title: Trends of HIV-1 incidence with credible intervals in Sweden 2002-09 reconstructed using a dynamic model of within-patient IgG growth.
abstract: BACKGROUND
  HIV-1 is a lifelong disease, often without serious symptoms for years after infection, and thus many infected persons go undetected for a long time. This makes it difficult to track incidence, and thus epidemics may go through dramatic changes largely unnoticed, only to be detected years later. Because direct measurement of incidence is expensive and difficult, several biomarker-based tests and algorithms have been developed to distinguish between recent and long-term infections. However, current methods have been criticized and demands for novel methods have been raised.
  
  
  METHODS
  We developed and applied a biomarker-based incidence model, joining a time-continuous model of immunoglobulin G (IgG) growth (measured by the IgG-capture BED-enzyme immunoassay) with statistical corrections for both sample size and unobserved diagnoses. Our method uses measurements of IgG concentration in newly diagnosed people to calculate the posterior distribution of infection times. Time from infection to diagnosis is modelled for all individuals in a given period and is used to calculate a sample weight to correct for undiagnosed individuals. We then used a bootstrapping method to reconstruct point estimates and credible intervals of the incidence of HIV-1 in Sweden based on a sample of newly diagnosed people.
  
  
  RESULTS
  We found evidence for: (i) a slowly but steadily increasing trend in both the incidence and incidence rate in Sweden; and (ii) an increasing but well-controlled epidemic in gay men in Stockholm. Sensitivity analyses showed that our method was robust to realistic levels (up to 15%) of BED misclassification of non-recently infected persons as early infections.
  
  
  CONCLUSIONS
  We developed a novel incidence estimator based on previously published theoretical work that has the potential to provide rapid, up-to-date estimates of HIV-1 incidence in populations where BED test data are available.
authors.affiliation: Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM, USA eoromero@lanl.gov.
authors.forename: Ethan Obie
authors.initials: EO
authors.lastname: Romero-Severson
authors.affiliation: Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM, USA.
authors.forename: Cody
authors.initials: C
authors.lastname: Lee Petrie
authors.affiliation: Department of Statistics, University of Michigan, Ann Arbor, MI, USA and.
authors.forename: Edward
authors.initials: E
authors.lastname: Ionides
authors.affiliation: Departments of Microbiology, Karolinska Institute and Clinical Microbiology, Karolinska University Hospital, Stockholm, Sweden.
authors.forename: Jan
authors.initials: J
authors.lastname: Albert
authors.affiliation: Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM, USA.
authors.forename: Thomas
authors.initials: T
authors.lastname: Leitner
mesh_terms: D001499:Bayes Theorem; D015415:Biomarkers / Q000097:blood*; D005260:Female; D015658:HIV Infections / Q000453:epidemiology*; D015497:HIV-1; D006801:Humans; D007124:Immunoenzyme Techniques; D007074:Immunoglobulin G / Q000097:blood*; D015994:Incidence; D008297:Male; D008962:Models, Theoretical; D012737:Sex Factors; D013548:Sweden / Q000453:epidemiology
chemical_list: D015415:Biomarkers; D007074:Immunoglobulin G
keywords: BED assay; Bayesian methods; HIV; bootstrapping; differential misclassification; incidence estimation