Dataset Information

Effect of Age on Opioid Prescribing, Overdose, and Mortality in Massachusetts, 2011 to 2015.

ABSTRACT: OBJECTIVES:To examine the effect of age on the likelihood of PIP of opioids and the effect of PIP on adverse outcomes. DESIGN:Retrospective cohort study. SETTING:Data from multiple state agencies in Massachusetts from 2011 to 2015. PARTICIPANTS:Adult Massachusetts residents (N=3,078,163) who received at least one prescription opioid during the study period; approximately half (1,589,365) aged 50 and older. MEASUREMENTS:We measured exposure to 5 types of PIP: high-dose opioids, coprescription with benzodiazepines, multiple opioid prescribers, multiple opioid pharmacies, and continuous opioid therapy without a pain diagnosis. We examined 3 adverse outcomes: nonfatal opioid overdose, fatal opioid overdose, and all-cause mortality. RESULTS:The rate of any PIP increased with age, from 2% of individuals age 18 to 29 to 14% of those aged 50 and older. Older adults also had higher rates of exposure to 2 or more different types of PIP (40-49, 2.5%; 50-69, 5%; ?70, 4%). Of covariates assessed, older age was the greatest predictor of PIP. In analyses stratified according to age, any PIP and specific types of PIP were associated with nonfatal overdose, fatal overdose, and all-cause mortality in younger and older adults. CONCLUSION:Older adults are more likely to be exposed to PIP, which increases their risk of adverse events. Strategies to reduce exposure to PIP and to improve outcomes in those already exposed will be instrumental to addressing the opioid crisis in older adults. J Am Geriatr Soc 67:128-132, 2019.

SUBMITTER: Rose AJ

PROVIDER: S-EPMC6448572 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Effect of Age on Opioid Prescribing, Overdose, and Mortality in Massachusetts, 2011 to 2015.

Rose Adam J AJ McBain Ryan R Schuler Megan S MS LaRochelle Marc R MR Ganz David A DA Kilambi Vikram V Stein Bradley D BD Bernson Dana D Chui Kenneth Kwan Ho KKH Land Thomas T Walley Alexander Y AY Stopka Thomas J TJ

Journal of the American Geriatrics Society 20181124 1

<h4>Objectives</h4>To examine the effect of age on the likelihood of PIP of opioids and the effect of PIP on adverse outcomes.<h4>Design</h4>Retrospective cohort study.<h4>Setting</h4>Data from multiple state agencies in Massachusetts from 2011 to 2015.<h4>Participants</h4>Adult Massachusetts residents (N=3,078,163) who received at least one prescription opioid during the study period; approximately half (1,589,365) aged 50 and older.<h4>Measurements</h4>We measured exposure to 5 types of PIP: h ...[more]

PMID: 30471102

Similar Datasets

Project description:BackgroundPotentially inappropriate prescribing (PIP) may contribute to opioid overdose.ObjectiveTo examine the association between PIP and adverse events.DesignCohort study.ParticipantsThree million seventy-eight thousand thirty-four individuals age ≥ 18, without disseminated cancer, who received prescription opioids between 2011 and 2015.Main measuresWe defined PIP as (a) morphine equivalent dose ≥ 100 mg/day in ≥ 3 months; (b) overlapping opioid and benzodiazepine prescriptions in ≥ 3 months; (c) ≥ 4 opioid prescribers in any quarter; (d) ≥ 4 opioid-dispensing pharmacies in any quarter; (e) cash purchase of prescription opioids on ≥ 3 occasions; and (f) receipt of opioids in 3 consecutive months without a documented pain diagnosis. We used Cox proportional hazards models to identify PIP practices associated with non-fatal opioid overdose, fatal opioid overdose, and all-cause mortality, controlling for covariates.Key resultsAll six types of PIP were associated with higher adjusted hazard for all-cause mortality, four of six with non-fatal overdose, and five of six with fatal overdose. Lacking a documented pain diagnosis was associated with non-fatal overdose (adjusted hazard ratio [AHR] 2.21, 95% confidence interval [CI] 2.02-2.41), as was high-dose opioids (AHR 1.68, 95% CI 1.59-1.76). Co-prescription of benzodiazepines was associated with fatal overdose (AHR 4.23, 95% CI 3.85-4.65). High-dose opioids were associated with all-cause mortality (AHR 2.18, 95% CI 2.14-2.23), as was lacking a documented pain diagnosis (AHR 2.05, 95% CI 2.01-2.09). Compared to those who received opioids without PIP, the hazard for fatal opioid overdose with one, two, three, and ≥ four PIP subtypes were 4.24, 7.05, 10.28, and 12.99 (test of linear trend, p < 0.001).ConclusionsPIP was associated with higher hazard for all-cause mortality, fatal overdose, and non-fatal overdose. Our study implies the possibility of creating a risk score incorporating multiple PIP subtypes, which could be displayed to prescribers in real time.

