3 years ago

Schistosomiasis mansoni incidence data in Rwanda can improve prevalence assessments, by providing high-resolution hotspot and risk factors identification

Schistosomiasis mansoni incidence data in Rwanda can improve prevalence assessments, by providing high-resolution hotspot and risk factors identification
A. Veldkamp, S. Amer, C. Karema, E. Nyandwi, I. Umulisa
Schistosomiasis mansoni constitutes a significant public health problem in Rwanda. The nationwide prevalence mapping conducted in 2007–2008 revealed that prevalence per district ranges from 0 to 69.5% among school children. In response, mass drug administration campaigns were initiated. However, a few years later some additional small-scale studies revealed the existence of areas of high transmission in districts formerly classified as low endemic suggesting the need for a more accurate methodology for identification of hotspots. This study investigated if confirmed cases of schistosomiasis recorded at health facility level can be used to, next to existing prevalence data, detect geographically more accurate hotspots of the disease and its associated risk factors. A GIS-based spatial and statistical analysis was carried out. Confirmed cases, recorded at primary health facilities level, were combined with demographic data to calculate incidence rates for each of 367 health facility service area. Empirical Bayesian smoothing was used to deal with rate instability. Incidence rates were compared with prevalence data to identify their level of agreement. Spatial autocorrelation of the incidence rates was analyzed using Moran’s Index, to check if spatial clustering occurs. Finally, the spatial relationship between schistosomiasis distribution and potential risk factors was assessed using multiple regression. Incidence rates for 2007–2008 were highly correlated with prevalence values (R2 = 0.79), indicating that in the case of Rwanda incidence data can be used as a proxy for prevalence data. We observed a focal distribution of schistosomiasis with a significant spatial autocorrelation (Moran’s I > 0: 0,05–0.20 and p ≤ 0,05), indicating the occurrence of hotspots. Regarding risk factors, it was identified that the spatial pattern of schistosomiasis is significantly associated with wetland conditions and rice cultivation. In Rwanda the high density of health facilities and the standardized microscopic laboratory diagnostic allow the derived data to be used to complement prevalence studies to identify hotspots of schistosomiasis and its associated risk factors. This type of information, in turn, can support disease control interventions and monitoring.
You might also like
Discover & Discuss Important Research

Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.