Non-fatal opioid-related overdoses among adolescents in Massachusetts 2012-2014.
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ABSTRACT:
SUBMITTER: Chatterjee A
PROVIDER: S-EPMC6312455 | biostudies-literature | 2019 Jan
REPOSITORIES: biostudies-literature
Publications
Non-fatal opioid-related overdoses among adolescents in Massachusetts 2012-2014.
Chatterjee Avik A Larochelle Marc R MR Xuan Ziming Z Wang Na N Bernson Dana D Silverstein Michael M Hadland Scott E SE Land Thomas T Samet Jeffrey H JH Walley Alexander Y AY Bagley Sarah M SM
Drug and alcohol dependence 20181025
<h4>Background</h4>Opioid-related overdoses and deaths among adolescents in the United States continue to increase, but little is known about adolescents who experience opioid-related non-fatal overdose (NFOD). Our objective was to describe (1) the characteristics of adolescents aged 11-17 who experienced NFOD and (2) their receipt of medications for opioid use disorder (MOUD) in the 12 months following NFOD, compared with adults.<h4>Methods</h4>We created a retrospective cohort using six Massac ...[more]
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Project description:PURPOSE:The study aims to develop and validate algorithms to identify and classify opioid overdoses using claims and other coded data, and clinical text extracted from electronic health records using natural language processing (NLP). METHODS:Primary data were derived from Kaiser Permanente Northwest (2008-2014), an integrated health care system (~n > 475 000 unique individuals per year). Data included International Classification of Diseases, Ninth Revision (ICD-9) codes for nonfatal diagnoses, International Classification of Diseases, Tenth Revision (ICD-10) codes for fatal events, clinical notes, and prescription medication records. We assessed sensitivity, specificity, positive predictive value, and negative predictive value for algorithms relative to medical chart review and conducted assessments of algorithm portability in Kaiser Permanente Washington, Tennessee State Medicaid, and Optum. RESULTS:Code-based algorithm performance was excellent for opioid-related overdoses (sensitivity = 97.2%, specificity = 84.6%) and classification of heroin-involved overdoses (sensitivity = 91.8%, specificity = 99.0%). Performance was acceptable for code-based suicide/suicide attempt classifications (sensitivity = 70.7%, specificity = 90.5%); sensitivity improved with NLP (sensitivity = 78.7%, specificity = 91.0%). Performance was acceptable for the code-based substance abuse-involved classification (sensitivity = 75.3%, specificity = 79.5%); sensitivity improved with the NLP-enhanced algorithm (sensitivity = 80.5%, specificity = 76.3%). The opioid-related overdose algorithm performed well across portability assessment sites, with sensitivity greater than 96% and specificity greater than 84%. Cross-site sensitivity for heroin-involved overdose was greater than 87%, specificity greater than or equal to 99%. CONCLUSIONS:Code-based algorithms developed to detect opioid-related overdoses and classify them according to heroin involvement perform well. Algorithms for classifying suicides/attempts and abuse-related opioid overdoses perform adequately for use for research, particularly given the complexity of classifying such overdoses. The NLP-enhanced algorithms for suicides/suicide attempts and abuse-related overdoses perform significantly better than code-based algorithms and are appropriate for use in settings that have data and capacity to use NLP.
| S-EPMC6767606 | biostudies-literature
Project description:IntroductionThe opioid epidemic is a modern public health emergency. Common interventions to alleviate the opioid epidemic aim to discourage excessive prescription of opioids. However, these methods often take place over large municipal areas (state-level) and may fail to address the diversity that exists within each opioid case (individual-level). An intervention to combat the opioid epidemic that takes place at the individual-level would be preferable.MethodsThis research leverages computational tools and methods to characterize the opioid epidemic at the individual-level using the electronic health record data from a large, academic medical center. To better understand the characteristics of patients with opioid use disorder (OUD) we leveraged a self-controlled analysis to compare the healthcare encounters before and after an individual's first overdose event recorded within the data. We further contrast these patients with matched, non-OUD controls to demonstrate the unique qualities of the OUD cohort.ResultsOur research confirms that the rate of opioid overdoses in our hospital significantly increased between 2006 and 2015 (P?<?0.001), at an average rate of 9% per year. We further found that the period just prior to the first overdose is marked by conditions of pain or malignancy, which may suggest that overdose stems from pharmaceutical opioids prescribed for these conditions.ConclusionsInformatics-based methodologies, like those presented here, may play a role in better understanding those individuals who suffer from opioid dependency and overdose, and may lead to future research and interventions that could successfully prevent morbidity and mortality associated with this epidemic.
