SynthesisFrontiers in public health2026
A systematic review investigating emerging trends between Extreme Weather Events (EWEs) and infectious disease outbreaks in South Africa.
Synthesis in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- A systematic review investigating emerging trends between Extreme Weather Events (EWEs) and infectious disease outbreaks in South Africa.Frontiers in public health · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Extreme weather events (EWEs) are increasing in frequency and intensity due to climate change, exacerbating health risks, particularly for vulnerable populations. Infectious diseases are climate-sensitive, yet microbial dynamics during and after EWEs remain poorly understood. This systematic review examines if there are emerging trends and associations in infectious disease outbreaks and health care following EWEs in South Africa. Methods: A comprehensive search across fifteen electronic databases was conducted using Cochrane systematic review principles, including double screening and double extraction. Studies describing outbreaks of infectious diseases related to EWEs in South Africa were included, considering all study designs. A PRISMA diagram details the screening process, and quality appraisal was conducted using JBI checklist tools or a mixed-methods assessment tool. Results: The review did not identify any primary studies that explicitly examined the presence of specific pathogens contaminating the environment in relation to EWEs in South Africa. Instead, the available literature predominantly focused on clinical symptom patterns or broader syndromic descriptions, with limited attention to pathogen-specific detection or characterisation. This highlights a significant evidence gap in microbial-level assessments of how EWEs influence environmental contamination. However, findings indicate the presence of infectious diseases post-EWEs, with three key themes emerging: (1) climate variables, (2) population vulnerabilities, and (3) policy effects. The review highlights the need for longitudinal microbiological data to improve outbreak prediction and preparedness. Discussion: This review underscores a significant research gap and calls for an integrated approach combining environmental monitoring with pathogen diagnostics. Large-scale longitudinal studies and enhanced collaboration public health, environmental surveillance, laboratory capacity, and disaster preparedness are needed. Early Warning Systems should incorporate climate variables to predict disease outbreaks, addressing the limited diagnostics, lack of environmental microbiology, fragmented surveillance data effectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.