ArticleTheoretical population biology2017
Theory of partitioning of disease prevalence and mortality in observational data.
Article in Theoretical population biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Decomposition of Heart Failure Prevalence and Mortality Among Older Adults in the United States: Medicare-Based Partitioning Analysis.JMIR public health and surveillance · 2024Article
- Spatiotemporal analysis of diarrhea-related hospitalizations of children in Brazil's Midwest region from 2011 to 2020.Revista brasileira de epidemiologia = Brazilian journal of epidemiology · 2024Article
- Decomposition of disparities in life expectancy with applications to administrative health claims and registry data.Theoretical population biology · 2023Article
- Forecasting prevalence and mortality of Alzheimer's disease using the partitioning models.Experimental gerontology · 2023Article
- Underlying mechanisms of change in cancer prevalence in older U.S. adults: contributions of incidence, survival, and ascertainment at early stages.Cancer causes & control : CCC · 2022Article
- Epidemiology of geographic disparities in heart failure among US older adults: a Medicare-based analysis.BMC public health · 2022Article
- Geographic disparities in mortality from Alzheimer's disease and related dementias.Journal of the American Geriatrics Society · 2021Article
- Analysis of Time Trends in Alzheimer's Disease and Related Dementias Using Partitioning Approach.Journal of Alzheimer's disease : JAD · 2021Article
- Partitioning of time trends in prevalence and mortality of bladder cancer in the United States.Annals of epidemiology · 2020Article
- Partitioning of time trends in prevalence and mortality of lung cancer.Statistics in medicine · 2019Article
- A forecasting model of disease prevalence based on the McKendrick-von Foerster equation.Mathematical biosciences · 2019Article
- Time trends in the prevalence of cancer and non-cancer diseases among older U.S. adults: Medicare-based analysis.Experimental gerontology · 2018Article
- Identifying the causes of the changes in the prevalence patterns of diabetes in older U.S. adults: A new trend partitioning approach.Journal of diabetes and its complications · 2018Article
- Time Trends in the Prevalence of Neurocognitive Disorders and Cognitive Impairment in the United States: The Effects of Disease Severity and Improved Ascertainment.Journal of Alzheimer's disease : JAD · 2018Article
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7 authors.
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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.
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