This study finds that the rich–poor gap, level of citizens’ trust in institutions, economic opportunity, and public welfare spending are all related to firearm homicide rates in the US. Further establishing the causal nature of these associations and modifying these social determinants may help to address the growing gun violence epidemic and reverse recent life expectancy declines among Americans.

This study used negative binomial regression models and geolocated gun homicide incident data from January 1, 2015, to December 31, 2015, to explore and compare the independent associations of key state-, county-, and neighborhood-level social determinants of health—social mobility, social capital, income inequality, racial and economic segregation, and social spending—with neighborhood firearm-related homicides and mass shootings in the United States, accounting for relevant state firearm laws and a variety of state, county, and neighborhood (census tract [CT]) characteristics. Latitude and longitude coordinates on firearm-related deaths were previously collected by the Gun Violence Archive, and then linked by the British newspaper The Guardian to CTs according to 2010 Census geographies. The study population consisted of all 74,134 CTs as defined for the 2010 Census in the 48 states of the contiguous US. The final sample spanned 70,579 CTs, containing an estimated 314,247,908 individuals, or 98% of the total US population in 2015. The analyses were based on 13,060 firearm-related deaths in 2015, with 11,244 non-mass shootings taking place in 8,673 CTs and 141 mass shootings occurring in 138 CTs. For area-level social determinants, lag periods of 3 to 17 years were examined based on existing theory, empirical evidence, and data availability. County-level institutional social capital (levels of trust in institutions), social mobility, income inequality, and public welfare spending exhibited robust relationships with CT-level gun homicide rates and the total numbers of combined non-mass and mass shooting homicide incidents and non-mass shooting homicide incidents alone. A 1–standard deviation (SD) increase in institutional social capital was linked to a 19% reduction in the homicide rate (incidence rate ratio [IRR] = 0.81, 95% CI 0.73–0.91, p < 0.001) and a 17% decrease in the number of firearm homicide incidents (IRR = 0.83, 95% CI 0.73–0.95, p = 0.01). Upward social mobility was related to a 25% reduction in the gun homicide rate (IRR = 0.75, 95% CI 0.66–0.86, p < 0.001) and a 24% decrease in the number of homicide incidents (IRR = 0.76, 95% CI 0.67–0.87, p < 0.001). Meanwhile, 1-SD increases in the neighborhood percentages of residents in poverty and males living alone were associated with 26%–27% and 12% higher homicide rates, respectively. Study limitations include possible residual confounding by factors at the individual/household level, and lack of disaggregation of gun homicide data by gender and race/ethnicity.

Gun violence has shortened the average life expectancy of Americans, and better knowledge about the root causes of gun violence is crucial to its prevention. While some empirical evidence exists regarding the impacts of social and economic factors on violence and firearm homicide rates, to the author’s knowledge, there has yet to be a comprehensive and comparative lagged, multilevel investigation of major social determinants of health in relation to firearm homicides and mass shootings.

Data Availability: The de-identified data are available through the openICPSR’s repository at https://www.openicpsr.org (Project ID # openicpsr-115063), which is maintained by the University of Michigan’s Inter-university Consortium for Political and Social Research. Users will need to log in and agree to the consortium’s terms of use.

Copyright: © 2019 Daniel Kim. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Notably, studies so far have focused analytically on single social determinants, which can introduce confounding by other social determinants at the same and other geographic levels, and can hamper our ability to directly compare the relative sizes of impacts across social determinants. To date, no studies to the author’s knowledge have comprehensively investigated and compared major social determinants of health as drivers of firearm homicides and mass shootings. To address this gap and to capitalize on the recent public release of geolocation data on reported gun homicides for the year 2015 in the US, this study explores and compares the independent associations of state-, county-, and neighborhood-level income inequality, racial and economic segregation, social spending, social capital, and social mobility with neighborhood firearm homicides and mass shootings, after taking into account relevant state firearm laws and state-, county-, and neighborhood-level characteristics. In contrast to previous studies, this study directly compares the magnitude of associations across multiple social determinants in the same models. This study further explores associations using multiple lag periods for 2 key social determinants, social spending and income inequality, to identify the most sensitive lag periods.

