A MULTIVARIATE SPATIOTEMPORAL CHANGE-POINT MODEL OF OPIOID OVERDOSE DEATHS IN OHIO
Ontology highlight
ABSTRACT:
SUBMITTER: Hepler S
PROVIDER: S-EPMC8553088 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description: Not available
| S-EPMC6902770 | biostudies-literature
Project description:PurposeOpioid misuse is a national epidemic, and Ohio is one of the states most impacted by this crisis. Ohio collects county-level counts of opioid-associated deaths and treatment admissions. We jointly model these two outcomes and assess the association of each rate with social and structural factors.MethodsWe use a joint spatial rates model of death and treatment counts using a generalized common spatial factor model. In addition to covariate effects, we estimate a spatial factor for each county that characterizes structural factors not accounted for by other covariates in the model that are associated with both outcomes.ResultsWe observed an association of health professional shortage area with death rates and the rate of people 18-64 on disability with treatment rates. The proportion of single female households was associated with both outcomes. We estimated the presence of unmeasured risk factors in the southwestern part of the state and unmeasured protective factors in the eastern region.ConclusionsWe described associations of social and structural covariates with the death and treatment rates. We also characterized counties with latent risk that can provide a launching point for future investigations to determine potential sources of that risk.
| S-EPMC6502680 | biostudies-literature
Project description:Pain management clinic (PMC) laws were enacted by 12 states to promote appropriate opioid prescribing, but their impact is inadequately understood. We analyzed county-level opioid overdose deaths (National Vital Statistics System) and patients filling long-duration (≥30 day) or high-dose (≥90 morphine milligram equivalents per day) opioid prescriptions (IQVIA, Inc.) in the United States in 2010-2018. We fitted Besag-York-Mollié spatiotemporal models to estimate annual relative rates (RRs) of overdose and prevalence ratios (PRs) of high-risk prescribing associated with any PMC law and 3 provisions: payment restrictions, site inspections, and criminal penalties. Laws with criminal penalties were significantly associated with reduced PRs of long-duration and high-dose opioid prescriptions (adjusted PR = 0.82, 95% credible interval (CrI): 0.82, 0.82, and adjusted PR = 0.73, 95% CI: 0.73, 0.74 respectively) and reduced RRs of total and natural/semisynthetic opioid overdoses (adjusted RR = 0.86, 95% CrI: 0.80, 0.92, and adjusted RR = 0.84, and 95% CrI: 0.77, 0.92, respectively). Conversely, PMC laws were associated with increased relative rates of synthetic opioid and heroin overdose deaths, especially criminal penalties (adjusted RR = 1.83, 95% CrI: 1.59, 2.11, and adjusted RR = 2.59, 95% CrI: 2.22, 3.02, respectively). Findings suggest that laws with criminal penalties were associated with intended reductions in high-risk opioid prescribing and some opioid overdoses but raise concerns regarding unintended consequences on heroin/synthetic overdoses.
| S-EPMC8796812 | biostudies-literature
Project description:Opioid misuse is a significant public health issue and a national epidemic with a high prevalence of associated morbidity and mortality. The epidemic is particularly severe in Ohio which has some of the highest overdose rates in the country. It is important to understand spatial and temporal trends of the opioid epidemic to learn more about areas that are most affected and to inform potential community interventions and resource allocation. We propose a multivariate spatio-temporal model to leverage existing surveillance measures, opioid-associated deaths and treatment admissions, to learn about the underlying epidemic for counties in Ohio. We do this using a temporally varying spatial factor that synthesizes information from both counts to estimate common underlying risk which we interpret as the burden of the epidemic. We demonstrate the use of this model with county-level data from 2007-2018 in Ohio. Through our model estimates, we identify counties with above and below average burden and examine how those regions have shifted over time given overall statewide trends. Specifically, we highlight the sustained above average burden of the opioid epidemic on southern Ohio throughout the 12 years examined.
| S-EPMC8300591 | biostudies-literature
Project description:IntroductionPrescription Drug Monitoring Program data can provide insights into a patient's likelihood of an opioid overdose, yet clinicians and public health officials lack indicators to identify individuals at highest risk accurately. A predictive model was developed and validated using Prescription Drug Monitoring Program prescription histories to identify those at risk for fatal overdose because of any opioid or illicit opioids.MethodsFrom December 2018 to July 2019, a retrospective cohort analysis was performed on Maryland residents aged 18-80 years with a filled opioid prescription (n=565,175) from January to June 2016. Fatal opioid overdoses were identified from the Office of the Chief Medical Examiner and were linked at the person-level with Prescription Drug Monitoring Program data. Split-half technique was used to develop and validate a multivariate logistic regression with a 6-month lookback period and assessed model calibration and discrimination.ResultsPredictors of any opioid-related fatal overdose included male sex, age 65-80 years, Medicaid, Medicare, 1 or more long-acting opioid fills, 1 or more buprenorphine fills, 2 to 3 and 4 or more short-acting schedule II opioid fills, opioid days' supply ≥91 days, average morphine milligram equivalent daily dose, 2 or more benzodiazepine fills, and 1 or more muscle relaxant fills. Model discrimination for the validation cohort was good (area under the curve: any, 0.81; illicit, 0.77).ConclusionsA model for predicting fatal opioid overdoses was developed using Prescription Drug Monitoring Program data. Given the recent national epidemic of deaths involving heroin and fentanyl, it is noteworthy that the model performed equally well in identifying those at risk for overdose deaths from both illicit and prescription opioids.
