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Predictors of Self-Report of Heart Failure in a Population-Based Survey of Older Adults

Originally published Cardiovascular Quality and Outcomes. 2012;5:396–402



    Little research has been conducted on the predictors of self-report or patient awareness of heart failure (HF) in a population-based survey. The objective of this study was to (1) test the agreement between Medicare administrative and Health and Retirement Study (HRS) survey data and (2) determine predictors associated with self-report of HF, using a validated Medicare claims algorithm as the reference standard. We hypothesized that those who self-reported HF were more likely to have a higher number of HF-related claims.

    Methods and Results—

    Secondary data analysis was conducted using the 2004 wave of the HRS linked to 2002 to 2004 Medicare claims (n=5573 respondents aged ≥67 years). Concordance between self-report of HF in the HRS and Medicare claims was calculated. Logistic regression was performed to identify predictors associated with self-report HF. HF prevalence by self-report was 4.6%. Self-report of HF and claims agreement was 87% (κ=0.34). The presence of >1 HF inpatient claims was associated with greater odds of self-report (odds ratio [OR], 1.92; 95% CI, 1.23–3.00). Greater odds of self-reporting HF was also associated with ≥4 HF claims (OR, 2.74; 95% CI, 1.36–5.52). Blacks (OR, 0.28; 95% CI, 0.14–0.55) and Hispanics (OR, 0.30; 95% CI, 0.11–0.83) were less likely to self-report HF compared with whites in the final model.


    Self-report of HF is an insensitive method for accurately identifying HF cases, especially in those with less-severe disease and who are nonwhite. There may be limited awareness of HF among older minority patients despite having clinical encounters during which HF is coded as a diagnosis.


    Chronic heart failure (HF) is a common and complex condition in older adults.15 HF is associated with increased disability and is frequently accompanied by other debilitating chronic conditions.68 Despite a number of initiatives designed to reduce the mortality and morbidity of older adults with HF, such as disease management programs and discharge planning services, outcomes have only slightly improved in older adults in the past decade.4,913 The lack of substantial improvement in mortality and morbidity has prompted further consideration of the impact of comorbid conditions and psychosocial contextual factors (eg, caregiver support, patient functional status) on the health status of older adults with HF.

    Population-based HF research can provide important information about the interrelationship of HF with psychosocial factors and other comorbid conditions, including geriatric syndromes. The Health and Retirement Study (HRS), a nationally representative survey of adults aged >50 years, is an ideal data source for investigating geriatric-relevant chronic conditions and psychosocial contextual factors of older adults with HF. However, respondents often underreport the prevalence of their chronic diseases.14 In prior studies with the HRS, we found a 4% prevalence of self-reported HF in older adults aged ≥65 years, which is less than the estimates of 8% to 10% found with other population-based surveys that used expert clinical adjudication.3,5,6 An HF case definition developed from prior work used self-report of HF by telephone interview as the gold standard to maximize the performance of claims-based algorithms.15 In it, 6% of 4600 members of a Medicare Choice plan reported having HF in the year 2000. The patient population identified solely by self-report data in the HRS (and perhaps in the Medicare Choice telephone survey) may have more-severe HF16; thus, individuals who are less likely to self-report because of milder disease may have been missed. Ascertainment of HF through self-report alone may lead to an overestimation or underestimation of outcomes of interest.

    Patient awareness of HF as a condition is likely influenced by multiple factors besides illness severity, including socioeconomic background, race, patient health awareness gained through encounters with healthcare providers, and effective self-management skills.1722 However, little research has been conducted on the predictors of the self-report or patient awareness of HF in a population-based survey. Furthermore, those who underreport having HF, despite having administrative claims that indicate receiving healthcare for HF, likely represent important segments of the population that may have lower health literacy and higher vulnerability. Understanding the sociodemographic and health status variables associated with self-report of HF may also have important implications for targeting self-management education programs toward certain subpopulations of older adults with HF.

    The objective of our study was to (1) test the agreement between Medicare claims and HRS survey data and (2) determine the patient and health status characteristics associated with self-report of HF by using a comparison with the presence of an HF diagnosis code in linked Medicare claims as a reference standard. We hypothesized that those who self-reported HF were more likely to have a higher number of inpatient clinical encounters related to care for HF.



    We used data from the 2004 wave of the HRS, a biennial, longitudinal survey of a nationally representative cohort of US older adults.23 The HRS provides detailed self-report information on chronic diseases and task-specific disabilities.

    Sample Definition

    Ninety percent of HRS respondents provided consent to link their Medicare claims to their HRS survey data. We were interested in viewing claims data 2 years before the 2004 interview to capture the greatest number of HRS respondents with HF in the Medicare claims data.15,24 Of the 20 129 respondents in the 2004 survey wave, we restricted our analysis to the 9663 respondents aged ≥67 years, ensuring that each individual had 2 full years as a Medicare beneficiary during the surveillance period. The sample was reduced to 8207 after exclusion of those HRS respondents who were not continuously enrolled in Medicare Parts A and B. We excluded 1650 respondents who were enrolled in Medicare managed care because by law, managed care plans are not required to report complete data. We excluded respondents who were represented in the survey by proxy informants or who were residing in a nursing home at the time of the analysis (n=804). The analytic sample included 5753 respondents with nonmissing self-reported HF data, which was representative of a population of ≈21.4 million adults aged ≥67 years with fee-for-service Medicare.

