Dataset Information

A MULTIVARIATE SPATIOTEMPORAL CHANGE-POINT MODEL OF OPIOID OVERDOSE DEATHS IN OHIO.

ABSTRACT: Ohio is one of the states most impacted by the opioid epidemic and experienced the second highest age-adjusted fatal drug overdose rate in 2017. Initially it was believed prescription opioids were driving the opioid crisis in Ohio. However, as the epidemic evolved, opioid overdose deaths due to fentanyl have drastically increased. In this work we develop a Bayesian multivariate spatiotemporal model for Ohio county overdose death rates from 2007 to 2018 due to different types of opioids. The log-odds are assumed to follow a spatially varying change point regression model. By assuming the regression coefficients are a multivariate conditional autoregressive process, we capture spatial dependence within each drug type and also dependence across drug types. The proposed model allows us to not only study spatiotemporal trends in overdose death rates but also to detect county-level shifts in these trends over time for various types of opioids.

SUBMITTER: Hepler SA

PROVIDER: S-EPMC8553088 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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A MULTIVARIATE SPATIOTEMPORAL CHANGE-POINT MODEL OF OPIOID OVERDOSE DEATHS IN OHIO.

Hepler Staci A SA Waller Lance A LA Kline David M DM

The annals of applied statistics 20210923 3

Ohio is one of the states most impacted by the opioid epidemic and experienced the second highest age-adjusted fatal drug overdose rate in 2017. Initially it was believed prescription opioids were driving the opioid crisis in Ohio. However, as the epidemic evolved, opioid overdose deaths due to fentanyl have drastically increased. In this work we develop a Bayesian multivariate spatiotemporal model for Ohio county overdose death rates from 2007 to 2018 due to different types of opioids. The log- ...[more]

PMID: 34721750

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Project description:IntroductionOpioid overdose deaths quintupled in Massachusetts between 2000 and 2016. Potentially inappropriate opioid prescribing practices (PIP) are associated with increases in overdoses. The purpose of this study was to conduct spatial epidemiological analyses of novel comprehensively linked data to identify overdose and PIP hotspots.MethodsSixteen administrative datasets, including prescription monitoring, medical claims, vital statistics, and medical examiner data, covering >98% of Massachusetts residents between 2011-2015, were linked in 2017 to better investigate the opioid epidemic. PIP was defined by six measures: ≥100 morphine milligram equivalents (MMEs), co-prescription of benzodiazepines and opioids, cash purchases of opioid prescriptions, opioid prescriptions without a recorded pain diagnosis, and opioid prescriptions through multiple prescribers or pharmacies. Using spatial autocorrelation and cluster analyses, overdose and PIP hotspots were identified among 538 ZIP codes.ResultsMore than half of the adult population (n = 3,143,817, ages 18 and older) were prescribed opioids. Nearly all ZIP codes showed increasing rates of overdose over time. Overdose clusters were identified in Worcester, Northampton, Lee/Tyringham, Wareham/Bourne, Lynn, and Revere/Chelsea (Getis-Ord Gi*; p < 0.05). Large PIP clusters for ≥100 MMEs and prescription without pain diagnosis were identified in Western Massachusetts; and smaller clusters for multiple prescribers in Nantucket, Berkshire, and Hampden Counties (p < 0.05). Co-prescriptions and cash payment clusters were localized and nearly identical (p < 0.05). Overlap in PIP and overdose clusters was identified in Cape Cod and Berkshire County. However, we also found contradictory patterns in overdose and PIP hotspots.ConclusionsOverdose and PIP hotspots were identified, as well as regions where the two overlapped, and where they diverged. Results indicate that PIP clustering alone does not explain overdose clustering patterns. Our findings can inform public health policy decisions at the local level, which include a focus on PIP and misuse of heroin and fentanyl that aim to curb opioid overdoses.

