

Milk or curd daily 0.78 0.54 1.14 0.88 0.68 1.14

Fruits daily 1.07 0.68 1.71 0.91 0.69 1.22

Green, leafy vegetables daily 0.71 0.53 0.95 0.96 0.74 1.23

Eggs at least weekly 1.29 0.83 2.01 1.23 0.91 1.66

Chicken, meat, or fish at least weekly 0.69 0.36 1.30 0.90 0.60 1.35

Work status

Works at home 0.92 0.38 2.23 0.85 0.52 1.38

Works outside 1.06 0.58 1.93 1.21 0.93 1.57

Does not worka 1.00 - - 1.00 - -

Religion

Hindua 1.00 - - 1.00 - -

Muslim 0.83 0.39 1.74 1.63 0.99 2.67

Sikh 0.61 0.41 0.90 0.91 0.69 1.21

Other 1.44 0.57 3.65 1.33 0.65 2.75

Caste/tribe

SC/ST 0.74 0.48 1.12 0.98 0.71 1.36

OBC 0.82 0.53 1.28 0.94 0.70 1.25

Othera 1.00 - - 1.00 - -

Standard of living

Lowa 1.00 - - 1.00 - -

Medium 0.44 0.21 0.90 1.08 0.60 1.97

High 0.43 0.19 0.98 0.84 0.43 1.64

State

Delhia 1.00 - - 1.00 - -

Punjab 1.45 0.94 2.22 1.08 0.82 1.41

Number of women 1,560 2,908

a Reference category

LL lower limit; UL: upper limit

J Public Health (2012) 20:387–397 395

We found positive association of overweight and obesitywith important covariates such as education, wealth status anddiet habits such as eating fruits daily contrary to our expect-ations. It is a fact in India that overweight/obesity is associatedwith high education, high wealth status and eating fruits daily.A study by the author (unpublished) shows a strong andstatistically significant positive effect of education and wealthon overweight/obesity among both men and women in India.The risk of overweight and obesity were almost 10 timeshigher among men and six times higher among women withhigh wealth and high education with reference to low wealthand low education. The risk increased to 14 times for men and8 times for women for age 40+ when analysed separately forage groups. One study on food intake patterns in westerncountries, indicated that a diet rich in fruits and vegetableswas associated with smaller gains in body mass index (BMI)(Newby et al 2003). However, other studies on dietary patternand BMI could not consistently predict changes in BMI orobesity development in either developed (Togo et al 2004;2001) or developing countries.

There are a number of measurement concerns that shouldbe kept in mind when considering the findings of this study.First, our analysis is based on cross-sectional data, whereprevalence of overweight and obesity is measured at the timeof the survey and the experience of an adverse pregnancyoutcome is from the past. A positive correlation betweenoverweight and obesity and adverse pregnancy outcome canresult if a woman became overweight or obese as a result ofthe pregnancy that resulted in a miscarriage or stillbirth.Although our analysis excludes pregnant women and thosewho gave birth in the preceding two months, the possibility ofsuch reverse causation cannot be ruled out. Second, the surveyonly measured height and weight of the respondents. Othermeasures such as waist circumference or waist-hip ratio maybetter capture overweight and obesity conditions in India,especially as they relate to adverse health effects. Third, theinformation on pregnancy outcomes is based on mother’srecall and it is possible that some women, especially olderwomen, may not recall an adverse pregnancy outcome in themore distant past. Others may not report an adverse pregnancyoutcome for cultural reasons. This may partly explain the lackof relationship between obesity and adverse pregnancy out-comes among older women in our analysis. Moreover, amongolder women, more educated women may be more likely torecall an adverse pregnancy outcome which explains theevidence of a positive effect of education on the likelihoodof adverse pregnancy outcomes.

Lastly, this study used relatively older data (NFHS-2 dataconducted in 1998–1999) rather than the latest round of thesurvey, i.e., NFHS-3 conducted in 2005–2006. This is largelybecause in NFHS-3 women were not specifically asked aboutthe adverse pregnancy outcomes such as miscarriage or stillbirth pregnancy, but were asked to provide a dichotomous

answer to the question, “Have you ever had a pregnancy thatmiscarried, was aborted, or ended in a stillbirth?” (availableat www.nfhsindia.org). Also, it was not possible to exclude theinduced abortion cases from the data, as a woman can haveany of the above pregnancy outcomes in her life. Therefore,NFHS-2 is the only survey where married women were spe-cifically asked about adverse pregnancy outcomes separatelyon still birth and miscarriage as well as induced abortion andlivebirths. We used only still birth and miscarriage as adversepregnancy outcomes as the main reponse variable in our studyand utilised NFHS-2 data for analyzing the adverse pregnancyoutcome and linked it with maternal overweight and obesityas its consequence. Therefore, irrespective of the data beingold, we believe that the covariates of overweight and obesityand consequence of higher BMI on adverse pregnancy out-come has not changed much during the last decade and wefound no study which has shown the effect of overweight andobesity on women’s risk of adverse pregnancy outcome inIndia with a nationally representative data.

Despite these limitations, our study found out a possiblecausal relationship between overweight/obesity and variouscovariates. In a developing country situation, where data onBMI and clinical data on women’s reproductive morbiditiesare usually not available, very weak, or not available for thesame sets of people, the NFHS-2 data provided a uniqueopportunity to study the covariates of obesity among adultIndian women and examine the consequences of overweightand obesity on adverse pregnancy outcomes. The findingsof our study are extremely useful in identifying populationgroups for public health campaigns to promote effectivediets and lifestyles to prevent the obesity epidemic and itsassociated adverse pregnancy outcomes.

Acknowledgements Authors are thankful to Gayle Yamashita forcomputer programming and Sally Dai for research assistance. Authorsare also thankful to Tim Dyson, P.N. Mari Bhat, and Kamla Gupta foruseful comments on an earlier draft. An earlier version of this paperwas presented at the IUSSP Asia-Pacific Regional Population Confer-ence in Bangkok, 10–13 June 2002.

Conflict of interest The authors declare that they have no conflict ofinterest.

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Author’s contribution

PA conceived the study, analyzed and interpreted the data, and wrotemanuscript. VM contributed to the conceptualization of the study, anal-ysis and interpretation of the results, discussion, and reviewed/editedmanuscript. SA contributed to introduction and discussion andreviewed/edited manuscript. All authors approve the final version of thepaper.

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