Abstract
Background:
Comparatively less research has been done on caregiving for persons with cardiovascular disease (CVD) than in other chronic conditions, leaving gaps in guidance for clinical care and interventions.
Objective:
We aimed to describe the care needs of older adults with CVD in the United States and identify determinants of stress and mental health in their care partners.
Methods:
This was a cross-sectional analysis using the National Health and Aging Trends Study (n = 1011 persons with CVD) and the National Study of Caregiving (n = 510 CVD care partners). We compared differences in health and care needs of persons with and without CVD and described care partners' caregiving activities. Using multivariate regression, we examined determinants of care partner stress and anxiety/depression.
Results:
Persons with CVD had higher comorbid burden, worse health, and more care needs than those without CVD. Most care partners helped with activities of daily living in addition to disease-related care. Worse stress was associated with worse care partner health (β = 0.357; 95% confidence interval [CI], 0.192–0.522), more care activities (β = 0.388; CI, 0.070–0.705), greater care coordination (β = 0.367; CI, 0.012–0.722), more medical/nursing tasks (β = 0.489; CI, 0.145–0.834), and poorer relationship quality with the care recipient (β = −0.269; CI, −0.365 to −0.173). Care partners were more likely to have anxiety/depression if their care recipient had poorer mental health (odds ratio [OR], 1.137; CI, 1.017–1.270), whereas better relationship quality and higher educational attainment appeared protective (OR, 0.837 [CI, 0.719–0.975], and OR, 0.170 [CI, 0.076–0.380], respectively).
Conclusions:
Our findings suggest a need for broader examination of caregiving burden in CVD, well beyond CVD-specific aspects.
Keywords: cardiovascular diseases, caregiver burden, caregivers, mental health
Cardiovascular disease (CVD) is the leading cause of death and a major source of disability globally, with both CVD-related mortality and disability rising as populations age.1,2 Persons living with CVD are at significantly higher risk for rapid functional decline in later life, and the vast majority of older persons with CVD also have multimorbidity that adversely impacts their health and quality of life and adds to the complexity of their care.3,4 With increasing burden of comorbidity and disability comes an increased need for caregiving support, particularly in our current era of higher-complexity care shifting to outpatient and community settings.5–7 To meet these needs, family care partners provide upward of $61 billion worth of care to persons with CVD annually, with the greatest proportion of care needs observed in persons aged 65 years and older.8 However, comparatively less research has been done on caregiving and care needs in persons with CVD than in other chronic conditions, leaving serious gaps in guidance for clinical care and interventions.9–11
Family care partners are defined with great heterogeneity in the literature and are sometimes also referred to as family or informal caregivers, carers, or caregiving partners.12,13 In this article, we define family care partners broadly as family members, close friends, or community members that provide a wide range of support to an older adult or person living with chronic illness. This broad definition aligns with the definition used by the National Academies of Science, Engineering, and Medicine in their Families Caring for an Aging America report, as well as definitions commonly used in the health services sector (eg, Centers for Medicare & Medicaid Services).5
The primary focus of most CVD caregiving research has been on heart failure and on CVD-specific aspects of caregiving, leaving serious gaps in our understanding of care needs and activities outside of CVD-specific care.10,14 Although we know that care partners of persons with CVD experience stress and adverse impacts on mental health related to caregiving, much of this knowledge comes from heart failure, with limited data on CVD more broadly. Taken together, these represent major knowledge gaps, particularly given the rising prevalence of CVD, its impact on older adults, and the growing public health concern around stress and mental health in care partners.2,5,15,16
To bridge these gaps, the overall goal of this article is to evaluate the health, care needs, and caregiving contexts of older adults with CVD in the United States and their care partners, and to identify determinants of care partner mental health and care-related stress. Using data from the National Health and Aging Trends Study (NHATS) and the paired National Study of Caregiving (NSOC), we address the following objectives: (1) to provide foundational context for how the health characteristics of persons with CVD contribute to caregiving complexity, we characterize the physical, social, emotional and cognitive health of older adults with a history of CVD and describe the degree to which they report needing assistance with daily activities and self-care; (2) we identify family care partners of older adults with CVD and describe their health, their caregiving activities, and the caregiving context (eg, caregiving relationship, caregiving impacts/benefits, support for caregiving); and (3) we quantify individual-level, interpersonal-level, and caregiving-related determinants of care partner mental health and care-related stress.
Methods
Data Source
Data for this analysis were from round 11 of NHATS, a nationally representative cohort study of Medicare beneficiaries in the United States, and NSOC, a cross-sectional study of their care partners.17 Data in NHATS are collected annually and include both surveys and physical/cognitive performance assessments collected during a home visit. As part of each NHATS survey, participants identify family members or unpaid nonrelatives that help them with mobility, self-care, or household activities. These helpers are then approached for participation in NSOC. Data collected in NSOC consist of surveys collected by phone interview or web survey. Round 11 data for NHATS were collected in 2021, and the associated NSOC data were collected from June 2021 through January 2022.
Sample
To be eligible for NHATS, participants needed to be Medicare beneficiaries 65 years or older representatively sampled from the Medicare enrollment file in 2011 (study start) or 2015 (sample replenishment year). The NHATS participant was eligible to have 1 or more care partners participate in NSOC if they reported receiving assistance from a relative (paid/unpaid) or nonrelative (unpaid) with mobility, self-care activities, or selected other tasks for health or functional reasons. Once identified, helpers were eligible to participate in NSOC if they were 18 years or older. Both studies were approved by the institutional review board at Johns Hopkins Bloomberg School of Public Health, and all participants gave informed consent.
