overweight and obesity among internal migrants in...

416 Asia Pac J Clin Nutr 2013;22 (3):416-425

Original Article Overweight and obesity among internal migrants in India Kiruba Sankar Varadharajan MD, Tinku Thomas PhD, Divya Rajaraman PhD, Anura V Kurpad MD, PhD, Mario Vaz MD St John’s Research Institute, Koramangala, Bangalore, Karnataka, India

Migration, chiefly from rural to urban areas has been linked to precursor conditions of cardiovascular dis-eases. We estimated the odds of overweight/obesity ( BMI ≥25 kg/m2) associated with different patterns of internal migration, using data from the National Family Health Survey 3, a cross-sectional survey that covered 29 states of India in 2005/06. A total of 56,498 non-pregnant women, aged 15 to 49 years, and 42,190 men, aged 15 to 54 years, with BMI >18.5 kg/m2 were included in the final analysis. Odds ratios (ORs) for over-weight/obesity were computed for different groups of migrants after adjusting for age, socioeconomic status and parity using binomial logistic regression models. All analyses were performed separately for men and women and weighted using national sampling weights. Rural to urban migrant women but not men, had higher odds of being overweight/obese (adjusted OR (AOR): 1.50, 95% confidence interval (CI): 1.36-1.65) com-pared to non-migrant rural residents. Increased odds were also noted among women migrating from one ur-ban area to another, compared to non-migrant urban women ( AOR: 1.10, 95% CI: 1.02-1.19). Women mi-grating from urban to rural areas, however had decreased odds (AOR: 0.75, 95% CI: 0.67-0.83) of over-weight/obesity. Thus, apart from confirming rural to urban migration as a risk factor for overweight, this study finds that other patterns of migration are also associated with overweight/obesity.

Key Words: internal migrants, overweight, obesity, India, NFHS 3 INTRODUCTION Chronic cardiovascular diseases have become the lead-ing killers in low and middle income countries today.1,2 These countries account for 80% of noncommunicable disease mortality worldwide. India is in the midst of this epidemiological transition, with an estimated 53% of deaths and 44% of disability adjusted life years lost (DALYs) attributed to chronic diseases.1-3 Several stud-ies have documented a rising prevalence of cardiovascu-lar risk factors such as type 2 diabetes mellitus, hyperten-sion and dyslipidemia, especially among urban Indians.4-8 As elsewhere, in India, this epidemiological transition has been linked to the demographic and nutrition transi-tion.9-11 Nationwide surveys conducted over the previ-ous decade have shown relatively high levels of overweight among women, urban residents and higher socioeconomic groups.12-14 This has been attributed to sedentary lifestyles and increased consumption of energy dense foods, occurring in the context of rapid urbaniza-tion.15 The Indian population remains predominantly rural; however, India has the second largest urban population in the world. Over 40% of the population is projected to live in urban regions by 2030.16 High urban growth rates can be attributed, in part, to internal migration of citizens from rural to urban areas, driven chiefly by prospects of better livelihood opportunities. Migrant studies can pro-vide valuable information on the effects of environ-mental changes on behavioural patterns and occurrence

of diseases.17 A recent review of cardiovascular effects of rural-to-urban migration in low and middle income coun-tries found that most but not all cardiovascular risk fac-tors are higher among migrants compared to rural resi-dents.18 Migration from rural to urban areas has been as-sociated with deleterious lifestyle and behavior patterns; increased consumption of saturated fats and lower con-sumption of complex carbohydrates, polyunsaturated fat-ty acids, and fibers, in addition to decreased physical ac-tivity.19-20 Rural to urban migrants have been shown to be at increased risk for cardiovascular risk factors such as hypertension, obesity and dyslipidemia in comparison with rural residents,5,21-23 or their rural siblings.4,24 Some recent studies however, have reported migration to be associated with decreased blood pressure and serum tri-glyceride levels.25,26 Further, relatively little is known in this regard about other streams of migrants. Yet, in devel-oping countries like India where more than 30% of the citizens have moved from their place of birth, other

Corresponding Author: Dr Kiruba Sankar Varadharajan, Sen-ior Resident, Division of Epidemiology & Biostatistics, St. John’s Research Institute, St. John’s National Academy of Health Sciences, Koramangala, Bangalore, India 560034. Tel: +91 80 22065059; Fax: +91 80 25532037 Email: [email protected] Manuscript received 23 December 2012. Initial review complet-ed 7 March 2013. Revision accepted 7 May 2013. doi: 10.6133/apjcn.2013.22.3.14

Overweight among internal migrants, India 417

forms of migration such as that between rural areas and between urban areas are also common.27 These forms of migration could also conceivably alter risks of over-weight/obesity through a change in diets or disruptions of social networks that could affect physical activity.28 Therefore, in this paper we sought to evaluate the asso-ciation between overweight/obesity and internal migra-tion using data from the National Family Health Survey 3 (NFHS 3) conducted in India during 2005/06,29 adjust-ing for other determinants that can influence body weight.

