title: Capture-Recapture Estimators in Epidemiology with Applications to Pertussis and Pneumococcal Invasive Disease Surveillance.
abstract: BACKGROUND
  Surveillance networks are often not exhaustive nor completely complementary. In such situations, capture-recapture methods can be used for incidence estimation. The choice of estimator and their robustness with respect to the homogeneity and independence assumptions are however not well documented.
  
  
  METHODS
  We investigated the performance of five different capture-recapture estimators in a simulation study. Eight different scenarios were used to detect and combine case-information. The scenarios increasingly violated assumptions of independence of samples and homogeneity of detection probabilities. Belgian datasets on invasive pneumococcal disease (IPD) and pertussis provided motivating examples.
  
  
  RESULTS
  No estimator was unbiased in all scenarios. Performance of the parametric estimators depended on how much of the dependency and heterogeneity were correctly modelled. Model building was limited by parameter estimability, availability of additional information (e.g. covariates) and the possibilities inherent to the method. In the most complex scenario, methods that allowed for detection probabilities conditional on previous detections estimated the total population size within a 20-30% error-range. Parametric estimators remained stable if individual data sources lost up to 50% of their data. The investigated non-parametric methods were more susceptible to data loss and their performance was linked to the dependence between samples; overestimating in scenarios with little dependence, underestimating in others. Issues with parameter estimability made it impossible to model all suggested relations between samples for the IPD and pertussis datasets. For IPD, the estimates for the Belgian incidence for cases aged 50 years and older ranged from 44 to58/100,000 in 2010. The estimates for pertussis (all ages, Belgium, 2014) ranged from 24.2 to30.8/100,000.
  
  
  CONCLUSIONS
  We encourage the use of capture-recapture methods, but epidemiologists should preferably include datasets for which the underlying dependency structure is not too complex, a priori investigate this structure, compensate for it within the model and interpret the results with the remaining unmodelled heterogeneity in mind.
authors.affiliation: Department Epidemiology of infectious diseases, Epidemiology, Scientific Institute of Public Health, Brussels, Belgium.
authors.forename: Toon
authors.initials: T
authors.lastname: Braeye
authors.affiliation: Department of Clinical Microbiology, University Clinic Leuven, Leuven, Belgium.
authors.forename: Jan
authors.initials: J
authors.lastname: Verheagen
authors.affiliation: Medical Affairs, Pfizer Vaccines, Brussels, Belgium.
authors.forename: Annick
authors.initials: A
authors.lastname: Mignon
authors.affiliation: Infectious Disease Control, Flemish Agency for Care and Health, Brussels, Belgium.
authors.forename: Wim
authors.initials: W
authors.lastname: Flipse
authors.affiliation: Institute of Medical Microbiology, University Hospital of Brussels, Brussels, Belgium.
authors.forename: Denis
authors.initials: D
authors.lastname: Pierard
authors.affiliation: Department immunology, Communicable and Infectious Diseases, Scientific Institute of Public Health, Brussels, Belgium.
authors.forename: Kris
authors.initials: K
authors.lastname: Huygen
authors.affiliation: Cellule de surveillance des maladies infectieuses, Direction générale de la santé, Brussels, Belgium.
authors.forename: Carole
authors.initials: C
authors.lastname: Schirvel
authors.affiliation: Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Hasselt, Belgium.|Centre for Health Economic Research and Modelling Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute (WHO Collaborating Centre), University of Antwerp, Wilrijk, Belgium.|Epidemiology and social medicine (ESOC), University of Antwerp, Wilrijk, Belgium.
authors.forename: Niel
authors.initials: N
authors.lastname: Hens
mesh_terms: D000328:Adult; D062665:Epidemiological Monitoring; D005240:Feasibility Studies; D005260:Female; D006760:Hospitalization; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D011008:Pneumococcal Infections / Q000453:epidemiology* / Q000628:therapy; D062206:Spatial Analysis; D014917:Whooping Cough / Q000453:epidemiology* / Q000628:therapy