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.
The proportion of the elderly population in USA is constantly growing. That prioritizes the task of determining the national trends in health and vital status of older adults making it a major public health concern. To better address these health demands and to reduce economic burdens on society, it is important to understand the key factors driving the onset and progression of aging-related chronic diseases in humans. Unfortunately, the identification of disease age patterns with sufficient precision requires large population-based databases that are costly to collect. This is a reason why the studies on disease age patterns, along with investigations of factors affecting them, are not common in the US elderly population. Among aging-associated diseases, cancer incidences are better studied at a national level, predominantly due to the existence of the Surveillance Epidemiology and End Results (SEER) Registry data [
To calculate the incidence rates, data from the national registers or from the surveys representing the US population should be used. For surveys, special procedures are needed to generalize the obtained results to the national level. It could be done by using a weight function assigned to each individual in the sample such that the “weighted” sums (means) over individuals in the sample give the quantities at the national level. For such analysis the 1982–2005 National Long-Term Care Survey (NLTCS) can be used. It focuses on the US elderly (65 and older) population. Since NLTCS is linked with the Medicare records and the Vital Statistics files, it represents a unique opportunity for analysis of the data with continuous recording of health services provided, age of death, and detailed reassessment of health status which are performed by a survey every five years (except for the first two waves in 1982 and 1984). The participants of the NLTCS were drawn from the Medicare enrollment lists. In 1982–1999, in the NLTCS there were about 400,000 person-years of exposure over age 65, including over 100,000 person-years of exposure over age 85. The sample weights are provided by the US Census Bureau and are available for each year of followup. Thus, the NLTCS design provides an excellent opportunity to study the incidence patterns of aging-associated diseases in the US elderly population. Earlier analyses of the NLTCS data provided consistent results on functional disability, active life expectancy, and chronic disease prevalence in the US elderly; for example, for 1982–1999 period, a 15% decline in chronic disability (1.1% per annum) has been reported [
The Medicare service use files linked to the NLTCS (NLTCS-M) contain the information about disease diagnoses. This information can be used in a computational algorithm for identification of the dates of disease onsets. The primary goal of this study is to estimate age-adjusted and age-specific (as well as disability- and comorbidity-specific) disease incidence in the US elderly population applying such an algorithm for the NLTCS-M data. Specific attention is paid to detailed analysis of stochastic and systematic uncertainties in these estimates: for example, we compare the evaluated rates with those obtained using alternative approaches for evaluating the incidence rates using the Medicare files. Also, we compare our estimates with the results obtained from other studies. Thus, the disease incidence estimates for advanced ages presented here should be very valuable for basic understanding of interrelations between chronic aging-related disease incidence and senescence and for practical implementations when analyzing the national health trends and forecasting future Medicare expenditures.
The primary data to be analyzed are the six waves of the NLTCS [
All individuals in the NLTCS are continuously tracked for Medicare Part A and Part B service use. Thus, for all persons we have continuous records of Medicare service use from 1991 (or from the time the person has passed the age of 65 after 1990) until his/her time of death. These records are available for each institutional (inpatient, outpatient, skilled nursing facility, hospice, or home health agency) and noninstitutional (carrier-physician-supplier and durable medical equipment providers) claim type.
The self-reported information about serious (i.e., activities of daily living; ADLs) and less serious (i.e., instrumental activities of daily living; IADLs) impairments was used to construct a disability index [
The date of onset of chronic disease is not defined with the same precision as mortality and there is always certain arbitrariness in defining the date of onset. Computational approaches of different complexity have been used for reconstruction of onsets of cancer and non-cancer diseases [
The computational algorithm is applied for each disease separately. First, the individual medical histories of an applicable disease were reconstructed from Medicare files combining all records with respective ICD-9 codes of that disease. Patients with the history of the disease before the date of interview in 1994 or in 1999 were excluded from the study for onset of this disease. Four variants of algorithms (Algorithms A, B, C, and D) of the identification of the disease onset from the disease-specific medical histories were considered. In Algorithm A, a date of a Medicare record (referred to as “ In addition to
In Algorithm B, the confirmation by the occurrence of the second record is not required; that is, only the first condition is valid. In Algorithm C, all codes (not necessary being primary) are considered valid and the confirmation is also not required. In Algorithm D, death is not considered as the second record.
