Predictive effect of determinants of health on prevalence of diseases in India. An approach for measurement in a multi-factorial and multi-level setting.

Abstract

The burden of acute and chronic diseases in India is higher than their respective global averages. To mitigate this in an effective and resource efficient way, the concerned health policy should undertake interventions against the determinants that can predict the prevalence of acute and chronic diseases in a multi-factorial setting. In addition, the policy should be customized at appropriate administrative levels to account for the variation attributed to the local context. To identify the appropriate determinants for intervention, and administrative levels for customization, data on 17 determinants from 274 district nested within 21 states was used, and analyzed using a combination of multiple regression analysis and multi-level analysis techniques. Consequently, 8 determinants were identified to have predictive ability on prevalence of acute diseases, while prevalence of chronic disease could be predicted by 10 determinants. State level was identified as the appropriate level for customization of the policies concerned with all the predictive determinants, while district level was identified as the appropriate level for customization for only half of the predictive determinants.