Income inequality and firearm homicide in the US: a county-level cohort study

Abstract

ObjectiveIncome inequality has been rising in the US and thought to be associated with violence especially homicide. About 75% of homicides involve firearms. We quantified the association between county-level income inequality and all-race/ethnicity and race/ethnicity-specific firearm homicide rates among individuals aged 14–39 years.MethodsWe conducted a cohort study of US counties to examine the association of Gini Index (ranging from 0 [perfect income equality] to 1.0 [perfect income inequality]) separately measured in 1990 and 2000 with all-race/ethnicity and race/ethnicity-specific firearm homicide rates in 2005–2015. Generalised linear mixed models with Poisson distribution including a random intercept for state provided IRRs and 95% CIs. Bayesian Poisson-lognormal hierarchical modelling with integrated nested Laplace approximations was used in exploratory spatial analyses. Models accounted for county-level age, sex and race/ethnicity composition, crime rate, deprivation, social capital, urbanicity, and firearm ownership.FindingsThe Gini Index was associated with firearm homicide rates among all races/ethnicities. After accounting for contextual determinants of firearm homicide, the association persisted among African–Americans. In this group, a 1 SD greater Gini Index in 1990 (IRR=1.09; 95% CI 1.02 to 1.16) and 2000 (IRR=1.09; 95% CI 1.01 to 1.17) was associated with greater firearm homicide rates in 2005–2015. Exploratory spatial analyses did not materially change the results.ConclusionPolicies addressing the gap between the rich and the poor deserve further considerations for reducing firearm homicide rates. Incorporating income inequality to refine measures of socioeconomic position may advance public health and clinical research and practice for firearm violence prevention.

Social determinants of health in relation to firearm-related homicides in the United States: A nationwide multilevel cross-sectional study

“…In a study among Chicago neighborhoods, there was a similar correlation of 0.75 between income inequality and the homicide rate [86]. A recent ecological, lagged cross-sectional study [22] observed a positive association between US county-level income inequality and the homicide rate, although the association only attained significance at the 5% level in black individuals. Each 1-SD unit increase in the county-level Gini coefficient was related to a 9% increase in the county-level firearm homicide rate among black individuals 5 to 15 years later, controlling for county-level age, gender, and race/ethnicity composition, crime rate, deprivation, social capital, urbanicity, and firearm ownership [22].…”

“…Likewise, to date, findings from empirical studies of income inequality and homicide have been mixed. Some studies have confirmed the theorized positive association [22,74], whereas other studies have found no association [75] or even an inverse relationship [61,76,77]. Possible explanations for this mixed evidence include the ecological and cross-sectional design of many of the studies, a study design which is prone to confounding and reverse causation.…”

Income Inequality across the Rural‐Urban Continuum in the United States, 1970–2016*

“…In contrast, virtually all of the counties characterized by low inequality in 1970 had transitioned to moderate or high levels of inequality by 2016. Fifth and finally, increases in the average level of inequality among counties were paralleled by non-trivial growth in the share of populations residing in high-inequality places and thus potentially subject to the adverse social and epidemiological effects of such places (Eckenrode et al 2014;Monnat 2018;Rowhani-Rahbar et al 2019). This growth has been particularly dramatic among the metropolitan population, such that the predominance of non-metropolitan counties in high-inequality contexts in 1970 was reversed by 2016.…”

“…Fifth and finally, increases in the average level of inequality among counties were paralleled by non‐trivial growth in the share of populations residing in high‐inequality places and thus, potentially subject to the adverse social and epidemiological effects of such places (Eckenrode et al 2014; Monnat 2018; Rowhani‐Rahbar et al 2019). This change has been particularly dramatic among the metropolitan population, which had very little (1.0 percent) exposure to high inequality (and 30.0 percent exposure to low inequality) in 1970.…”

“…Third and finally, an updated account of local inequality levels across the United States—and particularly one that tracks populations' exposure to inequality—is important given the second‐order consequences of inequality levels in one's place of residence. Exposure to high inequality in particular is correlated with a range of adverse outcomes, including drug‐related mortality, firearm homicides, child maltreatment, and restricted access to the democratic process (Eckenrode et al 2014; Monnat 2018; Rowhani‐Rahbar et al 2019; Smith 2012). Understanding where and to what degree sub‐national inequality has increased (or decreased) over time should therefore be of interest to a wide range of stakeholders.…”