Cardiovascular Disease and Health-Related Quality of Life Among Asian American,Native Hawaiian and Pacific Islander Older Adults

Abstract

Objectives: Assess the relationship between cardiovascular disease (CVD) and health-related quality of life (HRQOL) among Asian American, Native Hawaiian, and Pacific Islander (NH/PI) compared to white older adults. Methods: Data were from the 2011–2015 Health Outcomes Survey. HRQOL was assessed using the Veterans RAND 12-Item Survey, composed of physical (PCS) and mental component scores (MCS). Lower scores represent worse health. Multivariate regression was conducted to estimate PCS and MCS mean score differences related to self-reported CVD (coronary artery disease, congestive heart failure, myocardial infarction, other heart conditions, stroke) and race/ethnicity. Results: There were marked differences in PCS and MCS scores by disaggregated Asian American and NH/PI subgroups. After adjustment, Asian American and NH/PI older adults had better PCS but worse MCS than white older adults. Race/ethnicity moderated the relationship between CVD and HRQOL. Discussion: Asian American and NH/PI older adults with CVD had poorer mental health compared to their white counterparts.

Keywords

Race/ethnicity well-being health-related quality of life Asian American Pacific Islander

Introduction

Cardiovascular disease (CVD) is the leading cause of mortality in the United States (U.S.), accounting for over 25% of deaths in individuals 65 and older in 2017 (Heron, 2019; Virani et al., 2020), and is a major cause of morbidity (Lloyd-Jones et al., 2006), disability (Centers for Disease Control and Prevention (CDC), 2009), and healthcare expenditures (Yazdanyar & Newman, 2009). Research has demonstrated that significant racial/ethnic disparities exist in CVD mortality and CVD risk factors (Havranek et al., 2015; Virani et al., 2020), and these health disparities persist in the maintenance of chronic diseases in older age (Riegel et al., 2017). Understanding outcomes beyond mortality, like health-related quality of life (HRQOL), can help assess individual well-being and monitor clinical changes contributing to CVD (Wilson & Cleary, 1995). The American Heart Association (AHA) released the 2030 Impact Goals, highlighting their priorities to increase healthy life expectancy (e.g., HRQOL), center work around equity, and improve health and well-being (Angell et al., 2020).

HRQOL can measure the effects of chronic diseases like diabetes or stroke on daily physical and mental health burdens (Health Services Advisory Group, 2015). HRQOL is not uniformly operationalized in the literature and includes outcomes like general health perceptions, physical functioning psychological health (e.g., happiness and life satisfaction), social relationships, and cognitive functioning (Netuveli & Blane, 2008; Wilson & Cleary, 1995). As an example, HRQOL can be defined as general health perceptions or a combination of physical functioning and mental health. General health perceptions have been shown to be a strong predictor of mortality, declines in health, and hospitalization (Rumsfeld et al., 2013; Wilson & Cleary, 1995). Prior studies have demonstrated that individuals with CVD experience worse HRQOL (Pinheiro et al., 2019), increased depression (Hare et al., 2014) and physical disabilities (Yazdanyar & Newman, 2009). Disparities in HRQOL also exist across racial/ethnic (Ng et al., 2014) and socioeconomic (Clauser et al., 2008) groups. Greater awareness of the burdens of CVD risk could inform more meaningful prevention strategies among aging racial/ethnic minority populations.

The older adult population in the U.S. is also becoming more diverse, with the racial/ethnic minority population projected to surpass the white population by 2043 (Ortman et al., 2014). Asian Americans, Native Hawaiians, and Pacific Islanders (NH/PIs) are projected to have the greatest increases in the older adult population aged 65 and older (Ortman et al., 2014), however, evidence on HRQOL among Asian American and NH/PI older adults with CVD are limited. It is important to ensure that racial/ethnic minority populations are included in preventative and treatment efforts for CVD health and well-being to keep the health disparities gap from worsening. The unique experiences of the racially/ethnically diverse Asian American and NH/PI populations may explain CVD health disparities.

The few studies assessing HRQOL among Asian American and NH/PI subgroups report mixed evidence. For example, Asian American adults reported better physical health but worse mental health than white adults, whereas NH/PI adults reported worse physical and mental health among Medicare managed care members (Ng et al., 2014). However, despite reporting better physical functioning, this same study showed that Asian American adults reported fair or poor health more often than white adults (Ng et al., 2014). Adia and colleagues (2020) found Asian American adults reported fair or poor health more often than white adults using the California Health Interview Survey, and there was marked differences in perceived health across Asian American disaggregated ethnic groups (Adia et al., 2020). NH/PI adults have been reported to report lower percentages of excellent or very good health compared to the total U.S. population and their white and Asian counterparts, with poorer health reported among Samoan individuals (Galinsky et al., 2017). Some explanations for these differences between studies include how HRQOL is operationalized, how the Asian American and NH/PI racial/ethnic groups are categorized, the disease focus (e.g., cancer), and inclusion of different age categories. The relationship between HRQOL and CVD has not been examined by disaggregated Asian American and NH/PI subgroups and could identify ethnic groups at increased risk of worse overall health.

The purpose of this study was to assess the relationship between CVD and HRQOL using the Veterans RAND 12 Item Health Survey (VR-12) among 10 disaggregated Asian American and NH/PI subgroups compared to white Medicare Advantage enrollees using the Health Outcomes Survey (HOS). Characterizing the association between HRQOL and CVD status by race/ethnicity would provide evidence on the effects of chronic conditions on everyday life and inform health promotion and healthy aging interventions targeted towards Asian American and NH/PI communities.

Methods

Data Source

We used the Medicare HOS Limited Data Sets, a patient-reported outcomes survey administered by the Centers for Medicare & Medicaid Services (CMS). The purpose of the HOS is to collect data on Medicare Advantage enrollees to evaluate quality improvement efforts, health plan performance, and provide information to beneficiaries to make informed decisions (Ambs et al., 2008). The HOS randomly samples individuals enrolled in Medicare Advantage plans from participating Medicare Advantage organizations with a minimum of 500 members. Surveys are mailed to respondents in Chinese, Spanish, and English. For respondents who do not complete the survey, they are contacted up to 10 times via telephone calls in Spanish and English.

