The relationship between low social/emotional support and health care affordability among rural and urban residents

Abstract

Purpose

Social/emotional support can help to buffer the health and financial impacts of health care costs. However, little research examines differences in social/emotional support as it relates to health care affordability, and even less examines these issues by rurality despite rural/urban differences in health and health care access. This study addresses these gaps by examining differences in social/emotional support and health care affordability issues among rural and urban adults.

Methods

Using weighted data from the 2020 and 2021 National Health Interview Survey (n = 44,987), we examined differences in three health care affordability issues: worry about medical bills, problems paying medical bills, and inability to pay medical bills. We conducted bivariate and multivariate logistic regression analyses comparing these issues by rurality, social/emotional support, and other sociodemographic and health characteristics, generating adjusted odds ratios and predicted probabilities of these issues.

Findings

Rural residents were more likely to report problems paying and inability to pay medical bills (13.0% vs. 10.2%, p < 0.001; 8.2% vs 6.2%, p < 0.001). Sociodemographic and health covariates were differentially associated with adjusted odds of health care affordability issues, while low social/emotional support was associated with higher adjusted odds and adjusted predicted probabilities of all three health care affordability issues in both rural and urban areas.

Conclusions

Low social/emotional support is associated with higher odds and predicted probabilities of all health care affordability issues regardless of rurality. Future policy aimed at reducing medical debt in rural areas should consider the beneficial impacts of social/emotional support.

INTRODUCTION

Difficulty affording health care is widespread and problematic. One marker of affordability issues is medical debt, or debt accrued from health care expenses. Approximately one in 10 U.S. adults reported having issues with medical debt in 2020, ¹ and the Consumer Financial Protection Bureau estimated a total of $88 billion due to medical debt on consumer credit records in 2021—the largest single source of debt that year. ² Consequences of health care debt include financial hardship such as credit score decreases, inability to pay other bills, delaying higher education or purchasing homes, and changing housing situations, as well as difficulties accessing additional health care, such as being denied care by a provider due to unpaid bills or otherwise postponing or forgoing necessary health care due to cost. ³

Being unable to afford health care is related to a variety of sociodemographic and health factors. Groups with higher risk of medical debt and health care affordability issues include women, those in non‐Hispanic Black and Hispanic minoritized groups, those with minor children, those living in the Southern United States, low‐ and middle‐income households, those having private or no insurance, and those with chronic health conditions. ¹ , ⁴ , ⁵ , ⁶ In turn, medical debt acquisition may worsen existing population inequities in issues such as housing, food security, frailty at birth, and other key determinants of adverse health outcomes. ⁴ , ⁵ , ⁷

Difficulty affording health care is also not equally distributed geographically, and problems affording health care are more widespread among rural residents. ⁶ Health care access and health outcomes also tend to be worse among rural residents, partially due to rural health workforce shortages, hospital closures, and biases in health care funding favoring larger populations. ⁸ , ⁹ , ¹⁰ Moreover, rural residents experience higher rates of many adverse health outcomes such as heart disease, stroke, cancer, unintentional injuries, suicide risk, chronic lung disease, unintentional injury death rates, and COVID‐19 death rates, as compared to urban communities, ¹¹ , ¹² with the gap between urban and rural mortality widening. ¹³

High levels of social and emotional support may offset adverse consequences of financial stressors, including medical debt. By seeking help from a support network of family, friends, neighbors, and other community members, individuals experiencing financial difficulty may be better able to meet their essential needs. Previous research has shown that a lack of social support is a contributing factor in the relationship between financial hardship and multimorbidity ¹⁴ and that highly financially stressed individuals with low social support have increased odds of low psychological well‐being and psychosomatic symptoms compared to similarly stressed individuals with higher levels of social support, indicating a buffering effect of tangible social support. ¹⁵

