Evidence map›Paper›PMID 41834920›Full record

ArticleJournal of public health research2026

A mixed-methods evaluation to optimize the existing hospital based sickle cell disease care model from a tribal area of Gujarat, India.

Kapilkumar Dave, Abhay Nirgude, Gayatri Desai, Sumeet Devare, Shrey Desai

Abstract read
In one paragraph

Article in Journal of public health research, 2026. 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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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Kapilkumar DaveCHP Department, SEWA Rural, Bharuch, Gujarat, India.ORCID https://orcid.org/0000-0003-3815-7292
Abhay NirgudeYenepoya Medical College, Yenepoya (Deemed to be University), Mangalore, Karnataka, India.ORCID https://orcid.org/0000-0002-8687-7094
Gayatri DesaiCHP Department, SEWA Rural, Bharuch, Gujarat, India.ORCID https://orcid.org/0009-0009-7784-2955
Sumeet DevareCHP Department, SEWA Rural, Bharuch, Gujarat, India.ORCID https://orcid.org/0009-0000-0400-4349
Shrey DesaiCHP Department, SEWA Rural, Bharuch, Gujarat, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Sickle cell disease (SCD) remains a significant public health challenge among tribal communities in India, causing high morbidity and mortality. Despite expanded hospital-based care, clinical outcomes have shown limited improvement due to persistent barriers in care delivery. Design and methods: A formative, mixed-methods study was conducted at Kasturba Hospital, SEWA Rural, Gujarat, to refine an SCD care model tailored for a low-resource setting serving a tribal population. Quantitative analysis of SCD registry data (July 2021-June 2022) assessed intervention coverage, adherence, and outcomes. Qualitative data from in-depth interviews with patients, caregivers, and healthcare providers explored barriers and facilitators. The Consolidated Framework for Implementation Research (CFIR) guided gap identification, while Expert Recommendations for Implementing Change (ERIC) informed strategy selection. Literature review and expert consultation further refined the model. Results: Findings showed low clinic adherence, high hospitalization rates (64.4/100 person-years), and severe anemia mainly due to nutritional deficiencies. Hydroxyurea was prescribed to 56% of eligible patients but clinical outcomes remained poor. Key barriers included transport difficulties, long waits, stigma, limited health literacy, operational constraints, and insufficient supervision. Model refinements included expanding outreach clinics, introducing structured counseling and supervision, enhancing digital follow-up systems, and adding routine micronutrient supplementation. Literature and expert reviews affirmed these adaptations' feasibility and relevance. Conclusions: Using an implementation science framework, this study optimized an SCD care model tailored to a marginalized tribal population. By strengthening outreach, care protocols, counseling, and digital tracking, the model addresses persistent delivery gaps, illustrating the value of evidence-informed, context-specific intervention refinement in resource-limited settings.

Indexed as

implementation researchoptimizationSCDsickle cell diseasetribal health

Identifiers

PMID41834920
PMCPMC12988238

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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.