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Abstract

Introduction:

Racial minorities and people with low educational attainment, low income, or nonmetropolitan residence are underrepresented in research on Alzheimer’s disease and related dementias (ADRD). We evaluated care navigation, a program designed to address individual medical and nonmedical caregiver and patient needs, to support research participation of underrepresented groups.

Methods:

Patient/caregiver dyads recruited from the community received care navigation designed to help patients and families get the right support at the right time for medical and nonmedical needs. It consisted of initial discussion of individual unmet needs and available resources; evaluation for patient cognitive, behavioral, and affective signs; and assessment of caregiver burden and caregiver self-efficacy. The primary outcome measure was the rate of enrollment in a biomarker clinical research study. Diversity of participant pool was compared to local demographic data and publicly available clinical research enrollment information.

Results:

About 18 dyads were enrolled in care navigation. Patient cognitive impairment was moderate to severe, with low patient depression, anxiety, and behavioral symptoms. Reported caregiver burden and caregiver self-efficacy were low. Of the 18 dyads, 16 (89%) enrolled in a separate biomarker clinical research study. Of this group, 37% were Black, 31% had a high school education or less, 40% had a household income of $45,000 or less, and 25% were nonmetropolitan dwellers. Participant demographics were similar to those of the community.

Discussion:

Pilot findings suggest that individualized care navigation supports recruitment of underrepresented groups based on race, education level, income, and dwelling. Offering assistance to patients and families in medical crisis has the potential to increase receptivity to research participation as well as improve participant and community health and well-being.

Introduction

Health care disparities and research participation

Race, socioeconomic status, education level, and urban/rural dwelling impact risk for age-related neurodegenerative diseases like dementia (for review, see Ref.¹). Despite progress in research, practice, and policy, disparities in health care and research participation persist. For example, 80% of surveyed Black patients with Alzheimer’s disease and related dementias (ADRD) in the United States reported experiencing barriers to health care, resulting in delayed treatment and reduced access to clinical trials.² Lack of diversity in ADRD clinical research is particularly troubling because the incidence of ADRD in Black compared to White populations is about two times higher (for review, see Ref.³) and the Black population has the highest prevalence of ADRD among people 65 years and older.⁴ The World Health Organization Commission on Social Determinants of Health put forward a social framework for health policy and research which emphasizes equity, or the absence of unfair and avoidable or remediable differences in health among social groups, as a key concept when addressing disparities.⁵ Social determinants of health, including socioeconomic status, education level, and urban/rural dwelling, intersect with race,⁶ and observational research has documented the impact of these factors on clinical research participation.

For example, a recent meta-analysis showed that only 7% of trial participants diagnosed with AD were Black, and that retention was lower in Black compared to White populations.⁷ Additionally, review of a pre-clinical AD trial showed that only 15% of the participants had education levels of 12 years or fewer,⁸ compared to the national rate of 34.6%.⁹ Furthermore, previous work from cancer clinical trial enrollment indicates that low income was associated with a 27% reduction in clinical trials participation compared to those with income above $50,000.¹⁰ Finally, Tanner and colleagues¹¹ found that rural residents were perceived by clinical researchers to be underrepresented in research compared to both the general and Black populations. These issues are amplified in Louisiana (LA), which has long-standing health and social disparities linked to racism, poverty, education, and rural residence. Louisiana has a large Black population (32.6% in LA and 50% in Shreveport vs. 13.4% in the United States), a high poverty rate (18.6% vs. 11.5% nationally), and a large rural population (27% vs. 19% in the United States). Patient Social Vulnerability Index (SVI¹²) data from our institution, based on socioeconomic status, household characteristics, racial and ethnic minority status, and housing and transportation, showed “high vulnerability.” Specifically, 90% of dementia patients lived in regions that scored above the 50th percentile, 51.5% lived in regions that scored in the 80th percentile or above, and 35% lived in areas that scored in the 90th percentile or above. Representation in clinical research that reflects the demographics of the country is key to addressing health care disparities and therapeutic development in age-related neurodegenerative disease. However, barriers to research participation are multifactorial, including issues of mistrust, fear, limited access, financial constraints, poor health, lack of transportation, time, information, or family support, and concerns about adverse events,^13–17 and vary across groups and individuals, making them difficult to address.

Patient-centered care navigation and research participation

Care navigation is an emerging method of “patient-centered” aging and dementia care involving both patient and caregiver and providing personalized guidance, facilitating access to timely and coordinated care and information, as well as referral and support accessing nonmedical community resources.¹⁸ Specifically, patients and caregivers are assigned a care navigator who assists with timely access to care including specialty services, coordinates care across providers, provides information and addresses question in real time, and facilitates access to community resources to address issues such as food and housing security, safety, social support, transportation access, Medicare management, and disease- and care-related knowledge gaps. For age-related neurodegenerative diseases like dementia, patient-centered care navigation is particularly important because care is often fragmented and uncoordinated,¹⁹ and changes over the course of the disease (for review, see Ref.¹⁸). A strength of care navigation is the opportunity to deliver culturally competent support that can be designed to address the unique needs of underrepresented groups to combat inequity in support for patients and caregivers.¹⁹ Unmet needs represent barriers to equitable health care participation, and Black²⁰ and rural²¹ caregivers report greater unmet needs and less use of support services than their White and urban counterparts. Furthermore, Adams and colleagues²² noted that vulnerable patients, racial and ethnic minorities, and low-income families without health insurance did not benefit from patient-centered medical care until their nonmedical needs were addressed. They propose a hierarchical pyramid of health care needs, similar to Maslow’s Hierarchy of Needs,²³ a construct that was considered in the design of the study.

