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Spatiotemporal variations and determinants of overweight/obesity among women of reproductive age in urban India during 2005-2021



India has witnessed rapid urbanization in recent decades, leading to a worrisome surge in non-communicable diseases, particularly overweight/obesity, which now present a critical public health concern. Therefore, this study seeks to examine spatiotemporal variations and determinants of overweight/obesity among women of reproductive age (WRA) in urban India and its states during 2005-2021.


The study used 44,882, 171,443, and 135,272 WRA aged 15–49 from National Family Health Survey (NFHS)-3 (2005-06), NFHS-4 (2015-16), and NFHS-5 (2019-21), respectively. The outcome variable was overweight/obesity, defined as a Body Mass Index (BMI) of ≥ 25 kg/m2. Chi-squared test and multivariable logistic regression were used to identify the determinants of overweight/obesity.


Overweight/obesity prevalence among WRA in urban India has risen significantly, from 23% in 2005-06 to 33% in 2019-21. This increase is particularly pronounced among SC/ST women and women with lower educational levels. During the study period, overweight/obesity rates in different states exhibited varying increases, ranging from 3 percentage points (pp) in Rajasthan to 22 pp in Odisha. Certain southern (e.g., Tamil Nadu and Andhra Pradesh) and northeastern states saw a significant 15 pp or more increase. In contrast, several northern, central, and eastern states (e.g., Punjab, Haryana, Rajasthan, Madhya Pradesh, Chhattisgarh, Jharkhand, West Bengal) experienced relatively smaller increases ranging from 5 to 8 pp. As of 2019-21, two regions exhibited high prevalence rates of overweight/obesity, exceeding 35%: the southern region (Tamil Nadu, Andhra Pradesh, Kerala, and Karnataka) and the northern region (Punjab, Himachal Pradesh, Uttarakhand, and Haryana). In contrast, the Empowered Action Group states had relatively lower rates (25% or less) of overweight/obesity. Regression results showed that older women [AOR: 5.98, 95% CI: 5.71–6.27], those from the richest quintile [AOR: 4.23, 95% CI: 3.95–4.54], those living in south India [AOR: 1.77, 95% CI: 1.72–1.82], and those having diabetes [AOR: 1.92, 95% CI: 1.83–2.02] were more likely to be overweight/obese.


Considering the significant increase in overweight/obesity among urban WRA in India, along with substantial disparities across states and socioeconomic groups, it is imperative for the government to formulate state-specific strategies and policies based on determinants to effectively combat overweight/obesity.

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Overweight/obesity, defined as abnormal or excessive fat accumulation that may impair health, has emerged as a significant global health challenge in recent years [1]. Between 1975 and 2016, overweight/obesity rates saw a threefold increase worldwide [1]. Globally, 39% of adults aged 18 years and above were overweight/obese in 2016 [1]. Forecast suggests that by 2030, the population of overweight/obese adults might reach a staggering 1 billion [2]. India, the world’s most populous country, follows this global trend [3]. By 2030, it is projected that around 64 million individuals in India will be living with obesity alone, placing the country 3rd in rank after the United States and China [2]. The impact of overweight/obesity is substantial. It has emerged as an epidemic worldwide, with 2.8 million people dying yearly due to this condition [4]. Failing to address the issue not only jeopardizes the efforts to achieve Sustainable Development Goals (SDGs), but also brings a significant economic and societal repercussions [2, 5]. It is estimated that if left unchecked, overweight/obesity related medical costs in India will reach up to $479 billion by 2060 [2].

While urban areas often act as the engines of a nation’s economic prosperity and provide advanced healthcare facilities, previous evidence suggests that burden of non-communicable diseases (NCDs), including overweight/obesity is much higher in the urban area than in rural areas [6,7,8,9]. Existing research attributes this paradoxical phenomenon to several key factors, including the shifting dietary preferences, altered physical activity patterns, and evolving lifestyle choices among the urban population, all of which collectively contribute to an increased susceptibility to overweight/obesity [7, 10, 11]. It is important to highlight that the swift rise in the burden of overweight/obesity within urban areas is generally far from consistent. Instead, it exhibits significant variations among urban populations across various dimensions, including socioeconomic status, biodemographic factors, and geographic locations [12,13,14,15,16]. With India hosting the world’s second-largest urban population (approximately 500 million) and experiencing rapid urbanization characterized by the significant growth of urban centers and the presence of some of the world’s largest metropolitans, there is a heightened need to direct attention towards the burden of overweight/obesity among its urban population [17, 18].

Overweight/obesity is usually more prevalent among women than men [1, 19]. Within women, the women of reproductive age (WRA) emerge as a particularly crucial population to study. WRA’s health not only matters for their own well-being but profoundly influences future generations [20]. Overweight/obesity among WRA is linked to higher risks of gestational diabetes, pre-eclampsia, birthing complications, and maternal/infant mortality [12, 21, 22]. Furthermore, evidence suggests that this group exhibits an elevated vulnerability to a range of NCDs, encompassing cardiovascular disease, diabetes, hypertension, and diverse cancers, thereby contributing to the overall burden of NCD-related morbidity and mortality [1, 13, 23,24,25]. Addressing the overweight/obesity burden among this subgroup of the population is imperative to reduce NCD-related morbidity and mortality, thereby safeguarding the pursuit of healthy lives for all, as outlined in SDG-3 [26].

