title: National probability samples in studies of low-prevalence diseases. Part II: Designing and implementing the HIV cost and services utilization study sample.
abstract: OBJECTIVE
  The design and implementation of a nationally representative probability sample of persons with a low-prevalence disease, HIV/AIDS.
  
  
  METHODS
  One of the most significant roadblocks to the generalizability of primary data collected about persons with a low-prevalence disease is the lack of a complete methodology for efficiently generating and enrolling probability samples. The methodology developed by the HCSUS consortium uses a flexible, provider-based approach to multistage sampling that minimizes the quantity of data necessary for implementation.
  
  
  METHODS
  To produce a valid national probability sample, we combined a provider-based multistage design with the M.D.-colleague recruitment model often used in non-probability site-specific studies.
  
  
  METHODS
  Across the contiguous United States, reported AIDS cases for metropolitan areas and rural counties. In selected areas, caseloads for known providers for HIV patients and a random sample of other providers. For selected providers, anonymous patient visit records.
  
  
  RESULTS
  It was possible to obtain all data necessary to implement a multistage design for sampling individual HIV-infected persons under medical care with known probabilities. Taking account of both patient and provider nonresponse, we succeeded in obtaining in-person or proxy interviews from subjects representing over 70 percent of the eligible target population.
  
  
  CONCLUSIONS
  It is possible to design and implement a national probability sample of persons with a low-prevalence disease, even if it is stigmatized.
authors.affiliation: Baruch College, CUNY, USA.
authors.forename: M R
authors.initials: MR
authors.lastname: Frankel
authors.forename: M F
authors.initials: MF
authors.lastname: Shapiro
authors.forename: N
authors.initials: N
authors.lastname: Duan
authors.forename: S C
authors.initials: SC
authors.lastname: Morton
authors.forename: S H
authors.initials: SH
authors.lastname: Berry
authors.forename: J A
authors.initials: JA
authors.lastname: Brown
authors.forename: M A
authors.initials: MA
authors.lastname: Burnam
authors.forename: S E
authors.initials: SE
authors.lastname: Cohn
authors.forename: D P
authors.initials: DP
authors.lastname: Goldman
authors.forename: D F
authors.initials: DF
authors.lastname: McCaffrey
authors.forename: S M
authors.initials: SM
authors.lastname: Smith
authors.forename: P A
authors.initials: PA
authors.lastname: St Clair
authors.forename: J F
authors.initials: JF
authors.lastname: Tebow
authors.forename: S A
authors.initials: SA
authors.lastname: Bozzette
mesh_terms: D003625:Data Collection; D015658:HIV Infections / Q000191:economics*; D017048:Health Care Costs / Q000706:statistics & numerical data*; D006296:Health Services / Q000191:economics / Q000706:statistics & numerical data*; D006302:Health Services Research / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D018579:Patient Selection; D015995:Prevalence; D011336:Probability; D011897:Random Allocation; D015203:Reproducibility of Results; D012107:Research Design; D018401:Sample Size; D014481:United States