    Definition of HF by Self-Report in the HRS

    Self-report of HF was determined based on survey responses to questions in the HRS 2004 interview. Respondents were asked, “Has a doctor ever told you that you had a heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems?” Those who responded yes were further asked, “Has a doctor told you that you have congestive heart failure?” We categorized respondents as having HF if they answered yes to the latter question; those who responded no were categorized as not having HF.

    Definition of HF by Claims-Based Diagnosis

    HF was defined with Medicare claims by making use of the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), diagnosis codes for primary and secondary diagnoses. Using Medicare Part A and Part B claims files, we determined the claims-based diagnosis of HF for a 2-year period before each respondent's HRS interview date.15 We used ICD-9-CM coding algorithms to identify HF as a diagnosis in ≥1 claim from inpatient, outpatient, and carrier files using codes 398.91, 402.01 to 402.91, 404.01 to 404.93, and 428.0 to 428.90 within a 2-year period based on a previously published validated algorithm by Rector et al,15 who reported a positive predictive value of 38% for finding HF cases among Medicare Choice plan members, in whom the prevalence was 6%.

    Independent Variables

    The sociodemographic variables included in the analysis as independent variables were age, race, sex, net worth, and education. Self-reported chronic conditions were hypertension, lung disease, stroke, cancer, diabetes, and psychiatric (emotional and mental health) problems. We created a count variable of these conditions that ranged from 0 to 6. The self-reported number of physician encounters was also used as a covariate. Respondents in the HRS were first asked, “[Aside from any hospital or nursing home stays], during the last 12 months, since [month] of [2004], have you seen a medical doctor about your health?” Respondents who said yes were then asked, “How many times have you talked to a medical doctor [about your own health] in the last 12 months?” Responses were coded into 4 categories (0–3, 4–7, 8–12, and ≥13 visits) for analysis.

    For self-respondents, the HRS assesses cognitive function using a modified version of the Telephone Interview for Cognitive Status, a validated cognitive screening instrument patterned on the Mini-Mental State Examination.25 The modified Telephone Interview for Cognitive Status uses a 27-point scale that has high sensitivity and specificity for cognitive impairment and dementia in community samples of older adults.2527 The test items include (1) an immediate and delayed 10-word recall test to measure memory, (2) a serial 7 subtraction test to measure working memory, and (3) a counting backward test to measure speed of mental processing. Cut points to identify respondents with cognitive impairment were based on the findings from the Aging, Demographics, and Memory Study.28,29 Cognitive function scores were created and categorized as normal (≥12 points) or cognitively impaired (<12 points), with the cognitively impaired category divided into mild (7–11) and moderate to severe impairment (0–6) based on prior work validating these cut points.

    Physical function variables included in the analysis were activities of daily living (ADL). A respondent was considered to have an ADL (bathing, dressing, eating, transferring, toileting, and walking) disability if they reported having difficulty performing the ADL or required assistance. ADL limitations were dichotomized as 0, 1 to 2, and 3 to 6 to identify those with a low, medium, and high burden of ADL impairments, respectively.

    Claims-Based Covariates

    HF claims-based covariates were chosen as indicator variables to measure poor health status. We counted the number of HF-related claims present in all claims files of interest as a measure of healthcare utilization without defining discrete hospital or outpatient visits. This was operationalized 1, 2 to 3, 4 to 9, or ≥10. Additionally, we created a dichotomous variable indicating the presence of an inpatient claim for HF if there were >1 inpatient claims with HF as the primary or secondary diagnosis.

    Statistical Analysis

    All analyses were conducted using SAS (SAS Institute Inc) statistical software. Weighted analyses were conducted to calculate sample characteristics and multivariate logistic regression. Sample characteristics for the different groups were compared using χ2 and t tests, as appropriate, and were adjusted for the HRS complex sampling design.

    The κ statistic was used to measure the agreement between the HRS survey data and Medicare claims. We investigated the sensitivity and specificity of the self-report of HF in the HRS using the Medicare claims data as the reference standard rather than self-report because prior work indicated that older adults who have a particular condition tend to underreport its presence.30 We defined sensitivity as the proportion of HRS respondents with Medicare claims-linked data identified by a claims-based algorithm as having HF (denominator) who self-reported that a physician told them they had HF (numerator). We defined specificity as the proportion of HRS respondents without a claims-based diagnosis of HF who did not self-report having the condition.

