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Assessing whether individual characteristics of police officers such as age, race, and prior performance influence police behavior has been a long-standing topic of social science research. The effect of officer characteristics on their risk of shooting people is confounded by police assignments and by the environmental factors associated with those assignments. This article provides a method to separate out the influence of individual officer characteristics from environmental factors. Using data from the New York City Police Department (NYPD) and the Major Cities’ Chiefs Association (MCCA), the analysis finds that police officers who join the NYPD later in their careers have a lower shooting risk: for each additional year of their recruitment age, the odds of being shooters declines by 10 percent. Both officer race and prior problem behavior (e.g., losing a firearm, crashing a department vehicle) predict up to three times greater odds of shooting, yet officers who made numerous misdemeanor arrests were four times less likely to shoot.

Allegations of racially biased policing are a contentious issue in many communities. Processes that flag potential problem officers have become a key component of risk management systems at major police departments. We present a statistical method to flag potential problem officers by blending three methodologies that are the focus of active research efforts: propensity score weighting, doubly robust estimation, and false discovery rates. Compared with other systems currently in use, the proposed method reduces the risk of flagging a substantial number of false positives by more rigorously adjusting for potential confounders and by using the false discovery rate as a measure to flag officers. We apply the methodology to data on 500,000 pedestrian stops in New York City in 2006. Of the nearly 3,000 New York City Police Department officers regularly involved in pedestrian stops, we flag 15 officers who stopped a substantially greater fraction of black and Hispanic suspects than our statistical benchmark predicts.

\"Stop-and-frisk\" is a policing tactic in which officers briefly stop suspicious individuals to determine whether they are criminally involved and, if they have reasonable suspicion that the person has a weapon, pat down the detained individual. The Supreme Court instructed in Terry v. Ohio (1968) that this practice is constitutional. Yet stop-and-frisk has become a contentious legal and policy issue. The error-filled clash at the presidential debate demonstrates the intense disagreement - and misconceptions - about the issue. Yet there is a middle ground. We need police officers to intervene when they observe suspicious activity. We need criminals to know that they are at risk of being stopped if an officer notes that they match a suspect description or appear to be working a lock on a bicycle, car, home, or business. We want to ensure high-quality, non-discriminatory, professional stops. My role in the stop-and-frisk question began in 2007, before the 2008 stop-and-frisk of David Floyd, the lead plaintiff in the case that would cause stops in New York City to plummet by more than 95 percent from their peak. New York Police Department (NYPD) Commissioner Kelly asked me to conduct an analysis of NYPD's UF250 data, the data collected from the Stop, Question, and Frisk Report Worksheet that officers complete after conducting stops. In my analysis, while I \"found some evidence of unequal treatment across racial groups,\" I also concluded that the problem was \"not of a massive scale, but rather one that police management can address with effective supervision, monitoring of police activity, and effective interventions when problems are identified.\"

Objectives We use the temporary closings of subway stations in Philadelphia to examine the effects of public transit on crime in the nearby communities. Methods The Southeastern Pennsylvania Transportation Authority (SEPTA), a regional public transportation authority in Philadelphia, has experienced two labor strikes occurring in 2009 and 2016. During these two strikes, public transit was disrupted for nearly 1 week. We used the closings of 47 subway stations during strikes to evaluate the place-based effect of public transit on crime. We also examined whether these effects varied with the ridership level in each station. Results Total crime decreased by 38% within 100 m of the stations when subway stations were closed due to the strike and by 10% within 500 m. The primary drivers of the decrease were violent crimes, including assault and robbery. However, there is no obvious relationship between the closings of stations and the change in property crimes and mischief. In addition, crimes in stations with higher ridership declined less than those in stations with medium ridership when strikes occurred. Conclusions Public transit in Philadelphia appears to be associated with elevated violent crime in the surrounding community. Areas around subway stations may require greater security to control crime during its operational hours. Passengers may serve as guardians to deter some crimes when the public transit is operational.

Every policy decision involves trade-offs. Professor Fagan's point enumerates several of the key trade-offs that cities debating stop-and-frisk must make. As in my Point essay, I argue that there is a middle ground to stop-and-frisk where police continue to engage with suspicious individuals and take proactive steps to address crime problems. The trade-off is that police actions inevitably involve false positives. I do not think Professor Fagan and I would disagree that stop-and-frisk has a role in law enforcement, but perhaps would quibble on the trade-offs we would be willing to make. Differential valuation of true positives (correctly identifying a suspect) and false positives (detaining someone who is innocent) is not a debate just for Professor Fagan and me, but for every community weighing crime prevention against intrusiveness of police.

