Forecasting the prevalence of overweight and obesity in India to 2040.

BACKGROUND: In India, the prevalence of overweight and obesity has increased rapidly in recent decades. Given the association between overweight and obesity with many non-communicable diseases, forecasts of the future prevalence of overweight and obesity can help inform policy in a country where around one sixth of the world's population resides.
METHODS: We used a system of multi-state life tables to forecast overweight and obesity prevalence among Indians aged 20-69 years by age, sex and urban/rural residence to 2040. We estimated the incidence and initial prevalence of overweight using nationally representative data from the National Family Health Surveys 3 and 4, and the Study on global AGEing and adult health, waves 0 and 1. We forecasted future mortality, using the Lee-Carter model fitted life tables reported by the Sample Registration System, and adjusted the mortality rates for Body Mass Index using relative risks from the literature.
RESULTS: The prevalence of overweight will more than double among Indian adults aged 20-69 years between 2010 and 2040, while the prevalence of obesity will triple. Specifically, the prevalence of overweight and obesity will reach 30.5% (27.4%-34.4%) and 9.5% (5.4%-13.3%) among men, and 27.4% (24.5%-30.6%) and 13.9% (10.1%-16.9%) among women, respectively, by 2040. The largest increases in the prevalence of overweight and obesity between 2010 and 2040 is expected to be in older ages, and we found a larger relative increase in overweight and obesity in rural areas compared to urban areas. The largest relative increase in overweight and obesity prevalence was forecast to occur at older age groups.
CONCLUSION: The overall prevalence of overweight and obesity is expected to increase considerably in India by 2040, with substantial increases particularly among rural residents and older Indians. Detailed predictions of excess weight are crucial in estimating future non-communicable disease burdens and their economic impact.

Background

Approximately 39% of the global adult population were classified as overweight (Body Mass Index (BMI) 25.0–29.9 kg/m2) or obese (BMI > 29.9kg/m2) in 2014; a doubling since 1975[1]. Whereas the prevalence of obesity was 6.4% among women and 3.2% among men in 1975, it had risen to 14.9% and 10.8%, respectively by 2014[1]. In developing countries like India, the increasing prevalence of overweight and obesity has coincided with the demographic and epidemiological transitions, in which mortality and fertility have declined, and lifestyle-related diseases have become more common[2-4].

The prevalence of overweight and obesity in India is increasing faster than the world average. For instance, the prevalence of overweight increased from 8.4% to 15.5% among women between 1998 and 2015, and the prevalence of obesity increased from 2.2% to 5.1% over the same period[5-7]. This fast-paced growth has been accompanied by notable increases in the burden of non-communicable diseases (NCDs). Whereas in 1990 the number of life years lost to disability (DALYs) attributable to communicable, maternal, neonatal and nutritional disorders exceeded that attributable to NCDs in virtually all of India’s states, currently the opposite is true[3]. Given the extent of the increase in prevalence of overweight and obesity, and its relationships with NCDs[8], reliably predicting its future prevalence has become increasingly important.

Despite this, few studies have attempted to estimate future trends in overweight and obesity in India. One study that reports on global trends estimated that 27.8% of all Indians would be overweight, and 5.0% obese, by 2030[9]. Another study estimated that around 20% of rural Indian adults will be either overweight or obese by 2030[10]. However, these previous studies have merely extrapolated previous trends in prevalence without accounting for a changing population at risk of becoming overweight or obese which declines as the proportion of the population classified as overweight or obese increases.

Simulation models offer a more sophisticated alternative to the extrapolation of secular trends and may produce more accurate forecasts. For example, as an internally logical system, the population at-risk of becoming overweight or obese is regularly updated at each forecasted time interval. Such models therefore allow the incorporation of the impact on future prevalence of past increases in the incidence of overweight or obesity[11]. Additionally, the logical framework enables the estimation of potential impacts of policy decisions, directed at the incidence of overweight and obesity[11, 12], and identification of at-risk subpopulations[11, 13, 14]. This analysis brings together nationally-representative data from a range of publicly available sources in a dynamic simulation model to forecast the future prevalence of overweight and obesity in India to 2040 among adults aged 20–69 years.

National Family and Health Survey (NFHS). The nationally-representative NFHS collects health and demographic data among women aged 15–49 years and men aged 15–54 years. NFHS 3 (2005–06) interviewed 124,385 and 74,369 adult women and men respectively, and NFHS 4 (2015–16) contains data on 625,000 adult women and 93,065 adult men[6, 7].

The Study on global AGEing and adult health (SAGE). SAGE Waves 0 (2002–04) and 1 (2007–10) contain longitudinal health and demographic data on people aged 50 or more years from six states which are believed to be nationally-representative [15]. Wave 0 collected health information on 2559 adults aged 50 or more years, whereas Wave 1 collected data on approximately 3000 men and 3000 women aged 50 or more years.

Sample Registration System (SRS). The SRS reports sex- and residence-specific abridged life tables by five-year age groups for each state for every year between 1997 and 2015[16-21]. The SRS dually records deaths using representative samples from across the country[22].

United Nations World Population Prospects 2019 and World Urbanization Prospects 2018. The 2019 round of the World Population Prospects includes population projections and estimates by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat[23]. The Division uses the cohort-component method for each country and major geographical region to produce population projections under a number of different future fertility scenarios. Separate urban and rural projections that are consistent with the national projections are reported by the UN World Urbanization Prospects 2018[24].

Methods

Model inputs

From these data sources, we extracted the following model inputs for the age, sex and residence-specific forecasts of overweight prevalence in India:

Age-specific prevalence of overweight and obesity

We estimated the prevalence of overweight and obesity among individuals aged 20–49 using the BMI variable in NFHS-3 and NFHS-4, whereby individuals with a BMI>24.9kg/m2 and <30.0kg/m2 were classified as overweight, and those with a BMI>29.9kg/m2 were classified as obese following the World Health Organization’s (WHO) recommendations[8]. Pregnant women (5.2% of women in NFHS 3 and 4.4% in NFHS 4) were excluded from our sample as their pregnancy could give misleadingly high BMI scores. We used survey weights to calculate age-specific prevalence accounting for the complex survey design and based the baseline (2010) age-specific prevalence on the mid-point prevalence of the two surveys. Among individuals aged 50–69, we used the BMI variable, and the same cut-offs, in SAGE wave 0 and 1 to estimate the overall age-specific prevalence and applied the overall relative risk of overweight and obesity among urban and rural residents to obtain residence-specific prevalence estimates. The prevalence estimates from the data are included in the S2 File.