Rural residents differ from urban residents in their social well‐being. Rural communities tend to have greater affective and evaluative place identity—that is, placing significant emotional investment in belonging to a place‐related community—than urban communities. ¹⁶ Rural residents are more likely to say they are satisfied with their family life, ¹⁷ have more frequent interactions with neighbors leading to more connectedness in their community, ¹⁸ , ¹⁹ and report less social isolation and more social relationships than urban residents. ²⁰ However, despite the similar prevalence of serious mental illnesses and psychiatric disorders among rural and urban adults, ²¹ rural residents also exhibit greater geography‐specific risk factors for poor health outcomes than their urban counterparts, including transportation barriers and higher rates of living alone, ²² , ²³ opioid/other drug overdoses, ²⁴ , ²⁵ and suicide, ²⁶ all linked to loneliness and isolation. ²⁷ , ²⁸ This is exacerbated by limited access to mental health services. ²⁹ The recent COVID‐19 pandemic further hindered people's ability to make and maintain social connections and widened existing health outcome disparities between rural and urban residents. ³⁰ , ³¹

Despite these issues, very little research explores the association between social well‐being and health care affordability, as well as how that relationship differs by rurality. In this paper, we examine the association between social/emotional support and health care affordability, with a particular focus on rural residents.

METHODS

Data and sample

Data for this study come from the 2020 and 2021 National Health Interview Survey (NHIS), an annual, nationally representative study in the United States, accessed through IPUMS Health Surveys. ³² We limited our analysis to adults (18+) with information on all variables measured in this analysis, resulting in a final sample size of 44,987 (rural N = 6735).

Measures

Health care affordability was measured in three increasing levels of affordability issues: being worried about, having problems paying, and being unable to pay medical bills. The first question, “If you get sick or have an accident, how worried are you that you will be able to pay your medical bills?”, had response options of “very,” “somewhat,” and “not at all,” which we dichotomized to “yes” (“very” or “somewhat”) and “no” (“not at all”). The second and third questions, “In the past 12 months, did you or anyone in the family have problems paying or were unable to pay any medical bills?” and “Do you/does anyone in your family have any medical bills that you are unable to pay at all?”, already had dichotomized responses. Respondents were only asked the last question if they answered “yes” to the second question.

Subjective strength of social/emotional support was measured by respondents’ answers to the question: “How often do you get the social and emotional support you need? Would you say always, usually, sometimes, rarely, or never?” We collapsed these answers to “High Social/Emotional Support,” if they reported always or usually receiving support, and “Low Social/Emotional Support,” if they reported sometimes, rarely, or never receiving support.

Rural was defined using the 2013 NCHS Urban‐Rural Classification Scheme, with nonmetropolitan counties defined as “rural” and all other counties defined as “urban.” ³³ Health was self‐reported by respondents, which we collapsed to “excellent/good/very good” versus “fair/poor” health. We categorized health insurance statuses as receiving insurance from one of the following sources (in order of prioritization hierarchy): private, dual‐eligible (both Medicare and Medicaid), Medicare Advantage, Medicare only (no Advantage), Medicaid only, other government insurer (including state‐sponsored health plans or other government programs), uninsured (including Indian Health Service with no other source of health insurance), and unknown insurance status, based on previous research. ⁶

We also included other covariates associated with differences in social/emotional support and health care affordability issues, ⁶ including U.S. Census region of residence, ³⁴ sex (male and female), race and ethnicity (non‐Hispanic White, non‐Hispanic Black, Asian, Hispanic, American Indian/Alaska Native [AI/AN], and two or more), age (18–64 and 65+), marital status (married/partnered, separated/divorced, widowed, and never married), household income (as a ratio to the poverty threshold), and the type of usual place of care (doctor's office, urgent care/walk in, Emergency Room [ER], Veterans Affairs [VA], other/unknown, and no usual place of care).

Analysis

We conducted bivariate analyses comparing rural and urban adults by social/emotional support and various sociodemographic and health characteristics, using chi‐squared tests to detect significant differences. We then conducted multivariate logistic regression analyses to generate adjusted odds ratios of experiencing health care affordability issues by rurality, social/emotional support, and sociodemographic and health characteristics, using the full sample. Finally, we obtained predicted probabilities of all health care affordability issues in the rural and urban subsamples by social/emotional support, adjusted for sociodemographic covariates. Analyses were performed using pooled survey weights based on NHIS recommendations for combining multiple years of data in order to approximate national estimates and account for the complex survey design, and all underwent Bonferroni correction. Analyses were completed using SAS 9.3 and Stata 18.0.