Age patterns of incidence rates are assessed by stratifying the sample into relevant age categories (a year, or several years). Empirical age-specific risks (
Age-adjusted rates (or directly standardized incidence rates) are the averages of age-specific rates. For population aged 66+, they are calculated as
The numbers of individuals in the pooled cohort without prevalent cases for each disease are shown in Table
The total numbers of individuals followedup for onsets of chronic diseases (i) in two waves of NLTCS-M, that is, waves formed in 1994 and 1999, (ii) without individuals who had an additional coverage by the Health Maintenance Organization (HMO) and prevalent cases, and (iii) with the registered onsets.
Lung cancer | Colon cancer | Breast cancer* | Prostate cancer** | Melanoma | Heart failure | Alzheimer’s disease | Parkinson’s disease | Diabetes | Asthma | |
---|---|---|---|---|---|---|---|---|---|---|
Total in two waves | 34077 | 34077 | 20771 | 13306 | 34077 | 34077 | 34077 | 34077 | 34077 | 34077 |
Total without HMO | 27607 | 27607 | 16985 | 10622 | 27607 | 27607 | 27607 | 27607 | 27607 | 27607 |
Total without HMO |
27480 | 27313 | 16494 | 9990 | 27509 | 25921 | 27431 | 27378 | 25399 | 27099 |
The number of onsets | 752 | 525 | 546 | 605 | 189 | 3046 | 687 | 331 | 1231 | 415 |
*Females only.
**Males only.
Age-adjusted incidence rates per 100,000 of geriatric diseases with standard errors (in brackets).
Sex | Year | Lung cancer | Colon cancer | Breast cancer | Prostate cancer | Melanoma | Heart failure | Alzheimer’s disease | Parkinson’s disease | Diabetes | Asthma |
---|---|---|---|---|---|---|---|---|---|---|---|
Males | 1994 | 616 | 314 | 1146 | 163 | 1864 | 199 | 217 | 724 | 201 | |
(52) | (36) | (75) | (25) | (87) | (26) | (28) | (59) | (29) | |||
Males | 1999 | 491 | 311 | 924 | 117 | 1437 | 255 | 195 | 841 | 247 | |
(46) | (35) | (67) | (20) | (76) | (29) | (29) | (66) | (33) | |||
Male | Combined | 554 | 312 | 1033 | 139 | 1647 | 228 | 204 | 779 | 223 | |
(35) | (25) | (50) | (16) | (58) | (20) | (20) | (44) | (22) | |||
Female | 1994 | 293 | 262 | 555 | 53 | 1540 | 210 | 143 | 697 | 303 | |
(29) | (27) | (38) | (11) | (61) | (21) | (19) | (44) | (30) | |||
Female | 1999 | 331 | 196 | 508 | 71 | 1313 | 290 | 134 | 803 | 258 | |
(32) | (23) | (41) | (14) | (59) | (25) | (18) | (51) | (29) | |||
Female | Combined | 312 | 230 | 530 | 61 | 1426 | 250 | 139 | 744 | 282 | |
(21) | (18) | (28) | (9) | (42) | (16) | (13) | (33) | (21) | |||
Total | 1994 | 421 | 283 | 96 | 1667 | 205 | 173 | 707 | 263 | ||
(27) | (22) | (12) | (50) | (16) | (16) | (35) | (21) | ||||
Total | 1999 | 397 | 243 | 90 | 1366 | 276 | 159 | 818 | 253 | ||
(27) | (20) | (12) | (47) | (19) | (16) | (40) | (22) | ||||
Total | Combined | 410 | 264 | 92 | 1516 | 241 | 165 | 758 | 258 | ||
(19) | (15) | (8) | (34) | (13) | (11) | (27) | (15) |
Table
Estimates of
Lung cancer | Colon cancer | Breast cancer | Prostate cancer | Melanoma | Heart failure | Alzheimer’s disease | Parkinson’s disease | Diabetes | Asthma | |
