Município-Level Estimates of Child Mortality for Brazil: A New Approach Using Bayesian Statistics

Sarah McKinnon, University of Texas at Austin
Joseph E. Potter, University of Texas at Austin
Carl P. Schmertmann, Florida State University

Previous efforts to estimate child mortality levels in smaller geographical areas have been hampered by the relative rarity of child deaths which has often resulted in unstable and noisy estimates. However, with a spatial smoothing process based upon Bayesian Statistics it is possible to “borrow” information from neighboring areas in order to generate more stable estimates of mortality in smaller areas. The objective of this study was to use a spatial smoothing process to derive estimates of child mortality at the level of the município. Using data from the 2000 Brazil Census we derive both Bayesian and non-Bayesian estimates of mortality for each município. In comparing the smoothed and raw estimates of this parameter, we find that the Bayesian estimates yield a clearer spatial pattern of child mortality with smaller variances in less populated municípios, thus, more accurately reflecting the true mortality situation of those municípios.

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Presented in Session 32: Statistical, Spatial, and Network Methods