Sample

Our study population included Medicare Advantage beneficiaries from the annual 2011–2015 HOS baseline cohorts (n = 655,914). We included community-dwelling older adults who were 65 years or older at baseline and identified as Asian American, NH/PI or white as well as non-Hispanic ethnicity. The study sample included two groups of beneficiaries, those who reported any CVD condition and those who did not report a CVD condition. We excluded respondents who had incomplete baseline surveys (i.e., less than 79.5% of the survey was completed), respondents who had end stage renal disease, were in hospice or institutionalized and respondents who did not answer of the questions about CVD, stroke, or PCS and MCS scores. The final analytical sample included 618,154 respondents.

Dependent Variable

The primary health outcome was HRQOL and was operationalized using the Veterans RAND 12-Item Health Survey (VR-12). The VR-12 is comprised of 12 questions that summarize HRQOL into two scores, the physical component score (PCS) and mental component score (MCS). The HOS survey started collecting VR-12 data in 2006. The VR-12 was developed from the Veterans RAND 36-Item Health Survey (VR-36), that was originally adapted from the RAND 36-Item Short Form (SF-36) questionnaire (Iqbal et al., 2015). The main differences between the VR-12 and VR-36 surveys and the SF-36 survey are that the response categories were expanded for role limitations due to physical health and emotional problems and the change in health (Iqbal et al., 2015). The reliability and validity of the VR-12 has been tested among general patient populations, and a difference of 1- to 2-points has been reported to be clinically meaningful (Kazis et al., 2004, 2006).

The VR-12 questions cover eight domains, including perceptions of general health, physical functioning, limitations due to physical and emotional problems, bodily pain, mental health, vitality, and social functioning (Iqbal et al., 2015) (Supplemental Table 1). The PCS and MCS scores are calculated using all questions and weights created from use of the VR-36 during the 1999 Large Health Survey of Veteran Enrollees (Iqbal et al., 2015). The scores are standardized to a 1990 non-institutionalized US population using a t-score transformation where a score of 50 represents the national average and the standard deviation is 10 points (Health Services Advisory Group, 2014). The VR-12 uses the modified regression estimate (MRE) method that uses regression models to impute missing responses based on the patterns of missingness (Spiro et al., 2004).

The PCS and MCS scores range from 0 to 100, where higher scores indicate better physical and mental functioning. Individuals with high PCS scores have no physical limitations or disabilities, high energy, and an excellent health rating (Health Services Advisory Group, 2014). Individuals with high MCS have greater positive affect, no psychological distress, and no limitations due to emotional problems (Health Services Advisory Group, 2014).

Primary Independent Variables

The primary independent variables were self-report of any CVD condition and race/ethnicity. Respondents were asked if they were ever diagnosed with coronary artery disease (CAD), congestive heart failure (CHF), myocardial infarction (MI), other heart conditions (problems with heart valves or the rhythm of their heartbeat), or stroke. Respondents who answered yes to any of the CVD conditions were categorized as has CVD.

We conceptualized self-reported race/ethnicity as a social construct that represents the heterogeneity of cultural norms, sociopolitical history, and acculturation experiences of Asian American and NH/PI groups (Baumhofer & Yamane, 2019; Gee et al., 2019). We also theorize that self-identification into these racial/ethnic groups are linked to experiences of racism and discrimination that impact health outcomes and well-being (Jones, 2001). We categorized respondents into 11 racial/ethnic categories: non-Hispanic Asian Indian, non-Hispanic Chinese, non-Hispanic Filipino, non-Hispanic Japanese, non-Hispanic Korean, non-Hispanic Vietnamese, non-Hispanic Other Asian, Multiple race Asian, non-Hispanic Native Hawaiian, non-Hispanic Pacific Islander, and non-Hispanic white. Multiple race Asian included respondents who self-identified as an Asian group and another racial/ethnic group. Any respondent who identified as Native Hawaiian was categorized as Native Hawaiian. Because of small sample sizes, we had to combine Guamanian, Samoan, or Other Pacific Islander into the Pacific Islander category. Non-Hispanic white adults were included as the primary referent group for our analyses.

Covariates Selection

The Andersen and Newman’s theory of health services utilization was used as a framework to understand the drivers of health care use and barriers that exist that prevent access to health care services (Andersen & Newman, 2005). The main components of this behavioral model include predisposing characteristics such as age and gender, enabling resources such as income and health insurance, and need characteristics such as perceived and evaluated health status. This framework informed the selection of the model covariates. We hypothesized that predisposing characteristics, enabling resources, and need characteristics would independently impact use of health care services, and therefore be related to health care use associated with HRQOL among older adults with CVD. Covariates were also selected based on their known associations with CVD (Eaton, 2005) and health disparities (Kaplan & Keil, 1993).

Predisposing characteristics included age, gender, education level, and marital status. Age was categorized into young-old (65–74 years), middle-old (75–84 years), and old-old (85 years and over) groups. Marital status was dichotomized into married or not married (i.e., divorced/separated, widowed, and single and never married). Enabling resources included household income and whether respondents were enrolled in Medicare only or Medicare/Medicaid. Need characteristics included body mass index (BMI), diabetes, hypertension, smoking status, whether a proxy completed the survey, geographic region, and survey year. BMI was categorized using Asian-specific thresholds for Asian American groups and standard thresholds for NH/PI and white groups. The Asian-specific BMI categories have lower cutoffs for overweight and obese groups (overweight = 23–27.5 kg/m² and obese ≥ 27.5 kg/m²), compared to the standard BMI categories (overweight = 25–30 kg/m² and obese ≥30 kg/m²) (Jih et al., 2014). Respondents were asked whether they were ever diagnosed with diabetes or hypertension. Respondents were asked about their smoking status and the response categories were every day, some days, not at all and don’t know. Respondents were asked who completed the survey, and responses were grouped into no proxy (i.e., person to whom the survey was addressed) or proxy answered (i.e., family member or relative, friend, or professional caregiver to whom the survey was addressed). We adjusted for geographical region of the CMS plan and survey year to account for regional differences in Medicare Advantage organizations and differences in survey administration.

