Bayesian Multimodel Inference for Geostatistical Regression Models

The problem of simultaneous covariate selection and parameter inference for spatial regression models is considered. Previous research has shown that failure to take spatial correlation into account can influence the outcome of standard model selection methods. A Markov chain Monte Carlo (MCMC) method is investigated for the calculation of parameter estimates and posterior model probabilities for spatial regression models. The method can accommodate normal and non-normal response data and a large number of covariates. Thus the method is very flexible and can be used to fit spatial linear models, spatial linear mixed models, and spatial generalized linear mixed models (GLMMs). The Bayesian MCMC method also allows a priori unequal weighting of covariates, which is not possible with many model selection methods such as Akaike's information criterion (AIC). The proposed method is demonstrated on two data sets. The first is the whiptail lizard data set which has been previously analyzed by other researchers investigating model selection methods. Our results confirmed the previous analysis suggesting that sandy soil and ant abundance were strongly associated with lizard abundance. The second data set concerned pollution tolerant fish abundance in relation to several environmental factors. Results indicate that abundance is positively related to Strahler stream order and a habitat quality index. Abundance is negatively related to percent watershed disturbance

Race/ethnicity and potential suicide misclassification: window on a minority suicide paradox?

<p>Abstract</p> <p>Background</p> <p>Suicide officially kills approximately 30,000 annually in the United States. Analysis of this leading public health problem is complicated by undercounting. Despite persisting socioeconomic and health disparities, non-Hispanic Blacks and Hispanics register suicide rates less than half that of non-Hispanic Whites.</p> <p>Methods</p> <p>This cross-sectional study uses multiple cause-of-death data from the US National Center for Health Statistics to assess whether race/ethnicity, psychiatric comorbidity documentation, and other decedent characteristics were associated with differential potential for suicide misclassification. Subjects were 105,946 White, Black, and Hispanic residents aged 15 years and older, dying in the US between 2003 and 2005, whose manner of death was recorded as suicide or injury of undetermined intent. The main outcome measure was the relative odds of potential suicide misclassification, a binary measure of manner of death: injury of undetermined intent (includes misclassified suicides) versus suicide.</p> <p>Results</p> <p>Blacks (adjusted odds ratio [AOR], 2.38; 95% confidence interval [CI], 2.22-2.57) and Hispanics (1.17, 1.07-1.28) manifested excess potential suicide misclassification relative to Whites. Decedents aged 35-54 (AOR, 0.88; 95% CI, 0.84-0.93), 55-74 (0.52, 0.49-0.57), and 75+ years (0.51, 0.46-0.57) showed diminished misclassification potential relative to decedents aged 15-34, while decedents with 0-8 years (1.82, 1.75-1.90) and 9-12 years of education (1.43, 1.40-1.46) showed excess potential relative to the most educated (13+ years). Excess potential suicide misclassification was also apparent for decedents without (AOR, 3.12; 95% CI, 2.78-3.51) versus those with psychiatric comorbidity documented on their death certificates, and for decedents whose mode of injury was "less active" (46.33; 43.32-49.55) versus "more active."</p> <p>Conclusions</p> <p>Data disparities might explain much of the Black-White suicide rate gap, if not the Hispanic-White gap. Ameliorative action would extend from training in death certification to routine use of psychological autopsies in equivocal-manner-of-death cases.</p