Modelling the age-prevalence relationship in schistosomiasis: A secondary data analysis of school-aged-children in Mangochi District, Lake Malawi

Abstract

Schistosomiasis is an aquatic snail borne parasitic disease, with intestinal schistosomiasis (IS) and urogenital schistosomiasis (UGS) caused by Schistosoma mansoni and S. haematobium infections, respectively. School-aged-children (SAC) are a known vulnerable group and can also suffer from co-infections. Along the shoreline of Lake Malawi a newly emerging outbreak of IS is occurring with increasing UGS co-infection rates. Age-prevalence (co)infection profiles are not fully understood. To shed light on these (co)infection trends by Schistosoma species and by age of child, we conducted a secondary data analysis of primary epidemiological data collected from SAC in Mangochi District, Lake Malawi, as published previously. Available diagnostic data by child, were converted into binary response infection profiles for 520 children, aged 6–15, across 12 sampled schools. Generalised additive models were then fitted to mono- and dual-infections. These were used to identify consistent population trends, finding the prevalence of IS significantly increased [p = 8.45e-4] up to 11 years of age then decreasing thereafter. A similar age-prevalence association was observed for co-infection [p = 7.81e-3]. By contrast, no clear age-infection pattern for UGS was found [p = 0.114]. Peak prevalence of Schistosoma infection typically occurs around adolescence; however, in this newly established IS outbreak with rising prevalence of UGS co-infections, the peak appears to occur earlier, around the age of 11 years. As the outbreak of IS fulminates, further temporal analysis of the age-relationship with Schistosoma infection is justified. This should refer to age-prevalence models which could better reveal newly emerging transmission trends and Schistosoma species dynamics. Dynamical modelling of infections, alongside malacological niche mapping, should be considered to guide future primary data collection and intervention programmes.