Evidence map›Paper›PMID 33479017›Full record

ArticleBMJ global health2021

Estimating the burden of cardiovascular risk in community dwellers over 40 years old in South Africa, Kenya, Burkina Faso and Ghana.

Ryan G Wagner, Nigel J Crowther, Lisa K Micklesfield, Palwende Romauld Boua, Engelbert A Nonterah, Felistas Mashinya, Shukri F Mohamed, Gershim Asiki, Stephen Tollman, Michèle Ramsay and 1 more

Abstract read
In one paragraph

Article in BMJ global health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
–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

20 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Systematic review and meta-analysis of cardiovascular disease risk among hypertensive patients in Africa.International journal of cardiology. Cardiovascular risk and prevention · 2026
    Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

11 authors.

Ryan G WagnerMRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, Gauteng, South Africa Ryan.Wagner@wits.ac.za.ORCID 0000-0003-2741-3676
Nigel J CrowtherDepartment of Chemical Pathology, National Health Laboratory Service, Johannesburg, Gauteng, South Africa.
Lisa K MicklesfieldSAMRC/Wits Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, Gauteng, South Africa.ORCID 0000-0002-4994-0779
Palwende Romauld BouaClinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Sante, Ouagadougou, Centre, Burkina Faso.ORCID 0000-0001-8325-2665
Engelbert A NonterahNavrongo Health Research Centre, Ghana Health Service, Accra, Greater Accra, Ghana.
Felistas MashinyaDepartment of Pathology and Medical Sciences, Faculty of Health Sciences, University of Limpopo, Sovenga, Limpopo, South Africa.ORCID 0000-0002-4132-7558
Shukri F MohamedHealth and Systems for Health, African Population and Health Research Center, Nairobi, Kenya.ORCID 0000-0002-8693-1943
Gershim AsikiHealth and Systems for Health, African Population and Health Research Center, Nairobi, Kenya.
Stephen TollmanMRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, Gauteng, South Africa.ORCID 0000-0003-0744-7588
Michèle RamsaySydney Brenner Institute for Molecular Bioscience and Division of Human Genetics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Justine I DaviesMRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, Gauteng, South Africa.

Funding

Shared Core: Biobanking, Bioinformatics and Data ManagementU54HG006938 · NHGRI · WITS HEALTH CONSORTIUM (PTY), LTD · PI RAMSAY, MICHELE MICHELE · 2012 to 2022
$12.2M
NHGRI NIH HHS U54 HG006938Wellcome TrustWellcome Trust 058893/Z/99/AWellcome Trust 069683/Z/02/ZWellcome Trust 085477/B/08/ZWellcome Trust 085477/Z/08/Z
6 · The paper itself

Abstract

introductionCardiovascular disease (CVD) risk factors are increasing in sub-Saharan Africa. The impact of these risk factors on future CVD outcomes and burden is poorly understood. We examined the magnitude of modifiable risk factors, estimated future CVD risk and compared results between three commonly used 10-year CVD risk factor algorithms and their variants in four African countries.

methodsIn the Africa-Wits-INDEPTH partnership for Genomic studies (the AWI-Gen Study), 10 349 randomly sampled individuals aged 40-60 years from six sites participated in a survey, with blood pressure, blood glucose and lipid levels measured. Using these data, 10-year CVD risk estimates using Framingham, Globorisk and WHO-CVD and their office-based variants were generated. Differences in future CVD risk and results by algorithm are described using kappa and coefficients to examine agreement and correlations, respectively.

resultsThe 10-year CVD risk across all participants in all sites varied from 2.6% (95% CI: 1.6% to 4.1%) using the WHO-CVD lab algorithm to 6.5% (95% CI: 3.7% to 11.4%) using the Framingham office algorithm, with substantial differences in risk between sites. The highest risk was in South African settings (in urban Soweto: 8.9% (IQR: 5.3-15.3)). Agreement between algorithms was low to moderate (kappa from 0.03 to 0.55) and correlations ranged between 0.28 and 0.70. Depending on the algorithm used, those at high risk (defined as risk of 10-year CVD event >20%) who were under treatment for a modifiable risk factor ranged from 19.2% to 33.9%, with substantial variation by both sex and site.

conclusionThe African sites in this study are at different stages of an ongoing epidemiological transition as evidenced by both risk factor levels and estimated 10-year CVD risk. There is low correlation and disparate levels of population risk, predicted by different risk algorithms, within sites. Validating existing risk algorithms or designing context-specific 10-year CVD risk algorithms is essential for accurately defining population risk and targeting national policies and individual CVD treatment on the African continent.

Indexed as

Cardiovascular DiseasesAdultBurkina FasoGhanaHeart Disease Risk FactorsHumansKenyaRisk FactorsSouth Africacardiovascular diseasecommunity-based surveyepidemiologyhypertension

Identifiers

PMID33479017
PMCPMC7825268

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.