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A Multilevel Model for Continuous Time Population Estimation

Authors

  • Jason M. Sutherland,

    1. Center for Health Policy Research, The Dartmouth Institute for Health Policy and Clinical Practice, Dartmouth College, Hanover, New Hampshire 03766, U.S.A.
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  • Pete Castelluccio,

    1. Regenstrief Institute, Inc., 410 West 10th Street, Suite 2000, Indianapolis, Indiana 46202, U.S.A.
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  • Carl James Schwarz

    Corresponding author
    1. Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, Burnaby, B.C. V5A 1S6, Canada
      email: jason.sutherland@dartmouth.edu
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email: jason.sutherland@dartmouth.edu

Abstract

Summary Statistical methods have been developed and applied to estimating populations that are difficult or too costly to enumerate. Known as multilist methods in epidemiological settings, individuals are matched across lists and estimation of population size proceeds by modeling counts in incomplete multidimensional contingency tables (based on patterns of presence/absence on lists). As multilist methods typically assume that lists are compiled instantaneously, there are few options available for estimating the unknown size of a closed population based on continuously (longitudinally) compiled lists. However, in epidemiological settings, continuous time lists are a routine byproduct of administrative functions. Existing methods are based on time-to-event analyses with a second step of estimating population size. We propose an alternative approach to address the twofold epidemiological problem of estimating population size and of identifying patient factors related to duration (in days) between visits to a health care facility. A Bayesian framework is proposed to model interval lengths because, for many patients, the data are sparse; many patients were observed only once or twice. The proposed method is applied to the motivating data to illustrate the methods' applicability. Then, a small simulation study explores the performance of the estimator under a variety of conditions. Finally, a small discussion section suggests opportunities for continued methodological development for continuous time population estimation.

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