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The Invisible Barrier of Neighborhood Disadvantage in Clinical Trial Recruitment and Retention Among Older Adults

Year: 2026
Type: editorial
Abstract: Older adults are among the most common recipients of interventions tested in clinical research, yet they remain underrepresented in clinical trials [1-3]. Efforts to close this participation gap have grown in recent years, reflected in initiatives such as the National Institutes of Health (NIH) Inclusion Across the Lifespan clinical trial requirement encouraging age-diverse study designs [4]. Despite this momentum, significant barriers to adequate and equitable participation in clinical trials among older adults continue to persist. Different explanations for why this gap endures have been offered, including older adult preferences specific to undergoing or avoiding certain therapies [3], age limits on participation, exclusion criteria for conditions more commonly found among older adults [2, 3], and a number of other factors. In this issue of JAGS, Gill and colleagues draw attention to a recruitment and retention barrier that has received less attention: the social exposomes of the neighborhoods in which participants reside. Living in a resource-poor neighborhood, they argue, may challenge nearly every operational aspect of a clinical trial; yet, the relationship between neighborhood disadvantage (i.e., adverse social exposome) and clinical trial implementation has gone largely unexamined. This is a key gap as it is an easily measured construct that is fundamentally modifiable, providing ready pathways towards interventions and practices which could significantly enhance the inclusivity of clinical trials across the US. In this study, Gill and colleagues sought to determine whether recruitment and retention of community-living persons at risk of fall-related injuries in a large multi-site clinical trial (Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) Study) differed based on level of neighborhood disadvantage. To quantify neighborhood disadvantage, they used the Area Deprivation Index (ADI), a freely available composite metric that captures social exposome at the Census block group (i.e., “neighborhood”) level by integrating domains including income, education, employment, and housing quality from current Census and American Community Survey data [5]. ADI can be reported at the national level using percentiles (1–100), or at the state level using deciles (1–10), with higher scores indicating greater adverse social exposome (Figure 1). The results of this work offer profound insights on the role of neighborhood disadvantage in clinical trial recruitment and retention. Health system patients residing in more disadvantaged neighborhoods were less likely to return the clinical trial's screening postcard during the recruitment period from August 2015 to March 2017; however, they were more likely to screen positive. Those living in high-ADI areas were also less likely to enroll in the trial compared to those in less disadvantaged areas. Among those who did enroll, participants from higher-ADI neighborhoods were more likely to be Black, have a high school education or less, and have lower individual-level income. After enrollment, neighborhood disadvantage was associated with a significantly higher likelihood of death and interview refusal. Notably, there was no difference in study withdrawal or loss to follow-up among those with and without neighborhood disadvantage, which may have been due to reduced power to detect differences by ADI. Overall, the findings by Gill and colleagues are consistent with a growing body of research linking higher ADI to numerous adverse health outcomes, including poor postoperative and survival outcomes [6, 7], increased Alzheimer's disease neuropathology [8], more difficulty accessing care, and a greater likelihood of rehospitalization within 30 days [9]. Considered alongside this prior work, the findings of Gill et al. are not entirely surprising and underscore that these disparities likely stem from adverse social exposome-based structural limitations that constrain individuals' ability to participate in essential healthcare and research activities. The work of Gill and colleagues extends this understanding by implicating the neighborhood itself as one such structure. In doing so, their work speaks to a fundamental access problem long acknowledged in clinical trials, while adding a new dimension by detailing precisely where the participation gap widens. This precision is perhaps nowhere more apparent than in the composition of enrollment across site types in the Gill study. Individuals from high-ADI areas were underrepresented at academic sites and disproportionately concentrated at non-academic sites. This pattern reflects a broader and well-documented reality that academic medical institutions have historically been less accessible to certain populations. This is partly attributable to policies that either limit or refuse non-emergent treatment for the under-insured, non-insured, or those on Medicaid. Others may be less accessible because of pragmatic factors such as geographic location or clinical appointment wait times. For others, it may be a perceived lack of trust in the institution or safety for certain individuals and communities. Taken together, these factors can result in less diversity of patient populations across many academic medical settings. It is notable that Gill and colleagues seem to have observed just that—individuals from high ADI areas, who are more apt to be racially diverse, poor, and with fewer years of education, were underrepresented in academic sites within the study. Those with high ADI were much more likely to be patients in the non-academic sites. The barriers facing underrepresented older adults, however, extend beyond the institutional limitations of where enrollment occurs. Participation in clinical trials can be impacted by many multilevel resource barriers, including lack of transportation to attend scheduled visits, scheduling conflicts with the time or length of study visits, and difficulties coordinating with care partners. Alternative methods such as electronic consent forms, home-based visits, and telehealth can address some of these challenges, yet may introduce new barriers for those with limited or unreliable internet access, no personal computing devices, or reduced digital literacy. Critically, as a marker of the adverse social exposome, neighborhood disadvantage extends beyond present-day circumstances as it can be structured to measure an entire life course, including the cumulative adverse social and environmental exposures and the critical life stages in which they are experienced [10]. The downstream health effects of these exposures, including higher rates of chronic disease and comorbid conditions [11], may themselves restrict trial eligibility when studies seek to enroll healthier and thus typically more advantaged populations. This creates a paradoxical situation wherein those most affected by structural disadvantage-linked disease burden may be systematically excluded from the research needed to inform their care. This, in turn, can contribute to the underrepresentation found in this article and across the broader clinical trials literature. “The downstream health effects of these exposures, including higher rates of chronic disease and comorbid conditions, may themselves restrict