---|---|---|---|---|---|---|---|---|---|---|
Time Trend | ||||||||||
Males | −1.8 | −0.06 | −2.21 | −1.44 | −3.7 | 1.44 | −0.55 | 1.32 | 1.05 | |
Female | 0.88 | −1.86 | −0.84 | 1.01 | −2.67 | 2.45 | −0.34 | 1.57 | −1.08 | |
Total | −0.63 | −1.35 | −0.35 | −4.39 | 2.86 | −0.62 | 2.09 | −0.33 | ||
| ||||||||||
Male/female differences | ||||||||||
1994 | 5.42 | 1.16 | 4.03 | 3.05 | −0.33 | 2.19 | 0.37 | −2.44 | ||
1999 | 2.86 | 2.75 | 1.88 | 1.29 | −0.91 | 1.79 | 0.46 | −0.25 | ||
Pooled | 5.93 | 2.66 | 4.25 | 3.09 | −0.86 | 2.72 | 0.64 | −1.94 |
The age, disability, and comorbidity patterns were analyzed for all considered diseases. The results for cancer and non-cancer diseases are presented in Figures
Age (left columns), disability (central columns), and comorbidity (right columns) patterns of the incidence rates (per 100,000) of cancers. Arguments are years (age), disability groups (nondisabled, IADL only, 1-2 ADLs, 3-4 ADLs, 5-6 ADLs, and institutionalized), and comorbidity group in the units of Charlson index (0, 1, 2, and 3 and more). The sex-specific rates are given for lung cancer, colon cancer, breast cancer, and melanoma. The time period-specific (i.e., cohort specific) rates are given for prostate cancer. The rates are calculated using the basic strategy for disease onset identification (Algorithm A).
Age (left columns), disability (central columns), and comorbidity (right columns) patterns of the incidence rates (per 100,000) of non-cancer chronic diseases. Arguments are years (age), disability groups (nondisabled, IADL only, 1-2 ADLs, 3-4 ADLs, 5-6 ADLs, and institutionalized), and comorbidity group in the units of Charlson index (0, 1, 2, and 3 and more). The sex-specific rates are given for Parkinson’s disease and asthma. The time period-specific (i.e., cohort specific) rates are given for heart failure, Alzheimer’s disease, and diabetes. The rates are calculated using the basic strategy for disease onset identification (Algorithm A).
For several diseases (e.g., heart failure, diabetes, asthma, and Parkinson’s disease) the incidence rates were higher among individuals with severe disabilities, while for breast and prostate cancers the higher rate was registered among people with minor disabilities. The most dramatic increase of incidence with disability was for heart failure. Interestingly, that for many diseases institutionalized individuals have lower rates and for several (such as melanoma, lung cancer (males), colon cancer, and asthma) the lowest rates among all other disability groups including nondisabled individuals. However, for neurodegenerative diseases (i.e., Parkinson’s (females) and Alzheimer’s diseases) the rate for institutionalized individuals is the highest. Among individuals with high comorbidity indices (i.e., Charlson index) higher rates were observed for heart failure, melanoma, and Alzheimer’s disease. The incidence rates of cancers of breast and prostate and of diabetes had decreasing trends with increasing comorbidity index.
