Abstract
In this study, we used data from the Health and Retirement Study (HRS) to investigate factors associated with older adults’ engagement with advance care planning (ACP) across varying levels of cognitive functioning status. Our analysis used a sample of 17,698 participants in the HRS 2014 survey. Survey descriptive procedures (Proc SurveyMeans, Proc SurveyFreq) and logistic regression procedures (Proc SurveyLogistic) were used. Race, ethnicity, level of cognition, education, age, and number of chronic diseases consistently predicted ACP. Participants with lower levels of cognition were less likely to have a living will and durable power of attorney for healthcare (DPOAH). African American and Hispanic participants, younger participants, and those with lower cognition and education levels were less likely to engage in ACP. Marital status and loneliness predicted ACP engagement. Some results varied across the cognition cohorts. Our results indicated that sociodemographic status, together with health and cognitive status, has a significant role in predicting ACP. The results can provide valuable insights on ACP for older adults with or at risk of Alzheimer’s disease and related dementia and other cognitive impairments, caregivers, families, and healthcare providers.
Keywords: advance care planning, living will, durable power of attorney for healthcare, end-of-life care, health and retirement study, Alzheimer’s disease and related dementia, older adults, sociodemographic status
Introduction
In the United States, Alzheimer’s disease and related dementias (ADRD) are the fifth leading cause of death among adults aged 65 and older.1 In addition to Alzheimer’s disease, other types of related dementias include vascular, frontotemporal, Lewy bodies, Parkinson’s disease, mixed, and recently discovered limbic-predominant age-related TDP-43 encephalopathy (LATE) dementia.2,3 The prevalence and challenges of ADRD are magnified among ethnic4 and rural5,6 groups, women, and persons with older age, lower education, and previous health issues.7 Advance care planning (ACP) and end-of-life (EOL) care pose unique challenges in ADRD patients.8 As dementia progresses and decisional capacity declines, healthcare decisions are typically made by a family member or proxy, which may be challenging when life-expectancy is unknown and advanced care planning discussions have not occurred.9
Advance care planning includes formal mechanisms or written documents, such as advance directives, living wills, and durable power of attorney for healthcare (DPOAH), and informal mechanisms, such as verbal discussion of healthcare preferences with family and providers.10 The purposes and definitions of ACP across the time reflect a shift from a written document, such as an advance directive, to a process of ongoing support, communication, and in-the-moment decision making for understanding and sharing personal values, preferences, and goals of care in adults of any age and health status.11,12 Advance care planning provides opportunities for communication of healthcare decisions and choices ahead of the time when a person has impaired medical decisional capacity. There is controversy in the field about the benefits of ACP. Morrison contended that ACP may not help to achieve certain outcomes, such as reducing medical expenses.13 However, there is evidence indicating that ACP helps improve outcomes related to patients, surrogates, and clinicians, such as quality of care, satisfaction with communication, and satisfaction with patients’ care. Furthermore, ACP helps reduce surrogates’ and clinicians’ distress and enhance medical decision making congruent with personal values and goals.14,15 Moreover, as the baby boomer generation has aged, they have helped to raise awareness of the value and importance of autonomy in healthcare decision.16
In this study, we used data from the Health and Retirement Study (HRS) to investigate factors associated with older adults’ engagement with ACP across varying levels of cognitive status. We define ACP as having a living will and DPOAH, measures available in HRS 2014 survey (wave 12). A living will is a document expressing the person’s preferences for medical treatments that helps understand a person wishes to receive or decline specific healthcare in severe health conditions or when they have impaired medical decisional capacity.17 A DPOAH assigns a person to make healthcare decisions on behalf of a person when they are incapable of making decisions.17 Our research questions included: (1) How commonly do people with varying cognitive status engage in ACP? (2) What sociodemographic and health factors, including race and ethnicity, correlate with ACP engagement?
Methods
Our overall strategy was to conduct an observational, cross-sectional study using data sets from the HRS. The HRS is a nationally representative dataset and includes data from more than 43,000 respondents on four major components: health and well-being, work and retirement, social connections, and economic status.18 The setting is a population-based survey conducted by the Institute for Social Research at the University of Michigan and funded by the National Institute on Aging and the Social Security Administration. The HRS team collects information from adults older than 51 years every two years on questions related to health, functional status, family structure, demographics, and lifestyle activities using a probability sample method with oversampling of African American, Hispanic, and Floridian participants.15
Our analysis used a sample of 17,698 participants of the HRS 2014 survey (wave 12). We combined data from two different HRS datasets: the harmonized HRS version B and the 2016 Rand HRS Longitudinal version 2 to ensure pooled cross-sectional data to achieve statistical power and examine the relationships between study variables. Sub-data files were created from the original files, followed by procedures including data cleaning, computing, and recoding variables when appropriate. The final data file was created by merging all subfiles. We selected the 2014 wave because it was the latest and the most comprehensive wave used in selected harmonized data sets, specifically in EOL sections. Our University Institutional Review Board approved our study protocols.
