1. Introduction

People with mental illness (MI) in the United States (U.S.) face a multitude of barriers to accessing both mental and physical healthcare, resulting in the suboptimal receipt of care and contributing to disparities in health and healthcare outcomes [1–4]. Facilitating access to healthcare is therefore a critical component of improving health and healthcare outcomes for this population. Commonly cited barriers to accessing healthcare among people with MI include impaired cognition, general distress and disorganization, negative attitudes toward treatment, inadequate transportation, lack of care coordination, and the cost of health services [5–10].

Barriers to healthcare access significantly impact people with MI. For example, compared to individuals in the general population, individuals with MI are up to 50% less likely to report having a usual source of care (USC), an important enabling factor that has been linked to the adequate and timely receipt of care [11–13]. Additionally, individuals with MI are seven times more likely to report difficulty accessing healthcare than individuals in the general population [11]. Ultimately, the inadequate receipt of needed care among individuals with MI can result in increased acute care use, preventable healthcare costs, homelessness, incarceration, diminished quality of life, and suicide [2, 14–19].

A number of individual- and system-level approaches have been developed and deployed in response to findings of diminished access to care for individuals with MI (see [1] for a review of barriers to care and related interventions; [20–22]). While these efforts have shown promise, they largely benefit individuals who already have an existing connection to healthcare systems and pay little attention to the broader social and economic contexts that impact health and healthcare outcomes for individuals with MI [12, 23, 24]. Therefore, the “upstream” community-level social and economic contexts that prevent individuals from even approaching healthcare systems in the first place are considered a potentially potent, yet understudied, point of intervention [14, 25, 26].

A growing body of literature, emerging from increasingly popular discussions around the social determinants of health (see [27]), is investigating how social and economic aspects of communities impact healthcare access and utilization [12, 28–33]. For example, community-level socioeconomic status (SES) has been shown to influence the need for healthcare by influencing health behaviors via the availability of healthy foods and opportunities to exercise [33, 34]. Community-level SES has also been linked to the availability of quality healthcare, with poorer neighborhoods having greater difficulty recruiting and retaining the most qualified providers [12, 23, 35].

Residential stability can help facilitate access to services by strengthening social networks and support systems within communities which, in turn, can influence how individuals evaluate their own health and healthcare needs, affect an individual’s willingness to seek care, and facilitate the sharing of knowledge and resources necessary to initiate care [31, 36, 37]. In this vein, a systematic review by Derose & Vara [38]​ found that trusting and cooperative relationships within a person’s social network were related to overall improvements in healthcare access generally. However, less is known about how these community-level factors impact healthcare outcomes among individuals with MI specifically. In addition, previous research has largely focused on access to, and utilization of, mental health (MH) treatment specifically. There is much to be learned about the factors that impact the ability of individuals with MI to access healthcare services more generally.

The current study helps fill this gap by examining how indicators of community-level SES and residential stability are associated with access to healthcare among a sample of individuals with MI drawn from a nationally representative survey in the U.S. (see Methods section for a description of how we operationalize “access to healthcare”).

2. Methods

3. Results

4. Discussion

In the U.S., individuals with MI must often navigate fragmented systems of care to get their physical and MH needs met [21]. Unfortunately, many barriers exist that prevent these individuals from receiving adequate care, which can result in poor health and healthcare outcomes [46, 47]. While previous studies have extensively examined the individual-level factors that can help or hinder access to care, few studies have examined how the characteristics of the communities in which people with MI live influence healthcare access and utilization [25].

The current study found that, among a sample of adults with MI, community-level socioeconomic disadvantage was associated with less access to a USC and decreased access to prescription medication. Residential stability within the community was associated with increased access to a USC, decreased odds of delaying and forgoing needed care, and decreased odds of forgoing prescription medication. The association between residential stability and delaying or forgoing needed care remained significant even after including individual-level variables.

Findings from the current study lend support to a growing body of literature that suggests socioeconomic disadvantage at the community level hinders individuals with MI from accessing healthcare. First, a heightened need for care in disadvantage neighborhoods may be driven by a myriad of community-level social and economic characteristics that research has found to be potent social determinants of health [48–50]. Secondly, as Davidson et al. [12] theorize, poorer communities may struggle to retain quality care providers due to high rates of publicly and uninsured individuals. This may disproportionately impact individuals with MI who rely on safety net providers to get their mental and physical healthcare need met [12, 51]. A lack of available quality care, in turn, may exacerbate mental and physical health symptomatology which then increases both the need and demand for care [52].

Findings from the current study also suggest that residential stability can help keep individuals with MI linked to a USC and is a protective factor against delaying or forgoing needed care. One possible explanation for these findings is that residential stability helps to maintain healthy social networks and support systems at the community level. Healthy social networks and support systems act as a protective factor against community stressors that may otherwise negatively affect health and healthcare outcomes [53]. Social networks may also be an important mechanism through which information about health and healthcare services is shared among individuals within a community, and strong social networks may help contribute to norms and attitudes that facilitate treatment seeking, access, and engagement [38].

Interestingly, Black and Hispanic individuals in the current study reported less difficulty accessing care than their White counterparts. While racial and ethnic disparities in access to care are well documented in the literature, previous studies also suggest that social networks and social support systems, particularly among Black and Latinx communities, can influence the perceived need for care and the willingness of individuals to initiate care and serve as an informal source of care [36, 37, 54, 55]. At the systems level, disproportionately low rates of contact with providers may also contribute to low levels of perceived need for care among Black and Latinx individuals [28]. The way in which community-level factors may differentially impact healthcare access for individuals with MI based on race and ethnicity is an area for future research.

