person:cerdam01 or freids01 or hamill07 or krawcn01
Spatiotemporal Analysis of the Association between Pain Management Clinic Laws and Opioid Prescribing and Overdose Deaths
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 (â‰¥30 day) or high-dose (â‰¥90 morphine milligram equivalents) opioid prescriptions (IQVIA, Inc) in the United States from 2010-2018. We fit Besag-York-MolliÃ© spatiotemporal models to estimate annual relative rates (RR) of overdose and prevalence ratios (PR) of high-risk prescribing associated with any PMC law and three 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) 0.92-0.83, and 0.73, and 0.73, 0.74 respectively), and reduced RRs of total and natural/semi-synthetic opioid overdoses (adjusted RR: 0.86, 95% CrI: 0.80, 0.92; and 0.84, and 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 2.59, and 2.22, 3.02, respectively). Findings suggest 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.
Trajectories of and disparities in HIV prevalence among Black, White, and Hispanic/Latino High Risk Heterosexuals in 89 U.S. Metropolitan statistical areas, 1992-2013
PURPOSE/OBJECTIVE:Estimates of HIV prevalence, and how it changes over time, are needed to inform action (e.g., resource allocation) to improve HIV-related public health. However, creating adequate estimates of (diagnosed and undiagnosed) HIV prevalence is challenging due to biases in samples receiving HIV testing and due to difficulties enumerating key risk populations. To our knowledge, estimates of HIV prevalence among high risk heterosexuals in the United States produced for geographic areas smaller than the entire nation have to date been only for single years and/or for single cities (or other single geographic locations). METHODS:The present study addresses these gaps by using multilevel modeling on multiple data series, in combination with previous estimates of HIV prevalence among heterosexuals from the extant literature, to produce annual estimates of HIV prevalence among high risk heterosexuals for each of 89 metropolitan statistical areas, from 1992 to 2013. It also produces estimates for these MSAs and years by racial/ethnic subgroup to allow for an examination of change over time in racial/ethnic disparities in HIV prevalence among high risk heterosexuals. RESULTS:The resulting estimates suggest that HIV prevalence among high risk heterosexuals has decreased steadily, on average, from 1992 to 2013. Examination of these estimates by racial/ ethnic subgroup suggests that this trend is primarily due to decreases among Black and Hispanic/Latino high risk heterosexuals. HIV prevalence among white high risk heterosexuals remained steady over time at around 1% during the study period. Although HIV prevalence among Black and Hispanic/Latino high risk heterosexuals was much higher (approximately 3.5% and 3.3%, respectively) than that among whites in 1992, over time these differences decreased as HIV prevalence decreased over time among these subgroups. By 2013, HIV prevalence among Hispanic/Latino high risk heterosexuals was estimated to be very similar to that among white high risk heterosexuals (approximately 1%), with prevalence among Black high risk heterosexuals still estimated to be almost twice as high. CONCLUSIONS:It is likely that as HIV incidence has decreased among heterosexuals from 1992 to 2013, mortality due to all causes has remained disparately high among racial/ethnic minorities, thereby outpacing new HIV cases. Future research should aim to empirically examine this by comparing changes over time in estimated HIV incidence among heterosexuals to changes over time in mortality and causes of death among HIV-positive heterosexuals, by racial/ethnic subgroup.
A Systematic Review of Simulation Models to Track and Address the Opioid Crisis
The opioid overdose crisis is driven by an intersecting set of social, structural, and economic forces. Simulation models offer a tool to help us understand and address this complex, dynamic, and nonlinear social phenomenon. We conducted a systematic review of the literature on simulation models of opioid use and overdose up to September 2019. We extracted modeling types, target populations, interventions, and findings. Further, we created a database of model parameters used for model calibration, and evaluated study transparency and reproducibility. Of the 1,398 articles screened, we identified 88 eligible articles. The most frequent types of models were compartmental (36%), Markov (20%), system dynamics (16%), and Agent-Based models (16%). Over a third evaluated intervention cost-effectiveness (40%), and another third (39%) focused on treatment and harm reduction services for people with opioid use disorder (OUD). More than half (61%) discussed calibrating their models to empirical data, and 31% discussed validation approaches used in their modeling process. From the 63 studies that provided model parameters, we extracted the data sources on opioid use, OUD, OUD treatment, cessation/relapse, emergency medical services, and mortality parameters. This database offers a tool that future modelers can use to identify potential model inputs and evaluate comparability of their models to prior work. Future applications of simulation models to this field should actively tackle key methodological challenges, including the potential for bias in the choice of parameter inputs, investment in model calibration and validation, and transparency in the assumptions and mechanics of simulation models to facilitate reproducibility.
