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BRIEF METHODOLOGICAL REPORTS

Age Patterns of Incidence of Geriatric Disease in the U.S. Elderly Population: Medicare-Based Analysis

Igor Akushevich, PhD,* Julia Kravchenko, MD, PhD,† Svetlana Ukraintseva, PhD,*†

Konstantin Arbeev, PhD,* and Anatoliy I. Yashin, PhD, ScD*†

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 sub- sequent leveling off and decline was observed for myocar- dial infarction, stroke, heart failure, ulcer, and Alzheimer’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 circu- latory 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. J Am Geriatr Soc 60:323–327, 2012.

Key words: Medicare; chronic disease onset; comorbidity

The ability to understand morbidity and mortalitytrends in the U.S. population with growing propor- tions of elderly adults is a major public health concern and an important issue for policymakers and governmental institutions, as well as for health insurance programs such as Medicare and Medicaid. Studying age patterns of aging- related diseases requires large population-based data sets that are costly to collect and maintain; therefore, studies on age patterns of diseases in older U.S. adults are not common, especially regarding incidence of aging-associated chronic diseases. Incidence can be estimated using data from the national registries or from surveys representing the U.S. population; in the latter case, special procedures are required to generalize survey respondent results to the national level. One way to do that is to use a weight func- tion (possibly time dependent) assigned to each individual such that population estimates calculated using the weights give estimates at the national level. A national survey with such a design is the 1982 to 2005 National Long Term Care Survey (NLTCS), which focuses on the U.S. elderly (! 65) population.1 Another data set that can be used for such an analysis is the Surveillance, Epidemiology, and End Results (SEER) Registry data linked to the Medicare Service Use Files (SEER-M). Using these extensive sources of information allows specific algorithms of incidence identification from administrative data to be validated and their ability to evaluate the incidence of major aging- related diseases in older U.S. adults to be proved.

In this study, for the first time, age patterns of inci- dence of common geriatric diseases were evaluated at the national level.

From the *Center for Population Health and Aging, and †Duke Cancer Institute, Duke University, Durham, North Carolina.

Address correspondence to Igor Akushevich, Center for Population Health and Aging, Duke University, 002 Trent Dr., Durham, NC 27708. E-mail: [email protected]

DOI: 10.1111/j.1532-5415.2011.03786.x

DATA AND METHODS

Both Medicare-linked data sets (SEER-M and NLTCS-M) contain information from MFSU since 1991. These data sets allow for reconstruction of individual histories of med- ical service use and, therefore, for modeling of individual follow-up from age 65 to death or onset of a disease of interest. All individuals in the SEER-M and NLTCS-M are longitudinally tracked for Medicare Part A and Part B service use. The 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 provid- ers) claim type.

Two of the six NLTCS waves, the cohorts of 1994 and 1999, were used for analysis; they were chosen primarily because the high-quality Medicare follow-up data are avail- able only since 1991, and the complete 5-year follow-up after the NLTCS interview is accessible only for these two waves after 1991. The NLTCS uses a sample of individuals drawn from the national Medicare enrollment files. In total, 34,077 individuals were followed-up for 5 years. “Screener weights” released with the NLTCS were used in this study to produce the national population estimates.

The collection of SEER data began in 1973 and cur- rently covers approximately 26% of the U.S population. The SEER-M data set includes Medicare records for individuals with diagnosed breast (n = 353,285), colon (n = 222,659), lung (n = 342,961), and prostate (n = 448,410) cancers and skin melanoma (n = 101,123) and Medicare records for control containing individuals identi- fied from a random 5% sample of Medicare beneficiaries residing in the SEER areas who had none of above- mentioned cancers. Medicare records for 2,154,598 indi- viduals are available in SEER-M.

Date of Onset Definitions

Nineteen diseases with high prevalence in elderly adults were selected for analyses (none of the initially selected diseases were excluded from the analysis). The ages at onset of all studied diseases were reconstructed from the MFSU using the following scheme. First, the histories of the applicable disease for each individual were recon- structed from the Medicare files, combining all records

with their respective International Classification of Diseases, Ninth Revision (ICD-9) codes (Table 1). Then a special procedure was applied for individuals with a his- tory of the considered disease to separate the incident and prevalent cases. In the procedure used for identification of the date at onset, a date of a Medicare record (referred to as “this record” below in this subsection) was identified with the date of onset of applicable disease if both of the conditions mentioned below were met:

(i) This record was the earliest record with the respec- tive ICD code as a primary diagnosis in one of the four Medicare sources (inpatient care, outpatient care, physi- cian services, and skilled nursing facilities). This choice is in accordance with the general practice of reconstruction of the date at onset from Medicare data.2–4

(ii) In addition to this record, there was another record with its respective ICD code as the primary diagno- sis from one of the four Medicare sources listed in (i) that appeared with a date different from the date of this record and not later than 0.3 years after this record. Death that occurred during this period (0.3 years after this record) was also considered as the second record.

