Contact by Collection Agencies for Medical Debt: Rural–Urban Differences Among Older and Younger Medicare Beneficiaries

Abstract

Geographic disparities in health and health care are increasingly well-documented, as are financial barriers to accessing care. Still, less is known about whether Medicare beneficiaries differ in their ability to pay for care by rurality. Using data from the 2016 Medicare Current Beneficiary Survey (n = 12,688 U.S. community-dwelling beneficiaries), we analyzed rural–urban differences in rates of collection agency contact for unpaid medical bills using chi-square tests and multivariable logistic regression for the full sample and by age (65+ and <65). Nearly 10% of Medicare beneficiaries had been contacted by a collection agency for medical debt in the previous year, with higher percentages among rural beneficiaries (8% for urban vs 10% for rural micropolitan and 11% for rural noncore, p < .05). This difference attenuated after adjusting for educational attainment and income, suggesting that attention to socio-economic status among rural Medicare beneficiaries would help to address financial barriers to care and decrease medical debt.

Keywords

geographic disparities socio-economic status access to care Medicare age differences

Background

Seventy-nine million Americans have had difficulty paying medical bills and have accumulated medical debt, including 7 million adults age 65 and above (The Commonwealth Fund, 2019). Medical debt is the most common type of past-due bill for which consumers are contacted by collection agencies (Consumer Financial Protection Bureau, 2017). This problem affects individuals across the country, including one-third of rural adults who reported that their families have had problems paying for medical or dental bills in the past few years (NPR, Robert Wood Johnson Foundation & Harvard T.H. Chan School of Public Health, 2019).

Nonpayment of medical debt can result in collection agencies pursuing payment. Such collections can impact credit reports, and individuals can be sued for payment, with resolution including liens or garnishment of wages (though veterans’ benefits and Social Security cannot be garnished for medical debt) (Markowitz, 2019). Medical debt can be viewed through the lens of cumulative advantage/disadvantage, in which financial hardships and limited opportunities to accrue wealth begin in early life and accrue into late adulthood (Crystal et al., 2017), putting some Medicare beneficiaries at greater risk of medical debt than others. In turn, experiencing medical debt further serves to exacerbate socio-economic inequities, leading to poorer health and forgone care (Drentea & Reynolds, 2012; Kalousova & Burgard, 2013).

Medicare beneficiaries are not immune from having trouble with paying medical bills (Caswell & Goddeeris, 2020), and 53% of Medicare beneficiaries with a serious illness reported having difficulty paying medical bills (Kyle et al., 2019). Among individuals who are age 65 and above, those with only Medicare coverage have four times as much consumer debt, including from medical expenses, as those with Medicare and supplementary insurance; this difference is due to differences in out-of-pocket expenses as well as differences in the demographic characteristics of those with traditional versus supplemental Medicare coverage (Kim et al., 2012). Medicare beneficiaries younger than 65 may also experience difficulties paying for medical expenses due to higher disability rates and lower socio-economic status, compared with their older counterparts (Colligan et al., 2016; Lochner & Cox, 2013; Mehrotra et al., 2017).

Among traditional Medicare beneficiaries, the average out-of-pocket spending in 2016 was nearly $5,500 (Cubanski et al., 2019). Medicare payments vary widely between high- and low-cost areas in the United States, but out-of-pocket expenses do not (Chen et al., 2014). Due to this, rural beneficiaries pay co-pays and other out-of-pocket expenses similar to those in urban areas, even when cost of living and average incomes differ by geography. Furthermore, rural Medicare beneficiaries are in poorer health and have lower socio-economic statuses than their urban counterparts (Henning-Smith et al., 2017), which may make them more likely to need care and less likely to be able to afford it. While the rates of contact from collections agencies are lower in Medicare beneficiaries than the general population, they still signal troubling gaps in payment for medical care, and associated risk of accumulated debt among this growing population.

Still, limited research has explored rural–urban differences in the rate of Medicare beneficiaries being contacted by collection agencies, despite current policy discussions about expansion of Medicare. In this study, we seek to address that gap by examining rural–urban differences in the percentage of Medicare beneficiaries being contacted by collection agencies and whether socio-demographic and health characteristics attenuate any such differences. Furthermore, given the fact that younger Medicare beneficiaries (<65 years) differ from older beneficiaries (65+ years) in terms of health and socio-economic status, it is important to investigate risk of medical debt separately. Information about medical debt among younger Medicare beneficiaries may also foreshadow future financial trouble for them as they age, due to cumulative disadvantage. In this article, we look at overall risk of collection agency contact by rurality for Medicare beneficiaries, and examine risk separately for younger and older beneficiaries.

