Faculty Bibliography

BACKGROUND:Mandates for prescriber use of prescription drug monitoring programs (PDMPs), databases tracking controlled substance prescriptions, are associated with reduced opioid analgesic (OA) prescribing but may contribute to care discontinuity and chronic opioid therapy (COT) cycling, or multiple initiations and terminations. OBJECTIVE:To estimate risks of COT cycling in New York City (NYC) due to the New York State (NYS) PDMP mandate, compared to risks in neighboring New Jersey (NJ) counties. DESIGN/METHODS:We estimated cycling risk using Prentice, Williams, and Peterson gap-time models adjusted for age, sex, OA dose, payment type, and county population density, using a life-table difference-in-differences design. Failure time was duration between cycles. In a subgroup analysis, we estimated risk among patients receiving high-dose prescriptions. Sensitivity analyses tested robustness to cycle volume considering only first cycles using Cox proportional hazard models. PARTICIPANTS/METHODS:The cohort included 7604 patients dispensed 12,695 prescriptions. INTERVENTIONS/METHODS:The exposure was the August 2013 enactment of the NYS PDMP prescriber use mandate. MAIN MEASURES/METHODS:We used monthly, patient-level data on OA prescriptions dispensed in NYC and NJ between August 2011 and July 2015. We defined COT as three sequential months of prescriptions, permitting 1-month gaps. We defined recurrence as re-initiation of COT after at least 2 months without prescriptions. The exposure was enactment of the PDMP mandate in NYC; NJ was unexposed. KEY RESULTS/RESULTS:Enactment of the NYS PDMP mandate was associated with an adjusted hazard ratio (HR) for cycling of 1.01 (95% CI, 0.94-1.08) in NYC. For high-dose prescriptions, the risk was 1.16 (95% CI, 1.01-1.34). Sensitivity analyses estimated an overall risk of 1.01 (95% CI, 0.94-1.11) and high-dose risk of 1.09 (95% CI, 0.91-1.31). CONCLUSIONS:The PDMP mandate had no overall effect on COT cycling in NYC but increased cycling risk among patients receiving high-dose opioid prescriptions by 16%, highlighting care discontinuity.

IMPORTANCE:Benzodiazepines are prescribed for the treatment of adolescent sleep disorders; however, benzodiazepine overdoses occur, often in combination with opioids. OBJECTIVE:To evaluate whether benzodiazepine treatment for sleep disorders, compared with alternative pharmacologic treatments (trazodone, hydroxyzine, zolpidem, zaleplon, and eszopiclone), is associated with increased risk of drug overdose for young people. DESIGN, SETTING, AND PARTICIPANTS:This cohort study included privately insured people 10 to 29 years of age identified from a US commercial claims database (MarketScan), from January 1, 2009, to December 31, 2018. Young people with a sleep disorder diagnosis initiating benzodiazepine (n = 23 084) or comparator pharmacologic treatments (n = 66 706) were included in the study. Statistical analysis was performed from November 1, 2021, to May 16, 2022. EXPOSURES:New use of benzodiazepine treatment or comparator pharmacologic treatments (defined as ≥1 year without a prescription for benzodiazepine or comparator medications). MAIN OUTCOMES AND MEASURES:Incident diagnosed drug overdoses were identified from inpatient and emergency department records within 6 months of treatment initiation. The propensity score-adjusted cumulative incidence of overdose and hazard ratios (HRs) were estimated with intention-to-treat (analyzed based on initial treatment) and as-treated analyses (added censoring at treatment discontinuation). Results were stratified by prior prescription opioid fill. RESULTS:The cohort included 23 084 young people initiating benzodiazepine treatment (14 444 female participants [62.6%]; mean [SD] age, 23 [4.1] years) and 66 706 initiating a comparator treatment (38 446 female participants [57.6%]; mean [SD] age, 22 [4.4] years). Six months after treatment initiation, 9.7% (95% CI, 9.3%-10.1%) of benzodiazepine users and 12.3% (95% CI, 12.1%-12.6%) of the comparator group were still receiving treatment. The crude incidence of drug overdose at 6 months was 0.9% for benzodiazepine initiators and 0.8% for comparator treatment initiators. In adjusted analyses, an increased risk of drug overdose was associated with benzodiazepines vs comparator treatments (intention-to-treat analysis: HR, 1.25 [95% CI, 1.03-1.51]; as-treated analysis: HR, 1.44 [95% CI, 1.14-1.80]). This association was stronger among young people with a recent prescription opioid fill vs those without a recent prescription opioid fill (as-treated analysis: adjusted HR, 2.01 [95% CI, 1.24-3.25] vs adjusted HR, 1.31 [95% CI, 1.00-1.70]). CONCLUSIONS AND RELEVANCE:The findings of this study suggest that benzodiazepines, compared with alternative pharmacologic treatments for common sleep disorders, were associated with an increased risk of drug overdose among young people during the following 6-month period, especially among those with a recent opioid prescription. Drug overdose is an important safety consideration when treating young people with benzodiazepines.