The first condition allows for identification of the first occurrence of disease code, and the second condition is required for confirmation of disease presence. This algo- rithm was used in studies of recovery after stroke,5 medi- cal cost trajectories before and after disease onset,6 and the role of behavior factors in cancer risk.7 The algorithm was implemented using SAS (SAS Institute, Inc., Cary, NC). Required running time for the computational server (2 quad core Xeon X5570 processors running at 2.93 GHz with 32 GB of RAM) is several hours per disease.

RESULTS

For most of the studied diseases, age-adjusted incidence rates obtained using the NLTCS-M and SEER-M were in excellent agreement (Figure 1). As an additional control for reasonability of the obtained incidence rates, total mor- tality was also estimated. Of the studied diseases, incidence of Alzheimer’s disease, stroke, and heart failure increased with age, whereas the rates of cancers of the lung and breast, angina pectoris, diabetes mellitus, asthma, emphy- sema, arthritis, and goiter became lower at older ages. The rates obtained from the NLTCS-M were a little higher for

Table 1. International Classification of Diseases, Ninth Revision (ICD-9) Codes Used for the Considered Conditions

Group of Diseases Disease (ICD-9 Code)

Cardio- and cerebrovascular

Myocardial infarction (410.xx), angina pectoris (413.xx), stroke (431.xx, 433.x1, 434.x1, 436.xx), heart failure (428.xx)

Malignancy Lung cancer (162.xx), colon cancer (153.xx), breast cancer (females) (174.xx), prostate cancer (185.xx), skin melanoma (172.xx)

Neurogenerative Parkinson’s disease (332.xx), Alzheimer’s disease (331.0) Pulmonary Chronic obstructive pulmonary disease (COPD) (490.xx, 491.xx, 492.xx, 493.xx, 494.xx, 495.xx, 496.xx), asthma (493.xx),

emphysema (492.xx), Endocrine and metabolic

Diabetes mellitus (250.xx), goiter (240.xx, 241.xx, 242.0x, 242.1x, 242.2x, 242.3x)

Miscellaneous Chronic renal diseases with renal failure (403.xx, 404.xx, 585.xx, 250.4x, 249.4x), ulcer (531.xx, 532.xx, 533.xx, 534.xx), arthritis (714.0x, 714.1x, 714.2x, V82.1x)

324 AKUSHEVICH ET AL. FEBRUARY 2012–VOL. 60, NO. 2 JAGS

incidence of most noncancer diseases (such as myocardial infarction, stroke, heart failure, diabetes mellitus, and ulcer) than the rates calculated using the SEER-M; the same difference was observed for total mortality at age 85 and older. For possible explanations of these findings, methodological and substantive hypotheses are discussed in the Results (those that can be tested using the NLTCS- M and SEER-M data) and Discussion (those requiring usage of external data sets) sections. For the majority of diseases, a decline in incidence with age was detected. Because of the use of weights in this analysis, the age- specific rates and standard errors obtained could provide estimates that are valid for the general U.S. elderly population.

Several types of age patterns of disease incidence were observed in this study. The first was a monotonic increase until age 85 to 95, with a subsequent slowing down, level- ing off, and decline at age 100—for myocardial infarction,

stroke, heart failure, ulcer, and Alzheimer’s disease. The second type had an earlier maximum and a more-symmetric shape (an inverted U-shape)—for cancers of the lung and colon, Parkinson’s disease, ulcer, and renal failure. The majority of diseases (e.g., prostate cancer, asthma, and dia- betes mellitus) demonstrated the third shape: a monotonic decline or a decline after a short period of rate of increase. Melanoma and emphysema can also fit into this pattern, although their patterns could also be considered flat.

