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BackgroundIt has been suggested that cardiovascular disease exhibits a ‘social cross-over’, from greater risk in higher socioeconomic groups to lower socioeconomic groups, on economic development, but robust evidence is lacking. We used standardised data to compare the social inequalities in cardiovascular mortality across states at varying levels of economic development in Brazil.MethodsWe used national census and mortality data from 2010. We used age-adjusted multilevel Poisson regression to estimate the association between educational status and cardiovascular mortality by state-level economic development (assessed by quintiles of Human Development Index).ResultsIn 2010, there were 185 383 cardiovascular deaths among 62.5 million adults whose data were analysed. The age-adjusted cardiovascular mortality rate ratio for women with ConclusionsOur results do not support a ‘social crossover’ in cardiovascular mortality on economic development. Our analyses, based on a large standardised dataset from a country that is currently experiencing economic transition, provide strong evidence that low socioeconomic groups experience the highest risk of cardiovascular disease, irrespective of the stage of national economic development.

Aims/hypothesis We aimed to estimate the lifetime risk of diabetes and diabetes-free life expectancy in metropolitan cities in India among the population aged 20 years or more, and their variation by sex, age and BMI. Methods A Markov simulation model was adopted to estimate age-, sex- and BMI-specific lifetime risk of developing diabetes and diabetes-free life expectancy. The main data inputs used were as follows: age-, sex- and BMI-specific incidence rates of diabetes in urban India taken from the Centre for Cardiometabolic Risk Reduction in South Asia (2010–2018); age-, sex- and urban-specific rates of mortality from period lifetables reported by the Government of India (2014); and prevalence of diabetes from the Indian Council for Medical Research INdia DIABetes study (2008–2015). Results Lifetime risk (95% CI) of diabetes in 20-year-old men and women was 55.5 (51.6, 59.7)% and 64.6 (60.0, 69.5)%, respectively. Women generally had a higher lifetime risk across the lifespan. Remaining lifetime risk (95% CI) declined with age to 37.7 (30.1, 46.7)% at age 60 years among women and 27.5 (23.1, 32.4)% in men. Lifetime risk (95% CI) was highest among obese Indians: 86.0 (76.6, 91.5)% among 20-year-old women and 86.9 (75.4, 93.8)% among men. We identified considerably higher diabetes-free life expectancy at lower levels of BMI. Conclusions/interpretation Lifetime risk of diabetes in metropolitan cities in India is alarming across the spectrum of weight and rises dramatically with higher BMI. Prevention of diabetes among metropolitan Indians of all ages is an urgent national priority, particularly given the rapid increase in urban obesogenic environments across the country.

ObjectiveTo determine cause-specific and age-specific contributions to life expectancy changes between 2000 and 2015, separately by state and sex in Brazil, with a focus on homicides.DesignRetrospective cross-sectional demographic analysis of mortality.Setting and populationBrazilian population by age, sex and state from 2000 to 2015.Main outcome measureUsing mortality data from the Brazilian Mortality Information System and population estimates from the National Statistics Office, we used death distribution methods and the linear integral decomposition model to estimate levels and changes in life expectancy. We also examine how multiple causes of death, including those attributable to homicides and amenable/avoidable mortality, contributed to these changes from 2000 to 2015.ResultsBetween 2000 and 2015, life expectancy in Brazil increased from 71.5 to 75.1 years. Despite state-level variation in gains, life expectancy increased in almost all states over this period. However across Brazil, homicide mortality contributed, to varying degrees, to either attenuated or decreased male life expectancy gains. In Alagoas in 2000–2007 and Sergipe in 2007–2015, homicides contributed to a reduction in life expectancy of 1.5 years, offsetting gains achieved through improvements due to medically amenable causes. In the period 2007–2015, male life expectancy could have been improved by more than half a year in 12 of Brazil’s states if homicide mortality had remained at the levels of 2007.ConclusionsHomicide mortality appears to offset life expectancy gains made through recent improvements to mortality amenable to medical services and public health interventions, with considerable subnational heterogeneity in the extent of this phenomenon. Efforts combating the causes of homicides can increase life expectancy beyond what has been achieved in recent decades.

Background: To our knowledge, no estimates of lifetime risk of diabetes have been produced for India, a country with a high propensity to developing diabetes and a low overall body weight compared to high-income countries. Data: We estimated the lifetime risk of diabetes in metropolitan India, and its variation by sex, age and BMI. We used government mortality statistics, diabetes prevalence from the Indian Council for Medical Research INdia DIABetes study (2008-15) and incidence from the Centre for Cardiometabolic Risk Reduction in South Asia (2010-2018). We limited estimates Indians aged 20 or more years. Results: Overall lifetime risk of diabetes in women in metropolitan cities aged 20 was 74.7% (95%CI: 65.4-83.3%), and 69.0% (95%CI: 58.9-78.7%) in men. Remaining risk is highest among the obese (92.9% (95%CI: 88.0-96.0%) in women and 92.4% (95%CI: 86.6-96.1%) in men aged 20) and is lowest among underweight/normal weight people (56.4% (95%CI: 46.9-66.3%) in women and 54.2% (95%CI: 44.5-64.6%) in men). Lifetime risk declines with age (to 67.3% (95%CI: 57.4-77.1%) in women aged 40 to 42.5% (95%CI: 33.8-52.9%) at age 60). Conclusion: In metropolitan India, 1 in 2 underweight/normal weight, and 9 in 10 obese, people aged 20 are projected to develop diabetes during their life. This portends a serious epidemic and indicates what may develop elsewhere in India in the absence of urgent action. Table. Lifetime risk of diabetes by sex, age and BMI Disclosure S. Luhar: None. S. Kinra: None. V. Mohan: None. D. Kondal: None. R. Anjana: None. S.A. Patel: None. M.K. Ali: Research Support; Self; Merck & Co., Inc. D. Prabhakaran: None. N. Tandon: None. K. Narayan: None. Funding Economic and Social Research Council (ES/J500021/1)

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.