Early innovations in opioid use disorder treatment and harm reduction during the COVID-19 pandemic: a scoping review
BACKGROUND:The COVID-19 pandemic has exerted a significant toll on the lives of people who use opioids (PWUOs). At the same time, more flexible regulations around provision of opioid use disorder (OUD) services have led to new opportunities for facilitating access to services for PWUOs. In the current scoping review, we describe new services and service modifications implemented by treatment and harm reduction programs serving PWUO, and discuss implications for policy and practice. METHODS:Literature searches were conducted within PubMed, LitCovid, Embase, and PsycInfo for English-language studies published in 2020 that describe a particular program, service, or intervention aimed at facilitating access to OUD treatment and/or harm reduction services during the COVID-19 pandemic. Abstracts were independently screened by two reviewers. Relevant studies were reviewed in full and those that met inclusion criteria underwent final data extraction and synthesis (nâ€‰=â€‰25). We used a narrative synthesis approach to identify major themes around key service modifications and innovations implemented across programs serving PWUO. RESULTS:Reviewed OUD treatment and harm reduction services spanned five continents and a range of settings from substance use treatment to street outreach programs. Innovative service modifications to adapt to COVID-19 circumstances primarily involved expanded use of telehealth services (e.g., telemedicine visits for buprenorphine, virtual individual or group therapy sessions, provision of donated or publicly available phones), increased take-home medication allowances for methadone and buprenorphine, expanded uptake of long-acting opioid medications (e.g. extended-release buprenorphine and naltrexone), home delivery of services (e.g. MOUD, naloxone and urine drug screening), outreach and makeshift services for delivering MOUD and naloxone, and provision of a safe supply of opioids. CONCLUSIONS:The COVID-19 pandemic has posed multiple challenges for PWUOs, while simultaneously accelerating innovations in policies, care models, and technologies to lower thresholds for life-saving treatment and harm reduction services. Such innovations highlight novel patient-centered and feasible approaches to mitigating OUD related harms. Further studies are needed to assess the long-term impact of these approaches and inform policies that improve access to care for PWUOs.
Prescription opioid laws and opioid dispensing in U.S. counties: Identifying salient law provisions with machine learning
BACKGROUND:Hundreds of laws aimed at reducing inappropriate prescription opioid dispensing have been implemented in the United States, yet heterogeneity in provisions and their simultaneous implementation have complicated evaluation of impacts. We apply a hypothesis-generating, multi-stage, machine learning approach to identify salient law provisions and combinations associated with dispensing rates to test in future research. METHODS:Using 162 prescription opioid law provisions capturing prescription drug monitoring program (PDMP) access, reporting and administration features, pain management clinic provisions, and prescription opioid limits, we used regularization approaches and random forest models to identify laws most predictive of county-level and high-dose dispensing. We stratified analyses by overdose epidemic phases-the prescription opioid phase (2006-2009), heroin phase (2010-2012), and fentanyl phase (2013-2016)-to further explore pattern shifts over time. RESULTS:PDMP patient data access provisions most consistently predicted high dispensing and high-dose dispensing counties. Pain management clinic-related provisions did not generally predict dispensing measures in the prescription opioid phase but became more discriminant of high dispensing and high-dose dispensing counties over time, especially in the fentanyl period. Predictive performance across models was poor, suggesting prescription opioid laws alone do not strongly predict dispensing. CONCLUSIONS:Our systematic analysis of 162 law provisions identified patient data access and several pain management clinic provisions as predictive of county prescription opioid dispensing patterns. Future research employing other types of study designs is needed to test these provisions' causal relationships with inappropriate dispensing, and to examine potential interactions between PDMP access and pain management clinic provisions.
CDC Guideline For Opioid Prescribing Associated With Reduced Dispensing To Certain Patients With Chronic Pain
The Centers for Disease Control and Prevention's 2016 Guideline for Prescribing Opioids for Chronic Pain aimed to reduce unsafe opioid prescribing. It is unknown whether the guideline influenced prescribing in the target population: patients with chronic, noncancer pain, who may be at particular risk for opioid-related harms. To study this question, we used 2014-18 data from a commercial claims database to examine associations between the release of the guideline and opioid dispensing in a national cohort of more than 450,000 patients with four common chronic pain diagnoses. We also examined whether any reductions associated with the guideline were larger for diagnoses for which there existed stronger expert consensus against opioid prescribing. Overall, the guideline was associated with substantial reductions in dispensing opioids, including a reduction in patients' rate of receiving at least one opioid prescription by approximately 20Â percentage points by December 2018 compared with the counterfactual, no-guideline scenario. However, the reductions in dispensing did not vary by the strength of expert consensus against opioid prescribing. These findings suggest that although voluntary guidelines can drive changes in prescribing, questions remain about how clinicians are tailoring opioid reductions to best benefit patients.
