The economics of climate-sensitive infectious diseases affecting human health in Latin America and the Caribbean: a scoping review
Bibliographic record
Abstract
The incidence of climate-sensitive infectious diseases (CSIDs), such as dengue, chikungunya and Zika, has been rising in Latin America and the Caribbean (LAC). Reported cases of these diseases nearly doubled between 2022 and 2023, with the highest figure reported from Brazil. However, evidence regarding the economic cost of CSIDs is limited in the region. This scoping review aims to identify the available evidence on the economic impacts of CSIDs in LAC countries and the potential costs and benefits of adaptation interventions. We searched PubMed, the Virtual Health Library, Web of Science, Scopus, JSTOR and EconPapers and included peer-reviewed and grey-literature studies published between January 2015 and December 2023. 10 peer-reviewed studies were included in this review: 9 assessed the economic impacts of the health consequences of CSIDs and 1 assessed the economics of adaptation policies or programmes concerning human health in LAC countries. Most studies were conducted in Mexico and Brazil. Studies have focused primarily on the economic costs of arboviruses, influenza and enteritis. The outcomes most frequently studied were disability-adjusted life years and mortality. Only one study evaluated the economic impact of implementing an adaptation intervention for dengue. These findings highlight a significant gap in understanding the economic impacts of CSIDs, particularly because most studies fail to monetise the reported metrics, limiting their ability to provide a comprehensive assessment of economic costs and cost-effectiveness analyses of interventions. There is a need for further research in this field, incorporating data from a diverse range of countries in the region and analysing the economic impacts of various CSIDs, as well as the cost-effectiveness of interventions aimed at reducing their prevalence.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".