Project description:The US opioid overdose epidemic has been a major public health concern in recent decades. There has been increasing recognition that its etiology is rooted in part in the social contexts that mediate substance use and access; however, reliable statistical measures of social influence are lacking in the literature. We use Facebook's social connectedness index (SCI) as a proxy for real-life social networks across diverse spatial regions that help quantify social connectivity across different spatial units. This is a measure of the relative probability of connections between localities that offers a unique lens to understand the effects of social networks on health outcomes. We use SCI to develop a variable, called "deaths in social proximity", to measure the influence of social networks on opioid overdose deaths (OODs) in US counties. Our results show a statistically significant effect size for deaths in social proximity on OODs in counties in the United States, controlling for spatial proximity, as well as demographic and clinical covariates. The effect size of standardized deaths in social proximity in our cluster-robust linear regression model indicates that a one-standard-deviation increase, equal to 11.70 more deaths per 100,000 population in the social proximity of ego counties in the contiguous United States, is associated with thirteen more deaths per 100, 000 population in ego counties. To further validate our findings, we performed a series of robustness checks using a network autocorrelation model to account for social network effects, a spatial autocorrelation model to capture spatial dependencies, and a two-way fixed-effect model to control for unobserved spatial and time-invariant characteristics. These checks consistently provide statistically robust evidence of positive social influence on OODs in US counties. Our analysis provides a pathway for public health interventions informed by social network structures. The statistical robustness of our primary variable of interest, deaths in social proximity, supports the hypothesis of a social network effect on OODs. Using agent-based modeling (ABM) to simulate social networks can offer an effective method to design interventions that incorporate the dynamics of social networks for maximum impact.

Project description:BackgroundAs several regulatory and environmental changes have occurred in North America, trends in overdose deaths were examined in the United States (US), Ontario and British Columbia (BC), including changes in consumption levels of prescription opioids (PO) and overdose deaths, changes in correlations between consumption levels of PO and overdose deaths and modeled differences between observed and predicted overdose deaths if no changes had occurred.MethodsConsumption levels of PO included defined daily doses for statistical purposes per million inhabitants per day for the US and Canada (2001-2015). Overdose deaths included opioid overdose deaths for the US (2001-2017) and Ontario (2003-2017) and illicit drug overdose deaths for BC (2001-2017). The analytic techniques included structural break point analyses, Pearson product-moment correlations and multivariate Gaussian state space modeling.ResultsConsumption levels of PO changed in the US in 2010 and in Canada in 2012. Overdose deaths changed in the US in 2014 and in Ontario and BC in 2015. Prior to the observed changes in consumption levels of PO, there were positive correlations between consumption levels of PO and overdose deaths in the US (r = 0.99, p < 0.001) and Ontario (r = 0.92, p = 0.003). After the observed changes in consumption levels of PO, there was a negative correlation between consumption levels of PO and overdose deaths in the US (r = - 0.99, p = 0.002). Observed overdose deaths exceeded predicted overdose deaths by 5.7 (95% Confidence Interval [CI]: 4.8-6.6), 3.5 (95% CI: 3.2-3.8) and 21.8 (95% CI: 18.6-24.9) deaths per 100,000 people in the US, Ontario and BC, respectively in 2017. These excess deaths corresponded to 37.7% (95% CI: 31.9-43.6), 39.2% (95% CI: 36.3-42.1) and 72.2% (95% CI: 61.8-82.6) of observed overdose deaths in the US, Ontario and BC, respectively in 2017.ConclusionsThe opioid crisis has evolved in North America, as a sizeable proportion of overdose deaths are now attributable to the several regulatory and environmental changes. These findings necessitate substance use policies to be conceptualized more broadly as well as the continued expansion of harm reduction services and types of pharmacotherapy interventions.

Dataset Information

A MULTIVARIATE SPATIOTEMPORAL CHANGE-POINT MODEL OF OPIOID OVERDOSE DEATHS IN OHIO.

Publications

A MULTIVARIATE SPATIOTEMPORAL CHANGE-POINT MODEL OF OPIOID OVERDOSE DEATHS IN OHIO.

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