The date of chronic disease onset cannot be defined with the same precision as the date of death. A specific record with the disease code does not contain information for concluding whether the disease was diagnosed on the date of the record or whether it had been diagnosed ear- lier; in the latter case, the record relates to the visit to treat the already-diagnosed disease. Therefore, the date of onset can be identified using information collected in the MFSU with specific assumptions outlining a specific algorithm of

250

500

750

1000 Mortality 35

Heart Failure 7.5

Stroke 5.2

Myocardial Infarction 3

Alzheimer disease 1.7

250

500

750

1000

70 80 90 100

Renal 1.4

70 80 90 100

Ulcer 1

70 80 90 100

Lung cancer 0.8

70 80 90 100

Colon cancer 0.7

70 80 90 100

Parkinson disease 0.5

250

500

750

1000 COPD 2.8

Prostate cancer 2.5

Diabetes 2

Female Breast cancer 1.1

Angina Pectoris 1.1

250

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70 80 90 100

Asthma 0.8

70 80 90 100

Goiter 0.4

70 80 90 100

Emphysema 0.3

70 80 90 100

Arthritis 0.3

70 80 90 100

Melanoma 0.3

Age

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Figure 1. Age-specific total mortality and disease incidence calculated using National Long Term Care Survey (NLTCS)-Medicare (open dots) and Surveillance, Epidemiology, and End Results (SEER)-Medicare (close dots). Rates for different diseases are rescaled to use the same scale on all plots to compare rates for different diseases: the original rate (per 100,000) can be calcu- lated by multiplying the values obtained from the plot by the rescaled factor. Upper panel represents diseases with age patterns of the first and second types (a monotonic increase of incidence with age with subsequent leveling off and decline), and the lower panel represents the results of the age pattern of the third type (a monotonic decline in incidence with age or decline after short period of increase).

JAGS FEBRUARY 2012–VOL. 60, NO. 2 AGE PATTERNS OF INCIDENCE OF GERIATRIC DISEASE 325

calculation. In addition, the approaches to the registration of certain chronic disease onset could vary according to medical service provider and specialization of medical staff. Thus, there is certain arbitrariness in defining the date of onset that can be used for constructing a unified definition of date of onset appropriate for population stud- ies. The scheme used in this study resulted from review of the approaches used in several published studies for differ- ent diseases.2–4 The evaluated incidences are in close agree- ment with those obtained in other studies. For example, age-adjusted cancer rates (!65, per 100,000) for five con- sidered cancers calculated using the NLTCS-M and stan- dard SEER Registry data are 521/448 (in the NLTCS/ SEER, respectively) for female breast, 1,042/991 for pros- tate, 346/345 for lung, and 243/232 for colon. The age- specific rates of incidence of diabetes mellitus obtained in the current study are in agreement with those from the Canadian Study of Health and Aging, the Zwolle Outpa- tient Diabetes project Integrating Available Care8 (the Netherlands), and the UK Pooled Diabetes Study.9 For stroke, the current results are in a good agreement with those obtained in the Cardiovascular Health Study (CHS) and the Framingham Heart Study (FHS; reviewed in10), as well as from the Health Cost and Utilization Project study11 and the Greater Cincinnati-Northern Kentucky Stroke Study.12 The FHS and CHS results correspond bet- ter with the rates obtained for NLTCS-M than with the SEER-M data. Estimates for mortality from the Human Mortality database also correspond better with the estimates of the NLTCS-M.

When analyzing large administrative data sets, there is often a question of existence of factors that could produce systematic over- or underestimation of the number of diag- nosed diseases or of age at onset. The reasons for such uncertainties could be the incorrect date of disease onset, latent disenrollment, and incorrect reporting of date of birth and date of death; whereas the first affects the age at onset, the latter tends to reduce or increase the number of person-years at risk. To evaluate the effect of these uncer- tainties, the calculations were performed with different definitions of disease onset, and several alternative censor- ing schemes were used to define individual observation periods. The following calculations were performed: only inpatient records were kept, confirmation of the diagnosis was not required, confirmation of inpatient cases was not required, not only the primary code contributed to diagno- sis, and different options of the health maintenance organi- zation (HMO) coverage (i.e., coverage by an alternative insurance) were applied, such as rejecting individuals with a fraction of a month covered by the HMO exceeding a certain level. It was found that, qualitatively, the picture is similar for all considered cases. It could be concluded, that the reason for the differences between the circulatory dis- ease rates obtained from the NLTCS-M and SEER-M are probably not methodological.