As we discussed in Section
Age-adjusted incidence rates per 100,000 under alternative approaches to the definition of age at onset. Standard calculation (V0) of age-adjusted incidence rates (per 100,000) was performed according to the aforementioned rules, that is, screener NLTCS population, using the NLTCS weights, the 4 basic Medicare sources, only the primary diagnosis, at least two records (or death) in
Sex | Year | V0 | V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | V10 | V11 | V12 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Lung cancer |
M | 94 | 616 | 613 | 618 | 623 | 593 | 529 | 572 | 541 | 629 | 657 | 839 | 624 | 454 |
M | 99 | 491 | 490 | 488 | 495 | 484 | 432 | 511 | 462 | 496 | 612 | 711 | 532 | 373 | |
F | 94 | 293 | 295 | 280 | 281 | 269 | 248 | 278 | 258 | 288 | 368 | 455 | 323 | 220 | |
F | 99 | 331 | 330 | 332 | 333 | 322 | 289 | 342 | 302 | 324 | 393 | 454 | 330 | 264 | |
| |||||||||||||||
Colon cancer |
M | 94 | 314 | 306 | 302 | 304 | 280 | 253 | 269 | 267 | 289 | 427 | 483 | 362 | 224 |
M | 99 | 311 | 311 | 321 | 326 | 322 | 296 | 308 | 300 | 356 | 440 | 526 | 377 | 270 | |
F | 94 | 262 | 259 | 252 | 253 | 243 | 232 | 245 | 237 | 264 | 349 | 432 | 299 | 218 | |
F | 99 | 196 | 195 | 194 | 195 | 192 | 177 | 200 | 187 | 200 | 282 | 361 | 220 | 170 | |
| |||||||||||||||
Breast cancer | F | 94 | 555 | 559 | 572 | 573 | 541 | 498 | 508 | 511 | 544 | 802 | 906 | 680 | 485 |
F | 99 | 508 | 509 | 512 | 514 | 496 | 432 | 454 | 442 | 453 | 580 | 674 | 491 | 424 | |
Prostate cancer | M | 94 | 1146 | 1148 | 1116 | 1125 | 1098 | 984 | 996 | 1033 | 1102 | 1649 | 2044 | 1419 | 944 |
M | 99 | 924 | 922 | 950 | 967 | 939 | 808 | 817 | 876 | 913 | 1305 | 1675 | 1131 | 792 | |
| |||||||||||||||
Melanoma | M | 94 | 163 | 159 | 161 | 162 | 149 | 131 | 131 | 137 | 142 | 225 | 277 | 147 | 110 |
M | 99 | 117 | 117 | 130 | 132 | 135 | 113 | 113 | 119 | 131 | 204 | 257 | 137 | 106 | |
F | 94 | 53 | 51 | 49 | 49 | 45 | 42 | 44 | 46 | 57 | 139 | 166 | 73 | 36 | |
F | 99 | 71 | 70 | 76 | 76 | 76 | 63 | 63 | 64 | 65 | 99 | 127 | 68 | 63 | |
| |||||||||||||||
Heart fail. | M | 94 | 1864 | 1795 | 1869 | 1881 | 1784 | 1612 | 1706 | 1767 | 2713 | 3055 | 5345 | 4484 | 1389 |
M | 99 | 1437 | 1438 | 1465 | 1486 | 1501 | 1312 | 1367 | 1445 | 2292 | 2659 | 4670 | 3554 | 1215 | |
F | 94 | 1540 | 1484 | 1511 | 1515 | 1476 | 1346 | 1458 | 1511 | 2348 | 3009 | 5084 | 4128 | 1189 | |
F | 99 | 1313 | 1313 | 1323 | 1329 | 1349 | 1185 | 1274 | 1301 | 2042 | 2500 | 4397 | 3166 | 1084 | |
| |||||||||||||||
Parkinson disease | M | 94 | 217 | 208 | 221 | 223 | 213 | 190 | 199 | 205 | 272 | 289 | 465 | 408 | 183 |
M | 99 | 195 | 195 | 186 | 189 | 179 | 156 | 159 | 171 | 313 | 282 | 531 | 416 | 150 | |
F | 94 | 143 | 139 | 140 | 140 | 135 | 121 | 133 | 128 | 222 | 216 | 405 | 318 | 116 | |
F | 99 | 134 | 134 | 127 | 127 | 130 | 117 | 130 | 130 | 216 | 209 | 413 | 283 | 107 | |
| |||||||||||||||
Alzheimer disease | M | 94 | 199 | 187 | 200 | 201 | 192 | 174 | 179 | 194 | 396 | 343 | 737 | 607 | 151 |
M | 99 | 255 | 257 | 245 | 247 | 242 | 219 | 246 | 262 | 462 | 540 | 992 | 794 | 183 | |
F | 94 | 210 | 197 | 192 | 192 | 189 | 183 | 225 | 221 | 532 | 439 | 1048 | 876 | 157 | |
F | 99 | 290 | 291 | 287 | 288 | 296 | 264 | 301 | 323 | 717 | 670 | 1457 | 1114 | 239 | |
| |||||||||||||||
Diabetes | M | 94 | 724 | 721 | 718 | 723 | 725 | 681 | 715 | 772 | 1303 | 1787 | 3403 | 2529 | 646 |
M | 99 | 841 | 841 | 801 | 816 | 884 | 784 | 878 | 936 | 1478 | 2130 | 4158 | 2654 | 739 | |
F | 94 | 697 | 696 | 702 | 704 | 707 | 650 | 678 | 728 | 1179 | 1665 | 3197 | 2219 | 611 | |
F | 99 | 803 | 804 | 797 | 801 | 799 | 713 | 818 | 827 | 1260 | 1835 | 3636 | 2195 | 673 | |
| |||||||||||||||
Asthma | M | 94 | 201 | 200 | 202 | 204 | 198 | 174 | 198 | 211 | 395 | 744 | 1484 | 804 | 157 |
M | 99 | 247 | 248 | 263 | 267 | 260 | 228 | 260 | 255 | 478 | 723 | 1591 | 818 | 206 | |
F | 94 | 303 | 307 | 309 | 310 | 301 | 287 | 329 | 316 | 550 | 934 | 1692 | 1045 | 278 | |
F | 99 | 258 | 257 | 228 | 229 | 239 | 213 | 274 | 232 | 525 | 842 | 1679 | 901 | 200 |
Observed variations in incidence rates display that different definition of incidence rate extracted from administrative data can be used and they result in significantly different incidence rates. However, male/female differences, estimates of time trends, and ratios of rates of various diseases remain stable across the different definitions of incidence rates.
