Evidence map›Paper›PMID 39832161›Full record

ArticleJMIR cardio2025

Estimating Trends in Cardiovascular Disease Risk for the EXPOSE (Explaining Population Trends in Cardiovascular Risk: A Comparative Analysis of Health Transitions in South Africa and England) Study: Repeated Cross-Sectional Study.

Shaun Scholes, Jennifer S Mindell, Mari Toomse-Smith, Annibale Cois, Kafui Adjaye-Gbewonyo

Abstract readComparative StudyMulticenter Study
In one paragraph

Article in JMIR cardio, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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.

Shaun ScholesDepartment of Epidemiology and Public Health, University College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-7865-5264
Jennifer S MindellDepartment of Epidemiology and Public Health, University College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-7604-6131
Mari Toomse-SmithNational Centre for Social Research, London, United Kingdom.ORCID https://orcid.org/0009-0008-9130-4056
Annibale CoisBurden of Disease Research Unit, South African Medical Research Council, Cape Town, South Africa.ORCID https://orcid.org/0000-0002-7014-6510
Kafui Adjaye-GbewonyoFaculty of Education, Health and Human Sciences, University of Greenwich, London, United Kingdom.ORCID https://orcid.org/0000-0002-8919-6518

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCardiovascular diseases (CVDs) are the leading cause of death globally. Demographic, behavioral, socioeconomic, health care, and psychosocial variables considered risk factors for CVD are routinely measured in population health surveys, providing opportunities to examine health transitions. Studying the drivers of health transitions in countries where multiple burdens of disease persist (eg, South Africa), compared with countries regarded as models of "epidemiologic transition" (eg, England), can provide knowledge on where best to intervene and direct resources to reduce the disease burden.

objectiveThe EXPOSE (Explaining Population Trends in Cardiovascular Risk: A Comparative Analysis of Health Transitions in South Africa and England) study analyzes microlevel data collected from multiple nationally representative population health surveys conducted in these 2 countries between 1998 and 2017. Creating a harmonized dataset by pooling repeated cross-sectional surveys to model trends in CVD risk is challenging due to changes in aspects such as survey content, question wording, inclusion of boost samples, weighting, measuring equipment, and guidelines for data protection. This study aimed to create a harmonized dataset based on the annual Health Surveys for England to estimate trends in mean predicted 10-year CVD risk (primary outcome) and its individual risk components (secondary outcome).

methodsWe compiled a harmonized dataset to estimate trends between 1998 and 2017 in the English adult population, including the primary and secondary outcomes, and potential drivers of those trends. Laboratory- and non-laboratory-based World Health Organization (WHO) and Globorisk algorithms were used to calculate the predicted 10-year total (fatal and nonfatal) CVD risk. Sex-specific estimates of the mean 10-year CVD risk and its components by survey year were calculated, accounting for the complex survey design.

resultsLaboratory- and non-laboratory-based 10-year CVD risk scores were calculated for 33,628 and 61,629 participants aged 40 to 74 years, respectively. The absolute predicted 10-year risk of CVD declined significantly on average over the last 2 decades in both sexes (for linear trend; all P<.001). In men, the mean of the laboratory-based WHO risk score was 10.1% (SE 0.2%) and 8.4% (SE 0.2%) in 1998 and 2017, respectively; corresponding figures in women were 5.6% (SE 0.1%) and 4.5% (SE 0.1%). In men, the mean of the non-laboratory-based WHO risk score was 9.6% (SE 0.1%) and 8.9% (SE 0.2%) in 1998 and 2017, respectively; corresponding figures in women were 5.8% (SE 0.1%) and 4.8% (SE 0.1%). Predicted CVD risk using the Globorisk algorithms was lower on average in absolute terms, but the pattern of change was very similar. Trends in the individual risk components showed a complex pattern.

conclusionsHarmonized data from repeated cross-sectional health surveys can be used to quantify the drivers of recent changes in CVD risk at the population level.

Indexed as

Cardiovascular DiseasesAdultAgedCross-Sectional StudiesEnglandFemaleHealth SurveysHeart Disease Risk FactorsHumansMaleMiddle AgedRisk AssessmentRisk FactorsSouth Africacardiovascular diseasecross-country comparisonsCVDCVD risk scoresdata harmonizationEnglandpublic healthSouth Africatrends

Identifiers

PMID39832161
PMCPMC11791442

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Registered trials

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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.