DOI: 10.5061/dryad.s7h44j15p
title: Exposure, hazard and vulnerability all contribute to Schistosoma haematobium re-infection in northern Senegal

description: The risk of infectious diseases, including snail-borne schistosomiasis, is
 determined by three interrelated components: exposure, hazard and
 vulnerability. For schistosomiasis, exposure occurs through behaviors
 involving water contact, but not without the environmental hazard of
 snails and parasites in the water. Socio-economic vulnerability makes it
 difficult to reduce exposure in the presence of hazard, because it
 increases reliance on hazardous activities and environments. We aimed to
 quantify the contributions of exposure, hazard and vulnerability to
 schistosome re-infection presence and intensity. We used cross-sectional
 parasitological data from 821 school-aged children in 13 villages along
 the Senegal River, survey data from 411 households where those children
 resided and ecological data from all 24 village water contact sites. We
 fit mixed-effects logistic and negative binomial regressions with indices
 of exposure, hazard and vulnerability as explanatory variables of
 Schistosoma haematobium infection, along with demographic control
 variables. Multi-model inference was used to determine the relative
 importance of each component of risk and model averaging was used to
 quantify associations between infection outcomes and indices of hazard,
 exposure and vulnerability. The most important component of S. haematobium
 presence was hazard (Ʃwi = 0.95), followed by vulnerability (Ʃwi = 0.91),
 while the most important component of S. haematobium intensity was
 exposure (Ʃwi = 1.00), followed by hazard (Ʃwi = 0.77). Hazard was
 positively associated with infection presence (OR = 1.49, 95% CI 1.12,
 1.97), while exposure was positively associated with infection intensity.
 Our findings highlight how social (exposure and vulnerability) and
 environmental (hazard) processes act together to facilitate the
 acquisition and accumulation of schistosome infection from the environment
 across time and space. This approach can inform targeting of social and
 environmental interventions as complements to mass drug administration.
description: <strong>Parasitology data:</strong> <em>S.
 haematobium </em>infection in school-aged children were determined
 by detection of eggs in filtered urine samples. Infections were determined
 to be present when any eggs were detected; intensity of infection
 determined by number of eggs in sample.
 <strong>Household survey data: </strong>Household
 surveys were completed in all households where children enrolled in the
 parasitology study lived. Relevant survey modules include (1)
 individual-level demopgrahic information, (2) individual-level, two-week
 recall of contact with surface water for six primary domestic and
 occupational chores requiring contact with water, (3) average freuqency of
 each water contact activity reported at the household level, (4)
 socio-economic indicators, such as reported durable assets and access to
 improved water and sanitation infrastructure, and (5) geographic
 coordinates of houseohld locations. <strong>Body
 surface area interviews: </strong>Interviews with village residents
 at water access sites elicitied information on how much of the body
 typically comes into contact with water for each of six common water
 contact activities: (1) washing laundry, (2) washing dishes, (3)
 collecting water, (4) irrigated crops, (5) washing or watering livestock
 and (6) fishing from shore. Responses for each activity were registered on
 a diagram to used to measure burn size.
 <strong>Ecological surveys: </strong>Exhaustive snail
 surveys were performed at all water access sites in all study villages
 with sampling stratified by three microhabitat types (emergent vegetation,
 non-emergent vegetation and open water/mud bottom). Microhabitats were
 identified using imagery from Google Earth and an unmanned aerial vehicle
 equipped with a camera.
description: Individual- and household-level data have been aggregated to the
 village level in accordance with IRB privacy safeguards. The data needed
 to replicate the analysis can be made available upon request. README file
 with variable descriptions included as attachement. Code used to analyze
 data is freely available at
 https://github.com/andjanlund/ehv_schisto.