| S-EPMC7996003 | biostudies-literature
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.
| S-EPMC6685426 | biostudies-literature
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.
| S-EPMC7457770 | biostudies-literature
Project description:Background:Opioid-overdose deaths and opioid prescriptions have increased substantially within the past decade, leading to examinations of urban-rural differences for these opioid-related outcomes, and whether annual trends differ by urban-rural status. Most investigations have examined differences using national data, whereas few studies have identified patterns in hard-hit regions. Therefore, we examined urban-rural differences for opioid-related outcomes in Michigan, a state with overdose death and prescribing rates above the national average. Methods:This study used county-level public data on opioid prescribing and opioid-overdose death rates in Michigan. Bivariate and joinpoint regression analyses tested for annual differences and annual changes in opioid-related outcomes across 2013-2017. Rural Urban Continuum Codes classified urban-rural county status. Results:Bivariate analyses demonstrated that urban counties had consistently higher opioid-overdose death rates than rural, whereas rural counties had consistently higher opioid prescribing rates than urban. Joinpoint regression (2013-2017) revealed opioid-overdose death rates increased in urban (Annual Percent Change = 25.0%, p = .001) and rural counties (Annual Percent Change = 21.7%, p = .002), though no changes for opioid prescribing rates were observed among urban or rural counties. Conclusions:Our study highlights nuanced urban-rural patterns in Michigan, a hard-hit state, compared to trends in national data. Both urban and rural counties experienced rising rates of opioid-overdose deaths, and rural counties experienced higher opioid prescribing rates than urban. Though urban counties experienced higher opioid-overdose death rates than rural, the rise in both county types was similar. Future research directions, implications for public health, and healthcare policy recommendations are discussed.
| S-EPMC7244930 | biostudies-literature
Project description:Opioid-related deaths have severely increased since 2000 in the United States. This crisis has been declared a public health emergency, and among the most affected states is Ohio. We used statewide vital statistic data from the Ohio Department of Health (ODH) and demographics data from the U.S. Census Bureau to analyze opioid-related mortality from 2010 to 2016. We focused on the characterization of the demographics from the population of opioid-related fatalities, spatiotemporal pattern analysis using Moran's statistics at the census-tract level, and comorbidity analysis using frequent itemset mining and association rule mining. We found higher rates of opioid-related deaths in white males aged 25-54 compared to the rest of Ohioans. Deaths tended to increasingly cluster around Cleveland, Columbus and Cincinnati and away from rural regions as time progressed. We also found relatively high co-occurrence of cardiovascular disease, anxiety or drug abuse history, with opioid-related mortality. Our results demonstrate that state-wide spatiotemporal and comorbidity analysis of the opioid epidemic could provide novel insights into how the demographic characteristics, spatiotemporal factors, and/or health conditions may be associated with opioid-related deaths in the state of Ohio.
| S-EPMC7907120 | biostudies-literature
Project description:Drug overdose is now the leading cause of unintentional death nationwide, driven by increased prescription opioid overdoses. To better understand urban opioid overdose deaths, this paper examines geographic, demographic, and clinical differences between heroin-related decedents and prescription opioid decedents in San Francisco from 2010 to 2012. During this time period, 331 individuals died from accidental overdose caused by opioids (310 involving prescription opioids and 31 involving heroin). Deaths most commonly involved methadone (45.9%), morphine (26.9%), and oxycodone (21.8%). Most deaths also involved other substances (74.9%), most commonly cocaine (35.3%), benzodiazepines (27.5%), antidepressants (22.7%), and alcohol (19.6%). Deaths were concentrated in a small, high-poverty, central area of San Francisco and disproportionately affected African-American individuals. Decedents in high-poverty areas were significantly more likely to die from methadone and cocaine, whereas individuals from more affluent areas were more likely die from oxycodone and benzodiazepines. Heroin decedents were more likely to be within a younger age demographic, die in public spaces, and have illicit substances rather than other prescription opioids. Overall, heroin overdose death, previously common in San Francisco, is now rare. Prescription opioid overdose has emerged as a significant concern, particularly among individuals in high-poverty areas. Deaths in poor and affluent regions involve different causative opioids and co-occurring substances.
| S-EPMC4524842 | biostudies-other