title: Practical Implications of the Non-Linear Relationship between the Test Positivity Rate and Malaria Incidence.
abstract: BACKGROUND
  The test positivity rate (TPR), defined as the number of laboratory-confirmed malaria tests per 100 suspected cases examined, is widely used by malaria surveillance programs as one of several key indicators of temporal trends in malaria incidence. However, there have been few studies using empiric data to examine the quantitative nature of this relationship.
  
  
  METHODS
  To characterize the relationship between the test positivity rate and the incidence of malaria, we fit regression models using the confirmed malaria case rate as the outcome of interest and TPR as the predictor of interest. We varied the relationship between the two by alternating linear and polynomial terms for TPR, and compared the goodness of fit of each model.
  
  
  RESULTS
  A total of 7,668 encounters for malaria diagnostic testing were recorded over the study period within a catchment area of 25,617 persons. The semi-annual TPR ranged from 4.5% to 59% and the case rates ranged from 0.5 to 560 per 1,000 persons. The best fitting model was an exponential growth model (R2 = 0.80, AIC = 637). At low transmission levels (TPR<10%), the correlation between TPR and CMCR was poor, with large reductions in the TPR, for example from 10% to 1%, was associated with a minimal change in the CMCR (3.9 to 1.7 cases per 1,000 persons). At higher transmission levels, the exponential relationship made relatively small changes in TPR suggestive of sizeable change in estimated malaria incidence, suggesting that TPR remains a valuable surveillance indicator in such settings.
  
  
  CONCLUSIONS
  The TPR and the confirmed malaria case rate have a non-linear relationship, which is likely to have important implications for malaria surveillance programs, especially at the extremes of transmission.
authors.affiliation: Division of Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, United States of America.
authors.forename: Ross M
authors.initials: RM
authors.lastname: Boyce
authors.affiliation: Division of General Medicine, University of North Carolina at Chapel Hill, Chapel Hill, United States of America.
authors.forename: Raquel
authors.initials: R
authors.lastname: Reyes
authors.affiliation: Department of Community Health, Mbarara University of Science & Technology, Mbarara, Uganda.
authors.forename: Michael
authors.initials: M
authors.lastname: Matte
authors.affiliation: Department of Community Health, Mbarara University of Science & Technology, Mbarara, Uganda.
authors.forename: Moses
authors.initials: M
authors.lastname: Ntaro
authors.affiliation: Department of Community Health, Mbarara University of Science & Technology, Mbarara, Uganda.
authors.forename: Edgar
authors.initials: E
authors.lastname: Mulogo
authors.affiliation: Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, United States of America.
authors.forename: Feng-Chang
authors.initials: FC
authors.lastname: Lin
authors.affiliation: Department of Medicine, Harvard Medical School, and Massachusetts General Hospital, Boston, United States of America.
authors.forename: Mark J
authors.initials: MJ
authors.lastname: Siedner
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D002648:Child; D002675:Child, Preschool; D003955:Diagnostic Tests, Routine; D005260:Female; D006801:Humans; D015994:Incidence; D007223:Infant; D016014:Linear Models; D008288:Malaria / Q000175:diagnosis / Q000453:epidemiology*; D008297:Male; D008875:Middle Aged; D014454:Uganda / Q000453:epidemiology; D055815:Young Adult