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

Abstract

BackgroundSchistosomiasis 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.MethodsA 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.ResultsIncidence 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.ConclusionIn 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.

Modeling schistosomiasis spatial risk dynamics over time in Rwanda using zero-inflated Poisson regression

“…Moreover, in our study socio-economic risk factors were excluded from the explanatory risk factors (including access to improved water for domestic uses, proper sanitation, wearing shoes, bush defecation behavior, etc.) 14 . Those factors are represented by qualitative (%ge) and not spatially explicit figures—result of integrated living conditions surveys extrapolated at district level (10 times + bigger than a HFSA).…”

Approaches to defining health facility catchment areas in sub-Saharan Africa

“…The use of buffers was criticized because it is based on unrealistic straight-line distances which do not account for topography, transport modes, seasonality, mobility of people, the attractiveness of facilities, the inability of sick people to walk, and documented healthcare-seeking behaviour (such as bypassing the nearest facility). Straight-line distances are unrealistic, do not account for topography, transport modes, the likelihood of living beyond the threshold, lack of updated spatial and healthcareseeking behaviour data, the inability of sick people to walk, facilities are not uniformly attractive, seasonal mobility of people, bypassing of the nearest facility, the catchment is not a function of distance only Radial buffers accounting for geographical barriers [55], enumeration [56,57] or parish boundaries and road networks [34,58] Thiessen polygon, a region incorporating all points that are closer to a given facility than any other [6,[59][60][61] All points that are closer to a given facility than any other Health facility, coarse residential location Straight-line distances are unrealistic, bypassing the nearest facility, does not account for transport modes, healthcare-seeking behaviour and other factors beyond distance, percapita utilization rate is constant within the HFCA Thiessen polygon with boundaries, travel factors, buffers, and population [62] Modelled travel time or distance based on a leastcost path model [5,13,38,[63][64][65][66][67][68][69][70][71][72] or on network analysis [73] often adjusted for facility capacity [74], population [75], Thiessen polygon [76], boundary [77,78] [71] combined with patient address [88] and gravity models [89] considers interaction between supply and demand Facility, urban residence, travel factors, population, capa...…”

Molecular Tools and Schistosomiasis Transmission Elimination

“…Previously, it was reported that certain species of vegetation and other biota in snails’ habitat can affect transmission to man. It was, thus, shown that water lilies 37 and rice 38 affect snail abundance and support increased cercarial numbers in the corresponding sites. On the other hand, attachment of rotifers to snails inhibits development of schistosomes within them and reduces the viability of cercariae.…”