For the present study, we focused on community-dwelling NHATS participants who self-responded to the survey (ie, did not respond by proxy). Within this subsample of NHATS participants (N = 2921), our main population of interest was persons with a self-reported history of CVD (chronic heart disease or a history of myocardial infarction; n = 1011) and their associated care partners who participated in NSOC (n = 510).
Measurement
Sociodemographic characteristics of older adults and care partners were collected by survey/interview. Because of NHATS' cohort design and participant age at enrollment (65 years or older), the possible age range for older adults in round 11 was 71 years or older. Self-identified gender was collected as a binary variable only. Rural residence of the older adult was classified into metropolitan/nonmetropolitan based on Rural-Urban Continuum Codes.18
Self-reported health of both older adults and care partners was typically measured using identically worded items. Overall health was measured with a single item: “Would you say that in general your health is excellent, very good, good, fair, or poor?” Specific symptoms of dyspnea, pain, and fatigue were measured using single-item questions with binary (yes/no) responses (eg, “In the last month, did you have any breathing problems, including shortness of breath or difficulty breathing?”). Mental health was measured using the Patient Health Questionnaire 2 (symptoms of depression; PHQ-2) and the Generalized Anxiety Disorder 2 (symptoms of anxiety, GAD-2) screening instruments.19,20 A score of 3 or greater on the GAD-2 has a sensitivity of 86% and specificity of 83% for identifying generalized anxiety disorder, and a score of 3 or greater on the PHQ-2 has a sensitivity of 82.9% and a specificity of 90.0% for identifying major depressive disorder.19,20 To measure comorbidities, participants were asked whether a doctor had told them that they had any one of a list of health conditions. To quantify history of CVD for older adults, we created a composite variable based on their self-reported history (all rounds) of being told they had “a heart attack or myocardial infarction” and/or “any heart disease including angina or congestive heart failure.”
Physical function (older adults only) was measured using NHATS Short Physical Performance Battery, collected during a home visit.21 The Short Physical Performance Battery was based on prior large-scale studies of older adults. It is derived from performance on balance stands, walking speed, and chair stands. Specific procedures and validity/psychometric performance of the Short Physical Performance Battery algorithm have been previously published.21
Cognitive function (older adults only) was quantified using NHATS dementia classification criteria. A classification of probable, possible, or no dementia is derived from the participant’s self-reported history of dementia/Alzheimer’s disease, alongside their scores on a cognitive battery with tests of memory, orientation, and executive function.22 Specific procedures and the validity/psychometric performance of the classification algorithm have been previously published.22
Social function (older adults only) was described using two variables: social connectedness and loneliness. Social connectedness was measured using an approach developed by Cornwell and colleagues23 for the National Social Life Health and Aging Project. The older adult is asked to name the people that they “talk to about important things in your life. This may include good or bad things that happen to you, problems you are having, or important concerns you have.” Respondents list up to 5 individuals, from which NHATS derives a social network size variable with a possible range of 0 to 5. Loneliness was added to NHATS for the first time in this round of data collection (round 11) and was measured using a direct single-item measure of loneliness that is often used in national surveys of health (eg, the National Health Interview Survey, US Census Bureau’s Household Pulse Survey). Specifically, the older adult is asked a single-item question “During the last month, how often did you feel lonely?” Original responses were on a 5-point Likert scale, ranging from 1 (every day) to 5 (never). As very few endorsed loneliness “every day,” the category was collapsed and the scale was then reverse coded such that higher values represent greater loneliness, with the final scale ranging from 1 (never feels lonely) to 4 (feels lonely most days or every day).
Older adults' self-reported help received with care activities was measured using a series of single-item questions asking whether the older adult received help to do each activity within the last month. Items included help with mobility (outside the home, inside the home, getting out of bed), self-care (eating, bathing, toileting, dressing), household activities (shopping, meals), picking up and tracking medications, and medical visits (accompanying the older adult during a visit, remembering questions for the provider, communicating things to the provider, understanding the provider).
The caregiving context (as reported by the care partner) was measured as follows. Care partners reported their relationship to the older adult, as well as whether they lived together. Care partners were also asked a series of questions about the nature of their relationship with the older adult that have been used in prior research as a measure of relationship quality24: (1) “How much do you enjoy being with [the older adult]?” (2) “How much does [the older adult] argue with you?” (3) “How much does [the older adult] appreciate what you do for them?” and (4) “How often do they get on your nerves?” Responses were on a 4-point Likert scale ranging from 1 (a lot) to 4 (not at all). A sum score is generated based on these 4 items, with items 1 and 3 reverse coded such that higher scores indicate higher relationship quality. Caregiving activities were measured with a series of single-item questions asking about the types of things that the care partner helped the older adult with in the last month. Items included help with activities of daily living (ADLs; personal care, mobility), instrumental activities of daily living (IADLs; eg, transportation, shopping, meals), and care coordination. We also included medical/nursing tasks that have been associated with higher levels of stress in previous caregiving studies (tracking medications, special diets, complex medical tasks)25,26 and whether they had helped the older adult after a hospitalization (past year). Care-related impacts were measured with 3 binary-response items asking whether helping the older adult was financially, emotionally, or physically difficult. These items are similar to the single-item care-related strain measures used in a landmark study of care strain and mortality which are widely used in caregiving research.27 Care partners were also asked whether they experienced family disagreements related to care and whether caregiving adversely impacted their work. Care-related benefits were measured using 4 items that asked whether their experience caregiving: (1) “made you more confident about your abilities,” (2) “taught you to deal with difficult situations,” (3) “brought you closer to [the older adult],” and (4) “has given you satisfaction that [the older adult] is well-cared for.” Care partners were also asked whether they used care-related supports like respite, as well as whether they received care-related training or education or whether a provider had asked them if they needed help managing the older adult’s care.
