Dataset Information

Touchpoints - Opportunities to predict and prevent opioid overdose: A cohort study.

ABSTRACT: BACKGROUND:Medical care, public health, and criminal justice systems encounters could serve as touchpoints to identify and intervene with individuals at high-risk of opioid overdose death. The relative risk of opioid overdose death and proportion of deaths that could be averted at such touchpoints are unknown. METHODS:We used 8 individually linked data sets from Massachusetts government agencies to perform a retrospective cohort study of Massachusetts residents ages 11 and older. For each month in 2014, we identified past 12-month exposure to 4 opioid prescription touchpoints (high dosage, benzodiazepine co-prescribing, multiple prescribers, or multiple pharmacies) and 4 critical encounter touchpoints (opioid detoxification, nonfatal opioid overdose, injection-related infection, and release from incarceration). The outcome was opioid overdose death. We calculated Standardized Mortality Ratios (SMRs) and Population Attributable Fractions (PAFs) associated with touchpoint exposure. RESULTS:The cohort consisted of 6,717,390 person-years of follow-up with 1315 opioid overdose deaths. We identified past 12-month exposure to any touchpoint in 2.7% of person-months and for 51.8% of opioid overdose deaths. Opioid overdose SMRs were 12.6 (95% CI: 11.1, 14.1) for opioid prescription and 68.4 (95% CI: 62.4, 74.5) for critical encounter touchpoints. Fatal opioid overdose PAFs were 0.19 (95% CI: 0.17, 0.21) for opioid prescription and 0.37 (95% CI: 0.34, 0.39) for critical encounter touchpoints. CONCLUSIONS:Using public health data, we found eight candidate touchpoints were associated with increased risk of fatal opioid overdose, and collectively identified more than half of opioid overdose decedents. These touchpoints are potential targets for development of overdose prevention interventions.

SUBMITTER: Larochelle MR

PROVIDER: S-EPMC7020606 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Touchpoints - Opportunities to predict and prevent opioid overdose: A cohort study.

Larochelle Marc R MR Bernstein Ryan R Bernson Dana D Land Thomas T Stopka Thomas J TJ Rose Adam J AJ Bharel Monica M Liebschutz Jane M JM Walley Alexander Y AY

Drug and alcohol dependence 20190903

<h4>Background</h4>Medical care, public health, and criminal justice systems encounters could serve as touchpoints to identify and intervene with individuals at high-risk of opioid overdose death. The relative risk of opioid overdose death and proportion of deaths that could be averted at such touchpoints are unknown.<h4>Methods</h4>We used 8 individually linked data sets from Massachusetts government agencies to perform a retrospective cohort study of Massachusetts residents ages 11 and older. ...[more]

PMID: 31521956

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Project description:Background:Opioid overdose survivors have an increased risk for death. Whether use of medications for opioid use disorder (MOUD) after overdose is associated with mortality is not known. Objective:To identify MOUD use after opioid overdose and its association with all-cause and opioid-related mortality. Design:Retrospective cohort study. Setting:7 individually linked data sets from Massachusetts government agencies. Participants:17 568 Massachusetts adults without cancer who survived an opioid overdose between 2012 and 2014. Measurements:Three types of MOUD were examined: methadone maintenance treatment (MMT), buprenorphine, and naltrexone. Exposure to MOUD was identified at monthly intervals, and persons were considered exposed through the month after last receipt. A multivariable Cox proportional hazards model was used to examine MOUD as a monthly time-varying exposure variable to predict time to all-cause and opioid-related mortality. Results:In the 12 months after a nonfatal overdose, 2040 persons (11%) enrolled in MMT for a median of 5 months (interquartile range, 2 to 9 months), 3022 persons (17%) received buprenorphine for a median of 4 months (interquartile range, 2 to 8 months), and 1099 persons (6%) received naltrexone for a median of 1 month (interquartile range, 1 to 2 months). Among the entire cohort, all-cause mortality was 4.7 deaths (95% CI, 4.4 to 5.0 deaths) per 100 person-years and opioid-related mortality was 2.1 deaths (CI, 1.9 to 2.4 deaths) per 100 person-years. Compared with no MOUD, MMT was associated with decreased all-cause mortality (adjusted hazard ratio [AHR], 0.47 [CI, 0.32 to 0.71]) and opioid-related mortality (AHR, 0.41 [CI, 0.24 to 0.70]). Buprenorphine was associated with decreased all-cause mortality (AHR, 0.63 [CI, 0.46 to 0.87]) and opioid-related mortality (AHR, 0.62 [CI, 0.41 to 0.92]). No associations between naltrexone and all-cause mortality (AHR, 1.44 [CI, 0.84 to 2.46]) or opioid-related mortality (AHR, 1.42 [CI, 0.73 to 2.79]) were identified. Limitation:Few events among naltrexone recipients preclude confident conclusions. Conclusion:A minority of opioid overdose survivors received MOUD. Buprenorphine and MMT were associated with reduced all-cause and opioid-related mortality. Primary Funding Source:National Center for Advancing Translational Sciences of the National Institutes of Health.