| S-EPMC7309230 | biostudies-literature
Project description:BackgroundHospital patients who use illicit opioids such as heroin may use drugs during an admission or leave the hospital in order to use drugs. There have been reports of patients found dead from drug poisoning on the hospital premises or shortly after leaving the hospital. This study examines whether hospital admission and discharge are associated with increased risk of opioid-related death.Methods and findingsWe conducted a case-crossover study of opioid-related deaths in England. Our study included 13,609 deaths between January 1, 2010 and December 31, 2019 among individuals aged 18 to 64. For each death, we sampled 5 control days from the period 730 to 28 days before death. We used data from the national Hospital Episode Statistics database to determine the time proximity of deaths and control days to hospital admissions. We estimated the association between hospital admission and opioid-related death using conditional logistic regression, with a reference category of time neither admitted to the hospital nor within 14 days of discharge. A total of 236/13,609 deaths (1.7%) occurred following drug use while admitted to the hospital. The risk during hospital admissions was similar or lower than periods neither admitted to the hospital nor recently discharged, with odds ratios 1.03 (95% CI 0.87 to 1.21; p = 0.75) for the first 14 days of an admission and 0.41 (95% CI 0.30 to 0.56; p < 0.001) for days 15 onwards. 1,088/13,609 deaths (8.0%) occurred in the 14 days after discharge. The risk of opioid-related death increased in this period, with odds ratios of 4.39 (95% CI 3.75 to 5.14; p < 0.001) on days 1 to 2 after discharge and 2.09 (95% CI 1.92 to 2.28; p < 0.001) on days 3 to 14. 11,629/13,609 deaths (85.5%) did not occur close to a hospital admission, and the remaining 656/13,609 deaths (4.8%) occurred in hospital following admission due to drug poisoning. Risk was greater for patients discharged from psychiatric admissions, those who left the hospital against medical advice, and those leaving the hospital after admissions of 7 days or more. The main limitation of the method is that it does not control for time-varying health or drug use within individuals; therefore, hospital admissions coinciding with high-risk periods may in part explain the results.ConclusionsDischarge from the hospital is associated with an acute increase in the risk of opioid-related death, and 1 in 14 opioid-related deaths in England happens in the 2 weeks after the hospital discharge. This supports interventions that prevent early discharge and improve linkage with community drug treatment and harm reduction services.