The research landscape in India currently has few studies examining different aspects of overweight/obesity, particularly among WRA in urban settings over time [7, 8, 11, 27,28,29]. Earlier research has attempted to identify the determinants of overweight/obesity among WRA, and consistently indicated that the proportion of overweight/obesity grows with women’s age, level of education, urbanization, household wealth and parity [7, 8, 14, 30]. Furthermore, some studies have indicated that excessive consumption of alcohol, cigarette smoking, and sedentary lifestyle habits are also associated with overweight/obesity among WRA [12, 31]. It’s worth noting that while these studies have recognized a growing prevalence of overweight/obesity in urban India, they are relatively dated and lack inclusion of the most recent data, creating a significant void in our understanding of current trends and patterns of overweight/obesity among WRA in urban India.

Addressing this critical void, our study undertakes a comprehensive analysis of the trends of overweight/obesity among WRA in urban India. Our investigation spans a significant 15-year period from 2005 to 2021, with a specific emphasis on understanding state-level variations in prevalence of overweight/obesity over this time frame. Additionally, we attempt to understand the determinants of overweight/obesity in this demographic. We believe our findings will provide actionable insights for policymakers to combat overweight/obesity and enhance people’s health.


Data source

The study utilizes data from the National Family Health Survey (NFHS)-3, 4, and 5, conducted during 2005-06, 2015-16, and 2019-21, respectively. NFHS, India’s Demographic and Health Survey (DHS), is a large-scale, multi-round survey designed to collect data concerning a wide array of indicators associated with reproductive health, maternal, newborn, and child health, healthcare utilization, maternal and infant nutrition, substance abuse, domestic violence, menstrual hygiene, and various other interconnected domains. Using a two-stage stratified random sampling in all rounds, NFHS-3, NFHS-4 and NFHS-5 interviewed 124,385, 699,686 and 724,115 women aged 15–49 years, respectively. Notably, the response rates were high, with NFHS-3 achieving a rate of 98%, while NFHS-4 and NFHS-5 maintained response rates of 97% each [32,33,34].

Study samples

For the present study, data was extracted from three consecutive rounds of the NFHS. A total of 44,882, 171,443, and 135,272 sampled women (aged 15–49), who were non-pregnant during the survey and had not given birth within the last two months, were chosen from NFHS-3, NFHS-4, and NFHS-5 datasets, respectively. The detailed process of sample selection for this study is given in Fig. 1.

Fig. 1
figure 1

Flowchart showing the selection process of the study sample

In NFHS-3, there was a state named Andhra Pradesh, which was subsequently divided into Telangana and Andhra Pradesh in 2014. To maintain consistency in our findings, we combined the state of Telangana with Andhra Pradesh in our analysis for NFHS-4 and NFHS-5. This approach allowed us to ensure that our results were comparable across different survey periods. It is important to note that the state of Nagaland did not have anaemia observations in NFHS-3. Since anaemia is an important variable in our analysis, we opted to remove the observations of Nagaland from all NFHS datasets to maintain the consistency of the analysis.

Furthermore, we decided not to include data from Union Territories (UTs) in our analysis due to the lack of availability of data on UTs in NFHS-3. In addition, in subsequent NFHS rounds (NFHS-4 and NFHS-5), there were changes in the number of UTs, and certain states underwent transformation into new UTs. For example, Jammu and Kashmir were divided into two separate UTs, Jammu & Kashmir and Ladakh during NFHS-5. These administrative divisions posed challenges in the inclusion of UTs in our analysis, and hence, we chose to exclude UTs from the study. It is important to mention that in this study, the terms “women of reproductive age” (WRA) and “women” were used interchangeably.

Conceptual framework

The present study employs a conceptual framework adapted from the existing literature on overweight/obesity [6, 7, 9, 13, 35, 36]. This framework outlines various variables associated with overweight/obesity, which may influence its prevalence in urban India. The framework identifies three principal domains of variables (biodemographic, socioeconomic and geographic, health and behavioral) which are explained in subsequent sections. This framework guides the study analysis and is visually represented in Fig. 2.

Fig. 2
figure 2

Conceptual framework showing determinants of overweight/obesity among WRA

Dependent variable

The World Health Organization (WHO) defines overweight/obesity as having a Body Mass Index (BMI) of ≥ 25.0 kg/m² [37]. For our study, we categorized women into two groups according to their BMI: those with a BMI of 25 kg/m² or higher were coded as ‘1', indicating ‘overweight/obese’; while those with a BMI below 25 kg/m² were coded as ‘0', indicating ‘not overweight/obese’.

Independent variables

In this study, a set of independent variables was used to explore the determinants of overweight/obesity among WRA. We reviewed the previous literature on this issue and identified a number of potential variables to be included in the analysis [7, 8, 11, 28, 29, 38, 39]. These variables included age, marital status, parity, level of education, social groups, religion, household wealth, region, mass media exposure, currently having diabetes, anaemia level, current contraceptive use, alcohol consumption, and tobacco consumption in any form. We classified these variables under three major domains: (a) biodemographic, (b) socioeconomic and geographic, and (c) health and behavioral variables. Table 1 contains a comprehensive description of these variables.

Table 1 Description of independent variables

Statistical analysis

We used bivariate statistics to analyze the prevalence of overweight/obesity among WRA across various background characteristics. All estimates of overweight/obesity were appropriately weighted. We assessed the statistical significance of the association between each independent variable and the outcome variable using the Chi-squared test [40]. Moreover, our study employed multivariable binary logistic regression to quantify the independent effects of determinants of overweight/obesity [41].

