Chronic health conditions and health care affordability issues among U.S. rural and urban adults

Abstract

Purpose

Chronic health conditions and rurality are associated with each other and with higher risks of medical debt. Less is known about the intersection of chronic conditions and health care affordability issues as they relate to rurality. The objective of this study is to determine whether rural/urban differences exist in associations between chronic conditions and health care affordability issues.

Methods

Using data from the 2022 National Health Interview Survey, we conducted survey-weighted bivariate analyses comparing rates of chronic conditions and multimorbidity by rurality, followed by adjusted multivariate logistic regression models examining associations between health care affordability issues, chronic conditions, and multimorbidity.

Major Findings

Compared to urban residents, rural residents reported significantly higher rates of nearly all chronic conditions, multimorbidity (3+ chronic conditions: 26.7% vs 18.3%, P<.001), problems paying medical bills (13.7% vs 11.0%, P=.004), and inability to pay medical bills (9.6% vs 6.8%, P<.001). After adjustment, certain chronic conditions and multimorbidity were associated differentially with health care affordability issues across the full, urban-only, and rural-only subsamples.

Conclusions

Adults living in rural areas of the U.S. may have higher rates of chronic conditions and be more likely to experience health care affordability issues than their urban counterparts. Differing patterns by multimorbidity and rurality indicate that policy interventions addressing medical debt should be targeted to chronic condition types and tailored by rurality.

Keywords

chronic disease health care costs rural health health care disparities social determinants of health

Introduction

People with chronic health conditions experience higher medical debt burden than their counterparts without chronic conditions among the commercially insured,^1,2 and financial precarity is a risk factor for chronic conditions^3,4 leading to a vicious cycle of poor health and increasing medical debt. However, people with certain chronic conditions are more likely to incur medical debt than people with other chronic conditions. For example, severe mental illness and serious cardiovascular conditions have been associated with large increases in medical debt burden, measured by both the likelihood of having medical debt and the overall dollar amount of debt.¹ Conditions like depression, anxiety, diabetes, and hypertension are associated with comparatively smaller amounts, although being diagnosed with any of those conditions is still associated with an increase in medical debt compared to not having those conditions.¹ There are many reasons for this variability, including differing amounts of health care needed to treat different conditions.⁵ Further, some people experience more than one chronic condition simultaneously – a phenomenon known as multimorbidity – which is, in turn, associated with depression and anxiety,^6,7 leading to higher risk of additional chronic conditions. Managing multiple chronic conditions therefore increases health care usage and the potential for medical debt.

Further complicating the issue of chronic conditions and medical debt are rural disparities in health and financial resources. These disparities are due, in part, to intersecting social and economic factors experienced in rural areas⁸ and include higher prevalences of many chronic conditions such as diabetes, heart conditions, mental illness, and substance use disorders.⁹ Rural residents also face more barriers to accessing health care^10–12 are more likely to be uninsured or underinsured than their urban counterparts¹³ and have lower incomes,¹⁴ all of which can make affording timely, necessary care more challenging. Further, rural residence is associated with increased health care affordability issues, such as difficulty paying medical bills and inability to pay bills entirely.^15–17 This can lead to additional negative health consequences, such as delaying or forgoing necessary care, which can exacerbate chronic conditions.^16,18

More research is needed on the relationship between chronic conditions and difficulty affording health care, with particular attention to how this relationship varies by rurality. This study seeks to address these gaps by examining rural/urban differences in the association between various chronic conditions and three measures of health care unaffordability: worry about medical bills, problems paying medical bills, and being unable to pay medical bills entirely.

Methods

Data and sample

Data from this study came from the 2022 National Health Interview Survey (NHIS), an annual, nationally representative, cross-sectional survey of the non-institutionalized U.S. population.¹⁹ Our sample included adults, age 18-64, with complete information on all conditions and covariates, resulting in a final sample size of 17,389 individuals (rural N = 2,536 or 13.3%). We limited the sample to adults younger than 65 because access to health insurance and health care affordability is markedly different for those age 65 and older due to Medicare eligibility.

Measures

Health care affordability issues were measured by respondents’ answers to three survey questions: being worried about medical bills (“very worried” and “somewhat worried” dichotomized to “yes”, and “not at all worried” dichotomized to “no”), having problems paying medical bills within the past 12 months (“yes” or “no”), and being unable to pay medical bills within the past 12 months (“yes” or “no”). Respondents were only asked about inability to pay medical bills if they reported problems paying medical bills.

