Debt-Related Financial Hardship and Health

Abstract

Recent research shows personal financial debt is an important socioeconomic determinant of health, but the mechanisms through which it operates are not well understood. This article uses data from a mixed-methods study to explore how changes in spending and behavior that result from debt have salient health consequences in a cross-sectional sample of Boston area adults (n = 286). Findings show that a large majority of respondents had skipped medical care, housing payments, or consumer purchases at least once because of their debt. Controlling for multiple sociodemographic characteristics, each of these measures of debt-related behavior change was associated with worse self-rated health, and higher depressive symptoms, anxiety, and perceived stress. In models including all three measures, skipped medical care was associated with worse health across all outcomes, while skipped consumer purchases were associated with higher perceived stress and depression. These findings suggest that altered spending and care-seeking behaviors are potential pathways through which financial debt can negatively affect health and suggest areas in need of additional research.

Keywords

debt depression financial hardship skipped medical care stress

Personal financial debt has emerged in recent scholarship as an important socioeconomic determinant of health. Multiple studies have now found that being in financial debt, especially unsecured, nonmortgage debt, is strongly predictive of poor mental health, including higher depression, stress, and anxiety, as well as poor physical health indices like higher blood pressure and body mass, elevated inflammation, and worse self-rated general health and symptomatology (Alley et al., 2011; Bridges & Disney, 2010; Clayton et al., 2015; Drentea & Reynolds, 2012; Hojman et al., 2016; Pollack & Lynch, 2009; Sweet, 2018; Sweet, Kuzawa, & McDade, 2018; Sweet et al., 2013; Zurlo et al., 2014).

Psychosocial stress is an often cited (although not tested) mechanism linking indebtedness with poor health (Münster et al., 2009; Selenko & Batinic, 2011; Sweet, DuBois, & Stanley, 2018). Both quantitative and qualitative evidence suggest that carrying debt is stressful (Sweet, 2018; Sweet, DuBois, & Stanley, 2018), and since stress has known neurochemical and cardiovascular consequences (Cohen et al., 2007; Steptoe & Kivimäki, 2013), it is a logical vehicle through which debt could affect mental and physical health. However, the ways in which debt is experienced as stressful, and the other mechanisms by which debt may have health-relevant impacts on everyday life, are in need of elaboration and unpacking. For instance, research shows that having debt may lead to behavior changes such as forgoing medical care and cutting back on food (Alley et al., 2011; Pollack & Lynch, 2009; Sweet, DuBois, & Stanley, 2018). Qualitative research shows that spending behaviors change in a variety of salient ways in response to debt, including avoiding new medication and health care expenses, cutting back on essential expenses and utilities, delaying buying new clothes and shoes, and denying leisure and entertainment purchases (Sweet, DuBois, & Stanley, 2018). These behaviors could either directly affect health (particularly forgoing medical care) or contribute to the broader experiential matrix in which being in debt is felt as stressful. Indeed, research on general financial hardships (not necessarily because of debt) such as having trouble paying bills, making ends meet, and affording basic necessities suggests that these are important predictors of health outcomes like depression (Butterworth et al., 2009; Dijkstra-Kersten et al., 2015; Heflin & Iceland, 2009; Okechukwu et al., 2012; Price et al., 2002; Savoy et al., 2014) and worse self-rated health (Marshall & Tucker-Seeley, 2018; Tucker-Seeley et al., 2013). The health impacts of spending and behavior changes that are specifically induced by debt have not yet been explored.

This article draws on a mixed-methods study of debt experience and health among adults in Boston, Massachusetts, to explore whether debt-induced behavior changes that restrict household expenditures are associated with four health outcomes—depression, anxiety, perceived stress, and self-rated general health. It is hypothesized that more debt-induced behavior changes will be associated with worse health across these four indices. Testing this hypothesis will contribute to unpacking the experience of debt in everyday life and the mechanisms through which it may adversely affect health.