Care-related stress was measured using a composite variable based on 4 items that have been used in prior research as a measure of care-related stress24: (1) “You are exhausted when you go to bed at night”; (2) “You have more things to do than you can handle”; (3) “You don't have time for yourself”; and (4) “As soon as you get a routine going, [the older adult’s] needs change.” Care partners indicate their degree of agreement on a 3-point Likert scale, ranging from 1 (very much) to 3 (not so much). A sum score is generated based on these 4 items, with all items reverse coded such that higher scores indicate higher care-related stress.24
Analysis
To accomplish objectives 1 and 2, descriptive statistics accounting for NHATS/NSOC complex sampling design were used (weighted proportions, means, and linearized standard errors). To compare characteristics of older adults by CVD status, survey-adjusted χ2 tests or survey-adjusted Wald tests were used, applying Bonferroni corrections for multiple comparisons.
To accomplish objective 3, we first generated a composite care partner mental health endpoint quantifying the presence of anxiety or depression (score ≥3) on either the GAD-2 or the PHQ-2, in alignment with prior caregiving research.16 To inform model selection, a series of univariate tests were run between the dependent variables and individual-level, interpersonal-level, and caregiving-related factors. Factors associated with one or both dependent variables at P ≤ .10 were selected for inclusion in the final models. For the final models, survey-adjusted logistic regression was used to model caregiver mental health, and generalized linear modeling (gamma probability distribution) was used to model care stress. Because of the relative completeness of the data (n = 504 and n = 505 for each final model out of a possible n = 510), complete case analysis was used.
Analyses were conducted using Stata v18 (StataCorp LLC, College Station, TX), and NHATS/NSOC technical guidance was followed on appropriate application of survey weight and design variables.28 For analyses focusing on care partners, NSOC weights that account for potential clustering within NHATS respondents were applied.28 The first author (J.T.B.) had full access to the data and takes responsibility for its integrity and the data analysis.
Results
Objective 1: Characterize the Health of Older Adults With a History of CVD and Describe the Degree to Which They Report Needing Assistance With Daily Activities and Self-care
Characteristics of older adults are presented in Table 1. Of the 2291 older adult NHATS participants, 1011 (35%) reported a history of CVD. Older adults with CVD were in their late 70’s on average and were evenly balanced by sex (50% identified as female). Most participants (81%) identified as White and non-Hispanic, followed by Hispanic (8%) and Black non-Hispanic (8%). When comparing participants with and without a history of CVD, older adults with CVD were significantly older, reported poorer overall health, had worse physical and cognitive function, were more likely to have been hospitalized in the past year, and had greater depression, more distressing symptoms, and more comorbidities. In terms of care needs (Table 2), participants with CVD were significantly more likely to report needing help with mobility, bathing/toileting, getting dressed, and picking up and tracking medications. There were also significant differences in the help they received preparing meals, and they were more likely to have someone else accompany them in medical visits.
TABLE 1.
Characteristics of Community-Dwelling Older Adults With a History of CVD (Endorsed Chronic Heart Disease or Ever Had an Myocardial Infarction)
| Total Sample | No History of CVD | History of CVD | Comparisons by CVD History | |||||
|---|---|---|---|---|---|---|---|---|
| N = 2921 | n = 1910 | n = 1011 | ||||||
| Weighted N = 27 354 636 | Weighted n = 19 070 804 | Weighted n = 8 283 832 | ||||||
| Mean ± SE or n (%) | Mean ± SE or n (%) | Mean ± SE or n (%) | P | Adjusted P | ||||
| Age | 78.47 ± 0.10 | 77.95 ± 0.11 | 79.65 ± 0.20 | <.0001 | <.0001 | |||
| Female sex | 1661 (54.65) | 1138 (56.65) | 523 (50.04) | .0339 | .7458 | |||
| Race/ethnicity | .4805 | 1.0000 | ||||||
| White, non-Hispanic | 2105 (81.67) | 1367 (81.92) | 738 (81.11) | |||||
| Black, non-Hispanic | 582 (7.89) | 385 (7.78) | 197 (8.14) | |||||
| Other, non-Hispanic | 65 (3.39) | 47 (3.68) | 18 (2.72) | |||||
| Hispanic | 135 (7.05) | 85 (6.62) | 50 (8.03) | |||||
| Education level | .2010 | 1.0000 | ||||||
| Less than high school | 428 (12.21) | 257 (11.24) | 171 (14.41) | |||||
