2024
Understanding the impact of covariates on the classification of implementation units for soil-transmitted helminths control: a case study from Kenya
Puranik A, Diggle P, Odiere M, Gass K, Kepha S, Okoyo C, Mwandawiro C, Wakesho F, Omondi W, Sultani H, Giorgi E. Understanding the impact of covariates on the classification of implementation units for soil-transmitted helminths control: a case study from Kenya. BMC Medical Research Methodology 2024, 24: 294. PMID: 39614175, PMCID: PMC11606136, DOI: 10.1186/s12874-024-02420-1.Peer-Reviewed Original ResearchConceptsPredictive inferenceRemotely sensed covariatesSimulation studyModel-based geostatisticsGeostatistical modelImpact of covariatesSpatially referenced covariatesSample sizeModern statistical methodsModel-based geostatistical methodsCross-sectional surveyCovariatesReduced sample sizeClassification of areasPrevalence predictionsInferenceDisease riskMethodsThis studyPrevalenceSub-countyPrevalence levels
2023
Using a model-based geostatistical approach to design and analyse the prevalence of schistosomiasis in Kenya
Okoyo C, Minnery M, Orowe I, Owaga C, Wambugu C, Olick N, Hagemann J, Omondi W, Gichuki P, McCracken K, Montresor A, Fronterre C, Diggle P, Mwandawiro C. Using a model-based geostatistical approach to design and analyse the prevalence of schistosomiasis in Kenya. Frontiers In Tropical Diseases 2023, 4: 1240617. DOI: 10.3389/fitd.2023.1240617.Peer-Reviewed Original ResearchTreatment strategy changesSchool-based deworming programmePublic health problemPrevalence of schistosomiasisWorld Health Organization guidelinesCross-sectional surveyHealth Organization guidelinesPredictive probabilitySCH prevalenceDeworming programsSchistosoma haematobiumHealth problemsSchistosoma mansoniOrganization guidelinesStudy designCounties of KenyaPrevalenceSchistosomiasisHighest predictive probabilityParasitic wormsTreatment requirementsTreatmentGuidelinesMorbidityHaematobium