Comparing imputation approaches for immigration status in ED visits: Implications for using electronic medical records.

IF 3.1 2区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Sarah Axeen, Anna Gorman, Todd Schneberk, Annie Ro
{"title":"Comparing imputation approaches for immigration status in ED visits: Implications for using electronic medical records.","authors":"Sarah Axeen, Anna Gorman, Todd Schneberk, Annie Ro","doi":"10.1111/1475-6773.14397","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to compare imputation approaches to identify the likely undocumented patient population in electronic health record (EHRs). EHR are a promising source of information on undocumented immigrants' medical needs and care utilization, but there is no verified way to identify immigration status in the data. Different approaches to approximating immigration status in EHR introduce unique biases, which in turn has major implications on our understanding of undocumented immigrant patients.</p><p><strong>Study setting and design: </strong>We used a dataset of all emergency department (ED) visits from 2016 to 2019 in the Los Angeles Department of Health Services (LADHS) merged across patient medical records, demographic data, and claims data. We included all ED visits from our patient groups of interest and limited to patients at or over the age of 18 years at the time of their ED visit and excluded empty encounter records (n = 1,106,086 ED encounters).</p><p><strong>Data sources and analytic sample: </strong>We created three patient groups: (1) US-born, (2) foreign-born documented, and (3) undocumented using two different imputation approaches: a logical approach versus statistical assignment. We compared predicted probabilities for two outcomes: an ED visit related to a behavioral health (BH) disorder and inpatient admission/transfer to another facility.</p><p><strong>Principal findings: </strong>Both approaches provide comparable estimates among the three patient groups for ED encounters for a BH disorder and inpatient admission/transfer to another facility. Undocumented immigrants are less likely to have a BH diagnosis in the ED and are less likely to be admitted or transferred compared to the US-born.</p><p><strong>Conclusions: </strong>Researchers should consider expanding EHR with administrative data when studying the undocumented patient population and may prefer a logical approach to estimate immigration status. Researchers who rely on payer status alone (i.e., restricted Medicaid) as a proxy for undocumented immigrants in EHR should consider how this may bias their results. As Medicaid expands for undocumented immigrants, statistical assignment may become the preferred method.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Services Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/1475-6773.14397","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0

Abstract

Objective: This study aimed to compare imputation approaches to identify the likely undocumented patient population in electronic health record (EHRs). EHR are a promising source of information on undocumented immigrants' medical needs and care utilization, but there is no verified way to identify immigration status in the data. Different approaches to approximating immigration status in EHR introduce unique biases, which in turn has major implications on our understanding of undocumented immigrant patients.

Study setting and design: We used a dataset of all emergency department (ED) visits from 2016 to 2019 in the Los Angeles Department of Health Services (LADHS) merged across patient medical records, demographic data, and claims data. We included all ED visits from our patient groups of interest and limited to patients at or over the age of 18 years at the time of their ED visit and excluded empty encounter records (n = 1,106,086 ED encounters).

Data sources and analytic sample: We created three patient groups: (1) US-born, (2) foreign-born documented, and (3) undocumented using two different imputation approaches: a logical approach versus statistical assignment. We compared predicted probabilities for two outcomes: an ED visit related to a behavioral health (BH) disorder and inpatient admission/transfer to another facility.

Principal findings: Both approaches provide comparable estimates among the three patient groups for ED encounters for a BH disorder and inpatient admission/transfer to another facility. Undocumented immigrants are less likely to have a BH diagnosis in the ED and are less likely to be admitted or transferred compared to the US-born.

Conclusions: Researchers should consider expanding EHR with administrative data when studying the undocumented patient population and may prefer a logical approach to estimate immigration status. Researchers who rely on payer status alone (i.e., restricted Medicaid) as a proxy for undocumented immigrants in EHR should consider how this may bias their results. As Medicaid expands for undocumented immigrants, statistical assignment may become the preferred method.

比较急诊室就诊中移民身份的估算方法:使用电子病历的意义。
研究目的本研究旨在比较在电子健康记录(EHR)中识别可能的无证病人群体的估算方法。电子健康记录是有关无证移民医疗需求和护理利用情况的一个很有前景的信息来源,但目前还没有经过验证的方法来识别数据中的移民身份。在电子病历中近似确定移民身份的不同方法会带来独特的偏差,这反过来又会对我们了解无证移民患者产生重大影响:我们使用了洛杉矶卫生服务部(LADHS)从 2016 年到 2019 年所有急诊科(ED)就诊数据集,这些数据集合并了患者病历、人口统计数据和索赔数据。我们纳入了我们感兴趣的患者群体的所有急诊就诊记录,仅限于急诊就诊时年龄在 18 岁或以上的患者,并排除了空的就诊记录(n = 1,106,086 个急诊就诊记录):我们使用两种不同的估算方法创建了三个患者组:(1) 在美国出生的患者;(2) 在外国出生的有证患者;(3) 无证患者。我们比较了两种结果的预测概率:与行为健康(BH)障碍相关的急诊就诊和住院病人入院/转院:这两种方法对三个患者群体因行为健康障碍而去急诊室就诊和住院/转院的概率进行了估算,结果具有可比性。与美国出生的人相比,无证移民在急诊室被诊断为 BH 的可能性较小,入院或转院的可能性也较小:研究人员在研究无证病人群体时,应考虑扩大电子病历与行政数据的范围,并可能倾向于采用合理的方法来估计移民身份。在电子病历中仅依靠付款人身份(即受限制的医疗补助)来代表无证移民的研究人员应考虑这可能会使他们的研究结果产生偏差。随着无证移民医疗补助的扩大,统计分配可能成为首选方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Health Services Research
Health Services Research 医学-卫生保健
CiteScore
4.80
自引率
5.90%
发文量
193
审稿时长
4-8 weeks
期刊介绍: Health Services Research (HSR) is a peer-reviewed scholarly journal that provides researchers and public and private policymakers with the latest research findings, methods, and concepts related to the financing, organization, delivery, evaluation, and outcomes of health services. Rated as one of the top journals in the fields of health policy and services and health care administration, HSR publishes outstanding articles reporting the findings of original investigations that expand knowledge and understanding of the wide-ranging field of health care and that will help to improve the health of individuals and communities.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信