The age patterns (sex- and cohort-unspecific) calculated using two Medicare-based datasets (i.e., NLTCS-M as in this study and SEER-Medicare) were compared and an agreement was found for chronic diseases considered here [
In USA, about 60% of cancers are diagnosed at age 65 and older [
Age-specific cancer incidence rates: means and SE of NLTCS/Medicare (Algorithms A and D) and SEER data for 1994–2003. The rate for SEER above 85 is shown at mean age of cases above 85. The number in the right upper corner is the renormalization factor: true incidence rates are obtained by dividing plotted rates by this factor.
Figure
Age-specific incidence rates for heart failure obtained in this study (NLTCS/Medicare) in comparison with rates in other studies: the Atherosclerosis Risk in Communities (ARIC), the Cardiovascular Health Study (CHS), and the Framingham Health Study (FHS).
Figure
Age-specific incidence rates for diabetes mellitus obtained in this study (NLTCS/Medicare) in comparison with those studied for other countries.
Another study on diabetes was recently published by McBean et al. [
Asthma may occur in the elderly more frequently than it is usually appreciated and be, therefore, underdiagnosed [
The most common disorders in the NDD group are Alzheimer’s disease (and other dementia) and Parkinson’s disease. They have been in the focus of several studies and meta-analyses which estimated their incidence rates and age patterns in elderly populations. We compare the results of our calculation to the meta-analysis [
Age-specific incidence rates for Alzheimer’s disease obtained in this study (NLTCS/Medicare) using the Algorithms A and C in comparison with other studies.
The difficulties with studying the incidence data on Parkinson’s disease result from the difficulties in identifying a sufficiently large number of affected individuals in a well-defined or enumerated population. Low frequency of disease, difficulties in establishing diagnosis, and the absence of population-based disease registries contributed to the lack of its basic epidemiologic characteristics [
In this study the estimates on changes of incidence rates of cancer and non-cancer chronic diseases with (i) age among the older US adults (males and females), (ii) disability prevalence, and (iii) prevalence and severity of comorbidities were obtained using the NLTCS-Medicare-linked data. Disease incidences were analyzed for ten chronic conditions representing major groups of chronic conditions in the elderly; (i) circulatory: heart failure; (ii) cancer: breast, prostate, lung, and colon cancers and melanoma; (iii) neurodegenerative: Parkinson’s disease and Alzheimer’s disease; (iv) diabetes; and (v) asthma. We have demonstrated that NLTCS-M dataset could be very useful for answering the spectrum of questions on the elderly health in the USA from both medical and economical perspectives. Also, this dataset allows for bringing additional information which cannot be obtained from the other datasets: for example, comorbidity and disability. These data are population-based, minimizing selection bias with respect to geographic region, urban versus rural location, racial health disparities with a whole spectrum of race- and ethnic-specific populations, and socioeconomic characteristics. Each of these factors is an important predictor of disease risk, progression, treatment availability, and response. This information is limited in databases from more restricted populations [
Generally, the obtained results were in accordance with our expectations. The comparison of the age patterns with other studies, as well as their sex differences and time trends, demonstrated similarities of these patterns with those obtained in other population studies in USA and other countries. Patterns of the majority of diseases were well described by the base algorithm, the most important features of which included (i) the occurrence of primary diagnosis in one of four Medicare sources (inpatient care, outpatient care, physician services, and skilled nursing facilities) and (ii) the confirmation of the diagnoses by another record. The only exception was Alzheimer’s disease: its patterns required certain corrections to the base algorithm to be adequately described; that is, only one record is sufficient and it needs not be primary. Note that for Alzheimer’s disease the nonpostmortal diagnosis (i.e., largely subjective or included a subjective component in distinguishing between Alzheimer’s disease and dementia) is still implemented; therefore, our result that an algorithm without confirmation better fits data could tell us that the incidence of Alzheimer’s disease can be overestimated among the oldest elderly patients [
