Evidence map›Paper›PMID 34664641›Full record

SynthesisJournal of the American Medical Informatics Association : JAMIA2021

The quality of social determinants data in the electronic health record: a systematic review.

Lily A Cook, Jonathan Sachs, Nicole G Weiskopf

Abstract readSystematic Review
In one paragraph

Synthesis in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
95citing papers in PubMed, 4 pooled it
–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

95 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
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  13. Causal Inference in Health Disparities Research.Annual review of public health · 2026
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35 more citing papers are in PubMed but not listed here.

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

3 authors.

Lily A CookDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA.ORCID 0000-0002-2949-4978
Jonathan SachsDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA.
Nicole G WeiskopfDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA.ORCID 0000-0003-0365-909X

Funding

TRAINING PROGRAM IN HEALTH INFORMATICST15LM007088 · NLM · OREGON HEALTH & SCIENCE UNIVERSITY · PI WILLIAM R HERSH, Vishnu Mohan · 1992 to 2026
$23.4M
Measuring and improving data quality for clinical quality measure reliabilityK01LM012738 · NLM · OREGON HEALTH & SCIENCE UNIVERSITY · PI WEISKOPF, NICOLE GRAY · 2017 to 2019
$462k
Identifying and understanding drivers of selection bias and information bias in clinical COVID-19 dataR21LM013645 · NLM · OREGON HEALTH & SCIENCE UNIVERSITY · PI WEISKOPF, NICOLE GRAY · 2021 to 2022
$368k
NLM NIH HHS K01 LM012738NLM NIH HHS K01LM012738NLM NIH HHS R21 LM013645NLM NIH HHS T15 LM007088NLM NIH HHS T15LM007088
6 · The paper itself

Abstract

objectiveThe aim of this study was to collect and synthesize evidence regarding data quality problems encountered when working with variables related to social determinants of health (SDoH). MATERIALS AND

methodsWe conducted a systematic review of the literature on social determinants research and data quality and then iteratively identified themes in the literature using a content analysis process.

resultsThe most commonly represented quality issue associated with SDoH data is plausibility (n = 31, 41%). Factors related to race and ethnicity have the largest body of literature (n = 40, 53%). The first theme, noted in 62% (n = 47) of articles, is that bias or validity issues often result from data quality problems. The most frequently identified validity issue is misclassification bias (n = 23, 30%). The second theme is that many of the articles suggest methods for mitigating the issues resulting from poor social determinants data quality. We grouped these into 5 suggestions: avoid complete case analysis, impute data, rely on multiple sources, use validated software tools, and select addresses thoughtfully. DISCUSSION: The type of data quality problem varies depending on the variable, and each problem is associated with particular forms of analytical error. Problems encountered with the quality of SDoH data are rarely distributed randomly. Data from Hispanic patients are more prone to issues with plausibility and misclassification than data from other racial/ethnic groups.

conclusionConsideration of data quality and evidence-based quality improvement methods may help prevent bias and improve the validity of research conducted with SDoH data.

Indexed as

Electronic Health RecordsSocial Determinants of HealthEthnicityHispanic or LatinoHumansRacial Groupsbiasdata qualityhealthy equityHispanic Americanssocial determinants of health

Identifiers

PMID34664641
PMCPMC8714289

What OpenQuestion holds

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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.