Project description:OBJECTIVE:To compare the risk of mortality among people with opioid use disorder on and off opioid agonist treatment (OAT) in a setting with a high prevalence of illicitly manufactured fentanyl and other potent synthetic opioids in the illicit drug supply. DESIGN:Population based retrospective cohort study. SETTING:Individual level linkage of five health administrative datasets capturing drug dispensations, hospital admissions, physician billing records, ambulatory care reports, and deaths in British Columbia, Canada. PARTICIPANTS:55?347 people with opioid use disorder who received OAT between 1 January 1996 and 30 September 2018. MAIN OUTCOME MEASURES:All cause and cause specific crude mortality rates (per 1000 person years) to determine absolute risk of mortality and all cause age and sex standardised mortality ratios to determine relative risk of mortality compared with the general population. Mortality risk was calculated according to treatment status (on OAT, off OAT), time since starting and stopping treatment (1, 2, 3-4, 5-12, >12 weeks), and medication type (methadone, buprenorphine/naloxone). Adjusted risk ratios compared the relative risk of mortality on and off OAT over time as fentanyl became more prevalent in the illicit drug supply. RESULTS:7030 (12.7%) of 55?347 OAT recipients died during follow-up. The all cause standardised mortality ratio was substantially lower on OAT (4.6, 95% confidence interval 4.4 to 4.8) than off OAT (9.7, 9.5 to 10.0). In a period of increasing prevalence of fentanyl, the relative risk of mortality off OAT was 2.1 (95% confidence interval 1.8 to 2.4) times higher than on OAT before the introduction of fentanyl, increasing to 3.4 (2.8 to 4.3) at the end of the study period (65% increase in relative risk). CONCLUSIONS:Retention on OAT is associated with substantial reductions in the risk of mortality for people with opioid use disorder. The protective effect of OAT on mortality increased as fentanyl and other synthetic opioids became common in the illicit drug supply, whereas the risk of mortality remained high off OAT. As fentanyl becomes more widespread globally, these findings highlight the importance of interventions that improve retention on opioid agonist treatment and prevent recipients from stopping treatment.

Project description:BackgroundRapid naloxone administration is crucial in reversing an opioid overdose. We investigated whether equipping community members, including people who use opioids (PWUO), with a smartphone application enabling them to signal and respond to suspected overdose would support naloxone administration in advance of Emrgency Medical Services (EMS).MethodsThis observational cohort study of opioid overdose intervention used a dedicated smartphone app, UnityPhilly, activated by volunteers witnessing an overdose to signal other nearby volunteers in Philadelphia (March 2019 - February 2020). Alerted volunteers chose to respond, or declined to respond, or ignored/missed the alert. Witnessing volunteer was connected to 9-1-1 through a semi-automated telephone call. The primary outcome was layperson-initiated overdose reversal before EMS arrival, and a secondary outcome was hospital transfer. This study is registered with ClinicalTrials.gov, NCT03305497.Findings112 volunteers, including 57 PWUO and 55 community members, signaled 291 suspected opioid overdose alerts. 89 (30⸱6%) were false alarms. For 202 true alerts, the rate of layperson initiated naloxone use was 36⸱6% (74/202 cases). Most naloxone-use cases occurred in the street (58⸱11% (43/74)) and some in home settings (22⸱98% (17/74)). The first naloxone dose was provided by a nearby volunteer responding to the alert in 29⸱73% (22/74) of cases and by the signaling volunteer in 70⸱27% (52/74) of cases. Successful reversal was reported in 95⸱9% (71/74) of cases. Layperson intervention preceded EMS by 5 min or more in 59⸱5% of cases. Recovery without hospital transport was reported in 52⸱7% (39/74) of cases.InterpretationOur findings support the benefits of equipping community members, potentially witnessing suspected opioid overdose, with naloxone and an emergency response community smartphone app, alerting EMS and nearby laypersons to provide additional naloxone.FundingFunding provided by NIH through NIDA, grant number: 5R34DA044758.