RESULTS

Table 1 shows sociodemographic and health characteristics for the pooled 2020 and 2021 NHIS sample of rural and urban adults. Reports of low social/emotional support did not differ between rural and urban residents. Rural residents were significantly more likely than urban residents to report problems paying medical bills (13.0% vs. 10.2%, p < 0.001) and inability to pay medical bills (8.2% vs. 6.2%, p < 0.001). Additionally, rural residents were more likely to be older, identify as White non‐Hispanic or American Indian/Alaska Native (AI/AN), be in fair or poor health, be married/partnered or widowed, live in the North Central/Midwest or South, have lower income–poverty ratios, have traditional Medicare health insurance, and use the doctor's office as the usual place of care (all p < 0.001).

TABLE 1.

Sociodemographic and health characteristics, by rurality.

	Rural	Urban	p‐value
Weighted % of total	13.7%	86.3%
Low social/emotional support	19.0%	19.2%	0.827
Worried about medical bills	45.2%	45.9%	0.540
Problems paying medical bills	13.0%	10.2%	<0.001
Inability to pay medical bills	8.2%	6.3%	<0.001
Female	52.5%	51.6%	0.239
Age			<0.001
18–64	72.7%	78.9%
65+	27.3%	21.1%
Race/ethnicity			<0.001
White non‐Hispanic	80.7%	60.8%
Black non‐Hispanic	6.9%	12.0%
Asian	1.0%	6.6%
Hispanic	6.7%	18.3%
American Indian/Alaska Native (AI/AN)	2.6%	0.4%
Two or more	2.1%	1.9%
Health
Fair/poor	19.0%	12.2%	<0.001
Marital status			<0.001
Married/partnered	64.5%	60.1%
Separated/divorced	11.0%	9.9%
Widowed	7.8%	5.4%
Never married	16.7%	24.5%
Region			<0.001
Northeast	9.6%	18.4%
North Central/Midwest	32.6%	19.3%
South	41.6%	37.4%
West	16.1%	24.9%
Income–poverty ratio			<0.001
Below poverty line	13.3%	9.3%
1.00–1.99× above	23.0%	16.8%
2.00–2.99× above	19.7%	16.1%
3.00–3.99× above	14.5%	13.0%
4.00× above or more	29.5%	44.8%
Insurance type			<0.001
Private	59.3%	65.2%
Dual‐eligible	2.4%	1.3%
Medicare advantage	6.8%	7.1%
Medicare only (no Advantage)	7.2%	4.3%
Medicaid only	10.7%	9.0%
Other government insurer	0.6%	0.7%
Uninsured	11.2%	10.1%
Unknown insurance status	1.2%	2.2%
Usual place for care			<0.001
No usual place	0.3%	0.3%
Doctor's office	83.1%	79.2%
Urgent care/walk‐in	5.3%	6.3%
Emergency Room (ER)	1.7%	1.6%
Veterans Affairs (VA)	1.7%	1.3%
Other/unknown	8.1%	11.6%
Unweighted Ns	6,735	38,252

In the full‐sample multivariate logistic regression models predicting health care affordability issues (Table 2), higher odds of health care affordability issues were associated with low social/emotional support, female sex, fair or poor health, residing in the South, income–poverty ratios slightly above the poverty threshold, and lack of health insurance. Conversely, lower odds of health care affordability issues were associated with being ages 65+, never having been married, high income–poverty ratios, and unknown insurance status. After controlling for sociodemographics, there was no significant difference in health care affordability issues between rural and urban respondents. Certain covariates were associated with some health care affordability issues but not others; for example, Hispanic ethnicity was associated with higher odds of being worried about medical bills but was not associated with problems paying or inability to pay medical bills.

TABLE 2.

Adjusted odds of experiencing health care affordability issues (95% confidence intervals).