Rural was defined using the publicly available definition in the NHIS as residing in a nonmetropolitan county using the 2013 National Center for Health Statistics (NCHS) Urban-Rural Classification Scheme.²⁰ Chronic conditions were self-reported by the respondents, who answered yes/no questions on whether a health professional had ever said they had: cancer, diabetes, hypertension, coronary heart disease (CHD), heart attack, stroke, asthma, chronic obstructive pulmonary disorder (COPD), epilepsy or seizure disorder, chronic fatigue syndrome, lupus or arthritis (including rheumatoid arthritis, gout, and fibromyalgia), depression, and/or anxiety. We also examined multimorbidity, categorizing respondents into having 0, 1, 2, or 3+ conditions based on their responses to chronic condition questions.

We included other covariates associated with differences in chronic health conditions and health care unaffordability issues¹⁶ including sex (male and female), race and ethnicity (Black Non-Hispanic, white Non-Hispanic, Hispanic, Asian, American Indian/Alaska Native (AI/AN), and two or more races/ethnicities), age, household income (as a ratio to poverty line, from below the poverty line to 4.00x the poverty line or more), self-reported health (fair/poor vs excellent/good/very good), insurance type (Medicaid only, Medicare only, dual-eligible, other government insurance including VA, uninsured, and private insurance), and whether or not the individual had a usual place for health care. Respondents who reported multiple types of insurance were categorized using the following prioritization hierarchy based on prior research²¹: private, dual eligible, Medicare only (including Medicare Advantage), Medicaid only, other government insurance, or uninsured.

Analysis

Analyses were conducted using SAS 9.3 (SAS Institute Inc., Cary, NC). We first conducted bivariate χ² analyses comparing health care affordability issues among rural and urban adults by sociodemographic characteristics, chronic conditions, and multimorbidity, reporting P-values with significance thresholds set at P<.05. We then built multivariate logistic regression models, adjusting for all sociodemographic characteristics, to predict health care unaffordability issues in the full sample by rurality and multimorbidity. Given previous research findings showing significant differences in multimorbidity by rurality^22–24 we accounted for interaction between rurality and multimorbidity in the full sample model. We did this using two sets of models: Model 1 included an interaction term between rurality and multimorbidity, such that results show the adjusted odds ratios at each level of multimorbidity (0, 1, 2, and 3+) for rural residents compared to urban residents for each affordability issue. Model 2 was a set of stratified logistic regression models for each affordability measure, run separately for rural (Model 2a) vs. urban (Model 2b) in order to see how the adjusted odds of affordability issues differed at each level of multimorbidity, with the reference set at 0 conditions. Finally, we built similar multivariate logistic regression models predicting health care affordability issues in the full (Model 3), rural (Model 4a), and urban (Model 4b) samples, adjusting for sociodemographic covariates, chronic conditions, and multimorbidity, and accounting for interaction between rurality and multimorbidity in the full sample. We report adjusted odds ratios and 95% confidence intervals for each set of models.

All analyses are survey-weighted using the NHIS-provided survey weights to account for the complex survey design and in order to approximate nationally representative estimates. All of our analyses also underwent Bonferroni correction. The Institutional Review Board determined this study was not human subjects research.

Results

Table 1 presents sociodemographic, health, multimorbidity, and health care unaffordability characteristics of the population by rurality. Compared to urban counterparts, rural individuals were more likely to be older, non-Hispanic white or American Indian/Alaska Native, lower income, receiving public insurance, and be in fair or poor health. Approximately 36% of rural adults reported household poverty-to-income ratios below 2.00x the federal poverty level and approximately 25% of rural adults reported receiving public insurance. Approximately 17% of rural adults reported being in fair or poor health, however they were more likely to have a regular place for health care than urban counterparts (with 8.9% of rural vs 13.2% of urban adults reporting no usual place for care, P<.001).

Table 1.

Sociodemographic, health, multimorbidity, and health care affordability characteristics by rurality.