We constructed three logistic regression models, each comprising a distinct set of independent variables. We utilized a block-wise forward selection method to eliminate any variables that were statistically insignificant (p > 0.05). Variables were introduced in blocks, and only those with a p < 0.05 were included in the subsequent models. Model 1 included biodemographic variables, such as age, marital status, and parity. Model 2 expanded upon Model 1 by incorporating statistically significant variables from it, as well as socioeconomic and geographic variables, such as education level, social groups, religion, household wealth, and region of residence. Finally, Model 3 extended the analysis further by including significant variables from Model 2, as well as health and behavior-related variables, such as diabetes, anaemia level, mass media exposure, current contraceptive use, and tobacco consumption. We reported Adjusted Odds Ratios (AOR), p-values (< 0.05), and 95% Confidence Intervals (CIs) to present the outcomes of the logistic regression models. To ensure that the independent variables did not suffer from multicollinearity issues, we calculated the variance inflation factors (VIF) (see Supplementary Table 1) [42]. We found that the VIFs for all the independent variables were below 5. This indicates that multicollinearity was not a concern for our models. We also took into consideration the complex design of NFHS surveys by using the ‘svyset’ command in Stata 16 statistical software [43].


Respondent characteristics

Table 2 presents the socio-demographic profile of WRA in NFHS-3, NFHS-4, and NFHS-5. In each round of NFHS, about one-third and two-thirds of women were aged 20–29 and currently married, respectively. Regarding parity, more than one-third of women in NFHS-3 had 1–2 children, while this was close to half of women in NFHS-4 and NFHS-5. Half of the women had a secondary education, and only a tiny percentage of women belonged to Scheduled Tribe (ST) social group. Nearly 80% and 50% of women were Hindu and belonged to the richest quintile across all rounds of NFHS, respectively. The proportion of women was higher in the southern region of the country. More than 90% and 50% of women had exposure to mass media and use modern contraceptives in all rounds of NFHS, respectively. Most women in all rounds of NFHS did not use tobacco or drunk alcohol.

Table 2 Respondent characteristics

Prevalence of overweight/obesity among WRA by background characteristics

In NFHS-3, approximately 23% of WRA in urban India were reported as overweight/obese. However, this percentage increased to 30% in NFHS-4 and further to 33% in NFHS-5. Table 3 presents the prevalence of overweight/obesity during 2005–2021 among WRA in urban India by background characteristics. Across all rounds of the NFHS, there is a consistent trend of rising overweight/obesity rates with advancing age. Notably, around half of women aged 40–49 exhibited overweight/obesity, while this was observed in less than 10% of women aged 15–19. Furthermore, a distinct pattern emerges regarding marital status and parity. Across all survey rounds, persistently elevated prevalence of overweight/obesity was observed among currently married women and women with children as compared to those not married and with no children.

Table 3 Prevalence of overweight/obesity among WRA by background characteristics in urban India during NFHS 3, 4 and 5

Over time, there has been a reduction in the disparities in overweight/obesity rates among different educational groups. In 2019-21, these differences have narrowed significantly, ranging from 32 to 37%, compared to larger gaps observed in 2005-06 (17–31%). This shift can be attributed to a faster increase in overweight/obesity rates among those with lower education levels (rising from 17 to 33% for those with no formal education and from 21 to 37% for those educated up to the primary level) compared to individuals with higher education (who experienced a more modest increase from 31 to 33%).

The prevalence of overweight/obesity also varied among different social groups. Specifically, the rates were relatively lower among Scheduled Caste (SC) and ST women in comparison to Other Backward Class (OBC) and ‘Other’ category women. However, the rate of increase in overweight/obesity prevalence was more pronounced among SC and ST women. The prevalence of overweight/obesity demonstrated a consistent upward trend as household wealth increased. This pattern was observed consistently across all rounds of the NFHS. Particularly noteworthy was the significantly higher prevalence among the wealthiest segment of the population, the richest quintile, displaying rates of 32%, 37%, and 39% in consecutive survey rounds. However, the most substantial increase 14 to 15 percentage points (pp) was witnessed among the three quintiles in the middle of the wealth distribution (i.e. poorer, middle, and richer).

In addition, women with mass media exposure and those utilizing traditional contraception methods consistently displayed higher levels of overweight/obesity throughout all survey rounds. Overweight/obesity prevalence was notably higher among women with diabetes compared to those without diabetes. In the period of 2019-21, the southern region of India recorded a notably higher prevalence of overweight/obesity at 42%, in contrast to approximately 25–30% prevalence in other regions across the country. Remarkably, the southern region also exhibited the most pronounced shift in prevalence over the study period of about 14 pp (28–42%).

Prevalence of overweight/obesity among WRA across Indian states during 2005-2021

There was a notable 10 pp increase in the prevalence of overweight/obesity among WRA in urban India between NFHS-3 (23%) and NFHS-5 (33%) (see Fig. 3). However, there were significant state-level disparities in the prevalence of overweight/obesity among WRA of urban India during three rounds of NFHS. However, it is important to discuss the spatial patterns observed in the most recent survey (2019-21). Two regions demonstrated particularly high levels of overweight/obesity: the southern region, which included Tamil Nadu (46%), Andhra Pradesh (43%), Kerala (41%), and Karnataka (37%), and the northwestern region comprising Punjab (44%), Himachal Pradesh (38%), Uttarakhand (38%), and Haryana (37%). In addition to these regions, other states with a prevalence exceeding 35% included Sikkim, Odisha, and Manipur. Conversely, most of the Empowered Action Group states, such as Bihar, Rajasthan, Madhya Pradesh, Chhattisgarh, Jharkhand, and Assam, reported relatively lower prevalence rates, typically around 25% or less.

