Evidence map›Paper›PMID 39497349›Full record

ArticleHealth services research2025

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

Sarah Axeen, Anna Gorman, Todd Schneberk, Annie Ro

Abstract readComparative Study
In one paragraph

Article in Health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Sarah AxeenDepartment of Emergency Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.ORCID https://orcid.org/0000-0002-1538-0013
Anna GormanLos Angeles County Department of Health Services, Alhambra, California, USA.
Todd SchneberkDepartment of Emergency Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Annie RoDepartment of Health, Society, & Behavior, Wen School of Public Health, University of California, Irvine, Irvine, California, USA.ORCID https://orcid.org/0000-0001-9684-5566

Funding

Access to medical care and health care utilization among low-income immigrantsR21MD019086 · NIMHD · UNIVERSITY OF CALIFORNIA-IRVINE · PI RO, ANNIE EUN YOUNG · 2023 to 2024
$425k
NIMHD NIH HHS R21 MD019086NIMHD NIH HHS R21MD019086
6 · The paper itself

Abstract

objectiveThis 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

designWe 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

findingsBoth 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.

conclusionsResearchers 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.

Indexed as

Electronic Health RecordsEmergency Service, HospitalEmigrants and ImmigrantsUndocumented ImmigrantsAdolescentAdultAgedFemaleHumansLos AngelesMaleMiddle AgedUnited StatesYoung Adultelectronic medical recordshealth disparitiesimmigrantsimmigration statusimputation

Identifiers

PMID39497349
PMCPMC11782073

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.