Socioeconomic status has been associated with in-creased body weight in both men and women from de-veloping nations,30 and significant differences in the wealth and education levels were noted between mi-grants and non-migrants in the sample; hence we ad-justed for these variables in the models. Parity was included as an explanatory variable in the models for women, as gestational weight gain is known to increase the risk of overweight.31 All models were also adjusted for age as older migrants may have increased adiposity related to the effect of aging. MATERIALS AND METHODS National Family Health Survey 3 was conducted in 2005/06 as part of the Demographic and Health Surveys (DHS) program, funded primarily by the United States Agency for International Development (USAID). It cov-ered 29 states in India which account for 99% of the population. The Indian Institute of Population Sciences (IIPS), Mumbai coordinated data collection activities, with technical assistance from MEASURE DHS+ at ORC Macro, Calverton, MD and the East-West Center, Honolulu, HI. The ethical board of IIPS approved the study. Datasets for the analysis were provided by MEASURE DHS+, ORC Macro, Calverton, MD, USA. The design of the survey has been reported elsewhere.29 The interviews dealt with sociodemographic particulars, fertility behavior, and maternal and child health issues. All interviewees were asked about the number of years of stay at the current place of residence and if they had resided in any other place previously. Both current and previous places of residence were classified as urban or rural using Census of India 2001 definitions: all plac-es with a municipality, corporation, cantonment board or notified town area committee as well as all those that met the three following criteria simultaneously, were considered urban: a) a population of 5000 or above; b) at least 75% of male working population engaged in non-agricultural work; and c) population density of at least 400 per sq km.32 Movement within the same vil-lage, town or city was not recorded. Standing height was recorded to the nearest 0.1 cm. Weight was measured to the nearest 0.1 kg on a UNICEF electronic weigh-ing scale (Seca Corporation, Munich). A wealth index score was computed based on the possession of house-hold assets and households categorized into wealth quin-tiles at the national level. We classified all individuals who resided currently in a place different from the re-ported previous place of residence as migrants and cat-egorized them into one of four patterns of migration, rural to rural, rural to urban, urban to rural, and urban

to urban. BMI was computed and the body weight status of each individual was classified in accordance with the WHO classification; overweight was defined as BMI of 25.0-29.9 kg/m2 and obesity as ≥30 kg/m2.33

Statistical analysis Prevalence rates of overweight/obesity were estimated in all individuals aged 18 years or above for the different categories of migrants and non-migrants. Age-standardized prevalence rates were also calculated for different groups of migrants, using the age distribution of the respective non-migrant stream, to account for the younger age structure of non-migrants. To estimate the odds of overweight/obesity, two sets of logistic regres-sion analyses were performed with BMI category as the outcome variable: the first compared individuals who moved out of a rural area (into rural or urban areas) with those who had always resided in a rural area, and the second compared individuals who moved out of an urban area (into rural or urban areas) with those who had always resided in an urban area. Odds ratios (OR) of being overweight/obese were computed in comparison with normal weight individuals after adjusting for age, years of education, economic status (as classified by wealth index quintile categories) and parity (in women). After studying the main effects, interaction terms for migration status and socioeconomic status (education and wealth index) were introduced in the models. All analyses were done separately for males and females, after weighting the data using national sampling weights to account for over-sampling in urban areas and smaller states, and differential non-response rates. SPSS (Chica-go, IL) version 13.0 was used to analyze data. RESULTS A total of 124,385 women (aged 15 to 49 years) and 74,369 men (aged 15 to 54 years) residing in 109,041 households were interviewed in NFHS 3. Response rates were 95% for women and 87% for men. Individuals aged 18 years or above, who were usual residents of the sampled household were eligible for inclusion in our analysis. Patterns of migration, socio-demographic and dietary characteristics have been reported for 103,013 women and 64,265 men meeting the above criteria, for whom information on migration status could be ascer-tained (Tables 1 and 2). The prevalence of overweight in the sample has been reported for 144,518 individuals; pregnant women, women who had delivered a child in the past year, individuals with tuberculosis, and those with inconsistent or unrecorded anthropometric meas-urements were excluded (Table 3). A total of 98,688 individuals with a BMI ≥18.5 kg/m2 constituted the final sample for computing odds ratios (Figure 1).

As seen in Table 1, 65% of individuals resided in rural areas. Seventy percent of men and just over 50% of women were literate. Seventeen percent of women and 16% of men belonged to the lowest quintile of the wealth index (poorest), while 23% and 24% belonged to the topmost quintile. Individuals residing in rural areas were more likely to be poorer, less educated, married, and employed in skilled or unskilled manual labour as compared to their urban counterparts. Of these,

418 KS Varadharajan, T Thomas, D Rajaraman, AV Kurpad and M Vaz

Table 1. Characteristics of different groups of migrant and non-migrant individuals†

Variables All Urban Rural Urban non-migrants

Urban to urban migrants

Urban to rural migrants

Rural non-migrants

Rural to urban migrants

Rural to rural migrants

Women N (%) ‡ 103013 34621 (33.6) 68392 (66.4) 9937 (9.6) 13635 (13.2) 6938 (6.7) 14320 (13.9) 11049 (10.7) 47133 (45.8) Mean age (SD) 31.1 (8.8) 31.3 (8.8) 31.0 (8.8) 28.5 (9.2) 32.5 (8.4)‡‡ 31.0 (8.3)‡‡ 28.5 (9.4) 32.4 (8.4)‡‡ 31.8 (8.5)‡‡ Current residence >5 years, % 64.2 71.9 77.1 84.7 Mean years of education (SD) 4.8 (5.1) 7.5 (5.4) 3.5 (4.4)¶ 8.8 (5.1) 8.5 (5.3) 5.3 (4.9)‡‡ 5.0 (4.9) 5.1 (5.0) 2.73 (4.0)‡‡ Mean height in metres (SD) 1.52 (0.06) 1.52 (0.06) 1.52 (0.06)¶ 1.53 (0.06) 1.53 (0.06) 1.52 (0.06)‡‡ 1.52 (0.06) 1.52 (0.06)‡‡ 1.52 (0.06) Currently married, % 82.6 77.5 85.1** 48.0 88.1§§ 90.9§§ 51.9 90.9§§ 94.4§§ Parous % 80.8 75.8 83.4** 49.2 84.4§§ 87.1§§ 54.4 89.2§§ 91.7§§ Manual labourers (agricultural & non-agricultural) 35.9 14.0 47.0** 15.5 10.6 36.4§§ 47.0 16.9§§ 48.5

