Abstract
Objectives
To determine the extent to which disease-related symptoms and impairments, which constitute measures of disease severity or targets of therapy, account for the associations between chronic diseases and universal health outcomes.
Design
Cross-sectional
Setting
Cardiovascular Health Study (CHS) and Health ABC.
Participants
5,654 CHS, and 2,706 Health ABC, members.
Measurements
Diseases included heart failure (HF), chronic obstructive pulmonary disease (COPD), osteoarthritis, and cognitive impairment. The universal health outcomes included self-rated health, basic and instrumental activities of daily living (BADLs-IADLs), and death. Disease-related symptoms/impairments included HF symptoms and ejection fraction (EF) for HF; Dyspnea Scale and FEV1 for COPD; joint pain for osteoarthritis, and executive function for cognitive impairment.
Results
The diseases were associated with the universal health outcomes (p<0.001) except osteoarthritis with death (both cohorts) and cognitive impairment with self-rated health (Health ABC). Symptoms/impairments accounted for ≥30% of each disease’s effect on the universal health outcomes. In CHS, for example, HF, compared with no HF, was associated with one fewer (0.918) BADLs-IADL performed without difficulty; 27% of this effect was accounted for by HF symptoms, only 5% by EF. The hazard ratio for death with HF was 6.5 (95% CI, 4.7, 8.9) with 40% accounted for by EF and only 14% by HF symptoms.
Conclusion
Disease-related symptoms/impairments accounted for much of the significant associations between the 4 chronic diseases and the universal health outcomes. Results support considering universal health outcomes as common metrics across diseases in clinical decision-making, perhaps by targeting the disease-related symptoms/impairments that contribute most strongly to the effect of the disease on the universal health outcomes.
Keywords: chronic diseases, universal health outcomes, patient-reported outcomes, clinical decision-making
INTRODUCTION
Clinical decisions currently are aimed at diagnosing and treating individual diseases. Within each disease, treatments may be targeted at different disease-related symptoms, impairments or therapeutic endpoints, increasingly with multiple pharmacologic and nonpharmacologic therapies.1–3 As the number of diseases and treatment options escalate, decision-making can be confusing and burdensome.1–3 This concern is particularly relevant for older adults, the majority of whom experience multiple chronic diseases.4–6 Improving the effectiveness, safety, and efficiency of decision-making in patients with chronic diseases requires determining how individual diseases, and their treatments, contribute to overall health. One method for doing this would be to focus decision-making on a shared set of health outcomes – cross-disease or universal health outcomes - that are affected by most chronic diseases. Determining the effect of treatments on these universal health outcomes would facilitate the identification of those treatments with the greatest overall effects on patients’ health.
Previous work supports the existence of universal health outcomes. In addition to survival, an obvious universal health outcome, there is an extensive literature describing health-related quality of life (HRQOL), quality adjusted life years (QALYs), and other patient-reported outcomes (PROs). Some of these are summary measures that incorporate multiple domains; others address a single domain such as affect or physical function.7–10 A large body of research supports an association between individual chronic diseases and these cross-disease, universal health outcomes.11–21 Older adults volunteer that they are concerned about specific diseases because of their effect on these cross-disease, universal health outcomes.22 While there is as yet no single set of universal health outcomes, activities of daily living functioning, self-rated health, and survival are examples of outcomes that have been widely used in clinical research and are often included in disease guidelines.
Despite a compelling body of work linking chronic diseases with HRQOL, activities of daily living, self-rated health, and other PROs, they have not been broadly or systematically incorporated into clinical practice or clinical decision-making. At least one factor that may explain this lack of inclusion is that studies have not yet demonstrated whether diseases are linked to universal health outcomes through their effect on disease-related symptoms or impairments. This linkage is important to establish because disease-related symptoms/impairments, the usual therapeutic targets or measures of disease severity, are the focus of disease-specific decision-making. If disease-related symptoms/impairments account for much of the effect of chronic diseases on universal health outcomes, and patients endorse the central importance of universal health outcomes, then the focus of clinical decision-making might shift to considering the effect of disease-related symptoms and impairments - and their treatments - on universal health outcomes. As an initial step in determining the appropriateness of such a shift, we determined whether and to what extent disease-related symptoms and impairments accounted for significant associations between chronic diseases and universal health outcomes. We studied four common chronic conditions and three representative universal health outcomes.
METHODS
Study Populations
The study population included two cohorts of older adults, the Cardiovascular Health Study (CHS) and the Health, Aging, and Body Composition Study (Health ABC). We chose to study two cohorts to assess the robustness of results. We chose these two community-based cohorts because they have a wealth of disease-related symptom and impairment data. The CHS cohort was recruited from a gender and age-stratified (65–69, 70–74, 75–79, ≥ 80) random sample of Medicare eligible individuals in Forsyth County, North Carolina; Sacramento County, California; Washington County, Maryland, and Allegheny County (Pittsburgh), Pennsylvania.23 Eligibility criteria included age ≥65 years, not living in an institution, expected to remain in the area for 3 years and capable of giving informed consent. Participants needing a wheelchair or receiving hospice care, radiation treatment, or chemotherapy were excluded. The initial sample of 5,201 participants, recruited from 1989 to 1990, was enriched with the addition of 687 African American men and women meeting the same eligibility criteria who were recruited from 1992 to 1993, for a combined cohort of 5888 participants. The 5654 cohort members with data for at least one of our analytical models were included in the current study. Health ABC is a prospective cohort study involving all black and a random sample of white Medicare-eligible community-dwelling individuals living in Memphis, Tennessee or Pittsburgh, Pennsylvania who were aged 70–79 years at recruitment.24 Eligible participants reported no life-threatening cancers or difficulty walking one-quarter of a mile, climbing 10 steps, or performing activities of daily living (BADL-IADLs) at baseline. Of 3,075 participants, 2,733 (89%) had a year 5 (2001–2002) assessment; the 2,706 participants with data for at least one of our analytical models were included in the current study. The cohorts were followed every six months. All participants provided informed consent; respective Institutional Review Board approvals were received. The study was approved by the Yale School of Medicine’s Human Investigation Committee.
Data
Socio-demographic, functional, and health data were obtained from person-level interviews and clinical examinations. Information on diseases was collected via self-report and medical record abstraction for both cohorts and Medicare claims data for CHS. The CHS data were from the publicly-released files.
Chronic diseases of interest
The four chronic diseases, chosen because they are common in older adults and account for a large amount of morbidity, included heart failure (HF), chronic obstructive pulmonary disease (COPD), osteoarthritis, and cognitive impairment/dementia. Adjudicated algorithms were used to define HF in both cohorts.25,26 The combination of self-report and claims data was used for COPD (chronic bronchitis or emphysema) in CHS; self-report, medications, and medical records were used for Health ABC. Self-reported medical provider diagnosis alone was used for osteoarthritis because osteoarthritis is poorly reported in claims data. Cognitive impairment was defined in CHS by any claims data for dementia or a modified Mini-Mental State Exam (3MS) score that was at least 1.5 standard deviations (SD) below the strata mean for education (less than high school; at least high school) and race (Black; Other), criteria with a high specificity for dementia.27,28 Medical record data or 3MS score was used for Health ABC.
