title: Theory of partitioning of disease prevalence and mortality in observational data.
abstract: In this study, we present a new theory of partitioning of disease prevalence and incidence-based mortality and demonstrate how this theory practically works for analyses of Medicare data. In the theory, the prevalence of a disease and incidence-based mortality are modeled in terms of disease incidence and survival after diagnosis supplemented by information on disease prevalence at the initial age and year available in a dataset. Partitioning of the trends of prevalence and mortality is calculated with minimal assumptions. The resulting expressions for the components of the trends are given by continuous functions of data. The estimator is consistent and stable. The developed methodology is applied for data on type 2 diabetes using individual records from a nationally representative 5% sample of Medicare beneficiaries age 65+. Numerical estimates show excellent concordance between empirical estimates and theoretical predictions. Evaluated partitioning model showed that both prevalence and mortality increase with time. The primary driving factors of the observed prevalence increase are improved survival and increased prevalence at age 65. The increase in diabetes-related mortality is driven by increased prevalence and unobserved trends in time-periods and age-groups outside of the range of the data used in the study. Finally, the properties of the new estimator, possible statistical and systematical uncertainties, and future practical applications of this methodology in epidemiology, demography, public health and health forecasting are discussed.
authors.affiliation: Biodemography of Aging Research Unit, Center for Population Health and Aging, Duke University, Durham, NC, United States. Electronic address: igor.akushevich@duke.edu.
authors.forename: I
authors.initials: I
authors.lastname: Akushevich
authors.affiliation: Biodemography of Aging Research Unit, Center for Population Health and Aging, Duke University, Durham, NC, United States.
authors.forename: A P
authors.initials: AP
authors.lastname: Yashkin
authors.affiliation: Department of Surgery, Duke University School of Medicine, Durham, NC, United States.
authors.forename: J
authors.initials: J
authors.lastname: Kravchenko
authors.affiliation: Biodemography of Aging Research Unit, Center for Population Health and Aging, Duke University, Durham, NC, United States.
authors.forename: F
authors.initials: F
authors.lastname: Fang
authors.affiliation: Biodemography of Aging Research Unit, Center for Population Health and Aging, Duke University, Durham, NC, United States.
authors.forename: K
authors.initials: K
authors.lastname: Arbeev
authors.affiliation: Department of Economics, Duke University, Durham, NC, United States.
authors.forename: F
authors.initials: F
authors.lastname: Sloan
authors.affiliation: Biodemography of Aging Research Unit, Center for Population Health and Aging, Duke University, Durham, NC, United States.
authors.forename: A I
authors.initials: AI
authors.lastname: Yashin
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology* / Q000401:mortality; D005544:Forecasting; D006801:Humans; D015994:Incidence; D006278:Medicare / Q000706:statistics & numerical data*; D015995:Prevalence; D016019:Survival Analysis; D014481:United States / Q000453:epidemiology
keywords: Diabetes; Incidence; Mortality; Partitioning; Prevalence; Time trend