Predicting the Future Course of Opioid Overdose Mortality: An Example From Two US States

Abstract

Background: The rapid growth of opioid abuse and the related mortality across the United States has spurred the development of predictive models for the allocation of public health resources. These models should characterize heterogeneous growth across states using a drug epidemic framework that enables assessments of epidemic onset, rates of growth, and limited capacities for epidemic growth. Methods: We used opioid overdose mortality data for 146 North and South Carolina counties from 2001 through 2014 to compare the retrodictive and predictive performance of a logistic growth model that parameterizes onsets, growth, and carrying capacity within a traditional Bayesian Poisson space–time model. Results: In fitting the models to past data, the performance of the logistic growth model was superior to the standard Bayesian Poisson space–time model (deviance information criterion: 8,088 vs. 8,256), with reduced spatial and independent errors. Predictively, the logistic model more accurately estimated fatality rates 1, 2, and 3 years in the future (root mean squared error medians were lower for 95.7% of counties from 2012 to 2014). Capacity limits were higher in counties with greater population size, percent population age 45–64, and percent white population. Epidemic onset was associated with greater same-year and past-year incidence of overdose hospitalizations. Conclusion: Growth in annual rates of opioid fatalities was capacity limited, heterogeneous across counties, and spatially correlated, requiring spatial epidemic models for the accurate and reliable prediction of future outcomes related to opioid abuse. Indicators of risk are identifiable and can be used to predict future mortality outcomes.

Identifying counties at risk of high overdose mortality burden during the emerging fentanyl epidemic in the USA: a predictive statistical modelling study

“…Although similar models have been used to inform funding allocation, such as the CDC's drug-related HIV outbreak risk assessment model, 12,22 these models have not been validated against data and have not been designed to provide yearly predictions-studies by Sumetsky and colleagues 18 and Campo and colleagues 23 are two exceptions. Model validation is key to both ensuring that the tools used for policy guidance are providing accurate information and to improving our understanding of the epidemic processes.…”

“…We also included an offset term for the log of the population carrying capacity, similar to Sumetsky and colleagues. 18 We hypothesised that as more overdose deaths occur in a location, the population of susceptible individuals would diminish. Thus, we defined carrying capacity as 5% of the 2010 county population minus the number of overdoses in the county in the previous 3 years (or the previous available years in the data for years 2011 and 2012), setting 50 as the minimum possible carrying capacity (appendix pp 2-3).…”

“…We used the lme4 package in R for all analyses. 19,20 Further details and code for running the analyses are available in the appendix (pp [13][14][15][16][17][18][19].…”

Methodological approaches for the prediction of opioid use-related epidemics in the United States: a narrative review and cross-disciplinary call to action

“…An interesting study led by Sumetsky et al tested the performance of two statistical methods (standard logÀlinear vs logÀlogistic Bayesian hierarchical Poisson conditionally autoregressive (CAR) spatial models) in predicting overdose deaths by county in two states from 2001-2014. 67 They generated a countylevel "carrying capacity" (ie, the number of people at risk of overdose in given county) based on a set of factors, a well-founded decision given that a majority of individuals are not at risk of overdosing. However, one factor informing this carrying capacity was the proportion of county population that was white, replicating the issue highlighted by Robinson above and potentially limiting its validity to the prescription opioid wave of the opioid crisis.…”

“…This is reflected in the historical timeframes of the predictive studies presented here: Sumetsky, Cooper and Campo (each published in 2020) only predicted overdose deaths up to 2017. 67,69,72 Importantly, drug market data collected by law enforcement agencies is typically unavailable or restricted to public health research teams when it provides valuable information to monitor both changes in drug availability and properties. 68,89 Alternative strategies to monitor drug markets, such as wastewater testing have been investigated and hold promise.…”