<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sun,Dongchu</style></author><author><style face="normal" font="default" size="100%">Tsuakawa, R. K.</style></author><author><style face="normal" font="default" size="100%">Kim, H.</style></author><author><style face="normal" font="default" size="100%">Z. He</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian Analysis of Mortality Rates with Disease Maps</style></title><secondary-title><style face="normal" font="default" size="100%">Statistics in Medicine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><volume><style face="normal" font="default" size="100%">19</style></volume><pages><style face="normal" font="default" size="100%">2015-2035</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This article summarizes our research on estimation of age-specific and age-adjusted mortality rates for chronic obstructive pulmonary disease (COPD) for white males. Our objectives are more precise and informative displays (than previously available) of geographic variation of the age-specific mortality rates for COPD, and investigation of the relationships between the geographic variation in mortality rates and the corresponding variation in selected covariates. For a given age class, our estimates are displayed in a choropleth map of mean rates. We develop a variation map that identifies the geographical areas where inferences are reliable. Here, the variation is measured by considering a set of maps produced using samples from the posterior distribution of the population mortality rates. Finally, we describe the spatial patterns in the age-specific maps and relate these to patterns in potential explanatory covariates such as smoking rate, annual rainfall, population density, elevation, and measures of air quality.&lt;/p&gt;
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