Measuring the hidden burden of violence: Use of explicit and proxy diagnoses codes for violence identification and its association with economic hardship

Abstract

Violence is a common and serious public health problem. Substantial evidence suggests that economic hardship causes violence. However, a large majority of this research relies on traditional violence surveillance systems that may suffer from selection bias and potentially over-represent the most vulnerable populations, such as people of color. Emerging research has operationalized violence in new ways by identifying injuries highly correlated with violence (proxy-identified violence) in hospital discharge data, which may be more representative of the total population, in conjunction with explicitly identified violence (injuries identified in the medical record to be caused by a violent event). The three studies presented here investigated how economic hardship is associated with these different identifications of violence. Using Minnesota hospital discharge data from 2004 to 2014, this dissertation included three studies that investigated 1) the trends of child maltreatment, elder abuse, and intimate partner abuse in Minnesota by county from 2004 to 2014, and the association of county-level demographic characteristics with violence rates as measured through explicit codes, proxy codes, and a combination of the two; 2) the associations of a range of county-level economic hardship indicators (unemployment rate, male mass layoffs, female mass layoffs, foreclosure rate, and unemployment rate change) with rates of explicit- and proxy-identified violence-related child abuse, elder abuse, and intimate partner violence (IPV) injuries; and 3) the change of county-level violence victimization rates (child abuse, elder abuse, IPV, and all subtypes combined) as a function of the Great Recession comparing more- to less-affected counties with a quasi-experimental design. The main finding from paper one was that the patterns of county-level violence differed depending on whether one used explicit or proxy codes. In particular, explicit codes suggested that child abuse and IPV trends were flat or decreased slightly from 2004 to 2014, while proxy codes suggested the opposite. Elder abuse increased during this timeframe for both explicit and proxy codes, but more dramatically when using proxy codes. In regard to the associations between county level characteristics and each violence subtype, previously identified county-level risk factors were more strongly related to explicitly-identified violence than to proxy-identified violence. Given the larger number of proxy-identified cases as compared with explicit-identified violence cases, the trends and associations of combined codes align more closely with proxy codes, especially for elder abuse and IPV. Paper two further examined the association of five measures of economic hardship and their contemporaneous and lagged associations with explicit- and proxy-identified child abuse, elder abuse and IPV rates. After adjustment for county sociodemographic factors and all other measures of economic hardship, a county’s higher foreclosure rate was the factor most strongly and consistently associated with higher violence across subtypes. Unemployment rate was the second strongest and most consistent adverse measure in its relation to violence subtypes. Lastly, there appeared to be a gender-specific association of mass lay-offs with child abuse, i.e., male mass-lay-offs were associated with increased rates while female mass-lay-offs were associated with decreased rates. Paper three employed a quasi-experimental design to assess the impact of the Great Recession on explicit and proxy child maltreatment, IPV, and elder abuse. The findings suggested that the Great Recession had little or no impact on explicit-identified violence but was associated with an increased risk for proxy-identified violence. Specifically, over the course of the Great Recession, counties that were more highly affected by the Great Recession’s impact saw a greater increase in the average rate of proxy-identified child abuse, elder abuse, intimate partner, and combined violence when compared to less affected counties. This dissertation makes substantial contributions to violence surveillance and injury research. Hospital discharge data, particularly proxy codes, may identify cases of violence that traditional surveillance misses. Most importantly, explicit and proxy codes indicate different associations with county sociodemographic characteristics and with the impact of the Great Recession. Future research should examine hospital discharge data for violence identification to validate proxy codes that can be utilized to help to identify the hidden burden of violence. In addition, understanding the pathways to violence (proxy- and explicit-identified) through economic hardship is another step in developing and targeting more holistic prevention and intervention efforts.