title: Defining disease heterogeneity to guide the empirical treatment of febrile illness in resource poor settings.
abstract: BACKGROUND
  Malaria incidence is in decline in many parts of SE Asia leading to a decreasing proportion of febrile illness that is attributable to malaria. However in the absence of rapid, affordable and accurate diagnostic tests, the non-malaria causes of these illnesses cannot be reliably identified. Studies on the aetiology of febrile illness have indicated that the causes are likely to vary by geographical location within countries (i.e. be spatially heterogeneous) and that national empirical treatment policies based on the aetiology measured in a single location could lead to inappropriate treatment.
  
  
  METHODS
  Using data from Vientiane as a reference for the incidence of major febrile illnesses in the Lao People's Democratic Republic (Laos) and estimated incidences, plausible incidence in other Lao provinces were generated using a mathematical model for a range of national and local scale variations. For a range of treatment protocols, the mean number of appropriate treatments was predicted and the potential impact of a spatially explicit national empirical treatment protocol assessed.
  
  
  RESULTS
  The model predicted a negative correlation between number of appropriate treatments and the level of spatial heterogeneity. A spatially explicit national treatment protocol was predicted to increase the number of appropriate treatments by 50% for intermediate levels of spatial heterogeneity.
  
  
  CONCLUSIONS
  The results suggest that given even only moderate spatial variation, a spatially explicit treatment algorithm will result in a significant improvement in the outcome of undifferentiated fevers in Laos and other similar resource poor settings.
authors.affiliation: Centre for Clinical Vaccinology and Tropical Medicine, Nuffield Department of Clinical Medicine, Churchill Hospital, University of Oxford, Oxford, United Kingdom. lisa@tropmedres.ac
authors.forename: Lisa J
authors.initials: LJ
authors.lastname: White
authors.forename: Paul N
authors.initials: PN
authors.lastname: Newton
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Maude
authors.forename: Wirichada
authors.initials: W
authors.lastname: Pan-ngum
authors.forename: Jessica R
authors.initials: JR
authors.lastname: Fried
authors.forename: Mayfong
authors.initials: M
authors.lastname: Mayxay
authors.forename: Rapeephan R
authors.initials: RR
authors.lastname: Maude
authors.forename: Nicholas P J
authors.initials: NP
authors.lastname: Day
mesh_terms: D000465:Algorithms; D001208:Asia / Q000453:epidemiology; D006801:Humans; D007812:Laos / Q000453:epidemiology; D008288:Malaria / Q000175:diagnosis / Q000453:epidemiology*; D008962:Models, Theoretical