Evidence map›Paper›PMID 39375319›Full record

ArticleJournal of general internal medicine2025

Identifying Veterans with a Higher Risk of Social Needs Using Cluster Analysis.

Hannah Friedman, Mingfei Li, Kimberly L Harvey, Ida Griesemer, David Mohr, Amy M Linsky, Deborah Gurewich

Abstract read
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Article in Journal of general internal medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Hannah FriedmanCenter for Healthcare Organization and Implementation Research (CHOIR), VA Boston Healthcare System, Boston, MA, USA. Hannah.friedman2@va.gov.ORCID 0000-0002-5449-0987
Mingfei LiCHOIR, VA Bedford Healthcare System, Bedford, MA, USA.
Kimberly L HarveyCenter for Healthcare Organization and Implementation Research (CHOIR), VA Boston Healthcare System, Boston, MA, USA.
Ida GriesemerRural Health Resource Center, VA Medical Center, White River Junction, VT, USA.
David MohrDepartment of Health Policy and Management, Boston University School of Public Health, Boston, MA, USA.
Amy M LinskyCenter for Healthcare Organization and Implementation Research (CHOIR), VA Boston Healthcare System, Boston, MA, USA.
Deborah GurewichCenter for Healthcare Organization and Implementation Research (CHOIR), VA Boston Healthcare System, Boston, MA, USA.

Funding

Office of Rural Health XVA-11-052
6 · The paper itself

Abstract

importanceMany social need screening to advance population health and reduce health disparities, but barriers to screening remain. Improved knowledge of patient populations at risk for social needs based on administrative data could facilitate more targeted practices, and by extension, feasible social need screening and referral efforts.

objectiveTo illustrate the use of cluster analysis to identify patient population segments at risk for social needs.

designWe used clustering analysis to identify population segments among Veterans (N=2010) who participated in a survey assessing nine social needs (food, housing, utility, financial, employment, social disconnection, legal, transportation, and neighborhood safety). Clusters were based on eight variables (age, race, gender, comorbidity, region, no-show rate, rurality, and VA priority group). We used weighted logistic regression to assess association of clusters with the risk of experiencing social needs.

participantsNational random sample of Veterans with and at risk for cardiovascular disease who responded to a mail survey (N=2010). MAIN OUTCOMES AND MEASURES: Self-reported social needs defined as the risk of endorsing (1) each individual social need, (2) one or more needs, and (3) a higher total count of needs. KEY

resultsFrom the clustering analysis process with sensitivity analysis, we identified a consistent population segment of Veterans. From regression modeling, we found that this cluster, with lower average age and higher proportions of women and racial minorities, was at higher risk of experiencing ≥ 1 unmet need (OR 1.74, CI 1.17-2.56). This cluster was also at a higher risk for several individual needs, especially utility needs (OR 3.78, CI 2.11-6.78).

conclusionsThe identification of characteristics associated with increased unmet social needs may provide opportunities for targeted screenings. As this cluster was also younger and had fewer comorbidities, they may be less likely to be identified as experiencing need through interactions with healthcare providers.

Indexed as

Needs AssessmentVeteransAdultAgedCardiovascular DiseasesCluster AnalysisFemaleHumansMaleMiddle AgedSurveys and QuestionnairesUnited States

Identifiers

PMID39375319
PMCPMC11803015

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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.