| High school | 748 (24.28) | 504 (24.19) | 244 (24.48) | |||||
| Some college | 800 (29.21) | 512 (28.86) | 288 (30.02) | |||||
| Baccalaureate degree | 436 (16.73) | 282 (16.95) | 154 (16.24) | |||||
| Graduate degree | 478 (17.57) | 332 (18.76) | 146 (14.85) | |||||
| Rural residence | 562 (18.03) | 346 (16.60) | 216 (21.33) | .0295 | .6490 | |||
| Overall health (self-reported) | <.0001 | <.0001 | ||||||
| Excellent | 261 (10.71) | 205 (12.29) | 56 (7.07) | |||||
| Very good | 888 (32.44) | 644 (35.43) | 244 (25.55) | |||||
| Good | 1133 (37.60) | 738 (37.53) | 395 (37.79) | |||||
| Fair | 529 (15.99) | 272 (12.68) | 257 (23.60) | |||||
| Poor | 107 (3.26) | 49 (2.07) | 58 (5.99) | |||||
| Physical performance battery score | 7.19 ± 0.08 | 7.53 ± 0.09 | 6.42 ± 0.16 | <.0001 | <.0001 | |||
| Dementia classification | .0004 | .0088 | ||||||
| Probable dementia | 189 (4.76) | 97 (3.63) | 92 (7.38) | |||||
| Possible dementia | 230 (6.29) | 139 (5.85) | 91 (7.30) | |||||
| No dementia | 2502 (88.95) | 1674 (90.52) | 828 (85.33) | |||||
| Hospitalized in past year | 534 (17.01) | 248 (12.62) | 286 (27.15) | <.0001 | <.0001 | |||
| Social connectedness | 2.44 ± 0.04 | 2.47 ± 0.05 | 2.36 ± 0.05 | .1020 | 1.0000 | |||
| Loneliness | 1.81 ± 0.02 | 1.78 ± 0.02 | 1.87 ± 0.04 | .0709 | 1.0000 | |||
| Depression (PHQ-2 score) | 0.79 ± 0.03 | 0.73 ± 0.03 | 0.94 ± 0.05 | .0018 | .0396 | |||
| Positive depression screen | 315 (9.20) | 176 (7.93) | 139 (12.12) | .0032 | .0704 | |||
| Anxiety (GAD-2 score) | 0.74 ± 0.03 | 0.70 ± 0.04 | 0.85 ± 0.05 | .0119 | .2618 | |||
| Positive anxiety screen | 254 (8.23) | 140 (7.16) | 114 (10.70) | .0081 | .1782 | |||
| Pain in past month | 1652 (55.74) | 1023 (53.12) | 629 (61.78) | .0004 | .0088 | |||
| Breathing problems in past month | 624 (19.13) | 293 (14.56) | 331 (29.65) | <.0001 | <.0001 | |||
| Fatigue in past month | 1343 (42.35) | 762 (36.05) | 581 (56.84) | <.0001 | <.0001 | |||
| History of stroke | 363 (10.93) | 180 (8.76) | 183 (15.91) | <.0001 | <.0001 | |||
| History of cancer | 1076 (35.11) | 646 (32.34) | 430 (41.49) | .0001 | .0022 | |||
| History of diabetes | 887 (29.29) | 514 (25.79) | 373 (37.34) | <.0001 | <.0001 | |||
| History of lung disease | 703 (23.30) | 376 (19.35) | 327 (32.40) | <.0001 | <.0001 | |||
All descriptive statistics account for sampling design (weighted proportions, means, and standard errors); P values are for survey-adjusted χ2 tests (categorical variables) or survey-adjusted Wald tests (continuous variables); the adjusted P value is the P value with a Bonferroni correction applied for multiple comparisons.
TABLE 2.
Types of Caregiving Help Received by Older Adults With CVD
| Total Sample | No History of CVD | History of CVD | Comparisons by CVD History | |||||
|---|---|---|---|---|---|---|---|---|
| N = 2921 | n = 1910 | n = 1011 | ||||||
| Weighted N = 27 354 636 | Weighted n = 19 070 804 | Weighted n = 8 283 832 | ||||||
| Type of Help | Mean ± SE or n (%) | Mean ± SE or n (%) | Mean ± SE or n (%) | P | Adjusted P | |||
| Mobility outside the home | 300 (7.3) | 151 (5.2) | 149 (11.9) | <.0001 | <.0001 | |||
| Mobility inside the home | 151 (4.1) | 74 (2.7) | 77 (7.3) | <.0001 | <.0001 | |||
| Getting out of bed | 106 (3.1) | 60 (2.6) | 46 (4.5) | .0349 | .4537 | |||
| Eating | 80 (2.2) | 35 (1.5) | 45 (3.9) | .0056 | .0728 | |||
| Bathing/toileting | 196 (5.) | 101 (3.7) | 95 (8.) | .0002 | .0026 | |||
| Getting dressed | 318 (9.8) | 172 (7.9) | 146 (14.3) | <.0001 | <.0001 | |||
| Picking up medications | 451 (16.8) | 245 (13.4) | 206 (24.5) | <.0001 | <.0001 | |||
| Tracking medications | 333 (9.5) | 169 (7.2) | 164 (14.6) | <.0001 | <.0001 | |||
| Making hot meals | .0034 | .0442 | ||||||
| Always made by self | 1259 (42.6) | 891 (44.8) | 368 (37.6) | |||||
| Made with someone or it varied | 1192 (43.7) | 745 (42.7) | 447 (45.8) | |||||
| Someone else always made | 467 (13.8) | 271 (12.5) | 196 (16.6) | |||||
| Sit with them at provider visit | 926 (29.4) | 513 (25.2) | 413 (39.1) | <.0001 | <.0001 | |||
| Remember questions for the providera | 559 (59.2) | 288 (55.1) | 271 (65.3) | .0145 | .1885 | |||
| Ask or tell the provider thingsa | 525 (53.4) | 265 (48.7) | 260 (60.3) | .0137 | .1781 | |||
| Understand the providera | 500 (47.5) | 258 (45.) | 242 (51.1) | .2244 | 1.0000 | |||
All descriptive statistics account for sampling design (weighted proportions, means, and standard errors); P values are for survey-adjusted χ2 tests (categorical variables) or survey-adjusted Wald tests (continuous variables); the adjusted P value is the P value with a Bonferroni correction applied for multiple comparisons.
