Social Conditions and Disability Related to the Mortality of Older People in Rural South Africa
Bibliographic record
Abstract
BACKGROUND: South Africa is experiencing a health and social transition including an ageing population and an HIV epidemic. We report mortality experience of an older rural South African population. METHODS: Individual survey data and longer-term demographic data were used to describe factors associated with mortality. Individuals aged 50 years and over (n = 4085) answered a health and quality of life questionnaire in 2006 and were followed for 3 years thereafter. Additional vital events and socio-demographic data were extracted from the Agincourt Health and Demographic Surveillance System from 1993 to 2010, to provide longer-term trends in mortality. Cox regression analysis was used to determine factors related to survival. RESULTS: In 10 967 person-years of follow-up between August 2006 and August 2009, 377 deaths occurred. Women had lower mortality {hazard ratio [HR] 0.35 [95% confidence interval (CI) 0.28-0.45]}. Higher mortality was associated with being single [HR 1.48 (95% CI 1.16-1.88)], having lower household assets score [HR 1.79 (95% CI 1.28-2.51)], reporting greater disability [HR 2.40 (95% CI 1.68-3.42)] and poorer quality of life [HR 1.59 (95% CI 1.09-2.31)]. There was higher mortality in those aged under 69 as compared with those 70 to 79 years old. Census data and cause specific regression models confirmed that this was due to deaths from HIV/TB in the younger age group. CONCLUSIONS: Mortality due to HIV/TB is increasing in men, and to some extent women, aged over 50. Policy makers and practitioners should consider the needs of this growing and often overlooked group.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".