ArticleBMJ open2025
Spatial and spatio-temporal analysis for malaria hotspot identification: a scoping review protocol.
Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
- Analysis of clinical malaria hotspots according to transmission levels in two health areas of the Kolondieba health district, Mali.MalariaWorld journal · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionMalaria hotspots have been the focus of public health managers during the last two decades because of the potential elimination gains that can be obtained by targeting them. Advances in spatial technologies in the 20th century such as geographic information systems, remotely sensed satellite data on climate and ecology, and statistical methods for spatial cluster detection have enhanced our ability to map fine-scale patterns of malaria transmission. This led to the diversification of analytical approaches and a lack of consensus on methods and standardised indicators for malaria hotspot detection, raising challenges for comparing and synthesising findings across different studies. This review aims to fill this gap by identifying and summarising all publicly available peer-reviewed articles on spatial and spatio-temporal analytical approaches used to detect malaria hotspots while highlighting research gaps.
methodsThis scoping review will follow the Joanna Briggs Institute Framework. A comprehensive search will be conducted in PubMed, Medline, Web of Science, Scopus and Embase using keywords related to malaria, hotspots and detection. Retrieved articles published between 1 January 2000 and 31 December 2024 in English or French will be uploaded to Covidence for screening. Empirical studies that apply spatial or spatio-temporal analytical methods to detect malaria hotspots will be included. Studies will be excluded if they rely solely on geographical visualisation without formal spatial analysis. Data extraction will be performed by two independent reviewers, with disagreements resolved by discussion. Data will be summarised using descriptive statistics and thematic analysis. ETHICS AND DISSEMINATION: This scoping review will involve the secondary analysis of published literature on malaria hotspot analysis; therefore, ethics approval is not required. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews checklist will be used to ensure transparency and methodological rigour in reporting. The findings will be disseminated through publication in a peer-reviewed journal and presented at scientific conferences via abstracts, oral or poster presentations. TRIAL EGISTRATION NUMBER: This review has been registered on the Open Science Framework under the DOI: https://doi.org/10.17605/OSF.IO/C8KUN.
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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.