Age-specific incidence of overweight and obesity and age-specific rates of urbanization

We used the changing prevalence of overweight and obesity between 2005 and 2015 among the population aged 20–49 years to estimate the age-specific incidence of overweight among the underweight/normal weight population, and incidence of obesity among overweight individuals in our baseline year of 2010. We used the iterative intracohort interpolation procedure[25] whereby the observed changes in overweight status to specific cohorts are translated into age-specific rates for the inter-survey interval (a more detailed explanation is presented in S1 File). The age-specific rates were estimated separately by sex, residency (urban and rural) and age (20–24, 25–29, 30–34, 35–39, 40–44, 45–49 years).

Age-specific rates of urbanization were also calculated by the same method, using the age-specific proportions of the population in urban and rural areas in the two NFHS surveys.

For those aged 50–69, we calculated the incidence of overweight and obesity using longitudinal data from SAGE waves 0 and 1 for men and women separately by dividing the number of incident cases by the person-years of exposure. As information on the exact time an incident case occurred was not available, incident cases were assumed to have occurred at the halfway-point between the two waves. We calculated an overall incidence rate among men and women separately, and indirectly standardized the rates using the age-distribution of obesity incidence from a study in the United States[26] that used data from the Behavioral Risk Factor Surveillance System in order to obtain net rates for the following age groups: 50–54, 55–59, 60–64, and 65–69 years (S1 File).

We fitted a spline to smooth the age-specific incidence rates across the lifespan and used age-specific incidence by the five-year age groups in the final analysis.

Remission

We incorporated the potential for individuals to transition from overweight and obesity to lower BMI groups by modelling gross, rather than net incidence rates at all ages. Remission refers to reverse transitions, whereby the simulated population is able to transition from a state of ‘Obese’ to ‘Overweight’, and ‘Overweight’ to ‘Not Overweight/Obese’. We used rates of remission that allowed our model with gross rates to closely match the measured age-specific prevalence in 2015 from NFHS-4. To estimate remission in older ages, we applied an odds ratio of remission in older ages (50+ years), relative to younger ages. A prospective study in rural India carried out between 2008 and 2017[27] found an elevated odds of remission from higher to lower BMIs of 1.74 and 2.12 among older aged men and women, relative to younger counterparts.

Current and future age-, sex- and urban/rural residence- specific mortality rates

We converted conditional mortality probabilities reported by the SRS to age-specific mortality rates from 1997 to 2013 using standard demographic procedures[28] and used these rates to forecast future mortality to 2040 using the Lee-Carter method[29, 30]. In brief, the Lee-Carter method summarizes a series of sets of age-specific mortality rates for successive periods of time by its average age-schedule, age-specific deviations from the average age-schedule, and the trend in the overall level of mortality over time. The forecast is contingent on the extrapolation of this latter parameter (S1 File).

Relative risk of dying for those overweight or obese compared with those who are not

We adjusted the forecasted mortality rates to account for differential mortality between overweight and obese individuals and those who are in lower weight categories. Relative risks of dying, based on BMI group, were adopted from the findings reported in a study that examined the association between BMI and mortality in Mumbai[31]. This study reported relative risks of dying for those who are overweight excluding obesity (OW), relative to normal (N) weight, those who are obese (OB) relative to normal weight, and those who are underweight (UW) relative to normal weight. The authors report risk ratios, along with confidence intervals, for men and women aged 35–59 and 60 or more, separately. As the study did not calculate risk ratios for individuals aged 20–34, we assumed that the relative risk of dying at 35–59 also prevailed at these ages. We obtained separate relative risks of dying for those who are overweight and obese relative to new reference categories of ‘not overweight’ and ‘not obese’ using a basic algebraic approach (S1 File), and subsequently used these relative risks to calculate BMI specific rates of mortality using the population level mortality rate. Below we present an example of the calculation of obesity-specific mortality rates.

Where refers to the prevalence of obesity in age group a at time t, and R is the relative risk of dying among obese adults relative to non-obese counterparts aged a.

Age-, sex- and urban/rural residence-specific population in 2010

Estimates of the 2010 urban and rural population were taken from the World Urbanization Prospects[24] and disaggregated using the average age-group and sex structure of urban and rural populations separately, which we obtained from the NFHS-3 and NFHS-4[6, 7].

Population aged 20–24 entering the simulation at every interval

The new entrants aged 20–24 that join the urban and rural populations in each time interval were estimated using the projected population aged 20–24 from the medium fertility projection scenario by the United Nations’ World Population Prospects[23] and split into the projected proportions of the population in urban and rural areas from the World Urbanization Prospects[24].

The model

We estimated the future prevalence of overweight and obesity through 2040 using an age-stratified simulation model based on a system of multi-state lifetables[32], that moved individuals through mutually-exclusive health states depending on our estimated transition rates as they age. The model operated in discrete time, estimating the prevalence of overweight and obesity separately among men and women in urban and rural areas separately, at five-yearly intervals between 2010 and 2040. The system of multi-state lifetables is shown in Fig 1.

Fig 1

Compartmental model of forecasted overweight and obesity prevalence in India.

Most epidemiological studies apply transition probabilities to the population at risk of a transition at the beginning of a time period to determine the distribution of the population across health states in a succeeding time period, without taking account of a changing population at risk within a time interval. This is due to individuals being able to enter and re-enter a particular BMI group within a time interval. In order to sufficiently account for this, we employed a multi-state lifetable system first developed by Schoen and Nelson (1974) who addressed questions about flows in and out of marriage in the UK and USA. Rather than work with transition probabilities derived from the rates, this approach to forecasting health states uses the rates directly. A detailed description of this method is included in S1 File.

Assumptions

Firstly, between 2005 and 2015 the pace of increase in prevalence of overweight and obesity is faster in rural populations than in urban areas. As the prevalence of overweight and obesity among the 20–24 population is not determined within the model, we assumed that the rate of increase in this age group observed from the overweight and obesity prevalence in the NFHS data decreased and converged towards a 0% increase by 2040, so as not to overinflate our estimates. Additionally, our baseline forecasts, assumed that the empirically estimated overweight and obesity incidence for each demographic group for the baseline year (2010) applied throughout the forecasting period. This assumption provides a clear and easily interpretable counterfactual scenario against which to compare other scenarios whereby incidence is allowed to vary over the forecast period. For simplicity, we assumed that there is no migration in and out of India. Finally, it was assumed that the rate of urbanisation measured between 2005 and 2015 prevailed throughout the forecast period.

Uncertainty analyses

To obtain uncertainty bounds for our estimates we simultaneously selected random prevalence, incidence and mortality rates from the distributions that informed their uncertainty. We repeated the simulation 5000 times and we reported the median estimate as the final point estimate, whilst the range of estimates for each population subgroup informed our uncertainty bounds. The analysis was conducted in R version 3.5.1.

