Santaularia N Jeanie, Ramirez Marizen R, Osypuk Theresa L, Mason Susan M
Division of Epidemiology and Community Health, University of Minnesota School of Public Health, 300 West Bank Office Building, 1300 S. 2nd St., Minneapolis, MN, 55454, USA.
Minnesota Population Center, University of Minnesota, 225 19th Ave S #50th, Minneapolis, MN, 55455, USA.
Inj Epidemiol. 2021 Nov 1;8(1):63. doi: 10.1186/s40621-021-00354-6.
Commonly-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.
Using 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.
The 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.
Violence 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.
常用的暴力监测系统因报告过多或审查过度而偏向某些人群。医院出院数据通过使用替代编码（即与暴力相关的损伤诊断），可能提供更具代表性的暴力情况视图。本文的目的是比较明尼苏达州暴力事件的趋势，以及县级人口特征与暴力发生率之间的关联，通过明确诊断的暴力事件和替代编码来衡量。这是对某些亚人群在传统监测系统中如何被过度代表的一种探索。
利用2004年至2014年与人口普查数据相关联的明尼苏达州医院出院数据，本研究考察了虐待儿童、亲密伴侣暴力（IPV）和虐待老人的明确编码、替代编码以及组合编码（替代编码和明确编码）的分布和时间趋势。使用负二项回归模型和广义估计方程来估计县级风险因素（如贫困）与县级暴力发生率之间的关联，以考虑随时间的聚类情况。
主要发现是，县级暴力模式因使用明确编码还是替代编码而有所不同。特别是，明确编码表明2004年至2014年期间虐待儿童和IPV趋势持平或略有下降，而替代编码则显示相反情况。在此期间，明确编码和替代编码的虐待老人情况均有所增加，但使用替代编码时增加更为显著。关于县级特征与每种暴力亚型之间的关联，先前确定的县级风险因素与明确识别的暴力事件的相关性比与替代识别的暴力事件更强。鉴于与明确识别的暴力事件相比，替代识别的病例数量更多，组合编码的趋势和关联与替代编码更为接近，尤其是在虐待老人和IPV方面。
利用医院出院数据，特别是替代编码进行暴力监测，可能会补充传统监测遗漏的重要信息。最重要的是，明确编码和替代编码显示出与县级社会人口特征的不同关联。未来的研究应检查医院出院数据以进行暴力识别，以验证可用于帮助识别暴力隐藏负担的替代编码。