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Project description:ImportancePrescription opioids are involved in 40% of all deaths from opioid overdose in the United States and are commonly the first opioids encountered by individuals with opioid use disorder. It is unclear whether the pharmaceutical industry marketing of opioids to physicians is associated with mortality from overdoses.ObjectiveTo identify the association between direct-to-physician marketing of opioid products by pharmaceutical companies and mortality from prescription opioid overdoses across US counties.Design, setting, and participantsThis population-based, county-level analysis of industry marketing information used data from the Centers for Medicare & Medicaid Services Open Payments database linked with data from the Centers for Disease Control and Prevention on opioid prescribing and mortality from overdoses. All US counties were included, with data on overdoses from August 1, 2014, to December 31, 2016, linked to marketing data from August 1, 2013, to December 31, 2015, using a 1-year lag. Statistical analyses were conducted between February 1 and June 1, 2018.Main outcomes and measuresCounty-level mortality from prescription opioid overdoses, total cost of marketing of opioid products to physicians, number of marketing interactions, opioid prescribing rates, and sociodemographic factors.ResultsBetween August 1, 2013, and December 31, 2015, there were 434 754 payments totaling $39.7 million in nonresearch-based opioid marketing distributed to 67 507 physicians across 2208 US counties. After adjustment for county-level sociodemographic factors, mortality from opioid overdoses increased with each 1-SD increase in marketing value in dollars per capita (adjusted relative risk, 1.09; 95% CI, 1.05-1.12), number of payments to physicians per capita (adjusted relative risk, 1.18; 95% CI, 1.14-1.21, and number of physicians receiving marketing per capita (adjusted relative risk, 1.12; 95% CI, 1.08-1.16). Opioid prescribing rates also increased with marketing and partially mediated the association between marketing and mortality.Conclusions and relevanceIn this study, across US counties, marketing of opioid products to physicians was associated with increased opioid prescribing and, subsequently, with elevated mortality from overdoses. Amid a national opioid overdose crisis, reexamining the influence of the pharmaceutical industry may be warranted.
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Project description:BackgroundPrescription drug monitoring programs (PDMPs) that collect and distribute information on dispensed controlled substances have been adopted by nearly all US states. We know little about program characteristics that modify PDMP impact on prescription opioid (PO) overdose deaths.MethodsWe measured associations between adoption of any PDMP and changes in fatal PO overdoses in 2002-2016 across 3109 counties in 49 states and D.C. We then measured changes related to the adoption of "proactive PDMPs," which report outlying prescribing/dispensing patterns and provide broader access to PDMP data by law enforcement. Comparisons were made within 3 time intervals that broadly represent the evolution of PDMPs (2002-2004, 2005-2009, and 2010-2016). We modeled overdoses using Bayesian space-time models.ResultsAdoption of electronic PDMP access was associated with 9% lower rates of fatal PO overdoses after three years (rate ratio [RR] = 0.91, 95% credible interval [CI]: 0.88-0.93) with well-supported effects for methadone (RR = 0.86,95% CI: 0.82-0.90) and other synthetic opioids (RR = 0.82, 95% CI: 0.77-0.86). Compared with states with no/weak PDMPs, proactive PDMPs were associated with fewer deaths attributed to natural/semi-synthetic opioids (2002-2004: RR = 0.72 [0.66-0.78]; 2005-2009: RR = 0.93 [0.90-0.97]; 2010-2016: 0.89 [0.86-0.92]) and methadone (2002-2004: RR = 0.77 [0.69-0.85]; 2010-2016: RR = 0.90 [0.86-0.94]). Unintended effects were observed for synthetic opioids other than methadone (2005-2009: RR = 1.29 [1.21-1.38]; 2010-2016: RR = 1.22 [1.16-1.29]).ConclusionsState adoption of PDMPs was associated with fewer PO deaths overall while proactive PDMPs alone were associated with fewer deaths related to natural/semisynthetic opioids and methadone, the specific targets of these programs. See video abstract at, http://links.lww.com/EDE/B619.
| S-EPMC7027962 | biostudies-literature
Project description:The United States suffered a dramatic and well-documented increase in drug-related deaths from 2000 to 2018, primarily driven by prescription and non-prescription opioids, and concentrated in white and working-class areas. A growing body of research focuses on the causes, both medical and social, of this opioid crisis, but little work as yet on its larger ramifications. Using novel public records of accidental opioid deaths linked to behavioral political outcomes, we present causal analyses showing that opioid overdoses have significant political ramifications. Those close to opioid victims vote at lower rates than those less affected by the crisis, even compared to demographically-similar friends and family of other unexpected deaths. Moreover, among those friends and family affected by opioids, Republicans are 25% more likely to defect from the party than the statewide average Republican, while Democrats are no more likely to defect; Independents are moderately more likely to register as Democrats. These results illustrate an important research design for inferring the effects of tragic events and speak to the broad social and political consequences of what is becoming the largest public health crisis in modern United States history.