Sensitivity analyses

The future incidence of overweight may continue to increase due to economic development creating an increasingly obesogenic environment. To explore the implications of this potential trend, we included additional scenarios. Scenario 1 involved examining the effect on future prevalence the incidence parameter increasing at a constant annual rate of 1%. In Scenario 2, we examined the effect on future overweight and obesity of the urbanization rate being set at its upper confidence bound throughout the forecast period. Finally, Scenario 3 examined the extent to which the total prevalence of overweight and obesity prevalence would change if no further urbanization were to take place to 2040. Although unrealistic, this provides an understanding as to the extent to which the future increase in prevalence is driven by future urbanization.

We also performed additional analysis using the South Asian BMI cut-offs values. Some advocate the use of these BMI cut-offs due to a stronger positive association between BMI and body fat observed in South Asians compared to White Caucasians, and consequently an elevated disease risk at lower BMI levels[33, 34]. Under this assumption, a BMI between 23.0 kg/m2 and 27.5 kg/m2 was used to define individuals who are overweight, and a BMI greater than 27.5 kg/m2 was used to define obesity[35].

Ethics statement

The analysis of secondary data was approved by the London School of Hygiene & Tropical Medicine’s Research Ethics Committee (ref: 16190).

Results

Nationally, our model estimates that the prevalence of overweight among women will increase from 14.7% (13.7%-15.5%) to 27.4% (24.5%-30.6%) between 2010 and 2040, whereas the prevalence of obesity is forecasted to increase from 4.4% (4.0%-4.9%) to 14.0% (10.5%-16.9%) over the same period (Fig 2). Among men, the prevalence of overweight and obesity is forecasted to increase from 12.6% (11.6%-13.7%) and 2.4% (2.1%-2.8%) in 2010 to 30.5% (27.4%-34.4%) and 9.5% (5.4%-13.3%), respectively, by 2040 (Fig 2).

Fig 2

Forecasted prevalence of overweight and obesity at ages (20–69 years) 2010–2040.

The prevalence of overweight and obesity is forecasted to remain higher in urban areas, compared with rural areas, reaching 32.3% (27.8%-37.1%) and 19.7% (14.0%-24.5%), respectively among urban women and 37.1% (31.6%-43.8%) and 11.4% (5.7%-16.8%), respectively, among urban men by 2040. However, the relative increase will be larger in rural areas, where the baseline model forecasts that the prevalence of obesity among women will be 4 times higher in 2040 than in 2010 in rural areas, compared to a 2.2 times higher prevalence of obesity in urban areas over the same period.

The model also predicts larger increases in the prevalence of overweight and obesity in older age groups. Using the broad age groups in Tables 1 and 2, we find that, for example, among men, the prevalence of overweight in urban areas among 55-69-year-olds is predicted to almost quadruple from 10.8% (8.4%-13.6%) to 38.5% (31.3%-48.0%) between 2010 and 2040, whereas the prevalence of overweight in rural areas is predicted to increase from 4.7% (3.5%-5.9%) to 30.5% (23.6–37.0%). On the other hand, our model predicts that younger age groups in our model will experience the smallest absolute increase in the overweight (Figs 3 and 4).

Table 1

Forecast percentage of overweight and obese in the population to 2010–2040 (men).

Weight	Residence	Year	20–34			35–54			55–69			All
			Point est.	Lower	Upper	Point est.	Lower	Upper	Point est.	Lower	Upper	Point est.	Lower	Upper
Overweight	Rural	2010	8.4	6.6	10.2	12.3	10.0	14.5	4.7	3.5	5.9	9.2	7.9	10.5
		2020	14.5	11.7	17.9	21.8	17.5	25.5	18.4	14.6	22.0	18.1	15.0	21.3
		2030	17.6	14.2	21.8	26.1	20.8	31.7	26.6	20.7	31.9	22.9	18.5	27.9
		2040	19.0	15.3	23.6	28.1	22.3	34.7	30.5	23.6	37.0	25.6	20.4	31.4
Obese	Rural	2010	1.2	0.8	1.7	2.3	1.7	2.9	0.8	0.2	1.3	1.6	1.2	2.0
		2020	2.6	1.4	3.8	5.4	3.6	7.6	3.1	1.8	4.4	3.8	2.4	5.2
		2030	3.7	1.9	5.4	8.5	5.1	12.6	6.4	3.4	9.7	6.2	3.7	9.0
		2040	4.3	2.2	6.3	10.5	6.2	15.4	9.3	4.9	14.5	8.2	4.7	12.0
Overweight	Urban	2010	15.9	14.0	17.8	25.8	22.9	29.0	10.8	8.4	13.6	19.0	17.2	21.1
		2020	24.5	21.3	27.6	38.4	33.0	44.0	30.7	25.4	36.2	31.4	27.2	35.5
		2030	28.3	24.3	32.0	41.4	35.1	49.0	37.3	30.6	45.8	35.7	30.5	41.6
		2040	30.2	25.9	34.2	42.1	35.8	50.5	38.5	31.3	48.0	37.1	31.6	43.8
Obese	Urban	2010	3.2	2.2	4.2	5.7	4.4	7.0	1.8	0.6	2.8	4.0	3.2	4.9
		2020	5.1	3.3	7.2	8.3	4.7	12.0	6.2	2.5	9.3	6.6	4.1	9.1
		2030	6.9	4.4	9.7	11.0	5.5	16.4	10.1	3.4	16.4	9.4	5.0	13.7
		2040	7.8	5.0	11.0	13.4	6.7	19.7	12.8	3.6	22.0	11.4	5.7	16.8

Table 2

Forecast percentage of overweight and obese in the population to 2010–2040 (women).