These items were only asked of respondents who endorsed having someone sit with them at a provider visit.
We identified 510 matched care partners in NSOC who assisted the NHATS participant with their care needs. Because care partner eligibility for NSOC is based on the older adult’s need for help with ADLs and/or IADLs because of functional health issues, as expected, NHATS participants with CVD with a caregiver in NSOC had significantly poorer overall health, physical function, and cognitive function than those NHATS participants with CVD who did not have a caregiver (all P < .001). Sociodemographically, those with a caregiver were also significantly more likely to be older (P < .001), have lower educational attainment (P < .001), or identify as male (P = .001), Black (P = .001), or Hispanic (P = .003). There were no significant differences by rural versus urban status (P = .387).
Objective 2: Identify Family Care Partners of Older Adults With CVD and Describe Their Health, Their Caregiving Activities, and the Caregiving Context
Characteristics of care partners are presented in Table 3. Care partners were in their early 60’s on average, and just over two thirds identified as female. Most care partners (65%) identified as White and non-Hispanic, with the next most frequently endorsed racial/ethnic categories being Hispanic (14%) and Black non-Hispanic (12%). Just under half (46%) of care partners had a history of hypertension, and more than 1 in 10 had a history of CVD themselves. In terms of other serious comorbidities, 19% reported a history of diabetes, and 14% reported a history of lung disease or cancer. Approximately 15% of care partners scored positive on the depression screen, and 14% scored positive on the anxiety screen.
TABLE 3.
Characteristics of Care Partners of Older Adults With a History of CVD (n = 510 Care Partners, Weighted n = 5 949 200)
| Mean ± SE or n (%) | ||
|---|---|---|
| Age | 61.40 ± 0.81 | |
| Female sex | 373 (68.2) | |
| Race/ethnicity | ||
| White, non-Hispanic | 323 (65.1) | |
| Black, non-Hispanic | 125 (11.8) | |
| Other, non-Hispanic | 14 (4.3) | |
| Hispanic | 42 (13.8) | |
| Education level | ||
| Less than high school | 31 (7.6) | |
| High school | 138 (26.2) | |
| Some college | 188 (35.6) | |
| Baccalaureate degree | 90 (18.2) | |
| Graduate degree | 70 (12.4) | |
| Relationship to older adult | ||
| Spouse/partner | 107 (21.4) | |
| Adult child | 296 (48.0) | |
| Sibling | 14 (3.4) | |
| Adult grandchild | 42 (9.4) | |
| Other relative | 22 (5.3) | |
| Friend/nonrelative | 48 (12.5) | |
| Lives with older adult | 232 (41.1) | |
| Quality of relationship with older adult | 13.78 ± 0.14 | |
| Strain related to caregiving | 5.60 ± 0.12 | |
| Multimorbidity caregiving | ||
| Older adult has concomitant cognitive impairment/dementia | 145 (26.2) | |
| Older adult has concomitant diabetes | 235 (45.6) | |
| Older adult has concomitant lung disease | 216 (44.8) | |
| Older adult is a stroke survivor | 125 (20.2) | |
| Older adult is a cancer survivor | 222 (41.0) | |
| Overall health (self-reported) | ||
| Excellent | 79 (14.1) | |
| Very good | 185 (36.7) | |
| Good | 162 (29.0) | |
| Fair | 72 (15.7) | |
| Poor | 25 (4.5) | |
| Depression (PHQ-2 score) | 1.06 ± 0.08 | |
| Positive depression screen | 77 (15.1) | |
| Anxiety (GAD-2 score) | 1.11 ± 0.08 | |
| Positive anxiety screen | 62 (14.0) | |
| Pain in past month | 299 (58.2) | |
| Breathing problems in past month | 81 (19.0) | |
| Fatigue in past month | 198 (37.7) | |
| Serious visual impairment | 31 (6.2) | |
| Serious hearing impairment | 46 (7.2) | |
| History of high blood pressure | 253 (45.8) | |
| History of heart attack or heart disease | 67 (13.6) | |
| History of cancer | 66 (13.8) | |
| History of diabetes | 106 (19.0) | |
| History of lung disease | 71 (14.3) | |
All descriptive statistics account for sampling design (weighted proportions, means, and standard errors).
Just over 40% of care partners lived with the older adult. Nearly half were the adult child of the older adult, and one fifth were their spouse/partner. Caregiving activities, supports, and impacts/benefits are presented in Figures 1 and 2A to C. Most care partners (between 80% and 90%) reported that they helped with IADLs not related to disease management, such as transportation and shopping, and a substantial proportion also helped with ADLs. Many also assisted with disease management, such as help with medication tracking (40%) and adhering to special diets (24%). Care partners also commonly helped the older adult navigate the healthcare system by making medical appointments (45%), discussing care with providers (47%), coordinating care across providers (36%), interacting with medical record portals (21%), and helping with hospital-to-home transitions (44%).
FIGURE 1.