Overall, the empirical literature linking area-level social determinants of health to homicides is limited by factors including the study designs used (e.g., primarily ecological and cross-sectional rather than multilevel and lagged/longitudinal designs), studies’ controlling for only a limited number of covariates (with a greater possibility of residual confounding), and studies’ lack of specificity to firearm-related homicides. Investigations on social mobility are also sparse. Furthermore, results from cross-sectional studies that do not employ time lags are problematic due to the possibility of reverse causation; for example, higher levels of violent crime have been linked to lower levels of social trust in neighbors in the subsequent year [ 13 ], such that the direction of the association between social trust and violent crime is less clear if temporal order is not taken into account. Even in lagged cross-sectional or longitudinal studies of social determinants and homicide, the lack of exploration of varying lag periods to identify the most sensitive lag periods is a critical empirical gap.

Identifying the key drivers of firearm violence is crucial to its prevention. While a growing literature has explored the impacts of state gun control policies [ 4 – 7 ], there are increasing calls for comprehensive, multidisciplinary approaches to unpack and address the root causes—the social determinants—of gun homicides [ 7 – 9 ]. As described by the World Health Organization, the social determinants of health are the upstream social and economic conditions in which people are born, grow, live, work, and age that shape individual as well as group differences in health status [ 10 ]. These social determinants of health include social capital, reflecting informal and formal social ties within society; income inequality, the divide between the rich and poor; residential racial segregation and economic segregation, which correspond to the physical separation of 2 or more groups as defined by race/ethnicity and socioeconomic status, respectively, into different neighborhoods; non-medical social spending such as government spending on welfare and education; and intergenerational social mobility—the ability of children to climb higher on the social ladder than their parents [ 11 , 12 ].

The US is grappling with a worsening epidemic of gun homicides. Between 2001 and 2013, guns took the lives of more Americans than the total number killed by war, AIDS, illegal drug overdoses, and terrorism combined during the same period [ 1 ]. Gun homicides rose consecutively in 2015, 2016, and 2017 [ 2 ], and are the second leading cause of injury death among youths and young adults [ 3 ].

Methods

All study analyses were planned (without a formal prospective analysis plan document) prior to undertaking this lagged multilevel cross-sectional study, with the exception of the analysis of additional lag periods for social spending and income inequality, as noted in the statistical analysis section below.

Study population The study population consisted of all 74,134 census tracts (CTs) as defined for the 2010 Census in the 48 states of the contiguous US. CTs are geographic entities within counties (or the statistical equivalent of counties) that generally vary in population size from 1,200 to 8,000 residents.

Outcomes Primary outcomes were the 2015 firearm-related homicide rate and the total number of firearm homicide incidents, non-mass shooting homicide incidents, and mass shooting homicide incidents, all measured at the CT level. Latitude and longitude coordinates on firearm-related homicides were previously collected by the Gun Violence Archive (GVA), a non-profit organization that tracks shootings and gun deaths in the US using media reports [14]. The GVA uses automated queries, and manually searches over 2,000 media sources, aggregates, police blotters, police media outlets, and other sources daily. Each incident is verified by both initial researchers and secondary validation processes [14]. The geolocated gun homicide data do not include information to allow for disaggregating homicides by victim and perpetrator characteristics including race and gender, and whether the homicides were perpetrated by law enforcement versus other individuals. In 2017, the British newspaper The Guardian linked each of the geolocated gun murders in 2015 to a CT according to 2010 Census geographies and publicly released the data [15]. For each CT, the homicide rate was calculated as the total number of reported firearm homicides divided by the total population in 2015. Non-mass shooting homicide and mass shooting homicide incidents were defined as the total number of reported incidents in the CT in 2015 in which an individual used a firearm to kill 1–2 and 3 or more persons, respectively, not including the shooter. This definition of a mass shooting comes from the Investigative Assistance for Violent Crimes Act of 2012 signed into law by the US Congress in 2013 [16]. For the analysis of non-mass shooting homicide incidents, 138 CTs with at least 1 mass shooting incident were excluded from the sample. For the analysis of mass shooting homicides, incidents with 1–2 deaths were not included. For area-level social determinants, average lag periods of 3 years up to a maximum of 17 years were examined. With the exception of income inequality, for which evidence points to lag periods on the order of a decade or longer [17,18], and residential segregation, for which available data were for the year 2000, year-specific social determinant data corresponding to shorter lag periods (a decade or less) were selected, to better reflect plausible lag periods for hypothesized effects. A more detailed description of the primary evidence on and the rationale for the choice of lag periods used in the main models is provided in S2 Table [19–37]. For state and local (county and municipal) social spending and county-level income inequality, for which data across multiple years were available, lag periods of 5 and 7 years, respectively, were originally used. In response to the peer review process, additional average lag periods of 3, 7, and 10 years for social spending and 3 and 17 years for income inequality were investigated (using unitary changes of $250 per capita for social spending and a 0.1-Gini unit increase for income inequality), given that the most sensitive lag periods have yet to be established (S2 Table). Models using 3-year lags for social spending with otherwise the same model specifications did not converge. Estimates according to the 5-year average lag for social spending and the 17-year average lag for income inequality exhibited the most consistent and smallest p-values, signifying a lower likelihood of being observed due to chance (see S3 and S4 Tables). These lag periods were hence used for the main analyses.