Method

Study Design and Sample

Data come from the “Price of Debt” Study, a cross-sectional, mixed-methods, two-phase study of debt and health in Boston. As published elsewhere, Phase 1 semistructured qualitative interviews with Boston area adults (n = 31) informed the development of a comprehensive questionnaire assessing financial debt experience (see Sweet, DuBois, & Stanley, 2018, for details of Phase 1 interviews). This article focuses on data from Phase 2, in which the comprehensive debt questionnaire was administered via an online format to a larger sample (n = 286), along with additional self-reported measures of health and well-being. For both phases of research, participants were recruited via informational fliers posted in public spaces around Boston and on public transportation. Participants were screened via telephone to ensure they met eligibility criteria of age (18–64 years) and English fluency. All respondents gave informed consent before completing the questionnaire, and all were compensated $50 for their participation. All study activities and procedures were reviewed for ethical treatment of human subjects and approved by the institutional review board at the author’s institution.

Measure of Debt-Induced Behavior Change

Debt-induced behavior changes were measured with a set of eight questions asking respondents how frequently they had done the following because of their debt: “Decided not to go to the doctor for medical care,” “Decided not to get treatment for a medical issue”; “Decided to not buy medications for a medical issue”; “Skipped meals or lowered the quality or quantity of your food purchases”; “Decided to miss or delay a rental or home mortgage payment”; “Decided to reduce your utility bill by turning off the lights, turning down the heat or A/C, or reducing your hot water use”; “Been unable to purchase new clothing”; and “Been unable to do leisure activities (such as going to the movies or out to eat).” For each question, response options (“never,” “a couple of times,” “sometimes,” “very often”) were coded 0 through 3. In analyses, questions were grouped into three subscales of spending change: skipped medical (3 questions), skipped necessities (3 questions), and skipped consumer purchases (2 questions). Cronbach’s alpha tests showed good to fair scale reliability for these three subscales (“skipped medical” α = .89, “skipped necessities” α = .61, “skipped consumer” α = .92). Subscale scores were calculated as the sum of individual responses for the questions in the subscale (i.e., how often they engaged in the behavior changes or skipped the types of payments in each category). With three questions scored from never (0) to very often (3), the “skipped medical” and “skipped necessities” subscales thus each had a possible score range from 0 to 9, while “skipped consumer” had a possible range from 0 to 6.

Measures of Emotional and General Health

Depression was measured using the 20-item Center for Epidemiologic Studies–Depression scale (Radloff, 1977), asking respondents how frequently they experienced a set of feelings and behaviors, such as being lonely and being “bothered by things that don’t usually bother me,” in the past week. Symptoms of anxiety were measured using the 21-item Beck Anxiety Inventory (Beck et al., 1988), asking respondents how much they had been bothered by symptoms such as being unable to relax and experiencing heart racing in the past month. Perceived stress was measured using Cohen’s 10-item Perceived Stress Scale (Cohen et al., 1983), asking respondents how often they felt symptoms such as being “upset because of something that happened” and feeling “nervous or stressed” in the past few months. For each scale, a composite summary score was constructed following standard scoring procedures, with positively worded items reverse-coded. A single question measured general, overall self-rated health, with response options “excellent,” “good,” “fair,” “poor,” and “very poor.”

Sociodemographic Measures

Questionnaire items asked respondents to self-report their age, gender (male = 0, female = 1), highest level of education completed (none, primary or middle school, high school or GED, vocational or technical school, some college, college degree, graduate degree; treated as a continuous variable in analyses), whether they were currently a student (no = 0, yes = 1), their total personal income for the previous year (reported on a categorical scale ranging from 1 = <$5,000 to 12 = ≥$150,000,” with responses recoded to the mid-point dollar value of each category and treated as continuous in analyses for ease of interpretation), how they would describe their race (American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Pacific Islander, White, Multiple Race, or Other, recoded as White = 1 and non-White = 0 for analyses), and whether they consider themselves to be of Hispanic or Latino ethnicity (no = 0, yes = 1).