We extend the patient-centered care model, using a care navigator program to support the recruitment of underrepresented populations for clinical research. Outreach from trusted sources,²⁴ such as a care navigator, can improve care experience and engagement in clinical research in a diverse sample.²⁵ We predict that people with dementia and their families from underrepresented groups will respond favorably to a solicitation for research participation if their medical and nonmedical needs are addressed. We expect enrollment to reflect local demographics, improving on publicly available ADRD participation data. There is growing interest in patient-centered care navigation (such as the new Centers for Medicare and Medicaid Services GUIDE [Guiding an Improved Dementia Experience model]). Thus, this pilot study is a timely and important first step toward understanding the utility of care navigation in addressing underrepresentation in clinical research.

Methods

Study design

To address disparities in ADRD research participation, we conducted a prospective pilot study with patient/caregiver dyads identified in the community. We evaluated the effectiveness of the University of California, San Fransisco Care Ecosystem program (UCSF Care Ecosystem Program²⁶) in a pilot study conducted at LSU Health Sciences Center in Shreveport to address modifiable barriers to research participation. The UCSF Care Ecosystem is a proven and effective program^27,28 that provides care navigation resources and training to improve care, reduce medication risk, and lower Medicare costs, nationwide (https://memory.ucsf.edu/healthcare-professionals/care-ecosystem).

Consent statement

This study was approved by the institutional review board at Louisiana State University Health Sciences Center, Shreveport, and was completed in accordance with the Helsinki Declaration. All participants provided written informed consent.

Study sample

Participant caregivers were identified through calls to our dementia resource center (The Bridge; alzbridge.org), speaking engagements, community events such as health fairs, and events at churches, Councils on Aging, federally qualified health centers, and Shreveport Public Assembly and Recreation (SPAR) sites. Consecutive caregivers (specifically, all individuals who met eligibility criteria within the recruitment period and expressed interest in support) were offered assistance based on individual needs and asked if they were interested in free care navigation as part of a study. Assistance was provided with or without care navigation participation. Of the 20 caregiver/patient dyads approached, 18 enrolled in care navigation.

Inclusion criteria for the patient were

Male or female ≥55 years of age at baseline

All-cause dementia²⁹ diagnosis as evidenced by one or more of the following criteria: ∘

Score of ≥17 on the ADAS-Cog.

∘

Dementia listed on medical record problem list as of January 1, 2015, or later, or, in the opinion of the primary care provider, likely has dementia.

∘

Prescribed any of the following: donepezil, memantine, galantamine hydrobromide, rivastigmine, or tartrate; or, in the opinion of the referring provider, could take such medication.

Living in community (as opposed to assisted living, nursing home, etc.)

Available informal caregiver (e.g., spouse, sibling, adult child) willing and able to participate in the study.

Exclusion criteria were

Documented history of amyotropic lateral sclerosis (ALS), Huntington’s disease, schizophrenia, bipolar disorder, or current substance abuse disorder.

Inability to complete monthly telehealth or phone visits.

Care navigation

A single navigator provided support services for the cohort. The care navigator was a 73-year-old White female and long-time Shreveport resident recruited from the community, with previous experience teaching and working with underserved populations. Specific care navigation training was 20 h of Care Ecosystem webinar training (Care Ecosystem Program²⁶) over 4–6 weeks. The training curriculum included the following topics:

Aging 101, Dementia Basics

Medication Reconciliation and Review

Caregiver Well-Being

Behavior Management

Promoting Health & Safety

Legal/Financial Decision-Making

Medical Decision-Making

Long-Term Care

End of Life Care in Dementia.

An additional 20 h of training occurred via live weekly observation by trained navigators with a gradual transition from observer to educator and resource for study dyads.

At enrollment, the care navigator assessed participant needs to generate a tailored care plan incorporating dementia severity, education, and strategies to address common dementia issues. Clinical research participation was requested at month 3 or 4, which provided time to build rapport with the patient and caregiver and address medical and nonmedical needs. Navigator/dyad contact was maintained via phone call at least once a month, and care navigation was provided through the completion of the clinical research study and as long as caregivers indicated that they wanted to continue, usually for about 1 year.

Support services

Care navigation consisted of information and assistance connecting dyads with medical and nonmedical needs. Needs were evaluated via a semi-structured interview covering the following core components of a Care Plan in the Care Ecosystem: (1) Assessment of Needs, (2) Caregiver Support, (3) Medication Management, (4) Behavioral Management, (5) Safety Concerns, (6) Community Resources, and (7) Advance Care Planning. Specifically, dementia-related needs included medication reconciliation and access to primary, specialty, or free care. We also provided or facilitated access to dementia specific educational material (in-person, remote, and additional reading recommendations); information about support groups; dementia care education; home health, respite and day care programs and assisted living or nursing home transition; safety issues (driving, gun control, wandering, falls, safe bathing and toileting, cooking and fire safety, etc.); prescription expense assistance; counseling on social security, Medicare, and Community Choices Waiver Louisiana; and advanced care planning. Community resources included free memory screening, counseling services, Meals on Wheels, food banks, and local communal meals, dementia friendly social events, support groups, tech device support, balance, and other dementia friendly exercise classes.

Free community services were provided by multiple organizations as part of their normal, often grant supported operations including LSU Health Shreveport Center for Brain Health, The Bridge Alzheimer’s and Dementia Resource Center, Caddo and Bossier Parish Councils on Aging, Martin Luther King and David Raines Federally Qualified Health Centers, Mom’s Meals, YWCA, SPAR, Shreve Memorial Libraries, and Bossier and Caddo Parish Sheriffs SOS (Sheriff’s Operational Safeguard) programs.