OBJECTIVES/OBJECTIVE:We sought to understand how opioid treatment programs (OTPs) adapted OTP operations to the COVID-19 pandemic and new federal regulations around methadone and buprenorphine. METHODS:In fall 2020, we conducted an online survey of all 103 OTPs licensed by the Pennsylvania Department of Drug and Alcohol Programs, including clinical directors. Survey domains included changes to methadone take-home and telehealth practices; overdose and diversion prevention tactics; perceptions regarding how such changes influence patient well-being; and financial/operational concerns related to the new policies and practices. We calculated descriptive statistics and conducted Chi-square test to test for differences between not-for-profit versus for-profit and large versus small OTPs. RESULTS:Forty-seven percent (46%) OTPs responded to the survey. 10% and 25%, respectively, endorsed offering telephone and video-based telemedicine buprenorphine induction. Sixty-six percent endorsed extending take-home supplies of methadone, but most indicated that these extensions applied to a minority of their patients. Most respondents agreed that provision of buprenorphine via telehealth and extended take-home methadone reduced patient burden in accessing medications and prevented exposure to COVID-19, while not significantly increasing risk of overdose. We did not find major differences in COVID-19 practice modifications by nonprofit status or size of OTP. CONCLUSIONS:In Pennsylvania, the COVID-19 pandemic led to rapid changes in provision of opioid treatment services. Findings on relatively low uptake of longer methadone take-home regimens and virtual buprenorphine initiation despite general support for these practices imply a need to further develop guidelines for best clinical practices and understand/address barriers to their implementation.

BACKGROUND AND AIMS/OBJECTIVE:In 2013, Uruguay became the first country to legalize and regulate the production and distribution of cannabis for recreational use. We measured whether Uruguay's non-commercial model of recreational cannabis legalization was associated with changes in the prevalence of risky and frequent cannabis use among secondary school students. DESIGN/METHODS:We used data from repeated cross-sectional surveys of secondary students in Uruguay and Chile (2007-2018). Using a difference-in-difference approach, we evaluated changes in the prevalence of past-year, past-month, any risky and frequent cannabis use following enactment (2014) and implementation (2016) of cannabis legalization among the full sample of secondary students and among students who reported past-year/month use. We examined changes separately for students aged 12-17, and students for whom cannabis became legally accessible, ages 18-21. SETTING/METHODS:Uruguay and Chile (2007-2018). PARTICIPANTS/METHODS:grade (n=204,730). MEASUREMENTS/METHODS:Past-year and past-month cannabis use; any risky cannabis use measured with the Cannabis Abuse Screening Test (CAST); and frequent cannabis use (10+ days in the past-month). FINDINGS/RESULTS:We found a decrease in past-year and past-month use following enactment or implementation. Among students ages 18-21, post-enactment, we observed a transitory increase in 2014 that decreased thereafter for: any risky use among those who reported past-year use (prevalence difference [PD]=13.5%; 95% confidence interval [CI]: 2.0, 24.9), frequent use in the full sample (PD=4.5%; 95%CI: 1.0, 8.1), and frequent use among those who reported past-month use (PD=16.8%; 95%CI: 1.9, 31.8). CONCLUSION/CONCLUSIONS:The legalization of recreational cannabis in Uruguay was not associated with overall increases in either past-year/past-month cannabis use or with multi-year changes in any risky and frequent cannabis use among young people.

We developed an agent-based model using a trial emulation approach to quantify effect measure modification of spillover effects of pre-exposure prophylaxis (PrEP) for HIV among men who have sex with men (MSM) in the Atlanta-Sandy Springs-Roswell metropolitan area, Georgia. PrEP may impact not only the individual prescribed, but also their partners and beyond, known as spillover. We simulated a two-stage randomised trial with eligible components (≥3 agents with ≥1 HIV+ agent) first randomised to intervention or control (no PrEP). Within intervention components, agents were randomised to PrEP with coverage of 70%, providing insight into a high PrEP coverage strategy. We evaluated effect modification by component-level characteristics and estimated spillover effects on HIV incidence using an extension of randomisation-based estimators. We observed an attenuation of the spillover effect when agents were in components with a higher prevalence of either drug use or bridging potential (if an agent acts as a mediator between ≥2 connected groups of agents). The estimated spillover effects were larger in magnitude among components with either higher HIV prevalence or greater density (number of existing partnerships compared to all possible partnerships). Consideration of effect modification is important when evaluating the spillover of PrEP among MSM.

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OBJECTIVES/OBJECTIVE:Behavioral health diagnoses are frequently underreported in administrative health data. For a pragmatic trial of a hospital addiction consult program, we sought to determine the sensitivity of Medicaid claims data for identifying patients with opioid use disorder (OUD). METHODS:A structured review of electronic health record (EHR) data was conducted to identify patients with OUD in 6 New York City public hospitals. Cases selected for review were adults admitted to medical/surgical inpatient units who received methadone or sublingual buprenorphine in the hospital. For cases with OUD based on EHR review, we searched for the hospitalization in Medicaid claims data and examined International Classification of Diseases, Tenth Revision discharge diagnosis codes to identify opioid diagnoses (OUD, opioid poisoning, or opioid-related adverse events). Sensitivity of Medicaid claims data for capturing OUD hospitalizations was calculated using EHR review findings as the reference standard measure. RESULTS:Among 552 cases with OUD based on EHR review, 465 (84.2%) were found in the Medicaid claims data, of which 418 (89.9%) had an opioid discharge diagnosis. Opioid diagnoses were the primary diagnosis in 49 cases (11.7%), whereas in the remainder, they were secondary diagnoses. CONCLUSION/CONCLUSIONS:In this sample of hospitalized patients receiving OUD medications, Medicaid claims seem to have good sensitivity for capturing opioid diagnoses. Although the sensitivity of claims data may vary, it can potentially be a valuable source of information about OUD patients.

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