Weight	Residence	Year	20–34			35–54			55–69			All
			Point est.	Lower	Upper	Point est.	Lower	Upper	Point est.	Lower	Upper	Point est.	Lower	Upper
Overweight	Rural	2010	8.0	6.6	9.5	14.9	12.5	17.0	9.3	7.8	10.8	10.9	9.7	12.1
		2020	12.6	10.4	14.8	21.4	18.1	24.9	21.9	17.8	26.3	18.0	15.2	21.0
		2030	14.7	12.1	17.4	24.5	19.9	29.4	27.6	21.3	34.5	21.9	17.9	26.3
		2040	15.6	12.9	18.4	25.9	20.9	31.4	30.0	23.3	38.1	24.0	19.4	29.3
Obese	Rural	2010	1.7	1.2	2.2	3.9	2.8	4.9	2.5	1.6	3.5	2.7	2.3	3.3
		2020	3.3	2.3	4.2	7.1	5.3	8.8	5.7	3.0	8.3	5.3	3.9	6.6
		2030	4.4	3.1	5.7	10.0	6.9	12.7	9.7	5.1	13.9	8.0	5.3	10.4
		2040	5.0	3.5	6.4	11.9	8.0	15.4	12.6	6.3	17.7	10.0	6.5	13.3
Overweight	Urban	2010	17.4	15.5	19.0	30.6	28.9	32.4	21.3	17.8	24.8	23.2	21.8	24.6
		2020	21.4	19.2	23.6	35.1	31.3	38.9	32.7	25.7	40.3	29.2	25.6	32.7
		2030	24.1	21.6	26.7	35.8	31.6	40.2	34.9	27.6	44.1	31.4	27.2	35.8
		2040	25.1	22.5	27.9	35.9	31.7	40.4	35.3	28.0	44.8	32.3	27.8	37.1
Obese	Urban	2010	5.4	4.5	6.3	12.3	10.6	13.9	5.9	3.7	8.0	8.2	7.3	9.1
		2020	7.1	6.0	8.3	18.7	15.1	22.3	15.6	8.3	22.5	13.5	10.5	16.4
		2030	8.9	7.4	10.5	21.5	16.6	26.0	23.7	12.6	33.1	17.4	12.7	21.6
		2040	9.9	8.2	11.7	23.4	17.9	28.3	26.2	13.8	36.2	19.7	14.0	24.5

Fig 3

Forecasted age-specific prevalence of overweight and obesity to 2040 (men).

Fig 4

Forecasted age-specific prevalence of overweight and obesity to 2040 (women).

Under the assumption of a 1% annual increase in incidence of overweight and obesity from 2015, we expect the national prevalence of overweight to increase to 29.9% (26.7%-33.7%) by 2040 among women and to 33.1% (29.4%-37.3%) over the same period for men (Fig 5). Over the same period, we expect the national prevalence of obesity to increase to 16.9% (11.9%-21.3%) among women and 12.3% (7.8%-17.0%) among men. Under the high urbanization scenario, we find that the future national prevalence of overweight between 2010 and 2040 will increase to 28.4% (25.5%-31.8%) among women, compared to 27.0% (23.7%-30.5%) under an assumption of no further urbanization. The high urbanization scenario for men finds a 1.4% higher percentage point prevalence of overweight among men in 2040, compared to the scenario of no further urbanization. The prevalence of obesity in 2040 does not vary notably between these scenarios (Fig 5).

Fig 5

Forecasted prevalence overweight and obesity to 2040 under the four different scenarios tested.

Discussion

Overall, we predict that the prevalence of overweight will increase approximately double among Indian adults aged 20–69 years between 2010 and 2040, whilst the prevalence of obesity is expected to increase approximately three-fold over the same period. Specifically, amongst men, we predict that the prevalence of overweight and obesity respectively will reach around 30% and 10%, whilst 27% and 14% of women are expected to be overweight and obese, respectively, by 2040. Our model additionally predicts an ageing distribution of overweight and obesity, with the largest relative increases in prevalence observed among the 55-69-year age group (in this age group the prevalence of obesity among women is predicted to increase almost 6-fold in rural areas and almost 5-fold in urban areas over the forecast period). Whilst prevalence of overweight and obesity is expected to be higher in urban areas throughout the forecast period, we predict larger relative increases in their prevalence in rural areas.

Our forecasting model has a number of limitations. Firstly, we determine the future prevalence of the new cohorts of 20-24-year individuals outside of the model, where we applied a declining rate of increase in prevalence, so as to not grossly inflate future prevalence in this age group to unrealistic levels. Studies have documented increasing overweight prevalence among young adults in India, especially among men and high socioeconomic status individuals[36].

Secondly, we used standard global BMI thresholds over which there is some controversy. Some researchers advocate for using lower BMI thresholds for South Asians[35] due to a higher percentage of body fat among South Asians compared to Caucasians of the same BMI[33, 34]. Some research has documented a nearly 10–15% higher prevalence of overweight among individuals with Asian heritage if Asian-specific cut-offs are used[34]. Others have found no higher risk of mortality among obese Asians compared to obese non-Asians, and advocate for global consistency in the definition of overweight and obesity[9, 37, 38]. We opted to use global cut-offs for this reason and in order to facilitate direct comparison of the predictions with similar forecasting studies in Western countries[39-41]. We sought to remedy this limitation by performing sensitivity analysis using South-Asian BMI cut-offs, and identified potential underestimation of our results (S2 File). For instance, among urban men, we identified a potential underestimation of the 2040 obesity prevalence of around 20 percentage points, suggesting that using global cut-offs may underestimate the future overall public health challenge related to excess weight in India.

Thirdly, our assumption of no migration in and out of India may slightly bias our findings if individuals leaving India to elsewhere are more likely to be overweight or obese than individuals who remain or enter. Any bias attributable to our assumption of zero migration in an out of India is however likely to be negligible as the number of annual net migrants (minus 2.5 million in 2017 according to the World Bank[42]) currently represents less than 1% of the total population[23].

Finally, it would have been desirable to have accommodated the changing socioeconomic patterning of overweight and obesity in India in our forecasts. Studies have shown that rural and lower SEP Indians are at increasing risk of overweight and obesity, with significant variation in this patterning sub-nationally[43,44]. However, due to the uncertainty in how these socioeconomic patterning trends will evolve throughout the forecasting period, the inclusion of socioeconomic status in our model has the potential to, at best, make the predictions marginally more accurate, and at worst, considerably more uncertain, and consequently, we opted for a relatively parsimonious model.

Despite these limitations, our study has a number of strengths. Firstly, we have quantified the future prevalence of overweight and obesity in India using the most recent nationally-representative publicly available data. Our model is able to reflect the changing demographic profile of India in future estimates of the prevalence of overweight and obesity and, in addition, to incorporate future rates of urbanization. Additionally, it models the future age- and sex-specific prevalence of overweight as a function of past and current age- and sex-specific incidence and mortality; reflecting the real-life lag between demographic changes, changes in incidence and mortality and their effect on the overall prevalence at various ages in the future. Unlike previous studies predicting future prevalence of overweight and obesity in India[9, 10], we forecasted prevalence for age-stratified subgroups as well as generating aggregated forecasts, putting emphasis on demographic groups that are expected to experience particularly high increases in prevalence.

Few studies have attempted to forecast the future prevalence of overweight in India. One study from 2005 predicted that the prevalence of overweight among Indian adults, assuming a continuation of past trends, will increase to 27.8% by 2030, whist the prevalence of obesity is predicted to reach 5.0%[9]. The overweight estimations closely resemble our predictions for men and are slightly above what we predict for women. However, our model predicts a considerably higher prevalence of obesity by 2030, with 11.5% of women and 7.4% of men predicted to be obese by 2030.