Analysis Strategy

Basic associations between both demographic variables and health measures with debt-induced behavior change scores were examined using pairwise correlations and t tests, as appropriate. Multiple linear regression analyses were used to test associations between debt-induced behavior changes and each of the four health measures (depression, anxiety, perceived stress, and health rating). Models 1 through 3 tested each behavior change measure (skipped medical, skipped necessities, and skipped consumer) separately as predictors of health outcomes, controlling for the effects of age, gender, race, income, education, total debt, debt-to-income ratio, and student status. Model 4 included all three behavior change subscales in the same model in order to isolate their separate effects, and adjusted for all covariates. All analyses were performed using Stata statistical software.

Results

A description of the sample is provided in Table 1. This community sample of Boston adults had an average age of just under 32 years and was fairly highly educated, with 41% of the sample having completed a college degree or higher. Just over half of the sample was White (55%), and just under half were current students (47%). Incomes varied widely but averaged just over $25,000. Total amounts of financial debt also varied considerably but averaged just under $39,000. Most of the sample reported some degree of debt-induced behavior change. Skipping medical care, treatment or medication was the least common, with 51% reporting that they had never done this as a result of their debt (27% reporting 1–3 times and 21% reporting 4 or more times). Skipped necessities, in the form of skipped or reduced payments for food or housing, and altered utility usage were more common, with 36.5% of respondents having done this because of their debt one to three times and 41% having done it four or more times. Skipped consumer purchases were also fairly common, with only 18% reporting that they had never cut back on clothing or leisure spending because of their debt, 38% reporting one to three times and 54% reporting four or more times.

Table 1.

Descriptive Statistics for Sample, % (n) or M (SD), N = 280.

Characteristic	% (n)	M (SD)
Age, years		31.8 (12.8)
Gender
Female	54.1 (153)
Male	44.9 (127)
Race
White	54.8 (153)
Black	16.8 (47)
Asian	18.3 (51)
American Indian	0.3 (1)
Multiple Race	6.4 (18)
Other	3.2 (9)
Education
No college degree	58.6 (164)
≥College degree	41.4 (116)
Income		$25,106 (28,576)
Total debt		$38,975 (71,985)
Student	46.8 (131)
Center for Epidemiologic Studies–Depression score		17.5 (10.7)
Beck Anxiety Inventory score		12.2 (10.6)
Perceived Stress score		18.6 (5.6)
Health rating		3.9 (0.7)
Skipped medical care
Never	51.6 (139)
1−3 times	27.5 (74)
4 or more times	20.9 (56)
Skipped paying necessities
Never	22.5 (60)
1−3 times	36.5 (97)
4 or more times	41.0 (109)
Skipped consumer purchases
Never	17.9 (48)
1−3 times	28.0 (75)
4 or more times	54.1 (145)

Basic associations between the three behavior change variables and demographic characteristics are presented in Table 2. Age was positively correlated with both skipped medical care (r = .13) and skipped consumer purchases (r = .13), with older age associated with higher scores on these subscales. Relatedly, skipped medical care and consumer purchases scores were also lower among current students (M = 1.11 and 2.95 among students compared to 2.39 and 3.65 for nonstudents). Skipped medical care scores were positively correlated with higher education (r = .12) and higher income (r = .15). None of the three behavior change scores were significantly correlated with amount of debt, and were not statistically different across race/ethnicity.

Table 2.

Associations Between Demographic Characteristics and Debt-Induced Spending Changes: Average Number of Skipped Payments (SD) by Demographic Category, or Correlations With Demographic Variable.