    We used logistic regression to identify covariates associated with the likelihood of self-report of HF in the HRS. The final logistic models included the sociodemographic, health, and claims-based factors that influenced self-report of HF as a condition. Model 1 included age, sex, race and ethnicity, level of education, and net worth. Model 2 included model 1 covariates in addition to respondents' cognitive status, number of chronic medical conditions, and number of healthcare provider encounters. Model 3 included model 2 covariates in addition to the number of HF claims and the presence of HF inpatient claims.


    Sample Characteristics

    Among the 5753 respondents in the analytic sample, the mean age was 76.4 years, 39.9% were men, 7.0% were black, and 3.2% were Hispanic (Table 1). The mean number of years of education was 12.2. Ten percent of the entire sample had ≥4 chronic conditions; 35.6% had ≤1 chronic condition.

    Table 1. Demographic and Health Statistics of Sample Population of Older Adults Aged ≥67 Years With Health and Retirement Study Data Linked to Medicare Claims (n=5753)

    CharacteristicTrue Positive (n=216)False Positive (n=39)False Negative (n=680)True Negative (n=4818)TotalP Value
    Weighted no.830 649151 4752 465 04317 979 55821 426 725
        Prevalence, %3.9 (3.3–4.5)0.7 (0.4–1.00)11.6 (10.7–12.5)83.8 (82.7–84.8)
    Age, y78.2±6.676.7±5.978.7±6.876.0±6.476.4±6.5<0.001
        Hispanic8 (2.3)2 (1.9)49 (4.7)225 (3.1)284 (3.2)
        Black20 (5.2)6 (10.5)101 (10.5)517 (6.5)644 (7.0)
        White/other188 (92.5)31 (87.6)530 (84.8)4076 (90.4)4825 (89.8)
        Male90 (41.3)17 (45.7)308 (44.6)1920 (39.2)2335 (39.9)
        Female126 (58.7)22 (54.3)372 (55.4)2898 (60.8)3418 (60.1)
    Education, y11.6±3.011.6±3.511.5±3.512.4±3.112.2±3.1<0.001
    Net worth (quartile)<0.001
        ≤$61 00083 (36.7)15 (29.3)247 (34.8)1128 (21.5)1473 (23.7)
        $61 001–$203 40055 (26.2)14 (33.3)191 (27.6)1212 (24.9)1472 (25.3)
        $203 401–$512 00044 (19.8)7 (26.1)137 (20.2)1238 (26.5)1426 (25.5)
        ≥$512 00034 (17.3)3 (11.3)105 (17.3)1240 (27.1)1382 (25.5)
    No. ADL impairments<0.001
        0112 (55.2)23 (58.4)460 (68.4)4048 (83.9)4643 (80.8)
        1–272 (31.6)12 (29.5)155 (22.8)591 (12.7)830 (14.7)
        3–632 (13.2)4 (12.1)64 (8.8)179 (3.4)279 (4.5)
        Mean±SD1.0 (1.5)0.8 (1.2)0.7 (1.3)0.3 (0.8)0.4 (0.9)<0.001
    Cognitive function<0.001
        Normal139 (65.8)22 (54.3)407 (60.9)3559 (75.2)4127 (73.0)
        Mild18 (22.5)16 (40.3)201 (29.4)988 (19.6)1252 (21.0)
        Moderate/severe30 (11.7)1 (5.4)72 (9.6)271 (5.2)374 (5.9)
    Provider visits23.0±22.814.7±13.716.3±17.79.9±11.911.2±13.7<0.001
    Chronic conditions2.9±1.42.8±1.12.4±1.21.9±1.12.0±1.2<0.001

    Data are presented as median (95% CI), n (%), or mean±SD, unless otherwise indicated. ADL indicates activities of daily living.

    Characteristics of True-Positive and False-Negative Populations

    Respondents with true-positive findings (self-report of HF and ≥1 HF claim present) comprised 3.9% (n=216) of the total sample; respondents with false-negative findings (no self-report of HF and ≥1 HF claim) comprised 11.6% (Table 1). The proportion of whites was higher in the true-positive group than the false-negative group (92.5% versus 84.8%, P<0.001). Conversely, higher proportions of blacks and Hispanics were found in the false-negative group, although the number of Hispanics was small (true-positive blacks, row %, 2.9%; true-positive Hispanics, row %, 2.8%; false negative blacks, row %, 17.4%; false negative Hispanics, row %, 17.0%; not shown in Table 1). Although there were significant differences when comparing all 4 subgroups, there were no differences in the mean number of years of education, number of ADL limitations, or mean net worth between the true-positive and false-negative groups. There was no difference in the cognitive scores between the true-positive (normal, 65.8%; impaired, 34.2%) and false-negative (normal, 60.9%; impaired, 39.0%; P>0.10 in all between-group comparisons) groups. The true-positive group had a greater mean number of chronic conditions than the false-negative group (2.9 versus 2.4, P<0.001). Additionally, the true-positive group had more encounters with a health provider (23 versus 16.3, P<0.001). The true-positive group was more likely than the false-negative group to have ≥1 inpatient claim with a diagnosis of HF (63.6% versus 30.7%, P<0.001) (Table 2). Similarly, the true-positive group had more HF claims than the false-negative group (mean, 12.9 versus 5.4; P<0.001).