Richer & Richest (4th and 5th) quintiles of wealth index §, % 44.0 79.2 26.3** 80.1 87.3§§ 43.7§§ 28.9 68.4§§ 22.9§§

Men N (%) ‡ 64265 23831 (37.1) 40434 (62.9) 13182 (20.5) 6291 (9.8) 1616 (2.5) 34350 (53.5) 4497 (7.0) 4600 (7.2) Mean Age (SD) 33.0 (10.0) 32.7 (10.1) 33.2 (10.0) 31.7 (10.1) 34.1 (10.0)‡‡ 33.9 (10.0)‡‡ 32.9 (10.0) 33.9 (10.0)‡‡ 35.1 (9.8)‡‡ Current residence >5 years, % 54.0 55.7 69.2 67.3 Mean years of education (SD) 7.2 (5.0) 9.1 (4.8) 6.1 (4.8)¶ 8.9 (4.7) 10.0 (5.0) 8.3 (4.8) 6.0 (4.8) 8.4 (4.9)‡‡ 5.9 (5.0) Mean height in metres (SD) 1.65 (0.07) 1.65 (0.07) 1.64 (0.07)¶ 1.65 (0.07) 1.66 (0.07)‡‡ 1.64 (0.07)‡‡ 1.64 (0.07) 1.65 (0.07)‡‡ 1.64 (0.07)‡‡ Currently married, % 72.1 65.7 75.9†† 60.2 70.8§§ 75.7§§ 75.0 74.9 82.1§§ Manual labourers (agricultural & non-agricultural) 64.9 44.5 77.1†† 44.9 38.6 58.0§§ 78.3 51.9§§ 74.8

Richer & Richest (4th and 5th) quintiles of wealth index §, % 46.0 79.1 26.6†† 77.9 85.9§§ 53.2§§ 24.7 72.8§§ 31.6§§ † All displayed frequencies (and percentages) are obtained through weighted analysis and hence differ from actual numbers in the final sample ‡ Proportion of total women/men §The NFHS-3 wealth index is based on 33 assets and housing characteristics. Each household asset was assigned a weight (factor score) generated through principal components analysis, and the resulting asset scores were standardized. Each household was then assigned a score for each asset, and the scores were summed for each household; individuals were ranked according to the score of the household in which they reside. The sample was then divided into quintiles at the national level ¶ p<0.05 in comparison with the urban group using unpaired t test †† p<0.05 in comparison with the urban group, using chi-square test ‡‡ p<0.05 in comparison with the corresponding non-migrant group, using One way ANOVA and post-hoc tests §§ p<0.05 in comparison with the respective non-migrant group, using chi-square test


Table 2. Reported daily/weekly intakes of different food groups by migrant and non-migrant individuals†


Urban to urban migrants


Rural non-migrants



Women N (%)‡ 103013 34621 (33.6) 68392 (66.4) 9937 (9.6) 13635 (13.2) 6938 (6.7) 14320 (13.9) 11049 (10.7) 47133 (45.8) Dairy products, % 55.3 63.0 51.4§ 63.1 67.6 60.6 51.3 57.3¶ 50.0 Pulses or beans, % 89.3 91.4 88.2 88.6 93.4 89.6 83.9 91.4 89.4 Vegetables, % 93.1 94.5 92.4 93.8 95.3 92.3 91.5 93.9 92.7 Fruits, % 39.7 59.0 30.0§ 61.4 65.3 43.5¶ 37.4 49.2¶ 25.8¶ Eggs, % 32.4 39.2 29.0§ 44.1 38.3 29.9¶ 40.2 35.8 25.5¶ Fish, % 28.6 32.0 26.9§ 36.4 30.1 22.8¶ 36.4 30.3 24.7¶ Meat, % 22.9 31.1 18.7§ 36.1 30.7 35.5 28.9 26.8 15.2¶

Men N (%)‡ 64265 23831 (37.1) 40434 (62.9) 13182 (20.5) 6291 (9.8) 1616 (2.5) 34350 (53.5) 4497 (7.0) 4600 (7.2) Dairy products, % 66.9 74.5 62.3§ 74.9 76.5 74.8 62.8 70.5¶ 57.5 Pulses or beans, % 90.7 93.0 89.4 92.6 94.1 90.6 89.5 92.9 88.2 Vegetables, % 93.7 95.8 92.6 95.5 96.0 94.3 92.5 96.1 92.3 Fruits, % 47.2 63.1 37.8§ 62.9 67.6 55.3¶ 36.7 57.2¶ 39.9 Eggs, % 41.0 48.5 36.5§ 49.9 47.7 49.8 35.4 45.6¶ 40.7 Fish, % 31.6 35.4 29.3§ 35.7 35.0 38.6 27.8 35.2¶ 37.3¶ Meat, % 28.4 37.6 23.1§ 38.6 37.6 37.6 21.9 34.5¶ 26.6 † All displayed frequencies (and percentages) are obtained through weighted analysis and hence differ from actual numbers in the final sample ‡ Proportion of total women/men § p<0.05 in comparison with the urban group, using chi-square test ¶ p<0.05 in comparison with the corresponding non-migrant group using chi-square test


Table 3. Prevalence of overweight/obesity (obesity) by characteristics of migrants and non-migrants†