Project description:BackgroundIn response to the opioid overdose crisis in the United States, many states implemented policies to guide opioid prescribing, but their impact on overdose mortality (prescription and non-prescription) remains poorly understood. We examined the impact of U.S. state opioid-prescribing policies on opioid overdose mortality following implementation.MethodsWe calculated opioid overdose mortality rates from 1999-2016 by U.S. state using the CDC WONDER database, overall and separately for overdose deaths from prescription and non-prescription opioids. For each state, policies active on 1/1/2014 were reviewed for the presence and strength of six provisions recommending judicious opioid prescribing practices; "strong" provisions used the words "should," "shall," or "must". Interrupted time series (ITS) tested the association of each strong provision with overdose mortality, overall and separately for prescription and non-prescription opioids, in the two years following implementation. Sensitivity analyses compared between states, used time-lagged analyses, and excluded synthetic opioids from non-prescription opioid deaths.ResultsAll six provisions had consistent direction of effect in ITS and sensitivity analyses. Strong provisions for prescriber training and limits on opioid dose reduced the slope of overall and prescription opioid overdose mortality in both ITS and sensitivity analyses. Reduced non-prescription opioid overdose mortality was only associated with strong provision for prescriber training. Some provisions had a negative impact. In ITS, strong provision for prescriber response to misuse increased the slope of non-prescription opioid overdose mortality. Strong provision for mandatory prescription drug monitoring program use had no relationship with overdose mortality in ITS and was associated with increased overall, prescription and non-prescription opioid overdose mortality in between-state sensitivity analysis.ConclusionOpioid prescribing policies in U.S. states at the peak of the prescription opioid epidemic had modest mortality benefit, and did not reduce non-prescription opioid overdose mortality. A strong provision for prescriber training was the only provision associated with reduced prescription and non-prescription opioid overdose mortality. These findings can inform future efforts addressing prescription drug epidemics.

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:ImportanceSafe opioid prescribing practices are critical to mitigate the risk of prescription opioid overdose in adolescents and young adults. However, studies that examine opioid prescribing patterns associated with prescription opioid overdose have mostly focused on older adults. The generalizability of these studies to adolescents and young adults is unclear.ObjectiveTo identify opioid prescribing patterns associated with prescription opioid overdose in adolescents and young adults.Design, setting, and participantsThis retrospective cohort study assessed privately insured patients aged 12 to 21 years with opioid prescription claims in the IBM MarketScan Commercial Claims and Encounters database between July 1, 2009, and October 1, 2017, and no cancer diagnosis. Data analysis was performed from January 1 to April 30, 2019.Main outcomes and measuresThe outcome was a treated opioid overdose as indicated by diagnosis codes. On the basis of days supplied, opioid prescription claims were converted to person-days (the unit of analysis) on which opioid exposure would occur if patients took medications as prescribed. Logistic regression with clustered SEs at the patient level was used to model the occurrence of overdose on a person-day as a function of daily opioid dosage category (<30, 30-59, 60-89, 90-119, or ≥120 morphine milligram equivalents), concurrent benzodiazepine use, and extended-release or long-acting opioid use. Regressions controlled for demographic characteristics, year, opioid use within 180 days, and comorbidities (mental health disorder, substance use disorder, and other chronic condition).ResultsA total of 2 752 612 patients (mean [SD] age at cohort entry, 17.2 [2.5] years; 1 451 918 [52.8%] female) participated in the study. Patients had 4 686 355 opioid prescription claims, corresponding to 21 605 444 person-days. Overdose occurred on 255 person-days among 249 patients (0.01% of the sample). Each increase in daily opioid dosage category was associated with higher overdose risk (adjusted odds ratio [AOR], 1.18; 95% CI, 1.05-1.31). Compared with no use, both concurrent benzodiazepine use (AOR, 1.83; 95% CI, 1.24-2.71) and extended-release or long-acting opioid use (AOR, 2.01; 95% CI, 1.16-3.46) were associated with increased overdose risk.Conclusions and relevanceThe findings suggest that when prescribing opioids to adolescents and young adults, practitioners could potentially mitigate overdose risk by using the lowest effective daily dosage, avoiding concurrent opioid and benzodiazepine prescribing, and relying on short-acting opioids. Findings are broadly consistent with prior opioid safety studies focused on older adults.

Project description:IntroductionOpioid and sedative/hypnotic drug overdoses are major causes of morbidity in the U.S. This study compares 12-month incidence of fatal unintentional drug overdose, suicide, and other mortality among emergency department patients presenting with nonfatal opioid or sedative/hypnotic overdose.MethodsThis is a retrospective cohort study using statewide, longitudinally linked emergency department patient record and mortality data from California. Participants comprised all residents presenting to a licensed emergency department at least once in 2009-2011 with nonfatal unintentional opioid overdose, sedative/hypnotic overdose, or neither (a 5% random sample). Participants were followed for 1 year after index emergency department presentation to assess death from unintentional overdose, suicide, or other causes, ascertained using ICD-10 codes. Absolute death rates per 100,000 person years and standardized mortality ratios relative to the general population were calculated. Data were analyzed February-August 2019.ResultsFollowing the index emergency department visit, unintentional overdose death rates per 100,000 person years were 1,863 following opioid overdose, 342 following sedative/hypnotic overdose, and 31 for reference patients without an index overdose (respective standardized mortality ratios of 106.1, 95% CI=95.2, 116.9; 24.5, 95% CI=21.3, 27.6; and 2.6, 95% CI=2.2, 3.0). Suicide mortality rates per 100,000 were 319, 174, and 32 following opioid overdose, sedative/hypnotic overdose, and reference visits, respectively. Natural causes mortality rates per 100,000 were 8,058 (opioid overdose patients), 17,301 (sedative/hypnotic overdose patients), and 3,097 (reference patients).ConclusionsEmergency department patients with nonfatal opioid or sedative/hypnotic drug overdose have exceptionally high risks of death from unintentional overdose, suicide, and other causes. Emergency department-based interventions offer potential for reducing these patients' overdose and other mortality risks.

Dataset Information

Effect of Age on Opioid Prescribing, Overdose, and Mortality in Massachusetts, 2011 to 2015.

Publications

Effect of Age on Opioid Prescribing, Overdose, and Mortality in Massachusetts, 2011 to 2015.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets