No abstract

The controversy surrounding recent high-profile police shootings (e.g., Michael Brown in Ferguson, Missouri; Laquan McDonald in Chicago) has prompted inquiry into the possible existence of bias in officers’ use-of-force decisions. Using a balanced mix of shoot/don’t shoot cases from a large municipal police department in the Southwestern United States, this study analyzed the effect of suspect race on officers’ decisions to shoot—while accounting for other theoretically relevant factors. Findings suggest that Black suspects were not disproportionately the target of police shootings; Black suspects were approximately one third as likely to be shot as other suspects. This finding challenges the current bias narrative and is consistent with the other race-related findings in recently published research.

We analyze a set of 207 Dallas Police Department officer-involved shooting incidents in reference to 1,702 instances in which officers from the same agency drew their firearms but did not shoot at the suspect. We find that situational factors of whether the suspect was armed and whether an officer was injured were the best predictors of the decision to shoot. We also find that African Americans are less likely than Whites to be shot. It is important to collect data on encounters in which weapons are and are not discharged. Analyses examining only shootings is fundamentally limited in assessing racial bias.

Background: A key first step in optimising COVID-19 patient outcomes during future case-surges is to learn from the experience within individual hospitals during the early stages of the pandemic. The aim of this study was to investigate the extent of variation in COVID-19 outcomes between National Health Service (NHS) hospital trusts and regions in England using data from MarchÀJuly 2020. Methods: This was a retrospective observational study using the Hospital Episode Statistics administrative dataset. Patients aged 18 years who had a diagnosis of COVID-19 during a hospital stay in England that was completed between March 1st and July 31st, 2020 were included. In-hospital mortality was the primary outcome of interest. In secondary analysis, critical care admission, length of stay and mortality within 30 days of discharge were also investigated. Multilevel logistic regression was used to adjust for covariates. Findings: There were 86,356 patients with a confirmed diagnosis of COVID-19 included in the study, of whom 22,944 (26.6%) died in hospital with COVID-19 as the primary cause of death. After adjusting for covariates, the extent of the variation in-hospital mortality rates between hospital trusts and regions was relatively modest. Trusts with the largest baseline number of beds and a greater proportion of patients admitted to critical care had the lowest in-hospital mortality rates. Interpretation: There is little evidence of clustering of deaths within hospital trusts. There may be opportunities to learn from the experience of individual trusts to help prepare hospitals for future case-surges.

The “veil-of-darkness” method is an innovative and low-cost approach that circumvents many of the benchmarking issues that arise in testing for racial profiling. Changes in natural lighting are used to establish a presumptively more race-neutral benchmark on the assumption that after dark, police suffer an impaired ability to detect motorists’ race. Applying the veil-of-darkness method to vehicle stops by the Syracuse (NY) police between 2006 and 2009 and examining differences among officers assigned to specialized traffic units and crime-suppression units, we found that African Americans were no more likely to be stopped during daylight than during darkness, indicating no racial bias.

This study tests the broken windows theory of crime by examining the relationship between 311 calls for service and crime at the street segment and intersection level in Washington, D.C. Controlling for a set of micro-level covariates as well as unobserved neighborhood-level effects using negative binomial regression models, it is found that detritus- and infrastructure-related calls for service have a positive, but small effect on crime. The results suggest that 311 calls for service are a valid indicator of physical disorder where available, and the findings partially confirm the broken windows theory. Given the small effects though, reducing physical disorder is unlikely to result in appreciable declines in crime.

This article estimates the relationship between alcohol outlets and crime at micro place street units in Washington, D.C. The analysis tests several spatial hypotheses on the local and spatial diffusion effects of on-premise and off-premise alcohol outlets on crime motivated by routine activities theory as well as theories that emphasize individual alcohol consumption. Findings show that the spatial diffusion effects of alcohol outlets are larger than the local effects, the effects of on-premise and off-premise outlets are similar in magnitude, and alcohol outlets have larger effects on interpersonal crimes than burglary. These findings are interpreted as favoring routine activities theories for why alcohol outlets increase crime, as opposed to prior research which emphasizes individual alcohol consumption.

Andresen's spatial point pattern test (SPPT) compares two spatial point patterns on defined areal units; it identifies areas where the spatial point patterns diverge and aggregates these local (dis)similarities to one global measure. We discuss the limitations of the SPPT and provide two alternative methods to calculate differences in the point patterns. In the first approach we use differences in proportions tests corrected for multiple comparisons. We show how the size of differences matters, as with large point patterns many areas will be identified by SPPT as statistically different, even if those differences are substantively trivial. The second approach uses multinomial logistic regression, which can be extended to identify differences in proportions over continuous time. We demonstrate these methods by identifying areas where pedestrian stops by the New York City Police Department are different from violent crimes for 2006-2016.