Another study, focusing on rural India estimated that the prevalence of overweight will approach 20% among men and just exceed 20% among women aged 18 and over by 2030[10]. We, on the other hand, expect 29.1% of men and 29.9% of women to be either overweight or obese by 2030. The discrepancy between these two separate findings may indeed be due to the different methodologies adopted but is more likely explained the fact that our study included older age groups among whom overweight and obesity prevalence is expected to increase most substantially by 2030.

The differences between our results and previous forecasts of the prevalence of overweight in India may also be explained by our attempts to take into account some of the heterogeneity in the incidence of overweight an obesity and mortality sub-nationally, estimating urban and rural outcomes separately for men and women. Also, instead of making a priori assumptions about the future prevalence of overweight and obesity, for instance a linearly increasing prevalence rate, we model future prevalence as a function of a continuously updated ‘population at-risk’. Although we expect our baseline results to be relatively conservative, as we fix age-specific incidence rates over the forecast period, we expect them to be more accurate than previous attempts. This is due to our use of the most up-to-date data, and the fundamental differences in modelling approaches.

The ageing age distribution of overweight prevalence is likely to be driven by a cohort effect. Previous research has reported a peak in the prevalence of overweight in the 40–49 age group in 2005 in India, whereas in more economically developed countries, the prevalence in the same year peaks in the 60–69 age group[9]. Our finding of an older age distribution of both overweight and obesity prevalence in 2040, compared with 2010, may be associated with India’s increasing resemblance to higher-income countries in terms of overall prevalence of overweight and economic development. When we tested our forecasts holding future mortality rates at the 2010 level, future prevalence did not notably differ from the forecasts in which future mortality was allowed to decline. Consequently, previous and continuing increases in longevity are not likely to be an important driver of this ageing age distribution of overweight.

We confirmed that our model predictions were very similar to the 2015 age-specific prevalence estimates reported by the NFHS. Another way we assessed the ability of our model to accurately predict future overweight was to compare our output with collected data on overweight prevalence from a data source that was not used in the parameterization of our model. The National Nutrition Monitoring Bureau (NNMB) reports that in 2017 the prevalence of overweight in urban areas was 34% among men. In our model, the prevalence of overweight and obesity combined among urban men is 35%, and the NNMB estimate falls comfortably within our uncertainty bound of 29.5%–40.3%. The NNMB also reports a point estimate of 44.0% prevalence among urban women in 2017, falling within our uncertainty bound of 34.7%– 45.8%, although our point estimate is lower, at 40.4%. We would expect the interval around their estimate to considerably overlap with ours, however this interval was unavailable. Although NNMB estimates fall comfortably within our uncertainty bound, differences between point estimates can derive from a number of sources. Firstly, different sampling frames are used in the surveys, whereby the NNMB in urban areas selects a sampling frame from under half (16) of Indian states they believe to accurately reflect national trends[45]. Additionally, the NNMB included individuals aged 70 years or more, the majority of whom are likely to be urban women due to their higher life expectancy[21].

We have found that the prevalence of obesity in 2040 is expected to be lower than levels that are currently observed in some of the world’s most industrialized economies, implying that India could be susceptible to considerable further increases in obesity prevalence beyond 2040. For instance, a recent survey has found that using the same BMI cut-offs as in our study, 40.4% and 35.0% of women and men in the US, respectively were classified as obese in 2013–14[46], whereas we find that in urban India, a relatively obesogenic environment, 19% of women and 11% of men are likely to be obese by 2040, however, this is one of our most conservative estimates, assuming a constant rate of incidence over the forecast period. Nevertheless, a 1% annual increase in incidence, corresponding to a 35% overall increase in incidence over the forecast period, only leads to a 5 percentage point higher prevalence in combined overweight and obesity by 2040, suggesting that much of the future forecasted prevalence will be determined by the changing demographic profile and background BMI trends of India. Attempts to reduce the forecasted prevalence in 2040 may aim to target a reduction in overweight and obesity incidence, starting among children and adolescents yet to pass through the 20-69-year-old population.

The future task of tackling the increasing disease burden associated with the tripling of obesity prevalence will be particularly challenging in India, given its already high burden of infectious diseases[3], and given that it is soon expected to have the largest population in the world [23]. Obesity is the main risk factor for a range of NCDs, including diabetes. A meta-analysis of prospective cohort studies found a 7.19 times higher risk of diabetes among obese individuals compared to normal weight individuals[47]. Given that people with diabetes are at a high risk of diabetes related complications, including long-term vascular complications affecting the kidneys, heart, and nerves[48], addressing the growing obesity prevalence and ageing pattern of prevalence, is of great urgency. The demand for medical services to tackle the increasing burden of overweight/obesity related diseases is also likely to increase substantially into the near future. Potential interventions include preventative measures such as screening for diabetes among high risk overweight/obese individuals to increase the proportion of people with diabetes that are diagnosed[49]. Further efforts may also wish to improve the provision of already established initiatives, particularly the National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Disease and Stroke (NPCDCS), that in-part aims to reduce out of pocket expenditure on diabetes healthcare and promote behavioural and lifestyle improvements that reduce the risk of such diseases[50].

Although the overall prevalence of overweight is expected to be higher in urban areas, our baseline scenario suggests that in urban India future overweight prevalence may begin to plateau during the forecasting period if incidence remains at 2010 levels over the forecast period, while rural areas will continue to experience an increasing prevalence. On the other hand, our model has predicted an almost linear growth in the prevalence of obesity in both urban and rural areas. Irrespective of future incidence or urbanization rate however, our results suggest that a considerably larger proportion of the population in both urban and rural areas will be either overweight or obese by 2040 compared to 2010, driven by the ageing of overweight and obese younger people and increasing prevalence of overweight and obesity in younger ages.

Close monitoring of these populations may be warranted, and interventions to reduce the overall growth in prevalence way wish to target these populations, particularly among populations susceptible to becoming obese for whom the risk of NCDs is substantially higher[51]. Additionally, health policy planners may wish to pay particular attention to individuals at younger ages to avoid early onset of overweight and the accumulation of overweight prevalence in older age groups.

Given the considerable heterogeneity in customs, diet and economic development between India’s states, these forecasts are likely to mask subnational variation. In future work, an examination of how these forecasts may differ at the state level may be particularly useful for health policy planning as the constitution of India devolves the deliverance of health and nutrition policy to the state level[52].

Our model is simple enough to apply to other developing countries with similarly limited data, and its flexibility can be demonstrated by appropriately adjusting the transition rates[53]. Our predictions can also provide the basis of future modelling studies aiming to quantify both monetary costs and future disease burden associated with excess weight in India[54-58].