	Worried about medical bills	Problems paying medical bills	Inability to pay medical bills
Social/emotional support (ref: High)
Low	1.59 (1.49–1.69)	1.66 (1.52–1.80)	1.76 (1.57–1.97)
Rurality (ref: Urban)
Rural	0.93 (0.85–1.02)	1.00 (0.88–1.15)	0.97 (0.84–1.11)
Sex (ref: Male)
Female	1.27 (1.20–1.33)	1.28 (1.18–1.40)	1.30 (1.18–1.46)
Age (ref: 18–64)
65+	0.47 (0.43–0.50)	0.45 (0.40–0.52)	0.41 (0.34–0.48)
Race (ref: White non‐Hispanic)
Black non‐Hispanic	1.04 (0.95–1.13)	1.17 (1.04–1.32)	1.41 (1.21–1.63)
Asian	1.30 (1.16–1.45)	0.52 (0.41–0.66)	0.46 (0.33–0.64)
Hispanic	1.56 (1.43–1.70)	0.89 (0.79–1.01)	0.90 (0.77–1.05)
AI/AN	1.09 (0.65–1.83)	0.85 (0.52–1.39)	1.13 (0.70–1.82)
Two or more	1.02 (0.84–1.24)	1.54 (1.18–2.02)	1.59 (1.09–2.32)
Health (ref: Excellent/very good/good)
Fair/poor	1.67 (1.55–1.81)	2.42 (2.19–2.67)	2.59 (2.29–2.92)
Marital status (ref: Married/partnered)
Separated/divorced	1.05 (0.98–1.13)	1.04 (0.93–1.16)	0.97 (0.84–1.13)
Widowed	0.79 (0.72–0.88)	0.84 (0.72–0.99)	0.86 (0.71–1.05)
Never married	0.79 (0.73–0.84)	0.81 (0.72–0.90)	0.77 (0.67–0.88)
Region (ref: Northeast)
North Central/Midwest	1.01 (0.91–1.12)	1.26 (1.09–1.45)	1.38 (1.17–1.62)
South	1.16 (1.04–1.28)	1.36 (1.20–1.55)	1.56 (1.34–1.81)
West	1.10 (0.98–1.22)	1.00 (0.86–1.17)	0.92 (0.77–1.11)
Income–poverty ratio (ref: Below poverty line)
1.00–1.99× above	1.22 (1.11–1.35)	1.26 (1.09–1.46)	1.19 (1.00 a –1.43)
2.00–2.99× above	1.15 (1.04–1.28)	1.15 (0.99–1.35)	0.97 (0.80–1.18)
3.00–3.99× above	0.89 (0.80–1.01)	0.73 (0.61–0.87)	0.59 (0.48–0.73)
4.00× above or more	0.55 (0.49–0.61)	0.34 (0.29–0.40)	0.26 (0.21–0.32)
Insurance type (ref: Private)
Dual‐eligible	0.62 (0.50–0.75)	0.93 (0.65–1.33)	1.09 (0.72–1.65)
Medicare advantage	0.97 (0.87–1.07)	1.26 (1.07–1.49)	1.39 (1.12–1.72)
Medicare only (no Advantage)	0.96 (0.85–1.08)	1.32 (1.08–1.61)	1.30 (1.02–1.67)
Medicaid only	0.63 (0.56–0.70)	0.75 (0.63–0.89)	0.91 (0.74–1.11)
Other government insurer	0.78 (0.54–1.11)	1.15 (0.71–1.87)	1.22 (0.67–2.22)
Uninsured	2.34 (2.08–2.64)	1.46 (1.27–1.69)	1.78 (1.51–2.11)
Unknown insurance status	0.43 (0.35–0.53)	0.58 (0.42–0.78)	0.52 (0.35–0.75)
Type of usual place for care (ref: Usual—doctor's office)
No usual place	0.69 (0.43–1.10)	0.96 (0.51–1.82)	0.85 (0.38–1.88)
Usual: Urgent care/walk‐in	1.07 (0.96–1.19)	1.15 (0.98–1.36)	1.12 (0.90–1.37)
Usual: Emergency Room (ER)	1.19 (0.93–1.52)	1.61 (1.24–2.08)	1.71 (1.26–2.31)
Usual: Veterans Affairs (VA)	0.66 (0.53–0.81)	1.08 (0.77–1.51)	0.89 (0.59–1.36)
Usual: Other/unknown	1.20 (1.09–1.33)	0.93 (0.79–1.09)	0.98 (0.82–1.18)

^a 1.001; odds ratio is significant.

