The International journal on drug policy · 2022
Using synthetic controls to estimate the population-level effects of Ontario's recently implemented overdose prevention sites and consumption and treatment services.
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Panagiotoglou Dimitra, Lim Jihoon
Аннотация
BACKGROUND: Between 2017 and 2020, Ontario implemented overdose prevention sites (OPS) and consumption and treatment services (CTS) in nine of its 34 public health units (PHU). We tested for the effect of booth-hours (spaces within OPS/CTSs for supervised consumption) on opioid-related health service use and mortality rates at the provincial- (aggregate) and PHU-level.
METHODS: We used monthly rates of all opioid-related emergency department (ED) visits, hospitalizations, and deaths between January 2015 and March 2021 as our three outcomes. For each PHU that implemented OPS/CTSs, we created a synthetic control as a weighted combination of unexposed PHUs. Our exposure was the time-varying rate of booth-hours provided. We estimated the population-level effects of the intervention on each outcome per treated/synthetic-control pair using controlled interrupted time series with segmented regression; and tested for the aggregate effect using a multiple baseline approach. We adjusted for time-varying provision of prescription opioids for pain management, opioid agonist treatment (OAT), and naloxone kits; and corrected for seasonality and autocorrelation. All rates were per 100,000 population. For sensitivity analysis, we restricted the post-implementation period to before COVID-19 public health measures were implemented (March 2020).
RESULTS: Our aggregate analyses found no effect per booth-hour on ED visits (0.00, 95% CI: -0.01, 0.01; p-value=0.6684), hospitalizations (0.00, 95% CI: 0.00, 0.00; p-value=0.9710) or deaths (0.00, 95% CI: 0.00, 0.00; p-value=0.2466). However, OAT reduced ED visits (-0.20, 95% CI: -0.35, -0.05; p-value=0.0103) and deaths (-0.04, 95% CI: -0.05, -0.03; p-value=<0.0001). Conversely, prescription opioids for pain management modestly increased deaths (0.0008, 95% CI: 0.0002, 0.0015; p-value=0.0157) per 100,000 population, respectively. Except for a few treated PHU/synthetic control pairs, disaggregate results were congruent with overall findings.
CONCLUSION: Booth-hours had no population-level effect on opioid-related overdose ED visit, hospitalization, or death rates.
METHODS: We used monthly rates of all opioid-related emergency department (ED) visits, hospitalizations, and deaths between January 2015 and March 2021 as our three outcomes. For each PHU that implemented OPS/CTSs, we created a synthetic control as a weighted combination of unexposed PHUs. Our exposure was the time-varying rate of booth-hours provided. We estimated the population-level effects of the intervention on each outcome per treated/synthetic-control pair using controlled interrupted time series with segmented regression; and tested for the aggregate effect using a multiple baseline approach. We adjusted for time-varying provision of prescription opioids for pain management, opioid agonist treatment (OAT), and naloxone kits; and corrected for seasonality and autocorrelation. All rates were per 100,000 population. For sensitivity analysis, we restricted the post-implementation period to before COVID-19 public health measures were implemented (March 2020).
RESULTS: Our aggregate analyses found no effect per booth-hour on ED visits (0.00, 95% CI: -0.01, 0.01; p-value=0.6684), hospitalizations (0.00, 95% CI: 0.00, 0.00; p-value=0.9710) or deaths (0.00, 95% CI: 0.00, 0.00; p-value=0.2466). However, OAT reduced ED visits (-0.20, 95% CI: -0.35, -0.05; p-value=0.0103) and deaths (-0.04, 95% CI: -0.05, -0.03; p-value=<0.0001). Conversely, prescription opioids for pain management modestly increased deaths (0.0008, 95% CI: 0.0002, 0.0015; p-value=0.0157) per 100,000 population, respectively. Except for a few treated PHU/synthetic control pairs, disaggregate results were congruent with overall findings.
CONCLUSION: Booth-hours had no population-level effect on opioid-related overdose ED visit, hospitalization, or death rates.