Disease-related symptoms or impairments
The symptoms (i.e. subjective complaints) and impairments (i.e. physiological or other derangements such as ejection fraction) chosen for study are those considered to result from the disease and represent therapeutic targets and/or measures of disease severity. For the CHS cohort, disease-related symptoms/impairments for HF included the CHS HF symptom score;29 and ejection fraction (EF). HF-related impairments were not available in Health ABC. The candidate COPD-related impairments included the American Thoracic Society Dyspnea Scale30 and spirometry measures which included forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and FEV1/FVC. The candidate osteoarthritis impairments included total number of pain sites and site-specific pain (upper extremity, lower extremity, and back). The Digit Symbol Substitution Test (DSST), a measure of processing speed and executive function,31 was the cognition-related impairment. Short term memory was also considered but was not included because it was assessed with delayed recall as part of the 3MS, the test used to define cognitive impairment.
Universal health outcomes
The three representative universal health outcomes included self-rated health, basic and instrumental activities of daily living (BADL-IADL) functioning or disability, and death. Self-rated health was included because it represents a simple and reliable measure of self-perceived overall health. Functional disability and death represent widely accepted health outcomes.7–11 The 5-level self-reported health measure ranged from 1 = excellent to 5 = poor in both cohorts. BADL-IADL functioning, a commonly used measure of disability, was assessed with a scale that ranged from 0–12 in CHS and 0–9 in Health ABC based on the number of BADLs and IADLs with which the participant reported having difficulty or could not perform.32,33 The 12 activities included in CHS included walking, transferring (out of bed), eating, toileting, dressing, bathing, light housework, and heavy housework, shopping, preparing meals, paying bills, and using the phone. The activities in Health ABC were similar except eating, toileting, paying bills, and using the phone were not asked but any of working, volunteering, or providing care was asked.
Death was ascertained primarily by interviews with the next of kin.
Descriptive and covariate data
The covariates included age, gender, race, education, smoking status, social support, depressive symptoms assessed with the Center for Epidemiologic Studies Depression (CES-D) Scale (10 items, range 0–30),34 and comorbidity. Comorbidity was measured using the Functional Comorbidity Scale which includes the following 18 conditions, arthritis, osteoporosis, asthma, COPD, angina, HF, myocardial infarction, neurological disease, stroke, peripheral vascular disease, diabetes, upper gastrointestinal disease, depression, anxiety, visual impairment, hearing impairment, degenerative disc disease, and obesity.35 Cardiovascular diseases, COPD, dementia, and arthritis were excluded for the relevant analyses.
Analysis
Because the relationships of interest are likely to be strongest contemporaneously, cross sectional analyses were performed, except for the outcome of death. The most thorough ascertainment of disease-related impairments for the original CHS cohort was baseline for HF and COPD and first follow-up for osteoarthritis and cognitive impairment; in the African American cohort, first follow-up was used for COPD, cognitive impairment, and osteoarthritis and second follow-up for HF. For Health ABC, Years 5–6 had the best available symptom, impairment, and universal outcome data. For both cohorts, death was ascertained over the two years following the disease ascertainment.
We used multiple linear regression for the analysis of continuous universal health outcomes to quantify each disease’s association with a universal health outcome and then to assess how much of this association could be attributed to the disease-related symptoms and impairments. Correlations between candidate disease-related symptoms and impairments were determined. The correlation between FEV1 and ATS Dyspnea scale was .22; all other correlations were <.20. Multicollinearity was assessed by calculating variance inflation factors for all variables included in the regression models. Variables with values > 5 were excluded from the regression models.
In the multiple regression models, the universal health outcome was the dependent variable. The independent variables were the disease status (1 = present, 0 = absent), the disease-related impairments, and the covariates. Models were fitted hierarchically to the data in the following sequence: 1) disease status alone; 2) disease status adjusted for disease-related impairments, singly and then in combination; and 3) disease status adjusted for disease-related impairments and covariates. To estimate the amount of the association between a disease and a universal health outcome that was accounted for by the disease-related impairments, we calculated the percent change in the regression coefficients for disease status between the models unadjusted for disease-related impairments (β) and the adjusted model (β*). Specifically, percent change was calculated as [(β − β*)/β] × 100%, so that positive values represent the amount of a disease effect on the universal health outcome attributed to the impairment. The variance of the percent change was estimated using the Delta method and was used to assess its significance (i.e., percent change/standard error is approximately normally distributed).34 Regression diagnostics were used to assess the fit of the multiple regression models.
Time to death was analyzed by the Cox model using the same approach as for the multiple regression models. The percent change in the hazard ratio and its variance were calculated as above. The proportional hazard assumption was examined in the Cox models by including time by covariate interaction terms. Model fit was evaluated by graphical techniques and examination of residuals.
RESULTS
Characteristics of the cohorts are presented in Table 1. Compared with the CHS cohort, the Health ABC cohort was older and had a higher percentage of Blacks. The mean age of the CHS cohort was 72.8 (SD 5.6) years; 57.5% were female; and 14.4% were Black. The mean age of the Health ABC at Year 5 was 77.6 (SD 2.9) years; 53% were female; and 39.8% were Black. With the exception of more BADL-IADL difficulties and a lower prevalence of osteoarthritis in Health ABC, the two cohorts were similar in results for the universal health outcomes and in prevalence of the chronic diseases.
Table 1.
Characteristics of the Two Cohorts*
| Characteristics | Cardiovascular Health Study (CHS) N=5,654† |
Health ABC Study (ABC) N=2,706† |
|---|---|---|
| Age, mean (SD) | 72.8 (5.6) | 77.6 (2.9). |
| Female, n (%) | 3,249 (57.5) | 1,435 (53.0) |
| Black, n (%) | 815 (14.4) | 1,077 (39.8) |
| Less than high school education, n (%) | 1,640 (29.0) | 652 (24.1) |
| Married, n (%) | 3,750 (66.4) | 1,456 (54.2) |
| Current smoker, n (%) | 673 (11.9) | 246 (9.1) |
| Social support, mean (SD) | 8.3 (2.6) | 8.4 (1.7) |
| Depressive symptoms, mean (SD) | 4.7 (4.6) | 5.0 (4.4) |
| Functional comorbidity index score, mean (SD) | 2.3 (1.8) | 3.1 (1.9) |
| No. BADLS-IADLS does with difficulty or does not perform, mean (SD) | 0.5 (1.1) | 1.6 (1.6) |
| Self-reported health, mean (SD) | 2.8 (1.0) | 2.8 (1.0) |
| Died over two years follow-up n (%) | 282 (5.1) | 217 (8.2) |
| Heart failure, n (%) | 262 (4.6) | 141 (5.3) |
| COPD, n (%) | 608 (10.8) | 288 (10.7) |
| Osteoarthritis, n (%) | 3,344 (59.2) | 1,073 (40.0) |
| Cognitive impairment, n (%) | 419 (7.9) | 201 (7.7) |
BADL-IADL, basic and instrumental activities of daily living; COPD, chronic obstructive pulmonary disease
The Cardiovascular Health Study cohort includes both the original and the African American cohort as described in the Methods. Characteristics are at baseline for the original cohort and the first follow-up for the African-American cohort for the Cardiovascular Health Study and at Year 5–6 for the Health ABC cohort.
N reflects the number of participants included in at least one analysis model.