Urban to ur-ban migrants


Rural non-migrants



Women N (%)‡ 84375 28815 (34.2%) 55560 (65.8%) 8612 (10.2%) 11206 (13.3%) 5554 (6.6%) 12811 (15.2%) 8997 (10.7%) 37195 (44.1%) Overall prevalence 15.2 (3.5) 27.0 (7.3) 9.1 (1.6)§ 21.7 (5.5) 34.2 (9.9)¶ 14.9 (3.3)¶ 7.3 (1.3) 23.2 (5.7)¶ 8.8 (1.4)¶ Age-standardized prevalence†† 26.6 (7.2) 12.0 (2.7) 17.5 (4.0) 6.7 (1.0) Education

No education 8.5 19.7 5.9§ 21.4 23.9 8.9¶ 5.7 16.5¶ 5.7 Primary 15.0 24.9 10.9§ 20.1 28.0¶ 13.8¶ 9.3 25.4¶ 11.1 Secondary 21.1 30.0 13.3§ 22.7 37.4¶ 19.8¶ 7.8 27.4¶ 14.8¶ Higher 26.7 30.5 15.4§ 20.6 38.8¶ 20.9 8.6 31.0¶ 22.9¶ Occupation

Not employed 19.7 29.3 12.3§ 23.4 36.3¶ 19.0¶ 9.5 25.4¶ 12.2 Professional, Technical & Managerial 27.9 32.7 18.9§ 23.6 40.2¶ 31.7¶ 7.0 37.5¶ 22.9¶ Clerical, Sales & Services 20.8 23.5 16.3§ 22.0 27.7¶ 26.2¶ 15.3 19.6¶ 14.4 Manual labour 6.8 17.2 5.1§ 14.3 24.0¶ 6.4¶ 4.6 14.4¶ 5.1 Wealth Index

1st & 2nd quintiles (Poorer/Poorest) 3.6 7.2 3.3§ 9.0 8.9 4.1¶ 3.6 5.6 3.1 3rd quintile (Middle) 9.0 14.0 7.7§ 14.4 14.7 9.5¶ 5.6 13.2¶ 8.3 4th & 5th quintiles (Richer/Richest) 26.7 31.0 20.4§ 24.0 37.2¶ 25.2 14.2 28.8¶ 21.7¶

Men N (%)‡ 60143 21686 (36.1%) 38456 (63.9%) 12038 (20.0%) 5629 (9.4%) 1519 (2.5%) 32662 (54.3%) 4020 6.7%) 4276 7.1%) Overall prevalence 10.9 (1.5) 18.4 (2.9) 6.6 (0.8)§ 17.0 (2.7) 23.1 (3.9)¶ 13.3 (1.4)¶ 6.1 (0.7) 15.8 (1.9)¶ 8.1 (0.9)¶ Age-standardized prevalence†† 20.9 (3.5) 12.2 (1.2) 15.1 (1.8) 7.4 (0.8) Education

No education 4.1 8.1 3.2§ 8.1 10.8 8.7 3.1 5.8 3.0 Primary 6.5 11.6 4.6§ 11.0 13.5 8.5 4.5 11.5¶ 4.6 Secondary 11.8 17.5 8.0§ 16.5 21.0¶ 13.9 7.4 16.3¶ 10.0 Higher 23.2 28.5 14.8§ 26.4 33.1¶ 18.7¶ 13.1 24.9¶ 22.8¶ Occupation

Not employed 7.3 9.8 4.8§ 8.5 14.2¶ 12..2¶ 4.4 8.9¶ 4.7 Professional, Technical & Managerial 25.3 30.9 16.5§ 26.7 36.8¶ 13.2¶ 15.6 29.0¶ 22.1¶ Clerical, Sales & Services 18.2 22.9 11.5§ 22.1 27.0¶ 19.3¶ 10.6 19.1¶ 11.9 Manual labour 7.5 13.8 5.4§ 13.5 15.9 10.6 5.1 12.3¶ 6.3 Wealth Index

1st & 2nd quintiles (Poorer/Poorest) 2.1 3.9 1.9§ 4.4 3.3 3.2 1.9 3.0 2.0 3rd quintile (Middle) 5.8 7.5 5.3§ 7.4 7.5 5.6 5.4 7.7 4.7 4th & 5th quintiles (Richer/Richest) 19.7 21.7 16.5§ 20.1 26.0¶ 21.3 15.6 19.5¶ 19.1¶ † All displayed frequencies (and percentages) are obtained through weighted analysis and hence differ from actual numbers in the final sample ‡ Proportion of total women / men § p<0.05 in comparison with the urban group, using chi-square test ¶ p<0.05 in comparison with the corresponding non-migrant group using chi-square test †† Age distribution of non-migrants (urban non-migrant residents and rural non-migrant residents) was used to standardize prevalence rates for corresponding migrant groups


95,759 (57%) individuals (77% of women and 27% of men) reported moving from a previous place of resi-dence and were classified as migrants. Women com-prised 80% of the migrants. Sixty one percent of urban and 55% of rural dwellers were migrants. The most prominent stream of migration was that between rural areas, with more than half the migrant individuals (60% of migrant women and 27% of migrant men) reporting such a pattern of movement. This was followed by movement between urban areas (21%) and from rural to urban areas (16%). Less than 10% of the migrants reported having moved from urban to rural areas. Most migrants had been residing in the current place of resi-dence for more than five years, among all streams of mi-gration. Migrant women were more likely to be older, married, and parous, compared to non-migrant women. Women who moved from one rural area to another were the least educated (2.7±4.0 years), followed by rural non-migrant women (5.0±4.9 years). Women who moved from rural to urban areas were richer; 68% of such women belonged to the upper two wealth quintiles in comparison with 23% of rural to rural migrant wom-en and 29% of rural non-migrant women. They were also less likely to be employed in manual labour. Women who moved from urban to rural areas were much less educated (5.3±4.9 years) and were likely to belong to lower economic classes (55%) than non-migrant urban women (8.7±5.1 years and 20 % respec-tively). The characteristics of migrant men reflected the patterns seen in women among the respective streams.

