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@article{Marks:2021:10.1016/S2468-2667(21)00080-3,
author = {Marks, C and Abramovitz, D and Donnelly, C and Carrasco-Escobar, G and Carrasco-Hernandez, R and Ciccarone, D and González-Izquierdo, A and Martin, NK and Strathdee, SA and Smith, DM and Bórquez, A},
doi = {10.1016/S2468-2667(21)00080-3},
journal = {The Lancet Public Health},
pages = {e720--e728},
title = {Identifying counties at risk of high overdose mortality burden throughout the emerging fentanyl epidemic in the united states: a predictive statistical modeling study},
url = {http://dx.doi.org/10.1016/S2468-2667(21)00080-3},
volume = {6},
year = {2021}
}

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TY  - JOUR
AB  - 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
AU  - Marks,C
AU  - Abramovitz,D
AU  - Donnelly,C
AU  - Carrasco-Escobar,G
AU  - Carrasco-Hernandez,R
AU  - Ciccarone,D
AU  - González-Izquierdo,A
AU  - Martin,NK
AU  - Strathdee,SA
AU  - Smith,DM
AU  - Bórquez,A
DO  - 10.1016/S2468-2667(21)00080-3
EP  - 728
PY  - 2021///
SN  - 2468-2667
SP  - 720
TI  - Identifying counties at risk of high overdose mortality burden throughout the emerging fentanyl epidemic in the united states: a predictive statistical modeling study
T2  - The Lancet Public Health
UR  - http://dx.doi.org/10.1016/S2468-2667(21)00080-3
UR  - http://hdl.handle.net/10044/1/89389
VL  - 6
ER  -

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