Data Analysis
SAS Version 9.4 was used for data analyses. Descriptive statistics were calculated to examine frequencies and distributions of key variables. The HRS incorporates a complex sampling design that over-samples minorities and included multiple respondents from a household, thus sample individual and household weights were used for some analyses. Weighted analyses were performed for descriptive statistics (Proc SurveyMeans, Proc SurveyFreq); however, due to the potential for bias in properly designated models,19 we utilized non survey procedures (Proc Logistic) for inferential model analyses.
The primary outcomes modeled were whether the respondents had a DPOAH (“whether respondent has durable power of attorney” [1 = yes, 0 = no]) and a living will (“whether respondent has a living will” [1 = yes, 0 = no]). Independent variables included race, ethnicity, education, gender, marital status, number of chronic conditions (none, 1 to 3, 4 or more out of 8 [high blood pressure, diabetes, cancer, lung disease, heart disease, stroke, psychiatric disorders, and arthritis]), assistance required for activities of daily living (none, one or more [bathing, dressing, eating, getting in/out of bed, toilet use]), rurality (rural, urban), and loneliness score (mean summary of 4 items).
We utilized the Langa-Weir approach, which is a composite measure ranging from 0 to 27 to differentiate cognitive impairment from more advanced dementia.20,21 Using this approach, the cohort was split into three groups: dementia (Langa-Weir score < 7), impaired cognition (Langa-Weir score 7-11), and normal cognition (Langa-Weir score > 11). All analyses were stratified to create a prediction model for each cognition category, allowing for comparison of significant predictors of ACP across cognition cohorts. Prediction models based on preliminary analyses were developed for each outcome (DPOAH and living will). All predictor variables significant in univariate models were included in the full model for each outcome.
Results
Table 1 shows the descriptive results stratified by cognition level. Of the 17,698 participants, 77.8% had normal cognition, 17.4% had cognitive impairment, and 4.8% had dementia. With higher levels of education, the likelihood of cognitive impairment and dementia decreased. Only 17.7% of the participants with dementia had completed higher education (beyond high school), compared to 29.5% of the impaired cognition, and 61.1% of the normal cognition cohorts. The HRS survey oversamples Black and Hispanic adults, however, the majority of the cohort (>85%) was non-Hispanic white. Among the cohort with normal cognition, 7.9% were Black and 7.1% Hispanic, compared to the cohort with cognitive impairment (22.2% Black, 15.6% Hispanic) and dementia (23.4% Black, 22% Hispanic). Marital status also varied between cognition groups, ranging from 76% married or living with a partner in the normal cognition (76%), to 70.6% in the cognitive impairment, and 69.9% in the dementia cohorts. Similarly, a greater proportion of people who lived alone had dementia (34%) and cognitive impairment (32.1%), compared to normal cognition (22.6%).
Table 1.
Descriptive Statistics for the 2014 Heath Retirement Survey Cohort by Cognition Group, Weighted %.
| Cognition Group Langa-Weir Score Range |
Normal >11 N = 13774 |
Impaired 7-11 N = 3080 |
Dementia < 7 N = 844 |
P-Value |
|---|---|---|---|---|
| Race | ||||
| White | 86.3 | 67.8 | 63.2 | <.01 |
| Black | 7.9 | 22.2 | 23.4 | |
| Other | 5.9 | 10.0 | 13.5 | |
| Ethnicity | ||||
| Hispanic | 7.1 | 15.6 | 22.6 | <.01 |
| Education | ||||
| ≤12 years | 38.8 | 70.5 | 82.3 | <.01 |
| 13-14 years | 21.7 | 15.5 | 9.7 | |
| 15+ years | 39.4 | 14.0 | 8.0 | |
| Gender | ||||
| Male | 45.1 | 46.1 | 42.4 | .30 |
| Female | 54.8 | 53.9 | 57.6 | |
| Marital status | ||||
| Married/Living with a partner | 76.0 | 70.6 | 69.9 | <.01 |
| Living will | ||||
| Yes | 54.1 | 47.0 | 43.3 | <.01 |
| Durable power of attorney | ||||
| Yes | 54.2 | 50.3 | 51.9 | .06 |
| Chronic conditions | ||||
| 0 | 15.6 | 7.9 | 6.7 | <.01 |
| 1-3 | 68.9 | 64.5 | 59.9 | |
| 4+ | 15.5 | 28.2 | 33.4 | |
| Assistance required for activities of daily living | ||||
| None | 88.3 | 72.0 | 65.0 | <.01 |
| 1+ | 11.7 | 28.0 | 35.0 | |