Reported daily/weekly consumption of milk and dairy products, fruits, eggs, fish and meat was signifi-cantly higher in urban areas. Urban to rural migrant women reported a lower frequency of consumption of fruits, eggs and fish (44%, 30% and 23%) compared to non-migrants (61%, 44% and 36% respectively). Men migrating between urban areas consumed fruits more often (68%) while those moving from urban to rural areas did so less often (55%) compared to urban non-migrants. The reported pattern of consumption of eggs, fish and meat among men migrating from rural to urban areas was similar to urban non-migrants. Rural to urban mi-grant women, however, reported frequencies that were on par with rural residents who had not migrated. Con-sumption of eggs, fish and meat was lower in rural to rural migrant women (26%, 26%, 25% and 15% re-spectively) compared to rural non-migrant residents (40%, 36% and 29% respectively) but rural to rural mi-grant men reported more frequent consumption of these items (Table 2).

Approximately 15% of women and 11% of men were overweight/obese. Women were more likely to be over-weight than men among all categories of migrants and non-migrants. Prevalence of overweight/obesity was low-est among non-migrants in rural areas (6.1% of men, 7.3% of women) followed by migrants between rural areas (8.1% of men and 8.8% of women). About 16% and 23% of men and women migrants from rural to ur-ban areas were overweight/obese. In comparison to ur-ban non-migrants (17% of men and 21.7% of women), overweight/obesity was significantly lower among mi-grants from urban to rural areas (13.3% of men, 14% of

women) and higher among individuals who moved from one urban area to another (23.1% of men and 34.2% of women). When standardized to reflect the age distribu-tion of non-migrants, the prevalence rates in all groups of migrants were seen to be slightly lower than the crude prevalence rates; nevertheless, the pattern reflected in the crude rates was not altered. Education, occupation and wealth index categories were directly related to the prevalence of overweight/obesity, both overall, as well as among different streams of migrants and non-migrants. The incremental change in prevalence of overweight/obesity along these socioeconomic gradients however, was not uniform across the migrant streams, suggesting a possible interaction between migration and these socioeconomic variables. For instance, the difference in prevalence of overweight/obesity between the poor and rich was about 11% and 15% among non-migrant female rural and urban residents respectively, but was 23% in rural to urban migrants and 30% among urban to urban migrants (Table 3).

Figure 1 presents the O Rs of being overweight/ obese for different groups of migrants after adjustment for age, education, economic status and parity (in women). Women who migrated from a rural to an ur-ban environment had a 50% increased odds of being overweight/obese ( a djusted OR (AOR): 1.50, 95% con-fidence interval (CI): 1.36-1.65)) in comparison with non-migrant rural women; no such association was seen among men (AOR: 0.97, 95% CI: 0.87-1.09). Movement from one urban area to another was associated with a slightly increased odds of overweight/obesity ( AOR: 1.10, 95% CI: 1.02- 1.19) in women, but not in men (AOR: 1.08, 95% CI: 0.99-1.18), when compared to women and men who were non-migrant urban residents, respectively. Decreased odds of being overweight/obese were noted among female migrants from urban to rural areas but not in men (AOR: 0.75, 95% CI: 0.67-0.83 in women; AOR: 0.87, 95% CI: 0.73- 1.03 in men), when non-migrant, urban resident women and men were used as the reference groups respectively. No significant as-sociation of overweight/obesity was noted with move-ment from one rural area to another among both gen-ders. On introducing an interaction term between mi-gration status and economic status (wealth index quin-tiles) or education (years of education) in the models, the main effects of migration became statistically insig-nificant among females. In males, the interaction effects were not significant. DISCUSSION We analyzed nationally representative data from India to study the patterns of migration, dietary habits and risk of overweight/obesity in different streams of migrants. Rural to rural migration was the most prominent stream in this study, similar to that reported previously in India. Likewise, the proportion of urban to rural mi-grants was considerably small.27,34 However, these anal-yses indicate a slightly higher proportion of urban to urban migrants than rural to urban migrants. While information on reasons for migration was not collected in NFHS 3; as reported previously, most women seem to migrate after marriage, while men migrate seeking better


employment opportunities. This seems applicable for all streams of migrants. Significant socioeconomic differen-tials were noted between the various streams of migrants and non-migrant residents. Migrants from rural to urban areas, both males and females, were likely to be more educated, less likely to be employed as manual labourers and richer. In contrast, individuals migrating into rural areas fared worse than their non-migrant counterparts in terms of education, occupation and economic status. While migration to urban areas can improve socioeco-nomic status, it is also likely that individuals belonging to the upper strata of society migrate to urban areas. Such socioeconomic differentials could have accounted for the increased odds of overweight in rural to urban migrants and decreased odds in urban to rural mi-grants uncovered in this study; however these odds persisted even after adjusting for socioeconomic factors in women, but not in men. When we studied the inter-action of migration with socioeconomic factors such as education and wealth in the logistic regression model, the main effects of migration became statistically not significant, indicating possible over-adjustment or inade-quate sample size in the various sub-groups.