Our model predicts a considerable increase and an ageing cohort pattern in overweight and obesity across India to 2040, which could have serious implications for future levels of obesity-related diseases, such as diabetes. Initiatives, such as the Integrated National Health Mission[59], which aims to raise overall population health, may wish to use these forecasts to target sub-populations in which the prevalence of excess weight is likely to be highest in the future. Our findings can be extended to quantify the impact of reductions in the incidence of overweight and obesity among certain subgroups and ages. This information may be crucial in estimating the future burden of NCDs, as well as their economic impact.

Detailed methods—Overweight and obesity forecast model.

This file contains a detailed description of the estimation of model parameters and the procedure followed in the forecasts.

(DOCX)

Click here for additional data file.

Supporting information Spreadsheet.

This file contains prevalence of overweight and obesity estimated in the data sets, forecasts by five-year age groups, a table comparing results of forecasting studies in India, and forecasts of overweight and obesity using South Asian BMI cut-offs.

(XLSX)

Click here for additional data file.

13 Aug 2019

PONE-D-19-17017

Forecasting the Prevalence of Overweight and Obesity in India to 2040

PLOS ONE

Dear Mr Luhar,

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Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

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Reviewer #1: In this paper the author has used a system of multi-state life tables to forecast overweight and obesityprevalence among Indians aged 20-69 years by age, sex and urban/rural residence to2040. The author has improved over the existing methodologies employed for similar projection studies by incorporating the change in future risk of becoming overweight by accounting for change in projected population as obesity and overweight increases over time. They report estimates which are close to those provided by NFHS and NNMB, implying that their results are robust. The paper can prove useful for policy makers in the area of health since prevalence of overweight and obesity is associated with numerous non-communicable diseases. However, more information about the values of the inputs used for the study is desirable.

1) The descriptive analyses related with prevalence of obesity for the years considered (From NFHS data and SAGE) for projection exercise can be included before presenting the results. Moreover, results can be presented for 5 year age groups as existing burden is higher for 40+ age groups.

2) A table on comparison of results from projection exercise with that of previous studies for India, their methodology and limitations can be included. The estimated burden can be presented for all the studies with the author’s calculation.

3) A look at the NFHS-4 data reveals that the prevalence of obesity is higher among the upper wealth quintiles. Therefore, projecting the burden separately by wealth quintiles might be a better idea for both rural and urban areas. The risk factor being adjusted for calculating obesity will vary across these groups. The results might change.

4) There is too much variation in BMI levels across States. The percentage of men and women who are overweight is surprisingly low in the most populated States such as Uttar Pradesh and Bihar. Given the dietary habits and pace of urbanization, it might not be desirable to project overall burden with assumptions that urbanization will grow at a stagnant rate. Moreover, use of low BMI thresholds for Asia can be one of the scenarios for the sensitivity analysis.

Reviewer #2: This is a well-written paper (apart from minor typos) and the methods used seem to be reasonable to answer the research question. I have some minor comments:

- Regarding the paragraph on remission it is not clear to me what you mean by this. Please explain what is exactly meant with rates of remission.

- I do not understand the meaningfulness of the comparison of future prevalence rates in India to that of the US (page 19), as obesity rates in the US are already higher also at the baseline. Please explain why you are making this comparison.

-Increases in oweight/obesity do not only result in higher prevalence rates of associated diseases but also in the need for medical care and facilities, which is in the short run more important, as it seems unrealistic with regard to past trends that the trends in overweight and obesity can be reversed in the short term. This could be better elaborated in the discussion.

**********

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Submitted filename: PONE-D-19-17017_review_cw.pdf

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2 Sep 2019

Dear Editors,

We thank the reviewers for the comments given on the manuscript titled “Forecasting the Prevalence of Overweight and Obesity in India to 2040” We have edited the manuscript in Track Changes to address their concerns. Below we provide detailed responses to how we have addressed each of the comments. Additionally, the manuscript has been edited to match the journal’s preferred style.

We believe that the manuscript is now suitable for publication in PLoS One.

Best wishes,

Shammi Luhar

On behalf of all authors

Reviewer 1

1) The descriptive analyses related with prevalence of obesity for the years considered (From NFHS data and SAGE) for projection exercise can be included before presenting the results. Moreover, results can be presented for 5-year age groups as existing burden is higher for 40+ age groups.

We agree with the reviewer on this point and have reported the prevalence of overweight and obesity from the data sets in S2 Files. We chose to include this here rather than in the manuscript so as to not clutter the paper. In the section describing the input parameters, we have included the following on page 6 so that readers know they can refer to the Supporting Material for this information:

“The prevalence estimates from the data are included in the S2 File”.

2) A table on comparison of results from projection exercise with that of previous studies for India, their methodology and limitations can be included. The estimated burden can be presented for all the studies with the author’s calculation.

We agree with the reviewer. We have included a table in the S2 File that compares the results of the other forecasts of overweight and obesity in India, along with their limitations.

3) A look at the NFHS-4 data reveals that the prevalence of obesity is higher among the upper wealth quintiles. Therefore, projecting the burden separately by wealth quintiles might be a better idea for both rural and urban areas. The risk factor being adjusted for calculating obesity will vary across these groups. The results might change.

Wealth quintiles reported in survey data is a relative measure, and consequently, the definition of what it means to be in each quintile is likely to vary considerably over time. This, therefore, makes its inclusion in the model inappropriate in the author’s opinion.

The inclusion of a comparative wealth index may be a solution to the issue of comparability over time. However, the association of wealth with overweight and obesity is both complicated, extremely variable by various population subgroups, and has constantly changed over the past two decades. Additionally, the proportion of the population in each quintile of a comparative wealth index is likely to change considerably over time. We refer you to the article below that describes the trends in the association of socioeconomic position with overweight and obesity, and the extent of this variation in these trends sub-nationally[1]. Its inclusion, we believe, would add many extra layers of uncertainty that would detract from the straightforward and relatively elegant model we sought to build.

1. Luhar, S., Mallinson, P.A.C., Clarke, L. and Kinra, S., 2019. Do trends in the prevalence of overweight by socio-economic position differ between India’s most and least economically developed states?. BMC Public Health, 19(1), p.783.

4) There is too much variation in BMI levels across States. The percentage of men and women who are overweight is surprisingly low in the most populated States such as Uttar Pradesh and Bihar. Given the dietary habits and pace of urbanization, it might not be desirable to project overall burden with assumptions that urbanization will grow at a stagnant rate.

We take this opportunity to clarify our use of the term urbanization in the context of this study. In this study, we used urbanization to refer to an individual’s propensity to migrate from a rural area to an urban area, and thus do not assume that urbanization will grow at a stagnant rate.