| Living alone | ||||
| Yes | 22.6 | 32.1 | 34.6 | <.01 |
| Rurality | ||||
| Rural | 26.2 | 27.2 | 31.8 | .15 |
| Urban | 73.8 | 72.8 | 68.2 | |
| Langa-Weir score (mean) | 17.2 | 9.5 | 4.4 | |
| Depression score (mean) | 1.2 | 2.0 | 2.5 | |
| Loneliness score (mean) | 1.5 | 1.6 | 1.7 | |
| Neighborhood cohesion score (mean) | 2.3 | 2.9 | 2.9 | |
| Neighborhood social score (mean) | 2.5 | 3.0 | 2.9 |
As the cognition level decreased, the proportion of people with a living will also decreased, ranging from 54.1% of the normal cognition, to 47.0% of the impaired cognition, and 43.3% of the dementia cohorts. A similar, but non-significant pattern was observed for having a DPOAH (Table 1). Table 2 shows the likelihood of having a living will in participants with dementia, impaired cognition, and normal cognition, based on various predictors. The likelihood of having a living will increased with each year of age. Compared to participants with high school level education, participants with some college education were five times more, and the university graduates were four times more likely to have a living will in the dementia group. In the cognitive impairment group, participants with any level of a college education were two times more likely to have a living will than those with high school level. Additionally, participants in the normal cognition cohort who had some college or university education (OR = 1.61, P < .0001) were more likely to have a living will than those with high school level (OR = 2.14, P < .0001).
Table 2.
Summary of Logistic Regression Predicting Living Will Across Cognitive Levels.
| Predictor | Dementia N = 844 |
Cognitive Impairment N = 3080 |
Normal N = 13,774 |
||||||
|---|---|---|---|---|---|---|---|---|---|
| OR | 95% CI | P | OR | 95% CI | P | OR | 95% CI | P | |
| Race | |||||||||
| White | 1.00 | .048 | 1.00 | <.0001 | 1.00 | <.0001 | |||
| African American | .41 | [.20, .87] | .32 | [.25, .40] | .39 | [.30, .51] | |||
| Other | 1.81 | [.35, 9.46] | .62 | [.39, .99] | .70 | [.45, 1.08] | |||
| Ethnicity | |||||||||
| Non-Hispanic | 1.00 | .003 | 1.00 | <.0001 | 1.00 | <.0001 | |||
| Hispanic | .17 | [.05, .54] | .26 | [.18, .35] | .29 | [.20, 0,41] | |||
| Education | |||||||||
| Highschool or less | 1.00 | .004 | 1.00 | <.0001 | 1.00 | <.0001 | |||
| Some college | 5.16 | [1.74, 15.32] | 2.14 | [1.61, 2.85] | 1.61 | [1.32, 1.98] | |||
| College (4 years) | 4.09 | [.94, 17.79] | 2.21 | [1.63, 2.98] | 2.14 | [1.79, 2.55] | |||
| Gender | NSa | NSa | |||||||
| Female | 1.00 | ||||||||
| Male | .77 | [.66, .91] | .0015 | ||||||
| Marital status | |||||||||
| Married | NSa | NSa | 1.00 | .006 | |||||
| Divorced/Separated | NSa | NSa | .97 | [.76, 1.23] | |||||
| Widowed | NSa | NSa | 1.37 | [1.12, 1.68] | |||||
| Never married | NSa | NSa | .75 | [.45, 1.23] | |||||
| Loneliness score | 1.84 | [.91, 3.68] | .09 | NSa | .74 | [.62, .88] | .001 | ||
| Number of chronic diseases | .002 | ||||||||
| 0 | NSa | NSa | 1.00 | ||||||
| 1-3 | NSa | NSa | 1.51 | [1.13, 2.02] | |||||
| 4+ | NSa | NSa | 1.78 | [1.29, 2.46] | |||||
NS-Not Significant: Variables were removed for the final model.
Compared to white participants, African American and Hispanic participants were less likely to have a living will across all cognition cohorts: dementia (Black OR = .41, P = .048; Hispanic OR = .17, P < .003), cognitive impairment (Black OR = .32, P < .0001; Hispanic OR = .26, P < .0001), and normal cognition (Black OR = .39, P < .0001; Hispanic OR = .29, P < .0001). There were no significant differences across marital status groups in dementia and cognitive impairment cohorts in terms of having a living will. However, divorced and never-married participants were less likely to have a living will than married participants in the normal cognition cohort. In contrast, widowed participants were 37% more likely to have a living will than married participants. Furthermore, participants in the dementia group were 84% more likely to have a living will for every one-point increase in the loneliness scores. Participants with normal cognition were more likely to have a living will as the number of their chronic diseases increased. This relationship was not significant in dementia and cognitive impairment cohorts.
