Background. The emergence of fentanyl around 2013 represented a new, deadly stage in the US opioid epidemic. We developed a statistical regression approach to identify counties at the highest risk of high overdose mortality in the next year by predicting annual county-level overdose death rates across the contiguous US and validated it against observed overdose mortality data from 2013 to 2018.
Methods. We fit mixed effects negative binomial regression models to predict next year’s county-level overdose death rates for the years 2013 to 2018. We used publicly available county-level data related to healthcare access, drug markets, socio-demographics, and the geographic spread of opioid overdose as model predictors. The crude number of county-level overdose deaths was extracted from restricted Centers for Disease Control and Prevention mortality records. To predict county-level overdose rates for the year 201X: 1) a model was trained on county-level predictor data for the years 2010-201(X-2) paired with county-level overdose deaths for the year 2011-201(X-1); 2) county-level predictor data for the year 201(X-1) was then fed into the model to predict the 201(X) county-level crude number of overdose deaths; and 3) the latter was converted to a population-adjusted rate. For comparison, we generated a benchmark set of predictions by applying the observed slope of change in overdose death rates in the previous year to 201(X-1) rates. To assess the predictive performance of the model, we compared predicted values (of both the model and benchmark) to observed values by 1) calculating the mean average error, root mean squared error, and Spearman’s correlation coefficient and 2) assessing the proportion of counties in the top decile (10%) of overdose death rates that were correctly predicted as such. Finally, in a post-hoc analysis, we sought to identify variables with greatest predictive utility.
Findings. Across the entire US and through time, our modeling approach outperformed the benchmark strategy across all metrics. The average county-level overdose death rate rose from 11.8/100,000 to 15.4 in 2017 before falling to 14.8 in 2018. Our modeling approach similarly identified an increasing trend, predicting an average 11.8 deaths/100,000 in 2013 up to 15.1 in 2017 and still increasing to 16.4 in 2018. The benchmark model over-predicted average death rates each year, ranging from 13.0/100,000 in 2013 to 18.3 in 2018. Our modeling approach successfully ranked counties by overdose death rate identifying between 41.6% and 56.8% of counties in the top decile of overdose mortality (compared to 28.7% and 42.6% using the benchmark) each year and identified 194 of the 808 with emergent overdose outbreaks (i.e., newly entered the top decile) across the study period, versus 31 using the benchmark. In the post-hoc analysis, we identified geospatial proximity of overdose in nearby counties, opioid prescription rate, presence of an urgent care facility, and several economic indicators as the variables with the greatest predictive utility.
Interpretation. Our study demonstrates that a regression approach can effectively predict county-level overdose death rates and serve as a risk assessment tool to identify future high mortality counties throughout an emerging drug use epidemic.