Population‐based prevalence of human T‐lymphotropic virus type 1 in sub‐Saharan Africa: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Human T-cell lymphotropic virus type 1 (HTLV-1), the causative agent of adult T-cell leukaemia/lymphoma (ATL) and HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP), is endemic in sub-Saharan Africa (SSA) and poses a high morbidity and mortality risk. Its prevalence in the general population is poorly understood. The potential for prevention motivated us to do a systematic review and meta-analysis of population-based studies to estimate the prevalence of HTLV-1 in SSA. METHODS: A comprehensive, no-limit search was conducted in EMBASE, PubMed, Web of Science and the Cochrane Library from their inception dates to March 2019. Population-based studies presenting data on HTLV-1 in sub-Saharan Africa were included. Pooled prevalence was estimated using a random-effects meta-analysis. RESULTS: A total of 21 studies were included, representing 42 297 participants. The pooled HTLV-1 seroprevalence was 3.19% (95% CI 2.36-4.12%) with variations across year of study. Prevalence of HTLV-1 positively correlated with year of study (β = 0.0036, P = 0.007). Participants from Central, Western and Southern Africa had a seroprevalence of 4.16% (95% CI 2.43-6.31%), 2.66% (95% CI 1.80-3.68%) and 1.56% (95% CI 0.48-3.15%), respectively. CONCLUSIONS: Our findings suggest that HTLV-1 infection is a public health concern in SSA and highlight the need to implement effective preventive programmes and interventions aimed at reducing the burden of this common yet neglected infection.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".