Fig. 3
figure 3

State-level disparities in the prevalence of overweight/obesity among WRA in urban India during NFHS-3, NFHS-4, and NFHS-5

Spatiotemporal variation in overweight/obesity among WRA in urban India

Figure 4 illustrates state wise increase in overweight/obesity among WRA in urban India between NFHS-3 (2005-06) and NFHS-5 (2019-21). Within the spectrum of all states, Odisha emerged with the most prominent increase in overweight/obesity prevalence, registering a substantial 22 pp increase. Notably, Sikkim (21 pp) and Manipur (20 pp) closely trailed behind as states with significant increments. States in southern and northeastern India witnessed a relatively higher increase in prevalence over the study period. For instance, in the South, Tamil Nadu and Andhra Pradesh experienced a 17 to 18 pp increase. Among the seven northeastern states, Sikkim (21 pp), Arunachal Pradesh, Manipur (20 pp), and Mizoram witnessed an increase of 15 pp or more. Importantly, none of the states demonstrated a decrease in overweight/obesity prevalence during the course of the study period. In contrast, Rajasthan had the lowest increase in overweight/obesity by three pp, followed by Gujarat (5 pp), West Bengal (5 pp), Assam (6 pp), and Chhattisgarh (6 pp).

Fig. 4
figure 4

State-wise increase in overweight/obesity among WRA in urban India during 2005-2021

Determinants of overweight/obesity among WRA in urban India

Table 4 presents the adjusted odds ratios (AOR) for overweight/obesity among WRA in urban India. The final regression model (model 3) shows that the odds of overweight/obesity among women aged 40–49 years were were six times higher (AOR: 5.98, 95% CI: 5.71–6.27) than those aged 15–19. Likewise, the odds among currently married women were 1.86 times higher (AOR: 1.86, 95% CI: 1.79–1.93) than unmarried women. ST women had about 34% (AOR: 0.66, 95% CI: 0.63–0.68) lower odds than ‘Other’ category women. The odds among women with secondary education were 24% higher (AOR: 1.24, 95% CI: 1.21–1.28) than those with no education. In addition, women from the richest quintile had more than four times higher odds of being overweight/obese (AOR: 4.23, 95% CI: 3.95–4.54) compared to those from the poorest quintile. Women residing in the south were 1.77 times more (AOR: 1.77, 95% CI: 1.72–1.82) likely to be overweight/obese than those in the northern region. The odds of being overweight/obese were 1.92 times higher among women with diabetes (AOR: 1.92, 95% CI: 1.83–2.02). On the other hand, non-anemic women were 1.98 times more likely to be overweight/obese than women with severe anaemia.

Table 4 Adjusted odds ratios (with 95% CI) of overweight/obesity among WRA (15–49 years) in urban India, 2005-2021


Our study examined spatiotemporal variations and determinants of overweight/obesity among WRA living in urban areas of India during 2005-2021. Over time the prevalence of overweight/obesity among WRA in urban India has increased in all states, with substantial variation in magnitude of increase. Certain demographic groups, mainly, women with lower levels of education, women of SC/ST category, women from the three middle wealth quintiles (poorer, middle, and richer), and those living in the southern region have witnessed a more rapid increase in the overweight/obesity than remaining women. After controlling for a number of factors, the regression results revealed that being older, currently married, high parity, rich, higher educated, ‘Others’ social group,  region of residence, having diabetes and mass media exposure were important determinants of overweight/obesity among the WRA in urban India.

The increasing prevalence of overweight/obesity among WRA in urban India has become one of the most significant public health challenges facing the nation today [44]. However, over the past decade and a half, the growth of overweight/obesity has exhibited notably higher rates in some states along the eastern and northeastern regions in comparison to the rest of the country. The reasons behind this divergence remain unclear, necessitating further research to comprehend the factors contributing to the variable rates of overweight/obesity increase across different states. This finding underscores the imperative to incorporate geographical disparities in the rate of change in overweight/obesity among WRA in urban India into future policy frameworks designed to mitigate these elevated levels.

An important finding of this study is that overweight/obesity has surged at a notably higher rate among socioeconomically disadvantaged groups, specifically SC/ST women, and those with lower level of education. This observation contradicts the conventional expectation that overweight/obesity tend to increase more rapidly among the more affluent and educated segments of society [7, 8, 38, 45,46,47]. The factors driving this rapid rise in overweight/obesity within these vulnerable groups remain unclear. Consequently, further investigations are needed to shed light on this issue to guide future policy interventions.

The study reveals that age, parity, and marital status are significant biodemographic determinants of overweight/obesity among the WRA in urban India. The observation that older WRA are more likely to be overweight/obese aligns with findings from prior research conducted in Low and Middle-Income Countries, including India, Ethiopia, Zimbabwe, and China [6, 13, 48,49,50,51]. This increased risk may be attributed to several intertwined factors, including reduced physical activity, higher consumption of calorie-dense foods, the demands of child-rearing associated with advancing age, and age-related hormonal fluctuations [52,53,54,55,56,57]. The study also notes that married WRA were more likely to have overweight/obesity, a pattern consistent with previous research in countries like India, Maldives, Cambodia, and Greece [47, 58,59,60]. The increased likelihood of overweight/obesity among married WRA can be attributed to a mix of factors. These include gestational weight gain during pregnancy, the social expectations related to marriage that may encourage more frequent and calorie-rich meals, possibly resulting in higher calorie intake, and a reduced emphasis on monitoring body weight [38, 47]. A clear association between parity and overweight/obesity is also observed, which is consistent with similar results from studies conducted in China, the Maldives, and Iraq [53, 58, 61]. The relationship between the two is complex and can be influenced by various factors, including gestational weight gain, hormonal changes during pregnancy, and postpartum lifestyle adjustments [53, 58].

