Completeness of birth and death registration in a rural area of South Africa: the Agincourt health and demographic surveillance, 1992–2014
Bibliographic record
Abstract
BACKGROUND: Completeness of vital registration remains very low in sub-Saharan Africa, especially in rural areas. OBJECTIVES: To investigate trends and factors in completeness of birth and death registration in Agincourt, a rural area of South Africa covering a population of about 110,000 persons, under demographic surveillance since 1992. The population belongs to the Shangaan ethnic group and hosts a sizeable community of Mozambican refugees. DESIGN: Statistical analysis of birth and death registration over time in a 22-year perspective (1992-2014). Over this period, major efforts were made by the government of South Africa to improve vital registration. Factors associated with completeness of registration were investigated using univariate and multivariate analysis. RESULTS: Birth registration was very incomplete at onset (7.8% in 1992) and reached high values at end point (90.5% in 2014). Likewise, death registration was low at onset (51.4% in 1992), also reaching high values at end point (97.1% in 2014). For births, the main factors were mother's age (much lower completeness among births to adolescent mothers), refugee status, and household wealth. For deaths, the major factors were age at death (lower completeness among under-five children), refugee status, and household wealth. Completeness increased for all demographic and socioeconomic categories studied and is likely to approach 100% in the future if trends continue at this speed. CONCLUSION: Reaching high values in the completeness of birth and death registration was achieved by excellent organization of the civil registration and vital statistics, a variety of financial incentives, strong involvement of health personnel, and wide-scale information and advocacy campaigns by the South African government.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".