County- and commuting zone-level social determinant exposures County-level intergenerational social mobility was based on Chetty and colleagues’ measure of absolute upward income mobility, which they have described as an indicator of economic opportunity [11]. This measure was constructed using 2010–2012 income tax return data on over 10 million individuals born in 1980–1982 whose parents could be identified through tax return data and whose mean parent income between 1996 and 2000 was strictly positive (thereby excluding 1.2% of children). Counties were assigned on the basis of the child’s zip code of residence at the age of 15 years (1995–1997). For a given county, this measure signifies the average income rank that individuals born to the poorest quartile of parents were able to attain. Higher average income ranks reflect greater intergenerational social mobility [11]. County-level income inequality was measured using the Gini coefficient derived from household income reported in the 2006–2010 and 2010–2014 American Community Survey (ACS) [19] and the Internal Revenue Service Statistics of Income sample [20]. County-level Gini coefficients estimated using the latter data were calculated by Chetty and colleagues for mean family income in 1996–2000 according to federal income tax records for the parents included in their core sample used to calculate social mobility [11]. The Gini coefficient can assume theoretical values ranging from 0 (perfect equality) to 1 (perfect inequality). Measures of residential segregation according to race/ethnicity and income were constructed by Chetty and colleagues using the 2000 Census [11]. Racial segregation was measured using Theil’s entropy index, which captures the extent to which the racial distribution in each CT deviates from the overall racial distribution in the commuting zone (CZ), an aggregate of counties meant to spatially represent the local labor market. Following Iceland [38], Chetty et al. [11] calculated H, the degree of racial segregation in the CZ, using the formula where pop j indicates the total population of CT j and pop total denotes the total population of the CZ; E is an entropy index that measures the level of racial diversity in the CZ; j indexes CTs in the CZ; and E j denotes the level of racial diversity in tract j. H takes on a theoretical value of 1 when there is no racial heterogeneity within CTs (corresponding to complete segregation)—since E j = 0 in all tracts—and a value of 0 when all tracts have the same racial composition as the CZ as a whole, since E j = E [11]. Following Reardon and Firebaugh [39], Chetty et al. [11] estimated the degree to which individuals below the xth percentile of the local household income distribution were segregated from individuals above the xth percentile in each CZ. For each CZ, they then calculated income segregation as the weighted average of income segregation at each percentile, with greater weight placed on percentiles in the middle of the income distribution. Social capital was measured at the county level using 2 validated indices, a community social capital index and an institutional social capital index [24]. The community social capital index was designed to measure levels of both informal and formal civic engagement/participation at the community level, while the institutional social capital index was developed to capture levels of trust and confidence in institutions that extend beyond the community and include the government, the media, and corporations. To construct these county-level social capital indices, the developers first created state-level indices. Principal components analysis (PCA) was used to identify different domains of social capital [24], and then both content validity and internal consistency reliability were employed to delineate the items to include in the state-level community and institutional social capital indices. The state community and institutional social capital indices had Cronbach’s α values of 0.92 and 0.72, respectively [24]. Because indicators of informal civic engagement at the county level were unavailable, the developers created an index of informal civic engagement at the state level using available data from the Current Population Survey (CPS). The index score was the first principal component score combining 6 items. This index score was then assigned to each county within a state. Next, at the county level, the developers created 5 different candidate indices, using various combinations of the informal civic engagement index score and the numbers of county-level membership organizations per capita, non-religious non-profit organizations per capita, and congregations per capita. These indices were estimated using PCA. Finally, for each of the candidate indices, the developers computed the population-weighted average index score across a state’s counties, and calculated the correlation between each of the state averages and the state-level community social capital index. Out of the candidate indices, the index with the strongest correlation was selected [24]. A similar process was undertaken by the developers for the county-level institutional social capital index due to the lack of data at the county level on confidence in institutions. At the state level, an institutional confidence index was first created that included 3 institutional confidence variables [24]. Three versions of a county-level institutional social capital index were then developed using different combinations of presidential voting rate, census response rate, and the confidence index. The developers next calculated population-weighted state averages across a state’s counties and compared the correlations with the state-level institutional social capital index to determine the items in the final index [24]. Estimated correlations with demographic, socioeconomic, and health factors that measure criterion and construct validity indicated that the county-level community and institutional social capital indices performed comparably to or even better than the Penn State social capital index, another US county-level social capital index [24]. The community and institutional social capital indices also improved upon the Penn State social capital index by including indicators tapping into additional concepts related to social capital such as volunteerism and informal community engagement [24]. The county-level community social capital index combined the number of membership organizations per capita, the number of non-profit (religious and non-religious) organizations per capita, and the percentages of adults who reported in the past year volunteering for an organization, attending a public meeting to discuss community affairs, working with neighbors to improve the community, serving on a committee or as an officer of a group, attending a public meeting where political issues were discussed, and participating in a march, protest, rally, or demonstration. Data sources consisted of the 2008 and 2013 Civic Engagement Supplements to the CPS and the 2015 Volunteer Supplement to the CPS [24], the 2015 Internal Revenue Service Business Master File [26], and the 2010 Religious Congregations and Membership Study [24]. The county-level institutional social capital index was derived from the average rate at which citizen adults of voting age cast ballots in the 2012 and 2016 presidential elections, the response rate for residents returning the 2010 decennial census questionnaire through the mail, and the percentage of adults with “great” or “some” confidence in institutions (corporations, the media, and public schools) “to do what is right”. These data came from US Election Assistance Commission annual reports [27,28], the Census Bureau, and the 2013 Civic Engagement Supplement to the CPS [25].

