Spatiotemporal Analysis of the Association Between Pain Management Clinic Laws and Opioid Prescribing and Overdose Deaths.
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ABSTRACT:
SUBMITTER: Cerda M
PROVIDER: S-EPMC8796812 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionIn response to the role of opioid prescribing in the U.S. opioid crisis, states have enacted laws intended to curb high risk opioid prescribing practices. This study assessed the effects of state prescribing cap laws that limit the dose and/or duration of dispensed opioid prescriptions on opioid prescribing patterns and opioid overdose.MethodsWe identified 1,414,908 adults from a large U.S. administrative insurance claims database. Treatment states included 32 states that implemented a prescribing cap law between 2017 and 2019. Comparison states included 16 states and DC without a prescribing cap law by 2019. A difference-in-differences approach with staggered policy adoption was used to assess effects of these laws on opioid analgesic prescribing and opioid overdose.ResultsState opioid prescribing cap laws were not associated with changes in the proportion of people receiving opioid analgesic prescriptions, the dose or duration of opioid prescriptions, or opioid overdose. States with laws that imposed days' supply limits only versus days' supply and dosage limits, as well as with specific law provisions also showed no association with opioid prescribing or opioid overdose outcomes.ConclusionsState opioid prescribing cap laws did not appear to impact outcomes related to opioid analgesic prescribing or opioid overdose. These findings are potentially due to the limited scope of these laws, which often apply only to a subset of opioid prescriptions and include professional judgment exemptions.
| S-EPMC9893520 | 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: Not available
| S-EPMC10869443 | biostudies-literature
Project description: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.
| S-EPMC8553088 | biostudies-literature
Project description:BackgroundRising opioid-related death rates have prompted reductions of opioid prescribing, yet limited data exist on population-level associations between opioid prescribing and opioid-related deaths.ObjectiveTo evaluate population-level associations between five opioid prescribing measures and opioid-related deaths.DesignAn ecological panel analysis was performed using linear regression models with year and commuting zone fixed effects.ParticipantsPeople ≥10 years aggregated into 886 commuting zones, which are geographic regions collectively comprising the entire USA.Main measuresAnnual opioid prescriptions were measured with IQVIA Real World Longitudinal Prescription Data including 76.5% (2009) to 90.0% (2017) of US prescriptions. Prescription measures included opioid prescriptions per capita, percent of population with ≥1 opioid prescription, percent with high-dose prescription, percent with long-term prescription, and percent with opioid prescriptions from ≥3 prescribers. Outcomes were age- and sex-standardized associations of change in opioid prescriptions with change in deaths involving any opioids, synthetics other than methadone, heroin but not synthetics or methadone, and prescription opioids, but not other opioids.Key resultsChange in total regional opioid-related deaths was positively correlated with change in regional opioid prescriptions per capita (β=.110, p<.001), percent with ≥1 opioid prescription (β=.100, p=.001), and percent with high-dose prescription (β=.081, p<.001). Change in total regional deaths involving prescription opioids was positively correlated with change in all five opioid prescribing measures. Conversely, change in total regional deaths involving synthetic opioids was negatively correlated with change in percent with long-term opioid prescriptions and percent with ≥3 prescribers, but not for persons ≥45 years. Change in total regional deaths in heroin was not associated with change in any prescription measure.ConclusionsRegional decreases in opioid prescriptions were associated with declines in overdose deaths involving prescription opioids, but were also associated with increases in deaths involving synthetic opioids (primarily fentanyl). Individual-level inferences are limited by the ecological nature of the analysis.
| S-EPMC9905341 | 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:ObjectivesStates in the US are controlling opioid prescribing to combat the opioid epidemic. Prescription Drug Monitoring Programs (PDMPs) were widely adopted, whereas less attention was given to pain clinic laws. This study examined the associations of mandatory use of PDMPs and pain clinic laws with prescription opioid exposures.MethodsState-level quarterly prescription opioid exposures reported to the National Poison Data System during 2010-2017 were analyzed. The primary outcome was age-adjusted rates of prescription opioid exposures per 1,000,000 population. The primary policy variables included the implementation of mandatory use of PDMPs alone, the implementation of pain clinic laws alone, and the implementation of both mandatory use of PDMPs and pain clinic laws. Linear regressions were used to examine the associations, controlling for other opioid policies, marijuana policies, socioeconomic factors, state fixed effects, time fixed effects, and state-specific time trends.ResultsRequiring mandatory use of PDMPs alone was not associated with significant changes in prescription opioid exposures. The implementation of pain clinic laws with or without concurrent mandatory use of PDMPs was associated with 5 fewer prescription opioid exposures per 1,000,000 population or a 9 % reduction compared to the pre-policy period (p < 0.01). Further analysis revealed that the reduction associated with pain clinic laws was pronounced in exposures reported by healthcare facilities.ConclusionsThis multi-state study provided new evidence that the implementation of pain clinic laws was associated with a significant reduction in prescription opioid exposures. Pain clinic laws may deserve further evaluation and consideration.
