Axeen Sarah, Gorman Anna, Schneberk Todd, Ro Annie
Department of Emergency Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Leonard D. Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, California, USA.
Health Serv Res. 2025 Feb;60(1):e14397. doi: 10.1111/1475-6773.14397. Epub 2024 Nov 4.
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.
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).
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.
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.
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.
本研究旨在比较不同的归因方法，以识别电子健康记录（EHR）中可能未记录在案的患者群体。电子健康记录是了解未记录在案移民的医疗需求和医疗服务利用情况的一个有前景的信息来源，但在数据中没有经过验证的方法来识别移民身份。在电子健康记录中近似估计移民身份的不同方法会引入独特的偏差，这反过来又对我们对未记录在案移民患者的理解产生重大影响。
我们使用了洛杉矶卫生服务部（LADHS）2016年至2019年所有急诊科就诊的数据集，该数据集合并了患者病历、人口统计学数据和理赔数据。我们纳入了感兴趣患者群体的所有急诊科就诊记录，并将范围限制在急诊科就诊时年龄在18岁及以上的患者，排除了空就诊记录（n = 1,106,086次急诊科就诊）。
我们创建了三个患者群体：（1）美国出生的，（2）外国出生且有记录的，（3）未记录在案的，使用两种不同的归因方法：逻辑方法与统计赋值。我们比较了两个结果的预测概率：与行为健康（BH）障碍相关的急诊科就诊以及住院或转至另一机构。
对于与BH障碍相关的急诊科就诊以及住院或转至另一机构，两种方法在三个患者群体中提供了可比的估计。与美国出生的患者相比，未记录在案的移民在急诊科被诊断为BH障碍的可能性较小，住院或转院的可能性也较小。
研究未记录在案患者群体时，研究人员在利用行政数据扩展电子健康记录时应予以考虑，并且可能更倾向于采用逻辑方法来估计移民身份。仅依靠付款人身份（即受限医疗补助）作为电子健康记录中未记录在案移民的替代指标的研究人员，应考虑这可能如何使他们的结果产生偏差。随着医疗补助向未记录在案移民的扩展，统计赋值可能会成为首选方法。