Methods

For this study, we use data from the 2016 Medicare Current Beneficiary Survey (MCBS). The sample included Medicare beneficiaries enrolled at the time of the survey, not necessarily continuously enrolled for the entire year. These data include individuals enrolled in Part A, Part B, or both and with Medicare as their primary or secondary payer. Using the beneficiary’s county of residence, we grouped respondents by rurality based on the Office of Management and Budget’s classification system, resulting in three groups: metropolitan (urban), rural micropolitan (generally, counties with an town of 10,000–49,999 people), and rural noncore (generally, counties with no population center of more than 9,999 people; Cromartie, 2019). This is a widely used measure of rurality, with implications for policy at the federal, state, and local level (Bennett et al., 2019). Our analytic sample size was 12,688 beneficiaries.

To determine the percentage of Medicare beneficiaries contacted by a collection agency in the past year, we estimated bivariate differences by rurality using chi-square tests, first for the full sample and then by age (65+ and <65 years). We also used chi-square tests for categorical variables and t-tests for continuous variables to determine rural/urban differences in sample characteristics within older and younger age groups.

To identify correlates of being contacted by a collection agency (yes, no) by rurality, we used a series of four multivariable logistic regression models. We used a stepwise model to assess the influence of individual covariates on collection agency contact. The initial model was a crude model adjusted for only rurality (Model 1). Model 2 adjusted for race (non-Hispanic white, Hispanic, non-Hispanic black, or other), age (<65 or 65+), gender (female or male), and marital status (married/partnered, widowed, divorced/separated, or never married). In Model 3, additional adjustments were added for self-rated health status (fair/poor or good/very good/excellent), number of chronic conditions (out of a possible 11: heart disease, hypertension, stroke, cancer, diabetes, arthritis, Alzheimer’s disease, mental illness, osteoporosis, Parkinson’s disease, and chronic obstructive pulmonary disease), and health insurance (Medicaid and Medicare Advantage enrollment). Model 4 included a final adjustment adding household income and educational attainment (less than high school, high school, some college, or college and beyond). As household income was highly skewed to the right, we used a log-transformation of income in the multivariable model. We include both income and Medicaid enrollment to account for the impact of being lower or middle income. Following the fully adjusted model, we used the “margins” command in Stata to generate adjusted marginal effects of having been contacted by a collection agency for each level of rurality, first based off of the full sample and then based off of subanalyses by age group. To account for the complex survey design of the MCBS, and to approximate nationally representative estimates, we used survey weights for all analyses. All analyses were conducted in Stata, version 16.

Results

Table 1 shows sample characteristics for Medicare beneficiaries by age (65+ and <65) and by rurality. In general, rural Medicare beneficiaries were more likely to be non-Hispanic white, less likely to be enrolled in Medicare Advantage, and had higher counts of chronic conditions than their urban counterparts. Rural beneficiaries also had significantly lower levels of educational attainment than urban beneficiaries. Older beneficiaries reported significantly lower household income as rurality increased, while younger beneficiaries reported significantly worse health as rurality increased.

Table 1.

Sample Characteristics by Rurality.

Sample characteristics	Metropolitan (79.6%)		Rural micropolitan (13.1%)		Rural noncore (7.3%)		P value
Sample characteristics	65+	<65	65+	<65	65+	<65	65+	<65
Demographic characteristics
Mean age	74.1	52.1	73.9	52.9	74.7	53.5	.167	<.01
Female	0.55	0.51	0.52	0.47	0.52	0.43	<.05	.203
Marital status							<.001	.348
Married/partnered	0.57	0.34	0.58	0.36	0.58	0.38
Widowed	0.22	0.05	0.23	0.08	0.24	0.07
Divorced/separated	0.15	0.29	0.16	0.31	0.17	0.32
Never married	0.06	0.31	0.03	0.25	0.02	0.23
Race and ethnicity							<.001	<.05
Non-Hispanic white	0.76	0.61	0.86	0.78	0.93	0.84
Hispanic	0.10	0.15	0.03	0.05	0.01	0.01
Non-Hispanic black	0.10	0.19	0.07	0.13	0.04	0.15
Other	0.04	0.05	0.04	0.04	0.02	0.01
Health insurance and health status
Medicare advantage	0.46	0.41	0.26	0.28	0.33	0.28	<.001	<.001
Medicaid	0.19	0.58	0.17	0.55	0.15	0.59	.174	.577
Count of chronic conditions	2.9	3.1	3.0	3.5	3.1	3.7	<.01	<.01
Self-rated health							.185	<.05
Good/very good/excellent	0.83	0.45	0.82	0.44	0.79	0.33
Fair/poor	0.17	0.55	0.18	0.56	0.21	0.67
Socio-economic status
Mean household income (SE)	58,499 (1,076)	28,845 (1,321)	45,819 (2,156)	29,110 (3,091)	38,895 (2,222)	25,203 (2,107)	<.001	.373
Educational attainment							<.001	<.05
Less than high school	0.15	0.21	0.20	0.24	0.18	0.24
High school degree	0.24	0.33	0.29	0.28	0.39	0.48
Some college	0.23	0.28	0.25	0.27	0.23	0.19
College degree or higher	0.38	0.19	0.25	0.12	0.20	0.10
N	8,023	1,575	1,586	394	890	220

