Medical cost trajectories and onsets of cancer and noncancer diseases in US elderly population.

Abstract

Time trajectories of medical costs-associated with onset of twelve aging-related cancer and chronic noncancer diseases were analyzed using the National Long-Term Care Survey data linked to Medicare Service Use files. A special procedure for selecting individuals with onset of each disease was developed and used for identification of the date at disease onset. Medical cost trajectories were found to be represented by a parametric model with four easily interpretable parameters reflecting: (i) prediagnosis cost (associated with initial comorbidity), (ii) cost of the disease onset, (iii) population recovery representing reduction of the medical expenses associated with a disease since diagnosis was made, and (iv) acquired comorbidity representing the difference between post- and pre diagnosis medical cost levels. These parameters were evaluated for the entire US population as well as for the subpopulation conditional on age, disability and comorbidity states, and survival (2.5 years after the date of onset). The developed approach results in a family of new forecasting models with covariates.

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Citation

Published Version (Please cite this version)

10.1155/2011/857892

Publication Info

Akushevich, Igor, Julia Kravchenko, Lucy Akushevich, Svetlana Ukraintseva, Konstantin Arbeev and Anatoliy I Yashin (2011). Medical cost trajectories and onsets of cancer and noncancer diseases in US elderly population. Comput Math Methods Med, 2011. p. 857892. 10.1155/2011/857892 Retrieved from https://hdl.handle.net/10161/14855.

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Scholars@Duke

Igor Akushevich

Research Professor in the Social Science Research Institute
Ukraintseva

Svetlana Ukraintseva

Research Professor in the Social Science Research Institute

Dr. Ukraintseva studies causes of human aging and related decline in resilience, to identify genetic and other factors responsible for the increase in mortality risk with age eventually limiting longevity. She explores complex relationships, including trade-offs, between physiological aging-changes and risks of major diseases (with emphasis on Alzheimer’s and cancer), as well as survival, to find new genetic and other targets for anti-aging interventions and disease prevention. She also investigates possibilities of repurposing of existing vaccines and treatments for AD prevention and interventions into the aging. For this, Dr. Ukraintseva and her team use data from several large human studies containing rich genetic and phenotypic information (including longitudinal measurements) on thousands of individuals. Dr. Ukraintseva is a PI and Key Investigator on several NIH funded grants, and has more than 130 peer-reviewed publications, including in major journals such as Nature Reviews, Stroke, European Journal of Human Genetics, and some other.

Arbeev

Konstantin Arbeev

Associate Research Professor in the Social Science Research Institute

Konstantin G. Arbeev received the M.S. degree in Applied Mathematics from Moscow State University (branch in Ulyanovsk, Russia) in 1995 and the Ph.D. degree in Mathematics and Physics (specialization in Theoretical Foundations of Mathematical Modeling, Numerical Methods and Programming) from Ulyanovsk State University (Russia) in 1999. He was a post-doctoral fellow in Max Planck Institute for Demographic Research in Rostock (Germany) before moving to Duke University in 2004 to work as a Research Scientist and a Senior Research Scientist in the Department of Sociology and the Social Science Research Institute (SSRI).  He is currently an Associate Research Professor in SSRI. Dr. Arbeev's major research interests are related to three interconnected fields of biodemography, biostatistics and genetic epidemiology as pertains to research on aging. The focus of his research is on discovering genetic and non-genetic factors that can affect the process of aging and determine longevity and healthy lifespan. He is interested in both methodological advances in this research area as well as their practical applications to analyses of large-scale longitudinal studies with phenotypic, genetic and, recently, genomic information. Dr. Arbeev authored and co-authored more than 150 peer-reviewed publications in these areas.


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