Outcome measures

The following measures were evaluated at enrollment in care navigation: demographic data consisting of age, sex, race, educational attainment, and household income. Cognitive impairment severity was evaluated using the Montreal Cognitive Assessment (MoCA³⁰) and the Alzheimer’s Disease Assessment Scale–Cognitive subscale (ADAS-Cog; cutoff score ≥17, with higher scores indicating greater severity of cognitive impairment³¹). Other assessments included the Patient Health Questionnaire–9 (PHQ-9³²); the Generalized Anxiety Disorder–7 (GAD-7³³); the Geriatric Depression Scale (GDS³⁴); the Neuropsychiatric Inventory–Questionnaire (NPI-Q³⁵); the Short Form Zarit Burden Interview (ZBI-12^36,37); The Generalized Self-Efficacy Scale (GSE³⁸); and Consent to participate in clinical research. The blood biomarker clinical research study involved cognitive testing, blood draw, and an MRI that took place over two study visits. Outcomes were study enrollment and completion rate.

Analysis

Research Electronic Data Capture (REDCap), a web-based application developed by Vanderbilt University to capture data for clinical research that is Health Insurance Portability and Accountability Act (HIPAA)–compliant, was used to collect and store clinical data. Caregiver survey and ADAS-Cog data were analyzed by calculating frequencies and percentages for categorical variables using the Statistical Package for the Social Sciences (SPSS version 28, IBM, SPSS Inc.) Continuous outcome measures were evaluated using the nonparametric Mann–Whitney U test with an alpha level of p < 0.05. Scores are presented as means, standard deviations (SD) and medians.

Results

Care navigator program participation

Eighteen patient/caregiver dyads participated in the care navigation program. Demographic data are reported in Table 1. Of participating patients, 62.5% reported being diagnosed with ADRD before enrolling in the study, whereas 37.5% were classified as likely ADRD as part of the study screening process.

Table 1.

Demographic Data of the 18 Patient/Caregiver Dyads That Participated in the Care Navigation Program

	Patient		Caregiver
Variables	N	%	N	%
Sex
Male	7	38.9	7	38.9
Female	11	61.1	11	61.1
Race
White	11	61.1	11	61.1
Black/African American	7	38.9	7	38.9
Education
Less or high school	7	38.9	5	27.8
More or college	11	61.1	13	72.2
Household income
≤$45,000	8	44.4	8	44.4
>$45,000	9	50.0	9	50.0
Unknown	1	5.6	1	5.6
Geography
Metropolitan	13	72.2	13	72.2
Nonmetropolitan	4	22.2	4	22.2
Unknown	1	5.6	1	5.6

Patients were moderately to severely cognitively impaired. The mean MoCA score (11.9, SD 5.5) was at the low end of the moderate cognitive impairment range. The mean ADAS-Cog score (33.4, SD 7.7) indicated significant cognitive impairment (cutoff ≥17 for dementia). Patient depression was minimal with a mean PHQ-9 score of 1.9, which is below the criteria for mild depression, and a mean GDS score of 5.0, which falls in the “no depression” category (Table 2). Similarly, patient anxiety was within the “minimal anxiety range” with a mean GAD-7 score of 2.6, and behavioral symptoms as measured by the NPI-Q were mild to absent, with mean behavior (2.9), severity (6.5), and distress (6.5) scores falling within the first quartile of potential scores.

Table 2.

Patient Behavioral Scores

Measures	Mean	SD	Median
Patient Health Questionnaire	1.9	2.6	0.50
Generalized Anxiety Disorder	2.6	3.7	1.0
Generalized Depression Scale	5.0	4.0	4.5
The Neuropsychiatric Inventory Questionnaire–Behavior	2.9	2.5	2.0
The Neuropsychiatric Inventory Questionnaire–Severity	6.5	5.0	7.0
The Neuropsychiatric Inventory Questionnaire–Distress	6.5	6.3	4.0
Zarit Burden Interview	8.5	10.4	5.5
Generalized Self-efficacy	18.4	4.4	18.0

Caregivers reported mild burden (ZBI-12; mean 8.5, SD 10.4) and modest caregiver self-efficacy (GSE; mean 18.4, SD 4.4), which falls below the 50th percentile. There were no significant differences in scores between groups based on patients’/caregivers’ race and sex for the outcome measures.

Patients also reported several physical barriers to care, specifically: lack of access to transportation (31.3%), safety risk (12.5%), and mobility problems (6.3%). Fifty percent of patients reported no physical barriers, and limited finances (0%) was not identified as a significant barrier to participation. There were no significant differences between groups based on race and sex related to barriers.

Retention and secondary enrollment in clinical research study

Of the 18 dyads, 16 (89%) agreed to participate in and completed a separate biomarker clinical research study on ADRD involving neuropsychological testing, blood draw, and an MRI over two visits, not more than 2 months apart. All 16 dyads (100%) completed both study visits. Two did not participate, one declined due to family support difficulties, and one was withdrawn from care navigation upon admission into long-term care. Retention from care navigation to biomarker clinical research study enrollment was 86% of Black participants, 67% of participants with low educational attainment, 75% with household income below $45,000, and 80% nonmetropolitan dwelling participants.

For the 16 dyads that agreed to participate in the biomarker clinical research study, patient mean age was 73.4 (7.1) years, and caregiver mean age was 66.9 (9.8) years. Six patients were male and 10 were female, while 7 caregivers were male and 9 were female. Within all dyads, the patient and caregiver were related (spouse or adult child) and of the same race. Other demographic variables are reported in Figure 1. Participants enrolled in the biomarker clinical research study were 37.5% Black, 31.3% had no more than a high school education, 40% reported an annual household income of less than $45,000, and 25% lived in nonmetropolitan areas. One participant declined to report income, and one did not report geographic location.

