MétaCan
Menu
Back to cohort
Record W4324140254 · doi:10.1161/circ.147.suppl_1.p384

Abstract P384: Gender Disparities in the Co-Existence of Hypertension and Diabetes in South Africa: Results From the Nationwide Demographic and Health Household Survey Data

2023· article· en· W4324140254 on OpenAlexaff
Ngianga‐Bakwin Kandala, Saverio Stranges

Bibliographic record

VenueCirculation · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineDiabetes mellitusPublic healthEnvironmental healthPopulationDiseaseDemographyEpidemiologyGerontologyInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Introduction: The rapidly growing public health burden of cardiovascular disease and diabetes in low- and middle-income countries threatens the progress expected by many countries’ effort to combat endemic infectious diseases and achieve good health and wellbeing (SDG3). Specifically, in South Africa the burden of hypertension and diabetes is a preventable but still neglected public health issue, causing a large number of premature deaths among men and women. There are very few population-based studies examining the distribution of risk factors and the combined burden of hypertension and diabetes prevalence in South Africa to inform an effective public health response. Objectives: This study investigates potential gender disparities in the co-existence of hypertension and diabetes prevalence and provincial variation in South Africa in 2016, adjusting for individual level demographic, behavioural and socio-economic variables, while allowing for spatial autocorrelation and adjusting simultaneously for the hierarchical data structure and risk factors. Methods: The study sample was based on participants aged ≥15 years from the 2016 South Africa DHS. Hypertension was defined as blood pressure ≥ 140/90 mmHg or self-reported health professional diagnosis or on antihypertensive medication and diabetes was defined as self-reported health professional diagnosis or on diabetic medication. Bayesian geo-additive regression modelling investigated the association of various socio-economic factors on the prevalence of both hypertension and diabetes across SA’s nine provinces while controlling for the latent effects of geographical location. Results: The prevalence of hypertension, diabetes, and combined hypertension and diabetes were 48.2% (4212 of 8679), 4.5% (458 of 10255) and 3.8% (314 of 8195) successively in the DHS in 2016. The prevalence of hypertension increased with age and was significantly higher in male, in people of coloured ethnic group, in overweight and obese, in people with high blood cholesterol and varied with geographical location. Diabetes prevalence increased with age and was significantly higher in male, in overweight and obese and in people with high blood cholesterol. The co-existence of the prevalence of both combined hypertension and diabetes was significantly higher in male, in overweight and obese and in people with high blood cholesterol, in people with heart attack/angina and increased with age. Conclusions: The findings can inform public health policy and decision making regarding the allocation of public resources to tackle the growing burden of hypertension and diabetes in South Africa, particularly in the most affected areas and subgroups of the population. Public health education aiming at the prevention of CVDs should target both ailments at the same time as cost-effective measure to achieve SDG3.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.183

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.262
GPT teacher head0.319
Teacher spread0.057 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCirculationSame topicCardiovascular Health and Risk FactorsFrench-language works237,207