Statistical Analysis

All analyses were conducted with RStudio, version 1.1.453 (RStudio Team, 2016). We calculated frequencies and conducted bivariate analyses to describe characteristics of the total sample. The main comparison was PCS and MCS scores by racial/ethnic group and CVD status. Multivariate regression was conducted to estimate the PCS and MCS mean score differences and 95% confidence intervals (CI) in relation to CVD status and racial/ethnic group. The final model adjusted for predisposing characteristics, enabling resources, and need factors. We conducted regression analyses for the total sample to assess differences across Asian American and NH/PI groups compared to white respondents. We created an interaction term between CVD status and racial/ethnic group to examine the moderating effect of race/ethnicity on HRQOL and CVD status. A statistically significant interaction term suggests that the relationship between HRQOL and CVD status is stronger for Asian American and NH/PI ethnic groups than white adults. We also completed separate regression analyses that only included Asian American and NH/PI groups, to assess differences between Asian American and NH/PI groups.

Japanese individuals were chosen as the reference category because they were more likely to have report fewer CVD conditions (Đoàn et al., 2021) and lower rates of depressive symptoms (Hooker et al., 2018) compared to other Asian American and NH/PI groups. Furthermore, compared to other Asian American and NH/PI groups, Japanese individuals have historically had a different trajectory of acculturation and have the highest percent of US born individuals and lower immigration rates (Shibusawa, 2013). Therefore, we expect that Japanese individuals in the HOS are more acculturated (i.e., more years of life lived in the US, better English proficiency, and better overall health because of familiarity with the US healthcare system) than other Asian American and NH/PI groups.

Results

In total, 618,154 respondents were included in the analysis (Table 1). About 5% of the sample identified as Asian American or NH/PI. Most respondents were 65–74 years old, married, graduated from high school, did not have diabetes, had hypertension, did not smoke, and were overweight. Among adults with CVD, 38% reported having CAD, 20% had CHF, 26% had MI, 61% had other heart conditions, and 21% reported having a stroke. Compared to respondents without CVD, adults with CVD were older and less educated, and reported greater rates of diabetes, hypertension, and being obese.

Table 1.

Descriptive Characteristics of Study Population, by Total Sample and CVD Status, Medicare Health Outcomes Survey, 2011–2015 (n = 618,514).

	Total sample	CVD status
Characteristics	Total sample	Has CVD^a	No CVD	p-value^b
Number of participants	618,154	209,283 (33.9)	408,871 (66.1)
CVD condition, n (%)
Coronary artery disease	79,790 (12.9)	79,790 (38.1)	—
Congestive heart failure	42,089 (6.8)	42,089 (20.1)	—
Myocardial infarction	55,295 (9.0)	55,295 (26.4)	—
Stroke	43,042 (7.0)	43,042 (20.6)	—
Other heart conditions	128,576 (20.8)	128,576 (61.4)	—
Predisposing characteristics
Mean age (SD), years	74.2 (6.9)	75.6 (7.2)	73.5 (6.7)	< .001
Age group, n (%)
65–74 years	360,256 (58.3)	104,134 (49.8)	256,122 (62.6)
75–84 years	198,549 (32.1)	77,930 (37.2)	120,619 (29.5)
85 years and older	59,349 (9.6)	27,219 (13.0)	32,130 (7.9)	< .001
Sex, n (%)
Female	348,776 (56.4)	103,604 (49.5)	245,172 (60.0)
Male	269,378 (43.6)	105,679 (50.5)	163,699 (40.0)	< .001
Race/ethnicity, n (%)
Asian Indian	2739 (.4)	735 (.4)	2004 (.5)
Chinese	6016 (1.0)	1418 (.7)	4598 (1.1)
Filipino	5391 (.9)	1303 (.6)	4088 (1.0)
Japanese	3474 (.6)	834 (.4)	2640 (.6)
Korean	1668 (.3)	278 (.1)	1390 (.3)
Vietnamese	1931 (.3)	440 (.2)	1491 (.4)
Other Asian	1342 (.2)	325 (.2)	1017 (.2)
Multiple race Asian	3241 (.5)	976 (.5)	2265 (.6)
Native Hawaiian	2118 (.3)	720 (.3)	1398 (.3)
Other Pacific Islander	2519 (.4)	784 (.4)	1735 (.4)
White	587,715 (95.1)	201,470 (96.3)	386,245 (94.5)	< .001
Marital status, n (%)
Married	362,344 (59.0)	120,013 (57.7)	242,331 (59.6)
Not married	252,122 (41.0)	88,027 (42.3)	164,095 (40.4)	< .001
Education, n (%)
Less than high school diploma	85,694 (14.0)	33,806 (16.3)	51,888 (12.8)
High school graduate or GED	212,419 (34.7)	71,584 (34.5)	140,835 (34.8)
Some college or 2-year degree	163,459 (26.7)	54,787 (26.4)	108,672 (26.8)
Four-year degree or more	150,830 (24.6)	47,135 (22.7)	103,695 (25.6)	< .001
Enabling resources
Household income, n (%)
Less than $10, 000	44,575 (7.9)	17,147 (8.19)	27,428 (6.71)
$10, 000–19, 999	98,933 (17.5)	38,049 (18.2)	60,884 (14.9)
$20, 000–29, 999	98,465 (17.4)	35,458 (16.9)	63,007 (15.4)
$30, 000–49, 999	131,306 (23.2)	43,723 (20.9)	87,583 (21.4)
$50, 000 or more	135,513 (24.0)	40,156 (19.2)	95,357 (23.3)
Don’t know	56,891 (10.1)	8728 (8.95)	38,163 (9.33)	< .001
Medicare/Medicaid, n (%)	74,021 (12.0)	30,417 (14.5)	43,604 (10.7)	< .001
Healthcare need factors
Diabetes, n (%)	140,732 (22.9)	63,538 (30.5)	77,194 (19.0)	< .001
Hypertension, n (%)	391,281 (63.5)	158,761 (76.1)	232,520 (57.1)	< .001
Smoking status, n (%)
Not at all	555,099 (90.4)	188,301 (90.6)	366,798 (90.3)
Every day	39,333 (6.4)	12,988 (6.2)	26,345 (6.5)
Some days	17,260 (2.8)	57,69 (2.8)	11,491 (2.8)
Don’t know	2384 (.4)	796 (.4)	1588 (.4)	< .001
BMI category, n (%)^c
Normal	184,448 (30.6)	56,485 (27.6)	127,963 (32.2)
Underweight	11,133 (1.8)	3712 (1.8)	7421 (1.9)
Overweight	235,356 (39.1)	78,743 (38.5)	156,613 (39.3)
Obese	171,204 (28.4)	65,412 (32.0)	105,792 (26.6)	< .001
Proxy completed survey, n (%)	49,830 (8.2)	23,125 (11.2)	26,705 (6.6)	< .001