Caregiving activities. Survey-weighted proportions of care partners endorsing caregiving activities, listed by care task.
FIGURE 2.
Caregiving-related impacts, services/education, and perceived benefits. A, Survey-weighted proportions of care partners' endorsement of adverse impacts related to caring. B, Survey-weighted proportions of care partners who endorsed using respite/other services, received training/education, or had a provider ask them whether they needed help. For education after hospitalization, the proportion shown is only of those who helped after a hospitalization. For providers asking whether the care partner needed help, the proportion shown is only for those who discussed the older adult’s care with a provider. C, Survey-weighted proportions of care partners' endorsement of perceived benefits related to caring.
Relatively few used respite or other services, and few endorsed receiving any caregiving training. In terms of caregiving-related impacts, just over 40% endorsed at least 1 impact, with the most common being emotional difficulty related to caring. Caregivers also endorsed benefits related to caring, the most common being increased closeness with the older adult (70%) and satisfaction that the older adult was being well-cared for (87%).
Objective 3: Quantify Individual-Level, Interpersonal-Level, and Caregiving-Related Determinants of Care Partner Mental Health and Care-Related Stress
Multivariable regression models predicting care partner stress and mental health are presented in Tables 4 and 5. Significant determinants differed between the 2 models with the exception of relationship quality, which emerged as a significant predictor in both.
TABLE 4.
Generalized Linear Regression Model Predicting Care Partner Care-Related Stress (Model, n = 505 Care Partners; Weighted, n = 5 875 861)
| β | P | 95% CI | |
|---|---|---|---|
| Care partner characteristics | |||
| Age | −0.013 | .101 | −0.029 to 0.003 |
| Female sex | 0.125 | .462 | −0.208 to 0.458 |
| Race/ethnicity | |||
| Black, non-Hispanic | −0.312 | .087 | −0.670 to 0.046 |
| Hispanic or other | −0.239 | .221 | −0.621 to 0.143 |
| Education level | |||
| Some college | 0.031 | .879 | −0.371 to 0.433 |
| Baccalaureate degree or higher | 0.243 | .243 | −0.165 to 0.651 |
| Overall health | 0.357 | .000 | 0.192–0.522 |
| Older adult characteristics | |||
| Age | 0.009 | .544 | −0.020 to 0.039 |
| Female sex | 0.363 | .075 | −0.036 to 0.763 |
| Impaired mobility | 0.085 | .728 | −0.394 to 0.564 |
| Probable or possible dementia | 0.262 | .192 | −0.131 to 0.656 |
| Social connectedness | −0.055 | .266 | −0.153 to 0.042 |
| Anxiety/depression | −0.016 | .598 | −0.074 to 0.043 |
| Interpersonal characteristics | |||
| Relationship type | |||
| Adult child | −0.027 | .928 | −0.603 to 0.550 |
| Other family member or friend | −0.093 | .761 | −0.691 to 0.506 |
| Relationship quality | −0.269 | .000 | −0.365 to −0.173 |
| Caregiving activities | |||
| Helps with ADLs | 0.388 | .017 | 0.070–0.705 |
| Helps with care coordination | 0.367 | .043 | 0.012–0.722 |
| Helps with medical/nursing tasks | 0.489 | .005 | 0.145–0.834 |
| Helps with transitional care | −0.291 | .083 | −0.620 to 0.038 |
Model coefficients (β) are unstandardized. Care-related stress (dependent variable): higher scores indicate greater levels of care-related stress. Directionality of ordinal/continuous variables in the model is the following: overall health, higher values indicate poorer health; social connectedness, higher values indicate greater connectedness; anxiety/depression, higher values indicate greater symptoms of depression/anxiety; relationship quality, higher scores indicate better relationship quality.
TABLE 5.
Logistic Regression Model Predicting Care Partner Depression and/or Anxiety (Model, n = 504 Care Partners; Weighted, n = 5 865 356)
| OR | P | 95% CI | |
|---|---|---|---|
| Care partner characteristics | |||
| Age | 0.978 | .124 | 0.950–1.006 |
| Female sex | 1.200 | .596 | 0.611–2.354 |
| Race/ethnicity | |||
| Black, non-Hispanic | 1.132 | .734 | 0.553–2.319 |
| Hispanic or other | 0.550 | .200 | 0.220–1.374 |
| Education level | |||
| Some college | 0.672 | .189 | 0.371–1.217 |
| Baccalaureate degree or higher | 0.170 | .000 | 0.076–0.380 |
| Overall health | 1.112 | .509 | 0.812–1.522 |
| Older adult characteristics | |||
| Age | 0.995 | .852 | 0.940–1.052 |
| Female sex | 0.572 | .092 | 0.298–1.096 |
| Impaired mobility | 1.382 | .429 | 0.619–3.083 |
| Probable or possible dementia | 0.861 | .746 | 0.349–2.126 |
| Social connectedness | 0.846 | .165 | 0.667–1.072 |
| Anxiety/depression | 1.137 | .024 | 1.017–1.270 |
| Interpersonal characteristics | |||
| Relationship type | |||
| Adult child | 1.880 | .232 | 0.667–5.303 |
| Other family member or friend | 0.870 | .822 | 0.259–2.923 |
| Relationship quality | 0.837 | .022 | 0.719–0.975 |
| Caregiving activities | |||
| Helps with ADLs | 1.145 | .735 | 0.523–2.507 |
| Helps with care coordination | 0.525 | .091 | 0.249–1.108 |
| Helps with medical/nursing tasks | 1.768 | .126 | 0.852–3.669 |
| Helps with transitional care | 1.174 | .638 | 0.601–2.293 |
Care partner anxiety/depression (dependent variable) is a binary variable indicating the presence of depression and/or anxiety. Directionality of ordinal/continuous variables in the model is the following: overall health, higher values indicate poorer health; social connectedness, higher values indicate greater connectedness; anxiety/depression, higher values indicate greater symptoms of depression/anxiety; relationship quality, higher scores indicate better relationship quality.