The study reveals that three significant socioeconomic factors, namely wealth, education, social group, are linked to overweight/obesity among WRA in urban India. WRA with higher education and from wealthier households are more likely to be overweight/obese than their less educated and poorer counterparts. These findings are consistent with previous research works in the countries of global South, including India, Bangladesh, Sub-Saharan Africa, Zimbabwe, Saudi Arabia, and China [15, 31, 45,46,47, 51, 62,63,64]. Evidences indicate that well-educated and wealthier WRA in urban areas often adopt sedentary lifestyles, engage in less physically demanding occupations, consume energy-dense foods due to their greater purchasing power, spend more time sitting rather than being active, and rely on modern conveniences such as smartphones [38, 47, 49, 65]. These lifestyle factors collectively contribute to an elevated risk of overweight/obesity among women in urban India [38, 47, 64]. In our study, ST women were less likely to be overweight/obese compared to women of other social groups, a trend consistent with prior Indian research [38, 47, 50, 64, 66]. This phenomenon may be attributed to a combination of factors prevalence among ST women, including economic disadvantages, traditional dietary patterns that are less calorie-dense, higher levels of physical activity due to manual labor or agricultural practices, cultural norms promoting healthier lifestyles, and potential genetic variations [38, 64, 66].

The results indicate that women from the southern region face a greater risk of overweight/obesity. This observation is consistent with findings reported in prior studies [6, 35, 47, 50, 66,67,68]. It is worth noting that these states are generally more developed, affluent, and further along in demographic and epidemiological transitions compared to other Indian [8, 69, 70]. In this study, anaemia and diabetes are significant predictors of overweight/obesity among WRA in urban India. Similar findings have been reported in earlier studies conducted in India [71,72,73]. It is important to note here that the relationship between these health conditions and overweight/obesity is complex. Therefore, additional research is necessary to delve deeper into these intricate connections.

A number of efforts have been made in the past in India to reduce or control the rising prevalence of overweight/obesity. The National Action Plan Monitoring Framework for Prevention and Control of NCDs was developed in 2013, aiming to halt the rise in obesity and diabetes prevalence in India by 2025 [74]. In 2017, the Ministry of Health and Family Welfare brought in a National Multisectoral Action Plan for Prevention and Control of Common NCDs and reiterated the same target [75]. Additionally, the National Nutrition Mission (Poshan Abhiyaan) aims to reduce the prevalence of stunting, malnutrition, and overweight/obesity among women and children [76]. Furthermore, the government has recently initiated several programs to promote physical activity, including the Fit India Movement [77], which encourages citizens to adopt an active lifestyle. In addition, some states at their state level implemented campaigns and programs to reduce overweight/obesity prevalence. For instance, in 2016, Tamil Nadu introduced both the Amma Master Health Care Scheme, and Maharashtra initiated an anti-obesity campaign with a focus on fostering healthy lifestyles and preventing overweight/obesity [78, 79]. However, despite the multitude of programs and policies currently in place, the concerning surge in overweight/obesity rates among WRA in urban India remains unabated. Unfortunately, the country is far from meeting the 2025 overweight/obesity targets set by the WHO [80]. This stark reality underscores the urgent need for a comprehensive review and reformulation of existing strategies aimed at reducing overweight/obesity prevalence in the country.

This study has certain limitations. This study employed a cross-sectional design, which permits the establishment of associations between dependent and independent variables. However, it’s important to note that this design does not enable us to infer causality. The models employed in this study were built exclusively using variables accessible in the survey data. Notably, we could not include in the analysis certain factors such as dietary behaviors, physical activity, and sleep patterns that may affect overweight/obesity as they were unavailable in the NFHS datasets. This may have potentially led to omitted variable bias. In light of these limitations, future research should prioritize the collection of more extensive and detailed data to enhance our understanding of the multifaceted factors influencing overweight/obesity.


In conclusion, our study of spatiotemporal variations and determinants of overweight/obesity among WRA in urban India during 2005-2021 reveals a disconcerting trend of increasing prevalence, albeit with significant regional and demographic disparities. Rapid rise of overweight/obesity in some vulnerable groups of WRA and geographic regions, including less educated women, those from marginalized castes/social groups, those from the middle wealth quintiles, and residents of the southern region underscores the pressing need for targeted interventions and policies that address the specific challenges and disparities faced by these women. Future policies and interventions must prioritize these disadvantaged groups while promoting awareness and healthier lifestyles across WRA living in urban areas. A multifaceted approach is imperative to curbing the overweight/obesity epidemic and ensuring the well-being of India’s urban WRA population.

Data Availability

National Family Health Survey datasets used in the study are available at the official website of Demographic and Health Surveys (DHS): In addition, these datasets can be obtained by registering as a DHS data user and requesting access for legitimate research purposes:


  1. World Health Organization. Obesity and overweight. 2021. Accessed 7 Apr 2023.

  2. Lobstein T, Brinsden H, Neveux M, Barata OC, Barquera S, Baur L et al. World Obesity Atlas 2022. 2022. Accessed 22 Aug 2023.

  3. Hertog S, Gerland P, Wilmoth J. India overtakes China as the world’s most populous country. 2023. Accessed 22 Aug 2023.

  4. World Health Organization. Obesity. 2021. Accessed 22 Aug 2023.

  5. Global Panel. The cost of malnutrition. Why policy action is urgent. 2016;1–11. Accessed 20 Aug 2023.

  6. Asthana S, Rawat D, Labani S. Comparison of underweight, overweight and obesity prevalence among Indian women in different national health surveys. Curr Med Res Pract. 2019;9:138–44.

    Article  Google Scholar 

  7. Gouda J, Prusty RK. Overweight and obesity among women by economic stratum in Urban India. J Heal Popul Nutr. 2014;32:79–88.

    Google Scholar 

  8. Kumar P, Mangla S, Kundu S. Inequalities in overweight and obesity among reproductive age group women in India: evidence from National Family Health Survey (2015–16). BMC Womens Health. 2022;22:1–13.