Individuals who move to urban areas have been shown to become more sedentary and adopt unhealthy diets.35,36 This incurs a higher risk of obesity, hyper-tension, diabetes, and dyslipidemia compared to rural residents.21-23 Studies conducted in India have indicated disparities in food consumption patterns between rural and urban populations and between different socioeco-nomic groups. Gradual decrease in cereal grain con-sumption and increases in that of dairy products, sugar as well as meat has been documented. Such changes have been shown to be most prominent among urban dwellers and have been implicated in the rapid in-crease in noncommunicable diseases in the country.15

Higher energy and fat intake as well as higher fruit and vegetable intake has been reported among rural to ur-ban migrants in India.37 Reported consumption of dairy products, eggs, meat, fish and fruits was seen to be higher among urban population in NFHS 3. Men, but not women, who migrated from rural to urban areas had a pattern that was more similar to urban residents than rural residents in terms of consumption of eggs, meat and fish. Among all groups of migrants and non-migrants, women seemed to consume these food groups less often than men but were seen to have a higher burden of over-weight. Inadequate physical activity could play a signif-icant role in the development of overweight among women migrants. Physical activity levels of rural to ur-ban migrants have been shown to resemble that of long-term urban residents in India.38 While no measurement of physical activity was undertaken in NFHS 3, the smaller proportion of urban migrants involved in manual labour and the larger proportion of unemployed women in urban areas indicate lower occupation-related physical activity in urban migrants. Physical activity may also be reduced due to an unfamiliar urban environment or loss of social networks and support.39

This analysis is supportive of the hypothesis that ex-posure to urban environments is associated with over-weight/obesity. Lending further credence to the obe-sogenic nature of urban environments was the finding of decreased odds of overweight/obesity for women mi-grating from urban to rural areas. Urban to rural migra-tion has been less studied and could possibly be associ-ated with increased physical activity or less energy dense diets or both, especially in women. Gender was seen to play a vital role in moderating the risk of overweight/obesity among migrants. The role of gender based differentials in knowledge and perceptions, access to physical activity facilities and health information, safe-

Figure 1. Adjusted odds ratios of overweight/obesity (BMI ≥25 Kg/m2). Odds ratios with 95% confidence intervals were calculated in comparison with normal weight individuals (BMI 18.5 to 25 kg/m2) after adjustment for age, years of education, economic status (wealth index quintiles) and parity (in women) through weighted analysis. a In comparison with non-migrant urban resident men (n = 9,221) and women (n = 6,401) respectively (Odds Ratio = 1). b In comparison with non-migrant rural resident men (n = 21,127) and women (n = 7,750), respectively (Odds Ratio = 1).


ty, and associated costs that have been documented in moderating risk of overweight/obesity among migrants warrants further study.40,41

The main strength of this study is that it covered a large, representative sample and hence the findings can be generalized at the country level. Among the major limitations are that the data were collected in a cross-sectional survey; this fails to capture the occurrence of multiple movements over longer intervals.42 This could have led to potential misclassification of migration status in our study, akin to the salmon bias described by migration researchers discussing differentials in Hispanic mortality in the United States.43,44 Longitudinal studies on migrants would be ideally suited to study such issues. However, most migrants in this study were seen to have resided in the current place of residence for more than five years, and the effects of misclassification on the risk of over-weight/obesity are likely minimal, and would possibly pull down the risk measures, rather than inflate them. Due to limited dietary information collected and absence of physical activity data, the role of these proximate fac-tors through which migration could operate to increase risk, could not be modeled. This study indicates the need for further research on migrants, to clarify the role of urban environments and individual behaviors in promoting obesogenic and cardiovascular risk factors as well as interventions to promote healthy lifestyles tar-geting migrants in urban areas.

ACKNOWLEDGEMENTS The NFHS 3 datasets were downloaded through the Demo-graphic Health Surveys data distribution system at www. measuredhs.com. The authors wish to thank ORC Macro for providing the datasets for analysis. AUTHOR DISCLOSURES The authors received no funding/support for this research. The authors declare no competing financial or other interests in rela-tion to the research described. REFERENCES 1. Murray CJL, Lopez AD. Global Health Statistics. Global

Burden of Disease and Injury Series. Boston MA: Harvard School of Public Health; 1996.

2. Yusuf S, Reddy S, Ôunpuu S, Anand S. Global burden of cardiovascular diseases: Part I: General considerations, the epidemiologic transition, risk factors, and impact of urbani-zation. Circulation. 2001;104:2746-53. doi: 10.1161/hc4601. 099487

3. Joshi R, Cardona M, Iyengar S, Sukumar A, Raju CR, Raju KR et al. Chronic diseases now a leading cause of death in rural India—mortality data from the Andhra Pradesh Rural Health Initiative. Int J Epidemiol. 2006;35: 1522-9. doi: 10. 1093/ije/dyl168

4. Kinra S, Anderson E, Ben-Shlomo Y, Bowen L, Lyngdoh T, Prabhakaran D et al. Association between urban life-years and cardiometabolic risk the Indian migration study. Am J Epidemiol. 2011;174:154-64. doi: 10.1093/aje/kwr 053

5. Das M, Pal S, Ghosh A. Rural urban differences of cardio-vascular disease risk factors in adult Asian Indians. Am J Hum Biol. 2008;20:440-5. doi: 10.1002/ajhb.20757

6. Gupta R. Trends in hypertension epidemiology in India. J Hum Hypertens. 2004;18:73-8. doi:10.1038/sj.jhh.10016 33

7. Ramachandran A, Snehalatha C, Kapur A, Vijay V, Mohan V, Das AK et al. High prevalence of diabetes and impaired glu-

cose tolerance in India: National Urban Diabetes Survey. Diabetologia. 2001;44:1094-101.

