The Relationship Between Financial Stressors, Chronic Pain, and High-Impact Chronic Pain: Findings From the 2019 National Health Interview Survey

Abstract

Objectives:

Public health interventions to prevent financial stressors and reduce chronic pain and high-impact chronic pain (HICP) are important to potentially improve the health of the US population. The objectives of our study were to provide an update on the prevalence of chronic pain and HICP and to examine relationships between financial stressors and pain.

Methods:

We used data from a cross-sectional sample of adults aged ≥18 years (n = 31 997) collected by the 2019 National Health Interview Survey. We constructed bivariate and multivariate models to examine chronic pain and HICP in relation to financial worries, employment with wages, income, sociodemographic characteristics, number of chronic health conditions, and body mass index.

Results:

In fully adjusted multivariate regression models, having no employment with wages was strongly associated with increased risk for chronic pain (adjusted odds ratio [aOR] = 1.3; 95% CI, 1.2-1.5) and HICP (aOR = 1.6; 95% CI, 1.4-1.9). Worries about paying medical bills was associated with chronic pain (aOR = 1.1; 95% CI, 1.0-1.2) and HICP (aOR = 1.1; 95% CI, 1.0-1.3). Being unable to pay medical bills was associated with chronic pain (aOR = 2.1; 95% CI, 1.9-2.3) and HICP (aOR = 2.3; 95% CI, 2.0-2.6). Compared with having more income, having less income relative to the federal poverty level was associated with increased risk for chronic pain and HICP.

Conclusions:

We found a strong relationship between financial worries, employment for wages, income, and self-reported chronic pain and HICP independent of poor physical health and body mass index. Interventions to reduce chronic pain and HICP should address economic instability and financial stressors.

In 2019, the Centers for Disease Control and Prevention (CDC) reported that 1 in 5 US adults experienced chronic pain. ¹ In addition, an estimated 7.4% of US adults had high-impact chronic pain (HICP) that frequently limited life or work activities in the past 3 months.^1,2 Rates of chronic pain and HICP have been consistent in the United States over time. In 2016, approximately 20% of US adults reported chronic pain and 8% reported HICP. ³ The consequences of chronic pain include poor quality of life, depression, anxiety, interference with work and family roles, and increased health care costs.^3,4 Chronic pain has also been associated with midlife morbidity and mortality.^2,5 Associations between financial stress and physical pain have also been observed. ² In addition, long-term loss of earnings and unemployment are associated with declines in physical and psychological well-being. ⁶

Since 2008, US adults have experienced increased job and income loss. ⁷ Research has drawn attention to the rising prevalence of financial worries and serious psychological distress among US adults, with more than half of adults reporting 2 or more worries about paying their bills, serious medical events, expected medical costs, retirement, children’s college tuition, and maintaining their standard of living. ⁶ While the relationship between financial stress and mental health is documented, few studies have examined the relationship between financial stress and pain. A study on the relationship between financial stress and pain relied on data collected almost a decade ago and focused on suicide trends in relationship to chronic pain. ² A 2019 CDC data brief did not consider financial stressors and related economic worries as correlates of chronic pain and HICP. ¹ Elucidating how chronic pain and HICP vary by financial stressors would extend previously reported data and aid in the development of public health interventions.

Financial stressors and economic worries likely have a bidirectional relationship with pain and HICP. Chronic pain and HICP may precipitate financial stressors, resulting in serious psychological distress and diminishing one’s functional and work ability. ⁵ Conversely, perceptions of pain and access to pain care may be exacerbated in the presence of financial stressors. ⁷ Perceptions of pain have been shown to increase among individuals under stress.^8,9 However, studies on these topics did not examine the relationship between financial stress and pain.^10,11

We hypothesized that financial and employment stressors would be associated with a greater likelihood of chronic pain and HICP after accounting for sociodemographic characteristics, economic factors, and selected health-related characteristics, such as the number of chronic health conditions and body mass index (BMI). We used data from the National Health Interview Survey (NHIS), the largest US survey dedicated to health, to provide an update on the prevalence of chronic pain and HICP and to examine relationships between financial stressors and pain. ³

Methods

Analytic Sample and Data Source

Our sample included adults aged ≥18 years (n = 31 997) from the 2019 NHIS. The NHIS is conducted by CDC’s National Center for Health Statistics through home-based interviews using clustered sampling techniques to select dwelling units.^12-14 It uses a multistage probability sample that incorporates stratification and oversampling of some subpopulations. Because of the complex sampling design, sampling weights are used to produce representative estimates that allow each person to be inflated to represent the total US population.^12-15

Adult responses were based on questions asked of a sample adult selected from each household. Additional responses were based on the family questionnaire. Informed consent was obtained from all respondents. The institutional review board at New York University deemed that approval for this study was not required.