The associations between HF and the three universal health outcomes and the contribution of HF-related symptoms and EF to these associations are displayed in Table 2. HF was significantly associated with the three universal outcomes in both cohorts. The percentage of the associations accounted for by disease-related symptoms and EF in CHS varied for the universal health outcomes (Table 2). For example, HF symptoms accounted for 25% of the effect of HF on self-rated health, 27% on BADL-IADL functioning, but did not account for the association with death. Conversely, EF accounted for a marginally statistically significant 40% of the association of HF with death but did not contribute to the association with self-rated health or BADL-IADL functioning. The HF-related symptoms and EF were unavailable Health ABC.
Table 2.
Amount of Association between Heart Failure and Universal Health Outcomes Accounted for by Disease-related Symptoms and Impairments in Older Adults
| Universal health outcome modelsa | Cardiovascular Health Study | Health ABC | |
|---|---|---|---|
| Effect of HF on UO | % of HF effect on UO contributed by symptoms/impairment(s) | Effect of HF on UO | |
| β (SE)†, P-Value | (SE)‡, P-Value | β (SE)†, P-Value | |
| Self-rated health (excellent to poor) | |||
| HF alone | .946 (.066), P<.001 | --- | .426 (.089), P<.001 |
| HF + HF symptoms§ | .709 (.066) | 25% (8.4%), P=.003 | Not available |
| HF + EF¶ | .852 (.069) | 10% (9.2%), P=.28 | Not available |
| HF + HF symptoms + EF | .627 (.068) | 34% (8.3%), P<.001 | Not available |
| HF + HF symptoms + EF + covariates # | .489 (.063) | 48% (7.4%), P<.001 | Not available |
| No. BADL-IADLs not performed or performed with difficulty | |||
| HF alone | .918 (.073), P<.001 | --- | .449 (.147), P=.002 |
| HF + HF symptoms | .670 (.073) | 27% (9.3%), P=.004 | Not available |
| HF + EF | .869 (.076) | 5% (10.4%), P=.63 | Not available |
| HF + HF symptoms + EF | .631 (.076) | 31% (9.4%), P=.001 | Not available |
| HF + HF symptoms + EF + covariates | .493 (.071) | 46% (8.5%), P<.001 | Not available |
| Death over two years** | HR (95% CI) | ||
| HF alone | 6.5 (4.7, 8.9), P<.001 | --- | 3.0 (1.9, 4.7), P<.001 |
| HF + HF symptoms | 5.6 (4.0, 7.9) | 14% (28.7%), P=.63 | Not available |
| HF + EF | 3.9 (2.7, 5.6) | 40% (22.8%), P=.08 | Not available |
| HF + HF symptoms + EF | 3.4 (2.3, 5.0) | 47% (20.7%), P=.02 | Not available |
| HF + HF symptoms + EF + covariates | 2.9 (2.0, 4.2) | 55% (18.1%), P=.002 | Not available |
Abbreviations: SE, Standard Error; HR, hazard ratio; CI, confidence intervals; BADL-IADL, basic and instrumental activities of daily living; CHS, Cardiovascular Health Study; EF, ejection fraction; HF, heart failure; UO, universal health outcome
Two year death estimates are from Cox models; other estimates are from cross sectional regression models. Heart failure-related symptoms and impairments were not available in Health ABC. Self-rated health ranged from 1 = excellent to 5 = poor. BADL-IADL functioning was the number of basic and instrumental ADLs that participants reported having difficulty with or were unable to perform (range, 0 to 12 in CHS; 0–9 in Health ABC).
The initial Beta (β) coefficient for each universal health outcome can be interpreted as the effect of the disease on the universal health outcome; i.e. the difference in the universal health outcome among those with vs. without heart failure. For example, for CHS, the β coefficient for BADL-IADL functioning is .918 meaning that persons with HF do not do, or perform with difficulty, almost one more BADL-IADL than those without HF.
This is the percent change in the regression coefficient for heart failure when the disease-related symptoms/impairments and the covariates are added to the model. The percentage change can be interpreted as the amount of the effect of heart failure on the universal health outcomes that is contributed by the heart failure-related symptoms/impairment(s).
CHS heart failure symptom score (0 to 3) - sleeping with pillows, awakening with shortness of breath, and swelling of ankles.
Ejection fraction categorized by CHS investigators as ≥ 55%; 45–54%; <45%.
Covariates included age, gender, race, social support, education, smoking, depressive symptoms, and Functional Comorbidity Index score with cardiovascular diseases removed.
Hazard ratios and 95% confidence intervals for death over two years.
The associations between COPD, osteoarthritis, and cognitive impairment and the universal health outcomes, and the contribution of the disease-related symptoms/impairments to these associations, are displayed in Tables 3–5. Dyspnea contributed over 50% of COPD’s effect on self-rated health and BADL-IADLs and a borderline significant 33% percent of COPD’s effect on death (Table 3). Although not reaching statistical significance, FEV1 accounted for 20–30% of COPD’s effect on each universal health outcome except for BADL-IADLs in Health ABC. Osteoarthritis was not associated with an increased risk of death (Table 4). Pain accounted for 30–70% of the effect of osteoarthritis on self-rated health and BADL-IADLs (Table 4). Cognitive impairment had a strong association with BADL-IADLs and death in both cohorts (Table 5). DSST accounted for 44% to 95% of these effects. This achieved borderline statistical significance (p=.07).
Table 3.
Amount of Association between COPD and Universal Health Outcomes Accounted for by Disease-related Symptoms and Impairments in Older Adults
| Universal health outcome models* | Cardiovascular Health Study | Health ABC | ||
|---|---|---|---|---|
| Effect of COPD on UO | % of COPD effect on UO contributed by symptoms/impairment(s) | Effect of COPD on UO | % of COPD effect on UO contributed by symptoms/impairment(s) | |
| β (SE)†, P-Value | (SE), P-Value‡ | β (SE), P-Value | (SE), P-Value | |
| Self-rated health (excellent to poor) | ||||
| COPD alone | .556 (.045), P<.001 | --- | .393 (.069), P<.001 | --- |
| COPD + ATS dyspnea scale§ | .258 (.044) | 54% (6.5%), P<.001 | .142 (.068) | 64% (11.0%), P<.001 |
| COPD + FEV1¶ | .438 (.046) | 21% (6.9%), P=.002 | .274 (.070) | 30% (11.4%), P=.009 |
| COPD + ATS dyspnea + FEV1 | .215 (.045) | 61% (6.6%), P<.001 | .089 (.069) | 77% (11.1%), P<.001 |
| COPD + ATS dyspnea + FEV1 + covariates# | .176 (.042) | 68% (6.1%), P<.001 | .016 (.066) | 96% (10.6%), P<.001 |
| No. BADL-IADLs not performed or performed with difficulty | ||||
| COPD alone | .411 (.046), P<.001 | --- | .651 (.115), P<.001 | --- |
| COPD + ATS dyspnea scale | .119 (.045) | 71% (7.2%), P<.001 | .320 (.115) | 51% (16.0%), P=.001 |
| COPD + FEV1 | .283 (.047) | 31% (7.6%), P<.001 | .601(.117) | 8% (17.3%), P=.64 |
| COPD + ATS dyspnea+ FEV1 | .065 (.045) | 84% (7.2%), P<.001 | .328(.117) | 50% (16.3%), P=.002 |
| COPD + ATS dyspnea + FEV1 + covariates | .084 (.043) | 80% (6.8%), P<.001 | .190 (.117) | 71% (16.0%), P<.001 |
| Death over two years** | HR (95% Cl) | |||
| COPD alone | 3.1 (2.2, 4.2), P<.001 | --- | 2.2 (1.4, 3.6), P=.001 | --- |
| COPD + ATS dyspnea scale | 2.0 (1.5, 2.9) | 33% (19.2%), P=.09 | 1.8 (1.1, 3.0) | 19% (34.9%), P=.59 |
| COPD + FEV1 | 2.4 (1.8, 3.4) | 21% (20.8%), P=.31 | 1.8 (1.1, 2.9) | 20% (34.3%), P=.56 |
| COPD + ATS dyspnea + FEV1 | 1.8 (1.3, 2.6) | 40% (18.7%), P=.03 | 1.5 (0.9, 2.6) | 30% (34.5%), P=.38 |
| COPD + ATS dyspnea + FEV1 + covariates | 1.6 (1.1, 2.3) | 48% (17.8%), P=.007 | (0.6, 1.9) | 52% (35.0%), P=.14 |
Abbreviations: SE, Standard Error; CI, confidence intervals; ATS, American Thoracic Society; BADL-IADL, basic and instrumental activities of daily living; CHS, Cardiovascular Health Study; COPD, chronic obstructive pulmonary disease; FEV1, Forced expiratory volume in one second; HR, hazard ratio; UO, Universal health outcome.