    Table 2. Claims-Based Characteristics of Sample Population of Older Adults Aged ≥67 Years With Health and Retirement Study Data Linked to Medicare Claims (n=5753)

    CharacteristicTrue Positive (n=216)False Positive (n=39)False Negative (n=680)True Negative (n=4818)TotalPValue
    Weighted no.830 649151 4752 465 04321 426 725
    Inpatient HF claim<0.001
        No78 (36.4)n/a472 (69.3)n/a550 (61.0)
        Yes138 (63.6)n/a208 (30.7)n/a346 (39.0)
    No. HF claims<0.001
        119 (7.7)n/a212 (31.2)n/a231 (25.3)
        2–334 (16.1)n/a180 (26.5)n/a214 (23.9)
        4–952 (24.2)n/a172 (25.8)n/a224 (25.4)
        ≥10111 (52.0)n/a116 (16.5)n/a227 (25.5)
        Mean±SD12.9±12.45.4±7.07.3 (9.3)<0.001

    Data are presented as n (%), unless otherwise indicated. HF indicates heart failure; n/a, not applicable.

    Sensitivity and Specificity of Self-Report of HF

    Of the 5753 respondents, 15.5% had a Medicare claim with an HF diagnosis during the 2-year period preceding the 2004 HRS interview (Table 3). Self-reported prevalence of HF in the 2004 interview was 4.6%. Interestingly, few respondents (15% of those self-reporting HF) overreported HF (false positive). Underreporting was much more common: Although 896 respondents had ≥1 HF diagnostic claim, 75% did not self-report the condition. Sensitivity of self-report of HF was 25.2%, with a specificity of 99.2%. The agreement between self-report of HF and claims for HF diagnosis was 87.7% (κ=0.34). We found no notable differences in test operating characteristics with use of other ICD-9-CM coding algorithms as the reference standard, such as use of 428 diagnosis codes as primary diagnosis, 428 codes as 1° and 2° diagnosis, or ≥2 Medicare claims.

    Table 3. Concordance of Self-Report of HF and Presence of One or More HF Diagnosis Claims

    Medicare HF DiagnosisNo Medicare HF DiagnosisTotal, n (%)
    Self-report HF, n21639255 (4.6)
    No self-report HF68048185498 (95.4)
    Total, n (%)896 (15.5)4857 (83.7)5753 (100)

    Data are presented as weighted percentages, unless otherwise indicated. Sensitivity, 25.2%; specificity, 99.2%; positive predictive value, 84.6%; negative predictive value, 87.8%; κ=0.34; agreement, 87.7%. HF indicates heart failure.

    Sociodemographic, Health Status, and Claims-Based Predictors Associated With Self-Report of HF

    The adjusted odds ratios (ORs) from the logistic regression models for the 896 respondents classified as either true positive or false negative are shown in Table 4. These models predict the likelihood of self-report of HF or, in other words, the predictors of being classified as a true positive (self-report of HF and ≥1 Medicare claim with HF within a 2-year period as the diagnosis). Respondents with more comorbidity were more likely to self-report HF as a chronic condition. The presence of >1 HF inpatient claim was associated with greater odds of self-report (OR, 1.92; 95% CI, 1.23–3.00). Those with ≥4 HF claims of any kind also had greater odds of self-reporting the condition (OR, 2.74; 95% CI, 1.36–5.52). The joint contribution of the number of HF claims and presence of an inpatient claim, as an indication of poor health status, significantly contributed to the model 3 (Wald χ2 test, 81.69; degrees of freedom, 4).

    Table 4. Logistic Regression Models of Self-Report of HF Among Those With Medicare HF Claims (n=896)

