A statewide evaluation of seven strategies to reduce opioid overdose in North Carolina

Abstract

Background In response to increasing opioid overdoses, US prevention efforts have focused on prescriber education and supply, demand and harm reduction strategies. Limited evidence informs which interventions are effective. We evaluated Project Lazarus, a centralised statewide intervention designed to prevent opioid overdose. Methods Observational intervention study of seven strategies. 74 of 100 North Carolina counties implemented the intervention. Dichotomous variables were constructed for each strategy by county-month. Exposure data were: process logs, surveys, addiction treatment interviews, prescription drug monitoring data. Outcomes were: unintentional and undetermined opioid overdose deaths, overdose-related emergency department (ED) visits. Interrupted time-series Poisson regression was used to estimate rates during preintervention (2009–2012) and intervention periods (2013–2014). Adjusted IRR controlled for prescriptions, county health status and time trends. Time-lagged regression models considered delayed impact (0–6 months). Results In adjusted immediate-impact models, provider education was associated with lower overdose mortality (IRR 0.91; 95% CI 0.81 to 1.02) but little change in overdose-related ED visits. Policies to limit ED opioid dispensing were associated with lower mortality (IRR 0.97; 95% CI 0.87 to 1.07), but higher ED visits (IRR 1.06; 95% CI 1.01 to 1.12). Expansions of medication-assisted treatment (MAT) were associated with increased mortality (IRR 1.22; 95% CI 1.08 to 1.37) but lower ED visits in time-lagged models. Conclusions Provider education related to pain management and addiction treatment, and ED policies limiting opioid dispensing showed modest immediate reductions in mortality. MAT expansions showed beneficial effects in reducing ED-related overdose visits in time-lagged models, despite an unexpected adverse association with mortality.

Association Between Harm Reduction Strategies and Healthcare Utilization and Outcomes in Patients on Long-term Prescribed Opioid Therapy Presenting to Acute Healthcare Settings: A Systematic Review and Meta-analysis

“…Nearly all studies were performed in ED settings (n = 14). A single cohort study assessed mortality (11). The five RCTs (Table 1) were all performed in the ED, with the comparator being usual care.…”

“…Harm reduction strategies are defined as "any policy or program designed to reduce drug-related harm without requiring the cessation of drug use; these interventions may be targeted at the individual, the family, community or society (11)(12)(13)(14)(15)(16)(17)(18)(19)." Harm reduction strategies are traditionally thought of and applied to patients demonstrating high risk behaviors and the term has not typically been applied in relation to individuals on longterm opioid therapy.…”

Association between opioid analgesic therapy and initiation of buprenorphine management: An analysis of prescription drug monitoring program data

“…The 2011-2015 period of the analysis was likely to be influenced by underlying secular trends in opioid use (including buprenorphine) and overdose, changes in clinical practice, and other interventions administered. [21] To account for secular trends, we adjusted for calendar time as continuous month over the 60-month observation period.…”

“…Product dosage and formulation details were abstracted using natural language processing and merged with drug metadata previously collected by our team: the active pharmaceutical ingredient (API), class (e.g., immediate-release full mu-opioid receptor agonist, benzodiazepine, stimulant), formulation (e.g., tablet, sublingual film, liquid), and dose. [21,22] Oral morphine equivalents were calculated for oral and transdermal full mu-opioid receptor agonists, excluding liquid opioid preparations used primarily as cough suppressants.…”

“…In order to assess whether total exposure to opioid analgesics may influence subsequent buprenorphine MAT, cumulative morphine milligram equivalents (MMEs) per prescription were calculated by multiplying the number of units dispensed by the days supply by a conversion factor. [21,23] Cumulative MMEs over time for each patient were then calculated by adding MMEs from each prescription dispensed during the observation period. For each individual, the cumulative MMEs dispensed were divided by the cohort member's person-time at-risk, creating a time-varying index of the current OA exposure density, interpreted as average daily MME, taking into account overlapping prescriptions.…”