A Bayesian approach to simultaneous adjustment of misclassification and missingness in categorical covariates.

This study considers concurrent adjustment of misclassification and missingness in categorical covariates in regression models. Under various misclassification and missingness mechanisms, we derive a general mixture regression structure for regression models that can incorporate multiple surrogates of categorical covariates that are subject to misclassification and missingness. In simulation studies, we demonstrate that including observations with missingness and/or multiple surrogates of the covariate helps alleviate the efficiency loss caused by misclassification.

Disparities in health related quality of life among Illinoisans diagnosed with depressive disorder: findings from the 2017 BRFSS.

Background: The United States experienced severe mental health budget cuts in many states across the nation during the years of the largest recession since the Great Depression. Illinois had one of the hardest hit mental health budgets in the country. The massive mental health funding cuts in Illinois, combined with the state's budget impasse, left fewer facilities available to provide treatment and support to those in need.