Population-Based Analysis of Incidence Rates of Cancer and Noncancer Chronic Diseases in the US Elderly Using NLTCS/Medicare-Linked Database

Abstract

The age, disability, and comorbidity patterns of incidence rates of cancer and chronic noncancer diseases such as heart failure, diabetes mellitus, asthma, Parkinson's disease, Alzheimer's disease, skin melanoma, and cancers of breast, prostate, lung, and colon were studied for the US elderly population (aged 65+) using the National Long-Term Care Survey (NLTCS) data linked to Medicare records for 1991-2005. Opposite to breast cancer and asthma, incidence rates of heart failure and Alzheimer's diseases were increasing with age. Higher incidence rates of heart failure, diabetes, asthma, and Parkinson's and Alzheimer's diseases were observed among individuals with severe disabilities or/and comorbidities, while rates of breast and prostate cancers were higher among those with minor disabilities or fewer comorbidities. Our results were in agreement with those obtained from other epidemiological datasets, thus suggesting that Medicare administrative records can provide nationally representative incidence rates. Detailed sensitivity analysis that focused on the effects of alternative onset definitions, latent censoring, study design, and other procedural uncertainties showed the stability of reconstructed incidence rates. This Medicare-linked dataset can be used for studying highly debated effects of new medical technologies on aging-related diseases burden and future Medicare costs.

Identifying the causes of the changes in the prevalence patterns of diabetes in older U.S. adults: A new trend partitioning approach

“…Assessing the effect of incidence of T2D is less straightforward. Although time pattern of the incidence rate shows fluctuations, the trends demonstrate the point of change in 2008 found in a previous study 3 based on the National Health Interview Survey and do not contradict findings of other studies 11,18,23 on the subject. The effect of the fluctuations is not an essential part of the overall trend.…”

“…This allows us to avoid dealing with possible numeric instabilities that occur when derivatives are evaluated numerically, and ultimately improves the accuracy of our modeling approach. Specific parameterizations for the incidence rate (linear models in respect to both in age and time 8,11,12 ) and relative survival (Weibul models for survival time with coefficients dependent on time linearly, and age quadratically 13,14 ) were chosen based on empiric analysis of the data and prior studies. Model parameters were then estimated using least squares for incidence and a likelihood-based approach 15 for relative survival.…”

Biodemographic Analyses of Longitudinal Data on Aging, Health, and Longevity: Recent Advances and Future Perspectives

“…In [ 10 ] we performed detailed sensitivity analyses with different definitions of disease onset and several alternative censoring schemes used to define individual observation periods and found that the patterns are qualitatively similar in all cases. See also [ 11 ] where we thoroughly investigated alternative approaches to definition of ages at onset of different diseases that can be applied to extract this information from large administrative databases such as Medicare. We observed that, although there is variability in the rates in different scenarios (e.g., using different Medicare sources), the estimates of age-adjusted incidence rates were stable in terms of male/female ratios, time trends, and ratios of the rates for different diseases.…”

“…In [ 12 ] we evaluated age-specific as well as age-adjusted incidence rates of circulatory diseases such as acute coronary heart disease (that includes myocardial infarction and angina pectoris), stroke, and heart failure, in the US elderly population using the NLTCS-M data. In [ 11 ] similar analyses were performed for other diseases that represent major groups of chronic conditions in the elderly including cancer (breast, prostate, lung, and colon cancers and melanoma), neurodegenerative disorders (Parkinson’s disease and Alzheimer’s disease), diabetes, and asthma. These population-based estimates provide valuable information for analyzing the national health trends and forecasting future Medicare expenditures related to these diseases.…”

“…The NLTCS-M data provide detailed information on disability and also allow reconstructing incidence rates from the linked Medicare data thus allowing one to perform such analyses. In [ 11 , 12 ] we presented disability-specific patterns of incidence rates of major diseases mentioned above showing that, indeed, the risks are affected by disability levels. For some diseases (e.g., heart failure, diabetes, asthma, and Parkinson’s disease) the incidence rates were higher among individuals with severe disabilities, while for others (such as breast and prostate cancers) the higher rate was observed among people with minor disabilities.…”