Understanding Geographic Variations in Bmi in India

Comparison of Body Mass Index is a useful marker for energy imbalance and associated variations across populations. High BMI is associated with cardiovascular and metabolic diseases, whereas low BMI is associated with increased mortality. BMI comparisons across geographical locations may give us indication as to which direction the public health policies should head and what could be the corrective approach towards a more balanced and healthier energy level. The current study uses Indian National Family Health Survey (NFHS-3) data for women from 2005-06 to develop state-specific models of BMI and do inter-state comparisons. We also examined the individual versus contextual predictors of these variations. of the total sample (N = 118,734), 29% had a BMI lower than 18.5, and were classified as underweight, with Uttar Pradesh having the highest number of underweight women, followed by Maharashtra, West Bengal and Karnataka. North-eastern states of Arunachal Pradesh, Nagaland, Manipur, and Mizoram, collectively had lowest percentage of underweight residents. Female respondents who had higher levels of education, were married, and were employed, had a lower prevalence of being underweight (p\u3c0.000). Women who smoked and consumed alcohol were also more likely to be underweight. But addition of such individual level variables like income and wealth variables, educational and demographic variables, and health behaviors alter the odds of having a low BMI in some states (such as Punjab, Kerala, Goa & Delhi), but not in others (such as Bihar, Jharkhand, Arunachal Pradesh, Nagaland, Madhya Pradesh & Manipur). In former types of states where individual level variables change the odds of having low BMI, continued investments in education, health education targeted toward health-adverse behaviors, and access to public health resources may show improvement in levels of BMI. On the other hand, states where individual level variables did not influence the odds of having low BMI in our analysis might have different genotypical characteristics of the female respondents. It is also possible that these states might need intervention not only at individual level, but also at the level of macroeconomic and developmental factors such as food security, or to health-related factors such as the availability, accessibility, and quality of health care services, particularly those directed toward women. The current study shows the need for two-pronged policy interventions to alter the BMI imbalance in India