Evidence map›Paper›PMID 36620294›Full record

ArticleFrontiers in public health2022

Health systems strengthening through policy-level integration of community health worker programs into national health systems for improved health outcomes - scorecard metrics validation: A bifactor structural equation model approach.

Lucia Mungapeyi Mupara, John Jules O Mogaka, William R Brieger, Joyce M Tsoka-Gwegweni

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Article in Frontiers in public health, 2022. 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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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Lucia Mungapeyi MuparaDepartment of Public Health Medicine, University of KwaZulu Natal, Durban, KwaZulu-Natal, South Africa.
John Jules O MogakaDepartment of Public Health Medicine, University of KwaZulu Natal, Durban, KwaZulu-Natal, South Africa.
William R BriegerBloomberg School of Public Health, Johns Hopkins University, Baltimore, MA, United States.
Joyce M Tsoka-GwegweniDepartment of Public Health Medicine, University of KwaZulu Natal, Durban, KwaZulu-Natal, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Subsequent to the demonstrated potential of community health workers (CHWs) in strengthening health systems to improve health outcomes, recent literature has defined context and guidelines for integrating CHW programs into mainstream health systems. However, quantitative measures for assessing the extent of CHW program integration into national health systems need to be developed. The purpose of this study was to validate a newly developed scale, Community Health Worker Program Integration Scorecard Metrics (CHWP-ISM), for assessing the degree of integration of CHW programs into national health systems in Sub-Saharan Africa (SSA). Methods: Data obtained through a pilot study involving a purposively selected sample of 41 participants selected from populations involved in CHW programs work in selected countries of SSA formed the basis of a 31-item bifactor model. Data were collected between June and December 2019. By applying a latent variable approach implemented with structural equation modeling, data analysis was mainly done using the R statistical environment, applying factor analysis procedures. Results: Dimensionality, construct validity, and the CHWP-ISM scale's internal consistency were assessed. Confirmatory factor analysis of the CHW-ISM bifactor model supported a co-occurring CHW integration general factor and six unique domain-specific factors. Both the comparative fit index (CFI) and Tucker-Lewis Index (TLI) fit indices were above 0.9, while the root mean square of the residuals (RMSR) was 0.02. Cronbach's alpha (α), Guttman 6 (Lambda 6), and Omega total (ω Conclusion: Statistical significance of the bifactor model suggests that CHW integration has to be examined using factors that reflect a single common underlying integration construct, as well as factors that reflect unique variances for the identified six subject-specific domains. The validated CHWP-ISM could be useful to inform policy advisers, health systems, donors, non-governmental organizations, and other CHW program stakeholders with guidance on how to quantitatively assess the integration status of different components of CHW programs into respective critical functions of the health system. Improved integration could increase CHW program functionality, which could in turn strengthen the healthcare systems to improve health outcomes in the region.

Indexed as

Community Health ServicesCommunity Health WorkersHumansOutcome Assessment, Health CarePilot ProjectsPolicyReproducibility of Resultscommunity health workdelivery of health careintegratedorganization & administrationquality of health care (improved health outcomes)standards (integration metrics)structural equation modeling

Identifiers

PMID36620294
PMCPMC9822652

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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.