    Model 1Model 2Model 3
    Age0.99 (0.96–1.01)0.99 (0.96–1.01)0.98 (0.95–1.00)
    Race or ethnicity
        White/other (ref)
        Hispanic0.41 (0.15–1.13)0.34* (0.12–1.01)0.30* (0.11–0.83)
        Black0.37 (0.20–0.68)0.35 (0.19–0.64)0.28 (0.14–0.55)
        Male (ref)
        Female1.11 (0.77–1.60)0.97 (0.67–1.39)0.96 (0.63–1.46)
    Years of education
        0–110.97 (0.61–1.55)0.96 (0.57–1.63)0.90 (0.47–1.70)
        12 (ref)
        13–151.44 (0.88–2.35)1.49 (0.89–2.47)1.72 (0.93–3.16)
        ≥160.97 (0.59–1.59)0.94 (0.59–1.50)0.92 (0.51–1.66)
    Log (net worth)0.96 (0.93–0.99)0.96 (0.93–1.00)0.96 (0.93–1.00)
    No. of ADL limitations
        0 (ref)1.001.00
        1–21.55 (0.96–2.52)1.51 (0.91–2.49)
        3–61.52 (0.84–2.74)1.30 (0.70–2.41)
    Cognitive function
        Normal (ref)1.001.00
        Mild impairment0.71 (0.47–1.07)0.69 (0.45–1.06)
        Moderate/severe1.23 (0.79–1.93)1.20 (0.71–2.02)
    No. of chronic conditions
        0–1 (ref)1.001.00
        21.13 (0.71–1.79)1.15 (0.68–1.93)
        31.47 (0.87–2.48)1.57 (0.92–2.70)
        ≥41.96 (1.25–3.10)1.73 (0.93–3.22)
    Provider encounters
        0–3 (ref)1.001.00
        4–70.86 (0.39–1.90)0.74 (0.34–1.61)
        8–121.72 (0.93–3.16)1.28 (0.63–2.61)
        ≥132.26* (1.11–4.60)1.49 (0.71–3.13)
    Inpatient HF claim
        No (ref)1.00
        Yes1.92 (1.23–3.00)
    No. of HF claims
        1 (ref)1.00
        2–32.08 (0.97–4.47)
        4–92.74 (1.36–5.52)
        ≥108.15 (3.72–17.83)

    Data are presented as odds ratios (95% CIs), which are weighted and corrected for complex sampling design.

    HF indicates heart failure; ref, reference; log, log-transformed; ADL, activities of daily living.




    In all models, blacks were least likely to self-report HF compared with whites (final model 3 OR, 0.28; 95% CI, 0.14–0.55), even after controlling for number of health-provider encounters and poorer health status as measured by the presence of an inpatient HF claim and a greater number of HF claims. Similarly, a lower likelihood of self-reporting HF was noted for Hispanics compared with whites (final model 3 OR, 0.30; 95% CI, 0.11–0.83). The effect of the number of physician visits was decreased by including administrative claims and comorbidity burden as covariates in the final model. Net worth was a modest predictor of true-positive status in model 1 but had no significant effect in the final model. Sex, years of education, and cognitive function were not significant predictors of a true-positive HF diagnosis.


    Using nationally representative data from the HRS linked to Medicare claims, this study characterizes the predictors of self-report of HF, using a validated algorithm for HF diagnosis based on Medicare claims as a reference standard. In addition, we report the test operating characteristics of self-report of HF using the claims-based algorithm as the reference standard. The specificity of self-report of HF was 99%, with a false-positive rate of 1%. If a respondent reported having HF, then it was highly probable that the condition existed. However, the high false-negative rate (sensitivity, 25%) made it difficult to exclusively rely on self-report of the condition as an appropriate method for HF case finding. This study enhances the understanding of the strengths and limitations in use of self-report of HF alone. Additionally, this work further emphasizes the importance of future work to improve accuracy in identifying HF cases within population-based surveys.

    The agreement between self-report and claims for HF diagnosis found in this study (total agreement, 87.7%; κ=0.34) is consistent with prior work examining the concordance between self-report and the medical record. There is excellent concordance between claims and self-report data for other chronic conditions, such as diabetes and acute myocardial infarction. However, only fair concordance has been reported in ambulatory managed care medical records and patient self-report of HF (total agreement, 86%; κ=0.3).31

    The primary strength of this study is its use of linked HRS-Medicare claims data to investigate the predictors associated with underreporting of HF. The aim of this analysis was to identify factors associated with true-positive and false-negative reports by performing multivariate logistic regression to predict the likelihood of self-report of HF. Older adults were more likely to report HF if they had poorer health status suggested by the presence of inpatient claims, suggesting acute care delivery for HF and a greater number of total claims for which HF was a primary or secondary diagnosis. We also found that blacks and Hispanics were least likely to report having HF compared with whites. Even after controlling for the number of healthcare encounters with a provider, respondents from minority groups still had lower odds of self-reporting HF. Underreporting of HF in blacks is especially troubling given the disproportionate burden of HF in older blacks compared with whites.3237 HF is a condition that may be underrecognized in minority populations, despite having clinical encounters with healthcare providers during which HF is coded as a diagnosis.

    Several limitations of the present study warrant mention. Medicare claims data are a less-than-ideal reference standard measurement of HF. The validity of Medicare administrative data depends on many factors, including ICD-9-CM coding practices, organizational culture, coder experience, and the type of administrative file used.16 A particularly important bias is the financial incentive health systems have to code HF as a diagnosis. These claims could be upcoded for patients without HF or used for those with HF whose primary reason for the hospitalization or outpatient visit is another condition. The most likely effect was causing an overestimation of the HF case counts and compromising our accuracy in estimating concordance. Despite these limitations, the data describing the concordance between self-report of HF in older adults and Medicare claims-based diagnosis of HF are useful and provide us with an opportunity to further our HF case-finding methodological work. Ultimately, this will allow us to explore geriatric-specific and psychosocial outcomes in older adults with HF in ways that were previously limited with HRS survey data alone.

