Ye Shen1, Meng-Hsuan Sung1, Charles H King2, Sue Binder3, Nupur Kittur3, Christopher C Whalen1,4, Daniel G Colley3,5. 1. Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, Georgia, USA. 2. Center for Global Health and Diseases, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA. 3. Schistosomiasis Consortium for Operational Research and Evaluation (SCORE), Center for Tropical and Emerging Global Diseases, University of Georgia, Athens, Georgia, USA. 4. Global Health Institute, University of Georgia, Athens, Georgia, USA. 5. Department of Microbiology, University of Georgia, Athens, Georgia, USA.
Abstract
BACKGROUND: Some villages, labeled "persistent hotspots (PHS)," fail to respond adequately in regard to prevalence and intensity of infection to mass drug administration (MDA) for schistosomiasis. Early identification of PHS, for example, before initiating or after 1 or 2 years of MDA could help guide programmatic decision making. METHODS: In a study with multiple rounds of MDA, data collected before the third MDA were used to predict PHS. We assessed 6 predictive approaches using data from before MDA and after 2 rounds of annual MDA from Kenya and Tanzania. RESULTS: Generalized linear models with variable selection possessed relatively stable performance compared with tree-based methods. Models applied to Kenya data alone or combined data from Kenya and Tanzania could reach over 80% predictive accuracy, whereas predicting PHS for Tanzania was challenging. Models developed from one country and validated in another failed to achieve satisfactory performance. Several Year-3 variables were identified as key predictors. CONCLUSIONS: Statistical models applied to Year-3 data could help predict PHS and guide program decisions, with infection intensity, prevalence of heavy infections (≥400 eggs/gram of feces), and total prevalence being particularly important factors. Additional studies including more variables and locations could help in developing generalizable models.
BACKGROUND: Some villages, labeled "persistent hotspots (PHS)," fail to respond adequately in regard to prevalence and intensity of infection to mass drug administration (MDA) for schistosomiasis. Early identification of PHS, for example, before initiating or after 1 or 2 years of MDA could help guide programmatic decision making. METHODS: In a study with multiple rounds of MDA, data collected before the third MDA were used to predict PHS. We assessed 6 predictive approaches using data from before MDA and after 2 rounds of annual MDA from Kenya and Tanzania. RESULTS: Generalized linear models with variable selection possessed relatively stable performance compared with tree-based methods. Models applied to Kenya data alone or combined data from Kenya and Tanzania could reach over 80% predictive accuracy, whereas predicting PHS for Tanzania was challenging. Models developed from one country and validated in another failed to achieve satisfactory performance. Several Year-3 variables were identified as key predictors. CONCLUSIONS: Statistical models applied to Year-3 data could help predict PHS and guide program decisions, with infection intensity, prevalence of heavy infections (≥400 eggs/gram of feces), and total prevalence being particularly important factors. Additional studies including more variables and locations could help in developing generalizable models.
Authors: Nupur Kittur; Carl H Campbell; Sue Binder; Ye Shen; Ryan E Wiegand; Joseph R Mwanga; Safari M Kinung'hi; Rosemary M Musuva; Maurice R Odiere; Sultani H Matendechero; Stefanie Knopp; Daniel G Colley Journal: Am J Trop Med Hyg Date: 2020-07 Impact factor: 2.345
Authors: Charles H King; Nupur Kittur; Ryan E Wiegand; Ye Shen; Yang Ge; Christopher C Whalen; Carl H Campbell; Jan Hattendorf; Sue Binder Journal: Am J Trop Med Hyg Date: 2020-07 Impact factor: 2.345
Authors: Samuel K Kwofie; Kwasi Agyenkwa-Mawuli; Emmanuel Broni; Whelton A Miller Iii; Michael D Wilson Journal: Mol Divers Date: 2021-08-05 Impact factor: 2.943
Authors: Christine Tedijanto; Solomon Aragie; Zerihun Tadesse; Mahteme Haile; Taye Zeru; Scott D Nash; Dionna M Wittberg; Sarah Gwyn; Diana L Martin; Hugh J W Sturrock; Thomas M Lietman; Jeremy D Keenan; Benjamin F Arnold Journal: PLoS Negl Trop Dis Date: 2022-03-11