Current status and future prospects of epidemiology and public health training and research in the WHO African region
Bibliographic record
Abstract
BACKGROUND: To date little has been published about epidemiology and public health capacity (training, research, funding, human resources) in WHO/AFRO to help guide future planning by various stakeholders. METHODS: A bibliometric analysis was performed to identify published epidemiological research. Information about epidemiology and public health training, current research and challenges was collected from key informants using a standardized questionnaire. RESULTS: From 1991 to 2010, epidemiology and public health research output in the WHO/AFRO region increased from 172 to 1086 peer-reviewed articles per annum [annual percentage change (APC) = 10.1%, P for trend < 0.001]. The most common topics were HIV/AIDS (11.3%), malaria (8.6%) and tuberculosis (7.1%). Similarly, numbers of first authors (APC = 7.3%, P for trend < 0.001), corresponding authors (APC = 8.4%, P for trend < 0.001) and last authors (APC = 8.5%, P for trend < 0.001) from Africa increased during the same period. However, an overwhelming majority of respondents (>90%) reported that this increase is only rarely linked to regional post-graduate training programmes in epidemiology. South Africa leads in publications (1978/8835, 22.4%), followed by Kenya (851/8835, 9.6%), Nigeria (758/8835, 8.6%), Tanzania (549/8835, 6.2%) and Uganda (428/8835, 4.8%) (P < 0.001, each vs South Africa). Independent predictors of relevant research productivity were 'in-country numbers of epidemiology or public health programmes' [incidence rate ratio (IRR) = 3.41; 95% confidence interval (CI) 1.90-6.11; P = 0.03] and 'number of HIV/AIDS patients' (IRR = 1.30; 95% CI 1.02-1.66; P < 0.001). CONCLUSIONS: Since 1991, there has been increasing epidemiological research productivity in WHO/AFRO that is associated with the number of epidemiology programmes and burden of HIV/AIDS cases. More capacity building and training initiatives in epidemiology are required to promote research and address the public health challenges facing the continent.
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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 | 0.060 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.024 | 0.049 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".