Article
Modelling spatially heterogeneous Schistosoma haematobium infection in Southwestern Tanzania using generalized additive mixed models
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Published: | February 26, 2021 |
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Background: Urinary schistosomiasis caused by the parasitic blood fluke Schistosoma haematobium is an important public health concern in Sub-Saharan-Africa, where according to recent estimates 112 million cases of S. haematobium infection infection occur [1]. Freshwater snails are the intermediate hosts of the parasite and infection is acquired by contact with infested water. Understanding the disease ecology and environmental factors that influence its distribution is important to guide control efforts. We report prevalences of S. haematobium infection in Southwestern Tanzania and use remotely sensed satellite data and questionnaire data of 17280 study participants to identify environmental and socio-demographic factors that are associated with this parasitic infection.
Methods: Since the infection is clustered within households and study sites, cluster and spatial correlation is present in our data. We use geostatistical logistic regression models [2] with mixed effects and spatially smoothed effects based on the GPS-location of the household. Variable selection was performed by forward variable selection based on the Akaike Information Criterion (AIC). The analyses were done in R using the “gamm4” package [3]. The R package “buildmer” with its function “buildgamm4” [4] was additionally used to perform variable selection including the spatial component and compared to the variable selection procedure without the spatial component.
Results: The overall prevalence of S. haematobium infection was 5.3% (95% confidence interval (CI): 5.0-5.6%), ranging from 0.0 to 15.8% per study site. Multivariable analysis revealed increased odds of infection for school-aged children and the age groups 15-25 and 25-35 years compared to persons above 35 years of age, for increasing distance to water courses and for people living near the Lake Nyasa (closer than 4km). Odds of infection decreased with higher altitude and with increasing enhanced vegetation index EVI. When additionally adjusting for spatial correlation population density became a significant predictor of schistosomiasis infection and altitude turned non-significant.
Conclusion: We found significant associations of individually assessed and remotely sensed factors with S. haematobium infection in Mbeya region in Southwestern Tanzania. The spatially heterogeneous results, which are typical for schistosomiasis, show that despite low overall prevalence some of the study sites suffer from a considerable burden of S. haematobium infection, which is related to various socio-demographic and environmental factors. Our results could help to design more effective control strategies in the future. They suggest that preventive chemotherapy should be targeted especially at school-aged children living in low altitude sites and/or crowded regions.
The authors declare that they have no competing interests.
The authors declare that a positive ethics committee vote has been obtained.
References
- 1.
- World Health Organization. Schistosomiasis Fact sheet. [Accessed 2020 Mar 18]. Available from: https://www.who.int/en/news-room/fact-sheets/detail/schistosomiasis
- 2.
- Wood SN. Generalized Additive Models: An Introduction with R. 2nd edition. CRC/Taylor & Francis; 2017.
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- Wood S, Scheipl F. gamm4: Generalized Additive Mixed Models using 'mgcv' and 'lme4'. R package version 0.2-5. 2017. Available from: https://CRAN.R-project.org/package=gamm4.
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- Voeten CC. buildmer: Stepwise Elimination and Term Reordering for Mixed-Effects Regression. R package version 1.5. 2020. Available from: https://CRAN.R-project.org/package=buildmer