FIG. 1.

Demographics (race, education, income, and residence, where provided) for patients who agreed to participate in the biomarker clinical research study. HS, high school.

Discussion

In this pilot study, we extended existing work on patient-centered care navigation to show enrollment and retention of underrepresented groups in clinical dementia research. While patient cognitive impairment was moderate to severe, caregivers reported only mild burden and low depression scores. However, caregiver self-efficacy scores were low, with the average response in the “hardly true” range for statements of affirmative efficacy.

Evidence-based recruitment of underrepresented populations

Evidence-based recruitment strategies for effectively reaching underrepresented populations are understudied,^39,40 but existing literature consistently highlights community outreach as a key approach. Effective methods include snowball sampling, community events, and speaking engagements, all involving in-person interaction.⁴¹ Recent reviews⁴² and meta-analyses⁴³ have found community outreach yields higher engagement, particularly from minority groups.⁴⁴ For instance, recruitment of Black participants from community health fairs has been notably successful (42.9% in one study⁴⁵). Similarly, in our study, 37.5% of our initial sample was Black and recruited from community events. Conversely, though commonly used, recruitment through collaboration with health care providers tends to be less effective (7.9%).⁴² This discrepancy is likely due to barriers in access to health care and the need for culturally appropriate physician-patient trust and communication.^14,46–48 This conjecture is supported by our findings showing that 37.5% of participating patients were not formally diagnosed with dementia, consistent with health access barriers. Our enrollment and retention pilot findings suggest that the Care Ecosystem Model, including the core elements of personalized care team navigation, patient and caregiver-centered care planning, creating a supportive environment, and providing training and support for caregivers, can be used effectively to address modifiable barriers to research participation.

Care navigation versus national research participation

Care navigation can be used to support research recruitment and participation among underrepresented populations. Our pilot care navigation participant sample was 37.5% Black, which is consistent with the race distribution of the state of Louisiana (32.6%⁴⁹). Enrollment of Black individuals was lower than the Black population in Shreveport/Caddo Parish (49.8%) but higher than that of neighboring Bossier Parish (23%) where subjects were recruited. Furthermore, we retained 86% (6/7) of Black care navigation participants to complete a biomarker clinical research study. Nationally, the Black population remains underrepresented in clinical research despite the high disease burden in this group.⁵⁰ A meta-analysis of Alzheimer’s disease trials indicated just 2.4% of participants were Black,⁵¹ and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) registry reported 11.5% Black participation.⁵² This range (2.4–11.5% Black participation) highlights the difference in Black representation in research compared to our care navigation recruitment results, which achieved engagement that reflected local demographics.

The population with low educational attainment is also underrepresented in clinical research. Only 15% of the participants in the ADNI registry had education levels of 12 years or lower,⁸ while nationally 34.6% of the adult population has up to 12 years of education.⁹ In our study, 31% of care navigation participants had a high school education, and 67% (4/6) agreed to participate in our biomarker clinical research study, suggesting that the enrollment of people with moderate to low educational attainment was higher than that of existing databases, which is in line with national average education rates. While lower educational attainment may be linked with decreased motivation to participate in research, and motivation may differ across race and ethnicity,^40,53 and our work is consistent with previous research reporting that community-based ADRD education programs can facilitate research participation of underrepresented groups.^54,55

Previous work indicates that patients and caregivers with low income are also less likely to participate in clinical trials compared to those with income above $50,000.^10,40,56 A previous study of cancer clinical trial participation showed that patient income level was an independent predictor of research participation, even though Medicare, private payers and Medicaid have covered the costs of routine services that would have been covered without participation in a trial.⁵⁷ In our study, 40% of dyads reported an annual household income of less than $45,000, and the retention rate was 75% of this group (6/8). Hesitancy to participate may be due to concerns about compensation versus costs incurred for childcare and transportation, or absence from work.⁵⁸

Much of the existing data on participants living in rural areas comes from cancer clinical trials.⁵⁹ For example, eligibility for breast cancer clinical trials was lower for rural (6.0%) versus urban (18.7%) participants, indicating that an urban patient would be 3.56 times more likely to have access to a trial than a similar rural patient.⁶⁰ The difference was even larger for the eligibility of rural (10.5%) versus urban (43.0%) lung cancer patients. Rural residence has been associated with reduced access to health care and clinical trials, and increased research recruitment barriers in communication and awareness,¹¹ including clinicians’ lack of awareness and reluctance to participate in clinical research.^61,62

However, not all studies agree. Bharucha and colleagues⁶³ found that the clinical trial enrollment rate was higher for rural residents (32%) than the local rural population (19%), though the rate varied across states. In Louisiana, 27.8% of adults over the age of 65 years live in rural areas.⁶⁴ We found that of the 25% of participants who lived in a nonmetropolitan area, 80% (4/5) participated in the clinical trial. The sample is too small to draw firm conclusions, but the results are promising. Globally, dementia-related support and education services are often limited in rural areas despite rural-dwelling caregivers reporting greater need for information and support, especially for early stages of disease (for a scoping review, see Ref.⁶⁵).

Caregiver burden and self-efficacy

Caregiver burden in dementia has long been appreciated (for reviews, see Refs.^66–68). This burden is due to high levels of stress, emotional strain, and depression from the demands of caregiving roles,^67,69 and we anticipated that caregiver burden in our study would be high. Interestingly, we found that while patient disease severity was significant, caregivers rated their burden in the minimal range. Depression has also been linked to caregiver burden,^70,71 and depression scores in our sample were also low. Caregiver burden is based on the caregiver’s perception of the activities and stressors of caregiving and is thus influenced by psychosocial factors, including culture, kinship, and social environment.⁶⁶ It is possible that psychosocial support mitigated caregiver burden in our sample.

