The two costs of neglect: evaluating the health and economic impacts of anthrax in an endemic area of rural Africa
Bibliographic record
Abstract
. Data on the burden of anthrax on human and animal health are extremely limited, perpetuating neglect at the policy level in endemic areas and internationally. To address these data inadequacies, we quantified the widespread impacts of anthrax on highly affected communities, focusing on a hyper-endemic area of eastern Africa, the Ngorongoro Conservation Area (NCA) of northern Tanzania. Data were collected between 2016 and 2019 through cross-sectional household surveys, active surveillance, compilation of available hospital and human disease surveillance records, and willingness-to-pay choice experiments conducted within wider socioeconomic surveys. Human anthrax was reported by 34 (16%) of 209 households surveyed. Cases of anthrax in livestock were reported in 63 households (30%), and in all 20 households within Ngoile, the most affected area. The majority of sudden deaths in livestock (368 of 500 cases in 30 months of active surveillance) were confirmed to be caused by anthrax using microscopy or qPCR. Monetary losses due to livestock deaths were significant and during outbreaks commonly exceeded average monthly household income. Despite the dual burden, willingness to adopt anthrax mitigation measures was low. Likely due to resource limitations, households would only consider such measures if they had high efficacy, be it for animals or humans, but even then, price was still an important factor. We discuss the potential benefits that anthrax prevention could have on affected communities, and factors that might influence the success of possible interventions. This study demonstrates that in endemic settings where dependence on livestock productivity is high, the impacts of neglected zoonoses can be diverse, persistent and severe and highlights the challenge of identifying locally acceptable mitigation strategies.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".