As urbanization is commonly defined as an increase in the proportion of the total population living in urban areas, assuming that the individual propensity to migrate to urban areas will remain constant over time implies that the growth in the urban population, whilst substantial, will increase at a variable rate. With this assumption, in states with a lower baseline prevalence of overweight/obesity and lower proportion urban, the rate at which the urban population will continue to grow will be faster than in states that have a higher proportion urban at baseline.

Moreover, use of low BMI thresholds for Asia can be one of the scenarios for the sensitivity analysis.

We agree with the reviewer’s comments that including sensitivity using the BMI cut-off values specific to South Asian populations are desirable. This sensitivity analysis is included in the S2 File, and is also referred to in the text on page 11:

“We also performed additional analysis using the South Asian BMI cut-offs values. Some advocate the use of these BMI cut-offs due to a stronger positive association between BMI and body fat observed in South Asians compared to White Caucasians, and consequently an elevated disease risk at lower BMI levels[2,3]. Under this assumption, a BMI between 23.00 kg/m2 and 27.49 kg/m2 was used to define individuals who are overweight, and a BMI greater than 27.50 kg/m2 was used to define obesity[4].”

We have also included the following on page 17, regarding the limitation of using global-cut-offs:

“We sought to remedy this limitation by performing sensitivity analysis using South-Asian BMI cut-offs, and identified potential underestimation of our results (refer to S2 File). For instance, among urban men, we identified a potential underestimation of the 2040 obesity prevalence of nearly 20 percentage points, suggesting that using global cut-offs may underestimate the future overall public health challenge related to excess weight in India.”

Reviewer 2

1) Regarding the paragraph on remission it is not clear to me what you mean by this. Please explain what is exactly meant with rates of remission.

We agree with the reviewer that this point could benefit from further clarification. In the text we have included the following on page 7:

“Remission refers to reverse transitions, whereby the simulated population is able to transition from a state of ‘Obese’ to ‘Overweight’, and ‘Overweight’ to ‘Not Overweight/Obese.”

2) I do not understand the meaningfulness of the comparison of future prevalence rates in India to that of the US (page 19), as obesity rates in the US are already higher also at the baseline. Please explain why you are making this comparison.

The way in which I have phrased this is misleading due to my use of the word baseline (which refers to the baseline scenario and not the baseline year). I wanted to convey that even by 2040, India’s prevalence of obesity will not reach the prevalence levels that are currently observed in the United States or other industrialized countries, allowing potential for further increases in future prevalence in India. The sentence has been since altered to the following:

“Our study has found that the prevalence of obesity in 2040 is expected to be lower than levels that are currently observed in some of the world’s most industrialized economies, implying potential for considerable further increases beyond 2040.”

3) Increases in overweight/obesity do not only result in higher prevalence rates of associated diseases but also in the need for medical care and facilities, which is in the short run more important, as it seems unrealistic with regard to past trends that the trends in overweight and obesity can be reversed in the short term. This could be better elaborated in the discussion.

We agree with the reviewer and have included the following in the discussion to elaborate on this point:

“The demand for medical services to tackle the increasing burden of overweight/obesity related diseases is also likely to increase substantially into the near future. Potential interventions include preventative measures such as screening for diabetes among high risk overweight/obese individuals to increase the proportion of people with diabetes that are diagnosed[5]. Further efforts may also wish to improve the provision of already established initiatives, particularly the National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Disease and Stroke (NPCDCS), that in-part aims to reduce out of pocket expenditure on diabetes healthcare and promote behavioural and lifestyle improvements that reduce the risk of such diseases[6].”

1. Luhar, S., Alice, P., Mallinson, C., Clarke, L. & Kinra, S. Do trends in the prevalence of overweight by socio-economic position differ between India ’ s most and least economically developed states ? 1–12 (2019).

2. Misra, A. Ethnic-Specific Criteria for Classification of Body Mass Index: A Perspective for Asian Indians and American Diabetes Association Position Statement. Diabetes Technol. Ther. 17, 667–71 (2015).

3. Stegenga, H., Haines, A., Jones, K. & Professor, J. W. Identification, assessment, and management of overweight and obesity: Summary of updated NICE guidance. BMJ 349, g6608 (2014).

4. Nishida, C. et al. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 363, 157 (2004).

5. Basu, S. et al. The Health System and Population Health Implications of Large-Scale Diabetes Screening in India: A Microsimulation Model of Alternative Approaches. PLoS Med. 12, 1–21 (2015).

6. Ministry of Health and Family Welfare, G. of I. National Program for Prevention and Control of Cancer, Diabetes, CVD and Stroke( NPCDCS). Directorate General of Health Services Available at: https://dghs.gov.in/content/1363_3_NationalProgrammePreventionControl.aspx. (Accessed: 14th August 2019)

Submitted filename: PLoS One Response to Reviewers.docx

Click here for additional data file.

2 Oct 2019

PONE-D-19-17017R1

Forecasting the Prevalence of Overweight and Obesity in India to 2040

PLOS ONE

Dear Mr Luhar,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Kindly note that one of the reviewer has suggested revisions to incorporate the vast heterogeneity of India by incorporating features such as urbanization and socioeconomic differentials in the forecasting approach. The authors are requested to incorporate these concerns in the revision or in the authors' reply to the comments.

We would appreciate receiving your revised manuscript by Nov 16 2019 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

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Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

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William Joe

Academic Editor

PLOS ONE

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Comments to the Author

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Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I still do not agreee with the assumptions that have been made with respect to urbanization and wealth. Also, the process to arrive the burden is not clear.

Reviewer #2: The points raised in my first review have adequately been addressed. The authors have clarified what they mean with remission. The comparison to the US overweight and obesity rates makes sense now. However the last sentence regarding future potential of obesity beyond 2040 is still a bit misleading. I would suggest not to call it "potential" but e.g. "vulnerability" or "susceptibility". The discussion section has also been elaborated.

**********

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Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

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13 Nov 2019

Reviewer #1: I still do not agree with the assumptions that have been made with respect to urbanization and wealth.

We thank the reviewer for their comment and agree with the reviewer that forecasting the future prevalence of overweight and obesity by separate wealth quintiles would be ideal, however we opted against doing this for the following reasons:

Firstly, there are inherent inadequacies of a wealth index that is commonly used to capture socioeconomic position in cross sectional surveys in low- and middle-income countries. A wealth index is designed as a relative measure of wealth, rather than an absolute measure. Therefore, the index used to calculate quintiles is specific to the setting and the time period in which it is collected1. Consequently, it cannot be compared over time, and thus cannot be used in the forecasting of overweight and obesity over the specified forecast period.