Note. Frequencies and means generated using survey weights. P values denote significant differences by rurality.

Source. 2016 Medicare Current Beneficiary Survey respondents, n = 12,688.

More than one in 10 (11%) rural noncore beneficiaries had been contacted by a collection agency in the last year, as had 10% of rural micropolitan, compared with 8% of metropolitan beneficiaries (see Figure 1; p < .05 for noncore beneficiaries, compared with metropolitan). Rates of collection agency contact for younger beneficiaries were 3 to 4 times higher than for older beneficiaries, with the highest rates among younger, rural noncore beneficiaries (27%).

Figure 1.

Unadjusted rates of contact by collection agency by rurality and age.

Rural Medicare beneficiaries had significantly higher odds of having been contacted by a collection agency (rural micropolitan odds ratio [OR]: 1.3, p < .1; rural noncore OR: 1.4, p < .05; see Table 2). Rural beneficiaries continued to have elevated odds of being contacted by a collection agency after adjusting for demographic characteristics, health status, and health insurance type. However, the difference in ORs attenuated after adjusting for socio-economic status, with higher household income being associated with lower odds of having been contacted. Being younger than 65, non-Hispanic black, and in worse health were all consistently associated with higher odds of having been contacted by a collection agency across models.

Table 2.

Adjusted Odds of Being Contacted by Collection Agency.

Sample characteristics	Model 1^a	Model 2^b	Model 3^c	Model 4^d
Rurality
Metropolitan (reference)	1.0	1.0	1.0	1.0
Rural micropolitan	1.3^†	1.3*	1.3*	1.2
Rural noncore	1.4*	1.4*	1.3^†	1.2
Demographic characteristics
Age
65 and above		1.0	1.0	1.0
Under 65		5.0***	3.4***	3.2***
Female		1.0	0.8	0.8
Marital status
Married/partnered (reference)		1.0	1.0	1.0
Widowed		1.3*	1.1	0.9
Divorced or separated		1.4**	1.2^†	1.0
Never married		0.8	0.9	0.7*
Race and ethnicity
Non-Hispanic white (reference)		1.0	1.0	1.0
Hispanic		1.0	0.8	0.7
Non-Hispanic black		3.2***	3.2***	2.9***
Other		1.1	1.1	1.1
Health insurance and health status
Medicare advantage			1.1	1.1
Medicaid			1.2*	1.0
Count of chronic conditions			1.2***	1.2***
Self-rated health
Good/very good/excellent (reference)			1.0	1.0
Fair/poor			2.1***	2.0***
Socio-economic status
Household income				0.7***
Educational attainment
Less than high school (reference)				1.0
High school				0.9
Some college				1.1
College and beyond				0.8^†

Source. 2016 Medicare Current Beneficiary Survey; n = 12,688.

Model 1: Adjusted for rurality. ^bModel 2: Model 1+ age, sex, marital status, and race/ethnicity. ^cModel 3: Model 2 + Medicare Advantage, Medicaid enrollment, count of chronic conditions, and self-rated health status. ^dModel 4: Model 3 + log-transformed household income and educational attainment.

Odds significant at ^†p < .1. *p < .05. **p < .01. ***p < .001.

Table 3 shows adjusted marginal effects by rurality and age group. For the full sample and for respondents under 65, living in a rural area was not associated with a significantly higher probability of collection agency contact after accounting for socio-demographic and health characteristics. For adults age 65 and older, living in a rural micropolitan area was associated with a 2.7 percentage point increase of collection agency contact (p < .05), holding all else constant.

Table 3.

Adjusted Marginal Effects by Rurality and Age.

Rurality	Full sample		65+		Under 65
Rurality	AME	P value	AME	P value	AME	P value
Rural micropolitan	0.013	.126	0.027	.022	−0.017	.498
Rural non-core	0.011	.252	0.026	.133	−0.003	.930

Note. AME = adjusted marginal effects. P value denotes significant difference from metropolitan respondents. Adjusted marginal effects generated after model adjusting for age, gender, marital status, race and ethnicity, Medicare Advantage, Medicaid dual-eligibility, count of chronic conditions, self-rated health status, log-transformed household income, and educational attainment.