Two year death estimates are from Cox models; other estimates are from cross sectional regression models. Self-rated health ranged from 1 = excellent to 5 = poor. BADL-IADL functioning was the number of basic and instrumental activities of daily living that participants reported having difficulty with or were unable to perform (range, 0 to 12 in CHS and 0 to 9 in Health ABC).
The initial Beta (β) coefficient for each universal health outcome can be interpreted as the difference for those with vs. without COPD. For example, the β coefficient for BADL-IADL functioning in CHS is 0.411 meaning that persons with COPD do not do, or perform with difficulty, almost one half more ADL than those without COPD.
This is the percent change in the regression coefficient for COPD when the disease-related symptoms/impairments and the covariates are added to the model. The percentage change can be interpreted as the amount of the effect of COPD on the universal outcome that is contributed by the COPD-related symptoms/impairment(s).
ATS dyspnea scale used in CHS (0 = no dyspnea to 5 = too breathless to leave the house or breathless on dressing). A modified 0–3 dyspnea scale was used for Health ABC.
FEV1 was correlated (>.50) with forced vital capacity (FVC) and FEV1/FVC ratio. FEV1 was chosen as the spirometry measure because it is less dependent on effort and cognition than FVC.
Covariates included age, gender, race, social support, education, smoking, depressive symptoms, and comorbidity index with pulmonary diseases removed.
Hazard ratios and 95% confidence intervals for death over two years
Table 5.
Amount of Association between Cognitive Impairment and Universal Health Outcomes Accounted for by Disease-related Impairments in Two Cohorts of Older Adults
| Universal health outcome models* | Cardiovascular Health Study | Health ABC | ||
|---|---|---|---|---|
| Effect of cognitive impairment on UO | % of cognitive impairment effect on UO contributed by impairment(s) | Effect of cognitive impairment on UO | % of cognitive impairment effect on UO contributed by impairment(s) | |
| β (SE) P-Value† | (SE)‡ | β (SE), P-Value | (SE), P-Value | |
| Self-rated health (excellent to poor) | ||||
| Cognitive impairment alone§ | .308 (.050), P<.001 | --- | .095 (.089), P=.28 | --- |
| Cognitive impairment + DSST¶ | .016 (.050) | 95% (8.5%), P<.001 | Not applicable | |
| Cognitive impairment + DSST + covariates# | .078 (.047) | 75% (8.0%), P<.001 | Not applicable | |
| No. BADL-IADLs not performed or performed with difficulty | ||||
| Cognitive impairment alone | .488 (.065), P<.001 | --- | P=.02.357 (.149), | --- |
| Cognitive impairment + DSST | .267 (.067) | 45% (10.3%), P<.001 | .032 (.150) | 91%(24.8%), P<.001 |
| Cognitive impairment + DSST+ covariates | .188 (.063) | 61% (9.6%), P<.001 | .040 (.147) | 89% (24.3%), P<.001 |
| Death over two years ** | HR (95% CI) | |||
| Cognitive impairment alone | 3.5 (2.5,5.0), P<.001 | --- | 3.2 (2.0, 5.0), P<.001 | --- |
| Cognitive impairment + DSST | 1.9 (1.3, 2.8) | 46% (18.3%), P=.01 | 1.8 (1.1, 2.8) | 44% (24.5%), P=.07 |
| Cognitive impairment + DSST + covariates | 1.5 (1.0, 2.2) | 58% (16.8%), P<.001 | 1.5 (0.9, 2.5) | 52% (23.8%), P=.03 |
Abbreviations: SE, standard error; HR, hazard ratio; CI, confidence intervals; CHS, Cardiovascular Health Study; BADL-IADL, basic and instrumental activities of daily living; DSST, Digit Symbol Substitution Test; UO, universal health outcome.
Two year death estimates are from Cox models; other estimates are from cross sectional regression models. Self-rated health ranged from 1 = excellent to 5 = poor. BADL-IADL functioning was the number of basic and instrumental activities of daily living that participants reported having difficulty with or were unable to perform (range, 0 to 12 in CHS and 0–9 in Health ABC).
The initial Beta (β) coefficient for each universal health outcome can be interpreted as the difference for those with vs. without cognitive impairment. For example, the β coefficient for BADL-IADL functioning in CHS is 0.488 meaning that persons with cognitive impairment do not perform, or perform with difficulty, one half more BADL-IADL than those without cognitive impairment.
This is the percent change in the regression coefficient for cognitive impairment when the disease-related impairment and the covariates are added to the model. The percentage change can be interpreted as the amount of the effect of cognitive impairment on the universal health outcomes that is accounted for by the disease-specific impairment.
Cognitive impairment not associated with self-rated health in Health ABC so disease-related impairment models not applicable.
Assessed by Digit Symbol Substitution Test, a measure of psychomotor speed and executive function; scores range from 0–90.
Covariates included age, gender, race, social support, education, smoking, depressive symptoms, and comorbidity index with cognitive impairment removed.
Hazard ratios and 95% confidence intervals for death over two years.
Table 4.
