Objective: Sobriety checkpoints have strong empirical and theoretical support as an intervention to reduce alcohol-involved motor vehicle crashes. The purpose of this study was to examine whether checkpoint size (the number of police officers) and checkpoint duration (the amount of time in operation) affect associations between individual checkpoints and subsequent alcohol-related crash incidence.Method: Queensland Police Service provided latitude-longitude coordinates and date and time data for all breath tests that occurred in Brisbane, Australia, from January 2012 to June 2018. We applied hierarchical cluster analysis to the latitude-longitude coordinates for breath tests, identifying checkpoints as clusters of ≥25 breath tests conducted by ≥3 breath testing devices over a duration of 3 to 8 hours. Generalized linear autoregressive moving average (GLARMA) models related counts of alcoholinvolved motor vehicle crashes to the number of checkpoints conducted per week, as well as 1 week prior and 2 weeks prior.Results: A total of 3420 alcohol-related crashes occurred and 2069 checkpoints were conducted in Brisbane over the 6.5-year (339-week) study period. On average, checkpoints included a mean of 266.0 breath tests (SD = 216.3), 16.4 devices (SD = 13.7), and were 286.3 minutes in duration (SD = 104.2). Each 10 additional checkpoints were associated with a 12% decrease in crash incidence at a lag of 1 week (IRR = 0.88; 95%CI: 0.80, 0.97). We detected no differential associations according to checkpoint size or duration.Conclusions: Sobriety checkpoints are associated with fewer alcohol-related motor vehicle crashes for around 1 week. Checkpoint size and duration do not appear to affect this relationship.

No abstract

Background and Aims Sobriety checkpoints are an effective strategy to reduce alcohol‐impaired driving, motor vehicle crashes, injuries and fatalities. The aim of this study was to identify the geographic extent over which individual sobriety checkpoints affect alcohol‐impaired driving. Design, Setting, Participants Spatial ecological panel analysis using geolocated breath test data from the Queensland Police Service, Australia, for January 2012 to June 2018. Data were aggregated over 338 weeks within 528 Statistical Area level 2 (SA2) units (n = 178 464 SA2‐weeks) and 84 Statistical Area level 3 (SA3) units (n = 28 392 SA3‐weeks). SA2 units in Queensland contain a mean population of 8883.5 (SD = 55 018.3) and encompass 468.9 roadway kilometers (SD = 1490.0); SA3 units contain a mean population of 57 201.6 (SD = 29521.6) and encompass 2936.0 roadway kilometers (SD = 7025.0). Measurements Independent measures were the density of sobriety checkpoints conducted per 500 roadway kilometers within local and spatially adjacent space–time units. The dependent measure was the rate of tests that detected breath alcohol concentration (a proxy for blood alcohol concentration [BAC]) greater than the legal maximum value of 0.05% for fully licensed drivers in Queensland. Bayesian hierarchical spatial negative binomial models‐related sobriety checkpoints to the rate of breath tests with BAC ≥ 0.05% within and between space–time units. Findings One additional sobriety checkpoint conducted per 500 roadway kilometers was associated with 2.5% reduction in the rate of breath tests with BAC ≥ 0.05% within local SA2 units (incidence rate ratio [IRR] = 0.975; 95% credibility interval (CrI): 0.973–0.978), and with 5.5% reduction in the rate of breath tests with BAC ≥ 0.05% within local SA3 units (IRR = 0.945; 95%CrI: 0.937–0.953). Associations were attenuated towards null in spatially adjacent units and in temporally lagged units (e.g. SA3‐weeks; adjacent lagged 1 week: IRR = 0.969; 95%CrI: 0.937–1.003). Conclusions Individual sobriety checkpoints appear to be associated with reductions in nearby alcohol‐impaired driving. Relationships decay after approximately 1 week and beyond local areas containing approximately 60 000 residents and 3000 kilometers of roadway.