In contrast, reported caregiver self-efficacy, or an individual’s assessment of his or her ability to successfully master a specific task,⁷² was poor in our sample, which is consistent with previous work. For example eight defining attributes of self-efficacy pertain to dementia caregivers and the burden of caregiving, including caregivers’ confidence in their ability to: (1) manage behaviors and other stressors, (2) control upsetting thoughts, (3) obtain medical information, (4) manage medical problems, (5) manage self-care, (6) access community support, (7) manage patient activities of daily living, and (8) maintain a good patient/caregiver relationship.⁷³ Self-efficacy has been shown to be higher in groups who volunteer to participate in clinical trials compared to those who decline.⁷⁴ Furthermore, Miller and colleagues⁷⁵ showed that clinical trial-specific education mediated the relationship between self-efficacy and decisional conflict, where reduced conflict was associated with increased likelihood of enrolling in a clinical trial. Our findings indicate that future research should target caregiver self-efficacy, including before and after care navigation, to better understand caregiver barriers to clinical research participation.

Limitations

While our sample of dyads was diverse and reflective of the local population, the generalizability of our results is limited by our small cohort. The study was underpowered to make comparisons across groups based on race, education, income, and dwelling, or to consider intersectionality. Also, we had no control group that received a similar duration interaction on a different topic. While a scientifically sound study design, it was not practical or even ethical to put time demands on caregivers with no benefit to them. We did not collect quantitative data after the care-navigation intervention, precluding the formation of any conclusions about the effectiveness of any specific part of the care navigator program as it relates to reported caregiver burden, self-efficacy, or unmet needs. However, caregivers did report qualitatively having a positive experience, citing increased access to specialty care and referral to community resources. Finally, we did not collect demographic information on those who did not agree to participate in care navigation.

Conclusions

The concepts of care navigation and individualized support are not new.⁷⁶ However, they have been less frequently applied in evidence-based research of recruitment for clinical trials. Findings from our pilot study suggest that individualized care navigation supports recruitment of underrepresented groups based on race, education level, income, and dwelling. In addition, our findings indicate that caregiver self-efficacy may be a target for improving participation, as it is for reducing dementia caregiver burden.⁷³

Footnotes

Acknowledgments

The authors would like to thank Robert Sawyer and Beth Arredondo from the Ochsner Health Cognitive Disorders and Brain Health Program for care navigation training and support. The authors would also like to thank Frances Katzenstein Zadeck for her generous contribution to support dementia research participation.

Authors’ Contributions

T.R.: Conceptualization, investigation, and writing—review and editing. F.T.-D.: Formal analysis, writing—original draft, and visualization. N.G.: Conceptualization and writing—review and editing. C.A.: Conceptualization, methodology, and writing—review and editing. E.P.: Conceptualization, resources, and writing—review and editing. E.D.: Conceptualization, formal analysis, funding acquisition, methodology, resources, project administration, supervision, and writing—review and editing.

Author Disclosure Statement

The authors declare no competing interests.

Funding Information

Funding for this study was provided by the LSU Health Collaborative Intramural Research Program grant to E.D.

Abbreviations Used

References

Misiura

, Butts

, Hammerschlag

, et al. Intersectionality in Alzheimer’s disease: The role of female sex and black American race in the development and prevalence of Alzheimer’s disease. Neurotherapeutics, 2023; 20(4):1019–1036; doi: 10.1007/s13311-023-01408-x

Findley

, Cox

, Lipson

, et al. Health disparities in aging: Improving dementia care for Black women. Front Aging Neurosci, 2023; 15:1107372; doi: 10.3389/fnagi.2023.1107372

Quiñones

, Mitchell

, Jackson

, et al. Achieving health equity in embedded pragmatic trials for people living with dementia and their family caregivers. J Am Geriatr Soc, 2020; 68(Suppl 2):S8–S13; doi: 10.1111/jgs.16614

Matthews

, Xu

, Gaglioti

, et al. Racial and ethnic estimates of Alzheimer’s disease and related dementias in the United States (2015–2060) in adults aged ≥65 years. Alzheimers Dement, 2019; 15(1):17–24; doi: 10.1016/j.jalz.2018.06.3063

Solar

, Irwin

. A Conceptual Framework for Action on the Social Determinants of Health. WHO Document Production Services; 2010.

Palmer

, Ismond

, Rodriquez

, et al. Social determinants of health: Future directions for health disparities research. Am J Public Health, 2019; 109(S1):S70–S71; doi: 10.2105/AJPH.2019.304964

Kennedy

, Cutter

, Wang

, et al. Challenging assumptions about African American participation in Alzheimer Disease trials. Am J Geriatr Psychiatry, 2017; 25(10):1150–1159; doi: 10.1016/j.jagp.2017.04.013

Raman

, Quiroz

, Langford

, et al. Disparities by race and ethnicity among adults recruited for a preclinical Alzheimer Disease trial. JAMA Netw Open, 2021; 4(7):e2114364; doi: 10.1001/jamanetworkopen.2021.14364

Ryan

, Bauman

. Educational Attainment in the United States: 2015. 2015.

10.

Unger

, Gralow

, Albain

, et al. Patient income level and cancer clinical trial participation: A prospective survey study. JAMA Oncol, 2016; 2(1):137–139; doi: 10.1001/jamaoncol.2015.3924

11.