	Rural, weighted %	Urban, weighted %	P-value^a
Sociodemographic characteristics
Mean age in years (SD)	42.8 (18.1)	40.7 (17.1)	<.001
Sex			0.257
Female	49.0	50.6
Male	51.0	49.4
Race/ethnicity			<.001
American Indian/Alaska Native (AI/AN)	3.3	0.4
Asian	1.4	7.1
Black non-Hispanic	7.3	12.7
Hispanic	7.0	21.1
Two or more races/ethnicities	1.7	2.4
White non-Hispanic	79.3	56.3
Household poverty-to-income ratio			<.001
Less than 1.00 (below poverty line)	14.0	9.2
1.00-1.99	21.8	15.9
2.00-2.99	19.7	15.1
3.00-3.99	14.6	12.1
4.00+	29.9	47.8
Insurance type^b			<.001
Private	61.1	69.9
Dual-eligible	2.4	1.1
Medicaid only	16.6	12.6
Medicare only	3.4	1.8
Other government insurance	2.7	2.8
Uninsured	13.6	11.9
Health characteristics^b
Self-rated health (fair/poor)	16.9	10.7	<.001
No usual place for health care	8.9	13.2	<.001
Cancer	6.0	4.8	0.02
Diabetes	9.2	6.2	<.001
Hypertension	29.5	22.3	<.001
Coronary heart disease (CHD)	3.6	1.8	<.001
Heart attack	2.6	1.3	<.001
Stroke	2.2	1.2	0.004
Asthma	17.0	15.1	0.04
Chronic obstructive pulmonary disorder (COPD)	6.6	2.4	<.001
Epilepsy/seizure disorder	2.4	1.8	0.05
Chronic fatigue syndrome	2.9	1.7	<.001
Lupus/arthritis	20.6	13.1	<.001
Depression	23.9	18.1	<.001
Anxiety	23.4	18.6	<.001
Multimorbidity^c
0 conditions	33.4	41.3	<.001
1 condition	22.7	25.1	.028
2 conditions	17.2	15.4	.078
3+ conditions	26.7	18.3	<.001
Health care affordability issues^b
Worried about medical bills	47.3	49.0	0.27
Problems paying medical bills	13.7	11.0	0.004
Unable to pay medical bills	9.6	6.8	<.001

^aP-values were obtained from Rao-Scott χ² analyses. All analytic sample sizes are N = 17,389 (rural N: 2536; urban N = 14,853) unless otherwise indicated.

^bThe percentages in these rows are of the total population analyzed. All variables in these sections are dichotomous and the percentage of the population without the described condition is the remainder after subtracting the percentage in the cell from 100.0. For example, the percentage of rural population worried about medical bills is 47.3%, therefore the percentage of rural population not worried about medical bills is 100.0%-47.3%, or 52.7%.

^cSample size: N = 17,271 (rural N = 2511; urban N = 14,760).

Rural residents were more likely to experience higher rates of the following chronic conditions compared to urban counterparts: cancer; diabetes; cardiovascular conditions including hypertension, CHD, heart attack and stroke; asthma; COPD; chronic fatigue syndrome; lupus/arthritis; depression; and anxiety. The largest substantive differences were in the rates of depression and anxiety, which were 5-6 percentage points higher among rural adults, as well as lupus/arthritis and hypertension, with rates over 7 percentage points higher among rural adults. The only chronic condition included in this analysis that was not significantly different by rurality was epilepsy/seizure disorder; 2.4% of rural and 1.8% of urban adults reported being diagnosed with epilepsy/seizure disorders (P=0.05).

When examining multimorbidity by rurality, we found that rural residents were less likely to report having zero or one chronic conditions (33.4% vs 41.3%, P<.001 and 22.7% vs 25.1%, P=.028, respectively), and more likely to report having three or more conditions compared to their urban peers (26.7% vs 18.3%, P<.001).

Compared to their urban counterparts, rural adults reported higher rates of problems paying medical bills (13.7% vs 11.0%, P=.004) and being unable to be able to pay medical bills (9.6% vs 6.8%, P<.001). There was no statistically significant difference in reports of worry about medical bills; nearly half of all respondents reported such worry.

Table 2 shows results of multivariate logistic regression analyses predicting health care unaffordability issues by multimorbidity and rurality, adjusting for sociodemographic covariates and accounting for interaction between multimorbidity and rurality. In the first set of models (Model 1), rural residents did not experience differing odds of health care affordability issues as compared to urban residents at any level of multimorbidity. However, in the second set of models, stratified by rurality (Models 2a and 2b), both rural and urban residents with 3+ conditions experienced significantly higher odds of all health care affordability issues than those with 0 conditions. Rural and urban residents with 2 conditions experienced higher odds of all health care affordability issues than those with 0 conditions, and rural and urban residents with 1 condition experienced lower odds of all health care affordability issues than those with 0 conditions, although these odds were not significant except for urban residents with one condition having significantly lower odds of worrying about medical bills compared to their counterparts with no conditions.