Finally, the U.S. has been relatively slow to develop and incorporate empirically sound, standardized measures of community-level social and economic characteristics into health and healthcare data. Phillips et al. [56] drew attention to this fact in their article titled, “How Other Countries Use Deprivation Indices—And Why the United States Desperately Needs One.” The authors note that, for decades, countries like New Zealand and the United Kingdom (among others) have been using such measures to “assess community needs, inform research, adjust clinical funding, allocate community resources, and determinate policy impact” (p. 1). Fortunately, some recent progress has been made in the U.S. For example, the development and validation of an updated ADI, used in the current study, has been funded by the National Institute of Health and recently made available to the public [39]. Though it remains one of the only validated, multidimensional tools of its kind in the U.S., the utility of the ADI is evident in its increasingly widespread use in health services research and, more recently, its incorporation by the Centers for Medicare and Medicaid (CMS) Innovation into risk adjustment benchmarks [57]. While these advancements are promising, the continued development and use of such indices in the U.S. is needed to progress health equity frameworks and policies aimed at equitably allocating healthcare resources, improving population health, and decreasing health disparities, particularly among vulnerable groups including individuals with MI [57].

Toward this end, the current study specifically illustrates how community-level characteristics can be used to augment existing health and healthcare data in the U.S., creating a more holistic picture of the factors that influence an individual’s ability to access healthcare. As community-level characteristics have been increasingly recognized as important drivers of health and healthcare access, new opportunities to develop and deploy community-minded interventions are beginning to emerge in the U.S. For example, in 2021, the CMS announced that it would assist states in funding and delivering interventions aimed at addressing health-related social needs (HRSNs), which are envisioned as an extension of an individual’s social determinants of health such as nutrition, housing, transportation, neighborhood safety, social networks, and educational and employment opportunities [58]. Findings from the current study support the development of programs that both target community-level social and economic factors directly as well as interventions that are responsive to an individual’s social and economic environment when trying to optimize access to healthcare. The current study also highlights the potential of community-level social and economic indicators in helping identify the individuals and communities that might most benefit from the allocation of these additional resources and supports. As future research establishes the evidence base in this emerging area, researchers and policymakers will need to be intentional in considering the unique healthcare needs and community contexts of individuals with MI, for whom community-level social and economic forces may be particularly potent predictors of healthcare access.

5. Limitations and Future Directions

Findings from the current analysis should be considered within the context of several limitations. First, while the data are drawn from a nationally representative sample, these analyses did not use the MEPS person-level weights, which means that estimates may not be representative of the entire U.S. population. Sample weights in the MEPS data do not accommodate studies that use only a single panel for each year of MEPS data—as is the case when establishing diagnoses (in each respondent’s first year in MEPS) before outcomes are examined (in each respondent’s second year in MEPS).

Second, the most common MH diagnoses in the study sample were depressive disorders. While we acknowledge the pitfalls of trying to categorize MH diagnoses into distinct groups, future research should replicate these analyses with individuals who have MH diagnoses associated with higher levels of service need, such as schizophrenia and other psychotic disorders.

Third, the findings from the current analyses carry forward the same limitations inherent in the community-level data that were compiled to make the current study possible. Notably, geographic boundaries used across these datasets are defined by the federal government and may not reflect the true social, economic, and structural communities with which individuals in the study routinely interact [59]. On a related note, both access to healthcare and community dynamics are likely to vary significantly between geographic locations, including between urban and rural areas. As the current study included a sample of individuals primarily residing in urban areas (85.1%), our findings may not be generalizable to rural contexts. Future research is needed to examine how geographic context impacts the differences found in both access to care as well as community characteristics.

Fourth, our findings and conclusions are limited by the broad definitions, carried forward from the MEPS data, that were used to capture access to healthcare. Replicating these analysis using a more granular definition of access to care, and incorporating indicators of actual service use, would help strengthen the current body of literature.

Fifth, while the ADI provides a robust multidimensional measure of community-level socioeconomic disadvantage in the U.S., no such measure exists for residential stability. As such, the current study used a single unidimensional indicator of residential stability from U.S. census data which is likely limited in its ability to capture the complexities and nuances of this construct. Establishing a standardized measure of community-level residential stability in the U.S. is an area for future research.

Finally, the current study used relatively broad insurance coverage categories that limited our ability to provide a more detailed analysis of how healthcare access among individuals with MI is impacted by specific types of healthcare coverage and programs. The financing and delivery of both physical and mental healthcare in the U.S. is comprised of a complex patchwork of policies, involving both public and private insurers and providers, which change over time and vary between localities. For example, Medicaid, the largest payee of behavioral health services in the U.S., gives states vast discretion in determining program eligibility, the types of services covered, and how behavioral health services are administered and delivered [60–62]. While providing a comprehensive review of healthcare policy in the U.S. is beyond the scope of this paper, future research is needed that examines how community characteristics and access to healthcare are simultaneously shaped by healthcare policy, financing, and delivery at the local, state, and federal levels.

6. Conclusion

Findings from the current study suggest that community-level social and economic factors influence healthcare access and utilization among individuals with MI. Therefore, interventions and policies must consider individuals with MI within the context of their broader environments when trying to increase access to and engagement in healthcare services [12, 25].

Disclosure

The analysis presented in this manuscript is a continuation of work submitted to fulfill the dissertation requirements of the Ph.D. in Social Work program at the University of North Carolina at Chapel Hill (see [63]). The full dissertation can be found here: https://doi.org/10.17615/kp23-1603.

Conflicts of Interest

The authors declare no conflicts of interest.

Funding

No funding was received for this manuscript.