title: Estimating the magnitude and direction of bias in tuberculosis drug resistance surveys conducted only in the public sector: a simulation study.
abstract: BACKGROUND
  Accurate assessment of the burden of drug-resistant TB requires systematic efforts to quantify its magnitude and trend. In approximately half the countries where resistance has been reported, estimates are based on surveys conducted in public sector facilities. However, in locations where a substantial fraction of TB cases seek care with private providers, these surveys may not accurately measure resistance in the entire population.
  
  
  METHODS
  We describe a mathematical model to investigate biases associated with sampling only from public sector cases in India, where TB treatment is offered in both public and private sectors. We then propose and demonstrate a weighted estimator as an efficient method for including small numbers of cases from the private sector as a way to recover valid estimates of resistance in the population under study.
  
  
  RESULTS
  We find that public sector surveys rarely provide valid estimates of drug-resistance among new and retreatment cases. Further, the magnitude and direction of the bias are sensitive to many parameters describing the health-seeking behaviours and treatment outcomes of tuberculosis patients, disallowing simple adjustments to recover accurate estimates.
  
  
  CONCLUSIONS
  In locations where large numbers of tuberculosis patients are diagnosed and treated by private sector practitioners who are not typically included in drug resistance surveys, targeted surveys for assessing drug resistance are required to validly estimate resistance.
authors.affiliation: Division of Global Health Equity, Brigham and Women's Hospital, 641 Huntington Avenue, Boston 02115, USA. tcohen@hsph.harvard.edu
authors.forename: Ted
authors.initials: T
authors.lastname: Cohen
authors.forename: Bethany L
authors.initials: BL
authors.lastname: Hedt
authors.forename: Marcello
authors.initials: M
authors.lastname: Pagano
mesh_terms: D000328:Adult; D015982:Bias; D003625:Data Collection / Q000379:methods; D024881:Drug Resistance, Bacterial; D005260:Female; D006801:Humans; D007194:India; D008297:Male; D008962:Models, Theoretical; D017150:Public Sector; D014397:Tuberculosis, Pulmonary / Q000188:drug therapy*