Characteristic	Skipped medical care		Skipped necessities		Skipped consumer purchase
Characteristic	Correlation	M (SD)	Correlation	M (SD)	Correlation	M (SD)
Age	.13*		.06		.13*
Gender
Male		1.72 (2.4)		2.77 (2.3)		3.11 (2.15)
Female		1.78 (2.4)		2.76 (2.2)		3.45 (2.15)
Race
White		1.88 (2.5)		2.76 (2.2)		3.33 (2.1)
Black		1.53 (2.0)		2.91 (2.2)		3.61 (1.9)
Asian		1.56 (2.4)		2.57 (2.3)		3.10 (2.3)
American Indian		n too small		n too small		n too small
Multiple Race		2.72 (2.9)		3.47 (2.6)		3.82 (2.1)
Other		1.12 (2.1)		1.75 (2.9)		1.37 (1.7)
Student
Yes		1.11 (1.8)*		2.54 (2.2)		2.95 (2.2)*
No		2.39 (2.8)*		2.97 (2.2)		3.65 (2.1)*
Education (years)	.12*		.08		.09
Income	.15*		.04		.04
Total debt	.05		.09		.04
Debt-to-income ratio	−.00		.11		.08

p < .05.

In separate multiple linear regression analyses (Models 1–3, Table 3) controlling for the effects of sociodemographic covariates, each of the debt-induced behavior change measures were significantly associated with each of the health outcomes, such that more behavior change was associated with worse self-rated health, and higher depressive symptoms, anxiety, and perceived stress. In combined multiple regression models that included all three of the behavior change measures together (Model 4, Table 4), skipped medical care scores were associated with worse health for all outcomes, while skipped consumer purchases scores were associated with higher perceived stress and marginally higher depressive symptoms. Skipped necessities scores were not associated with health outcomes when the other behavior change measures were included in the same models.

Table 3.

Multiple Regression Results: Separate Models (Models 1–3).

Behavior change measure	Center for Epidemiologic Studies–Depression		Anxiety		Perceived stress		Health rating
Behavior change measure	B [95% CI]	p	B [95% CI]	p	B [95% CI]	p	B [95% CI]	p
Skipped medical	1.72 [1.18, 2.26]	.00	1.67 [1.16, 2.19]	.00	0.71 [0.42, 0.99]	.00	−0.06 [−0.09, −0.03]	.00
Skipped necessities	1.63 [1.03, 2.23]	.00	1.41 [0.82, 2.00]	.00	0.88 [0.58, 1.19]	.00	−0.05 [−0.09, −0.01]	.00
Skipped consumer	1.77 [1.14, 2.41]	.00	1.52 [0.90, 2.14]	.00	0.96 [0.63, 1.28]	.00	−0.06 [−0.09, −0.02]	.00

Note. Separate regression models testing each debt-induced spending change score as a predictor of each health outcome. Unstandardized regression coefficients, controlling for age, gender, race, income, education, total debt, debt to income ratio, and student status (covariates not shown).

Table 4.

Multiple Regression Results: Combined Models (Model 4).