BMI, body mass index; CVD, cardiovascular disease.

Note: Column percentages are calculated and may not total 100% due to rounding

^aThis category includes respondents who answered yes to having angina pectoris or coronary artery disease, congestive heart failure, myocardial infarction, or other heart conditions (e.g., problems with heart valves or the rhythm of their heartbeat), or stroke.

^bp-values reported compare those who reported not having been diagnosed with any CVD to those with CVD. p-values < .05 were considered statistically significant.

^cAsian-specific BMI thresholds (overweight = 23–27.5 kg/m², obese ≥ 27.5 kg/m²) were applied to Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, Other Asian, and Multiple-race Asian groups. Standard BMI thresholds (overweight = 25–30 kg/m², obese ≥ 30 kg/m²) were applied white, Native Hawaiian, and Other Pacific Islander groups.

VR-12 PCS and MCS Scores

Table 2 presents the VR-12 PCS and MCS scores by total sample, CVD status and race/ethnicity. Lower scores represent worse health and a difference of 1- to 2-points in scores is considered clinically meaningful. PCS scores were lower than MCS scores. The mean PCS score (standard deviation) was 41.1 (SD = 12.1) and mean MCS score was 53.8 (SD = 9.9) for the total sample. PCS and MCS scores varied across Asian American and NH/PI groups, and the majority of Asian American and NH/PI groups had lower PCS and MCS scores compared to white adults.

Table 2.

VR-12 Physical and Mental Component Scores, by Total Sample, CVD Status and Racial/Ethnic Group, Medicare Health Outcomes Survey, 2011–2015.

	Total sample		CVD status
	Total sample		Has CVD		No CVD
	Mean PCS score (SD)	Mean MCS score (SD)	Mean PCS score (SD)	Mean MCS score (SD)	Mean PCS score (SD)	Mean MCS score (SD)
Number of participants	639,660	639,629	209,199	209,182	408,757	408,742
Total sample	41.1 (12.1)	53.8 (9.9)	36.4 (12.3)	52.1 (11.0)	43.5 (11.2)	54.6 (9.2)
White (reference)	41.1 (12.1)	53.9 (9.9)	36.5 (12.4)	52.2 (11.0)	43.5 (11.3)	54.8 (9.2)
Asian Indian	40.5 (11.1)	52.2 (10.7)	36.5 (11.4)	49.0 (11.8)	42.0 (10.7)	53.4 (10.0)
Chinese	41.4 (10.9)	49.9 (10.8)	36.0 (11.0)	46.3 (11.9)	43.0 (10.3)	51.0 (10.2)
Filipino	40.7 (11.0)	51.3 (10.1)	36.0 (11.0)	48.6 (11.0)	42.2 (10.5)	52.1 (9.7)
Japanese	43.2 (11.1)	53.6 (9.7)	39.0 (11.3)	52.1 (10.8)	44.5 (10.7)	54.1 (9.3)
Korean	43.6 (9.8)	50.8 (9.8)	39.2 (10.4)	46.9 (10.8)	44.4 (9.4)	51.6 (9.4)
Vietnamese	39.5 (10.2)	49.0 (9.9)	35.4 (10.3)	45.2 (9.9)	40.6 (9.9)	50.1 (9.6)
Other Asian	39.5 (10.9)	49.2 (11.2)	35.5 (11.5)	46.1 (11.8)	40.8 (10.4)	50.2 (10.9)
Multiple race Asian	39.7 (11.1)	50.2 (11.1)	34.9 (11.2)	47.6 (11.9)	41.9 (10.4)	51.4 (10.4)
Native Hawaiian	40.7 (11.7)	51.4 (11.1)	35.7 (11.7)	48.7 (12.2)	43.2 (10.8)	52.7 (10.3)
Other Pacific Islander	37.3 (11.7)	47.6 (12.1)	33.3 (11.7)	44.2 (12.3)	39.2 (11.4)	49.1 (11.6)
p-value	<.001	<.001	<.001	<.001	<.001	<.001

CVD, cardiovascular disease; MCS, mental component score; PCS, physical component score; SD, standard deviation; VR-12, Veteran’s RAND 12-item survey.

Note: The PCS and MCS scores range from 0 to 100, where higher scores indicate better physical and mental health functioning. Dark gray boxes represent lower scores (worse health) and light gray boxes represent higher scores (better health) than whites. ANOVA tests were conducted to test for differences in PCS and MCS scores by race/ethnicity. Non-Hispanic white is the reference group.

As expected, PCS and MCS scores were lower among adults with CVD compared to adults without CVD (Table 2). Regardless of CVD status, compared to white adults, the majority of Asian American and NH/PI groups reported significantly lower PCS and MCS scores. The two exceptions were Japanese and Korean adults who reported higher PCS scores than white adults. Among Asian American adults with CVD, multiple race Asian adults reported the lowest PCS scores and Vietnamese adults reported the lowest MCS scores. Pacific Islander adults reported the lowest PCS and MCS scores compared to Native Hawaiian, white and Asian American adults. There were notable differences in the magnitude of scores across ethnic groups, with a range of 6 PCS points between Korean and Pacific Islander adults and range of 8 MCS points between Japanese and Pacific Islander adults.