8. Ramachandran A, Snehalatha C, Latha E, Manoharan M, Vijay V. Impacts of urbanisation on the lifestyle and on the prevalence of diabetes in native Asian Indian population. Diabetes Res Clin Pract. 1999;44:207-13. doi: 10.1016/ S0168-8227(99)00024-8

9. Misra A, Sivakumar B, Bhagat N, Jaiswal A, Khurana L. Nutrition transition in India: Secular trends in dietary intake and their relationship to diet-related non-communicable dis-eases. J Diabetes. 2011;3:278-92. doi: 10.1111/j.1753-0407. 2011.00139.x.

10. Misra A, Ganda OP. Migration and its impact on adiposity and type 2 diabetes. Nutrition. 2007;23:696-708. doi: 10. 1016/j.nut.2007.06.008

11. Popkin BM, Horton S, Kim S, Mahal A, Shuigao J. Trends in diet, nutritional status, and diet-related noncommunicable diseases in China and India: the economic costs of the nutri-tion transition. Nutr Rev. 2001;59:379-90. doi: 10.1111/j. 1753-4887.2001.tb06967.x

12. Griffiths PL, Bentley ME. The nutrition transition is under-way in India. J Nutr. 2001;131:2692-700.

13. Garg C, Khan SA, Ansari SH, Garg M. Prevalence of obesi-ty in Indian women. Obes Rev. 2010;11:105-8. doi: 10.1111 /j.1467-789X.2009.00666.x

14. Wang Y, Chen H-J, Shaikh S, Mathur P. Is obesity becoming a public health problem in India? Examine the shift from under- to overnutrition problems over time. Obes Rev. 2009; 10:456-74. doi:10.1111/j.1467-789X.2009.0056 8.x

15. Shetty PS. Nutrition transition in India. Public Health Nutr. 2002;5:175-82. doi: 10.1079/PHN2001291

16. United Nations. World Urbanization Prospects: The 2009 Revision. New York: Population Division, Department of Economic and Social Affairs, United Nations; 2010. [cited 2013/6/24]; Available from: http://esa.un.org/unpd/wup/ doc_highlights.htm

17. Kinra S. Can conventional migration studies really identify critical age-period effects? Int J Epidemiol. 2004;33: 1226-7. doi: 10.1093/ije/dyh340

18. Hernández AV, Pasupuleti V, Deshpande A, Bernabé-Ortiz A, Miranda JJ. Effect of rural-to-urban within-country mi-gration on cardiovascular risk factors in low- and middle-income countries: a systematic review. Heart. 2012;98:185-94. doi: 10.1136/heartjnl-2011-300599

19. Yamauchi T, Umezaki M. Rural-Urban migration and chang-ing physical activity among Papua New Guinea Highlanders from the perspective of energy expenditure and time use. Environ Sci. 2005;12:155-66.

20. He J, Klag JM, Wu Z, Qian M, Chen J, Mo P et al. Effect of migration and related environmental changes on serum lipid levels in southwestern Chinese Men. Am J Epidemiol. 1996; 144:839-48. doi: 10.1093/oxfordjournals.aje.a0090 18

21. Kusuma Y, Gupta S, Pandav C. Migration and hypertension: a cross-sectional study among neo-migrants and settled-migrants in Delhi, India. Asia Pac J Public Health. 2009;21: 497-507. doi: 10.1177/1010539509344114

22. Schooling M, Leung GM, Janus ED, Ho SY, Hedley AJ, Lam TH. Childhood migration and cardiovascular risk. Int J Epidemiol. 2004;33:1219-26. doi: 10.1093/ije/dyh221

23. Sobngwi E, Mbanya J, Unwin NC, Porcher R, Kengne A, Fezeu L et al. Exposure over the life course to an urban envi-ronment and its relation with obesity, diabetes, and hyper-tension in rural and urban Cameroon. Int J Epidemiol. 2004; 33:769-76. doi: 10.1093/ije/dyh044

24. Ebrahim S, Kinra S, Bowen L, Andersen E, Ben-Shlomo Y, Lyngdoh T et al. The effect of rural-to-urban migration on


obesity and diabetes in India: a cross-sectional study. PLoS Med 2010;7:e1000268. doi: 10.1371/journal.pmed.1000268.

25. Miranda JJ, Gilman RH, Smeeth L. Differences in cardio-vascular risk factors in rural, urban and rural-to-urban mi-grants in Peru. Heart. 2011;97:787-96. doi: 10.1136/hrt.20 10.218537

26. Unwin N, McLarty D, Machibya H, Aspray T, Tamin B, Carlin L, Patel S, Walker M, Alberti K G. Changes in blood pressure and lipids associated with rural to urban migration in Tanzania. J Hum Hypertens. 2006;20:704-6. doi: 10.1038/ sj.jhh.1002056

27. Registrar General and Census Commissioner of India. Cen-sus of India 2001, Data Highlights – Migration tables [Table D1, D1 (Appendix), D2 & D3 Tables]. New Delhi: Office of the Registrar General and Census Commissioner of India. [cited 2012/8/10]; Available from: http://censusindia. gov.in/Data_Products/Data_Highlights/Data_Highlights_link/data_highlights_D1D2D3.pdf

28. Stahl T, Rutten A, Nutbeam D, Bauman A, Kannas L, Abel T et al. The importance of the social environment for physical-ly active lifestyle-results from an international study. Soc Sci Med. 2001;52:1-10. doi: 10.1016/S0277-9536(00)00116-7

29. International Institute for Population Sciences (IIPS) and Macro International. National Family Health Survey (NFHS-3), 2005–06 India: Volume I. Mumbai: IIPS; 2007. p. 590. Available from http://www.nfhsindia.org/NFHS-3%20Data/VOL1/India_volume_I_corrected_17oct08.pdf.