Behavior change measure	Center for Epidemiologic Studies–Depression		Anxiety		Perceived stress		Health rating
Behavior change measure	B [95% CI]	p	B [95% CI]	p	B [95% CI]	p	B [95% CI]	p
Skipped medical	1.26 [0.62, 1.89]	.00	1.32 [0.69, 1.94]	.00	0.32 [−0.00, 0.64]	.05	−0.05 [−0.09, −0.01]	.02
Skipped necessities	0.51 [−0.37, 1.39]	.26	0.21 [−0.65, 1.05]	.64	0.28 [−0.17, 0.74]	.22	−0.02 [−0.07, 0.04]	.55
Skipped consumer	0.87 [−0.07, 1.83]	.07	0.72 [−0.20, 1.63]	.13	0.58 [0.08, 1.07]	.02	−0.02 [−0.08, 0.04]	.54
Age	−0.11 [−0.23, 0.01]	.09	−0.18 [−0.31, −0.06]	0.01	−0.10 [−0.17, −0.04]	.00	−0.00 [−0.01, 0.00]	.26
Female	0.87 [−1.88, 3.63]	.53	3.98 [1.38, 6.58]	.00	−0.01 [−1.39, 1.36]	.98	−0.01 [−0.18, 0.16]	.92
White	0.21 [−2.66, 3.07]	.89	0.15 [−0.92, 1.22]	.78	0.49 [−0.06, 1.04]	.08	0.02 [−0.04, 0.09]	.48
Education	−0.57 [−1.49, 0.35]	0.22	−0.77 [−1.65, 0.11]	.09	−0.11 [−0.59, 0.37]	.65	0.07 [0.01, 0.13]	.01
Income	−0.08 [−0.66, 0.49]	.77	0.18 [−0.39, 0.76]	.53	−0.01 [−0.30, 0.29]	.97	0.04 [−0.00, 0.07]	.07
Debt	0.00 [−0.00, 0.00]	.35	0.00 [−0.00, 0.00]	.83	0.00 [−0.00, 0.00]	.21	0.00 [−0.00, 0.00]	.28
Debt-to-income ratio	0.09 [−0.12, 0.30]	.39	0.03 [−0.17, 0.23]	.76	−0.02 [−0.12, 0.09]	.76	0.01 [−0.00, 0.03]	.08
Student	0.29 [−3.14, 3.71]	.87	−0.67 [−3.94, 2.59]	.68	−0.15 [−1.95, 1.65]	.86	0.10 [−0.11, 0.32]	.35
R ²	.19		.20		.17		.10

Note. Regression models testing debt-induced spending change scores as predictors of health outcomes, controlling for covariates. Unstandardized regression coefficients.

Discussion

The findings of this article show that debt-induced behavior and spending changes are significantly associated with worse outcomes across multiple indices of general and mental health. These results support previous research showing that general financial hardships (not necessarily debt-specific), such as not being able to afford to pay bills and buy necessities, are associated with depression and other measures of poor health (Butterworth et al., 2009; Dijkstra-Kersten et al., 2015; Heflin & Iceland, 2009; Okechukwu et al., 2012; Savoy et al., 2014). The findings presented here also support growing scholarship demonstrating that personal debt is adversely associated with health (Alley et al., 2011; Bridges & Disney, 2010; Clayton et al., 2015; Drentea & Reynolds, 2012; Hojman et al., 2016; Richardson et al., 2013; Sweet, Kuzawa, & McDade, 2018; Walsemann et al., 2015; Zurlo et al., 2014) but expand that literature to show that behavior changes induced by debt are also predictive of poor health.

In prior research showing financial strain and hardships to be strong predictors of adverse mental health outcomes, there is indication that food insufficiencies and struggles to pay for utilities and other material necessities may be especially significant parameters (Heflin & Iceland, 2009; Okechukwu et al., 2012). In this study, however, skipped necessities (utilities and food) were not significantly associated with health outcomes when other forms of spending and behavior changes were included in the same model. While being unable to afford necessities is an important indicator of general financial strain, it may be that when induced specifically by being in debt, other types of changes in spending habits and health-relevant behaviors have more important implications.

Skipped medical care resulting from debt was the most consistent predictor of health outcomes in this analysis, remaining significantly associated with each of the four health outcomes in fully adjusted models. Marshall and Tucker-Seeley (2018) have also found skipped medication due to general financial hardship to be a strong predictor of worse self-rated health. When medical care and treatment are skipped because of being in debt, it may worsen existing physical and mental health issues that require regular or long-term management, or may lead to new health problems by delaying necessary intervention when an issue first arises. If so, policies and programs aimed at relieving debt burdens, as well as health care cost burdens for those in debt or with financial vulnerabilities, may be especially important for preventing this kind of care avoidance.