Tanner

, Kim

S-H

, Friedman

, et al. Barriers to medical research participation as perceived by clinical trial investigators: Communicating with Rural and African American Communities. J Health Commun, 2015; 20(1):88–96; doi: 10.1080/10810730.2014.908985

12.

Anonymous. CDC/ATSDR Social Vulnerability Index (CDC/ATSDR SVI). 2024. Available from: https://www.atsdr.cdc.gov/placeandhealth/svi/index.html [Last accessed: September 4, 2024].

13.

Williams

, Scharff

, Mathews

, et al. Barriers and facilitators of African American participation in Alzheimer disease biomarker research. Alzheimer Dis Assoc Disord, 2010; 24(Suppl):S24–S29; doi: 10.1097/WAD.0b013e3181f14a14

14.

Caldwell

, Ford

, Wallace

, et al. Intersection of living in a Rural Versus Urban Area and race/ethnicity in explaining access to health care in the United States. Am J Public Health, 2016; 106(8):1463–1469; doi: 10.2105/AJPH.2016.303212

15.

Chao

, Lai

, Tse

, et al. Recruitment of Chinese American elders into dementia research: The UCSF ADRC experience. Gerontologist, 2011; 51(Suppl 1):S125–S133; doi: 10.1093/geront/gnr033

16.

Gelman

. Learning from recruitment challenges: Barriers to diagnosis, treatment, and research participation for latinos with symptoms of Alzheimer’s Disease. J Gerontol Soc Work, 2010; 53(1):94–113; doi: 10.1080/01634370903361847

17.

Schnieders

, Danner

, McGuire

, et al. Incentives and barriers to research participation and brain donation among African Americans. Am J Alzheimers Dis Other Demen, 2013; 28(5):485–490; doi: 10.1177/1533317513488922

18.

Kallmyer

, Bass

, Baumgart

, et al. Dementia care navigation: Building toward a common definition, key principles, and outcomes. Alzheimers Dement (N Y), 2023; 9(3):e12408; doi: 10.1002/trc2.12408

19.

Saragosa

, MacEachern

, Chiu

, et al. Mapping the evidence on dementia care pathways – A scoping review. BMC Geriatr, 2024; 24(1):690; doi: 10.1186/s12877-024-05250-4

20.

Black

, Johnston

, Rabins

, et al. Unmet needs of community-residing persons with dementia and their informal caregivers: Findings from the maximizing independence at home study. J Am Geriatr Soc, 2013; 61(12):2087–2095; doi: 10.1111/jgs.12549

21.

Arsenault-Lapierre

, Bui

, Le Berre

, et al. Rural and urban differences in quality of dementia care of persons with dementia and caregivers across all domains: A systematic review. BMC Health Serv Res, 2023; 23(1):102; doi: 10.1186/s12913-023-09100-8

22.

Adams

, Sarnak

, Lewis

, et al. What do clinicians who deliver maternity services think patient-centered care is and how is that different for vulnerable women? A qualitative study. J Pregnancy, 2018; 2018:e5853235; doi: 10.1155/2018/5853235

23.

Maslow

. A dynamic theory of human motivation. In: Understanding Human Motivation. Howard Allen Publishers: Cleveland, OH, US; 1958; pp. 26–47; doi: 10.1037/11305-004

24.

Natale-Pereira

, Enard

, Nevarez

, et al. The role of patient navigators in eliminating health disparities. Cancer, 2011; 117(15 Suppl):3543–3552; doi: 10.1002/cncr.26264

25.

Eliacin

, Hathaway

, Wang

, et al. Factors influencing the participation of Black and White Americans in Alzheimer’s disease biomarker research. Alzheimers Dement (Amst), 2022; 14(1):e12384; doi: 10.1002/dad2.12384

26.

Possin

, Merrilees

, Bonasera

, et al. Development of an adaptive, personalized, and scalable dementia care program: Early findings from the Care Ecosystem. PLoS Med, 2017; 14(3):e1002260; doi: 10.1371/journal.pmed.1002260

27.

Rosenbloom

, Kashyap

, Diaz-Ochoa

, et al. Implementation and review of the care ecosystem in an integrated healthcare system. BMC Geriatr, 2023; 23(1):515; doi: 10.1186/s12877-023-04146-z

28.

Sideman

, Dulaney

, Merrilees

, et al. Dissemination of the care ecosystem collaborative care model for dementia. J Am Geriatr Soc, 2025; 73(7):2220–2228; doi: 10.1111/jgs.19363

29.

McKhann

, Knopman

, Chertkow

, et al. The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement, 2011; 7(3):263–269; doi: 10.1016/j.jalz.2011.03.005

30.

Nasreddine

, Phillips

, Bédirian

, et al. The Montreal cognitive assessment, MoCA: A brief screening tool for mild cognitive impairment. J Am Geriatr Soc, 2005; 53(4):695–699; doi: 10.1111/j.1532-5415.2005.53221.x

31.

Rosen

, Mohs

, Davis

. A new rating scale for Alzheimer’s disease. Am J Psychiatry, 1984; 141(11):1356–1364; doi: 10.1176/ajp.141.11.1356

32.

Spitzer

, Kroenke

, Williams

. Validation and utility of a self-report version of PRIME-MD: The PHQ primary care study. Primary Care Evaluation of Mental Disorders. Patient Health Questionnaire. JAMA, 1999; 282(18):1737–1744; doi: 10.1001/jama.282.18.1737

33.

Spitzer

, Kroenke

, Williams

JBW

, et al. A brief measure for assessing generalized anxiety disorder: The GAD-7. Arch Intern Med, 2006; 166(10):1092–1097; doi: 10.1001/archinte.166.10.1092

34.