State and local social determinant exposures State and local government social spending (public welfare, education, protection, and total per capita) corresponded to the 2005, 2008, 2010, and 2012 fiscal years as reported by the Annual Survey of State and Local Government Finances [29]. Welfare spending encompasses state supplements for unemployment insurance, workers’ compensation, work incentive programs, public assistance programs (e.g., Aid to Families with Dependent Children), and state supplements for the Supplemental Security Income (SSI) program for the aged, blind, and disabled. Education spending consists of local spending on elementary and secondary school education and financial aid to college students, with higher spending conceivably reducing the financial burden of parents. Protection spending reflects spending on state and local police, corrections/prisons, and local fire protection. S2 Table provides a list of the key social determinants examined, their data sources, and their average lag periods in relation to the outcomes, both in the current study and in previous studies. Additional details on how lag periods were selected are given within and beneath the table.

Covariates All models controlled for multiple CT-, county-, CZ-, and state-level characteristics. At all geographic levels, median household income and the percentage of black individuals were covariates, since they were considered key potential confounders, particularly for social determinants (e.g., social mobility) measured at the same geographic level. For example, all models controlled for median household income at the county level. This should have had the effect of reducing confounding by county-level socioeconomic status of the associations between the social capital indices and gun homicides. CT-level demographic and socioeconomic covariates included median household income, percentage with high school education, percentage black, percentage male, percentage unemployed, percentage receiving cash assistance, percentage in poverty, percentage age 20–34 years, percentage of males living alone, and total population centered in the year 2012 (based on 5-year estimates from the 2010–2014 ACS). Many of these covariates have been found to be related to violent crime or have been included as control variables in other homicide studies [5,40–43]. At the county level, covariates included median household income and the percentage black (5-year averages based on the 2006–2010 ACS), and population density and property crime rates, based on Area Resource File data obtained through the Inter-university Consortium for Political and Social Research archive [44]. At the CZ level, models were adjusted for median household income, the percentage black, and a dichotomous variable to indicate whether the CZ corresponded to an urban area. State-level covariates included total state and local social spending per capita and state gun control policy indicators for concealed weapon carry laws, requirements for gun dealers to report records to the state, and state background check laws—with each of these indicators being shown in previous studies to be associated with firearm-related homicide rates [4–6]. State fixed effects or fixed effects for the 9 census statistical divisions were further included to control for area-level socioeconomic and institutional factors that might be jointly correlated with the social determinants examined and firearm-related homicides.