    Article  CAS  Google Scholar 

  9. Niessen LW, Mohan D, Akuoku JK, Mirelman AJ, Ahmed S, Koehlmoos TP, et al. Tackling socioeconomic inequalities and non-communicable diseases in low-income and middle-income countries under the Sustainable Development agenda. Lancet. 2018;391:2036–46.

    Article  PubMed  Google Scholar 

  10. Kelly M. The Nutrition Transition in Developing Asia: Dietary Change, Drivers and Health Impacts. In: Eating, Drinking: Surviving The International Year of Global Understanding - IYGU. 2016. p. 83–90.

  11. Siddiqui Z, Donato R. The dramatic rise in the prevalence of overweight and obesity in India: obesity transition and the looming health care crisis. World Dev. 2020;134:105050.

    Article  Google Scholar 

  12. Ahmed KY, Abrha S, Page A, Arora A, Shiferaw S, Tadese F, et al. Trends and determinants of underweight and overweight/obesity among urban Ethiopian women from 2000 to 2016. BMC Public Health. 2020;20:1–13.

    Article  Google Scholar 

  13. Kassie AM, Abate BB, Kassaw MW. Prevalence of Underweight and Its Associated Factors among Reproductive Age Group Women in Ethiopia: Analysis of the 2016 Ethiopian Demographic and Health Survey Data. BMC Public Health. 2020; 20:1–11.

    Google Scholar 

  14. Hashan MR, Das Gupta R, Day B, Al Kibria GM. Differences in prevalence and associated factors of underweight and overweight/obesity according to rural-urban residence strata among women of reproductive age in Bangladesh: evidence from a cross-sectional national survey. BMJ Open. 2020. 10.

  15. Neupane S, Prakash KC, Doku DT. Overweight and obesity among women: analysis of demographic and health survey data from 32 sub-saharan african countries. BMC Public Health. 2016;16:1–9.

    Google Scholar 

  16. Luhar S, Mallinson PAC, Clarke L, Kinra S. Trends in the socioeconomic patterning of overweight/obesity in India: a repeated cross-sectional study using nationally representative data. BMJ Open. 2018;8:e023935.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Government of India. Census of India 2011: Rural Urban distribution of population. 2011.

  18. United Nations Department of Economic and Social Affairs. World Urbanization Prospects: The 2007 Revision. 2008.

  19. Cooper AJ, Gupta SR, Moustafa AF, Chao AM. Sex/Gender differences in obesity prevalence, Comorbidities, and treatment. Curr Obes Rep. 2021;10:458–66.

    Article  PubMed  Google Scholar 

  20. Onarheim KH, Iversen JH, Bloom DE. Economic benefits of investing in women’s health: a systematic review. PLoS ONE. 2016;11:1–23.

    Article  Google Scholar 

  21. Stubert J, Reister F, Hartmann S, Janni W. The Risks Associated with obesity in pregnancy. Dtsch Arztebl Int. 2018;115:276–83.

    PubMed  PubMed Central  Google Scholar 

  22. Butwick AJ, Abreo A, Bateman BT, Lee HC, El-Sayed YY, Stephansson O, et al. Effect of Maternal Body Mass Index on Postpartum Hemorrhage. Perioper Med. 2018;128:774–83. Accessed 06 Aug 2023.

  23. Development Initiatives. 2018 Global Nutrition Report: Shining a light to spur action on nutrition. 2018. Accessed 22 Aug 2023.

  24. Gonzalez AB de, Hartge P, Cerhan JR, Flint AJ, Hannan L, MacInnis RJ, et al. Body-Mass Index and Mortality among 1.46 Million White Adults. N Engl J Med. 2010;363:2211–9.

    Article  Google Scholar 

  25. Jiwani SS, Carrillo-Larco RM, Hernández-Vásquez A, Barrientos-Gutiérrez T, Basto-Abreu A, Gutierrez L, et al. The shift of obesity burden by socioeconomic status between 1998 and 2017 in Latin America and the Caribbean: a cross-sectional series study. Lancet Glob Heal. 2019;7:e1644–54.

    Article  Google Scholar 

  26. Food and Agriculture Organisation of the United Nations. Sustainable Development Goals. 2017.

  27. Siddiqui MZ, Donato R. Overweight and obesity in India: policy issues from an exploratory multi-level analysis. Health Policy Plan. 2016;31:582–91.

    Article  PubMed  Google Scholar 

  28. Sengupta A, Angeli F, Syamala TS, Dagnelie PC, van Schayck CP. Overweight and obesity prevalence among Indian women by place of residence and socio-economic status: contrasting patterns from underweight states and overweight states of India. Soc Sci Med. 2015;138:161–9.

    Article  PubMed  Google Scholar 

  29. Aiyar A, Dhingra S, Pingali P. Transitioning to an obese India: demographic and structural determinants of the rapid rise in overweight incidence. Econ Hum Biol. 2021;43:101041.

    Article  PubMed  Google Scholar 

  30. Khalid M, Shoaib A, Akhtar J, Alqarni MH, Badruddeen, Siddiqui HH, et al. Anti obesity prospective of Dalbergia latifolia (Roxb.) Hydroalcoholic bark extract in high fat diet induced obese rats. 3 Biotech. 2020;10:493.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Sarma H, Saquib N, Hasan MM, Saquib J, Rahman AS, Khan JR, et al. Determinants of overweight or obesity among ever-married adult women in Bangladesh. BMC Obes. 2016;3:1–11.

    Article  Google Scholar 

  32. International Institute for Population Sciences. National Family Health Survey (NFHS-3), 2005-06, India. 2007.

  33. International Institute for Population Sciences (IIPS). and ICF. National Family Health Survey (NFHS-4) 2015-16, India. 2015.