Multivariate Model for Total Sample

Table 3 shows the results of the adjusted regression models assessing the differences in PCS and MCS scores across Asian American and NH/PI groups compared to white respondents. Negative estimates represent lower scores and worse health. After adjustment, having CVD was significantly associated with a decrease of 4.94 PCS (95% CI = -5.00, -4.88) and 1.84 MCS (95% CI = -1.90, -1.78) points compared to adults without CVD. Compared to white adults, Asian American and NH/PI ethnic groups had significantly higher PCS scores, except for Asian Indian adults. For MCS scores, Chinese, Korean, Multiple race Asian and Pacific Islander groups had significantly lower MCS scores compared to white adults, whereas Filipino adults had higher MCS scores. We observed the greatest PCS mean score difference (2.56 points, 95% CI = 2.21, 2.92) in Filipino adults and the greatest MCS mean score difference (-.96 points, 95% CI = -1.40, -.52) in Multiple race Asian adults.

Table 3.

Adjusted PCS and MCS Mean Score Differences for the Overall Study Population (white is the Reference), Medicare Health Outcomes Survey, 2011–2015 (n = 618,154).

	PCS mean score difference	95% CI	MCS mean score difference	95% CI
Intercept	56.21		60.88
CVD status
No	Reference		Reference
Yes	−4.94	−5.00, −4.88	−1.84	−1.90, −1.78
Race/ethnicity
White	Reference		Reference
Asian Indian	−.06	−.55, .43	.31	−.13, .76
Chinese	2.51	2.18, 2.84	−.56	−.86, −.26
Filipino	2.56	2.21, 2.92	.40	.08, .73
Japanese	2.17	1.74, 2.60	.17	−.22, .56
Korean	2.16	1.58, 2.75	−.71	−1.24, −.17
Vietnamese	1.76	1.18, 2.35	−.32	−.85, .21
Other Asian	1.71	.99, 2.42	−.53	−1.18, .11
Multiple-race Asian	.74	.26, 1.23	−.96	−1.40, −.52
Native Hawaiian	2.25	1.64, 2.86	−.31	−.86, .24
Other Pacific Islander	1.21	.64, 1.77	−.80	−1.31, −.29
Race/ethnicity $\times$ CVD status
White $\times$ has CVD	Reference		Reference
Asian Indian $\times$ has CVD	1.44	.49, 2.38	−1.79	−2.64, −.93
Chinese $\times$ has CVD	.50	−.17, 1.17	−1.89	−2.50, −1.28
Filipino $\times$ has CVD	.63	−.07, 1.32	−1.13	−1.76, −.50
Japanese $\times$ has CVD	1.50	.64, 2.35	.11	−.66, .89
Korean $\times$ has CVD	2.03	.61, 3.44	−2.32	−3.60, −1.04
Vietnamese $\times$ has CVD	1.50	.29, 2.71	−2.36	−3.46, −1.27
Other Asian $\times$ has CVD	1.98	.55, 3.40	−1.52	−2.81, −.23
Multiple-race Asian $\times$ has CVD	.03	−.83, .90	−1.05	−1.83, −.27
Native Hawaiian $\times$ has CVD	−.42	−1.44, .60	−.62	−1.55, .31
Other Pacific Islander $\times$ has CVD	1.31	.32, 2.30	−2.28	−3.18, −1.38
Age categories
65–74 years	Reference		Reference
75–84 years	−2.63	−2.69, −2.57	.42	.36, .48
85 years or more	−6.71	−6.82, −6.60	−.21	−.31, −.12
Sex
Male	Reference		Reference
Female	−1.38	−1.44, −1.32	−.47	−.53, −.42
Education
Less than high school	Reference		Reference
HS/GED	.73	.64, .82	1.25	1.17, 1.33
Some college	1.04	.94, 1.14	1.51	1.42, 1.60
College+	2.11	2.00, 2.21	1.64	1.54, 1.73
Marital status
Married	Reference		Reference
Not married	.37	.31, .44	−.58	−.64, −.52
Medicaid
Medicare only	Reference		Reference
Medicare/Medicaid	−3.87	−3.97, −3.76	−2.37	−2.46, −2.28
Income
Less than $10,000	−4.34	−4.48, −4.21	−3.51	−3.63, −3.38
$10,000–$19,999	−3.98	−4.09, −3.88	−2.76	−2.85, −2.66
$20,000–$29,999	−3.12	−3.22, −3.03	−1.94	−2.03, −1.86
$30,000–$49,999	−1.82	−1.90, −1.73	−1.03	−1.11, −.95
$50,000 or more	Reference		Reference
Don’t know	−2.72	−2.83, −2.61	−1.26	−1.36, −1.16
Diabetes
No	Reference		Reference
Yes	−2.72	−2.79, −2.65	−1.29	−1.36, −1.23
Hypertension
No	Reference		Reference
Yes	−1.98	−2.04, −1.92	−.38	−.43, −.32
Smoking status
Never smoked	Reference		Reference
Smoke every day	−2.76	−2.88, −2.65	−1.78	−1.88, −1.67
Smoke some days	−2.04	−2.21, −1.87	−1.92	−2.07, −1.76
Don’t know	.23	−.23, .69	−2.70	−3.12, −2.28
BMI category^a
Normal	Reference		Reference
Underweight	−2.87	−3.08, −2.65	−1.88	−2.07, −1.68
Overweight	−1.03	−1.10, −.96	.31	.25, .37
Obese	−5.03	−5.11, −4.96	−.16	−.22, −.09
Proxy completed survey
No	Reference		Reference
Yes	−5.81	−5.92, −5.70	−4.83	−4.93, −4.73

BMI, body mass index; CI, confidence interval; CVD, cardiovascular disease; MCS, mental component score; PCS, physical component score.

