Information systems for enhancing multisectoral and multilevel preparedness and response to climate-sensitive infectious diseases in Latin America and the Caribbean: a scoping review
Bibliographic record
Abstract
Background Climate variability influences the spread of diseases such as dengue, malaria, leishmaniasis and Chagas in the Latin America and the Caribbean (LAC) region, increasing health risks for marginalised groups. Effective information systems are crucial for integrating data and implementing targeted interventions. However, despite efforts such as early warning and response and surveillance systems, gaps remain in data integration and stakeholder engagement. This scoping review focuses on the LAC region, assessing research gaps and exploring the development of information systems that use climate and health data to address climate-sensitive infectious diseases (CSIDs), drawing on frameworks that emphasise co-production, equity and cross-sectoral collaboration. Methods We conducted a scoping review following Joanna Briggs Institute guidelines and Preferred Reporting Items for Systematic review and Meta-Analysis extension for Scoping Reviews. We selected studies on CSIDs in LAC from 2015 to December 2023, focusing on a broad definition of climate–health information systems based on the framework proposed by Shumake et al (2023), which outlines seven principles of good practice for climate-informed health decision-making. We included a variety of study designs and excluded abstracts. Data sources included indexed databases (Scopus, Web of Science, PubMed, Virtual Health Library) and grey literature (regional development banks, preprints). Evidence was screened in Covidence, data on study characteristics and information-system types were extracted and quality was assessed qualitatively. We performed a narrative and thematic synthesis to analyse the extracted information. Results Our scoping review identified Brazil and Mexico as leaders in CSIDs research, with a primary focus on dengue. National or local surveillance systems were central to data collection, but data quality and utilisation challenges remain. Analyses by sex or vulnerable groups were under-represented in studies, highlighting gaps in understanding and the need for inclusive research approaches. Interpretation In contrast with the integrated climate and health-service practices advocated by Shumake et al , our review found limited evidence of co-production strategies in LAC countries, underscoring the importance of collaborative, resilience-building efforts across sectors for future research and policy development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".