Yesavage

, Brink

, Rose

, et al. Development and validation of a geriatric depression screening scale: A preliminary report. J Psychiatr Res, 1982; 17(1):37–49; doi: 10.1016/0022-3956(82)90033-4

35.

Kaufer

, Cummings

, Ketchel

, et al. Validation of the NPI-Q, a brief clinical form of the Neuropsychiatric Inventory. J Neuropsychiatry Clin Neurosci, 2000; 12(2):233–239; doi: 10.1176/jnp.12.2.233

36.

Bédard

, Molloy

, Squire

, et al. The Zarit burden interview: A new short version and screening version. Gerontologist, 2001; 41(5):652–657; doi: 10.1093/geront/41.5.652

37.

Branger

, O’Connell

, Morgan

. Factor analysis of the 12-Item Zarit burden interview in caregivers of persons diagnosed with dementia. J Appl Gerontol, 2016; 35(5):489–507; doi: 10.1177/0733464813520222

38.

Schwarzer

, Jerusalem

. General Self-Efficacy Scale. APA PsycNet; 2012; doi: 10.1037/t00393-000

39.

Grill

, Galvin

. Facilitating Alzheimer disease research recruitment. Alzheimer Dis Assoc Disord, 2014; 28(1):1–8; doi: 10.1097/WAD.0000000000000016

40.

Kurt

, Kincaid

, Semler

, et al. Impact of race versus education and race versus income on patients’ motivation to participate in clinical trials. J Racial Ethn Health Disparities, 2018; 5(5):1042–1051; doi: 10.1007/s40615-017-0452-z

41.

Walker

AIB

, Trani

J-F

, Banks

, et al. Recruiting a prospective community cohort to study Alzheimer’s disease and structural and social determinants of health among adults racialized as Black: The ARCHES cohort. Alzheimers Dement (N Y), 2024; 10(2):e12473; doi: 10.1002/trc2.12473

42.

Wong

, Amano

, Lin

S-Y

, et al. Strategies for the recruitment and retention of racial/ethnic minorities in Alzheimer disease and dementia clinical research. Curr Alzheimer Res, 2019; 16(5):458–471; doi: 10.2174/1567205016666190321161901

43.

Graham

, Ngwa

, Ntekim

, et al. Best strategies to recruit and enroll elderly Blacks into clinical and biomedical research. Clin Interv Aging, 2018; 13:43–50; doi: 10.2147/CIA.S130112

44.

Fritsch

, Adams

, Redd

, et al. Use of live theater to increase minority participation in Alzheimer disease research. Alzheimer Dis Assoc Disord, 2006; 20(2):105–111; doi: 10.1097/01.wad.0000213806.66811.ea

45.

Manjunath

, Ifelayo

, Jones

, et al. Addressing cardiovascular health disparities in Minnesota: Establishment of a community steering committee by FAITH! (Fostering African-American Improvement in Total Health). Int J Environ Res Public Health, 2019; 16(21):4144; doi: 10.3390/ijerph16214144

46.

Jetty

, Jabbarpour

, Pollack

, et al. Patient-Physician racial concordance associated with improved healthcare use and lower healthcare expenditures in minority populations. J Racial Ethn Health Disparities, 2022; 9(1):68–81; doi: 10.1007/s40615-020-00930-4

47.

Saha

, Beach

. Impact of physician race on patient decision-making and ratings of physicians: A randomized experiment using video vignettes. J Gen Intern Med, 2020; 35(4):1084–1091; doi: 10.1007/s11606-020-05646-z

48.

Takeshita

, Wang

, Loren

, et al. Association of racial/ethnic and gender concordance between patients and physicians with patient experience ratings. JAMA Netw Open, 2020; 3(11):e2024583; doi: 10.1001/jamanetworkopen.2020.24583

49.

Anonymous. U.S. Census Bureau QuickFacts: Louisiana. n.d. Available from: https://www.census.gov/quickfacts/fact/table/LA/PST045222 [Last accessed: April 2, 2024].

50.

Vyas

, Raval

, Watt

, et al. Representation of ethnic groups in dementia trials: Systematic review and meta-analysis. J Neurol Sci, 2018; 394:107–111; doi: 10.1016/j.jns.2018.09.012

51.

Mayeda

, Glymour

, Quesenberry

, et al. Inequalities in dementia incidence between six racial and ethnic groups over 14 years. Alzheimers Dement, 2016; 12(3):216–224; doi: 10.1016/j.jalz.2015.12.007

52.

Weiner

, Veitch

, Miller

, et al.; Alzheimer’s Disease Neuroimaging Initiative. Increasing participant diversity in AD research: Plans for digital screening, blood testing, and a community-engaged approach in the Alzheimer’s Disease Neuroimaging Initiative 4. Alzheimer’s & Dementia, 2023; 19(1):307–317; doi: 10.1002/alz.12797

53.

Ashford

, Eichenbaum

, Williams

, et al. Effects of sex, race, ethnicity, and education on online aging research participation. Alzheimers Dement (N Y), 2020; 6(1):e12028; doi: 10.1002/trc2.12028

54.

Perales-Puchalt

, Shaw

, McGee

, et al. Preliminary efficacy of a recruitment educational strategy on Alzheimer’s disease knowledge, research participation attitudes, and enrollment among Hispanics. Hisp Health Care Int, 2020; 18(3):144–149; doi: 10.1177/1540415319893238

55.

Shaw

, Perales-Puchalt

, Moore

, et al. Recruitment of older African Americans in Alzheimer’s disease clinical trials using a community education approach. J Prev Alzheimers Dis, 2022; 9(4):672–678; doi: 10.14283/jpad.2022.82

56.