Table 3 shows the likelihood of having a DPOAH based on several predictors in participants with dementia, impaired cognition, and normal cognition. For every one-year increase in age, the likelihood of having a DPOAH increased significantly. Participants with some college education and university graduates were more likely to have a DPOAH in cognitive impairment and normal cognition cohorts, but not in the dementia cohort, compared to those with high school level education.
Table 3.
Summary of Logistic Regression Predicting Durable Power of Attorney Across Cognitive Levels.
| Cognitive Level |
Dementia N = 844 |
Cognitive Impairment N = 3080 |
Normal N = 13,774 |
||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictor | OR | 95% CI | P | OR | 95% CI | P | OR | 95% CI | P |
| Race | |||||||||
| White | 1.00 | .0008 | 1.00 | <.0001 | 1.00 | <.0001 | |||
| African American | .27 | [.13, .54] | .47 | [.37, .59] | .48 | [.40, .56] | |||
| Other | 1.92 | [.46, 8.03] | .74 | [.48, 1.14] | .79 | [.60, 1.05] | |||
| Ethnicity | |||||||||
| Non-Hispanic | 1.00 | .0002 | 1.00 | <.0001 | 1.00 | <.0001 | |||
| Hispanic | .16 | [.06, .41] | .34 | [.25, .45] | .39 | [.31, .49] | |||
| Age | NSa | 1.07 | [1.06, 1.08] | <.0001 | |||||
| Education | |||||||||
| Highschool or less | NSa | 1.00 | <.0001 | 1.00 | <.0001 | ||||
| Some college | 1.68 | [1.28, 2.22] | 1.39 | [1.21, 1.59] | |||||
| College (4 yrs) | 2.78 | [2.02, 3.72] | 1.99 | [1.76, 2.24] | |||||
| Gender | NSa | NSa | |||||||
| Female | 1.00 | <.0001 | |||||||
| Male | .79 | [.71, .88] | |||||||
| Marital status | NSa | .001 | |||||||
| Married | 1.00 | <.0001 | |||||||
| Divorced/Separated | 1.13 | [.85, 1.51] | .89 | [.76, 1.04] | |||||
| Widowed | 1.93 | [1.57, 2.38] | 1.21 | [1.06, 1.38] | |||||
| Never married | .63 | [.33, 1.19] | .78 | [.56, 1.08] | |||||
| Loneliness score | 2.29 | [1.18, 4.41] | .0137 | NSa | NSa | ||||
| Number of chronic diseases | |||||||||
| 0 chronic disease | 1.00 | .0391 | 1.00 | .04 | 1.00 | .003 | |||
| 1-3 chronic disease | 4.28 | [.49, 37.28] | 1.56 | [.91, 2.67] | 1.40 | [1.15, 1.72] | |||
| 4 and more chronic disease | 8.22 | [.92, 73.08] | 1.87 | [1.08, 3.24] | 1.46 | [1.17, 1.82] | |||
NS-Not Significant: Variables demonstrated a crude relationship with the outcome but were not significant in the full model. They were removed for the final model.
Compared to white participants, African American and Hispanic participants were less likely to have a DPOAH across all cognition cohorts: dementia (Black OR = .27, P = .0008; Hispanic OR = .16, P = .0002), cognitive impairment (Black OR = .47, P < .0001; Hispanic OR = .34, P < .0001), and normal cognition (Black OR = .48, P < .0001; Hispanic OR = .39, P < .0001). Divorced and widowed participants (OR = 1.13, P < .0001) were more likely to have DPOAH than married participants (OR = 1.93, P < .0001) in the cognitive impairment cohort, whereas never-married participants were 37% less likely to have DPOAH. In the normal cognition cohort, divorced and never married participants were less likely to have a DPOAH, while widowed participants were 21% more likely to have a DPOAH. For every one-point increase in loneliness scores, participants in the dementia cohort were about 2.5 times more likely to have a DPOAH. As the number of chronic diseases increased, participants’ likelihood of having DPOAH increased significantly. This relationship was observed more drastically in the dementia cohort.
