Evidence map›Paper›PMID 41768266›Full record

ArticlePublic health in practice (Oxford, England)2026

How can integrated neighbourhood teams (INTs) tackle health and care inequalities? A policy evidence brief.

Anna Gkiouleka, Lee Melo, Emily Clark, Amy Dehn Lunn, Serge Engamba, Camille Gajria, Liam Loftus, Payam Torabi, Adnaan Ghanchi, John Ford

Abstract read
In one paragraph

Article in Public health in practice (Oxford, England), 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

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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Anna GkioulekaWolfson Institute for Population Health, Queen Mary University of London, UK.
Lee MeloLancaster Medical School, Lancaster University, UK.
Emily ClarkNorwich Medical School, University of East Anglia, UK.
Amy Dehn LunnWolfson Institute for Population Health, Queen Mary University of London, UK.
Serge EngambaExeter Collaboration for Academic Primary Care (APEx), University of Exeter, UK.
Camille GajriaInstitute of Health Sciences Education, Queen Mary University of London, UK.
Liam LoftusWolfson Institute for Population Health, Queen Mary University of London, UK.
Payam TorabiThe Aberfeldy Practice, UK.
Adnaan GhanchiDepartment of Public Health and Primary Care, University of Cambridge, UK.
John FordWolfson Institute for Population Health, Queen Mary University of London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The policy challenge: Tackling health inequalities requires sustained, multi-sectoral collaboration across health care, social care, and local and national authorities. Integrated neighbourhood teams (INTs) are increasingly discussed as a key component of an integrated strategy which can increase equity in health and health care - these involve micro-integration of health and care at the neighbourhood level. However, despite their potential, there is currently no evidence-informed approach guiding their design, implementation, and evaluation in relation to health inequalities. Key evidence to inform policy: There are four key principles for designing and delivering INTs to maximise their impact on reducing health inequalities: 1) locally sensitive, autonomous, and needs-based approaches, 2) trust between health care services and disadvantaged communities, 3) accessible, co-located, multi-sector services for disadvantaged groups, 4) integrated IT systems and information governance. Further considerations and implications: Further research is needed to establish a robust, equity-focused framework for the implementation and evaluation of INTs. Policy makers and health care leaders should focus on ensuring funding allocation according to local need, fostering community partnerships, co-locating health and social care services, and standardising IT systems and information governance to maximise the potential of INTs to reduce health inequalities.

Indexed as

Co-located servicesCommunity healthHealth equityHealth inequalitiesIntegrated neighbourhood teamsMultidisciplinary teamsPlace-based carePrimary care integrationSocial determinants of health

Identifiers

PMID41768266
PMCPMC12937141

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LicenceCC BY-NC-ND
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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.