Note: A negative mean score difference indicates worse health and a positive mean score difference indicates better health. Bold values denote statistical significance at the p < .05 level. This analysis adjusted for predisposing factors (sex, age, marital status, education level), enabling factors (income and Medicaid eligibility), and need factors (hypertension, diabetes, smoking status, BMI, whether a proxy completed the survey, Medicare Advantage Organization region).

^aAsian-specific BMI thresholds (overweight = 23–27.5 kg/m², obese ≥27.5 kg/m²) were applied to Asian American ethnic groups. Standard BMI thresholds (overweight = 25–30 kg/m², obese ≥30 kg/m²) were applied to white and Native Hawaiian and Other Pacific Islander groups.

For the moderating effect of race/ethnicity (interaction term), the direction of the associations between PCS and MCS and CVD remained the same, but the magnitude of mean score difference increased for MCS. For the relationship between PCS and CVD status, the interaction terms for Asian Indian, Japanese, Korean, Vietnamese, Other Asian, and Pacific Islander groups were statistically significant. This suggests that having CVD in these ethnic groups were more likely to report better PCS scores than white adults. For the MCS, except for Japanese and Native Hawaiian adults, Asian American and Pacific Islander adults with CVD were more likely to report worse MCS scores.

Multivariate Model for Asian American and NH/PI Only

Table 4 shows the adjusted regression models for the Asian American and NH/PI sample only (white adults are not included), to examine the differences in PCS and MCS scores between Asian American and NH/PI ethnic groups compared to Japanese adults. After adjustment, having CVD was associated with significantly lower PCS (mean score difference = -3.87; 95% CI = -4.66, -3.07) and MCS (mean score difference = -1.70; 95% CI = -2.52, -.88) scores. Most Asian American and NH/PI ethnic groups had lower PCS and MCS scores compared to Japanese adults but statistically significant differences varied by ethnic group. This indicates that there is heterogeneity in PCS and MCS scores among the Asian American and NH/PI sample. For example, Pacific Islanders had a PCS score that was 2.37 points (95% CI = -3.07, -1.67) and MCS score than was 2.08 (95% CI = -2.80, -1.36) points lower than Japanese adults.

Table 4.

Adjusted PCS and MCS Mean Score Differences for Asian American and NH/PI Groups (Japanese is the Reference), Medicare Health Outcomes Survey, 2011–2015 (n = 31,779).

	PCS mean score difference	95% CI	MCS mean score difference	95% CI
Intercept	54.59		58.61
CVD status
No	Reference		Reference
Yes	−3.87	−4.66, −3.07	−1.70	−2.52, −.88
Race/ethnicity
Japanese	Reference		Reference
Asian Indian	−2.23	−2.88, −1.57	−.49	−1.16, .18
Chinese	−.38	−.90, .14	−1.42	−1.95, −.88
Filipino	−.43	−.96, .10	−.16	−.71, .38
Korean	−.34	−1.03, .36	−1.89	−2.61, −1.17
Vietnamese	−1.87	−2.59, −1.16	−1.56	−2.30, −.83
Other Asian	−1.62	−2.42, −.82	−1.80	−2.63, −.98
Multiple-race Asian	−1.76	−2.37, −1.14	−1.50	−2.13, −.87
Native Hawaiian	−.77	−1.46, −.07	−.52	−1.24, .20
Other Pacific Islander	−2.37	−3.07, −1.67	−2.08	−2.80, −1.36
Race/ethnicity $\times$ CVD status
Japanese $\times$ has CVD	Reference		Reference
Asian Indian $\times$ has CVD	−.16	−1.34, 1.03	−1.82	−3.04, −.60
Chinese $\times$ has CVD	−1.11	−2.12, −.10	−2.01	−3.05, −.97
Filipino $\times$ has CVD	−.85	−1.88, .17	−1.32	−2.37, −.26
Korean $\times$ has CVD	.54	−1.00, 2.07	−2.29	−3.87, −.71
Vietnamese $\times$ has CVD	.11	−1.26, 1.49	−2.52	−3.93, −1.10
Other Asian $\times$ has CVD	.42	−1.12, 1.96	−1.60	−3.20, −.01
Multiple-race Asian	−1.62	−2.75, −.50	−1.23	−2.40, −.07
Native Hawaiian $\times$ has CVD	−2.05	−3.28, −.81	−.76	−2.04, .51
Other Pacific Islander $\times$ has CVD	−.23	−1.44, .99	−2.51	−3.77, −1.26
Age categories
65–74 years	Reference		Reference
75–84 years	−2.47	−2.74, −2.20	−.36	−.64, −.08
85 years or more	−6.88	−7.35, −6.41	−1.76	−2.25, −1.28
Sex
Male	Reference		Reference
Female	−1.79	−2.05, −1.53	−.24	−.51, .03
Education
Less than high school	Reference		Reference
HS/GED	.28	−.08, .64	1.4	1.03, 1.77
Some college	.44	.04, .84	1.41	1.00, 1.82
College+	1.1	.72, 1.47	2.04	1.65, 2.43
Marital status
Married	Reference		Reference
Not married	.11	−.16, .39	−.19	−.47, .10
Medicaid
Medicare only	Reference		Reference
Medicare/Medicaid	−3.17	−3.48, −2.85	−2.22	−2.54, −1.90
Income
Less than $10,000	−2.88	−3.33, −2.44	−3.71	−4.17, −3.25
$10,000–$19,999	−2.97	−3.39, −2.55	−3.15	−3.58, −2.72
$20,000–$29,999	−2.52	−2.97, −2.07	−2.19	−2.65, −1.72
$30,000–$49,999	−1.39	−1.81, −.98	−1.58	−2.01, −1.16
$50,000 or more	Reference		Reference
Don’t know	−2.42	−2.88, −1.97	−1.75	−2.21, −1.29
Diabetes
No	Reference		Reference
Yes	−2.34	−2.61, −2.08	−1.48	−1.75, −1.21
Hypertension
No	Reference		Reference
Yes	−1.52	−1.78, −1.25	−.59	−.86, −.31
Smoking status
Never smoked	Reference		Reference
Smoke every day	−.52	−1.13, .09	−1.23	−1.85, −.60
Smoke some days	−.69	−1.48, .09	−1.9	−2.70, −1.09
Don’t know	1.74	.59, 2.89	−2.47	−3.65, −1.29
BMI category^a
Normal	Reference		Reference
Underweight	−1.65	−2.27, −1.03	−2.43	−3.07, −1.79
Overweight	−.53	−.81, −.25	.02	−.27, .30
Obese	−3.09	−3.44, −2.74	−.45	−.81, −.09
Proxy completed survey
No	Reference		Reference
Yes	−2.9	−3.23, −2.58	−1.32	−1.66, −.99

