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Community effects on public health in India: A hierarchical model
Authors: Sada Nand Dwivedi, Shahina Begum, Alok Kumar Dwivedi, and Arvind Pandey
Source: Health, 4(8):526-536, doi:10.4236/health.2012.48084
Topic(s): Infant mortality
Service utilization
Country: Asia
Published: APR 2012
Abstract: The data on any aspect of public health, including that on infant mortality, has inbuilt hierarchical structure. Using traditional regression approach in data analysis, i.e., ignoring hierarchical structure, either at micro (individual) or at macro (community) level will be avoiding de-sired assumption related to independence of records. Accordingly, this may result into distortion in the results due to probable underestimation of standard error of the regression coefficients. To be more specific, an irrelevant covariate may emerge as an important covariate leading to inappropriate public health implications. To overcome this problem, the objective of the present work was to deal with multilevel analysis of the data on infant mortality available under second round of National family Health Survey and notify changes in results under traditional regression analysis that ignores hierarchical structure of data. This method provides more accurate results leading to meaningful public health implications. In addition, estimation of variability at different levels and their covariance are also obtained. The results indicate that the community (e.g., state) level characteristics still have major role regarding infant mortality in India. Further, if computational facilities are available, multilevel analysis may be preferred in dealing with data involving hierarchical structure leading to accurate results having meaningful public health implications.