ImportanceCannabis use is increasingly viewed by adolescents as not harmful. Youths with cannabis use disorder (CUD) are recognized by clinicians as being at risk for adverse outcomes, yet little is known about the associations between subclinical cannabis use (ie, nondisordered cannabis use [NDCU]) and adverse psychosocial events.ObjectiveTo describe the prevalence and demographics of NDCU and to compare associations of cannabis use with adverse psychosocial events among adolescents with no cannabis use, NDCU, and CUD.Design, Setting, and ParticipantsThis cross-sectional study used a nationally representative sample derived from the 2015 to 2019 National Survey on Drug Use and Health. Participants were adolescents aged 12 to 17 years, separated into 3 distinct groups: nonuse (no recent cannabis use), NDCU (recent cannabis use below diagnostic threshold), and CUD. Analysis was conducted from January to May 2022.ExposuresCUD, NDCU, or cannabis nonuse. NDCU was defined as endorsing recent cannabis use but not meeting the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) CUD criteria. CUD was defined using DSM-5 criteria.Main Outcomes and MeasuresThe main outcomes were prevalence of adolescents meeting criteria for NDCU and associations between adverse psychosocial events and NDCU, adjusted for sociodemographic characteristics.ResultsThe 68 263 respondents (mean [SD] age, 14.5 [1.7] years; 34 773 [50.9%] males) included in the analysis represented an estimated yearly mean of 25 million US adolescents during 2015 to 2019. Among respondents, 1675 adolescents (2.5%) had CUD, 6971 adolescents (10.2%) had NDCU, and 59 617 adolescents (87.3%) reported nonuse. Compared with nonusers, individuals with NDCU had approximately 2 to 4 times greater odds of all adverse psychosocial events examined, including major depression (adjusted odds ratio [aOR], 1.86; 95% CI, 1.67-2.08), suicidal ideation (aOR, 2.08; 95% CI, 1.88-2.29), slower thoughts (aOR, 1.76; 95% CI, 1.58-1.96), difficulty concentrating (aOR, 1.81; 95% CI, 1.65-2.00), truancy (aOR, 1.90; 95% CI, 1.67-2.16), low grade point average (aOR, 1.80; 95% CI, 1.62-2.00), arrest (aOR, 4.15; 95% CI, 3.17-5.43), fighting (aOR, 2.04; 95% CI, 1.80-2.31), and aggression (aOR, 2.16; 95% CI, 1.79-2.62). Prevalence of adverse psychosocial events was greatest for adolescents with CUD (range, 12.6% to 41.9%), followed by NDCU (range, 5.2% to 30.4%), then nonuse (range, 0.8% to 17.3%).Conclusions and RelevanceIn this cross-sectional study of US adolescents, past-year NDCU was approximately 4 times as prevalent as past-year CUD. A stepwise gradient association was observed for odds of adverse psychosocial events between adolescent NDCU and CUD. In the context of US normalization of cannabis use, prospective research into NDCU is necessary.

BackgroundRidesharing services (eg, Uber, Lyft) have facilitated over 11 billion trips worldwide since operations began in 2010, but the impacts of ridesharing on motor vehicle injury crashes are largely unknown.—MethodsThis spatial ecological case-cross over used highly spatially and temporally resolved trip-level rideshare data and incident-level injury crash data for New York City (NYC) for 2017 and 2018. The space-time units of analysis were NYC taxi zone polygons partitioned into hours. For each taxi zone-hour we calculated counts of rideshare trip origins and rideshare trip destinations. Case units were taxi zone-hours in which any motor vehicle injury crash occurred, and matched control units were the same taxi zone from 1 week before (−168 hours) and 1 week after (+168 hours) the case unit. Conditional logistic regression models estimated the odds of observing a crash (separated into all injury crashes, motorist injury crashes, pedestrian injury crashes, cyclist injury crashes) relative to rideshare trip counts. Models controlled for taxi trips and other theoretically relevant covariates (eg, precipitation, holidays).ResultsEach additional 100 rideshare trips originating within a taxi zone-hour was associated with 4.6% increased odds of observing any injury crash compared with the control taxi zone-hours (OR=1.046; 95% CI 1.032 to 1.060). Associations were detected for motorist injury and pedestrian injury crashes, but not cyclist injury crashes. Findings were substantively similar for analyses conducted using trip destinations as the exposure of interest.ConclusionsRidesharing contributes to increased injury burden due to motor vehicle crashes, particularly for motorist and pedestrian injury crashes at trip nodes.

No abstract