ReviewAdvances in parasitology2011
The applications of model-based geostatistics in helminth epidemiology and control.
Review in Advances in parasitology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 2 of them syntheses 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
61 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Mapping Soil Transmitted Helminths and Schistosomiasis under Uncertainty: A Systematic Review and Critical Appraisal of Evidence.PLoS neglected tropical diseases · 2016Pooled it
- Global mapping of infectious disease.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2013Pooled it
- Analysis of Schistosomiasis haematobium infection prevalence and intensity in Chikhwawa, Malawi: an application of a two part model.PLoS neglected tropical diseases · 2013Trial
- Spatial patterns and risk mapping of opisthorchiasis and soil-transmitted helminth infections in Thailand using Bayesian geostatistical models.Scientific reports · 2026Article
- An intuitive sampling framework for setting-specific decision-making in soil-transmitted helminthiasis control programs.PLoS neglected tropical diseases · 2026Article
- Using Model-Based Geostatistics to Refine Population-Based Estimates of Trachoma Prevalence: Update from a Technical Consultation.The American journal of tropical medicine and hygiene · 2025Article
- Prevalence and Hematological Changes in Urogenital Schistosomiasis: Infection Persistence in the Phase of Mass Drug Administration in Sempoa (Kwahu East, Ghana): A Cross-Sectional Study.Health science reports · 2025Article
- Using ESPEN data for evidence-based control of neglected tropical diseases in sub-Saharan Africa: A comprehensive model-based geostatistical analysis of soil-transmitted helminths.PLoS neglected tropical diseases · 2025Article
- Spatial co-distribution of tuberculosis prevalence and low BCG vaccination coverage in Ethiopia.Scientific reports · 2024Article
- Enabling targeted mass drug administration for schistosomiasis in north-western Tanzania: Exploring the use of geostatistical modeling to inform planning at sub-district level.PLoS neglected tropical diseases · 2024Article
- Estimating the burden of mycetoma in Sudan for the period 1991-2018 using a model-based geostatistical approach.PLoS neglected tropical diseases · 2022Article
- Enterobiasis and its risk factors in urban, rural and indigenous children of subtropical Argentina.Parasitology · 2022Review
- Spatial codistribution of HIV, tuberculosis and malaria in Ethiopia.BMJ global health · 2022Article
- Model building and assessment of the impact of covariates for disease prevalence mapping in low-resource settings: to explain and to predict.Journal of the Royal Society, Interface · 2021Article
- Geographical Variation inPathogens (Basel, Switzerland) · 2021Article
- Parasite associations predict infection risk: incorporating co-infections in predictive models for neglected tropical diseases.Parasites & vectors · 2020Article
- Modelling the impact of MAUP on environmental drivers for Schistosoma japonicum prevalence.Parasites & vectors · 2020Article
- Geographical distribution and risk factors forInternational journal for parasitology. Parasites and wildlife · 2019Article
- Mapping Schistosoma mansoni endemicity in Rwanda: a critical assessment of geographical disparities arising from circulating cathodic antigen versus Kato-Katz diagnostics.PLoS neglected tropical diseases · 2019Article
- Mapping Soil-Transmitted Helminth Parasite Infection in Rwanda: Estimating Endemicity and Identifying At-Risk Populations.Tropical medicine and infectious disease · 2019Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Funding agencies are dedicating substantial resources to tackle helminth infections. Reliable maps of the distribution of helminth infection can assist these efforts by targeting control resources to areas of greatest need. The ability to define the distribution of infection at regional, national and subnational levels has been enhanced greatly by the increased availability of good quality survey data and the use of model-based geostatistics (MBG), enabling spatial prediction in unsampled locations. A major advantage of MBG risk mapping approaches is that they provide a flexible statistical platform for handling and representing different sources of uncertainty, providing plausible and robust information on the spatial distribution of infections to inform the design and implementation of control programmes. Focussing on schistosomiasis and soil-transmitted helminthiasis, with additional examples for lymphatic filariasis and onchocerciasis, we review the progress made to date with the application of MBG tools in large-scale, real-world control programmes and propose a general framework for their application to inform integrative spatial planning of helminth disease control programmes.
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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.