Dataset Information

A Multi-Level Bayesian Analysis of Racial Bias in Police Shootings at the County-Level in the United States, 2011-2014.

ABSTRACT: A geographically-resolved, multi-level Bayesian model is used to analyze the data presented in the U.S. Police-Shooting Database (USPSD) in order to investigate the extent of racial bias in the shooting of American civilians by police officers in recent years. In contrast to previous work that relied on the FBI's Supplemental Homicide Reports that were constructed from self-reported cases of police-involved homicide, this data set is less likely to be biased by police reporting practices. County-specific relative risk outcomes of being shot by police are estimated as a function of the interaction of: 1) whether suspects/civilians were armed or unarmed, and 2) the race/ethnicity of the suspects/civilians. The results provide evidence of a significant bias in the killing of unarmed black Americans relative to unarmed white Americans, in that the probability of being {black, unarmed, and shot by police} is about 3.49 times the probability of being {white, unarmed, and shot by police} on average. Furthermore, the results of multi-level modeling show that there exists significant heterogeneity across counties in the extent of racial bias in police shootings, with some counties showing relative risk ratios of 20 to 1 or more. Finally, analysis of police shooting data as a function of county-level predictors suggests that racial bias in police shootings is most likely to emerge in police departments in larger metropolitan counties with low median incomes and a sizable portion of black residents, especially when there is high financial inequality in that county. There is no relationship between county-level racial bias in police shootings and crime rates (even race-specific crime rates), meaning that the racial bias observed in police shootings in this data set is not explainable as a response to local-level crime rates.

SUBMITTER: Ross CT

PROVIDER: S-EPMC4634878 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundFatal mass shootings, defined as four or more people killed by gunfire, excluding the perpetrator, account for a small percentage of firearm homicide fatalities. Research has not extensively focused on the role of domestic violence (DV) in mass shootings in the United States. This study explores the role of DV in mass shootings in the United States.MethodsUsing 2014-2019 mass shooting data from the Gun Violence Archive, we indexed our data by year and mass shooting and collected the number of deaths and injuries. We reviewed news articles for each mass shooting to determine if it was 1) DV-related (i.e., at least one victim of a mass shooting was a dating partner or family member of the perpetrator); 2) history of DV (i.e., the perpetrator had a history of DV but the mass shooting was not directed toward partners or family members); or 3) non-DV-related (i.e., the victims were not partners or family members, nor was there mention of the perpetrator having a history of DV). We conducted descriptive analyses to summarize the percent of mass shootings that were DV-related, history of DV, or non-DV-related, and analyzed how many perpetrators died during the incidents. We conducted one-way ANOVA to examine whether there were differences in the average number of injuries or fatalities or the case fatality rates (CFR) between the three categories. One outlier and 17 cases with unknown perpetrators were excluded from our main analysis.ResultsWe found that 59.1% of mass shootings between 2014 and 2019 were DV-related and in 68.2% of mass shootings, the perpetrator either killed at least one partner or family member or had a history of DV. We found significant differences in the average number of injuries and fatalities between DV and history of DV shootings and a higher average case fatality rate associated with DV-related mass shootings (83.7%) than non-DV-related (63.1%) or history of DV mass shootings (53.8%). Fifty-five perpetrators died during the shootings; 39 (70.9%) died by firearm suicide, 15 (27.3%) were killed by police, and 1 (1.8%) died from an intentional overdose.ConclusionsMost mass shootings are related to DV. DV-related shootings had higher CFR than those unrelated to DV. Given these findings, restricting access to guns by perpetrators of DV may affect the occurrence of mass shootings and associated casualties.

Project description:ObjectiveTo determine which county-level social, economic, demographic, epidemiologic and access to care factors are associated with Latino/non-Latino White disparities in prevalence of diagnosed HIV infection.Methods and findingsWe used 2016 county-level prevalence rates of diagnosed HIV infection rates for Latinos and non-Latino Whites obtained from the National HIV Surveillance System and factors obtained from multiple publicly available datasets. We used mixed effects Poisson modeling of observed HIV prevalence at the county-level to identify county-level factors that explained homogeneous effects across race/ethnicity and differential effects for Latinos and NL-Whites. Overall, the median Latinos disparity in HIV prevalence is 2.4; 94% of the counties have higher rates for Latinos than non-Latinos, and one-quarter of the counties' disparities exceeded 10. Of the 41 county-level factors examined, 24 showed significant effect modification when examined individually. In multi-variable modeling, 11 county-level factors were found that significantly affected disparities. Factors that increased disparity with higher, compared to lower values included proportion of HIV diagnoses due to injection drug use, percent Latino living in poverty, percent not English proficient, and percent Puerto Rican. Latino disparities increased with decreasing percent severe housing, drug overdose mortality rate, percent rural, female prevalence rate, social association rate, percent change in Latino population, and Latino to NL-White proportion of the population. These factors while significant had minimal effects on diminishing disparity, but did substantially reduce the variance in disparity rates.ConclusionsLarge differences in HIV prevalence rates persist across almost all counties even after controlling for county-level factors. Counties that are more rural, have fewer Latinos, or have lower NL-White prevalence rates tend to have higher disparities. There is also higher disparity when community risk is low.

Dataset Information

A Multi-Level Bayesian Analysis of Racial Bias in Police Shootings at the County-Level in the United States, 2011-2014.

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