Amount of Association between Osteoarthritis and Universal Health Outcomes Accounted for by Disease-related Symptoms in Older Adults
| Universal health outcome models* | Cardiovascular Health Study | Health ABC | ||
|---|---|---|---|---|
| Effect of Osteoarthritis on UO | % of Osteoarthritis effect on UO attributed to symptoms | Effect of Osteoarthritis on UO | % of Osteoarthritis effect on UO attributed to symptoms | |
| β (SE), P-Value† | (SE)‡, P-Value | β (SE), P-Value | (SE), P-Value | |
| Self-rated health (excellent to poor) | ||||
| Osteoarthritis alone | .376 (.025), P<.001 | --- | .249 (.041), P<.001 | --- |
| Osteoarthritis + pain§ | .264 (.026) | 30% (4.3%), P<.001 | .103 (.042) | 59% (7.4%), P<.001 |
| Osteoarthritis + pain + covariates¶ | .157 (.024) | 58% (3.9%), P<.001 | .115 (.039) | 54% (6.8%), P<.001 |
| No. BADL-IADLs not performed or performed with difficulty | ||||
| Osteoarthritis alone | .483 (.035), P<.001 | --- | .345 (.072), P<.001 | --- |
| Osteoarthritis + pain | .275 (.037) | 43% (5.7%), P<.001 | .134 (.074) | 61% (12.3%), P<.001 |
| Osteoarthritis + pain + covariates | .137 (.036) | 72% (5.5%), P<.001 | .135 (.072) | 61% (12.0%), P<.001 |
| Death over two years #,** | HR (95% CI) | |||
| Osteoarthritis alone | 0.9 (0.7,1.2), P=.41 | --- | 0.5 (0.3, 1.0), P=.04 | --- |
Abbreviations: SE, standard error; HR, hazard ratio; CI, confidence intervals; BADL-IADL, basic and instrumental activities of daily living; CHS, Cardiovascular Health Study; UO, universal health outcome
Two year death estimates are from Cox models; other estimates are from cross sectional regression models. Self-rated health ranged from 1 = excellent to 5 = poor. BADL-IADL functioning was the number of basic and instrumental activities of daily living that participants reported having difficulty with or were unable to perform (range, 0 to 12 in CHS and 0–9 in Health ABC).
The initial Beta (β) coefficient for each universal health outcome can be interpreted as the difference for those with vs. without osteoarthritis. For example, the β coefficient for BADL-IADL functioning in CHS is .483 meaning that persons with osteoarthritis do not do, or perform with difficulty, almost one half more BADL-IADL than those without osteoarthritis.
This is the percent change in the regression coefficient for osteoarthritis when the disease-related symptoms and the covariates are added to the model. The percentage change can be interpreted as the amount of the effect of osteoarthritis on the universal health outcome that is accounted for by the number of joint sites affected by pain.
Number of joint sites with self-reported pain (0–8 in CHS and 0–7 in Health ABC). The correlation between the total number of pain sites and localized pain sites (i.e. upper extremity, lower extremity, back) was ≥ 0.50 in CHS and ≥ 0.30 in Health ABC. The percent change in coefficient was similar for the total pain site score as for the three categories of pain site scores.
Covariates included age, gender, race, social support, education, smoking, depressive symptoms, and comorbidity index with osteoarthritis removed.
Hazard ratios and 95% confidence intervals for death over two years.
Osteoarthritis was not associated with increased risk of death in either cohort so disease-related symptom models were not applicable.
DISCUSSION
The four chronic diseases were significantly associated with each universal health outcome except, as expected, osteoarthritis with death. These observed effects of chronic diseases on self-rated health, BADL-IADL functioning, and death confirm previous findings in older adults.12–18, 20–22,37–40 The current study adds to our understanding of the relationship between chronic diseases and universal health outcomes by determining how much of the association is accounted for by the disease-related symptoms and impairments which constitute therapeutic targets and indicators of disease severity.
The percent of disease association with the universal health outcomes attributed to disease-related symptoms and impairments ranged from almost a third to over 95% for the four diseases. The amount contributed by the symptoms/impairments, were similar, although not identical, in the two cohorts for most of the analyses. These similarities were in spite of differences in the populations and in the measures of disease-related symptoms/impairments. The amount that individual symptoms or impairments contributed to each disease’s association with the universal health outcomes appeared to vary with some symptoms or impairments accounting for a greater or lesser amount of the relationship for some universal outcome than for others.
Heart failure symptoms accounted for the largest amount of the effect of HF on self-rated health and BADL-IADL functioning while reduced EF had the strongest effect on death. A continuous rather than categorical measure of EF may have shown stronger associations with self-rated health or functioning. Finding that most individuals with HF had normal EFs, 41–43 but that lower EF was associated with a higher risk of death, 37 confirms earlier studies. Some of the findings regarding HF may relate to differences between diastolic and systolic heart failure although we could not address this possibility in the current study. HF-related symptoms/impairments were not assessed in Health ABC so we could not corroborate the relationships in this cohort.
The ATS dyspnea scale accounted for the greatest percentage of the association between COPD and all universal health outcomes except death in Health ABC. The lack of statistical significance for FEV1 in Health ABC may reflect the smaller sample size. There is no current consensus on the best spirometry measure for airflow limitations. FEV1, which is highly correlated with FEV1/FVC ratio, is more reproducible45 and less affected by effort and cognition than FVC, and has been used as a measure of decline in lung function in observational studies.38,45,46 Our results do not discern the effects of height and waist circumference on spirometric measurements.
For osteoarthritis, pain was the only symptom assessed in CHS and in the entire Health ABC cohort. No measure of pain severity was available. We cannot comment on osteoarthritis-related impairments such as in range of motion or on radiographic measures. Although we cannot ensure that the pain reported was due to osteoarthritis, we did include only joint sites. While arthritic involvement of specific joints likely have unique effects,47 the sites of pain (lower extremity, upper extremity, back) were highly correlated and had similar relationships with the universal health outcomes in the current study. These findings suggest that all joint sites with pain contributed to the effect of osteoarthritis on the universal health outcomes.
Cognitive impairment was the most challenging condition to study but was included because of its frequency and morbidity. There is inherent circularity because the diagnosis of dementia requires impairment in memory and other cognitive domains as well as effect on functioning, one of the universal health outcomes. Using education-race strata cutoffs for the 3MS, a test with good specificity for dementia27 to define cognitive impairment, avoided tautology with the BADL-IADL functioning outcome. DSST,31 a measure of executive function and processing speed, contributed much of the effect of cognitive impairment on BADL-IADL functioning and death as has been shown previously.48–51 The contribution of other cognitive deficits, including short term memory, could not be determined in the current study but have been assessed previously, with variable results. 39,52 Reliability of patient-reported outcomes such as self-rated health and BADL-IADL functioning may be limited in individuals with cognitive impairment. Previous work has shown that, while there is good agreement, cognitively-impaired individuals tend to overestimate their functional abilities relative to proxy reports, suggesting that we may have underestimated the contribution of cognitive impairment to BADL-IADL functioning.53
Other methods require comment. The use of two large community-based cohorts with disease and health outcome data was a strength. Diagnostic accuracy is always an issue in epidemiological studies. The combination of medical record or claims data plus self-report likely improved reliability. The similar, albeit not identical, relationships in the two cohorts strengthen confidence in the findings. While precluding determination of temporal precedence, the effect of the disease-related symptoms and impairments on the universal health outcomes is likely to be most potent contemporaneously, supporting cross sectional analysis. For example, current arthritic pain or heart failure symptoms will have a stronger effect on BADL-IADL functioning today than in six months when the universal health outcomes were next ascertained in the cohorts. Because temporal sequence and establishment of causality can only be determined longitudinally, the current analyses only ascertained associations, not causality. A wealth of previous longitudinal research, upon which this work builds, has shown longitudinal associations between the diseases and the universal health outcomes and between disease-related symptoms and impairments and the outcomes.11–20
We reported the percent effects, based on the regression coefficients, to determine how much of the disease-universal health outcome association was accounted for by disease-related symptoms and impairments.54, 55 Our approach is similar to determining the mediating effect of the symptoms and impairments.54, 55 The focus was on the changes that occurred in the disease-universal outcome association (regression coefficient) not explaining the overall variation in outcomes. Some effect of each disease remained for most of the universal health outcomes after accounting for the disease-related symptoms and impairments. The disease-related symptom and impairment data available for the cohorts were not exhaustive. Accounting for more symptoms or impairments might have explained more disease effects on the universal health outcomes. Furthermore, there likely are physiological, anatomical, and other disease effects that are not yet known or measurable. Not surprisingly, some of the disease effect was reduced after adjusting for covariates suggesting that, as is well accepted, social, psychological, and other factors, influence the effect of diseases on health outcomes. We cannot comment on whether controlling for treatment of the diseases would increase or reduce the effect of the diseases on the universal health outcomes or on the amount of these effects accounted for by the symptoms/impairments. Given the magnitude of the effects, however, it is unlikely that the relationships would be eliminated by controlling for treatments.