To limit the social, economic and psychological damage caused by strict social distancing interventions, many low and middle-income countries (LMICs) are seeking to ease restrictions. However, it is unknown what a ‘safe reopening’ entails in LMICs given suboptimal diagnostic and surveillance capabilities. Here we discuss three community-based public health measures (sustained mitigation, zonal lockdown and dynamic rolling lockdowns) which seek to adequately balance the public health and economic priorities. Each of these options have limitations and prerequisites that may be context-specific and should be considered before implementation, including implementation and maintenance costs, social and economic costs, context-specific epidemic growth and the existing health resources.

Background India’s economic development and urbanisation in recent decades has varied considerably between states. Attempts to assess how overweight (including obesity) varies by socioeconomic position at the national level may mask considerable sub-national heterogeneity. We examined the socioeconomic patterning of overweight among adults in India’s most and least economically developed states between 1998 and 2016. Methods We used state representative data from the National Family Health Surveys from 1998 to 99, 2005–06 and 2015–16. We estimated the prevalence of overweight by socioeconomic position in men (15–54 years) and women (15–49 years) from India’s most and least economically developed states using multilevel logistic regressions. Results We observed an increasing trend of overweight prevalence among low socioeconomic position women. Amongst high socioeconomic position women, overweight prevalence either increased to a smaller extent, remained the same or even declined between 1998 and 2016. This was particularly the case in urban areas of the most developed states, where in the main analysis, the prevalence of overweight increased from 19 to 33% among women from the lowest socioeconomic group between 1998 and 2016 compared to no change among women from the highest socioeconomic group. Between 2005 and 2016, the prevalence of overweight increased to similar extents among high and low socioeconomic status men, irrespective of residence. Conclusions The converging prevalence of overweight by socioeconomic position in India’s most developed states, particularly amongst urban women, implies that this subpopulation may be the first to exhibit a negative association between socioeconomic position and overweight in India. Programs aiming to reduce the increasing overweight trends may wish to focus on poorer women in India’s most developed states, amongst whom the increasing trend in prevalence has been considerable. Electronic supplementary material The online version of this article (10.1186/s12889-019-7155-9) contains supplementary material, which is available to authorized users.

Objectives We aimed to examine trends in prevalence of overweight/obesity among adults in India by socioeconomic position (SEP) between 1998 and 2016. Design Repeated cross-sectional study using nationally representative data from India collected in 1998/1999, 2005/2006 and 2015/2016. Multilevel regressions were used to assess trends in prevalence of overweight/obesity by SEP. Setting 26, 29 and 36 Indian states or union territories, in 1998/99, 2005/2006 and 2015/2016, respectively. Participants 628 795 ever-married women aged 15–49 years and 93 618 men aged 15–54 years. Primary outcome measure Overweight/obesity defined by body mass index >24.99 kg/m2. Results Between 1998 and 2016, overweight/obesity prevalence increased among men and women in both urban and rural areas. In all periods, overweight/obesity prevalence was consistently highest among higher SEP individuals. In urban areas, overweight/obesity prevalence increased considerably over the study period among lower SEP adults. For instance, between 1998 and 2016, overweight/obesity prevalence increased from approximately 15%–32% among urban women with no education. Whereas the prevalence among urban men with higher education increased from 26% to 34% between 2005 and 2016, we did not observe any notable changes among high SEP urban women between 1998 and 2016. In rural areas, more similar increases in overweight/obesity prevalence were found among all individuals across the study period, irrespective of SEP. Among rural women with higher education, overweight/obesity increased from 16% to 25% between 1998 and 2016, while the prevalence among rural women with no education increased from 4% to 14%. Conclusions We identified some convergence of overweight/obesity prevalence across SEP in urban areas among both men and women, with fewer signs of convergence across SEP groups in rural areas. Efforts are therefore needed to slow the increasing trend of overweight/obesity among all Indians, as we found evidence suggesting it may no longer be considered a ‘diseases of affluence’.

Table S1. Percentage of households with the following assets/characteristics by survey and urban/rural residence – presents the ownership of assets and household characteristics by urban and rural residence across India in the three NFHS surveys used. Table S2. Percentage of the full women’s sample (including pregnant and never-married women) in each strata of the SEP exposures and the outcome – presents the distribution of the data across the SEP variables and main outcome in the full sample of women. Table S3. Predicted prevalence of overweight from the regression model in India’s least developed states (using the full sample of women including pregnant and never married women) – demonstrates potential underestimation of the convergence in socioeconomic patterning of overweight in least developed states when including never-married women and pregnant women. (DOCX 17 kb)

Table S1. Percentage of households with the following assets/characteristics by survey and urban/rural residence – presents the ownership of assets and household characteristics by urban and rural residence across India in the three NFHS surveys used. Table S2. Percentage of the full women’s sample (including pregnant and never-married women) in each strata of the SEP exposures and the outcome – presents the distribution of the data across the SEP variables and main outcome in the full sample of women. Table S3. Predicted prevalence of overweight from the regression model in India’s least developed states (using the full sample of women including pregnant and never married women) – demonstrates potential underestimation of the convergence in socioeconomic patterning of overweight in least developed states when including never-married women and pregnant women. (DOCX 17 kb)