Several types of age patterns were observed in our study. The first type was flat or plateau. The diseases manifesting this shape were prostate cancer, melanoma, and diabetes. Note that in the analyses of the shape of age patterns the first and the last point can be cut. The first point can still have a mixture of prevalence case and the last point typically has larger statistical uncertainty. The second type of the shape was monotonic increase with age. Diseases with this shape were heart failure and Alzheimer’s disease. The third type had the shape with a maximum or inverted U-shape. Respective diseases were lung cancer, colon cancer, and Parkinson’s disease. Age at maximal rate was in the region 80–90 years for all cases. The fourth shape appeared in the analysis had monotonic rates decline. Breast cancer and asthma possessed these shapes.
Occurrences of the shapes with a maximum and, especially, with monotonic decline contradict the hypothesis that risk of geriatric diseases correlates with accumulation of adverse health events (genetic mutations, deterioration of vascular system, immunosenescence, etc.). Three basic concepts could be considered to explain this phenomenon. The appearance of such effects can be attributed to the effect of selection [
Also, note that the shape of age pattern can depend on how broad or, alternatively, how narrow the definition of the set of disease is. Akushevich et al. [
Medicare claim data have certain limitations that are with a matter of the determination of diagnoses. Sensitivity analysis is one possible way to deal with such uncertainties. In this paper we analyzed several sources of possible uncertainties such as different definitions of disease onset and different censoring schemes. The rates obtained for different schemes of onset identification can be significantly different, but this simply corresponds to different definitions of incidence rates, many of which are used in epidemiology (e.g., fatal and nonfatal incidence rates). The following specific sources of potential uncertainties were in the focus of this study: (i) approaches to identification of incident cases (all Medicare sources, keeping only primary diagnoses, different definitions of the onset), (ii) censoring strategies (dependence between recovery and death), (iii) disenrollment from Medicare and coverage by HMO, and (iv) study design effects (e.g., using NLTCS weights). Also, the impact of factors of observed heterogeneity (e.g., age, disability, and comorbidity) on the incidence rates is also investigated.
Modern models of forecasting the health state and the associated medical costs include three essential components or submodels: (i) the model of medical cost projections conditional on health state, (ii) health state projections, and (iii) description of initial health state of a cohort to be projected [
The age-, disability-, and comorbidity-specific incidence rates of ten highly prevalent aging-related chronic diseases were analyzed using the NLTCS-M data. The most appropriate approach for identification of the disease onset required forthcoming occurrence of repeated claims containing chosen ICD codes as a prime diagnosis in basic Medicare sources. Comparing the age patterns obtained using this computational approach with those available in the literature showed a good agreement for the majority of diseases. Thus, the national incidence rates can be adequately evaluated from the Medicare service use files. Usefulness of the Medicare data for evaluation of the national incidence rates is very important because of limited data sources for evaluation of incidence patterns at advanced ages in the national population. These timely results can inform current scientific and policy debates about the effects of biomedical research and therapeutic innovations on disease incidence at increasingly advanced ages.
The research reported in this paper was supported by the National Institute on Aging Grants R01AG027019, R01AG032319, and R01AG028259. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Aging or the National Institutes of Health.