Determinants of mortality status and population attributable risk fractions of the North West Province, South African site of the international PURE study
Bibliographic record
Abstract
BACKGROUND: Mortality data and comparative risk assessments from sub-Saharan Africa are limited. There is an urgent need for high quality population health surveys to be conducted, to improve the national health surveillance system. Our aim was to perform a comparative risk assesment and report on the mortality status and cause of death data of participants from a South African site of the international Prospective Urban Rural Epidemiology study. METHODS: 1 921 Black participants were included, with a median observational time of 13 years resulting in 21 525 person-years. We performed a comparative risk assessment considering four health status domains: locality (rural vs. urban), socio-economic status (SES) (education and employment), lifestyle factors (physical activity, smoking and alcohol consumption) and prevalent diseases (human immunodeficiency virus (HIV), type 2 diabetes mellitus and hypertension). Next, population-attributable fractions (PAFs) were calculated to determine the mortality risk attributable to modifiable determinants. RESULTS: 577 all-cause deaths occurred. Infectious diseases (28.1% of all deaths) were the most frequent cause of death, followed by cardiovascular disease (CVD) (22.4%), respiratory diseases (11.6%) and cancer (11.1%). The three main contributors to all-cause mortality were HIV infection, high SES and being underweight. HIV infection and underweight were the main contributors to infectious disease mortality and hypertension, the urban environment, and physical inactivity to CVD mortality. HIV had the highest PAF, followed by physical inactivity, alcohol and tobacco use and hypertension (for CVD mortality). CONCLUSION: This African population suffers from a quadruple burden of disease. Urban locality, high SES, prevalent disease (HIV and hypertension) and lifestyle factors (physical inactivity, tobacco and alcohol use) all contributed in varying degrees to all-cause and cause-specific mortalities. Our data confirm the public health importance of addressing HIV and hypertension, but also highlights the importance of physical inactivity, tobacco use and alcohol consumption as focal points for public health strategies to produce the most efficient mortality reduction outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".