title: Multilevel and geo-statistical modeling of malaria risk in children of Burkina Faso.
abstract: BACKGROUND
  Previous research on determinants of malaria in Burkina Faso has largely focused on individual risk factors. Malaria risk, however, is also shaped by community, health system, and climatic/environmental characteristics. The aims of this study were: i) to identify such individual, household, community, and climatic/environmental risk factors for malaria in children under five years of age, and ii) to produce a parasitaemia risk map of Burkina Faso.
  
  
  METHODS
  The 2010 Demographic and Health Survey (DHS) was the first in Burkina Faso that tested children for malaria parasitaemia. Multilevel and geo-statistical models were used to explore determinants of malaria using this nationally representative database.
  
  
  RESULTS
  Parasitaemia was collected from 6,102 children, of which 66.0% (95% confidence interval (CI): 64.0-68.0%) were positive for Plasmodium spp. Older children (>23 months) were more likely to be parasitaemic than younger ones, while children from wealthier households and whose mother had higher education were at a lower risk. At the community level, living in a district with a rate of attendance to health facilities lower than 2 visits per year was significantly associated with greater odds of being infected. Malaria prevalence was also associated with higher normalized difference vegetation index, lower average monthly rainfall, and lower population densities. Predicted malaria parasitaemia was spatially variable with locations falling within an 11%-92% prevalence range. The number of parasitaemic children was nonetheless concentrated in areas of high population density, albeit malaria risk was notably higher in the sparsely populated rural areas.
  
  
  CONCLUSIONS
  Malaria prevalence in Burkina Faso is considerably higher than in neighbouring countries. Our spatially-explicit population-based estimates of malaria risk and infected number of children could be used by local decision-makers to identify priority areas where control efforts should be enhanced.
authors: Pôle Epidémiologie et Biostatistique (EPID), Institut de Recherche Expérimentale et Clinique (IREC), Faculté de Santé Publique (FSP), Université catholique de Louvain (UCL), Clos Chapelle-aux-champs 30, bte B1,30,13, 1200 Bruxelles, Belgium. sekou.samadoulougou@uclouvain.be.
authors: Sekou
authors: S
authors: Samadoulougou
authors: Mathieu
authors: M
authors: Maheu-Giroux
authors: Fati
authors: F
authors: Kirakoya-Samadoulougou
authors: Mathilde
authors: M
authors: De Keukeleire
authors: Marcia C
authors: MC
authors: Castro
authors: Annie
authors: A
authors: Robert
mesh_terms: D001499:Bayes Theorem; D002050:Burkina Faso / Q000453:epidemiology; D002675:Child, Preschool; D004777:Environment; D005260:Female; D006801:Humans; D007223:Infant; D008288:Malaria / Q000453:epidemiology*; D008297:Male; D008954:Models, Biological; D012307:Risk Factors