BMI, body mass index; CI, confidence interval; CVD, cardiovascular disease; MCS, mental component score; NH/PI, native Hawaiian and other Pacific Islander PCS, physical component score.

For PCS, the interaction term for Chinese, Multiple race Asian, and Native Hawaiian adults were statistically significant. This suggests that having CVD in these ethnic groups was associated with worse PCS scores than Japanese adults. Compared to Japanese adults with CVD, the majority of Asian American and Pacific Islander adults with CVD had significantly worse MCS scores, except for Native Hawaiian adults.

Discussion

This study assessed the association between CVD status and HRQOL among 10 disaggregated Asian American and NH/PI older adults enrolled in Medicare Advantage health plans. Overall, adults who self-reported a CVD condition had lower PCS and MCS scores than adults without CVD. We observed notable differences in the magnitude of PCS and MCS scores across Asian American and NH/PI ethnic groups, indicating differences in HRQOL when data are disaggregated. After adjusting for covariates, compared to white adults, Asian American and NH/PI ethnic groups had better PCS but worse MCS, though the differences were not statistical for all ethnic groups.

Our results indicate that having CVD was consistently associated with worse HRQOL, and having CVD impacted physical health more than mental health. For example, having CVD decreased PCS scores by 5 points but only decreased MCS scores by 2 points. The trend with PCS scores might be explained in part because individuals who suffer from a CVD event could experience a greater daily burden from physical disabilities and comorbidities than mental health (Rijken et al., 2005). Need characteristics included in the analyses such as diabetes and BMI may be more strongly associated with changes in physical than mental health. The estimated prevalence of adults aged ≥ 20 years with CVD was 48% in 2016 (121.5 million people) (Virani et al., 2020), and understanding acute and chronic impacts of CVD conditions and stroke on HRQOL is increasingly important to anticipate the needs of the aging adult population.

We found distinct patterns in PCS and MCS scores by disaggregated Asian American and NH/PI ethnic groups. Our findings indicate that some Asian American groups, including Vietnamese, Other Asian and Multiple race Asian adults, and Pacific Islander adults may be at greater risk for worse HRQOL, given that a score difference of 1–2 points have demonstrated meaningful clinical differences in general patient populations (Kazis et al., 2004, 2006). CMS previously reported similar findings where 25% of Other Asian and 42% of Samoan adults screened positive for depression, and 26% of Other Asian and 36% of Pacific Islander adults reported having 14–30 days with activity limitations in the past 30 days (Ritenour et al., 2017). We expect that the Other Asian category includes recent immigrants to the US or smaller Asian subgroups, like Cambodian or Laotian (Hoeffel et al., 2012), which could explain the worse HRQOL. Multiple race Asians are the fastest growing group within the Asian American racial category (Hoeffel et al., 2012), and are a population worth examining in detail in future research. The variation in HRQOL across Asian American and NH/PI groups is likely multifactorial, and could be explained by the diverse histories, cultures, and languages of these communities that subsequently impact health behaviors and health outcomes (Braun et al., 2015; Islam et al., 2010). For example, differences in health status for a Vietnamese American adult in comparison to a Japanese American adult could be explained in part by lower socioeconomic position, lower English language proficiency, and discriminatory experiences related to the context of migration (e.g., Vietnam War, Japanese internment during World War II). Assari and Kumar (2018) reported differences in overall self-rated health across Asian ethnic groups and in the effect of the socioeconomic factors (e.g., income, education, employment) on self-rated health in the National Asian American Survey, which is consistent with our results. Another study found that limited-English proficient adults were more likely to report poorer health and worse health care compared to adults who were English proficient (Ponce et al., 2006). The determinants of HRQOL are complex and require that future interventions and policies address the intersectional nature of an individual’s history.

Compared to white respondents, we found that the majority of Asian American and NH/PI groups had worse mental health but better physical health, although not all differences were statistically significant. This finding may be partially explained by the predisposing, enabling and need covariates. We previously reported that Asian American and NH/PI ethnic groups had greater prevalence of being overweight or obese, diabetes and hypertension than white adults (Đoàn et al., 2021). Asian American and NH/PI groups had lower HRQOL scores compared to white adults (Table 2). However, after we adjusted for covariates, we observed higher PCS scores among Asian American and NH/PI groups compared to white adults. Thus, perhaps the varying PCS scores among Asian American and NH/PI adults were more related to being overweight or obese, diabetes and hypertension than having a CVD condition. The younger age of Asian American and NH/PI ethnic groups compared to white adults might also explain the better PCS scores (Đoàn et al., 2021). Prior research suggests that there may be a healthy migrant effect, where Asian American immigrants who have lived fewer years in the US had lower risk of disabilities compared to native-born individuals and immigrants who have lived longer in the US (Cho & Hummer, 2001). The consistently lower MCS scores could be attributable to trauma experience pre-immigration, for instance, the refugee experience of the Vietnam War (Palaniappan et al., 2010). In contrast to our findings, prior research found that Asian Americans had significantly better MCS scores compared to white respondents (Clauser et al., 2008). However, this difference might be explained because the study did not examine HRQOL by Asian American ethnic groups and the variation in MCS scores could have been masked by the aggregate grouping.

