Economic impact of harmful algal blooms on human health: a systematic review
Bibliographic record
Abstract
Harmful algal blooms (HABs) damage human activities and health. While there is wide literature on economic losses, little is known about the economic impact on human health. In this review, we systematically retrieved papers which presented health costs following exposure to HABs. A systematic review was conducted up to January 2019 in databases such as ScienceDirect and PubMed, and 16 studies were selected. Health costs included healthcare and medication expenses, loss of income due to illness, cost of pain and suffering, and cost of death. Two categories of illness (digestive and respiratory) were considered for health costs. For digestive illness cost, we found $86, $1,015 and $12,605, respectively, for mild, moderate and severe cases. For respiratory illness, costs were $86, $1,235 and $14,600, respectively, for mild, moderate and severe cases. We used Quality-Adjusted Life Years (QALYs) to access the loss of well-being due to illness caused by HABs. We found that breathing difficulty causes the most loss of QALYs, especially in children, with a loss of between 0.16 and 0.771 per child. Having gastroenteritis could cause a loss of between 2.2 and 7.1 QALYs per 1,000 children. Misleading symptoms of illness following exposure to HABs could cause bias in health costs estimations.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".