    We analyzed HRS-Medicare claims data for respondents aged ≥67 years, using 2 years of Medicare claims as described in a prior validated algorithm15; however, there may be bias associated with omitting those who were Medicare eligible at age 65 years. Medical records were not available for survey respondents with claims data to further adjudicate HF cases. Approximately 11% of the HRS respondents did not consent to the linkage of their survey data to Medicare claims. The sample did not include persons with Medicare health maintenance organization plans because their claims data were not accessible. These factors may introduce selection bias because the population enrolled in managed care programs greatly differs from that with traditional Medicare fee-for-service plans with respect to health and socioeconomic status.38 Our estimates are conservative because survey respondents represented by a proxy were excluded; that is, those respondents with the most-severe cognitive impairment were excluded from the analysis.

    The prevalence of HF based on self-report alone is ≈5%, an undercount of the true prevalence based on prior published work.3,5 Self-report of HF is predicted by white race and severity of HF as measured by number of HF claims, the use of inpatient care, and the degree of comorbidity burden. Respondent report of HF is an insensitive method for accurately identifying HF cases, especially in patients with less-severe disease and who are nonwhite. The results suggest that ≈2.5 million individuals aged ≥67 years with HF are missed if only true-positive self-reported cases are counted, including 266 000 blacks and 120 000 Hispanics. The present study suggests that caution should be exercised when self-report of HF is exclusively relied on for health services research. Furthermore, current claims-based algorithms to ascertain HF cases based on a previously validated method by Rector et al15 may not reliably capture a broad disease mix of HF cases or an ethnically diverse population. More research is needed to develop effective claims-based algorithms that allow for the study of heterogeneous (clinically and ethnically) HF populations within unique data sources, such as the HRS linked to Medicare claims data. Finally, this study suggests that there may be limited awareness of HF within older black and Hispanic populations despite having clinical encounters with healthcare providers during which HF is coded as a diagnosis.

    Sources of Funding

    The National Institute on Aging (NIA) provides funding for the Health and Retirement Study (U01 AG09740), which is performed at the Institute for Social Research, University of Michigan. Dr Gure was supported by an NIA Diversity Supplement award (R01 AG027010–02S1). Drs Gure and Cigolle were supported by the John A. Hartford Foundation Center of Excellence, the NIA Claude D. Pepper Center Research Career Development Core, and the National Center for Research Resources (UL1 RR024986). Drs Cigolle and Blaum were supported by the Ann Arbor VA Geriatric Research, Clinical and Education Center. Dr Cigolle was supported by a Mentored Clinical Scientist Research Career Development Award (K08 AG031837). Dr Koelling was supported by Medtronic, Inc, and MESPERE, Inc. Dr Langa was supported by NIA grants R01 AG027010 and R01 AG030155. The content is solely the responsibility of the authors and does not necessarily represent the official views of National Center for Research Resources or the National Institutes of Health.


    Dr Koelling received research funding from Medtronic, Inc, and MESPERE, Inc. He received an honorarium from Abbott Vascular and served as an expert witness for a myocardial infarction case.


    An earlier version of this work was presented at the American Geriatrics Society National Meeting, May 14, 2010.

    Correspondence to Tanya R. Gure, MD,
    300 North Ingalls Bldg, Room 901, Ann Arbor, MI 48109-2007
    . E-mail