When we included an interaction term to test the moderating effect of race/ethnicity, there was a stronger relationship with having worse mental health among Asian American and NH/PI groups with CVD than their white counterparts. These findings demonstrate a need to focus on mental health among this population and that the response to improving HRQOL among Asian American and NH/PI ethnic groups is not one size fits all. Future work should improve current available data and collect high-quality, ethnic specific data for Asian Americans and NH/PIs; this would include collecting or merging of population level datasets (e.g., American Community Survey data) that include sociocultural, psychosocial, environmental, and lifestyle information and detailed CVD information and subclinical disease measures to understand disease progression across the lifecourse by specific ethnic groups (Kanaya et al., 2022). Taken together, advancing the empirical evidence on the burden of CVD risk among Asian Americans and NH/PIs will inform tailored prevention and healthy aging programming and interventions. Furthermore, existing interventions on CVD risk factors and disease management are rarely focused on older adults (Riegel et al., 2017), Asian Americans and NH/PIs, and reducing health disparities (Davis et al., 2007; Graham, 2014).

A potential area of focus for promoting healthy aging among Asian American and NH/PI adults is improving mental health. Asian American and NH/PI groups experience disparities in access to mental health care (Cook et al., 2017) and health services utilization (Lim et al., 2019), and intervention efforts are required to address current disparities and to keep them from widening as the population increases. Framing interventions around an integrated care approach could improve prevention and treatment of mental and physical health problems (e.g., older adult who had a stroke and has depression) for Asian American and NH/PI older adults, as well as other elderly populations (American Psychological Association, Presidential Task Force on Integrative Health Care for an Aging Population, 2008; Ida et al., 2012). For example, an intervention focusing on improving positive psychological well-being could subsequently improve perceptions of overall health, and thus CVD outcomes and management of CVD risk factors among older adults (Kubzansky et al., 2018). Interventions must consider the cultural traditions and identities of Asian American and NH/PI populations by training health professionals to provide culturally- and linguistically-competent care and could increase the use of mental health services by focusing programming on Asian American and NH/PI older adults and expanding the workforce to include ethnically diverse providers who are able to provide services in patient’s preferred languages, have received cultural competency training, and may be knowledgeable about cultural practices and beliefs (Tseng, 2016).

When considering our results, there are some limitations to note. First, the HOS data is self-reported, which may have resulted in higher HRQOL scores and underreporting of CVD due to the desire to report better health outcomes. Second, the HOS is only administered in English, Spanish, and Chinese (mail-only survey) among individuals with Medicare Advantage, so the HOS study population could lack generalizability and be biased towards respondents who were more fluent in English and towards respondents eligible for Medicare Advantage. The VR-12 instrument has not been validated for limited-English proficient individuals (Kazis et al., 2004, 2006). Thus, the VR-12 scores calculated from the HOS survey may not be equivalent across racial/ethnic groups (i.e., VR-12 scores may not be a meaningful comparison). Third, the VR-12 scores in the HOS also uses the scoring algorithm that normalizes the score to a 1990 US population standard (Selim et al., 2009), which is not representative of the current population composition and distribution. There are updated scoring algorithms that normalizes VR-12 scores to a 1998 US population standard and using the 2000 to 2002 Medical Expenditure Panel Survey (MEPS), however the first algorithm is proprietary and the second algorithm was based on the SF-12 questionnaire (Selim et al., 2009). Nevertheless, the VR-12 instrument has been utilized for the HOS since 2006 (Selim et al., 2009) and measures several domains of HRQOL, including general health perceptions, that provides a comprehensive summary of how CVD conditions are impacting health in older age. Fourth, the HOS baseline data we analyzed is cross-sectional. We are unable to infer causation and directionality in the relationship between HRQOL and CVD and rely on self-report information that may be biased.

Our study had several strengths that have important implications for the Asian American and NH/PI older adult population, particularly those with CVD. Our study includes a robust, nationally representative sample of older adults enrolled in Medicare Advantage plans and has disaggregated data for eight Asian American and two NH/PI groups. The large size of Asian American and NH/PI ethnic groups allowed us to estimate HRQOL among adults enrolled in Medicare Advantage plans, who are a gradually increasing group in Medicare (Neuman & Jacobson, 2018). Few other data sources provide the sample size and variation necessary to conduct a disaggregated Asian American and NH/PI ethnic group study like what we present in this analysis.

Data collection and epidemiological efforts should prioritize and invest in Asian American and NH/PI communities, who have been historically ignored in research (Morey et al., 2021; Yi et al., 2021). Future research could use a social determinants of health framework (Russo et al., 2021) to assess longitudinal data that detail major life and adverse events that impact HRQOL (e.g., immigration pathway, COVID-19 pandemic, acute symptoms from a stroke, chronic symptoms related to other heart conditions) and how HRQOL is changing in the context of these events and aging. Given the unique needs of older adults, particularly given the synergistic impacts of the ongoing COVID-19 pandemic and rising anti-Asian hate (Ma et al., 2021; Saw et al., 2022), understanding mental health could provide context on health behaviors that could inform how to best tailor interventions and healthcare services to improve health for racial/ethnic minority older adults.

Supplemental Material

Supplemental Material - Cardiovascular Disease and Health-Related Quality of Life Among Asian American, Native Hawaiian and Pacific Islander Older Adults

Supplemental Material for Cardiovascular Disease and Health-Related Quality of Life Among Asian American, Native Hawaiian and Pacific Islander Older Adults by Lan N. Đoàn, Yumie Takata, Carolyn Mendez-Luck, Karen Hooker, and Veronica L. Irvin in Journal of Aging and Health

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institute on Minority Health and Health Disparities (U54MD000538), Jo Anne Leonard Petersen fund in Gerontology and Family Studies, National Institute on Aging (5P30AG059302, R36AG060132), Centers for Disease Control and Prevention (1NH23IP922639-01-00, CFDA number 93.185, NU38OT2020001477, CFDA number 93.421).

ORCID iD

Lan N. Doán

Supplemental Material

Supplemental material for this article is available online.

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