Unintended reductions in assaults near sobriety checkpoints: A longitudinal spatial analysis

BACKGROUND

Sobriety checkpoints are a form of proactive policing in which law enforcement officers concentrate at a point on the roadway to systematically perform sobriety tests for all passing drivers. We investigated whether sobriety checkpoints unintentionally reduce assaults in surrounding areas.

METHODS

Exposures of interest were sobriety checkpoints conducted by the Los Angeles Police Department between 2012 and 2017. Comparison units were matched 1:2 to sobriety checkpoints, selected as the same point location temporally lagged by exactly ±168 hours. The outcome was the density of police-reported assaults around the checkpoint location.

RESULTS

In mixed effects regression analyses, assault incidence was lower when sobriety checkpoints were in operation compared to the same location ±168 hours [b= -0.0108, 95% CI: (-0.0203, -0.0012)].

CONCLUSIONS

Sobriety checkpoints were associated with decreased assault incidence, but estimated effect sizes were small and effects did not endure long after checkpoints ended.

The unknown denominator problem in population studies of disease frequency

Problems related to unknown or imprecisely measured populations at risk are common in epidemiologic studies of disease frequency. The size of the population at risk is typically conceptualized as a denominator to be used in combination with a count of disease cases (a numerator) to calculate incidence or prevalence. However, the size of the population at risk can take other epidemiologic properties in relation to an exposure of interest and the count outcome, including confounding, modification, and mediation. Using spatial ecological studies of injury incidence as an example, we identify and evaluate five approaches that researchers have used to address "unknown denominator problems": ignoring, controlling for a proxy, approximating, controlling by study design, and measuring the population at risk. We present a case example and recommendations for selecting a solution given the data and the hypothesized relationship between an exposure of interest, a count outcome, and the population at risk.

Ridesharing and motor vehicle crashes: a spatial ecological case-crossover study of trip-level data

BACKGROUND

Ridesharing 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.

- METHODS

This 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).

RESULTS

Each 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.

CONCLUSIONS

Ridesharing contributes to increased injury burden due to motor vehicle crashes, particularly for motorist and pedestrian injury crashes at trip nodes.