Chronic Pain and High-Impact Chronic Pain

Chronic pain was defined as responses of “most days” or “every day” to the question, “In the past 3 months, how often did you have pain? Would you say never, some days, most days, or every day?” HICP included adults with chronic pain responding “most days” or “every day” to the question, “In the past 3 months, how often did your pain limit your life or work activities? Would you say never, some days, most days, or every day?” Missing data for the HICP variable were coded as 0 to indicate the absence of HICP (n = 12 735). This approach allowed for an estimation of HICP prevalence, although it likely underestimated its true prevalence.

Economic Sources of Stress

Financial worries

Respondents were asked, “If you get sick or have an accident, how worried are you that you will be able to pay your medical bills? Are you very worried, somewhat worried, or not at all worried?” A response of “very worried” or “somewhat worried” was recorded as 1. A response of “not at all worried” was recorded as 0. A second item, unable to pay medical bills, was recorded as 1 if the respondent answered yes to having problems paying or being unable to pay any medical bills in the past 12 months.

Employment with wages

A response to the question about employment with wages was recorded as yes or 1 if the respondent or any member of the family aged ≥18 years received income from wages, salaries, commissions, bonuses, tips, or self-employment. If a respondent answered no (recorded as 0), the respondent was asked to indicate any of the following reasons for not working for pay: unemployed, laid off, seasonal/contract work, retired, unable to work for health reasons, taking care of house or family, going to school, and working at a job or business but not for pay. We did not include this variable in the multivariate analysis because of collinearity with the measure for whether the respondent worked last week (P < .001).

Worked last week

This variable corresponds to a question about whether the respondent worked in the last week. A yes response was coded as 1, and a no response was coded as 0.

Main reason for not working

This variable captures data on reasons for not working in the past week, including unemployment, seasonal/contract work, retirement, health reasons or disability, taking care of the house or family, going to school, or working at a job or business but not for pay.

Income

We grouped annual family income by poverty index ratio (PIR): <100% of the federal poverty level (FPL), 100%-199% FPL, 200%-399% FPL, ≥400% FPL. Poverty-level percentage was based on imputed annual family income, the number of children in the family, and the age of the family adults. NHIS multiple imputation files included income levels with missing data allowing for imputation estimates.^15,16

Sociodemographic Characteristics

Survey respondents were categorized by sex (male or female), race and ethnicity (Hispanic, non-Hispanic White [White], non-Hispanic Black [Black], and non-Hispanic Asian [Asian]), age (18-29, 30-44, 45-64, ≥65 years), marital status (living with spouse or partner, married or have a partner but not living with spouse or partner, widowed, divorced/separated, never married), region of residence at the time of interview (Northeast, Midwest, South, West), and education level (<high school graduate, high school graduate or General Educational Development [GED] equivalent, some college or an associate’s degree, bachelor’s or master’s degree, doctoral or professional degree). We included region of residence at the time of interview because health varies regionally. ¹⁷ The relationship between residence in an urban or rural area and US region of residence was significant (P < .001). Therefore, we included US regions in the final analysis. Because marital status is associated with health outcomes, ¹⁸ we included this variable in our analysis.

Health-Related Characteristics

Anxiety and depression

Respondents were asked, “Have you ever been told by a health professional that you had an anxiety disorder?” Respondents were also asked, “Have you ever been told by a health professional that you had depression?” Responses were coded as 1 if they answered yes and 0 if they answered no.

Health insurance type

Definitions of health insurance types were informed by previous National Center for Health Statistics reports that used NHIS data. ¹⁹ The categories included no health insurance, Medicaid, private health insurance, Medicare, and combined Medicare and private health insurance. Private health insurance was defined as coverage through employer(s), through union(s), or by purchase. Respondents categorized as having no health insurance reported not having health insurance at the time of the interview. Adults were considered to have no health insurance if they did not have private health insurance, Medicare, Medicaid, state Children’s Health Insurance Program (CHIP), a state-sponsored health plan, other government programs, or a military health plan.