Discussion
In this study, we used the HRS to investigate the extent to which older adults with varying levels of cognitive status engage in ACP and identify the factors associated with their ACP engagement. We addressed two critical legal documents that are available in HRS 2014 survey (wave 12) as ACP measures: a living will and DPOAH. Despite current controversies, ACP measures can help individuals to improve resilience, feeling of peace, and satisfaction with EOL care and reduce grief and anxiety about surrogate decision-making.14,22 This early healthcare management and decision making can substantially increase the clarity about patient preferences and improve care for patients with cognitive impairments and reduce the anxiety and burdens of last-minute decisions for everyone involved in patient care.10,23
Regarding our first research question, the results indicated that participants with lower levels of cognition were less likely to have a living will and DPOAH. Results regarding the second research question indicated that race, ethnicity, level of cognition, education, age, and number of chronic diseases consistently predicted ACP. African American and Hispanic participants, those with lower cognition and education levels, and younger participants were less likely to engage in ACP. Marital status and loneliness frequently predicted ACP engagement. Some results were varied across the cognition cohorts. In the dementia group, with an increase in loneliness score, the participants were more likely to have a living will and DPOAH. As the number of chronic diseases increased, participants’ likelihood of having DPOAH increased across all cognition cohorts. However, regarding having a living will, we found similar results only in normal cognition group. While the results regarding marital status across the cognition levels were varied, widowed participants were more likely to engage in ACP. This result aligns with studies indicating that an experience of a loved one’s death is associated with ACP engagement.24–26 Our results supported previous research documenting social factors as fundamental causes of inequalities in health and ACP.27–30
Multiple factors may serve as barriers to the initiation of ACP in people with ADRD: patients’ difficulties to engage in discussions; stress and fear associated with the diagnosis of ADRD and loss of self; avoidance of the topic; caregiver-patient disagreements related to the patient’s denial of the disease; behavioral changes related to the disease; a feeling of being late for the discussions after ADRD diagnosis; a feeling that involved family members would make arrangements if needed; a lack of knowledge about ACP, expected dementia trajectory, and potential medical decisions; and being a member of underserved or ethnic/racial minority groups.8,31–33 Racial/ethnic disparities in the extent of ACP and preferences for EOL care have been well documented. Consistent with our results, not only is the prevalence of ADRD higher in racial/ethnic minority populations,3,34 but minorities also experience disparities in EOL care and ACP.35 African Americans and Hispanic Americans are less likely to engage in ACP and more likely to prefer aggressive rather than comfort care.28,36 Studies indicate that other related factors that serve as barriers to ACP were sociodemographic factors, living conditions and residential areas (eg, urban vs rural settings), acculturation, spirituality, religiosity, a lack of proxy to appoint if needed, cultural beliefs (eg, adverse effects of EOL discussion on loved ones and God’s power over people’s decisions and planning).24,25,28,35,37,38 However, we did not find a relationship between rurality and ACP.
It is important to understand and acknowledge the barriers to the initiation of ACP in people with ADRD. Accordingly, patients, family members, and healthcare providers can initiate ACP before the decisional capacity starts to decline. The knowledge of other factors, such as race/ethnicity and cultural beliefs, will help involved people in the patient care to understand how to approach ACP. For example, the use of indirect communication approaches for ACP is recommended for discussion with Chinese older adults.39 For instance, they can discuss personal EOL care preferences using a hypothetical scenario that focuses on a time when the person ages a hundred years old instead of a direct discussion about EOL care preferences (cite).40
There were contradictory results in related research studies. In a longitudinal data analysis of HRS exit interviews (2002-2010), Khosla et al reported limited support for the significance of socioeconomic characteristics to predict odds of ACP.41 Choi et al’s results using HRS data were also contradictory with our results. They indicated that older adults with AD were more likely to have a living will or DPOAH compared to those without AD.42 These contradictory results can be a related to differences in measurement tools and study designs. For example, Choi et al used participants’ self-reported responses to a single question, “Has a doctor ever told you that you have Alzheimer’s disease?” Moreover, Khosla et al used HRS exit interviews which included data from proxy respondents for deceased participants who might have provided inaccurate information. As Khosla et al indicated, their focused analysis controlled for many social factors, which could conceal interactions among various social factors that may lead to inequities in ACP. They suggested that future research can focus on intersectionality of a variety of factors that are not limited to socioeconomic factors.41 We used data collected from the participants and considered the intersectionality of their sociodemographic attributes and health and cognition status.
