Mapping rates of inpatient hospitalizations related to mental disorders in the state of Missouri: A conditional autoregressive model with zip code-level data

Nearly one in five American adults suffers from mental illness in a given year. Mental health conditions are known to be spatially clustered, but no prior work has examined the clustering of mental health related hospitalizations. This analysis uses Bayesian hierarchical models to predict rates of inpatient hospitalizations attributed to mental disorders within zip codes in Missouri, USA. Eight separate models were run, and all models yielded similar estimates for the average rate of mental health related hospitalizations (around 13 per 1000 population). The percent of families receiving food stamps and percent of vacant housing were found to be significantly associated with hospitalization rates, after controlling for age, gender, and race. These rates were also significantly spatially clustered (Moran's I > 0.3 and p < 0.05 for all models). Health professionals can use these results to prioritize regions throughout the state that have the greatest need for mental health service providers and interventions.