Two Approaches for Estimating Disease Prevalence from Population-Based Registries of Incidence and Total Mortality

Authors

  • Mitchell H. Gail,

    Corresponding author
    1. National Cancer Institute, Division of Cancer Epidemiology and Genetics, Executive Plaza South, Room 8032, 6120 Executive Boulevard, MSC 7244, Bethesda, Maryland 20892-7244, U.S.A.
      *email:mit@cu.nih.gov
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  • Larry Kessler,

    1. Food and Drug Administration, 1350 Piccard Drive, Rockville, Maryland 20855, U.S.A.
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  • Douglas Midthune,

    1. 3National Cancer Institute, Division of Cancer Prevention, 6130 Executive Boulevard, EPN 344, Bethesda, Maryland 20892-7354, U.S.A.
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  • Steven Scoppa

    1. Information Management Services, 12501 Prosperity Drive, Suite 200, Silver Spring, Maryland 20904, U.S.A.
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*email:mit@cu.nih.gov

Abstract

Summary. Two approaches are described for estimating the prevalence of a disease that may have developed in a previous restricted age interval among persons of a given age at a particular calendar time. The prevalence for all those who ever developed disease is treated as a special case. The counting method (CM) obtains estimates of prevalence by dividing the estimated number of diseased persons by the total population size, taking loss to follow-up into account. The transition rate method (TRM) uses estimates of transition rates and competing risk calculations to estimate prevalence. Variance calculations are described for CM and TRM as well as for a variant of CM, called counting method times 10 (CM10), that is designed to yield more precise estimates than CM. We compare these three estimators in terms of precision and in terms of the underlying assumptions required to justify the methods. CM makes fewer assumptions but is typically less precise than TRM or CM10. For common diseases such as breast cancer, CM may be preferred because its precision is excellent even though not as high as for TRM or CM10. For less common diseases, such as brain cancer, however, TRM or CM10 and other methods that make stabilizing assumptions may be preferred to CM.

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