Some of the disease-related impairments such as FEV1 could be considered general measures of health as well as disease-related impairments. In addition, some symptoms, such as dyspnea, result from more than one disease. While perhaps not unique to specific diseases, the symptoms and impairments are those clinically ascribed to the diseases we studied.
We limited the current study to four common chronic diseases. It is possible, indeed likely, that similar relationships among diseases, disease-related symptoms/impairments, and universal health outcomes exist with most diseases. We investigated only three universal health outcomes. These are not the only important universal health outcomes. Determining the full set of universal health outcomes remains an important clinical and research challenge. Health-related quality of life, active life expectancy, depression, and symptom burden are examples of other potential universal health outcomes that should be explored. Eventually a set of reliable, valid, reproducible, clinically feasible universal health outcomes will hopefully to be chosen for use in clinical practice. We chose to consider symptoms to be disease-related and to include depression as a comorbidity. The fact that symptoms can be viewed as both specific to individual diseases and common across diseases and that depression is both a disease and a potential universal health outcome exemplifies the overlap between certain disease-specific and universal health outcomes.
In addition to identifying the complete contingent of universal health outcomes, much work is needed before proposing that universal health outcomes should serve as the focus of clinical decision-making. The effects of other diseases and disease-related impairments on universal health outcomes must be verified. Findings must be replicated in other populations. The temporal relationships among the symptoms/impairments and universal health outcomes need to be established.
Eventually, it will need to be determined which diseases or disease-related symptoms/impairments have the strongest effect on each universal health outcome. This latter information, along with knowledge of individuals’ outcome priorities, could help to establish treatment priorities. The effects of treatment on the disease-related symptoms/impairments and on the universal health outcomes also must be determined. While in many cases, it is known that a treatment may affect all disease-related symptoms and impairments and universal health outcomes similarly, there are examples in which this is not the case. For example, beta-blockers reduce cardiovascular mortality but their effects on heart failure symptoms, function, and quality of life are less clear.56 In the case of COPD, bronchodilators, oxygen, and exercise are the treatments most recommended for dyspnea with consideration of anxiolytics to treat the perception of dyspnea.57 The comparative effectiveness of bronchodilators or steroids at improving dyspnea, FEV1, or function is not yet known. To acquire this evidence, and to ensure that treatment is targeted toward the most important outcome to the patient and to the disease manifestations that affect that outcome, universal health outcome domains as well as disease-related symptoms/impairments will have to be assessed routinely in clinical trials and observational studies.
Once this evidence is acquired, current findings suggest a possible approach to more effective and efficient clinical decision-making for older patients. At the population level, the potential effect of recommended treatments on universal health outcomes could be considered in disease management guidelines. At the patient level, clinical decision-making could begin with the patient prioritizing which universal outcomes, e.g. functioning versus prolonged survival, was most important. The clinician then would choose treatments that target the disease-related symptom or impairment that most strongly affects this priority outcome. For example, treatment might prioritize maximizing EF for patients with HF for whom prolonged survival is most important, while symptoms should be the focus for those who prioritize function. These decisions will need to be made within the context of an individual’s overall disease burden; consideration needs to be given to priorities across as well as within diseases. Focusing on cross-disease, universal health outcomes might constitute one step toward effective and efficient clinical decision-making for older patients.
Acknowledgments
Sponsor’s Role:
None of the funding organizations had any say in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.
This research was supported by R21 AG033244 and the Yale Pepper Center (P30 AG021342), both from the National Institute on Aging, and by CTSA 1 UL1 RR024139-04 from The National Institutes of Health. Funding for Health ABC study is from N01AG-6-2101; N01AG-6-2103; and N01AG-6-2106. This research was also supported in part by the Intramural Research Program of the NIH, National Institute on Aging. CHS was supported by contract numbers N01-HC-85079 through N01-HC-85086, N01-HC-35129, N01 HC-15103, N01 HC-55222, N01-HC-75150, N01-HC-45133, grant number U01 HL080295 from the National Heart, Lung, and Blood Institute, with additional contribution from the National Institute of Neurological Disorders and Stroke.
Footnotes
Author Contributions:
All authors’ contributed to the conceptualization of the study, to interpretation of the data, and to preparation, review, and approval of the manuscript. There are no conflicts of interest, including financial interests and relationships.
Corresponding author had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Conflict of Interest Disclosures:
| Elements of Financial/Personal Conflicts | * Author 1 MT | Author 2 GM | Author 3 SC | Author 4 YN | ||||
|---|---|---|---|---|---|---|---|---|
| Yes | No | Yes | No | Yes | No | Yes | No | |
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| Grants/Funds | X | X | X | X | ||||
| Honoraria | X | X | X | X | ||||
| Speaker Forum | X | X | X | X | ||||
| Consultant | X | X | X | X | ||||
| Stocks | X | X | X | X | ||||
| Royalties | X | X | X | X | ||||
| Expert Testimony | X | X | X | X | ||||
| Board Member | X | X | X | X | ||||
| Patents | X | X | X | X | ||||
| Personal Relationship | X | X | X | X | ||||
| Elements of Financial/Personal Conflicts | * Author 5 AN | Author 6 AF | Author 7 TF | Author 8 TH | ||||
|---|---|---|---|---|---|---|---|---|
| Yes | No | Yes | No | Yes | No | Yes | No | |
| Employment or Affiliation | X | X | X | X | ||||
| Grants/Funds | X | X | X | X | ||||
| Honoraria | X | X | X | X | ||||
| Speaker Forum | X | X | X | X | ||||
| Consultant | X | X | X | X | ||||
| Stocks | X | X | X | X | ||||
| Royalties | X | X | X | X | ||||
| Expert Testimony | X | X | X | X | ||||
| Board Member | X | X | X | X | ||||
| Patents | X | X | X | X | ||||
| Personal Relationship | X | X | X | X | ||||
| Elements of Financiafl/Personal Conflicts | * Author 9 MN | Author 10 SS | Author 11 KY | Author 12 PP | ||||
|---|---|---|---|---|---|---|---|---|
| Yes | No | Yes | No | Yes | No | Yes | No | |
| Employment or Affiliation | X | X | X | X | ||||
| Grants/Funds | X | X | X | X | ||||
| Honoraria | X | X | X | X | ||||
| Speaker Forum | X | X | X | X | ||||
| Consultant | X | X | X | X | ||||
| Stocks | X | X | X | X | ||||
| Royalties | X | X | X | X | ||||
| Expert Testimony | X | X | X | X | ||||
| Board Member | X | X | X | X | ||||
| Patents | X | X | X | X | ||||
| Personal Relationship | X | X | X | X | ||||
References
- 1.Boyd CM, Darer J, Boult C, et al. Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases: implications for pay for performance. JAMA. 2005;294:716–724. doi: 10.1001/jama.294.6.716. [DOI] [PubMed] [Google Scholar]
- 2.Van Weel C, Schellevis FG. Comorbidity and guidelines: conflicting interests. Lance. 2006;367:550–551. doi: 10.1016/S0140-6736(06)68198-1. [DOI] [PubMed] [Google Scholar]
- 3.Tinetti ME, Bogardus ST, Jr, Agostini JV. Potential pitfalls of disease-specific guidelines for patients with multiple conditions. N Engl J Med. 2004;351:2870–2874. doi: 10.1056/NEJMsb042458. [DOI] [PubMed] [Google Scholar]
- 4.Centers for Disease Control and Prevention, National Center for Health Statistics. National Health Interview Survey, sample adult questionnaire. [Accessed February 21, 2009];Trends in Health and Aging. Available at: http://www.cdc.gov/nchs/agingact.htm.