Unger

, Hershman

, Albain

, et al. Patient income level and cancer clinical trial participation. J Clin Oncol, 2013; 31(5):536–542; doi: 10.1200/JCO.2012.45.4553

57.

Takvorian

, Guerra

, Schpero

. A hidden opportunity — medicaid’s role in supporting equitable access to clinical trials. N Engl J Med, 2021; 384(21):1975–1978; doi: 10.1056/NEJMp2101627

58.

Price

, Lyons

, Hamzavi

, et al. Facilitating clinical trials participation of low socioeconomic status patients. Dermatology, 2021; 237(5):843–846; doi: 10.1159/000511889

59.

Ratcliffe

, Burd

, Holder

, et al. Defining Rural at the U.S. Census Bureau. American Community Survey and Geography Brief; 2016.

60.

Mudaranthakam

, Gajewski

, Krebill

, et al. Barriers to clinical trial participation: Comparative study between rural and urban participants. JMIR Cancer, 2022; 8(2):e33240; doi: 10.2196/33240

61.

Comis

, Miller

, Aldigé

, et al. Public attitudes toward participation in cancer clinical trials. J Clin Oncol, 2003; 21(5):830–835; doi: 10.1200/JCO.2003.02.105

62.

Kadam

, Borde

, Madas

, et al. Challenges in recruitment and retention of clinical trial subjects. Perspect Clin Res, 2016; 7(3):137–143; doi: 10.4103/2229-3485.184820

63.

Bharucha

, Wi

, Srinivasan

, et al. Participation of rural patients in clinical trials at a multisite academic medical center. J Clin Transl Sci, 2021; 5(1):e190; doi: 10.1017/cts.2021.813

64.

Anonymous. Explore Rural Population - Ages 65+ in Louisiana. AHR. n.d. Available from: https://www.americashealthrankings.org/explore/measures/rural_senior/LA [Last accessed: April 2, 2024].

65.

Bayly

, Morgan

, Chow

, et al. Dementia-Related education and support service availability, accessibility, and use in rural areas: Barriers and solutions. Can J Aging, 2020; 39(4):545–585; doi: 10.1017/S0714980819000564

66.

Etters

, Goodall

, Harrison

. Caregiver burden among dementia patient caregivers: A review of the literature. J Am Acad Nurse Pract, 2008; 20(8):423–428.

67.

Steenfeldt

VØ

, Aagerup

, Jacobsen

, et al. Becoming a family caregiver to a person with dementia: A literature review on the needs of family caregivers. SAGE Open Nurs, 2021; 7:23779608211029073; doi: 10.1177/23779608211029073

68.

van den Kieboom

, Snaphaan

, Mark

, et al. The trajectory of caregiver burden and risk factors in dementia progression: A systematic review. J Alzheimers Dis, 2020; 77(3):1107–1115; doi: 10.3233/JAD-200647

69.

Alves

LCdS

, Monteiro

, Bento

, et al. Burnout syndrome in informal caregivers of older adults with dementia: A systematic review. Dement Neuropsychol, 2019; 13(4):415–421; doi: 10.1590/1980-57642018dn13-040008

70.

Gilliam

, Steffen

. The relationship between caregiving self-efficacy and depressive symptoms in dementia family caregivers. Aging Ment Health, 2006; 10(2):79–86; doi: 10.1080/13607860500310658

71.

Schulz

, O’Brien

, Bookwala

, et al. Psychiatric and physical morbidity effects of dementia caregiving: Prevalence, correlates, and causes. Gerontologist, 1995; 35(6):771–791; doi: 10.1093/geront/35.6.771

72.

Bandura

. Self-efficacy: Toward a unifying theory of behavioral change. Psychol Rev, 1977; 84(2):191–215; doi: 10.1037/0033-295X.84.2.191

73.

Khan

, Hirschman

, McHugh

, et al. Self-efficacy of family caregivers of older adults with cognitive impairment: A concept analysis. Nurs Forum, 2021; 56(1):112–126; doi: 10.1111/nuf.12499

74.

Gouveia

, Cruz

, Almeida

. Sociodemographic and psychological characteristics influencing patients’ willingness to participate in clinical trials. BMJ Open Qual, 2022; 11(4):e002044; doi: 10.1136/bmjoq-2022-002044

75.

Miller

, Hudson

, Egleston

, et al. The relationships among knowledge, self-efficacy, preparedness, decisional conflict and decisions to participate in a cancer clinical trial. Psychooncology, 2013; 22(3):481–489; doi: 10.1002/pon.3043

76.

Bernstein

, Merrilees

, Dulaney

, et al. Using care navigation to address caregiver burden in dementia: A qualitative case study analysis. Alzheimers Dement (N Y), 2020; 6(1):e12010; doi: 10.1002/trc2.12010

Care Navigation Supports Research Participation in Underrepresented Populations with Alzheimer’s Disease and Related Dementias: A Pilot Study

Abstract

Introduction:

Methods:

Results:

Discussion:

Introduction

Health care disparities and research participation

Patient-centered care navigation and research participation

Methods

Study design

Consent statement

Study sample

Inclusion criteria for the patient were

Exclusion criteria were

Care navigation

Support services

Outcome measures

Analysis

Results

Care navigator program participation

Retention and secondary enrollment in clinical research study

Discussion

Evidence-based recruitment of underrepresented populations

Care navigation versus national research participation

Caregiver burden and self-efficacy

Limitations

Conclusions

Footnotes

Acknowledgments

Authors’ Contributions

Author Disclosure Statement

Funding Information

Abbreviations Used

References