In the stress model (Table 4), greater care-related stress was associated with poorer care partner health (β = 0.357 ± 0.084, P < .001) and lower relationship quality (β = −0.269 ± 0.049, P < .001), as well as caregiver support of ADLs (mobility and/or personal care; β = 0.388 ± 0.162, P = .017), care coordination (making medical appointments, discussing care with providers, interacting with patient portals, and/or coordinating care across providers; β = 0.367 ± 0.181, P = .043), and doing medical/nursing tasks (medication management, helping with special diets, and/or doing wound/ostomy care, managing intravenous medications, or doing blood testing; β = 0.489 ± 0.175, P = .005).
In the mental health model (Table 5), care partner anxiety/depression was significantly associated with care partner education level (lower odds of anxiety/depression for care partners with a Baccalaureate degree or higher; odds ratio [OR], 0.170; 95% confidence interval [CI], 0.076–0.380), symptoms of depression/anxiety in the older adult (greater odds of anxiety/depression in care partners caring for an older adult who also had greater symptoms of depression/anxiety; OR, 1.137; 95% CI, 1.017–1.270), and relationship quality (lower odds of anxiety/depression for care partners who reported better relationship quality; OR, 0.837; 95% CI, 0.719–0.975).
Discussion
Our analysis provides a multidimensional data-based picture of the health and care needs of older adults with CVD in the United States, as well as much-needed insights on the health and caregiving activities of the care partners who support them. Our analyses also provide a broader understanding of the individual, interpersonal, and care-related factors that are significantly associated with care partner mental health and care-related stress — two endpoints that are critically important to the overall well-being of our nation’s family caregiving workforce.
First, we found that older adults with CVD had worse self-reported and objective indicators of health in multiple domains, including physical health/function, cognitive health, and mental health. This aligns with prior research demonstrating associations between CVD and functional decline, cognitive impairment/dementia, and increased (often concomitant) depression and anxiety.4,29,30 They also had significantly greater comorbid burden, which is also in alignment with prior studies showing high levels of multimorbidity in older persons with CVD.3,31 What has not been previously reported are specific care needs of persons with CVD, and we found that those with CVD were more likely to report needing help with ADLs and key disease management behaviors like tracking medications. Persons with CVD were also more likely to report distressing symptoms (pain, fatigue, dyspnea), which are key drivers of healthcare use, clinical outcomes, and quality of life.32 Taken together, this suggests a need for holistic assessment focused on functioning and “what matters” to older adults and their family care partners, in alignment with the Age Friendly Health Systems 4Ms Model and other person-centered care models.6,33 Mental health should also be a priority, especially given evidence that depression predicts future functional decline and poor clinical outcomes/mortality for persons with CVD.30,34
Second, we turned our attention to CVD care partners. Although generally younger and healthier than care recipients, a substantial proportion had health concerns of their own. Perhaps most notably, the proportion of care partners with a positive screen for major depression was 15%, which is higher than the average national prevalence of 8.3% for all US adults and 4.5% for adults 50 years or older.35 We also found a similar proportion with a positive screen for anxiety. These findings underscore the importance of understanding risk factors for care partner mental health, particularly in the broader context of rising public health concerns around this issue.5,16 The proportion of care partners endorsing distressing physical symptoms of their own was also notable, with over half endorsing symptoms of pain and more than a third endorsing fatigue. Care partners also reported serious comorbidities, including CVD, which aligns with recent summative work demonstrating increased CVD risk associated with caregiving.36
When examining caregiving activities, almost all care partners engaged in tasks that were not specific to disease management, such as help with ADLs and IADLs. This is not surprising given that CVD itself is responsible for substantial functional decline, often compounded by multimorbidity that increases care complexity, healthcare utilization, and further disability.3,31 Understanding the full scope of care activities addresses an important gap in CVD caregiving research, which has focused primarily on understanding care partners' contributions to CVD-specific care tasks despite substantial qualitative work suggesting a broader scope of care activities.10 Filling this gap is important in shaping our understanding of CVD care partner stress and mental health, as supporting a loved one with ADLs and increasing intensity of ADL/IADL support have been associated with risk for care partner depression and stress.37,38
Notably, despite substantial contributions to both general and disease-specific care, few care partners (just over 5%) had received any caregiving training. However, if the care partner had helped after a hospitalization, the proportion of those who endorsed receiving adequate education was much higher, at just over half. The higher proportion of training/education endorsed by care partners who helped with transitional care may be an indicator of the positive impact of policy, for example, the CARE Act, which mandates post-discharge training for care partners, on supporting delivery of caregiving education.39
Another important finding is that more than half of care partners did not endorse adverse impacts related to caregiving, and in fact, most were able to identify care-related benefits. This finding aligns with calls from the broader caregiving literature to take a balanced perspective on the caregiving experience and acknowledge that caregiving can have both positive and negative impacts for care partners.40 Instead of treating caregiving a universal stressor, the goal of this more balanced perspective is to understand variability in caregiving, thus improving risk stratification to support sustainable healthy caregiving.40
When care partners did report adverse impacts, the most common was emotional difficulty related to caring, which was endorsed by nearly a third of care partners. Poor psychological health related to caregiving is also common in the broader literature and represents an important area for deeper examination and intervention.5 Although care partner mental health and stress are often discussed together, our analyses revealed unique determinants of each (with the exception of relationship quality, which was significant in both models).