Additionally, the changing nature of the association of overweight and obesity with socioeconomic position makes it very difficult to predict in the future which would be set externally to the model. Whilst it is true that survey data shows a higher prevalence of overweight and obesity in higher wealth quintiles, we are unable to firstly determine the proportion of the population that is expected to be in pre-specified wealth quintiles over the forecasting period (2010-2040), and secondly, it is not possible to ascertain what the exact association between overweight/obesity and socioeconomic position will be in the future. A number of systematic reviews2–5, in addition to recent studies in India6–8, have documented and provided evidence that in low and middle income countries, like India, the socioeconomic patterning of overweight and obesity changes considerably over time. The higher prevalence initially observed among high socioeconomic status groups will turn negative along with continued economic development. Due to the likely uncertain nature and considerable subnational heterogeneity in the future socioeconomic patterning of overweight and obesity throughout the forecasting period, we strongly believe that its inclusion in the model would be inappropriate and what results would come out of the model would be uninterpretable due to escalating uncertainty in the data and projections of SES distribution.

We have also included the following on page 18 under the limitations section:

“Finally, it would have been desirable to have accommodated the changing socioeconomic patterning of overweight and obesity in India in our forecasts. Studies have shown that rural and lower SEP Indians are at increasing risk of overweight and obesity, with significant variation in this patterning sub-nationally[43],[44]. However, due to the uncertainty in how these socioeconomic patterning trends will evolve throughout the forecasting period, the inclusion of socioeconomic status in our model has the potential to, at best, make the predictions marginally more accurate, and at worst, considerably more uncertain, and consequently, we opted for a relatively parsimonious model.”

References

1. Rutstein SO, Johnson K. DHS Comparative reports No.6. The DHS Wealth Index. Calverton, Maryland, USA; 2004.

2. Dinsa GD, Goryakin Y, Fumagalli E, Suhrcke M. Obesity and socioeconomic status in developing countries: A systematic review. Obes Rev. 2012;13(11):1067–79.

3. McLaren L. Socioeconomic status and obesity. Epidemiol Rev. 2007;29(1):29–48.

4. McLaren L. Socioeconomic status and obesity. Epidemiol Rev. 2007;29(1):29–48.

5. Monteiro CA, Moura EC, Conde WL, Popkin BM. Public Health Reviews Socioeconomic status and obesity in adult populations of developing countries : a review. Public Health Rev. 2004;82(12):940–6.

6. 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(10):e023935.

7. Luhar S, Alice P, Mallinson C, Clarke L, Kinra S. Do trends in the prevalence of overweight by socio-economic position differ between India ’ s most and least economically developed states ? 2019;1–12.

8. Sengupta A, Angeli F, Syamala TS, Dagnelie PC, Schayck CP van. 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.

In regards urbanisation, this study does not make any assumption that urbanisation (the proportion of the population living in urban areas) will continue at a stagnant rate. In the paper, we use the term ‘urbanisation’ to refer to any one individual’s propensity to migrate to an urban area, not the population level of urbanisation, which is commonly defined as an increase in the proportion of the population residing in urban areas.

The highly populated and low overweight/obesity states such as Uttar Pradesh and Bihar also have relatively lower proportion of the population in urban areas. In these states, with comparatively lower levels of overweight and obesity, the increase in the proportion of the population residing in urban areas will be relatively fast compared in these low overweight/obesity prevalence areas compared to high overweight/obesity prevalence areas, addressing the reviewer’s initial concerns.

Reviewer #1: Also, the process to arrive the burden is not clear.

We agree with the reviewer that the calculation of the burden was not clear. We have clarified this in the S2 File in a table footnote under the ‘Table of Comparison’ tab. Specifically, we included the following:

“For consistency across the different studies, we calculated the burden using the United Nations World Population Prospects (2017) population projections, and United Nations World Urbanization Prospects (2018), and applying the proportions identified in the studies”

Reviewer #2: The points raised in my first review have adequately been addressed. The authors have clarified what they mean with remission. The comparison to the US overweight and obesity rates makes sense now. However, the last sentence regarding future potential of obesity beyond 2040 is still a bit misleading. I would suggest not to call it "potential" but e.g. "vulnerability" or "susceptibility". The discussion section has also been elaborated.

We thank the reviewer for clarifying that we have adequayely addressed what was meant by remission. We agree with the reviewer’s additional concerns and have amended the sentence in the manuscript.

The original sentence:

“Our study has found that the prevalence of obesity in 2040 is expected to be lower than levels that are currently observed in some of the world’s most industrialized economies, implying the potential for considerable further increases beyond 2040.”

Has been changed to:

“We have found that the prevalence of obesity in 2040 is expected to be lower than levels that are currently observed in some of the world’s most industrialized economies, implying that India could be susceptible to considerable further increases in obesity prevalence beyond 2040.”

Submitted filename: Response_to_Reviewers_Shammi .docx

Click here for additional data file.

15 Jan 2020

PONE-D-19-17017R2

Forecasting the Prevalence of Overweight and Obesity in India to 2040

PLOS ONE

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Reviewer #1: It would have been desirable to include a number of socio-economic determinants such as marital status, job status, income status, smoking, alcohol consumption, sleep duration, psychological factors, dietary intake, and fertility rate which in literature are identified as key determinants of overweight/obesity. But it is also true that it can be very challenging to forecast using these parameters which keep changing. However, the study can be a good starting point for those who want to do further research in this area. At the outset, margin of error should be expected since the values are being projected for such a long period of time. Also, the paper uses the WPP 2017 revision available on the population projection from the UN population projection. Since, the new version (2019) of the WPP and World Urbanization Prospects data is available, I leave it up to the authors to decide whether they want to update the analyses since it can be time consuming and the broad inference might not change.

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27 Jan 2020

Reviewer #1: It would have been desirable to include a number of socio-economic determinants such as marital status, job status, income status, smoking, alcohol consumption, sleep duration, psychological factors, dietary intake, and fertility rate which in literature are identified as key determinants of overweight/obesity. But it is also true that it can be very challenging to forecast using these parameters which keep changing. However, the study can be a good starting point for those who want to do further research in this area. At the outset, margin of error should be expected since the values are being projected for such a long period of time. Also, the paper uses the WPP 2017 revision available on the population projection from the UN population projection. Since, the new version (2019) of the WPP and World Urbanization Prospects data is available, I leave it up to the authors to decide whether they want to update the analyses since it can be time consuming and the broad inference might not change.

Response: We thank the reviewer for the feedback on our study and agree that the study can be a good starting point for those who want to do further research in this area. We also thank the reviewer for the opportunity to update our analyses with newly published data. We have since re-run the models with the new data and the changes have been included in the manuscript, graphs, tables and the supplementary information.

7 Feb 2020

Forecasting the Prevalence of Overweight and Obesity in India to 2040

PONE-D-19-17017R3

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11 Feb 2020

PONE-D-19-17017R3

Forecasting the Prevalence of Overweight and Obesity in India to 2040

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