Figure 1 describes the predicted probabilities of experiencing health care affordability issues by rural and urban location and social/emotional support level, adjusting for all covariates. In both rural and urban areas, those with high social/emotional support had lower predicted probabilities of all health care affordability issues than their low social/emotional support counterparts, averaging about 10 percentage points lower for worried about paying medical bills (43.2% and 44.0% vs. 53.5% and 54.3%), about 5 percentage points lower for problems paying medical bills (11.7% and 9.2% vs. 17.5% and 13.9%), and about 4 percentage points lower for inability to pay medical bills (7.1% and 5.5% vs. 11.5% and 8.9%).

FIGURE 1.

Adjusted predicted probabilities of experiencing health care affordability issues, rural and urban subsamples (95% confidence interval).

DISCUSSION

This study demonstrates the relationship between social/emotional support and health care affordability issues among rural and urban residents. We found that low social/emotional support was associated with higher odds and predicted probabilities of all health care affordability issues regardless of rurality, which adds to the growing body of evidence that the subjective strength of social/emotional support is correlated with financial issues. ¹⁴ , ¹⁵ The association between health care affordability and low social/emotional support is cause for concern, as this combination of factors may increase the risk of further financial issues, along with adverse mental health outcomes due to financial stress. Given existing disparities in access to mental health care, especially in rural areas, this research points to the need for further work focused on social and emotional support as a protective factor against the stress of health care affordability issues.

There are many ways that having social/emotional support could lessen the stress of health care affordability issues. For example, an individual with a strong social support network may be more likely to have a close relationship with someone who they can ask for financial support from, including to pay or loan money for medical bills. Strong social/emotional support may also translate to having someone who could set up a fundraiser on their behalf. This is commonly seen on crowdfunding campaign websites such as GoFundMe, the website that houses approximately one third of fundraisers for medical bills (although they are largely unsuccessful at raising the desired amount and are best positioned to help the populations least in need of support). ³⁵ Alternatively, those who have strong social/emotional support may naturally have access to more financial resources. For example, compared to single, divorced, or widowed individuals, married/partnered individuals may simultaneously experience stronger social/emotional support ³⁶ , ³⁷ , ³⁸ and several financial benefits such as marriage wage premiums, ³⁹ higher wealth accumulation, ⁴⁰ tax advantages, ⁴¹ retirement and social security benefits, ⁴² , ⁴³ higher rates of homeownership (likely due to pooling incomes together for down payments leading to an easier time qualifying for loans, although loan discrimination based on marital status is illegal), ⁴⁴ , ⁴⁵ and more opportunities for health and other workplace benefits. ⁴⁶ , ⁴⁷ , ⁴⁸ , ⁴⁹

Beyond social/emotional support, sociodemographic covariates were associated differentially with health care affordability issues within the rural population. Unsurprisingly, individuals with low income, poorer health outcomes, and inadequate or no insurance in rural communities experienced higher rates of health care affordability issues given the high cost of health care. In terms of geographic region, residing in the rural South was associated with higher rates of health care affordability issues than rural residents in other regions of the country. This may be due to higher rates of uninsurance (in part due to lack of Medicaid expansion) and poorer health outcomes in the Southern region, ⁵⁰ which could increase the risk of health care affordability issues. Black non‐Hispanic and American Indian/Alaska Native individuals in rural communities experience higher rates of health care affordability due to economic and health inequities stemming from discriminatory policies. ⁵¹ Our results also show that women in rural communities also experience higher rates of health care unaffordability than in urban areas. This may be due to women in rural areas being more likely than both women in urban areas and men in rural areas to have low income and limited access to health care. ⁵² , ⁵³ Compared to men, women experience higher rates of mental health concerns, including anxiety, ⁵⁴ and also have fewer health care benefits, ⁵⁵ more concerns regarding childcare, ⁵⁶ , ⁵⁷ and less social capital, ⁵⁸ with rural women experiencing higher rates of these issues than urban women. ⁵⁹ , ⁶⁰ , ⁶¹ Moreover, recent analyses demonstrate women's out of pocket health care costs are disproportionately higher than men's, even when excluding pregnancy‐related expenses. ⁵⁵ All of this may contribute to rural women being more likely to worry about the ability to pay their medical bills.