BMI and number of chronic health conditions

BMI (kg/m²) was defined as underweight (<18.5), normal weight (18.5-24.9), overweight (25.0-29.9), and obese (≥30.0). The number of chronic conditions (0, 1, or ≥2) included chronic obstructive pulmonary disease (COPD), diabetes, heart disease, hypertension, stroke, and cancer. ²⁰ Hypertension was defined as respondents being told by a health professional that they met criteria for hypertension on 2 different visits. Heart disease was based on being told by a health professional that they met criteria for coronary heart disease, angina, a heart attack, or any other kind of heart disease or heart condition. Diabetes and stroke were also based on yes or no responses to questions about being told by a health professional that they had these chronic conditions. Cancer was based on having been told by a health professional that they had a malignancy. COPD was based on positive responses to questions about being told by a health professional that they had COPD. The question on COPD had 50 missing responses, and the question on stroke had 54 missing responses. We excluded records with missing data from the multivariable analyses but retained them in the overall study population.

Statistical Analysis

We used SAS (SAS Institute, Inc) and SAS-callable SUDAAN version 11.0.3 (RTI International) to calculate point estimates and 95% CIs. Procedures for weighted data considered survey design and weights. We used Rao–Scott χ² statistics for weighted surveys to evaluate categorical variables at the α = .05 level (2-sided). The estimates reported in bivariate analysis did not require correction for multiple comparisons. ²¹

Several sociodemographic variables with known relationships to mental health, physical health, and pain were examined, including BMI. BMI was included because, unlike other poor health habits, it may not necessarily be a consequence of chronic pain as much as a cause of chronic pain.^22,23 We compared the proportions of adults with chronic pain and HICP across anxiety, depression, BMI, sociodemographic characteristics, number of health conditions, employment with wages, employed last week, reasons for unemployment, financial worries, and income. Variables nonsignificant at the bivariate level were still considered for multivariate models.^23,24

Multivariate logistic regression models predicted chronic pain and HICP. Independent variables included sex, race and ethnicity, age group, marital status, education level, employment status, region, BMI, number of chronic health conditions, employment with wages, financial worries, and income. We examined anxiety and depression in bivariate analysis but not in final multivariate models. We included BMI and the number of chronic health conditions in the final multivariable models to examine sociodemographic and economic characteristics independent of the respondent’s health status.^25,26 We found a significant relationship between financial worries, employment with wages, and income. We retained all 3 variables in the multivariate models because they addressed different dimensions of financial stress.

Results

Sample Characteristics

The unweighted sample size included 31 994 adults aged ≥18 years representing 250 896 150 US adults. The mean age was 52.1 years (SD = 18.4; range, 18-99 years). Most were female (51.7%) and White (63.9%), followed by Hispanic (16.7%), Black (11.8%), and Asian (7.3%). The greatest proportion resided in the South (37.6%), followed by the West (23.5%), Midwest (21.0%), and Northeast (17.7%). The greatest proportion (35.7%) had a bachelor’s or master’s degree, 27.5% were high school graduates or had a GED equivalent, 21.8% had some college or an associate’s degree, 12.0% did not graduate from high school, and 2.7% had a doctoral or professional degree.

Bivariate Analysis and Multivariate Model

Women were more likely than men to report chronic pain (21.3% vs 18.7%) and HICP (9.4% vs 6.9%) (Table 1). Differences between men and women for chronic pain risk were attenuated once sociodemographic, health-related, and economic characteristics were included in multivariate models (chronic pain among women: aOR = 1.0 [95% CI, 1.0-1.1]; HICP among women: aOR = 1.2 [95% CI, 1.0-1.3]). White adults were more likely than adults in other racial and ethnic groups to report chronic pain and HICP (Tables 1 and 2). Middle-aged adults (30-44 years, 45-64 years) were more likely than adults in the youngest group (18-29 years) to report chronic pain and HICP. Although adults aged ≥65 years were more likely than adults aged 18-29 years to report chronic pain and HICP, their relative risk was less than that of the middle-aged group aged 45-64 years compared with adults aged 18-29 years (Table 2).