| S-EPMC6980704 | biostudies-literature
Project description:BACKGROUND:High opioid dosage has been associated with overdose, and clinical guidelines have cautioned against escalating dosages above 100 morphine-equivalent mg (MEM) based on the potential harm and the absence of evidence of benefit from high dosages. However, this 100 MEM threshold was chosen somewhat arbitrarily. OBJECTIVE:To examine the association of prescribed opioid dosage as a continuous measure in relation to risk of unintentional opioid overdose to identify the range of dosages associated with risk of overdose at a detailed level. METHODS:In this nested case-control study with risk-set sampling of controls, cases (opioid overdose decedents) and controls were identified from a population of patients of the Veterans Health Administration who were prescribed opioids and who have a chronic pain diagnosis. Unintentional fatal opioid analgesic overdose was measured from National Death Index records and prescribed opioid dosage from pharmacy records. RESULTS:The average prescribed opioid dosage was higher (P<0.001) for cases (mean=98.1 MEM, SD=112.7; median=60, interquartile range, 30-120), than controls (mean=47.7 MEM, SD=65.2; median=25, interquartile range, 15-45). In a ROC analysis, dosage was a moderately good "predictor" of opioid overdose death, indicating that, on average, overdose cases had a prescribed opioid dosage higher than 71% of controls. CONCLUSIONS:A clear cut-point in opioid dosage to distinguish between overdose cases and controls was not found. However, lowering the recommended dosage threshold below the 100 MEM used in many recent guidelines would affect proportionately few patients not at risk for overdose while potentially benefitting many of those at risk for overdose.
| S-EPMC6626611 | biostudies-literature
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.
| S-EPMC6990690 | biostudies-literature
Project description:ImportanceTime pressure to provide a quick fix is commonly cited as a reason why opioids are frequently prescribed in the United States, but there is little evidence of an association between appointment timing and clinical decision-making. As the workday progresses and appointments run behind schedule, physicians may be more likely to prescribe opioids.ObjectiveTo estimate whether characteristics of appointment timing are associated with clinical decision-making about pain treatment.Design, setting, and participantsThis cross-sectional study of physician behavior used data from electronic health record systems in primary care offices in the United States to analyze primary care appointments occurring in 2017 for patients with a new painful condition who had not received an opioid prescription within the past year.Main outcomes and measuresThe association between treatment decisions and 2 dimensions of appointment timing (order of appointment occurrence and delay relative to scheduled start time) were assessed. The rates of opioid prescribing were measured and compared with rates of nonopioid pain medication (ie, nonsteroidal anti-inflammatory drugs) prescribing and referral to physical therapy. All rates were estimated within the same physician using physician fixed effects, adjusting for patient, appointment, and seasonal characteristics.ResultsAmong 678 319 primary care appointments (642 262 patients; 392 422 [61.1%] women) with 5603 primary care physicians, the likelihood that an appointment resulted in an opioid prescription increased by 33% as the workday progressed (1st to 3rd appointment, 4.0% [95% CI, 3.9%-4.1%] vs 19th to 21st appointment, 5.3% [95% CI. 5.1%-5.6%]; P < .001) and by 17% as appointments ran behind schedule (0-9 minutes late, 4.4% [95% CI, 4.3%-4.6%] vs ≥60 minutes late, 5.2% [95% CI, 5.0%-5.4%]; P < .001). Prescribing of nonsteroidal anti-inflammatory drugs and referral to physical therapy did not display similar patterns.Conclusions and relevanceThese findings suggest that, even within an individual physician's schedule, clinical decision-making for opioid prescribing varies by the timing and lateness of appointments.
| S-EPMC6724149 | biostudies-literature