Source. 2016 Medicare Current Beneficiary Survey.

Discussion

We found that nearly one in 10 of all Medicare beneficiaries across the country had been contacted by a collection agency for unpaid medical bills in the past year. This should concern policy-makers, insurers, and health care providers alike. Overall, Medicare beneficiaries have greater needs for health care, compared with the general population. Being contacted by a collection agency can put one’s access to care and/or financial stability in jeopardy, and may make individuals less likely to seek subsequent care, even when such care is necessary (Kalousova & Burgard, 2013). Indeed, individuals with medical bill problems reported delaying or forgoing health care in the past year because of cost at a rate two or three times higher than individuals who did not have trouble paying medical bills (Hamel et al., 2016). For Medicare beneficiaries, a population largely consisting of older adults or individuals with disabilities, these can be particularly troubling issues, given the heightened importance of timely medical care.

We also identified differences by rurality, age, and socio-demographic characteristics in the risk of collection agency contact. For example, the finding that rural Medicare beneficiaries are more likely to have been contacted by a collection agency than their urban counterparts is especially troubling. Rural Medicare beneficiaries are already in worse health and face more economic constraints and barriers to care, relative to their urban counterparts (Chan et al., 2006; Henning-Smith et al., 2017). And, while rates of collection agency contact are particularly high for beneficiaries younger than 65 in all locations, they are even more pronounced among rural noncore beneficiaries in that cohort. Finally, in multivariable models, non-Hispanic black beneficiaries and those in poorer health both faced higher risk of collection agency contact. The former is indicative of the pernicious role of structural racism, which systematically puts people of color at risk of poor economic and health outcomes, including within rural areas (Henning-Smith et al., 2019). The latter is likely a result of increased need for care, coupled with more tenuous financial status of people with worse health (Cubanski et al., 2019).

After adjusting for income and education, the relationship between rurality and collection agency contact attenuated for all but rural micropolitan beneficiaries age 65+. This suggests that the risk of collection agency contact is more closely related to socio-economic status than geography. However, our results show that rural Medicare beneficiaries have lower socio-economic status than urban and tend to be in poorer health. This suggests that the beneficiaries at highest risk of medical debt and its implications are concentrated in rural locations. As such, efforts to increase access to care in rural areas should include a focus on the economic well-being of rural residents and on decreasing out-of-pocket costs for care. Decreasing out-of-pocket expenditures for Medicare beneficiaries might include expanding coverage of services to those not included in traditional Medicare (e.g., vision, dental, and long-term care services), and/or ensuring affordable access to supplemental plans to reduce out-of-pocket costs (Cubanski et al., 2019).

Limitations

While we identified rural/urban disparities in experiences of medical debt among Medicare beneficiaries, we did not account for additional contextual factors that might influence one’s risk of debt. These include state-level policies, such as the generosity of the state Medicaid program, and community economic and occupational landscape. Future research should seek to explore additional contextual and geographic factors impacting risk of medical debt to design effective and targeted interventions.

Conclusion

In a time of heightened attention to rural health and heated policy debates about the future and potential expansion of the Medicare program (e.g., “Medicare for All”), the results of this study carry particular meaning. While the rate of collection agency contact for unpaid medical bills is generally lower among Medicare beneficiaries than among their younger or uninsured counterparts, it is not negligible. The fact that nearly one in 10 of all Medicare beneficiaries have been contacted by a collection agency for unpaid bills, and the fact that rural beneficiaries are harder hit than their urban peers suggests room for improvement in making medical care accessible and affordable for all. Furthermore, younger beneficiaries in all locations—but especially in rural noncore counties—have particularly high risk of collection agency contact, with more than one-in-four rural noncore beneficiaries under age 65 experiencing such contact. The first step might be to address high out-of-pocket costs, especially for middle- and lower-income rural beneficiaries and those younger than 65, to ensure access to timely medical care, with the goal of avoiding costlier emergency care and poor health outcomes later on.

Footnotes

Authors’ Note

The information, conclusions, and opinions expressed in this manuscript are those of the authors and no endorsement by FORHP, HRSA, or HHS is intended or should be inferred.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Federal Office of Rural Health Policy (FORHP), Health Resources and Services Administration (HRSA), U.S. Department of Health and Human Services (HHS) under PHS grant no. 5U1CRH03717.

Institutional Review Board Approval

This study was approved by the University of Minnesota Institutional Review Board, under study number STUDY00005985.

ORCID iD

Carrie Henning-Smith

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