Care-related stress was primarily associated with engagement in care tasks, specifically ADLs, care coordination, and medical/nursing tasks. Although studies of specific care tasks in relation to caregiving stress are relatively limited in CVD, prior studies in the broader literature have found relationships between stress and higher-intensity support with ADLs/IADLs, health management and medical/nursing tasks, and engagement with the health system.26,41,42 Although less research has been done on care coordination as a predictor of stress, the significant effect we observed may be reflective of higher concomitant multimorbidity and subsequent downstream complexity of care coordination. In addition to care activities, the quality of the caregiving relationship and care partner health also predicted stress. Other studies have also found relationships between care-related stress and care partner health, although the temporal ordering of these relationships remains elusive.41,42
In contrast to care-related stress, care partner mental health was not associated with care tasks but was instead predicted by care partner educational attainment, care recipient mental health, and relationship quality. Education level can act as a proxy for socioeconomic status, and socially/economically marginalized groups have long experienced greater burden of mental health disorders, which was further exacerbated in the pandemic era.43,44 In terms of care recipient mental health, this finding was not surprising, as interdependence of depression/anxiety in close relationships is an established phenomenon in the wider literature that has also been observed in CVD care dyads.11,45 One potential clinical implication of shared mental health risk may be to find novel ways to integrate care partner assessments into clinical visits, as care partners often accompany the older adult. Although structures and payment models exist to support care partner assessments in certain specialty care settings (ie, dementia), this is not the case in most cardiovascular specialty care settings.46
One significant predictor shared across both models was relationship quality, with better relationship quality predicting lower care-related stress and lower odds of depression/anxiety in the care partner. This is in alignment with multiple studies in CVD caregiving that have found protective effects of relationship quality on care partner stress and mental health, in addition to associations with better health-related quality of life and disease management behaviors.11,47–50 It is also a central component of two interventions for couples managing CVD, both still in trials: the Taking Care of Us intervention and the Healing Hearts Together program.51,52 Interventions like these are relatively novel in CVD caregiving, which has a comparatively small interventional literature consisting primarily of disease-specific psychoeducational interventions with overall mixed success.9,11,53 Our findings and the broader literature suggest a need for expanded intervention development beyond CVD-specific psychoeducation. For example, the Families Caring for an Aging America report found that caregiving interventions were most effective when they were tailorable multicomponent interventions based on personalized assessment of care partner needs, risk factors, and preferences.9 One potential avenue for future research that may be helpful in supporting adaptations of existing interventions to CVD care partners would be to further explore comparisons in caregiving across conditions. If we can better understand similarities and differences in caregiving experiences, resources, care needs, and outcomes between caregiving in CVD and caregiving in other illness contexts, especially dementia and cancer, we may gain important insight into which components of existing caregiving interventions might also be useful for CVD care partners.
This study has limitations. First, our analyses are cross sectional, precluding intimations about directionality/cause. Second, NHATS does not allow for categorization by specific cardiovascular condition, an important limitation given potential differences in care needs and experiences. We also did not control for geographic variations, which may be important given substantial regional differences in health and access to care across the United States. Third, we had hoped to include more health system-level factors in our models (eg, caregiver interactions with the medical team) in line with higher levels of influence in the socioecological framework. Whereas NSOC includes several pertinent items related to the quality of interactions with health professionals, over half of the care partners in our sample did not endorse any interactions with the older adult’s care team. This is an interesting and potentially clinically meaningful finding that warrants further study.
Our analysis also has strengths. First, NHATS is a nationally representative sample of Medicare beneficiaries in the United States, which enhances generalizability to a large portion of American adults and their care partners. The paired NSOC design also lends strength, as it enables examination of interpersonal-level effects (ie, associations between patient and care partner factors) that are not possible in caregiving studies that do not include in-depth care recipient information. Our use of NHATS/NSOC also allowed for analysis of a much larger and more diverse sample of CVD care partners than is typical in the CVD caregiving literature.
Conclusions
Older adults with CVD in the United States have poorer health, more comorbidities, and more care needs than those without CVD, and their care partners have physical and mental health concerns of their own. Although the CVD caregiving literature focuses primarily on CVD-specific care activities, we found that almost all care partners support the older adult with ADLs/IADLs in addition to disease management. We also found associations between care partner stress and engagement in a broad spectrum of care tasks. In alignment with prior work, better relationship quality appeared protective, underlining the value of studying and supporting interpersonal relationships to advance health. Given the comparatively small interventional literature in CVD caregiving and its mixed success,9,11,53 our findings also demonstrate a need for investment in additional research to support the development of care partner clinical assessments. Our results suggest that these assessments should include both individual and relational components, alongside a range of supportive intervention elements that can be tailored to care partner and care dyad needs.5
Footnotes
Published online 28 March 2025
Dr. Bidwell is supported by NIH/NINR K01NR011880. The content is solely the responsibility of the authors and does not represent official views of the funders.
The authors have no conflicts of interest to disclose.
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