title: A new general biomarker-based incidence estimator.
abstract: BACKGROUND
  Estimating disease incidence from cross-sectional surveys, using biomarkers for "recent" infection, has attracted much interest. Despite widespread applications to HIV, there is currently no consensus on the correct handling of biomarker results classifying persons as "recently" infected long after the infections occurred.
  
  
  METHODS
  We derive a general expression for a weighted average of recent incidence that-unlike previous estimators-requires no particular assumption about recent infection biomarker dynamics or about the demographic and epidemiologic context. This is possible through the introduction of an explicit timescale T that truncates the period of averaging implied by the estimator.
  
  
  RESULTS
  The recent infection test dynamics can be summarized into 2 parameters, similar to those appearing in previous estimators: a mean duration of recent infection and a false-recent rate. We identify a number of dimensionless parameters that capture the bias that arises from working with tractable forms of the resulting estimator and elucidate the utility of the incidence estimator in terms of the performance of the recency test and the population state. Estimation of test characteristics and incidence is demonstrated using simulated data. The observed confidence interval coverage of the test characteristics and incidence is within 1% of intended coverage.
  
  
  CONCLUSIONS
  Biomarker-based incidence estimation can be consistently adapted to a general context without the strong assumptions of previous work about biomarker dynamics and epidemiologic and demographic history.
authors.affiliation: DST/NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), University of Stellenbosch, Stellenbosch, South Africa. rkassanjee@sun.ac.za
authors.forename: Reshma
authors.initials: R
authors.lastname: Kassanjee
authors.forename: Thomas A
authors.initials: TA
authors.lastname: McWalter
authors.forename: Till
authors.initials: T
authors.lastname: Bärnighausen
authors.forename: Alex
authors.initials: A
authors.lastname: Welte
mesh_terms: D015415:Biomarkers / Q000378:metabolism*; D003430:Cross-Sectional Studies; D003627:Data Interpretation, Statistical; D015658:HIV Infections / Q000175:diagnosis / Q000453:epidemiology* / Q000378:metabolism; D006306:Health Surveys; D006801:Humans; D015994:Incidence; D016013:Likelihood Functions; D013997:Time Factors
chemical_list: D015415:Biomarkers