Although there were no significant differences for health care affordability issues between rural and urban respondents after controlling for rurality, we observed higher odds of health care affordability issues among those with low social/emotional support, female sex, fair or poor health, household income–poverty ratios slightly above the poverty threshold, and no insurance. Overall, our findings align with previous research indicating that certain rural subpopulations experience greater health inequities and higher rates of medical debt. These findings also demonstrate the need for tailored approaches to addressing health care affordability among different rural populations and add to existing research showing that social/emotional support is an important social and financial determinant of health in rural areas. ¹⁴ , ¹⁵ , ¹⁷ , ⁶² , ⁶³

Qualitative analyses have shown that patient medical debt negatively impacts the financial health of rural hospitals as well as their patients. ⁶⁴ When examining reasons as to why their patient population is struggling to pay medical bills, rural practitioners and health care organizations should consider social/emotional support, and health policy aimed at alleviating health care affordability issues should take people with low social/emotional support and rural subpopulations who experience higher rates of health care affordability issues into account. One potential policy solution is to increase collaboration between rural health care and local community resources. Collaboration is already happening in many rural health care settings to connect patients with resources for essential needs like food and housing, ⁶⁵ , ⁶⁶ and similar collaboration could happen to connect patients with mental health resources, community events, and other existing community social supports.

Financial assistance policy publicization, screening for social isolation, and creating tax‐deductible opportunities for health systems may facilitate this collaboration. Rural practitioners and health systems are currently required to maintain and widely publicize their financial assistance policies, ⁶⁷ and our study's findings highlight that the availabilit of this assistance is especially important for socially isolated patients. In addition, rural practitioners can screen for social isolation as a component of social determinants of health screenings and may be able to remedy some factors that lead to isolation, such as transportation barriers. ⁶⁸ Rural providers may also want to connect socially isolated patients with additional financial counseling and assistance, based on results of that screening. Lastly, health systems can create tax‐deductible opportunities for community social connection as a component of their Community Health Needs Assessments. ⁶⁹

Study limitations

This study is cross sectional, so the direction of effect between variables cannot be inferred; more research is needed to better understand the drivers of within‐rural differences in social/emotional support and health care affordability issues. The NHIS relies on self‐rated health and self‐rated social/emotional support, both of which may be subject to respondent recall bias. Additionally, the measurement of social/emotional support is limited to an overall indicator and does not differentiate between types of support (financial, verbal, instrumental, informational, etc.).

CONCLUSIONS

In this study, we found that adults with low social/emotional support had higher odds of reporting being worried about paying medical bills, problems paying medical bills, and inability to pay medical bills, regardless of rural or urban location. Among both rural and urban residents, predicted probabilities of experiencing health care affordability issues were higher by several percentage points among those with low social/emotional support.

Rural practitioners and health care organizations should consider the protective effects of tangible social/emotional support on their patient populations’ financial health, and health policy aimed at reducing widespread medical debt should be framed to consider social/emotional support's association with lessened health care affordability issues across both rural and urban settings. Rural health care systems’ leveraging of local community social support resources is a potential solution to this issue and also presents rural health care facilities with opportunities for tax deductions.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

Jacobson I, Rydberg K, Swendener A, MacDougall H, Henning‐Smith C. The relationship between low social/emotional support and health care affordability among rural and urban residents. J Rural Health. 2025;41:1–9. 10.1111/jrh.70034