Trends in the sequence of initiation of alcohol, tobacco, and marijuana use among adolescents in Argentina and Chile from 2001 to 2017
BACKGROUND:Variation in drug policies, norms, and substance use over time and across countries may affect the normative sequences of adolescent substance use initiation. We estimated relative and absolute time-varying associations between prior alcohol and tobacco use and adolescent marijuana initiation in Argentina and Chile. Relative measures quantify the magnitude of the associations, whereas absolute measures quantify excess risk. METHODS:We analyzed repeated, cross-sectional survey data from the National Surveys on Drug Use Among Secondary School Students in Argentina (2001-2014) and Chile (2001-2017). Participants included 8th, 10th, and 12th grade students (NÂ =Â 680,156). Linear regression models described trends over time in the average age of first use of alcohol, tobacco, and marijuana. Logistic regression models were used to estimate time-varying risk ratios and risk differences of the associations between prior alcohol and tobacco use and current-year marijuana initiation. RESULTS:Average age of marijuana initiation increased and then decreased in Argentina and declined in Chile. In both countries, the relative associations between prior tobacco use and marijuana initiation weakened amid declining rates of tobacco use; e.g., in Argentina, the risk ratio was 19.9 (95% CI: 9.0-30.8) in 2001 and 11.6 (95% CI: 9.0-13.2) in 2014. The relative association between prior alcohol use and marijuana initiation weakened Chile, but not in Argentina. On the contrary, risk differences (RD) increased substantially across both relationships and countries, e.g., in Argentina, the RD for tobacco was 3% (95% CI: 0.02-0.03) in 2001 and 12% (95% CI: 0.11-0.13) in 2014. CONCLUSION/CONCLUSIONS:Diverging trends in risk ratios and risk differences highlight the utility of examining multiple measures of association. Variation in the strength of the associations over time and place suggests the influence of environmental factors. Increasing risk differences indicate alcohol and tobacco use may be important targets for interventions to reduce adolescent marijuana use.
Temporal Trends in Opioid Prescribing Practices in Children, Adolescents, and Younger Adults in the US From 2006 to 2018
Importance/UNASSIGNED:Prescription opioids are involved in more than half of opioid overdoses among younger persons. Understanding opioid prescribing practices is essential for developing appropriate interventions for this population. Objective/UNASSIGNED:To examine temporal trends in opioid prescribing practices in children, adolescents, and younger adults in the US from 2006 to 2018. Design, Setting, and Participants/UNASSIGNED:A population-based, cross-sectional analysis of opioid prescription data was conducted from January 1, 2006, to December 31, 2018. Longitudinal data on retail pharmacy-dispensed opioids for patients younger than 25 years were used in the analysis. Data analysis was performed from December 26, 2019, to July 8, 2020. Main Outcomes and Measures/UNASSIGNED:Opioid dispensing rate, mean amount of opioid dispensed in morphine milligram equivalents (MME) per day (individuals aged 15-24 years) or MME per kilogram per day (age <15 years), duration of prescription (mean, short [â‰¤3 days], and long [â‰¥30 days] duration), high-dosage prescriptions, and extended-release or long-acting (ER/LA) formulation prescriptions. Outcomes were calculated for age groups: 0 to 5, 6 to 9, 10 to 14, 15 to 19, and 20 to 24 years. Joinpoint regression was used to examine opioid prescribing trends. Results/UNASSIGNED:From 2006 to 2018, the opioid dispensing rate for patients younger than 25 years decreased from 14.28 to 6.45, with an annual decrease of 15.15% (95% CI, -17.26% to -12.99%) from 2013 to 2018. The mean amount of opioids dispensed and rates of short-duration and high-dosage prescriptions decreased for all age groups older than 5 years, with the largest decreases in individuals aged 15 to 24 years. Mean duration per prescription increased initially for all ages, but then decreased for individuals aged 10 years or older. The duration remained longer than 5 days across all ages. The rate of long-duration prescriptions increased for all age groups younger than 15 years and initially increased, but then decreased after 2014 for individuals aged 15 to 24 years. For children aged 0 to 5 years dispensed an opioid, annual increases from 2011 to 2014 were noted for the mean amount of opioids dispensed (annual percent change [APC], 10.58%; 95% CI, 1.77% to 20.16%) and rates of long-duration (APC, 30.42%; 95% CI, 14.13% to 49.03%), high-dosage (APC, 31.27%; 95% CI, 16.81% to 47.53%), and ER/LA formulation (APC, 27.86%; 95% CI, 12.04% to 45.91%) prescriptions, although the mean amount dispensed and rate of high-dosage prescriptions decreased from 2014 to 2018. Conclusions and Relevance/UNASSIGNED:These findings suggest that opioid dispensing rates decreased for patients younger than 25 years, with decreasing rates of high-dosage and long-duration prescriptions for adolescents and younger adults. However, opioids remain readily dispensed, and possible high-risk prescribing practices appear to be common, especially in younger children.