| S-EPMC7402477 | biostudies-literature
Project description:INTRODUCTION:Prescription opioids were responsible for approximately 17,000 deaths in the U.S. in 2016. One in five prescription opioid deaths also involve alcohol. Drinkers who misuse prescription opioids (i.e., use without a prescription or use only for the experience or feeling it causes) are at a heightened risk of overdose. However, little is known about the relationship between drinking patterns and prescription opioid misuse. METHODS:Data were analyzed from 160,812 individuals (aged ?12 years) who responded to questions about prescription opioid misuse and alcohol consumption in the 2012, 2013, or 2014 National Survey on Drug Use and Health (analyzed in 2017-2018). The prevalence of self-reported past-30-days prescription opioid misuse was assessed by sociodemographic characteristics, other substance use (i.e., cigarettes, marijuana), and drinking patterns. Multiple logistic regression analyses were used to calculate AORs. RESULTS:From 2012 to 2014, 1.6% (95% CI=1.5, 1.7) of all individuals aged ?12 years (estimated 4.2 million) and 3.5% (95% CI=3.3, 3.8) of binge drinkers (estimated 2.2 million) reported prescription opioid misuse. Prescription opioid misuse was more common among binge drinkers than among nondrinkers (AOR=1.7, 95% CI=1.5, 1.9). Overall, the prevalence of prescription opioid misuse increased significantly with binge drinking frequency (p-value<0.001). CONCLUSIONS:More than half of the 4.2 million people who misused prescription opioids during 2012-2014 were binge drinkers, and binge drinkers had nearly twice the odds of misusing prescription opioids, compared with nondrinkers. Widespread use of evidence-based strategies for preventing binge drinking might reduce opioid misuse and overdoses involving alcohol.
| S-EPMC6642832 | biostudies-literature
Project description:BACKGROUND:Understanding relationships between individual-level demographic, socioeconomic status (SES) and U.S. opioid fatalities can inform interventions in response to this crisis. METHODS:The Mortality Disparities in American Community Study (MDAC) links nearly 4 million 2008 American Community Survey responses to the 2008-2015 National Death Index. Univariate and multivariable models were used to estimate opioid overdose fatality hazard ratios (HR) and 95% confidence intervals (CI). RESULTS:Opioid overdose was an overrepresented cause of death among people 10 to 59 years of age. In multivariable analysis, compared to Hispanics, Whites and American Indians/Alaska Natives had elevated risk (HR = 2.52, CI:2.21-2.88) and (HR = 1.88, CI:1.35-2.62), respectively. Compared to women, men were at-risk (HR = 1.61, CI:1.50-1.72). People who were disabled were at higher risk than those who were not (HR = 2.80, CI:2.59-3.03). Risk was higher among widowed than married (HR = 2.44, CI:2.03-2.95) and unemployed than employed individuals (HR = 2.46, CI:2.17-2.79). Compared to adults with graduate degrees, those with high school only were at-risk (HR = 2.48, CI:2.00-3.06). Citizens were more likely than noncitizens to die from this cause (HR = 4.62, CI:3.48-6.14). Compared to people who owned homes with mortgages, those who rented had higher HRs (HR = 1.36, CI:1.25-1.48). Non-rural residents had higher risk than rural residents (HR = 1.46, CI:1.34, 1.59). Compared to respective referent groups, people without health insurance (HR = 1.30, CI:1.20-1.41) and people who were incarcerated were more likely to die from opioid overdoses (HR = 2.70, CI:1.91-3.81). Compared to people living in households at least five-times above the poverty line, people who lived in poverty were more likely to die from this cause (HR = 1.36, CI:1.20-1.54). Compared to people living in West North Central states, HRs were highest among those in South Atlantic (HR = 1.29, CI:1.11, 1.50) and Mountain states (HR = 1.58, CI:1.33, 1.88). DISCUSSION:Opioid fatality was associated with indicators of low SES. The findings may help to target prevention, treatment and rehabilitation efforts to vulnerable groups.
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