Lifetime risk of diabetes in metropolitan cities in India

Abstract

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.

Cost-effectiveness of population-based screening for diabetes and hypertension in India: an economic modelling study

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“…At 65%, the lifetime risk of diabetes in women in Indian cities is among the highest in the world, and the rise of overweight/obesity in rural women may usher in similar risks. 38 Our study has numerous methodological strengths. Using a Bayesian GLMM has numerous advantages from providing stable estimates incorporating the hierarchy of space and time, better convergence to point estimates and modelling a cross-classified framework.…”

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