  34. International Institute for Population. Sciences (IIPS) and ICF. National Family Health Survey (NFHS-5), India. 2019.

  35. Siddiqui ST, Kandala NB, Stranges S. Urbanisation and geographic variation of overweight and obesity in India: a cross-sectional analysis of the Indian Demographic Health Survey 2005–2006. Int J Public Health. 2015;60:717–26.

    Article  PubMed  Google Scholar 

  36. Tebekaw Y, Teller C, Colón-Ramos U. The burden of underweight and overweight among women in Addis Ababa, Ethiopia. BMC Public Health. 2014;14.

  37. World Health Organization. A healthy lifestyle - WHO recommendations. 2010. Accessed 12 Apr 2023.

  38. Al Kibria GM, Swasey K, Hasan MZ, Sharmeen A, Day B. Prevalence and factors associated with underweight, overweight and obesity among women of reproductive age in India. Glob Heal Res Policy. 2019;4:1–12.

    Google Scholar 

  39. Hasan E, Khanam M, Shimul SN. Socio-economic inequalities in overweight and obesity among women of reproductive age in Bangladesh: a decomposition approach. BMC Womens Health. 2020;20:1–11.

    Article  Google Scholar 

  40. Chen Y-T, Chen MC. Using chi-square statistics to measure similarities for text categorization. Expert Syst Appl. 2011;38:3085-90.

  41. Mertler CA, Vannatta RA, LaVenia KN. Binary Logistic Regression. In: Advanced and Multivariate Statistical Methods. New York: Routledge; 2021. p. 270–88.

  42. Midi H, Sarkar SK, Rana S. Collinearity diagnostics of binary logistic regression model. J Interdiscip Math. 2010;13:253–67.

    Article  Google Scholar 

  43. StataCorp. Stata Survey Data Reference Manual - Introduction to survey commands. 2017.

  44. Luhar S, Timæus IM, Jones R, Cunningham S, Patel SA, Kinra S, et al. Forecasting the prevalence of overweight and obesity in India to 2040. PLoS ONE. 2020;15:e0229438.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Subramanian SV, Smith GD. Patterns, distribution, and determinants of under- and overnutrition: a population-based study of women in India. Am J Clin Nutr. 2006;84:633–40.

    Article  PubMed  CAS  Google Scholar 

  46. Wang Y, Chen HJ, 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.

    Article  PubMed  CAS  Google Scholar 

  47. Verma M, Das M, Sharma P, Kapoor N, Kalra S. Epidemiology of overweight and obesity in Indian adults - A secondary data analysis of the National Family Health surveys. Diabetes Metab Syndr. 2021;15:102166.

    Article  PubMed  CAS  Google Scholar 

  48. Biswas T, Uddin MJ, Mamun A, Al, Pervin S, Garnett SP. Increasing prevalence of overweight and obesity in bangladeshi women of reproductive age: findings from 2004 to 2014. PLoS ONE. 2017;12:1–12.

    Article  Google Scholar 

  49. Gupta R, Das, Haider SS, Sutradhar I, Hashan MR, Sajal IH, Hasan M, et al. Association of frequency of television watching with overweight and obesity among women of reproductive age in India: evidence from a nationally representative study. PLoS ONE. 2019;14:1–13.

    CAS  Google Scholar 

  50. Dandapat B, Biswas S, Patra B. Religion, nutrition and birth weight among currently married women (15–49) in India: a study based on NFHS-5. Clin Epidemiol Glob Heal. 2023;20:101218.

    Article  CAS  Google Scholar 

  51. Mangemba NT, San Sebastian M. Societal risk factors for overweight and obesity in women in Zimbabwe: a cross-sectional study. BMC Public Health. 2020;20:1–8.

    Article  Google Scholar 

  52. Subramanian SV, Jessica M, Perkins. Do burdens of underweight and overweight coexist among lower socioeconomic groups in India? Am J Clin Nutr. 2009;90:369–76.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Li W, Wang Y, Shen L, Song L, Li H, Liu B, et al. Association between parity and obesity patterns in a middle-aged and older Chinese population: a cross-sectional analysis in the Tongji-Dongfeng cohort study. Nutr Metab. 2016;13:1–8.

    Article  CAS  Google Scholar 

  54. Bastian LA, West NA, Corcoran C, Munger RG. Number of children and the risk of obesity in older women. Prev Med (Baltim). 2005;40:99–104.

    Article  Google Scholar 

  55. Mosha D, Paulo HA, Mwanyika-Sando M, Mboya IB, Madzorera I, Leyna GH, et al. Risk factors for overweight and obesity among women of reproductive age in Dar es Salaam, Tanzania. BMC Nutr. 2021;7:1–10.

    Article  Google Scholar 

  56. Karvonen-Gutierrez C, Kim C. Association of mid-life changes in body size, body composition and obesity status with the menopausal transition. Healthc. 2016;4.

  57. Colpani V, Baena CP, Jaspers L, van Dijk GM, Farajzadegan Z, Dhana K, et al. Lifestyle factors, cardiovascular disease and all-cause mortality in middle-aged and elderly women: a systematic review and meta-analysis. Eur J Epidemiol. 2018;33:831–45.

    Article  PubMed  CAS  Google Scholar 

  58. Hashan MR, Rabbi MF, Haider SS, Das Gupta R. Prevalence and associated factors of underweight, overweight and obesity among women of reproductive age group in the Maldives: Evidence from a nationally representative study. PLoS ONE. 2020;15:e0241621

    Article  Google Scholar 

  59. Um S, Yom A, Muir JA, Sopheab H. Overweight and obesity among women at reproductive age in Cambodia: Data analysis of Cambodia Demographic and Health Survey 2014. PLOS Glob Public Heal. 2023;3:1–10.