The aim of this study was to examine hospital performance measures that account more comprehensively for unique mixes of patients' characteristics. Nationwide cohort registry-based study within a population-based health care system. In this study, 331,513 patients discharged with a primary cardiovascular diagnosis from 1 of 26 Danish hospitals during 2011-2015 were included. Data covering all Danish hospitals were drawn from the Danish National Patient Registry and the Danish National Health Service Prescription Database. Thirty-day post-admission mortality rates, 30-day post-discharge readmission rates, and the associated numbers needed to harm were measured. For each index hospital, we used a non-parametric logistic regression model to compute propensity scores. Propensity score weighted patients treated at other hospitals collectively resembled patients treated at the index hospital in terms of age, sex, primary discharge diagnosis, diagnosis history, medications, previous cardiac procedures, and comorbidities. Outcomes for the weighted patients treated at other hospitals formed benchmarks for the index hospital. Doubly robust regression formally tested whether the outcomes of patients at the index hospital differed from the outcomes of the patients used to form the benchmarks. For each index hospital, we computed the false discovery rate, ie, the probability of being incorrect if we claimed the hospital differed from its benchmark. Five hospitals exceeded their benchmark for 30-day mortality rates, with the number needed to harm ranging between 55 and 137. Seven hospitals exceeded their benchmark for readmission, with the number needed to harm ranging from 22 to 71. Our benchmarking approach flagged fewer hospitals as outliers compared with conventional regression methods. Conventional methods flag more hospitals as outliers than our benchmarking approach. Our benchmarking approach accounts more thoroughly for differences in hospitals' patient case mix, reducing the risk of false-positive selection of suspected outliers. A more comprehensive system of hospital performance measurement could be based on this approach.

The key problem in testing for racial profiling in traffic stops is estimating the risk set, or \"benchmark,\" against which to compare the race distribution of stopped drivers. To date, the two most common approaches have been to use residential population data or to conduct traffic surveys in which observers tally the race distribution of drivers at a certain location. It is widely recognized that residential population data provide poor estimates of the population at risk of a traffic stop; at the same time, traffic surveys have limitations and are more costly to carry out than the alternative that we propose herein. In this article we propose a test for racial profiling that does not require explicit, external estimates of the risk set. Rather, our approach makes use of what we call the \"veil of darkness\" hypothesis, which asserts that police are less likely to know the race of a motorist before making a stop after dark than they are during daylight. If we assume that racial differences in traffic patterns, driving behavior, and exposure to law enforcement do not vary between daylight and darkness, then we can test for racial profiling by comparing the race distribution of stops made during daylight to the race distribution of stops made after dark. We propose a means of weakening this assumption by restricting the sample to stops made during the evening hours and controlling for clock time while estimating daylight/darkness contrasts in the race distribution of stopped drivers. We provide conditions under which our estimates are robust to a substantial nonreporting problem present in our data and in many other studies of racial profiling. We propose an approach to assess the sensitivity of our results to departures from our maintained assumptions. Finally, we apply our method to data from Oakland, California and find that in this example the data yield little evidence of racial profiling in traffic stops.

Objectives. To determine if remediating blighted vacant urban land reduced firearm shooting incidents resulting in injury or death. Methods. We conducted a cluster randomized controlled trial in which we assigned 541 randomly selected vacant lots in Philadelphia, Pennsylvania, to 110 geographically contiguous clusters and randomly assigned these clusters to a greening intervention, a less-intensive mowing and trash cleanup intervention, or a no-intervention control condition. The random assignment to the trial occurred in April and June 2013 and lasted until March 2015. In a difference-in-differences analysis, we assessed whether the 2 treatment conditions relative to the control condition reduced firearm shootings around vacant lots. Results. During the trial, both the greening intervention, −6.8% (95% confidence interval [CI] = −10.6%, −2.7%), and the mowing and trash cleanup intervention, −9.2% (95% CI = −13.2%, −4.8%), significantly reduced shootings. There was no evidence that the interventions displaced shootings into adjacent areas. Conclusions. Remediating vacant land with inexpensive, scalable methods, including greening or minimal mowing and trash cleanup, significantly reduced shootings that result in serious injury or death. Public Health Implications. Cities should experiment with place-based interventions to develop effective firearm violence–reduction strategies. Trial Registration. This trial was registered with the International Standard Randomized Controlled Trial Number (study ID ISRCTN92582209; http://www.isrctn.com/ISRCTN92582209 ).