Table 1.

Sample characteristics by chronic pain, high-impact chronic pain, and selected sociodemographic and health-related characteristics of US adults (N = 31 994), National Health Interview Survey, 2019 ^a

Characteristic	Chronic pain b		High-impact chronic pain c
	Unweighted no. of respondents to survey question d	Weighted % (95% CI) with chronic pain e	Unweighted no. of respondents to survey question d	Weighted % (95% CI) with high-impact chronic pain e
Total	7184	20.1 (19.5-20.7)	2977	8.2 (7.8-8.6)
Sex
Male	3093	18.7 (17.9-19.5)	1162	6.9 (6.4-7.4)
Female	4090	21.3 (20.5-22.1)	1815	9.4 (8.9-9.9)
Race and ethnicity
Hispanic	612	12.9 (11.6-13.9)	298	6.2 (5.4-7.0)
Non-Hispanic White	5475	23.6 (22.5-24.0)	2188	9.1 (8.6-9.5)
Non-Hispanic Black	748	18.8 (17.3-20.3)	344	8.3 (7.3-9.3)
Non-Hispanic Asian	115	11.4 (9.7-13.1)	126	5.3 (4.1-6.4)
Age group, y
18-29	405	8.3 (7.4-9.2)	140	3.0 (2.4-3.6)
30-44	1082	14.3 (13.3-15.2)	371	4.9 (4.3-5.5)
45-64	1711	28.4 (27.0-29.9)	785	12.8 (11.7-13.9)
≥65	2876	30.0 (28.9-31.2)	1219	12.9 (12.0-13.7)
Marital status
Living with spouse or partner	3288	19.8 (19.0-20.6)	1222	7.4 (6.9-7.9)
Married or have a partner but not living with spouse or partner	233	21.6 (18.4-24.8)	108	9.8 (7.6-11.9)
Widowed	1047	35.0 (32.8-37.2)	496	17.4 (15.5-19.3)
Divorced or separated	1511	31.7 (30.0-33.4)	690	14.6 (13.4-15.8)
Never married	1045	12.8 (11.8-13.8)	428	5.4 (4.7-6.1)
Region of residence at time of interview
Northeast	1122	18.4 (16.5-19.5)	462	7.4 (6.5-8.3)
Midwest	1747	22.8 (21.1-23.9)	662	8.4 (7.6-9.2)
South	2637	20.4 (19.4-21.4)	1139	8.7 (8.0-9.3)
West	1678	18.9 (17.8-20.0)	714	7.8 (7.1-8.6)
Education level
<High school graduate	870	25.4 (23.5-27.4)	464	13.4 (12.0-14.8)
High school graduate or GED equivalent	2076	22.1 (21.0-23.2)	899	9.2 (8.5-9.9)
Some college or an associate’s degree	1698	22.3 (21.1-23.2)	704	9.0 (8.2-9.8)
Bachelor’s or master’s degree	2322	15.9 (15.1-16.6)	828	5.3 (4.8-5.8)
Doctoral or professional degree	157	12.5 (10.4-14.7)	52	4.3 (2.9-5.7)
Someone in the household has employment with wages (family variable)	4164	17.1 (16.4-17.7)	1513	18.3 (17.3-19.3)
No one in the household has employment with wages (family variable)	2925	35.3 (33.9-36.6)	1415	6.1 (5.7-6.4)
Worked last week	2963	9.3 (8.9-9.7)	767	2.4 (2.2-2.6)
Did not work last week	4177	11.0 (10.6-11.5)	2185	5.9 (5.6-6.2)
Main reason for not working
Unemployed/laid off/seasonal contract work	95	17.7 (13.5-21.9)	29	5.7 (3.2-8.2)
Seasonal contract work	21	11.3 (6.3-16.3)	9	6.1 (1.4-10.9)
Retired	2416	30.8 (29.5-32.0)	1019	13.1 (12.2-14.0)
Unable to work for health reasons/disabled	1354	60.8 (58.0-63.6)	1005	45.6 (42.9-48.3)
Taking care of house or family	176	15.5 (12.9-18.0)	72	6.5 (4.8-8.3)
Going to school	32	5.4 (3.3-7.5)	9	1.2 (0.4-2.0)
Going to work at a job or business but not for pay	5	89.2 (78.9-99.5)	1	3.4 (0-10.2)
Financial worries
Worried about paying medical bills	3514	22.4 (21.5-23.3)	1586	9.8 (9.2-10.4)
Having problems paying medical bills	1560	35.8 (33.9-37.6)	830	17.9 (16.6-19.3)
Mental health
Anxiety	2056	41.3 (39.5-43.1)	1099	22.0 (20.5-23.4)
Depression	2416	43.0 (41.2-44.7)	1322	23.4 (22.0-24.8)
Body mass index, kg/m2
Underweight (<18.5)	107	18.8 (14.1-22.2)	62	9.7 (6.4-13.0)
Normal weight (18.5-24.9)	1687	14.8 (13.9-15.6)	709	6.1 (5.6-6.7)
Overweight (25.0-29.9)	2224	19.1 (18.2-20.0)	840	7.1 (6.5-7.7)
Obese (≥30.0)	2993	26.7 (25.7-27.8)	1295	11.5 (10.7-12.2)
No. of chronic health conditions f
0	2309	12.2 (11.6-12.7)	755	3.9 (3.6-4.3)
1	2187	24.8 (23.5-26.0)	841	9.8 (9.0-10.6)
≥2	2667	41.9 (40.4-43.4)	1369	21.3 (20.0-22.6)
Poverty index ratio g
<100% FPL	— h	29.5	— h	29.5
100%-199% FPL	— h	25.1	— h	25.1
200%-399% FPL	— h	20.4	— h	20.4
≥400% FPL	— h	15.8	— h	15.8

