Evidence map›Paper›PMID 37727122›Full record

ArticleHealth services research2024

Differential impact of hospital and community factors on breadth and depth of hospital population health partnerships.

Neeraj Puro, Cory E Cronin, Berkeley Franz, Simone Singh, Scott Feyereisen

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Article in Health services research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

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7citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Neeraj PuroCollege of Business, Health Administration Department, Florida Atlantic University, Boca Raton, Florida, USA.ORCID 0000-0002-8893-2317
Cory E CroninCollege of Health Sciences and Professions, Ohio University, Athens, Ohio, USA.ORCID 0000-0001-6381-9695
Berkeley FranzHeritage College of Osteopathic Medicine, Ohio University, Irvine, California, USA.ORCID 0000-0003-2091-1891
Simone SinghDepartment of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor, Michigan, USA.ORCID 0000-0002-3647-107X
Scott FeyereisenCollege of Business, Health Administration Department, Florida Atlantic University, Boca Raton, Florida, USA.ORCID 0000-0001-8327-3749

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe aim was to identify hospital and county characteristics associated with variation in breadth and depth of hospital partnerships with a broad range of organizations to improve population health. DATA SOURCES: The American Hospital Association Annual Survey provided data on hospital partnerships to improve population health for the years 2017-2019.

designThe study adopts the dimensional publicness theory and social capital framework to examine hospital and county characteristics that facilitate hospital population health partnerships. The two dependent variables were number of local community organizations that hospitals partner with (breadth) and level of engagement with the partners (depth) to improve population health. The independent variables include three dimensions of publicness: Regulative, Normative and Cultural-cognitive measured by various hospital factors and presence of social capital present at county level. Covariates in the multivariate analysis included hospital factors such as bed-size and system membership.

methodsWe used hierarchical linear regression models to assess various hospital and county factors associated with breadth and depth of hospital-community partnerships, adjusting for covariates. PRINCIPAL

findingsNonprofit and public hospitals provided a greater breadth (coefficient, 1.61; SE, 0.11; p < 0.001 and coefficient, 0.95; SE, 0.14; p < 0.001) and depth (coefficient, 0.26, SE, 0.04; p < 0.001 & coefficient, 0.13; SE, 0.05; p < 0.05) of partnerships than their for-profit counterparts, partially supporting regulative dimension of publicness. At a county level, we found community social capital positively associated with breadth of partnerships (coefficient, 0.13; SE, 0.08; p < 0.001).

conclusionsAn environment that promotes collaboration between hospitals and organizations to improve population health may impact the health of the community by identifying health needs of the community, targeting social determinants of health, or by addressing patient social needs. However, findings suggest that publicness dimensions at an organizational level, which involves a culture of public value, maybe more important than county factors to achieve community building through partnerships.

Indexed as

Hospitals, PublicPopulation Health ManagementPublic-Private Sector PartnershipsUnited Stateshospital-community partnershipspopulation healthpublicness theorypublic value outcomessocial capital

Identifiers

PMID37727122
PMCPMC10796292

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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.