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Theory of partitioning of disease prevalence and mortality in observational data.

dc.contributor.author Akushevich, Igor
dc.contributor.author Arbeev, Konstantin
dc.contributor.author Fang, F
dc.contributor.author Kravchenko, J
dc.contributor.author Sloan, Frank A
dc.contributor.author Yashin, Anatoli I
dc.contributor.author Yashkin, Arseniy
dc.coverage.spatial United States
dc.date.accessioned 2017-06-02T15:52:56Z
dc.date.available 2017-06-02T15:52:56Z
dc.date.issued 2017-04
dc.identifier https://www.ncbi.nlm.nih.gov/pubmed/28130147
dc.identifier S0040-5809(17)30007-2
dc.identifier.uri http://hdl.handle.net/10161/14750
dc.description.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.
dc.language eng
dc.relation.ispartof Theor Popul Biol
dc.relation.isversionof 10.1016/j.tpb.2017.01.003
dc.subject Diabetes
dc.subject Incidence
dc.subject Mortality
dc.subject Partitioning
dc.subject Prevalence
dc.subject Time trend
dc.title Theory of partitioning of disease prevalence and mortality in observational data.
dc.type Journal article
pubs.author-url https://www.ncbi.nlm.nih.gov/pubmed/28130147
pubs.begin-page 117
pubs.end-page 127
pubs.organisational-group Center for Child and Family Policy
pubs.organisational-group Center for Population Health & Aging
pubs.organisational-group Clinical Science Departments
pubs.organisational-group Duke
pubs.organisational-group Duke Cancer Institute
pubs.organisational-group Duke Population Research Center
pubs.organisational-group Duke Population Research Institute
pubs.organisational-group Economics
pubs.organisational-group Institutes and Centers
pubs.organisational-group Institutes and Provost's Academic Units
pubs.organisational-group Nursing
pubs.organisational-group Physics
pubs.organisational-group Sanford
pubs.organisational-group Sanford School of Public Policy
pubs.organisational-group School of Medicine
pubs.organisational-group School of Nursing
pubs.organisational-group Social Science Research Institute
pubs.organisational-group Staff
pubs.organisational-group Surgery
pubs.organisational-group Surgery, Surgical Sciences
pubs.organisational-group Trinity College of Arts & Sciences
pubs.organisational-group University Institutes and Centers
pubs.publication-status Published
pubs.volume 114
dc.identifier.eissn 1096-0325


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