  60. Tzotzas T, Vlahavas G, Papadopoulou SK, Kapantais E, Kaklamanou D, Hassapidou M. Marital status and educational level associated to obesity in Greek adults: data from the National Epidemiological Survey. BMC Public Health. 2010;10:732.

  61. Mansour AA, Ajeel NAH. Parity is associated with increased waist circumference and other anthropometric indices of obesity. Eat Weight Disord - Stud Anorexia, Bulim Obes. 2009;14:e50–5.

  62. Al-Hussaini A, Bashir M, Khormi M, Alturaiki M, Alkhamis W, Alrajhi M, et al. Overweight and obesity among saudi children and adolescents: where do we stand today? Saudi J Gastroenterol. 2019;25:229–35.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Feng Y, Ding L, Tang X, Wang Y, Zhou C. Association between Maternal Education and School-Age Children Weight Status: A Study from the China Health Nutrition Survey, 2011. Int J Environ Res Public Health. 2019;16:2543.

  64. Pengpid S, Peltzer K. Prevalence and correlates of underweight and overweight/obesity among women in India: Results from the National Family Health Survey 2015–2016. Diabetes, Metab Syndr Obes. 2019;12:647–53.

  65. Khandelwal S, Reddy KS. Eliciting a policy response for the rising epidemic of overweight-obesity in India. Obes Rev. 2013;14:114–25.

    Article  PubMed  Google Scholar 

  66. Biswas S, Hansda CK, Singh N, Malla VR, Pal AK. Determinants of nutritional status among scheduled tribe women in India. Clin Epidemiol Glob Heal. 2022;17:101119.

    Article  CAS  Google Scholar 

  67. Singh G, Agrawal R, Tripathi N. Overweight and obesity, the clock ticking in India? A secondary analysis of trends of prevalence, patterns, and predictors from 2005 to 2020 using the National Family Health Survey. Int J Noncommunicable Dis. 2017;2:45–8.

    Article  Google Scholar 

  68. Ahirwar R, Mondal PR. Prevalence of obesity in India: a systematic review. Diabetes Metab Syndr Clin Res Rev. 2019;13:318–21.

    Article  Google Scholar 

  69. Dandona L, Dandona R, Kumar GA, Shukla DK, Paul VK, Balakrishnan K, et al. Nations within a nation: variations in epidemiological transition across the states of India, 1990–2016 in the global burden of Disease Study. Lancet. 2017;390:2437–60.

    Article  Google Scholar 

  70. Kumar N, Rani R. Regional Disparities in Social Development: evidence from States and Union Territories of India. South Asian Surv. 2019;26:1–27.

    Article  Google Scholar 

  71. Cepeda-Lopez AC, Baye K. Obesity, iron deficiency and anaemia: a complex relationship. Public Health Nutr. 2020;23:1703–4.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Qin Y, Melse-Boonstra A, Pan X, Yuan B, Dai Y, Zhao J, et al. Anemia in relation to body mass index and waist circumference among Chinese women. Nutr J. 2013;12:10–2.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Gupta S, Bansal S. Does a rise in BMI cause an increased risk of diabetes? Evidence from India. PLoS ONE. 2020;15:1–20.

    Article  Google Scholar 

  74. Banerjee B. WHO Global Action Plan and Monitoring Framework for Prevention and Control of Non-Communicable Diseases. DK Taneja’s Health Policies and Programmes in India. 2017;:442–442.

  75. Ministry of Health and Family Welfare. National Multisectoral Action Plan for Prevention and Control of Common Noncommunicable Diseases. Supported by WHO Country Office for India; 2018. p. 74.

  76. Kumar A, Kaur S, Singh A, Menon P. Accelerating Progress on Nutrition in India: What Will it Take? Third Progress Report. 2020.

  77. Ministry of Youth Affairs and Sports. Fit India Movement. Sports Authority of India. 2023. Accessed 11 Apr 2023.

  78. Ankita, Gagan, Rachana, Pawan. Tamil Nadu Extends Amma Master Health Care scheme. Pradhan Mantri Yojana. 2023. Accessed 11 Apr 2023.

  79. Indo-Asian News Service. Minister Launches Anti-Obesity Campaign in Maharashtra. NDTV Food. 2016. Accessed 11 Apr 2023.

  80. World Health Organization. Global NCD Target: Halt the Rise in Obesity. 2016.

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Dr. Aditya Singh acknowledges the support of Institute of Eminence Seed Grant (R/Dev/D/IoE/Equipment/Seed Grant-II/2022-23/48726) by Banaras Hindu University. Subhojit Let (Ref. No: 200510160983) and Mahashweta Chakrabarty (Ref. No: 200510082749) acknowledge the support of Junior Research Fellowship provided by University Grants Commission, India.


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Aditya Singh: Investigation; Methodology; Validation; Visualization; Project administration; Supervision; Writing – original draft; Writing – review & editing. Subhojit Let: Data curation; Formal analysis; Resource; Methodology; Software; Validation; Visualization; Writing – original draft; Writing – review & editing. Seema Tiwari: Investigation; Validation; Supervision; Project administration. Mahashweta Chakrabarty: Conceptualization; Methodology; Software; Writing – original draft; Validation; Visualization; Writing – review & editing. All authors read and approved the final draft.

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Correspondence to Seema Tiwari or Mahashweta Chakrabarty.

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Singh, A., Let, S., Tiwari, S. et al. Spatiotemporal variations and determinants of overweight/obesity among women of reproductive age in urban India during 2005-2021. BMC Public Health 23, 1933 (2023).

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