Our results indicated that cognition level was directly and indirectly associated with the ACP engagement. As ADRD progresses, caregivers and families may face challenges related to care management and EOL care.43 Therefore, communication between patients, caregivers, and providers about EOL care preferences and plans before severe cognitive conditions is essential. Evidence suggests ACP disengagement is a missed opportunity to discuss goals of care and EOL wishes, which can result in lower utilization of supportive care and higher rates of curative interventions, increased family and caregiver stress, anxiety, and grief, and reduced care satisfaction.4–8,14,44 Advance care planning can help improve shared decisions and discussion of EOL care options, such as hospice services; care management aligned with the patient and family wishes;37 and EOL care outcomes, such as reduced hospital death in persons with ADRD.43 Involvement of older adults in medical decision making can improve their sense of dignity.45 Legislative initiatives, national organizations, and scholars emphasize early ACP before severe cognitive decline or developing signs of dementia.23,46 For example, in the state of Maryland, Medical Orders for Life-Sustaining Treatment (MOLST) must be completed for newly admitted patients to all assisted living programs, home health agencies, hospices, kidney dialysis centers, and nursing homes.47
Limitations and Strengths
There were several limitations in this study. First, secondary data analysis is limited to available data and measurement tools. For example, ACP measures in this study were limited to having a living will and DPOA; however, ACP encompasses a variety of formal and informal measures and methods for sharing personal values and goals of care. Second, due to the HRS study design, African Americans, Hispanics, and Florida residents are overrepresented in this dataset. As has been shown to be appropriate in the literature, we were intentional in utilizing the survey weights for descriptive statistics while not using them for the modeling analyses. This ensured that we have presented to most accurate and least biased estimates of the associations. Third, while we used a variety of sociodemographic and health factors, other variables that may influence ACP engagement, such as income and healthcare expenditure, were not included in our analysis. The strength of the study was using nationally representative survey of older U.S. adults with a low potential for sampling bias. Health and Retirement Study includes randomly selected participants using a multistage area probability sampling design.
Conclusion
Despite the benefits of care planning, ACP engagement has not been adequately studied across cognitive levels and racial/ethnic backgrounds. Lack of knowledge in this area may contribute to missed opportunities for patients, families, and caregivers regarding shared goal setting and decision making for EOL.10 Prior studies frequently addressed individuals in nursing home settings, limiting the results’ generalizability to individuals living in community settings. This study can fill the gap in knowledge about trends of ACP among older adults across levels of cognitive status and other factors using a nationally representative data of U.S. older adults. This investigation can provide valuable insights on ACP for older adults with or at risk of ADRD, caregivers, families, and healthcare providers. The findings can inform educational interventions regarding EOL care and planning for diverse older adults across cognitive functioning levels. The knowledge of barriers to the initiation of ACP in people with ADRD can help clinicians prepare for discussions about ACP with patients and their caregivers early in the disease progression.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This paper was supported by the National Institute on Aging (NIA)-funded Carolina Center on Alzheimer’s Disease and Minority Research (CCADMR, 5P30AG059294).
Footnotes
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
References
- 1.Alzheimer’s Association Report. 2021 Alzheimer’s disease facts and figures. Alzheimer’s Dement. 2021;17(3):327–406. doi: 10.1002/alz.12328 [DOI] [PubMed] [Google Scholar]
- 2.National Institute of Neurological Disorders Stroke NI of H. Focus on Alzheimer’s Disease and Related Dementias; 2022. https://www.ninds.nih.gov/Current-Research/Focus-Disorders/Alzheimers-Related-Dementias. Accessed April 21, 2022.