- 5.Schappert SM, Burt CW. Ambulatory care visits to physician offices, hospital outpatient departments, and emergency departments: United States, 2001–02. National Center for Health Statistics Vital Health Stat. 2006;13:1–66. [PubMed] [Google Scholar]
- 6.Wolff JL, Starfield B, Anderson G. Prevalence, expenditures, and complications of multiple chronic conditions in elderly. Arch Inter Med. 2002;162:2269–2276. doi: 10.1001/archinte.162.20.2269. [DOI] [PubMed] [Google Scholar]
- 7.Ware JE, Jr, Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care. 1992;30:473–483. [PubMed] [Google Scholar]
- 8.Rahimi K, Malhotra A, Banning AP, et al. Outcome selection and role of patient reported outcomes in contemporary cardiovascular trials: systematic review. BMJ. 2010;341:c570. doi: 10.1136/bmj.c5707. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Cieza A, Stucki G. Content comparison of health-related quality of life (HRQOL) instruments based on the international classification of functioning, disability and health (ICF) Qual Life Res. 2005;14:1225–1237. doi: 10.1007/s11136-004-4773-0. [DOI] [PubMed] [Google Scholar]
- 10.World Health Organisation. Basic Documents. 39. Geneva: WHO; 1992. [Google Scholar]
- 11.Boult C, Kane RL, Louis TA, et al. Chronic conditions that lead to functional limitation in the elderly. J Gerontol A Biol Sci Med Sci. 1994;49:M28–M36. doi: 10.1093/geronj/49.1.m28. [DOI] [PubMed] [Google Scholar]
- 12.Furner SE, Rudberg MA, Cassel CK. Medical conditions differentially affect the development of IADL disability: implications for medical care and research. Gerontologist. 1995;35:444–450. doi: 10.1093/geront/35.4.444. [DOI] [PubMed] [Google Scholar]
- 13.Stuck AE, Walthert J, Nikolaus T, et al. Risk factors for functional status decline in community-living elderly people: A systematic literature review. Soc Sci Med. 1998;48:445–467. doi: 10.1016/s0277-9536(98)00370-0. [DOI] [PubMed] [Google Scholar]
- 14.Lee SJ, Lindquist K, Segal MR, et al. Development and validation of a prognostic index for 4-year mortality in older adults. JAMA. 2006;295:801–808. doi: 10.1001/jama.295.7.801. [DOI] [PubMed] [Google Scholar]
- 15.Fried LP, Kronmal RA, Newman AB, et al. Risk factors for 5-year mortality in older adults: the Cardiovascular Health Study. JAMA. 1998;279:585–592. doi: 10.1001/jama.279.8.585. [DOI] [PubMed] [Google Scholar]
- 16.Newman AB, Sachs MC, Arnold AM, et al. Total and cause-specific mortality in the cardiovascular health study. J Gerontol A Biol Sci Med Sci. 2009;64A:1251–1261. doi: 10.1093/gerona/glp127. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Ettinger WH, Jr, Fried LP, Harris T, et al. Self-reported causes of physical disability in older people: The Cardiovascular Health Study. J Am Geriatr Soc. 1994;42:1035–1044. doi: 10.1111/j.1532-5415.1994.tb06206.x. [DOI] [PubMed] [Google Scholar]
- 18.Maty SC, Fried LP, Volpato S, et al. Patterns of disability related to diabetes mellitus in older women. J Gerontol A Biol Sci Med Sci. 2004;59:148–153. doi: 10.1093/gerona/59.2.m148. [DOI] [PubMed] [Google Scholar]
- 19.Verbrugge LM, Patrick DL. Seven chronic conditions: Their impact on US adults’ activity levels and use of medical services. Am J Public Health. 1995;85:173–182. doi: 10.2105/ajph.85.2.173. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Fried LP, Bandeen-Roche K, Kasper JD, et al. Association of Comorbidity with disability in older women: The Women’s Health and Aging Study. J Clin Epidemiol. 1999;52:27–37. doi: 10.1016/s0895-4356(98)00124-3. [DOI] [PubMed] [Google Scholar]
- 21.Sullivan MD, O’Meara ES. Heart failure at the end of life: symptoms, function, and medical care in the Cardiovascular Health Study. Am J Geriatr Cardiol. 2006;15:217–225. doi: 10.1111/j.1076-7460.2006.05196.x. [DOI] [PubMed] [Google Scholar]
- 22.Fried TR, McGraw S, Agostini JV, et al. Views of Older Persons with Multiple Morbidities on Competing Outcomes and Clinical Decision-Making. J Am Geriatr Soc. 2008;56:1839–1844. doi: 10.1111/j.1532-5415.2008.01923.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Fried LP, Borhani NO, Enright P, et al. The Cardiovascular Health Study: design and rationale. Ann Epidemiol. 1991;1:263–276. doi: 10.1016/1047-2797(91)90005-w. [DOI] [PubMed] [Google Scholar]
- 24.Cesari M, Penninx BW, Newman AB, et al. Inflammatory markers and onset of cardiovascular events: results from the Health ABC study. Circulation. 2003;108:2317–2322. doi: 10.1161/01.CIR.0000097109.90783.FC. [DOI] [PubMed] [Google Scholar]
- 25.Ives DG, Fitzpatrick AL, Bild DE, et al. Surveillance and ascertainment of cardiovascular events. The Cardiovascular Health Study. Ann Epidemiol. 1995;5:278–285. doi: 10.1016/1047-2797(94)00093-9. [DOI] [PubMed] [Google Scholar]
- 26. [Accessed January 5, 2010];Health, Aging, and Body Composition website. Available at: https://rds185.epi-ucsf.org/ucsf_cc/version3.1/habc/docs/D&D/CVD_adjudication_protocol.pdf.