Project description:ObjectivesTo study the association between accidental opioid overdose and neurological, respiratory, cardiac and other serious adverse events and whether risk of these adverse events was elevated during hospital readmissions compared with initial admissions.DesignRetrospective cohort study.SettingPopulation-based study using linked administrative data in British Columbia, Canada.ParticipantsThe primary analysis included 2433 patients with 2554 admissions for accidental opioid overdose between 2006 and 2015, including 121 readmissions within 1 year of initial admission. The secondary analysis included 538 patients discharged following a total of 552 accidental opioid overdose hospitalizations and 11 040 matched controls from a cohort of patients with ≥180 days of prescription opioid use.Outcome measuresThe primary outcome was encephalopathy; secondary outcomes were adult respiratory distress syndrome, respiratory failure, pulmonary haemorrhage, aspiration pneumonia, cardiac arrest, ventricular arrhythmia, heart failure, rhabdomyolysis, paraplegia or tetraplegia, acute renal failure, death, a composite outcome of encephalopathy or any secondary outcome and total serious adverse events (all-cause hospitalisation or death). We analysed these outcomes using generalised linear models with a logistic link function.Results3% of accidental opioid overdose admissions included encephalopathy and 25% included one or more adverse events (composite outcome). We found no evidence of increased risk of encephalopathy (OR 0.57; 95% CI 0.13 to 2.49) or other outcomes during readmissions versus initial admissions. In the secondary analysis, <5 patients in each cohort experienced encephalopathy. Risk of the composite outcome (OR 2.15; 95% CI 1.48 to 3.12) and all-cause mortality (OR 2.13; 95% CI 1.18 to 3.86) were higher for patients in the year following overdose relative to controls.ConclusionsWe found no evidence that risk of encephalopathy or other adverse events was higher in readmissions compared with initial admissions for accidental opioid overdose. Risk of serious morbidity and mortality may be elevated in the year following an accidental opioid overdose.

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:BackgroundLittle is known about whether machine-learning algorithms developed to predict opioid overdose using earlier years and from a single state will perform as well when applied to other populations. We aimed to develop a machine-learning algorithm to predict 3-month risk of opioid overdose using Pennsylvania Medicaid data and externally validated it in two data sources (ie, later years of Pennsylvania Medicaid data and data from a different state).MethodsThis prognostic modelling study developed and validated a machine-learning algorithm to predict overdose in Medicaid beneficiaries with one or more opioid prescription in Pennsylvania and Arizona, USA. To predict risk of hospital or emergency department visits for overdose in the subsequent 3 months, we measured 284 potential predictors from pharmaceutical and health-care encounter claims data in 3-month periods, starting 3 months before the first opioid prescription and continuing until loss to follow-up or study end. We developed and internally validated a gradient-boosting machine algorithm to predict overdose using 2013-16 Pennsylvania Medicaid data (n=639 693). We externally validated the model using (1) 2017-18 Pennsylvania Medicaid data (n=318 585) and (2) 2015-17 Arizona Medicaid data (n=391 959). We reported several prediction performance metrics (eg, C-statistic, positive predictive value). Beneficiaries were stratified into risk-score subgroups to support clinical use.FindingsA total of 8641 (1·35%) 2013-16 Pennsylvania Medicaid beneficiaries, 2705 (0·85%) 2017-18 Pennsylvania Medicaid beneficiaries, and 2410 (0·61%) 2015-17 Arizona beneficiaries had one or more overdose during the study period. C-statistics for the algorithm predicting 3-month overdoses developed from the 2013-16 Pennsylvania training dataset and validated on the 2013-16 Pennsylvania internal validation dataset, 2017-18 Pennsylvania external validation dataset, and 2015-17 Arizona external validation dataset were 0·841 (95% CI 0·835-0·847), 0·828 (0·822-0·834), and 0·817 (0·807-0·826), respectively. In external validation datasets, 71 361 (22·4%) of 318 585 2017-18 Pennsylvania beneficiaries were in high-risk subgroups (positive predictive value of 0·38-4·08%; capturing 73% of overdoses in the subsequent 3 months) and 40 041 (10%) of 391 959 2015-17 Arizona beneficiaries were in high-risk subgroups (positive predictive value of 0·19-1·97%; capturing 55% of overdoses). Lower risk subgroups in both validation datasets had few individuals (≤0·2%) with an overdose.InterpretationA machine-learning algorithm predicting opioid overdose derived from Pennsylvania Medicaid data performed well in external validation with more recent Pennsylvania data and with Arizona Medicaid data. The algorithm might be valuable for overdose risk prediction and stratification in Medicaid beneficiaries.FundingNational Institute of Health, National Institute on Drug Abuse, National Institute on Aging.

Dataset Information

Touchpoints - Opportunities to predict and prevent opioid overdose: A cohort study.

Publications

Touchpoints - Opportunities to predict and prevent opioid overdose: A cohort study.

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