Sex-Specific Risk Profiles for Suicide Among Persons with Substance Use Disorders in Denmark
BACKGROUND AND AIMS/OBJECTIVE:Persons with substance use disorders (SUDs) are at elevated risk of suicide death. We identified novel risk factors and interactions that predict suicide among men and women with SUD using machine learning. DESIGN/METHODS:Case-cohort study. SETTING/METHODS:Denmark. PARTICIPANTS/METHODS:The sample was restricted to persons with their first SUD diagnosis during 1995 to 2015. Cases were persons who died by suicide in Denmark during 1995 to 2015 (n = 2774) and the comparison subcohort was a 5% random sample of individuals in Denmark on 1 January 1995 (n = 13 179). MEASUREMENTS/METHODS:Suicide death was recorded in the Danish Cause of Death Registry. Predictors included social and demographic information, mental and physical health diagnoses, surgeries, medications, and poisonings. FINDINGS/RESULTS:Persons among the highest risk for suicide, as identified by the classification trees, were men prescribed antidepressants in the 4Â years before suicide and had a poisoning diagnosis in the 4Â years before suicide; and women who were 30+Â years old and had a poisoning diagnosis 4Â years before and 12Â months before suicide. Among men with SUD, the random forest identified five variables that were most important in predicting suicide; reaction to severe stress and adjustment disorders, drugs used to treat addictive disorders, age 30+Â years, antidepressant use, and poisoning in the 4 prior years. Among women with SUD, the random forest found that the most important predictors of suicide were prior poisonings and reaction to severe stress and adjustment disorders. Individuals in the top 5% of predicted risk accounted for 15% of all suicide deaths among men and 24% of all suicides among women. CONCLUSIONS:In Denmark, prior poisoning and comorbid psychiatric disorders may be among the most important indicators of suicide risk among persons with substance use disorders, particularly among women.
Association of substance use disorders and drug overdose with adverse COVID-19 outcomes in New York City: January-October 2020
BACKGROUND:Evidence suggests that individuals with history of substance use disorder (SUD) are at increased risk of COVID-19, but little is known about relationships between SUDs, overdose and COVID-19 severity and mortality. This study investigated risks of severe COVID-19 among patients with SUDs. METHODS:We conducted a retrospective review of data from a hospital system in New York City. Patient records from 1 January to 26 October 2020 were included. We assessed positive COVID-19 tests, hospitalizations, intensive care unit (ICU) admissions and death. Descriptive statistics and bivariable analyses compared the prevalence of COVID-19 by baseline characteristics. Logistic regression estimated unadjusted and sex-, age-, race- and comorbidity-adjusted odds ratios (AORs) for associations between SUD history, overdose history and outcomes. RESULTS:Of patients tested for COVID-19 (nÂ =Â 188Â 653), 2.7% (nÂ =Â 5107) had any history of SUD. Associations with hospitalization [AORs (95% confidence interval)] ranged from 1.78 (0.85-3.74) for cocaine use disorder (COUD) to 6.68 (4.33-10.33) for alcohol use disorder. Associations with ICU admission ranged from 0.57 (0.17-1.93) for COUD to 5.00 (3.02-8.30) for overdose. Associations with death ranged from 0.64 (0.14-2.84) for COUD to 3.03 (1.70-5.43) for overdose. DISCUSSION/CONCLUSIONS:Patients with histories of SUD and drug overdose may be at elevated risk of adverse COVID-19 outcomes.