- 3.Nelson P, Brayne C, Flanagan M, et al. Frequency of LATE neuropathologic change across the spectrum of Alzheimer’s disease neuropathology: combined data from 13 community-based or population-based autopsy cohorts. Acta Neuropathol. 2022. Published online. doi: 10.1007/s00401-022-02444-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Matthews KA, Xu W, Gaglioti AH, et al. Racial and ethnic estimates of Alzheimer ’ s disease and related dementias in the United States (2015–2060) in adults aged 65 years. Alzheimer’s Dement. 2019;15(1):17–24. doi: 10.1016/j.jalz.2018.06.3063 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Amjad H, Roth DL, Sheehan OC, Lyketsos CG, Wolff JL, Samus QM. Underdiagnosis of dementia: An observational study of patterns in diagnosis and awareness in US older adults. J Gen Intern Med. 2018;33(7):1131–1138. doi: 10.1007/s1160 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Gottesman RF, Albert MS, Alonso A, et al. Associations between midlife vascular risk factors and 25-year incident dementia in the atherosclerosis risk in communities (ARIC) cohort. JAMA Neurol. 2017;47(10):1246–1254. doi: 10.1001/jamaneurol.2017.1658 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Lipnicki DM, Sachdev PS, Crawford J, et al. Risk factors for late-life cognitive decline and variation with age and sex in the sydney memory and ageing study. PLoS One. 2013;8(6): e65841. doi: 10.1371/journal.pone.0065841 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Jones K, Birchley G, Huxtable R, Walter T, Dixon J. End of life care: A scoping review of experiences of advance care planning for people with dementia. Dementia. 2019;18(3):825–845. doi: 10.1177/1471301216676121 [DOI] [PubMed] [Google Scholar]
- 9.Ryan T, M-amen K, Mckeown J. The advance care planning experiences of people with dementia, family caregivers and professionals: A synthesis of the qualitative literature. 2017; 6(4):380–389. doi: 10.21037/apm.2017.06.15. [DOI] [PubMed] [Google Scholar]
- 10.Pettigrew C, Brichko R, Black B, et al. Attitudes toward advance care planning among persons with dementia and their caregivers. Int Psychogeriatr. 2020;32(5):585–599. doi: 10.1017/S1041610219000784 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Sudore R, Lum H, You J, et al. Defining advance care planning for adults: A consensus definition from a multidisciplinary Delphi panel. J Pain Symptom Manag. 2017;53(5):821–832. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Sudore RL, Fried TR. Redefining the “planning” in advance care planning: Preparing for end-of-life decision making. Ann Intern Med. 2010;153(4):256–261. doi: 10.7326/0003-4819-153-4-201008170-00008 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Morrison S, Meier DE, Arnold RM. What’s wrong with advance care planning? J Am Med Assoc. 2021;326(15):1575–1576. doi: 10.1001/jama.2021.16430 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.McMahan RD, Tellez I, Sudore RL. Deconstructing the complexities of advance care planning outcomes: What do we know and where do we go? A scoping review. J Am Geriatr Soc. 2021; 69(1):234–244. doi: 10.1111/jgs.16801 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Jacobsen J, Bernacki R, Paladino J. Shifting to serious illness communication. J Am Med Assoc. Published online. 2022. doi: 10.1001/jama.2021.23695. [DOI] [PubMed] [Google Scholar]
- 16.Golant SM. Self-reliant older baby boomers: Are now better connected to goods, services, and care. J Am Soc Aging. 2017; 41(2):79–87. https://www.jstor.org/stable/26556287 [Google Scholar]
- 17.Dalton MA, Langdon TP. Estate Planning. 13th ed. New York, NY: Money Education; 2022. [Google Scholar]
- 18.Sonnega A, Faul J, Ofstedal M, Langa K, Phillips J, Weir D. Cohort profile: The health and retirement study (HRS). Int J Epidemiol. 2014;43(2):576–585. doi: 10.1093/ije/dyu067. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Winship C, Radbill L. Sampling weights and regression analysis. Socio Methods Res. 1994;23(2):230–257. doi: 10.1177/0049124194023002004 [DOI] [Google Scholar]
- 20.Langa KM, Larson EB, Crimmins EM, et al. A comparison of the prevalence of dementia in the United States in 2000 and 2012. JAMA Intern Med. 2017;177(1):51–58. doi: 10.1001/jamainternmed.2016.6807 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Crimmins EM, Kim JK, Langa KM, Weir DR. Assessment of cognition using surveys and neuropsychological assessment: The health and retirement study and the aging, demographics, and memory study. J Gerontol. 2011(suppl 1):i162–i171. doi: 10.1093/geronb/gbr048 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Curtis JR. Three stories about the value of advance care planning. JAMA. 2021;326(21):2133–2134. doi: 10.1001/jama.2021.21075 [DOI] [PubMed] [Google Scholar]
- 23.Gaster B, Larson EB, Curtis JR. Advance directives for dementia meeting a unique challenge. J Am Med Assoc. 2017; 318(22):2175–2176. doi: 10.1001/jama.2017.16473 [DOI] [PubMed] [Google Scholar]
- 24.Rahemi Z, Williams CL. Older adults of underrepresented populations and their end-of-life preferences: An integrative review. Adv Nurs Sci. 2016;39(4). doi: 10.1097/ANS.0000000000000148 [DOI] [PubMed] [Google Scholar]
- 25.Rahemi Z. Planning ahead for end-of-life healthcare among Iranian-American older adults: Attitudes and communication of healthcare wishes. J Cross Cult Gerontol. 2019. Published online. doi: 10.1007/s10823-019-09371-x [DOI] [PubMed] [Google Scholar]
