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

Injury Epidemiology

Marizen R. Ramirez¹  Susan M. Mason²  Theresa L. Osypuk³  N. Jeanie Santaularia³ 

[1] Division of Environmental Health Sciences, University of Minnesota School of Public Health, 1260 Mayo Building, MMC 807, 420 Delaware St. SE, 55455, Minneapolis, MN, USA;Division of Epidemiology and Community Health, University of Minnesota School of Public Health, 300 West Bank Office Building, 1300 S. 2nd St., 55454, Minneapolis, MN, USA;Division of Epidemiology and Community Health, University of Minnesota School of Public Health, 300 West Bank Office Building, 1300 S. 2nd St., 55454, Minneapolis, MN, USA;Minnesota Population Center, University of Minnesota, 225 19th Ave S #50th, 55455, Minneapolis, MN, USA;

关键词: Violent injury;    Surveillance;    Hospital data;    Child abuse;    Intimate partner violence;    Elder abuse;   

DOI  :  10.1186/s40621-021-00354-6

来源: Springer

【 摘 要 】

PurposeCommonly-used violence surveillance systems are biased towards certain populations due to overreporting or over-scrutinized. Hospital discharge data may offer a more representative view of violence, through use of proxy codes, i.e. diagnosis of injuries correlated with violence. The goals of this paper are to compare the trends in violence in Minnesota, and associations of county-level demographic characteristics with violence rates, measured through explicitly diagnosed violence and proxy codes. It is an exploration of how certain sub-populations are overrepresented in traditional surveillance systems.MethodsUsing Minnesota hospital discharge data linked with census data from 2004 to 2014, this study examined the distribution and time trends of explicit, proxy, and combined (proxy and explicit) codes for child abuse, intimate partner violence (IPV), and elder abuse. The associations between county-level risk factors (e.g., poverty) and county violence rates were estimated using negative binomial regression models with generalized estimation equations to account for clustering over time.ResultsThe main finding 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.ConclusionsViolence surveillance utilizing hospital discharge data, and particularly proxy codes, may add important information that traditional surveillance misses. Most importantly, explicit and proxy codes indicate different associations with county sociodemographic characteristics. 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.

【 授权许可】

CC BY