Evidence map›Paper›PMID 34596232›Full record

ArticleHealth services research2022

Distinguishing neighborhood and individual social risk factors in health care.

Megan K Beckett, Steven C Martino, Denis Agniel, Megan Mathews, Sarah Hudson Scholle, Cara James, Shondelle Wilson-Frederick, Nate Orr, Biayna Darabidian, Marc N Elliott

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In one paragraph

Article in Health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.1field-weighted citation impact, top 19% of its field
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

10 citing papers in PubMed, 13 citations in OpenAlex.

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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

10 authors at 4 institutions in 1 country.

Megan K BeckettDivision of Health Care, RAND Corporation, Santa Monica, California, USA.
Steven C MartinoDivision of Health Care, RAND Corporation, Pittsburgh, Pennsylvania, USA.ORCID 0000-0002-1514-4133
Denis AgnielDivision of Health Care, RAND Corporation, Santa Monica, California, USA.
Megan MathewsDivision of Health Care, RAND Corporation, Arlington, Virginia, USA.
Sarah Hudson ScholleResearch and Analysis, National Committee for Quality Assurance, Washington, DC, USA.
Cara JamesGrantmakers In Health, Washington, DC, USA.
Shondelle Wilson-FrederickOffice of Minority Health, Centers for Medicare and Medicaid Services, Baltimore, Maryland, USA.
Nate OrrDivision of Health Care, RAND Corporation, Santa Monica, California, USA.
Biayna DarabidianDivision of Health Care, RAND Corporation, Santa Monica, California, USA.
Marc N ElliottDivision of Health Care, RAND Corporation, Santa Monica, California, USA.ORCID 0000-0002-7147-5535
RAND Corporation · USGrantmakers in the Arts · USNational Committee for Quality Assurance · USOffice of Minority Health · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo investigate (a) the magnitude of the independent associations of neighborhood-level and person-level social risk factors (SRFs) with quality, (b) whether neighborhood-level SRF associations may be proxies for person-level SRF associations, and (c) how the association of person-level SRFs and quality varies by neighborhood-level SRFs. DATA SOURCES: 2015-2016 Medicare Advantage HEDIS data, Medicare beneficiary administrative data, and 2016 American Community Survey (ACS). STUDY

designMixed effects linear regression models (1) estimated overall inequities by neighborhood-level and person-level SRFs, (2) compared neighborhood-level associations to person-level associations, and (3) tested the interactions of person-level SRFs with corresponding neighborhood-level SRFs. DATA COLLECTION/EXTRACTION

methodsBeneficiary-level SES and disability administrative data and five-year ACS neighborhood-level SRF information were each linked to HEDIS data. PRINCIPAL

findingsFor all or nearly all HEDIS measures, quality was worse in neighborhoods lower in SES and in neighborhoods with higher proportions of residents with a disability. Quality by neighborhood racial and ethnic composition was mixed. Accounting for corresponding person-level SRFs reduced neighborhood SRF associations by 25% for disability, 43% for SES, and 74%-102% for racial and ethnic groups. Person-level SRF coefficients were not consistently reduced in models that added neighborhood-level SRFs. In 19 of 35 instances, there were significant (p < 0.05) interactions between neighborhood-level and corresponding person-level SRFs. Significant interactions were always positive for disability, SES, Black, and Hispanic, indicating more negative neighborhood effects for people with SRFs that did not match their neighborhood and more positive neighborhood effects for people with SRFs that matched their neighborhood.

conclusionsRelying solely on neighborhood-level SRF models that omit similar person-level SRFs overattributes inequities to neighborhood characteristics. Neighborhood-level characteristics account for much less variation in these measures' scores than similar person-level SRFs. Inequity-reduction programs may be most effective when targeting neighborhoods with a high proportion of people with a given SRF.

Indexed as

Medicare Part CResidence CharacteristicsAgedDelivery of Health CareEthnicityHumansRisk FactorsSocioeconomic FactorsUnited Statesdisabilityinequitiesneighborhood disadvantagepatient experiencesquality of carerace and ethnicitysocial risk factors

Identifiers

PMID34596232
PMCPMC9108057
OpenAlexW3203685209

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.