AUTHOR=Okoyo Collins , Minnery Mark , Orowe Idah , Owaga Chrispin , Wambugu Christin , Olick Nereah , Hagemann Jane , Omondi Wyckliff P. , Gichuki Paul M. , McCracken Kate , Montresor Antonio , Fronterre Claudio , Diggle Peter , Mwandawiro Charles 

TITLE=Using a model-based geostatistical approach to design and analyse the prevalence of schistosomiasis in Kenya

JOURNAL=Frontiers in Tropical Diseases

VOLUME=Volume 4 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/tropical-diseases/articles/10.3389/fitd.2023.1240617

DOI=10.3389/fitd.2023.1240617

ISSN=2673-7515

ABSTRACT=Background: Kenya is endemic for both Schistosoma mansoni and Schistosoma haematobium, with over six million children being at-risk. A national school-based deworming programme was launched in 2012 with a goal to eliminate parasitic worms as a public health problem. This study used model-based geostatistical (MBG) approach to design and analyse the impact of the programme and inform treatment strategy changes for schistosomiasis (SCH).
Methods: A cross-sectional survey of 200 schools across 27 counties of Kenya was utilized. The study design, selection of the schools and analysis followed the MBG approach that incorporated historical data on treatment, morbidity and environmental covariates.
Results: The overall SCH prevalence was 5.0% (95%CI: 4.9-5.2) and was estimated with a high predictive probability of 0.999 to sit within 1% to <10%. Predictive probabilities at county level revealed county heterogeneity, with four counties estimated to sit within 0% to <1%, twenty counties within 1% to <10%, two counties within 10% to <20%, and one county within 20% to <50%.
Conclusion: SCH treatment requirements can now be confidently refined based on the World Health Organization guidelines. The four counties with prevalence within 0% to <1% may consider suspending treatment only in areas (sub-counties and wards) where prevalence is <1%.