30. McLaren L. Socioeconomic status and obesity. Epidemiol Rev. 2007;29:29-48. doi: 10.1093/epirev/mxm001

31. Gunderson EP, Abrams B. Epidemiology of gestational weight gain and body weight changes after pregnancy. Epi-demiol Rev. 2000;22:261-74. doi: 10.1093/oxfordjournals. epirev.a018001

32. Registrar General and Census Commissioner of India. Cen-sus of India 2001, Census data 2001- Metadata. New Delhi: Office of the Registrar General and Census Commissioner of India. [cited 2012/8/10]; Available from: http://censusindia. gov.in/Metadata/ Metada.htm#2b

33. WHO Expert committee. Physical status: The use and inter-pretation of anthropometry. Geneva: World Health Organiza-tion; 1995. p. 47. WHO Technical Report Series 894.

34. National Sample Survey Organization (NSSO). Migration in India 2007-2008 NSS 64th round. New Delhi: National Sample Survey Office, Ministry of Statistics and Programme

Implementation, Government of India; 2010. NSS Report No 533 (64/10.2/2).

35. Torun B, Stein AD, Schroeder D, Grajeda R, Conlisk A, Rodriguez M et al. Rural to urban migration and cardiovas cular disease risk factors in young Guatemalan adults. Int J Epidemiol. 2002;31:218-26. doi: 10.1093/ije/31.1.218

36. Gregory CO, Dai J, Ramirez-Zea, Stein AD. Occupation is more important than rural or urban residence in explaining the prevalence of metabolic and cardiovascular disease risk in Guatemalan adults. J Nutr. 2007;137:1314-9.

37. Bowen L, Ebrahim S, De Stavola B, Ness A, Kinra S, Bharathi AV et al. Dietary intake and rural-urban migration in India: a cross-sectional study. PLoS ONE. 2011;6:e14822. doi: 10.1371/ journal.pone.0014822

38. Sullivan R, Kinra S, Ekelund U, Bharathi AV, Vaz M, Kurpad A et al. Socio-demographic patterning of physical activity across migrant groups in India: results from the In-dian Migration Study. PLoS ONE. 2011;6:e24898. doi: 10. 1371/ journal.pone.0024898

39. Hanson BS, Isaacson SO. Social network, social support and regular leisure-time physical activity in elderly men. A popu-lation study of men born in 1914, Malmö, Sweden. Eur J Public Health. 1992;2:16-23. doi: 10.1093/eurpub/2.1.16

40. Juarbe T, Turok XP, Perez-Stable EJ. Perceived benefits and barriers to physical activity among older Latina women. West J Nurs Res. 2002;24:868-86. doi: 10.1177/01939450 2237699

41. Wilcox S, Richter DL, Henderson KA, Greaney ML, Ains-worth BE. Perceptions of physical activity and personal bar-riers and enablers in African-American women. Ethn Dis. 2002;12:353-362.

42. Bell M, Muhidin S. Cross-national comparisons of internal migration. New York: United Nations Development Pro-gramme (UNDP); 2009. p:67. UNDP Human Development Research Paper 2009/30. [cited 2013/6/24]; Available from: http://hdr.undp.org/es/informes/mundial/idh2009/trabajos/HDRP_2009_30.pdf

43. Razum O. Commentary: Of salmon and time travellers—musing on the mystery of migrant mortality. Int J Epidemiol. 2006;35:919-21. doi: 10.1093/ije/dyl143

44. Abraído-Lanza AF, Dohrenwend BP, Ng-Mak DS, Turner JB. The Latino mortality paradox: a test of the "salmon bias" and healthy migrant hypotheses. Am J Public Health. 1999; 89:1543-8. doi: 10.2105/AJPH.89.10.1543


Original Article Overweight and obesity among internal migrants in India Kiruba Sankar Varadharajan MD, Tinku Thomas PhD, Divya Rajaraman PhD, Anura V Kurpad MD, PhD, Mario Vaz MD St John’s Research Institute, Koramangala, Bangalore, Karnataka, India

印度境內移居者的體重過重與肥胖狀況

境內移居，主要是從鄉村到都市地區，被推測與心血管疾病前兆有關。本研究

資料來自 2005/06 年印度全國家庭健康調查 3，這是一個涵蓋 29 個州的橫斷性

調查。評估體重過重及肥胖(BMI25 kg/m2)的風險與不同模式的境內移居之相

關性。總共有 56,498 名年齡介於 15-49 歲的非懷孕女性及 42,190 名 15-54 歲的

男性，合乎 BMI>18.5 kg/m2，被納入最後的分析。校正年齡、社經狀況及生育

數，使用二項式羅吉斯回歸模式，計算不同移居組別的過重及肥胖的勝算比

(ORs)。所有分析均將男女性分開計算，並使用全國抽樣權數加權。從鄉村移

居到都市的女性比起非移居的鄉村女性，有較高的體重過重及肥胖的風險(校正 OR(AOR): 1.05，95% CI: 1.36-1.65)，男性則無差異。從一個都市移居到另

一個都市的女性比起非移居的都市女性，其風險也增加(AOR: 1.10，95% CI: 1.02-1.19)。女性從都市移居到鄉村地區，其過重及肥胖的風險則是降低(AOR: 0.75，95% CI: 0.67-0.83)。因此，除了確認鄉村移居到都市為體重過重的危險

因子以外，這個研究亦發現，其他移居模式也與體重過重/肥胖有關。關鍵字：境內移居、體重過重、肥胖、印度、全國家庭健康調查 3

overweight and obesity among internal migrants in...

Documents