Influence of landscape heterogeneity on plasmodium infection in Nyakach sub-county, western Kenya

Abstract

Despite scaled up intervention strategies in Kenya, existing control and treatment tools have not suppressed Plasmodium infection. Persistence of malaria has been attributed to submicroscopic infection with densities too low to be detected by standard diagnostic methods. Although landscape heterogeneity may contribute to the persistence of malaria, its role in persistence of submicroscopic infections is unknown. Landscape heterogeneity is defined here as variation in topography and rainfall seasonality. The topography of Nyakach Sub-County ranges from Lake Victoria's shores to the highland plateau, with habitat stability thought to influence diverse vector ecology and Plasmodium infection. Dynamic changes in vector ecology will always pose a challenge to intervention strategies. It is unknown, however, what effect landscape heterogeneity has on malaria entomological indices in maintaining year round vector population. Variation in the ecological landscape may result in differential risk exposures to malaria contributing to variation in febrile incidences in the community. It remains to be seen whether year-round clinical malaria persistence is influenced by landscape heterogeneity. The current study investigated the influence of landscape heterogeneity on Plasmodium infection in Nyakach Sub-County, western Kenya. The specific objectives were to determine the influence of topography and seasonality on prevalence of submicroscopic malaria, entomological indices of malaria, and incidence of clinical malaria in Nyakach Sub-County, western Kenya. A cross-sectional study design was used to collect data on prevalence of submicroscopic infection and entomological survey while longitudinal study design was used to collect data on the incidences of clinical malaria in lakeshore, hillside and highland plateau throughout wet and dry seasons. 1,777 finger prick blood smears and dry blood spots on filter paper were collected for microscopic inspection and real time-PCR diagnosis of Plasmodium infection over the wet and the dry season of 2019 and 2020. Larval sampling was conducted in all larval habitats using a standard dipper and adult Anopheles mosquitoes sampled using Pyrethrum Spray Catches. Finger-prick blood samples were collected from 2,205 febrile cases and tested for malaria parasites using ultra-sensitive Alere® malaria rapid diagnostic tests. Mixed effect model, negative binomial, and binary logistic regression determined the influence of topography and seasonality on: prevalence of submicroscopic infection; Anopheles larval and adult vector densities and abundance; and incidence of clinical malaria. The prevalence of submicroscopic infection was 14.2% (253/1,777). The likelihood of submicroscopic infection was higher in the lakeshore in both the wet and dry seasons (AOR: 2.71, 95% CI=1.85-3.95; p<0.0001) and hillside (AOR: 1.74, 95% CI=1.17–2.61, p=0.007) than in the highland plateau zones. Anopheles larval densities were 3.23 (95% CI=2.50-4.18, p<0.0001) and 1.81 (95% CI=1.32-2.48, p<0.0001) times higher in the lakeshore and hillside zones, respectively, than on the highland plateau and 4.59 (95% CI=3.61-5.83, p<0.0001) higher in wet season than dry season. Adult Anopheles abundance were 1.72 (95% CI=1.02-2.90, p=0.041) times higher in the lakeshore zone than on the highland plateau, and 2.17 (95% CI=1.48-3.20, p<0.0001) times higher in wet season than in dry season. Clinical malaria incidences were 2.02 (95% CI=1.62-2.50, p<0.0001) times higher lakeshore and 1.4 (OR: 1.42, 95% CI=1.13-1.79, p=0.002) times higher in hillside zone than on the highland plateau, and 1.49 (95% CI=1.24-1.80, p<0.0001) times higher in wet season, than in dry season. Landscape heterogeneity influenced prevalence of submicroscopic infection, entomological indices of malaria, and incidence of clinical malaria. Empirical evidence on the role of landscape heterogeneity on malaria, emphasizes importance of: developing strategies for identifying malaria transmission determinants in diverse landscapes; tailoring malaria control interventions to specific landscape attributes and improve the accuracy of malaria diagnosis and treatment.