Evidence map›Paper›PMID 40664712›Full record

ArticleScientific data2025

An inclusive economy dataset for wards in Great Britain using administrative and synthetic data sources.

Hugh P Rice, Andreas Höhn, Petra Meier, Alison Heppenstall, Nik Lomax

Abstract readDataset
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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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

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.

Hugh P RiceInstitute for Spatial Data Science, School of Geography, University of Leeds, Leeds, LS2 9JT, UK. h.p.rice@leeds.ac.uk.ORCID 0000-0002-6895-8325
Andreas HöhnSchool of Health and Wellbeing, University of Glasgow, Glasgow, G12 8TB, UK.
Petra MeierSchool of Health and Wellbeing, University of Glasgow, Glasgow, G12 8TB, UK.ORCID 0000-0001-5354-1933
Alison HeppenstallSchool of Health and Wellbeing, University of Glasgow, Glasgow, G12 8TB, UK.ORCID 0000-0002-0663-3437
Nik LomaxInstitute for Spatial Data Science, School of Geography, University of Leeds, Leeds, LS2 9JT, UK.

Funding

Chief Scientist Office (CSO) SPHSU20RCUK | Medical Research Council (MRC) MC_UU_00022/5Wellcome Trust
6 · The paper itself

Abstract

To address the scarcity of small-area datasets focused on economic inclusion, we created a harmonised dataset describing the extent and enablers of economic inclusion in Great Britain. The result, the SIPHER (Systems Science in Public Health and Health Economics Research) Inclusive Economy (Ward Level) dataset, consists of 13 indicators describing economic inclusion at electoral ward level (N = 7,973 of 8,020 wards, 2022 boundaries), for 2019-2021. The dataset was curated based on administrative statistics (mostly open-source) and the SIPHER Synthetic Population, a validated, survey-based, full-scale synthetic population dataset derived from the UK Household Longitudinal Study (UKHLS): Understanding Society, and aggregate-level population statistics. The dataset also includes summary measures of population health - age-standardised Short Form Health Survey (SF-12) mental and physical health component scores - and supplementary demographic indicators describing the population structure. For validation, a range of comparisons against deprivation indices and other data provide strong evidence of the dataset's added value and utility for applications in research and policy requiring high-quality estimates at a granular spatial resolution.

Indexed as

EconomicsHumansInformation SourcesLongitudinal StudiesUnited Kingdom

Identifiers

PMID40664712
PMCPMC12264001

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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.