title: Can slide positivity rates predict malaria transmission?
abstract: BACKGROUND
  Malaria is a significant threat to population health in the border areas of Yunnan Province, China. How to accurately measure malaria transmission is an important issue. This study aimed to examine the role of slide positivity rates (SPR) in malaria transmission in Mengla County, Yunnan Province, China.
  
  
  METHODS
  Data on annual malaria cases, SPR and socio-economic factors for the period of 1993 to 2008 were obtained from the Center for Disease Control and Prevention (CDC) and the Bureau of Statistics, Mengla, China. Multiple linear regression models were conducted to evaluate the relationship between socio-ecologic factors and malaria incidence.
  
  
  RESULTS
  The results show that SPR was significantly positively associated with the malaria incidence rates. The SPR (β = 1.244, p = 0.000) alone and combination (SPR, β = 1.326, p < 0.001) with other predictors can explain about 85% and 95% of variation in malaria transmission, respectively. Every 1% increase in SPR corresponded to an increase of 1.76/100,000 in malaria incidence rates.
  
  
  CONCLUSIONS
  SPR is a strong predictor of malaria transmission, and can be used to improve the planning and implementation of malaria elimination programmes in Mengla and other similar locations. SPR might also be a useful indicator of malaria early warning systems in China.
authors.affiliation: School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Victoria Park Road, Kelvin Grove, 4059, Brisbane, Australia.
authors.forename: Yan
authors.initials: Y
authors.lastname: Bi
authors.forename: Wenbiao
authors.initials: W
authors.lastname: Hu
authors.forename: Huaxin
authors.initials: H
authors.lastname: Liu
authors.forename: Yujiang
authors.initials: Y
authors.lastname: Xiao
authors.forename: Yuming
authors.initials: Y
authors.lastname: Guo
authors.forename: Shimei
authors.initials: S
authors.lastname: Chen
authors.forename: Laifa
authors.initials: L
authors.lastname: Zhao
authors.forename: Shilu
authors.initials: S
authors.lastname: Tong
mesh_terms: D002681:China / Q000453:epidemiology; D004812:Epidemiologic Methods; D006801:Humans; D015994:Incidence; D008288:Malaria / Q000175:diagnosis / Q000453:epidemiology* / Q000635:transmission*; D008853:Microscopy / Q000379:methods*; D010961:Plasmodium / Q000166:cytology*; D012959:Socioeconomic Factors