Table 2.

Adjusted odds^a of health care affordability issues by rurality and multimorbidity.

Model 1	Number of chronic conditions	Worried about medical bills (95% CI)	Problems paying medical bills (95% CI)	Unable to pay medical bills (95%CI)
Rural (ref: urban)	0	0.94 (0.88 – 1.00)	0.97 (0.88 – 1.06)	1.03 (0.92 – 1.16)
	1	0.90 (0.80 – 1.01)	1.04 (0.87 – 1.23)	1.13 (0.91 – 1.39)
	2	0.96 (0.85 – 1.08)	0.94 (0.77 – 1.16)	1.10 (0.85 – 1.42)
	3+	0.95 (0.86 – 1.06)	0.94 (0.82 – 1.08)	0.99 (0.86 – 1.13)
Model 2a	Rural sample only
Count of conditions (ref: 0 conditions)	1	0.84 (0.70 – 1.02)	0.94 (0.71 – 1.23)	0.93 (0.68 – 1.28)
	2	1.12 (0.94 – 1.32)	1.15 (0.83 – 1.59)	1.29 (0.88 – 1.89)
	3+	1.31 (1.12 – 1.52)	1.76 (1.39 – 2.24)	1.76 (1.38 – 2.25)
Model 2b	Urban sample only
Count of conditions (ref: 0 conditions)	1	0.89 (0.80 – 0.98)	0.87 (0.75 – 1.02)	0.85 (0.71 – 1.01)
	2	1.10 (0.99 – 1.20)	1.18 (0.99 – 1.40)	1.21 (0.99 – 1.49)
	3+	1.29 (1.18 – 1.41)	1.82 (1.58 – 2.08)	1.85 (1.59 – 2.14)

^aOdds ratios with 95% CIs containing 1.00 are non-significant. Odds ratios are adjusted for sociodemographic characteristics, self-rated health, and usual source of care.

As reported in Table 3, multivariate logistic regression analyses (Models 3, 4a, and 4b) adjusting for rurality, sociodemographic and health characteristics, and all chronic conditions revealed that certain conditions are significantly associated with health care affordability issues, and that patterns differ in rural and urban populations.

Table 3.

Adjusted odds^a of health care affordability issues by chronic conditions and multimorbidity; full, rural, and urban samples.

