title: Geo-spatial factors associated with infection risk among young children in rural Ghana: a secondary spatial analysis.
abstract: BACKGROUND
  Determining the spatial patterns of infection among young children living in a malaria-endemic area may provide a means of locating high-risk populations who could benefit from additional resources for treatment and improved access to healthcare. The objective of this secondary analysis of baseline data from a cluster-randomized trial among 1943 young Ghanaian children (6-35 months of age) was to determine the geo-spatial factors associated with malaria and non-malaria infection status.
  
  
  METHODS
  Spatial analyses were conducted using a generalized linear geostatistical model with a Matern spatial correlation function and four definitions of infection status using different combinations of inflammation (C-reactive protein, CRP > 5 mg/L) and malaria parasitaemia (with or without fever). Potentially informative variables were included in a final model through a series of modelling steps, including: individual-level variables (Model 1); household-level variables (Model 2); and, satellite-derived spatial variables (Model 3). A final (Model 4) and maximal model (Model 5) included a set of selected covariates from Models 1 to 3.
  
  
  RESULTS
  The final models indicated that children with inflammation (CRP > 5 mg/L) and/or any evidence of malaria parasitaemia at baseline were more likely to be under 2 years of age, stunted, wasted, live further from a health facility, live at a lower elevation, have less educated mothers, and higher ferritin concentrations (corrected for inflammation) compared to children without inflammation or parasitaemia. Similar results were found when infection was defined as clinical malaria or parasitaemia with/without fever (definitions 3 and 4). Conversely, when infection was defined using CRP only, all covariates were non-significant with the exception of baseline ferritin concentration. In Model 5, all infection definitions that included parasitaemia demonstrated a significant interaction between normalized difference vegetation index and land cover type. Maps of the predicted infection probabilities and spatial random effect showed defined high- and low-risk areas that tended to coincide with elevation and cluster around villages.
  
  
  CONCLUSIONS
  The risk of infection among young children in a malaria-endemic area may have a predictable spatial pattern which is associated with geographical characteristics, such as elevation and distance to a health facility. Original trial registration clinicaltrials.gov (NCT01001871).
authors.affiliation: Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, ON, M5T 3M7, Canada.
authors.forename: Ashley M
authors.identifier: http://orcid.org/0000-0001-9812-8640
authors.initials: AM
authors.lastname: Aimone
authors.affiliation: Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, ON, M5T 3M7, Canada.
authors.forename: Patrick E
authors.initials: PE
authors.lastname: Brown
authors.affiliation: Centre for Global Child Health, Hospital for Sick Children, Peter Gilgan Centre for Research and Learning, 686 Bay Street, Toronto, ON, M5G 0A4, Canada.
authors.forename: Stanley H
authors.initials: SH
authors.lastname: Zlotkin
authors.affiliation: Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, ON, M5T 3M7, Canada.
authors.forename: Donald C
authors.initials: DC
authors.lastname: Cole
authors.affiliation: Kintampo Health Research Centre, Kintampo, Ghana. Seth.owusu-agyei@kintampo-hrc.org.
authors.forename: Seth
authors.initials: S
authors.lastname: Owusu-Agyei
mesh_terms: D002675:Child, Preschool; D003141:Communicable Diseases / Q000453:epidemiology*; D005260:Female; D005869:Ghana / Q000453:epidemiology; D006297:Health Services Accessibility; D006801:Humans; D007223:Infant; D008297:Male; D015233:Models, Statistical; D018570:Risk Assessment; D012424:Rural Population; D062206:Spatial Analysis; D019371:Topography, Medical
keywords: Bayesian inference; Children; Geostatistical modelling; Infection; Malaria; Spatial