    • 1. Gill TM, Allore HG, Holford TR, Guo Z. Hospitalization, restricted activity, and the development of disability among older persons. JAMA. 2004; 292: 2115– 2124. CrossrefMedlineGoogle Scholar
    • 2. Wolinsky FD, Overhage JM, Stump TE, Lubitz RM, Smith DM. The risk of hospitalization for congestive heart failure among older adults. Med Care. 1997; 35: 1031– 1043. CrossrefMedlineGoogle Scholar
    • 3. Kitzman DW, Gardin JM, Gottdiener JS, Arnold A, Boineau R, Aurigemma G, Marino EK, Lyles M, Cushman M, Enright PL; Cardiovascular Health Study Research Group. Importance of heart failure with preserved systolic function in patients ≥65 years of age. CHS Research Group Cardiovascular Health Study. Am J Cardiol. 2001; 87: 413– 419. CrossrefMedlineGoogle Scholar
    • 4. Jessup M, Abraham WT, Casey DE, Feldman AM, Francis GS, Ganiats TG, Konstam MA, Mancini DM, Rahko PS, Silver MA, Stevenson LW, Yancy CW, Hunt SA, Chin MH. 2009 focused update: ACCF/AHA guidelines for the diagnosis and management of heart failure in adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation. 2009; 119: 1977– 2016. LinkGoogle Scholar
    • 5. Gillum RF. Epidemiology of heart failure in the United States. Am Heart J. 1993; 126: 1042– 1047. CrossrefMedlineGoogle Scholar
    • 6. Gure TR, Kabeto MU, Blaum CS, Langa KM. Degree of disability and patterns of caregiving among older Americans with congestive heart failure. J Gen Intern Med. 2008; 23: 70– 76. CrossrefMedlineGoogle Scholar
    • 7. Braunstein JB, Anderson GF, Gerstenblith G, Weller W, Niefeld M, Herbert R, Wu AW. Noncardiac comorbidity increases preventable hospitalizations and mortality among Medicare beneficiaries with chronic heart failure. J Am Coll Cardiol. 2003; 42: 1226– 1233. CrossrefMedlineGoogle Scholar
    • 8. Wolff JL, Starfield B, Anderson G. Prevalence, expenditures, and complications of multiple chronic conditions in the elderly. Arch Intern Med. 2002; 162: 2269– 2276. CrossrefMedlineGoogle Scholar
    • 9. Fonarow GC, Abraham WT, Albert NM, Stough WG, Gheorghiade M, Greenberg BH, O'Connor CM, Pieper K, Sun JL, Yancy CW, Young JB; OPTIMIZE-HF Investigators and Hospitals. Influence of a performance-improvement initiative on quality of care for patients hospitalized with heart failure: results of the Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients With Heart Failure (OPTIMIZE-HF). Arch Intern Med. 2007; 167: 1493– 1502. CrossrefMedlineGoogle Scholar
    • 10. Curtis LH, Greiner MA, Hammill BG, Kramer JM, Whellan DJ, Schulman KA, Hernandez AF. Early and Long-term outcomes of heart failure in elderly persons, 2001–2005. Arch Intern Med. 2008; 168: 2481– 2488. CrossrefMedlineGoogle Scholar
    • 11. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009; 360: 1418– 1428. CrossrefMedlineGoogle Scholar
    • 12. Kosiborod M, Lichtman JH, Heidenreich PA, Normand S-LT, Wang Y, Brass LM, Krumholz HM. National trends in outcomes among elderly patients with heart failure. Am J Med. 2006; 119: 616.e1– 616.e7. CrossrefGoogle Scholar
    • 13. Bueno H, Ross JS, Wang Y, Chen J, Vidán MT, Normand SL, Curtis JP, Drye EE, Lichtman JH, Keenan PS, Kosiborod M, Krumholz HM. Trends in length of stay and short-term outcomes among Medicare patients hospitalized for heart failure, 1993–2006. JAMA. 2010; 303: 2141– 2147. CrossrefMedlineGoogle Scholar
    • 14. Bowlin SJ, Morrill BD, Nafziger AN, Lewis C, Pearson TA. Reliability and changes in validity of self-reported cardiovascular disease risk factors using dual response: the behavioral risk factor survey. J Clin Epidemiol. 1996; 49: 511– 517. CrossrefMedlineGoogle Scholar
    • 15. Rector TS, Wickstrom SL, Shah M, Thomas Greenlee N, Rheault P, Rogowski J, Freedman V, Adams J, Escarce JJ. Specificity and sensitivity of claims-based algorithms for identifying members of Medicare plus Choice health plans that have chronic medical conditions. Health Serv Res. 2004; 39: 1839– 1857. CrossrefMedlineGoogle Scholar
    • 16. Quach S, Blais C, Quan H. Administrative data have high variation in validity for recording heart failure. Canadian journal of cardiology. 2010; 26: 306– 312. CrossrefMedlineGoogle Scholar
    • 17. Bersamin A, Stafford RS, Winkleby MA, Bersamin A, Stafford RS, Winkleby MA. Predictors of hypertension awareness, treatment, and control among Mexican American women and men. J Gen Intern Med. 2009; 24: 521– 527. CrossrefMedlineGoogle Scholar
    • 18. Huttin C, Moeller JF, Stafford RS. Patterns and costs for hypertension treatment in the United States: clinical, lifestyle and socioeconomic predictors from the 1987 National Medical Expenditures Survey. Clin Drug Invest. 2000; 20: 181– 195. CrossrefGoogle Scholar