	Worried about medical bills (95% CI)	Problems paying medical bills (95% CI)	Unable to pay medical bills (95%CI)
Model 3	Full sample
0 condition * rural (ref: 0 conditions * urban)	0.94 (0.88 – 1.01)	0.96 (0.88 – 1.06)	1.03 (0.92 – 1.16)
1 condition * rural (ref: 0 conditions * urban)	0.84 (0.69 – 1.03)	1.09 (0.83 – 1.44)	1.09 (0.79 – 1.50)
1 condition * urban (ref: 0 conditions * urban)	0.89 (0.80 – 0.996)^b	1.02 (0.87 – 1.19)	0.99 (0.83 – 1.20)
2 conditions * rural (ref: 0 conditions * urban)	1.08 (0.91 – 1.28)	1.10 (0.80 – 1.51)	1.23 (0.84 – 1.80)
2 conditions * urban (Ref: 0 conditions * urban)	1.05 (0.96 – 1.16)	1.11 (0.94 – 1.32)	1.15 (0.94 – 1.41)
3 conditions * rural (ref: 0 conditions * urban)	1.39 (1.15 – 1.69)	1.22 (0.92 – 1.63)	1.21 (0.90 – 1.63)
3 conditions * urban (ref: 0 conditions)	1.35 (1.17 – 1.56)	1.28 (1.05 – 1.57)	1.29 (1.03 – 1.62)
Cancer	0.81 (0.68 – 0.96)	1.02 (0.77 – 1.35)	1.20 (0.87 – 1.66)
Diabetes	0.91 (0.77 – 1.08)	1.40 (1.08 – 1.80)	1.37 (1.02 – 1.86)
Hypertension	0.90 (0.80 – 1.01)	0.99 (0.80 – 1.21)	1.09 (0.85 – 1.42)
CHD	0.91 (0.68 – 1.21)	0.85 (0.53 – 1.36)	1.00 (0.59 – 1.69)
Heart attack	0.89 (0.63 – 1.24)	1.39 (0.87 – 2.21)	1.38 (0.81 – 2.33)
Stroke	0.79 (0.57 – 1.10)	0.91 (0.60 – 1.38)	0.80 (0.50 – 1.26)
Asthma	0.85 (0.76 – 0.95)	1.00 (0.83 – 1.21)	0.95 (0.75 – 1.20)
COPD	0.80 (0.64 – 0.98)	1.26 (0.91 – 1.75)	1.22 (0.84 – 1.77)
Epilepsy/seizure disorder	0.93 (0.70 – 1.23)	0.90 (0.59 – 1.36)	1.15 (0.73 - 1.81)
Chronic fatigue syndrome	0.84 (0.64 – 1.11)	1.15 (0.75 – 1.75)	1.00 (0.64 – 1.54)
Lupus/arthritis	0.98 (0.87 – 1.11)	1.34 (1.09 – 1.64)	1.38 (1.07 – 1.78)
Depression	1.09 (0.96 – 1.25)	1.37 (1.11 – 1.71)	1.27 (0.98 – 1.65)
Anxiety	1.20 (1.05 – 1.37)	1.29 (1.02 – 1.64)	1.32 (0.99 – 1.75)
Model 4a	Rural sample only
1 condition (ref: 0 conditions)	0.91 (0.67 – 1.23)	1.47 (0.89 – 2.45)	1.65 (0.95 – 2.84)
2 conditions (ref: 0 conditions)	1.01 (0.71 – 1.42)	1.39 (0.75 – 2.57)	1.74 (0.93 – 3.28)
3+ conditions (ref: 0 conditions)	1.08 (0.64 – 1.80)	1.42 (0.67 – 3.02)	1.58 (0.72 – 3.44)
Cancer	1.10 (0.72 – 1.70)	1.11 (0.64 – 1.93)	1.09 (0.59 – 2.01)
Diabetes	0.82 (0.55 – 1.21)	1.24 (0.77 – 2.02)	1.26 (0.72 – 2.20)
Hypertension	1.08 (0.83 – 1.40)	1.00 (0.69 – 1.45)	1.05 (0.69 – 1.60)
CHD	0.75 (0.45 – 1.23)	0.70 (0.31 – 1.61)	0.72 (0.30 – 1.76)
Heart attack	0.86 (0.43 – 1.69)	1.09 (0.51 – 2.31)	0.81 (0.35 – 1.90)
Stroke	0.64 (0.32 – 1.29)	1.34 (0.75 – 2.38)	1.31 (0.73 – 2.36)
Asthma	0.93 (0.70 – 1.23)	1.06 (0.72 – 1.56)	0.92 (0.59 – 1.45)
COPD	0.63 (0.42 – 0.95)	1.07 (0.62 – 1.83)	1.32 (0.76 – 2.27)
Epilepsy/seizure disorder	1.24 (0.68 – 2.24)	1.87 (0.96 – 3.64)	2.26 (1.14 – 4.49)
Chronic fatigue syndrome	1.18 (0.69 –2.02)	1.18 (0.64 – 2.20)	1.49 (0.72 – 3.06)
Lupus/arthritis	1.18 (0.94 – 1.47)	1.43 (0.95 – 2.14)	1.25 (0.80 – 1.96)
Depression	1.19 (0.89 – 1.58)	1.26 (0.87 – 1.83)	1.58 (1.03 – 2.44)
Anxiety	1.62 (1.15 – 2.28)	1.62 (1.05 – 2.49)	1.45 (0.89 – 2.37)
Model 4b	Urban sample only
1 condition (ref: 0 conditions)	1.25 (1.11 – 1.41)	1.39 (1.13 – 1.72)	1.23 (0.94 – 1.61)
2 conditions (ref: 0 conditions)	1.41 (1.19 – 1.67)	1.69 (1.31 – 2.19)	1.48 (1.06 – 2.07)
3+ conditions (ref: 0 conditions)	1.86 (1.44 – 2.41)	2.07 (1.46 – 2.94)	1.98 (1.26 – 3.09)
Cancer	0.77 (0.64 – 0.92)	1.01 (0.78 – 1.31)	1.25 (0.93 – 2.68)
Diabetes	0.94 (0.78 – 1.13)	1.43 (1.13 – 1.80)	1.40 (1.07 – 1.85)
Hypertension	0.86 (0.76 – 0.98)	0.98 (0.81 – 1.18)	1.10 (0.86 – 1.40)
CHD	0.96 (0.69 – 1.35)	0.91 (0.59 – 1.42)	1.13 (0.68 – 1.86)
Heart attack	0.91 (0.61 – 1.35)	1.44 (0.92 – 2.25)	1.49 (0.90 – 2.45)
Stroke	0.83 (0.57 – 1.19)	0.81 (0.54 – 1.23)	0.68 (0.42 – 1.09)
Asthma	0.83 (0.73 – 0.95)	0.98 (0.82 – 1.16)	0.94 (0.76 – 1.17)
COPD	0.84 (0.65 - 1.08)	1.32 (0.97 – 1.80)	1.17 (0.80 – 1.69)
Epilepsy/seizure disorder	0.87 (0.63 – 1.20)	0.72 (0.48 – 1.08)	0.94 (0.60 – 1.48)
Chronic fatigue syndrome	0.78 (0.57 – 1.06)	1.16 (0.77 – 1.73)	0.91 (0.60 – 1.38)
Lupus/arthritis	0.95 (0.83 – 1.08)	1.32 (1.10 – 1.59)	1.41 (1.12 – 1.79)
Depression	1.07 (0.93 – 1.24)	1.39 (1.14 - 1.69)	1.20 (0.94 – 1.53)
Anxiety	1.13 (0.98 – 1.31)	1.24 (0.99 – 1.54)	1.29 (0.996 – 1.68)

