Age Patterns of Incidence of Geriatric Disease in the <scp>U.S.</scp> Elderly Population: Medicare‐Based Analysis

Abstract

Objectives To use the Medicare Files of Service Use ( MFSU ) to evaluate patterns in the incidence of aging‐related diseases in the U.S. elderly population. Design Age‐specific incidence rates of 19 aging‐related diseases were evaluated using the National Long Term Care Survey ( NLTCS ) and the Surveillance, Epidemiology, and End Results ( SEER ) Registry data, both linked to MFSU ( NLTCS‐M and SEER‐M , respectively), using an algorithm developed for individual date at onset evaluation. Setting A random sample from the entire U.S. elderly population (Medicare beneficiaries) was used in NLTCS , and the SEER Registry data covers 26% of the U.S. population. Participants Thirty‐four thousand seventy‐seven individuals from NLTCS‐M and 2,154,598 from SEER‐M. Measurements Individual medical histories were reconstructed using information on diagnoses coded in MFSU , dates of medical services and procedures, and Medicare enrollment and disenrollment. Results The majority of diseases (e.g., prostate cancer, asthma, and diabetes mellitus) had a monotonic decline (or decline after a short period of increase) in incidence with age. A monotonic increase in incidence with age with a subsequent leveling off and decline was observed for myocardial infarction, stroke, heart failure, ulcer, and A lzheimer's disease. An inverted U ‐shaped age pattern was detected for lung and colon carcinomas, Parkinson's disease, and renal failure. The results obtained from the NLTCS‐M and SEER‐M were in agreement (excluding an excess for circulatory diseases in the NLTCS‐M ). A sensitivity analysis proved the stability of the incidence rates evaluated. Conclusion The developed computational approaches applied to the nationally representative Medicare‐based data sets allow reconstruction of age patterns of disease incidence in the U.S. elderly population at the national level with unprecedented statistical accuracy and stability with respect to systematic biases.

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Affiliated Institutions

• China Population and Development Research Center CN
• Duke Medical Center US

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