    • 19. Ostchega Y, Dillon CF, Hughes JP, Carroll M, Yoon S. Trends in hypertension prevalence, awareness, treatment, and control in older US adults: data from the National Health and Nutrition Examination Survey 1988 to 2004. J Am Geriatr Soc. 2007; 55: 1056– 1065. CrossrefMedlineGoogle Scholar
    • 20. Ostchega Y, Hughes JP, Wright JD, McDowell MA, Louis T. Are demographic characteristics, health care access and utilization, and comorbid conditions associated with hypertension among US adults?American J Hypertens. 2008; 21: 159– 165. CrossrefMedlineGoogle Scholar
    • 21. Sudman S, Bradburn NM. Response Effects in Surveys. Chicago, IL: Aldine; 1974.Google Scholar
    • 22. Andersen RM, Kasper J, Frankel MR. Total Survey Error: Applications To Improve Health Surveys. San Francisco, CA: Jossey-Bass; 1979.Google Scholar
    • 23. Myers GC, Juster FT, Suzman RM. Asset and Health Dynamics Among the Oldest Old (AHEAD): initial results from the longitudinal study. Introduction. J Gerontol B Psychol Sci Soc Sci. 1997; 52: v– viii. CrossrefMedlineGoogle Scholar
    • 24. Birman-Deych E, Waterman AD, Yan Y, Nilasena DS, Radford MJ, Gage BF. Accuracy of ICD-9-CM codes for identifying cardiovascular and stroke risk factors. Med Care. 2005; 43: 480– 485. CrossrefMedlineGoogle Scholar
    • 25. Plassman BL, Newman TT, Welsh KA, Helms M, Breitner JCS. Properties of the telephone interview for cognitive status: application in epidemiologic and longitudinal studies. Neuropsychiatr Neuropsychol Behav Neurol. 1994; 7: 235– 241. Google Scholar
    • 26. Welsh KA, Breitner JCS, Magruderhabib KM. Detection of dementia in the elderly using telephone screening of cognitive status. Neuropsychiatr Neuropsychol Behav Neurol. 1993; 6: 103– 110. Google Scholar
    • 27. de Jager CA, Budge MM, Clarke R. Utility of TICS-M for the assessment of cognitive function in older adults. Int J Geriatr Psychiatry. 2003; 18: 318– 324. CrossrefMedlineGoogle Scholar
    • 28. Langa KM, Kabeto MU, Weir D; Alzheimer's Association. Percentage of Americans aged 55 and older with selected diseases by race/ethnicity and cognitive status, Health and Retirement Study, 2006. 2010 Alzheimer's disease facts and figures. Alzheimers Dement. 2010; 6: 56. Google Scholar
    • 29. Langa KM, Plassman BL, Wallace RB, Herzog AR, Heeringa SG, Ofstedal MB, Burke JR, Fisher GG, Fultz NH, Hurd MD, Potter GG, Rodgers WL, Steffens DC, Weir DR, Willis RJ. The aging, demographics, and memory study: Study design and methods. Neuroepidemiol. 2005; 25: 181– 191. CrossrefMedlineGoogle Scholar
    • 30. Turner CF, Smith TK, Fitterman LK, Reilly T, Pate K, Witt MB, McBean AM, Lessler JT, Forsyth BH. The quality of health data obtained in a new survey of elderly Americans: a validation study of the proposed Medicare Beneficiary Health Status Registry (MBHSR). J Gerontol B Psychol Sci Soc Sci. 1997; 52: S49– S58. CrossrefMedlineGoogle Scholar
    • 31. Tisnado DM, Adams JL, Liu H, Damberg CL, Chen W-P, Hu FA, Carlisle DM, Mangione CM, Kahn KL. What is the concordance between the medical record and patient self-report as data sources for ambulatory care?Med Care. 2006; 44: 132– 140. CrossrefMedlineGoogle Scholar
    • 32. Alexander M, Grumbach K, Remy L, Rowell R, Massie BM. Congestive heart failure hospitalizations and survival in California: patterns according to race/ethnicity. Am Heart J. 1999; 137: 919– 927. CrossrefMedlineGoogle Scholar
    • 33. Alexander M, Grumbach K, Selby J, Brown AF, Washington E. Hospitalization for congestive heart failure. Explaining racial differences. JAMA. 1995; 274: 1037– 1042. CrossrefMedlineGoogle Scholar
    • 34. Dries DL, Exner DV, Gersh BJ, Cooper HA, Carson PE, Domanski MJ. Racial differences in the outcome of left ventricular dysfunction. New Engl J Med. 1999; 340: 609– 616. CrossrefMedlineGoogle Scholar
    • 35. Loehr LR, Rosamond WD, Chang PP, Folsom AR, Chambless LE. Heart failure incidence and survival (from the Atherosclerosis Risk in Communities study). Am J Cardiol. 2008; 101: 1016– 1022. CrossrefMedlineGoogle Scholar
    • 36. Bahrami H, Kronmal R, Bluemke DA, Olson J, Shea S, Liu K, Burke GL, Lima JA. Differences in the incidence of congestive heart failure by ethnicity: the multi-ethnic study of atherosclerosis. Arch Intern Med. 2008; 168: 2138– 2145. CrossrefMedlineGoogle Scholar
    • 37. Kalogeropoulos A, Georgiopoulou V, Kritchevsky SB, Psaty BM, Smith NL, Newman AB, Rodondi N, Satterfield S, Bauer DC, Bibbins-Domingo K, Smith AL, Wilson PW, Vasan RS, Harris TB, Butler J. Epidemiology of incident heart failure in a contemporary elderly cohort: the health, aging, and body composition study. Arch Intern Med. 2009; 169: 708– 715. CrossrefMedlineGoogle Scholar
    • 38. Shimada SL, Zaslavsky AM, Zaborski LB, O'Malley AJ, Heller A, Cleary PD. Market and beneficiary characteristics associated with enrollment in Medicare managed care plans and fee-for-service. Med Care. 2009; 47: 517– 523. CrossrefMedlineGoogle Scholar