^aAll odds ratios describe odds of health care affordability issue in condition population vs no condition population (reference category). Odds ratios with 95% CIs containing 1.00 are non-significant. Odds are adjusted for sociodemographic characteristics, health, and usual source of care, as well as all other chronic conditions and multimorbidity level.

^bRounded to the third decimal place to illustrate significance.

In the full sample (Model 3), when compared to condition-free counterparts, we found that people who had anxiety had higher odds of worrying about medical bills (anxiety aOR: 1.20, 95% CI: 1.05 – 1.37); people with diabetes, lupus/arthritis, depression, or anxiety had higher odds of problems paying medical bills (diabetes aOR: 1.40, 95%: 1.08 – 1.80; lupus/arthritis aOR: 1.34, 95% CI: 1.09 – 1.64; depression aOR: 1.37, 95% CI: 1.11 – 1.71; anxiety aOR: 1.29, 95% CI: 1.02 – 1.64); and people who had diabetes or lupus/arthritis had higher odds of being unable to pay medical bills (diabetes aOR: 1.37, 95% CI: 1.02 – 1.86; lupus/arthritis aOR: 1.38, 95% CI: 1.07 – 1.78). We also found that people diagnosed with cancer, asthma, or COPD were significantly less likely to report worry about medical bills than their condition-free counterparts (cancer aOR: 0.81, 95% CI: 0.68 – 0.96; asthma aOR: 0.85, 95% CI: 0.76 – 0.95; COPD aOR: 0.80, 95% CI: 0.64 – 0.98).

With regards to multimorbidity and rurality, urban residents with 1 condition had lower odds of worrying about medical bills (aOR: 0.89, 95% CI: 0.80 – 0.996) and rural and urban residents with 3 conditions had higher odds of worrying about medical bills (rural aOR: 1.39, 95% CI: 1.15 – 1.96; urban aOR: 1.35, 95% CI: 1.17 – 1.56) than their counterparts with 0 conditions; and urban residents with 3 conditions had higher odds of problems paying medical bills (aOR: 1.28, 95% CI: 1.05 – 1.57) and being unable to pay medical bills (aOR: 1.29, 95% CI: 1.03 – 1.62) than urban residents with 0 conditions.

In the rural subsample (Model 4a), when compared to specific condition-free counterparts, people who had anxiety were significantly more likely to report worry about medical bills (rural anxiety aOR: 1.62, 95% CI: 1.15 – 2.28) and problems paying medical bills (rural anxiety aOR: 1.62, 95% CI: 1.05 – 2.49); people who had epilepsy/seizure disorders or depression were significantly more likely to report being unable to pay medical bills (rural epilepsy/seizure aOR: 2.26, 95% CI: 1.14 – 4.49; rural depression aOR: 1.58, 95% CI: 1.03 – 2.44); and people diagnosed with COPD were significantly less likely to report worry about medical bills (rural COPD aOR: 0.63, 95% CI: 0.42 – 0.95). Multimorbidity was not significantly associated with any health care affordability issues in the rural subsample.

