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Estimating the Population Impact of Preventive Interventions from Randomized Trials
Filetype[PDF - 476.19 KB]


Details:
  • Pubmed ID:
    21238868
  • Pubmed Central ID:
    PMC3042774
  • Funding:
    K24 MH086814/MH/NIMH NIH HHS/United States
    K24 MH086814-02/MH/NIMH NIH HHS/United States
    K24/MH086814/MH/NIMH NIH HHS/United States
    R01 MH073613/MH/NIMH NIH HHS/United States
    R01 MH073613-05/MH/NIMH NIH HHS/United States
    R01/MH073613/MH/NIMH NIH HHS/United States
    R49/CE000197/CE/NCIPC CDC HHS/United States
  • Document Type:
  • Collection(s):
  • Description:
    Growing concern about the limited generalizability of trials of preventive interventions has led to several proposals concerning the design, reporting, and interpretation of such trials. This paper presents an epidemiologic framework that highlights three key determinants of population impact of many prevention programs: the proportion of the population at risk who would be candidates for a generic intervention in routine use, the proportion of those candidates who are actually intervened on through a specific program, and the reduction in incidence produced by that program among recipients. It then describes how the design of a prevention trial relates to estimating these quantities. Implications of the framework include the following: (1) reach is an attribute of a program, whereas external validity is an attribute of a trial, and the two should not be conflated; (2) specification of a defined target population at risk is essential in the long run and merits greater emphasis in the planning and interpretation of prevention trials; (3) with due attention to sampling frame and sampling method, the process of subject recruitment for a trial can yield key information about quantities that are important for assessing its potential population impact; and (4) exclusions during subject recruitment can be conceptually separated into intervention-driven, program-driven, and trial-design-driven exclusions, which have quite different implications for trial interpretation and for estimating population impact of the intervention studied.