- 27.Teng EL, Chui HC. The Modified Mini-Mental State (3MS) examination. J Clin Psychiatry. 1987;48:314–318. [PubMed] [Google Scholar]
- 28.Yaffe K, Fiocco AJ, Lindquist K, et al. Health ABC Study. Predictors of maintaining cognitive function in older adults: the Health ABC study. Neurology. 2009;72:2029–2035. doi: 10.1212/WNL.0b013e3181a92c36. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. [Accessed January 5, 2010];Cardiovascular Health Study Data Documentation: Calculated Variables. Available at: https://biolincc.nhlbi.nih.gov/static/studies/chs/Other_Documents.htm.
- 30.Ferris BG., Jr Epidemiology standardization project. Am Rev Resp Dis. 1978;118:7–53. [PubMed] [Google Scholar]
- 31.Wechsler D. Wechsler Adult Intelligence Scale (WAIS-III) 3. San Antonio, TX: The Psychological Corporation; 1997. [Google Scholar]
- 32.Katz S, Ford AB, Moskowitz AW, et al. Studies of illness in the aged. The index of ADL: A standardized measure of biological and psychosocial function. JAMA. 1963;185:914–919. doi: 10.1001/jama.1963.03060120024016. [DOI] [PubMed] [Google Scholar]
- 33.Lawton MP, Brody EM. Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontologist. 1969;9:179–186. [PubMed] [Google Scholar]
- 34.Burman MA, Wells KB, Leake B, et al. Development of a brief screening instrument for detecting depressive disorders. Med Care. 1988;26:775–789. doi: 10.1097/00005650-198808000-00004. [DOI] [PubMed] [Google Scholar]
- 35.Groll DL, To T, Bombardier C, et al. The development of a comorbidity index with physical function as the outcome. J Clin Epidemiol. 2005;58:595–602. doi: 10.1016/j.jclinepi.2004.10.018. [DOI] [PubMed] [Google Scholar]
- 36.Chiang CL. Introduction to Stochastic Processes in Biostatistics. New York: John Wiley & Sons, Inc; 1968. p. 18. [Google Scholar]
- 37.Gottdiener JS, McClelland RL, Marshall R, et al. Outcome of congestive heart failure in elderly persons: influence of left ventricular systolic function. The Cardiovascular Health Study. Ann Intern Med. 2002;137:631–639. doi: 10.7326/0003-4819-137-8-200210150-00006. [DOI] [PubMed] [Google Scholar]
- 38.Mannino DM, Davis KJ. Lung function decline and outcomes in an elderly population. Thorax. 2006;61:472–477. doi: 10.1136/thx.2005.052449. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Portin R, Muuriaisniemi ML, Joukamaa M, et al. Cognitive impairment and the 10-year survival probability of a normal 62-year-old population. Scandinavian J Psychol. 2001;42:359–366. doi: 10.1111/1467-9450.00247. [DOI] [PubMed] [Google Scholar]
- 40.Lavery LL, Dodge HH, Snitz B, et al. Cognitive decline and mortality in a community-based cohort: the Monongahela Valley Independent Elders Survey. J Am Geriatr Soc. 2009;57:94–100. doi: 10.1111/j.1532-5415.2008.02052.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Maurer MS, Burkhoff D, Fried LP, et al. Ventricular structure and function in hypertensive participants with heart failure and a normal ejection fraction: the Cardiovascular Health Study. J Am Coll Cardiol. 2007;49:972–981. doi: 10.1016/j.jacc.2006.10.061. [DOI] [PubMed] [Google Scholar]
- 42.Kitzman DW, Gardin JM, Gottdiener JS, et al. Cardiovascular Health Study Research Group. Importance of heart failure with preserved systolic function in patients > or = 65 years of age. Am J Cardiol. 2001;87:413–419. doi: 10.1016/s0002-9149(00)01393-x. [DOI] [PubMed] [Google Scholar]
- 43.Ahmed A. Association of diastolic dysfunction and outcomes in ambulatory older adults with chronic heart failure. J Gerontol A Biol Sci Med Sci. 2005;60:1339–1344. doi: 10.1093/gerona/60.10.1339. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Herpel LB, Kanner RE, Lee SM, et al. Variability of spirometry in chronic obstructive pulmonary disease: results from two clinical trials. Am J Respir Crit Care Med. 2006;173:1106–1113. doi: 10.1164/rccm.200506-975OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Mannino DM, Sonia Buist A, Vollmer WM. Chronic obstructive pulmonary disease in the older adult: what defines abnormal lung function? Thorax. 2007;62:237–241. doi: 10.1136/thx.2006.068379. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Morone NE, Karp JF, Lynch CS, et al. Impact of chronic musculoskeletal pathology on older adults: a study of differences between knee OA and low back pain. Pain Med. 2009;10:693–701. doi: 10.1111/j.1526-4637.2009.00565.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Farias ST, Harrell CN, Neumann C, et al. The relationship between neuropsychological performance and daily functioning in individuals with Alzheimer’s disease: Ecological validity of neuropsychological tests. Arch Clin Neuropsychol. 2003;18:655–672. [PubMed] [Google Scholar]
- 49.Cahn-Weiner DA, Farias ST, Julian L, et al. Cognitive and neuroimaging predictors of instrumental activities of daily living. J Int Neuropsychol Soc. 2007;13:747–757. doi: 10.1017/S1355617707070853. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Rosano C, Newman AB, Katz R, et al. Association between lower digit symbol substitution test score and slower gait and greater risk of mortality and of developing incident disability in well-functioning older adults. J Am Geriatr Soc. 2008;56:1618–1625. doi: 10.1111/j.1532-5415.2008.01856.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Hall PA, Dubin JA, Crossley M, et al. Does executive function explain the IQ-mortality association? Evidence from the Canadian study on health and aging. Psychosom Med. 2009;71:196–204. doi: 10.1097/PSY.0b013e318190d7f0. [DOI] [PubMed] [Google Scholar]
- 52.Vecchione L, Golus E. Mortality risk assessment in the elderly: the utility of delayed word recall. J Insur Med (Seattle) 2006;38:111–115. [PubMed] [Google Scholar]
- 53.Neumann PJ, Araki SS, Gutterman EM. The use of proxy respondents in studies of older adults: lessons, challenges, and opportunities. J Am Geriatr Soc. 2000;48:1646–1654. doi: 10.1111/j.1532-5415.2000.tb03877.x. [DOI] [PubMed] [Google Scholar]
- 55.Kutner MH, Nachtsheim CJ, Neter J, et al. Applied Linear Statistical Models. 5. Boston: McGraw-Hill; 2005. [Google Scholar]
- 56.Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic and statistical considerations. J Personality Soc Psychol. 1986;51:1173–1182. doi: 10.1037//0022-3514.51.6.1173. [DOI] [PubMed] [Google Scholar]
- 57.Dobre D, van Jaarsveld CH, deJongste MJ, et al. The effect of beta-blocker therapy on quality of life in heart failure patients: a systematic review and meta-analysis. Pharmacoepidemiology & Drug Safety. 2007;16:152–159. doi: 10.1002/pds.1234. [DOI] [PubMed] [Google Scholar]
- 58.Rabe KF. Improving Dyspnea in Chronic Obstructive Pulmonary Disease. Optimal Treatment Strategies. Proc Am Thorac Soc. 2006;3:270–275. doi: 10.1513/pats.200601-002SF. [DOI] [PubMed] [Google Scholar]