Title: A latent spatial factor approach for synthesizing opioid associated deaths and treatment admissions in Ohio counties,
Abstract: Background: Opioid misuse is a major public health issue in the United States
and in particular Ohio. However, the burden of the epidemic is challenging to
quantify as public health surveillance measures capture different aspects of
the problem. Here we synthesize county-level death and treatment counts to
compare the relative burden across counties and assess associations with social
environmental covariates. Methods: We construct a generalized spatial factor
model to jointly model death and treatment rates for each county. For each
outcome, we specify a spatial rates parameterization for a Poisson regression
model with spatially varying factor loadings. We use a conditional
autoregressive model to account for spatial dependence within a Bayesian
framework. Results: The estimated spatial factor was highest in the southern
and southwestern counties of the state, representing a higher burden of the
opioid epidemic. We found that relatively high rates of treatment contributed
to the factor in the southern part of the state; whereas, relatively higher
rates of death contributed in the southwest. The estimated factor was also
positively associated with the proportion of residents aged 18-64 on disability
and negatively associated with the proportion of residents reporting white
race. Conclusions: We synthesized the information in the opioid associated
death and treatment counts through a spatial factor model to estimate a latent
factor representing the consensus between the two surveillance measures. We
believe this framework provides a coherent approach to describe the epidemic
while leveraging information from multiple surveillance measures.