DISCUSSION AND CONCLUSION

Age-specific incidence of the major groups of chronic diseases in elderly adults (therefore, resulting in high medical expenditures) were calculated using the NLTCS-M and SEER-M data. These data sets cover the same time

range (1991–2005) and are both designed to represent esti- mates at the national level. The strategy for identifying the dates of onset is based on the analysis of complete trajec- tories of individual records associated with the selected diseases. The most appropriate scheme for the onset identi- fication requires forthcoming occurrence of repeated claims containing chosen ICD codes as a primary diagnosis in basic Medicare sources. Comparison of age patterns observed in both data sets revealed a qualitative (i.e., the form of age patterns of incidence rates) and quantitative (i.e., the magnitude of rates) agreement for almost all dis- eases. Specifically, excellent agreement was found for all types of cancers, angina pectoris, asthma, arthritis, and goiter. For Parkinson’s disease, Alzheimer’s disease, emphysema, and renal disease, the agreement was also good: slightly higher for NLTCS but not exceeding 2rE (corresponds to P = .05). For diabetes mellitus, chronic obstructive pulmonary disease, and ulcer, several age-spe- cific rates were greater than the level of 2rE. For myocar- dial infarction, stroke, and heart failure, a systematic increase in rates observed in the NLTCS was detected. This increase correlated with an increase in total mortality. One hypothesis for the trend is that different populations were represented in the data sets: the entire U.S. popula- tion in NLTCS and the population of the SEER regions in the SEER-M data set.

The patterns observed suggest a declining incidence of aging-related diseases with age. This is in agreement with the results of several recent studies on declining cancer incidence in the oldest old (e.g., !85).13–19 This is also in agreement with autopsy studies20 and with observed mor- bidity profiles in elderly adults (e.g., in centenarians, 87% of men and 83% of women delayed onset or completely bypassed the most-lethal diseases21,22).

An occurrence of a maximum of the age-specific incidence rates and, especially, of a monotonic decline contradicts the hypothesis that risk of geriatric diseases correlates with accumulation of adverse health events such as genetic mutations, deterioration of vascular system, and immunosenescence. Three basic concepts could help explain such shapes. First, it could be attributed to the effect of selection,23 with frail individuals not surviving to advanced ages; this theory is popular in cancer modeling and was successfully applied to the SEER data.19,24,25 The second explanation is related to the question of possible underdiagnosis of chronic diseases at advanced ages (because of less-pronounced disease symptoms in elderly adults and infrequent doctor’s office visits), but it cannot be proven with the available data.26,27 It has also been suggested28 that three components contributing to mortal- ity with aging (basal, ontogenetic, and time dependent) may interact, leading to different patterns of morbidity and mortality in the elderly population.

Comparing the age patterns obtained with the results of other studies demonstrated similarities between the United States and other countries. The patterns of the majority of diseases could be described well using the algorithm developed in which occurrence of primary diag- nosis in one of four Medicare sources (inpatient care, out- patient care, physician services, and skilled nursing facilities) and confirmed diagnoses by a subsequent record are taken into account. The analyses performed suggest

326 AKUSHEVICH ET AL. FEBRUARY 2012–VOL. 60, NO. 2 JAGS

that the national age-specific incidence patterns can be evaluated adequately using the MFSU.

The usefulness of the Medicare data is important, because there are few data sources to study such incidence patterns at advanced ages in the U.S. population. For example, heart disease and stroke account for more than 40% of all deaths in persons aged 65 to 74 and almost 60% of those aged 85 and older, but there are no nation- ally representative data available on incidence, severity, or recurrence of acute coronary or stroke events in inpatient or outpatient settings. Therefore, the nationally representa- tive data sets linked to Medicare data could be useful in estimating the incidence of aging-related diseases and associated medical costs. An advantage of the approach is in using specific information in data sets to which the Medicare data are linked (e.g., the NLTCS can provide disability-specific incidence), allowing for projection of the estimates for the whole U.S. population (so that the rates are valid at the national level), and the SEER-M allows effects of comorbidity and specific characteristics of cancer (such as histotype-specific cancer rates) to be investigated. Disease-specific age at onset evaluated from these data could be used for estimation of the screening strategies, with population groups at the highest risk being evaluated at specific ages or age intervals. In addition, the Medicare data also allow the relationship between these age-specific incidence patterns and Medicare costs, including future Medicare cost projection, to be investigated. Thus, the results reported in this study are timely and important because they may inform current scientific and policy debates about the effects of biomedical research and thera- peutic innovations on disease incidence at increasingly advanced ages when the effective therapeutic interventions became actively introduced in recent decades.

ACKNOWLEDGMENTS

Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other kind of personal conflicts with this paper.

The research reported in this article was supported by National Institute on Aging (NIA) Grants R01AG027019, R01AG032319, and R01AG028259. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIA or the National Institutes of Health.

Author Contributions: IA and AIY: Study design, data analysis and interpretation, manuscript preparation. JK: Data analysis and interpretation, manuscript preparation. SU and KA: Data analysis and interpretation.

Sponsor’s Role: None.

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