trial eligibility when studies seek to enroll healthier, and thus typically more advantaged populations. This creates a paradoxical situation wherein those most affected by structural disadvantage-linked disease burden may be systematically excluded from the research needed to inform their care.” Further, by measuring the social exposome, neighborhood disadvantage as measured by ADI serves as a ready proxy for some of the policies and practices that have historically shaped opportunities for health, access, and resources. The participation gaps in modern clinical trials are, in many ways, natural consequences of deeply entrenched historical policies and practices such as redlining [12], exploitative research practices including the Tuskegee Syphilis Study [13], and environmental racism [14] that have impacted communities that remain underrepresented in research today. These patterns have resulted in a well-founded mistrust of the medical and research institutions now asking older adults impacted by such histories for their participation. Collectively, these barriers contextualize the findings of Gill and colleagues and illuminate why neighborhood disadvantage by ADI is more than a geographic marker. ADI captures the collective resource burdens and structural disadvantages that can significantly affect an individual's ability or willingness to participate in and remain in clinical research. Thus, this study challenges us to carefully consider where our research participants come from, not as another demographic factor to document, but as a critical and fundamental component of clinical trial design. Ultimately, the findings of Gill et al. raise an urgent question: what infrastructure investments are needed to meaningfully reach and serve participants in high-ADI neighborhoods? Because neighborhood disadvantage is multifaceted, there will not be a single, simple solution. Further, as Gill and colleagues aptly note, some trial outcomes, such as mortality, are unlikely to be amenable to intervention regardless of recruitment strategy. Yet the magnitude and complexity of this call are not reasons to shy away from answering. As a starting point, Gill et al. offer several practical strategies that can be applied immediately in clinical trials. First, they suggest using the Neighborhood Atlas to identify areas where members of disadvantaged groups are well represented, thereby enabling more targeted recruitment efforts. They also propose that postcard mailings, a common clinical trial recruitment method for older adults, can be enhanced through tailored materials that can be preferentially sent and, if needed, re-sent to individuals in disadvantaged neighborhoods. Finally, they note that barriers identified post-enrollment, such as lack of transportation, could be proactively anticipated and addressed by trial teams to help facilitate continued participation. In addition to these strategies, we would offer several longer-term infrastructure investments for research teams, funders, and policymakers to consider. Fundamental to increasing the representation of older adults from disadvantaged areas in clinical trials is establishing trust within these communities. Such an endeavor will require a paradigm shift wherein researchers move from finding participants to forming sustained partnerships. Rather than relying on episodic approaches to recruitment, research institutions and teams should seek to establish a service-oriented presence in high-ADI neighborhoods well before a trial opens. This often entails connecting with local healthcare agencies serving underserved older adults, establishing Community Advisory Boards (CABs) comprised of community members to help guide research questions and trial design, and partnering with community health workers to liaise with community members to identify opportunities for needed resources and services. Short- and long-term community investment strategies of beginning the research process with intentionality around representativeness, establishing a foundation of trust, anticipating and removing barriers to participation, and optimizing the study team are among a number of pathways to improve research inclusion detailed within a recent National Academies of Science, Engineering, and Medicine report on Improving Representation in Clinical Trials and Research [15]. To further address the fundamental access gap highlighted by Gill and colleagues, policymakers should consider approaches that expand access to care through improved insurance coverage and requirements, including broader acceptance of Medicaid across healthcare settings. While funders such as the NIH already have comprehensive requirements around clinical trial inclusion, several components could be further enhanced. Beyond inclusion enrollment plans that account for age, race, and ethnicity, funders could require ADI-stratified enrollment reporting with explicit targets for high-ADI neighborhoods. Recruitment and retention plans could also be expanded to include documentation of longer-term community investment, such as efforts already undertaken in underserved communities, plans for sustained presence and dissemination following the trial's conclusion, or a combination of both. In summary, the work of Gill and colleagues is a critical step in understanding neighborhood disadvantage as a novel determinant of recruitment and retention in clinical trials. This work has made explicit a barrier that, while deeply ingrained in and impactful to the structures of society, has remained largely invisible in clinical trial research. By anchoring to the Neighborhood Atlas and ADI, Gill and colleagues offer an easily accessible means by which to assess this type of inclusion within any US study. Rectifying the enduring effects of neighborhood disadvantage will require multifaceted approaches that prioritize research infrastructure, community partnerships, and novel approaches to close the participation gap among older adults in high ADI areas. For too long, this participation gap has been a function of place; an invisible marker of unfair access tied to a system that tends to prioritize academic medical center-based studies. It is time to ensure that all have access to the benefits of clinical trials, regardless of where they live. All authors conceptualized the content of this editorial. R.D.L. wrote and prepared the initial draft, A.K. and N.R.-P. reviewed and edited the draft. All authors approved the final version of this editorial. The authors wish to acknowledge Dr. William Buckingham for providing a map from the Neighborhood Atlas to accompany this editorial. This work was partially supported by the William S. Middleton Veteran Affairs Hospital, Madison, WI (Dr. Nicole Rogus-Pulia, GRECC Manuscript 06-2026); and the National Institute on Aging (R01AG070883 to Dr. Amy Kind). The funding organizations were not involved in the design or conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication. Dr. Amy Kind is the recipient of multiple grants from the National Institutes of Health, including support (R01AG070883) for the Neighborhoods Study and Neighborhood Atlas. N.R.-P. is a VA employee. This publication is linked to a related article by Gill et al. To view this article, visit https://doi.org/10.1111/jgs.70359.
DOI: 10.1111/jgs.70518
Language: English
Field: Medicine
Open Access status: green
OpenAlex ID: W7161441362
Cites: 8
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