The urban subsample (Model 4b) more closely resembled the full sample. Although there were no chronic health conditions associated with being more likely to worry about medical bills, people with diabetes, lupus/arthritis, or depression were significantly more likely to report problems paying medical bills (diabetes aOR: 1.43, 95% CI: 1.13 – 1.80; lupus/arthritis aOR: 1.32, 95% CI: 1.10 – 1.59; depression aOR: 1.39, 95% CI: 1.14 – 1.69) and people with diabetes or lupus/arthritis were significantly more likely to report inability to pay medical bills (diabetes aOR: 1.40, 95% CI: 1.07 – 1.85; lupus/arthritis aOR: 1.41, 95% CI: 1.12 – 1.79), than those without these conditions. Additionally, people with cancer, hypertension, or asthma were significantly less likely to report worrying about medical bills (cancer aOR: 0.77, 95% CI: 0.64 – 0.92; hypertension aOR: 0.86, 05% CI: 0.76 – 0.98; asthma aOR: 0.83, 95% CI: 0.73 – 0.95) than condition-free counterparts. With regard to urban residents experiencing multimorbidity, similar to the full sample, those reporting one chronic condition were significantly more likely to report experiencing worry about (aOR: 1.25, 95% CI: 1.11 – 1.41) and problems paying medical bills (aOR: 1.39, 95% CI: 1.13 – 1.72), and those reporting either 2 or 3+ chronic conditions were significantly more likely to report experiencing all health care affordability issues, than those reporting 0 chronic conditions.

Discussion

This study demonstrates the interconnected nature of chronic health conditions, living in a rural area, and experiencing health care affordability issues. Specifically, we found that rural residents reported significantly higher rates of having three or more conditions as well as having problems paying bills and being unable to pay medical bills in the bivariate analyses. The findings about medical bills align with past studies examining rural health care affordability issues for postpartum people²⁵ and speaks to the concerns regarding rural individuals having lower average incomes²⁶ and more health concerns.²⁷ This finding also supports research indicating rural residents have higher levels of medical debt on average.²⁸

In multivariate analyses, rurality was not significantly associated with higher odds of health care affordability issues after adjusting for sociodemographic characteristics. This suggests that socioeconomic status (e.g., poverty status) and insurance status, both of which were significantly different between rural and urban residents in the bivariate analyses, play an important role in impacting the association between rurality, multimorbidity, and health care affordability. However, we found that both rural and urban residents with 3+ conditions experienced higher odds of health care affordability issues than those with 0 conditions. Additionally, we found that certain chronic conditions and the experience of multimorbidity were associated differentially with health care affordability concerns across the rural, urban, and full-sample populations. These findings demonstrate the need for targeted public health policy efforts based on condition-specific needs. For example, those diagnosed with diabetes were significantly more likely in the full sample to report concern about paying medical bills and being unable to pay medical bills. This is consistent with prior research showing an increased probability of medical debt among people with diabetes,¹ although our finding was even larger. This finding could be related to current concerns about the cost of insulin and glucose monitoring technologies²⁹ and/or social drivers of health, such as limited food access, which may cause low-income individuals to be more likely to be diagnosed with diabetes.³⁰

Certain conditions were associated with lower odds of health care affordability issues when compared to those without said conditions. For example, while individuals diagnosed with cancer still experienced health care affordability issues, they had lower odds of worrying about medical bills than individuals without cancer in the full and urban-only samples. This was surprising, as cancer is known to carry a high financial burden.³¹ We offer several reasons that individuals with certain chronic conditions may experience lower odds of health care affordability issues than individuals without these conditions, including but not limited to: availability of condition-specific financial assistance; individuals with certain chronic conditions being less likely to receive health care in the first place, therefore not experiencing health care affordability issues; or, particularly for individuals with cancer and other serious chronic conditions, feeling that they have other issues to worry about beyond medical bills. Previous research has examined cancer-specific financial assistance programs,³² and policies exist to extend Medicaid coverage to uninsured individuals with certain types of cancer, in theory decreasing the burden of health care affordability issues.³³ Future research should investigate possible reasons as to why certain chronic conditions are associated with lower odds of health care affordability issues compared to those without said chronic conditions, and if these reasons are due to factors like the existence of condition-specific financial assistance programs, such as those available for cancer patients, and ascertain whether these programs could be replicated for other chronic conditions.

