title: Use of prospective hospital surveillance data to define spatiotemporal heterogeneity of malaria risk in coastal Kenya.
abstract: BACKGROUND
  Malaria in coastal Kenya shows spatial heterogeneity and seasonality, which are important factors to account for when planning an effective control system. Routinely collected data at health facilities can be used as a cost-effective method to acquire information on malaria risk for large areas. Here, data collected at one specific hospital in coastal Kenya were used to assess the ability of such passive surveillance to capture spatiotemporal heterogeneity of malaria and effectiveness of an augmented control system.
  
  
  METHODS
  Fever cases were tested for malaria at Msambweni sub-County Referral Hospital, Kwale County, Kenya, from October 2012 to March 2015. Remote sensing data were used to classify the development level of each monitored community and to identify the presence of rice fields nearby. An entomological study was performed to acquire data on the seasonality of malaria vectors in the study area. Rainfall data were obtained from a weather station located in proximity of the study area. Spatial analysis was applied to investigate spatial patterns of malarial and non-malarial fever cases. A space-time Bayesian model was performed to evaluate risk factors and identify locations at high malaria risk. Vector seasonality was analysed using a generalized additive mixed model (GAMM).
  
  
  RESULTS
  Among the 25,779 tested febrile cases, 28.7 % were positive for Plasmodium infection. Malarial and non-malarial fever cases showed a marked spatial heterogeneity. High risk of malaria was linked to patient age, community development level and presence of rice fields. The peak of malaria prevalence was recorded close to rainy seasons, which correspond to periods of high vector abundance. Results from the Bayesian model identified areas with significantly high malaria risk. The model also showed that the low prevalence of malaria recorded during late 2012 and early 2013 was associated with a large-scale bed net distribution initiative in the study area during mid-2012.
  
  
  CONCLUSIONS
  The results indicate that the use of passive surveillance was an effective method to detect spatiotemporal patterns of malaria risk in coastal Kenya. Furthermore, it was possible to estimate the impact of extensive bed net distribution on malaria prevalence among local fever cases over time. Passive surveillance based on georeferenced malaria testing is an important tool that control agencies can use to improve the effectiveness of interventions targeting malaria (and other causes of fever) in such high-risk locations.
authors.affiliation: Department of Environmental Sciences, Emory University, Atlanta, GA, USA. donal.bisanzio@zoo.ox.ac.uk.|Department of Zoology, University of Oxford, Oxford, UK. donal.bisanzio@zoo.ox.ac.uk.
authors.forename: Donal
authors.initials: D
authors.lastname: Bisanzio
authors.affiliation: Department of Environment and Health Sciences, Technical University of Mombasa, Mombasa, Kenya. fmutuku73@gmail.com.
authors.forename: Francis
authors.initials: F
authors.lastname: Mutuku
authors.affiliation: Department of Pediatrics, Stanford University, Stanford, CA, USA. dlabeaud@stanford.edu.
authors.forename: Angelle D
authors.initials: AD
authors.lastname: LaBeaud
authors.affiliation: Center for Global Health and Diseases, Case Western Reserve University, Cleveland, OH, USA. plmungai@yahoo.com.
authors.forename: Peter L
authors.initials: PL
authors.lastname: Mungai
authors.affiliation: Vector-Borne Diseases Control Unit, Msambweni County Referral Hospital, Kwale, Msambweni, Kenya. jmwandi@yahoo.com.
authors.forename: Jackson
authors.initials: J
authors.lastname: Muinde
authors.affiliation: Ministry of Health Kwale County, Kwale, Mombasa, Kenya. hajarabusaidy13@gmail.com.
authors.forename: Hajara
authors.initials: H
authors.lastname: Busaidy
authors.affiliation: Vector-Borne Diseases Control Unit, Ministry of Health, Nairobi, Kenya. dunstan.Mukoko29@gmail.com.
authors.forename: Dunstan
authors.initials: D
authors.lastname: Mukoko
authors.affiliation: Center for Global Health and Diseases, Case Western Reserve University, Cleveland, OH, USA. chk@cwru.edu.
authors.forename: Charles H
authors.initials: CH
authors.lastname: King
authors.affiliation: Department of Environmental Sciences, Emory University, Atlanta, GA, USA. ukitron@emory.edu.
authors.forename: Uriel
authors.initials: U
authors.lastname: Kitron
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D062665:Epidemiological Monitoring; D005260:Female; D006761:Hospitals; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007630:Kenya; D008288:Malaria / Q000453:epidemiology*; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D018570:Risk Assessment; D012621:Seasons; D062211:Spatio-Temporal Analysis; D019371:Topography, Medical; D055815:Young Adult