Measuring the hidden burden of violence: use of explicit and proxy codes in Minnesota injury hospitalizations, 2004-2014

Economic Hardship and Violence: A Comparison of County-Level Economic Measures in the Prediction of Violence-Related Injury

Economic hardship may lead to a wide range of negative outcomes, including violence. However, existing literature on economic hardship and violence is limited by reliance on official reports of violence and conflation of different measures of economic hardship. The goals of this study are to measure how violence-related injuries are associated with five measures of county-level economic shocks: unemployment rate, male mass layoffs, female mass layoffs, foreclosure rate, and unemployment rate change, measured cross-sectionally and by a 1-year lag. This study measures three subtypes of violence outcomes (child abuse, elder abuse, and intimate partner violence). Yearly county-level data were obtained on violence-related injuries and economic measures from 2005 to 2012 for all 87 counties in Minnesota. Negative binomial models were run regressing the case counts of each violence outcome at the county-year level on each economic indicator modeled individually, with population denominator offsets to yield incidence rate ratios. Crude models were run first, then county-level socio-demographic variables and year were added to each model, and finally fully-adjusted models were run including all socio-demographic variables plus all economic indicators simultaneously. In the fully-adjusted models, a county's higher foreclosure rate is the strongest and most consistently associated with an increase in all violence subtypes. Unemployment rate is the second strongest and most consistent economic risk factor for all violence subtypes. Lastly, there appears to be an impact of gender specific to economic impacts on child abuse; specifically, male mass-lay-offs were associated with increased rates while female mass-lay-offs were associated with decreased rates. Understanding the associations of different types of economic hardship with a range of violence outcomes can aid in developing more holistic prevention and intervention efforts.

Violence in the Great Recession

Substantial evidence suggests that economic hardship causes violence. However, a large majority of this research relies on observational studies that use traditional violence surveillance systems that suffer from selection bias and over-represent vulnerable populations, such as people of color. To overcome limitations of prior work, we employed a quasi-experimental design to assess the impact of the Great Recession on explicit violence diagnoses (injuries identified to be caused by a violent event) and proxy violence diagnoses (injuries highly correlated with violence) for child maltreatment, intimate partner violence, elder abuse, and their combination. We used Minnesota hospital data (2004-2014), conducting a difference-in-differences analysis at the county level (n = 86) using linear regression to compare changes in violence rates from before the recession (2004-2007) to after the recession (2008-2014) in counties most affected by the recession, versus changes over the same time period in counties less affected by the recession. The findings suggested that the Great Recession had little or no impact on explicitly identified violence; however, it affected proxy-identified violence. Counties that were more highly affected by the Great Recession saw a greater increase in the average rate of proxy-identified child abuse, elder abuse, intimate partner violence, and combined violence when compared with less-affected counties.