The association of skipped consumer purchases with higher depression and perceived stress in fully adjusted models is perhaps less intuitively explained than the results for skipped medical care. However, the psychology of debt and social status helps to contextualize these findings. For instance, recent qualitative research suggests that being in debt is associated with strong feelings of personal shame and failure (Sweet, DuBois, & Stanley, 2018), and these feelings in turn appear to be predictive of several adverse physical and emotional health indices, including higher blood pressure, adiposity, depression, anxiety, and perceived stress (Sweet, 2018). It is possible that when indebtedness prohibits purchases of clothes, shoes, and other needed consumer items, it exacerbates feelings of shame and failure. Consumer items, and especially highly visible ones like clothes and shoes, are also important vectors through which social position and status are conveyed (Sweet, 2010). Being unable to afford these items when needed could be experienced as quite stressful in the broader context of social positioning and presentation of self. In this case, again, policies and programs that help to make debts more manageable or to make these types of expenses more affordable for the most financially vulnerable would be helpful.

While plausible mechanisms exist to directly link both skipped medical care and consumer purchases with adverse health outcomes, other pathways are also possible. For instance, the severity or stressfulness of an individual’s debt situation could affect spending decisions and behaviors while also leading to higher depression, anxiety, perceived stress, and poor self-rated health. While analyses controlled for total amount of debt and debt-to-income ratio, they did not account for the type(s) of debt held. Recent research suggests that some types of debt, such as unsecured debts and predatory loans (e.g., payday loans), are more stressful and more predictive of poor health than secured loans (e.g., home mortgages; Berger & Houle, 2016; Sweet, Kuzawa, & McDade, 2018; Zurlo et al., 2014). These types of debt are also likely to be held in smaller amounts. It is possible that those carrying debt from predatory loans are more likely to feel overwhelmed by their debt situation and to alter their spending behaviors, even if their total debt is not greater than those with other types of loans.

Ultimately, broad political-economic interventions that reduce personal debt burdens nationally and diminish reliance on consumer credit are needed. Debt has risen dramatically in American households since the 1980s due to neoliberal economic policies that favor high-interest credit lending as an alternative to fair living wages (Soederberg, 2014). By deregulating the credit industry and opening avenues for state-by-state inconsistencies in predatory lending, interest and fees, debt has been “democratized” in ways that most heavily burden the poor and racial and ethnic minorities (Williams, 2005, 2008). With growing evidence that this growth in debt and its cascading consequences for household finances are adversely associated with health, it is clear that much more research is needed to fully understand this complex socioeconomic determinant of health and the mechanisms through which it operates. Furthermore, the Covid-19 pandemic will likely add another layer of urgency and complexity to this situation, since predatory lenders are already taking advantage of the economic downturn to target vulnerable borrowers (Mosley & Hagan, 2020). Research going forward should pay close attention to these dynamics.

This study has several limitations, most notably the small sample size and cross-sectional design that limit broad-scale generalizability and prevent analysis of long-term trends and patterns. In addition to being small, the sample was also nonrepresentative in some key ways, namely, being young and highly educated compared to the broader U.S. population. Furthermore, recruitment strategies targeting public spaces and transportation may have skewed the sample in particular ways. These sampling issues may have introduced biases that either under- or overestimate the effects found in this article. For instance, those who drive rather than take public transportation may have different debt profiles or more robust assets and resources. An older and less educated population may be more likely to suffer under the weight of chronic debt, or may simply have a different cognitive and sociological experience of debt compared to the largely millennial sample of this study. Despite sample biases, however, the study’s participants were quite diverse with respect to age, race, ethnicity, and socioeconomic status. Furthermore, the mixed-methods design of the broader project provided a rich ethnographic basis for the construction of debt-related questionnaire items, including the questions about behavior changes reported on here. By drawing on these qualitative insights in the formulation of questions and interpretation of findings, this study helps to provide a rich understanding of the specific ways in which debt burdens are experienced by American households and the particular financial hardships that are most salient for those living with debt.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the National Institutes of Health, National Institute on Minority Health and Health Disparities (Grant No. R01-MD007723).

ORCID iD

Elizabeth Sweet

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