Prevalence and risk distribution of schistosomiasis among adults in Madagascar: a cross-sectional study.

BACKGROUND: The goal to eliminate the parasitic disease of poverty schistosomiasis as a public health problem is aligned with the 2030 United Nations agenda for sustainable development goals, including universal health coverage (UHC). Current control strategies focus on school-aged children, systematically neglecting adults. We aimed at providing evidence for the need of shifting the paradigm of schistosomiasis control programs from targeted to generalized approaches as key element for both the elimination of schistosomiasis as a public health problem and the promotion of UHC. METHODS: In a cross-sectional study performed between March 2020 and January 2021 at three primary health care centers in Andina, Tsiroanomandidy and Ankazomborona in Madagascar, we determined prevalence and risk factors for schistosomiasis by a semi-quantitative PCR assay from specimens collected from 1482 adult participants. Univariable and multivariable logistic regression were performed to evaluate odd ratios. RESULTS: The highest prevalence of S. mansoni, S. haematobium and co-infection of both species was 59.5%, 61.3% and 3.3%, in Andina and Ankazomborona respectively. Higher prevalence was observed among males (52.4%) and main contributors to the family income (68.1%). Not working as a farmer and higher age were found to be protective factors for infection. CONCLUSIONS: Our findings provide evidence that adults are a high-risk group for schistosomiasis. Our data suggests that, for ensuring basic health as a human right, current public health strategies for schistosomiasis prevention and control need to be re-addressed towards more context specific, holistic and integrated approaches.

Climatic, land-use and socio-economic factors can predict malaria dynamics at fine spatial scales relevant to local health actors: Evidence from rural Madagascar.

While much progress has been achieved over the last decades, malaria surveillance and control remain a challenge in countries with limited health care access and resources. High-resolution predictions of malaria incidence using routine surveillance data could represent a powerful tool to health practitioners by targeting malaria control activities where and when they are most needed. Here, we investigate the predictors of spatio-temporal malaria dynamics in rural Madagascar, estimated from facility-based passive surveillance data. Specifically, this study integrates climate, land-use, and representative household survey data to explain and predict malaria dynamics at a high spatial resolution (i.e., by Fokontany, a cluster of villages) relevant to health care practitioners. Combining generalized linear mixed models (GLMM) and path analyses, we found that socio-economic, land use and climatic variables are all important predictors of monthly malaria incidence at fine spatial scales, via both direct and indirect effects. In addition, out-of-sample predictions from our model were able to identify 58% of the Fokontany in the top quintile for malaria incidence and account for 77% of the variation in the Fokontany incidence rank. These results suggest that it is possible to build a predictive framework using environmental and social predictors that can be complementary to standard surveillance systems and help inform control strategies by field actors at local scales.