- 26.Rahemi Z, Dunphy L, Newman D. Preferences regarding and communication about end-of-life care among older Iranian-American adults. West J Nurs Res. 2019. Published online. doi: 10.1177/0193945919832304 [DOI] [PubMed] [Google Scholar]
- 27.Orlovic M, Smith K, Mossialos E. Racial and ethnic differences in end-of-life care in the United States: Evidence from the health and retirement study (HRS). SSM-Popul Heal. 2019;7:100331. doi: 10.1016/j.ssmph.2018.100331 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Rahemi Z, Williams CL. Does ethnicity matter—cultural factors underlying older adults’ end-of-life care preferences: A systematic review. Geriatr Nurs. 2020;41(2). doi: 10.1016/j.gerinurse.2019.07.001 [DOI] [PubMed] [Google Scholar]
- 29.Allen JY, Hilgeman MM, Allen RS. Prospective end-of-life treatment decisions and perceived vulnerability: Future time left to live and memory self-efficacy. Aging Ment Health. 2011;15(1): 122–131. doi: 10.1080/13607863.2010.505229 [DOI] [PubMed] [Google Scholar]
- 30.Allen JO, Solway E, Kirch M, et al. Experiences of everyday ageism and the health of older US adults. JAMA Netw Open. 2022; 5(6):E2217240. doi: 10.1001/jamanetworkopen.2022.17240 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Financial advisors and Alzheimer’s disease. Financial Advisors and Alzheimer’s Disease: What You Need to Know; 2022. https://www.alz.org/advisors/alz-fy16-pg.pdf
- 32.Noh H, Kwak J. End-of-life decision making for persons with dementia: Proxies ’ perception of support. Dementia. 2018. Published online. doi: 10.1177/1471301216648473. [DOI] [PubMed] [Google Scholar]
- 33.Fried TR, Cohen AB, Harris JE, Moreines L. Cognitively impaired older persons’ and caregivers’ perspectives on dementia-specific advance care planning. J Am Geriatr Soc. 2021;69(4): 932–937. doi: 10.1111/jgs.16953 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Alzheimer’s Association Report. Alzheimer’s disease facts and figures. Alzheimer’s Dement. 2020;16:391–460. doi: 10.1002/alz.12068 [DOI] [Google Scholar]
- 35.Koss CS, Baker TA. Where there’s a will: The link between estate planning and disparities in advance care planning by white and Black older adults. Res Aging. 2018;40(3):281–302. doi: 10.1177/0164027517697116 [DOI] [PubMed] [Google Scholar]
- 36.Hong M, Hye E, Kimberly Y, Margaret JJ. Facilitators and barriers for advance care planning among ethnic and racial minorities in the U. S.: A systematic review of the current literature. J Immigr Minor Health. 2018;20(5):1277–1287. doi: 10.1007/s10903-017-0670-9 [DOI] [PubMed] [Google Scholar]
- 37.Nelson-Brantley H, Buller C, Befort C, Ellerbeck E, Shifter A, Ellis S. Using implementation science to further the adoption and implementation of advance care planning in rural primary care. J Nurs Scholarsh. 2020;52(1):55–64. doi: 10.1111/jnu.12513 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Rahemi Z, Parker V. Does culture matter? Young and middle-aged Iranian-American adults’ perspectives regarding end-of-life care planning. Am J Hospice Palliat Med. 2021. Published online. doi: 10.1177/10499091211036894. [DOI] [PubMed] [Google Scholar]
- 39.Cheng H Advance care planning in Chinese seniors: Cultural perspectives. J Palliat Care. 2018;33(4):242–246. doi: 10.1177/0825859718763644 [DOI] [PubMed] [Google Scholar]
- 40.Centre for Biomedical Ethics. A Singapore Bioethics Casebook: Making Difficult Decisions with Patients and Families; 2017. http://www.bioethicscasebook.sg/
- 41.Khosla N, Curl AL, Washington KT. Trends in engagement in advance care planning behaviors and the role of socioeconomic status. Am J Hospice Palliat Med. 2016;33(7):651–657. doi: 10.1177/1049909115581818 [DOI] [PubMed] [Google Scholar]
- 42.Choi S, Kim M, McDonough IM. Do older adults with Alzheimer’s disease engage in estate planning and advance care planning preparation? Aging Ment Health. 2019;23(7):872–879. doi: 10.1080/13607863.2018.1461192 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Dixon J, Karagiannidou M, Knapp M. The effectiveness of advance care planning in improving end-of-life outcomes for people with dementia and their carers: A systematic review and critical discussion. J Pain Symptom Manag. 2018;55(1):132–150. doi: 10.1016/j.jpainsymman.2017.04.009 [DOI] [PubMed] [Google Scholar]
- 44.Teno JM, Gozalo P, Mitchell SL, Kuo S, Fulton AT, Mor V. Feeding tubes and the prevention or healing of pressure ulcers. Arch Intern Med. 2012;172(9):697–701. doi: 10.1001/archinternmed.2012.1200 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Rahemi Z, Williams CL. Development of a dignity-enhancing model of caring for older adults. Int J Hum Caring. 2015;19(3): 36–42. [Google Scholar]
- 46.Rahemi Z, Williams CL, Tappen RM, Engstrom GA. Health-related decisions for serious illness among ethnically diverse older adults. Adv Nurs Sci. 2018;41(1):84–97. doi: 10.1097/ANS.0000000000000192 [DOI] [PubMed] [Google Scholar]
- 47.Maryland MOLST Training Task Force. Maryland MOLST FAQs; 2018. https://marylandmolst.org/docs/Maryland_MOLST_FAQs.pdf