Modeling Approaches to Predicting Persistent Hotspots in SCORE Studies for Gaining Control of Schistosomiasis Mansoni in Kenya and Tanzania
| Autor: | Christopher C. Whalen, Daniel G. Colley, Sue Binder, Charles H. King, Ye Shen, Meng-Hsuan Sung, Nupur Kittur |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Male 030231 tropical medicine Schistosomiasis Trees (plant) Tanzania 03 medical and health sciences Major Articles and Brief Reports Feces 0302 clinical medicine Environmental health schistosomiasis medicine Prevalence Immunology and Allergy persistent hotspots Animals Humans Parasites Mass drug administration Child Anthelmintics mass drug administration Models Statistical biology praziquantel Neglected Diseases modeling Schistosoma mansoni medicine.disease biology.organism_classification Kenya Schistosomiasis mansoni 030104 developmental biology Infectious Diseases Feasibility Studies Female |
| Zdroj: | The Journal of Infectious Diseases |
| ISSN: | 1537-6613 0022-1899 |
| Popis: | 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. Mass drug administration for schistosomiasis identified persistent hotspots villages that failed to respond adequately in regard to prevalence and intensity of infection.We applied statistical models to predict such persistent hotspots and found several important factors. |
| Databáze: | OpenAIRE |
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