Neglected Tropical Diseases in the Context of Climate Change in East Africa: A Systematic Scoping Review
Bibliographic record
Abstract
East Africa is highly affected by neglected tropical diseases (NTDs), which are projected to be exacerbated by climate change.Consequently, understanding what research has been conducted and what knowledge gaps remain regarding NTDs and climate change is crucial to informing public health interventions and climate change adaptation.We conducted a systematic scoping review to describe the extent, range, and nature of publications examining relationships between NTDs and climatic factors in East Africa.We collated all relevant English and French publications indexed in PubMed ® , Web of Science™ Core Collection, and CAB Direct © databases published prior to 2019.Ninety-six publications were included for review.Kenya, Tanzania, and Ethiopia had high rates of publication, whereas countries in the Western Indian Ocean region were underrepresented.Most publications focused on schistosomiasis (n = 28, 29.2%), soil-transmitted helminthiases (n = 16, 16.7%), or human African trypanosomiasis (n = 14, 14.6%).Precipitation (n = 91, 94.8%) and temperature (n = 54, 56.3%) were frequently investigated climatic factors, whereas consideration of droughts (n = 10, 10.4%) and floods (n = 4, 4.2%) was not prominent.Publications reporting on associations between NTDs and changing climate were increasing over time.There was a decrease in the reporting of Indigenous identity and age factors over time.Overall, there were substantial knowledge gaps for several countries and for many NTDs.To better understand NTDs in the context of a changing climate, it would be helpful to increase research on underrepresented diseases and regions, consider demographic and social factors in research, and characterize how these factors modify the effects of climatic variables on NTDs in East Africa.
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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 | 0.018 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".