Our findings demonstrate the need for targeted efforts based on geographic context. Concerns about health care unaffordability among rural populations is especially troubling when paired with our findings that rural residents reported higher prevalences of almost all chronic conditions examined in this study, as well as a higher prevalence of multimorbidity in the form of three or more chronic conditions. And although the prevalence of epilepsy/seizure disorders did not differ between rural and urban residents, rural people with epilepsy/seizure disorders experienced significantly higher odds of being unable to pay their medical bills compared to rural people without epilepsy/seizure disorders. Contrastingly, people with epilepsy/seizure disorders did not experience different odds of health care affordability issues compared to those without these disorders in the urban subsample. The financial impact of epilepsy/seizure disorders on patients is high,³⁴ and our findings indicate that the financial burden of these disorders may disproportionately affect rural residents.

Our findings additionally demonstrate the cyclical nature of chronic conditions and affordability concerns, which may, in some circumstances exacerbate each other. For example, we found that rural adults experiencing anxiety were significantly more likely to experience worry about medical bills and problems paying medical bills than those without anxiety, even after adjusting for other chronic conditions. Because some mental health concerns may be rooted in, or exacerbated by, social drivers of health such as economic stress,^35–37 adults experiencing mental health conditions may be particularly affected by health care affordability issues. Further ties between health care costs related to chronic physical conditions negatively affecting both mental health and exacerbating physical health conditions³⁸ emphasize the wide-reaching association of social drivers of comorbidity.

Limitations

Our study has limitations that should be explored in future research. First, our study is cross-sectional and thus unable to make causal inferences. Additionally, respondent recall bias is introduced any time people are asked to recall information about their lives. Our sample was nationally representative, but as such, smaller sample sizes are found among some sociodemographic groups, reducing the statistical power needed to analyze specific subgroups. In particular, the rural sample was relatively small (n=2536), especially compared to the urban sample (n=14,853), which likely made it more difficult to detect significant findings in the rural-only analyses. In addition, rural health experiences and access to care are more nuanced than a dichotomous categorization of rural-urban can capture, and we encourage future research to keep examining these social issues among different types of rural and urban locations.

Future research should expand to investigate the complex relationships between chronic conditions and health care affordability among other age groups. For example, as health care costs continue to rise, the needs of a growing aging population – and more rapidly growing rural aging population – require attention as well. Public health policies that promote and support healthy aging and disease prevention across the life course, healthy aging in place, and cost-effective and evidence-based care of older adults with multiple conditions will be important in reducing health care affordability among this group. Future research should also examine health care affordability issues by length of time; longer times with chronic conditions may increase health care affordability issues. We also encourage future research into rural-urban differences in the associations between chronic conditions and health care affordability issues by socioeconomic status. Previous research has shown that disease burden varies by both chronic condition and socioeconomic status.³⁹ Given that rural residents typically report lower incomes than their urban counterparts, this line of investigation would be particularly important.

Conclusions

Using a nationally representative sample, we find that working age (age 18-64) adults living in rural areas of the U.S. are more likely than their urban peers to experience issues with health care affordability, and more likely to have one or more chronic health conditions. After adjusting for sociodemographic and health characteristics, associations between chronic conditions and health care affordability remained significant for several chronic conditions. Patterns differed between the full, urban-only, and rural-only subsamples, indicating that interventions addressing widespread issues of medical debt and health care affordability should be targeted to certain chronic condition types and tailored by rurality. This study provides the necessary granularity to understand condition-specific concerns within different geographic contexts. Results from this study can be used to inform ongoing policy efforts to address widespread issues of medical debt and health care unaffordability across the population, with targeted investments to address financial risks associated with chronic conditions.

Footnotes

ORCID iD

Carrie Henning-Smith

Ethical considerations

Ethical approval was not required. This study did not include human subjects.

Funding

This study was supported by the Federal Office of Rural Health Policy (FORHP), Health Resources and Services Administration (HRSA), US Department of Health and Human Services (HHS) under PHS Cooperative Agreement No. 5U1CRH03717. This research was also supported by the NIH National Center for Advancing Translational Sciences, grant UM1TR004405. The information, conclusions, and opinions expressed in this manuscript are those of the authors and no endorsement by FORHP, HRSA, NIH, or HHS is intended or should be inferred.

Declaration of conflicting interests

The authors declare no conflict of interest.

Data Availability Statement

The data used in this study are publicly available from

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