Abbreviations: FPL, federal poverty level; GED, General Educational Development.

^aData source: National Center for Health Statistics. ³

^bChronic pain was defined as responses of “most days” or “every day” to the question, “In the past 3 months, how often did you have pain? Would you say never, some days, most days, or every day?” Missing responses were included in the denominator to calculate prevalence.

^cHigh-impact chronic pain was defined as responses of “most days” or every day” to the question, “In the past 3 months, how often did your pain limit your life or work activities? Would you say never, some days, most days, or “every day?” Missing responses were included in the denominator to calculate prevalence.

^dValues in cells may not add to total because not all respondents answered all questions.

^ePercentages in each category were significantly different at P < .001, except for region/high-impact pain; determined by Rao–Scott χ² test; P < .05 considered significant.

^fResponse options included chronic obstructive pulmonary disease (COPD), diabetes, heart disease, hypertension, stroke, and cancer. The question on COPD had 50 missing responses, and the question on stroke had 54 missing responses.

^gPoverty index ratio was defined as the ratio of family income to the FPL.

^hImputed datasets were used to calculate poverty index ratio; thus, sample numbers were not available.

Table 2.

Multivariate logistic regression results, by chronic pain and high-impact chronic pain as dependent variables and other selected sociodemographic and health-related characteristics as independent variables, US adults (N = 31 994), National Health Interview Survey, 2019 ^a

Characteristic	Chronic pain b	High-impact chronic pain c
Sex
Female	1.09 (1.0-1.18) [.049]	1.22 (1.09-1.37) [<.001]
Male	1 [Reference]	1 [Reference]
Race and ethnicity
Non-Hispanic White	1 [Reference]	1 [Reference]
Hispanic	0.45 (0.38-0.52) [<.001]	0.59 (0.48-0.72) [<.001]
Non-Hispanic Black	0.64 (0.56-0.74) [<.001]	0.67 (0.56-0.80) [<.001]
Non-Hispanic Asian	0.48 (0.40-0.58) [<.001]	0.68 (0.52-0.88) [.004]
Age group, y
18-29	1 [Reference]	1 [Reference]
30-44	1.60 (1.36-1.88) [<.001]	1.43 (1.09-1.86) [<.001]
45-64	2.53 (2.11-3.02) [<.001]	2.38 (1.80-3.14) [<.001]
≥65	2.12 (1.74-2.58) [<.001]	1.66 (1.24-2.24) [<.001]
Marital status
Married or living with a partner	1 [Reference]	1 [Reference]
Married or have a partner but not living with a spouse or partner	1.04 (0.83-1.31) [.74]	1.22 (0.89-1.68) [.21]
Widowed	1.02 (0.90-1.17) [.72]	1.06 (0.89-1.27) [.49]
Divorced or separated	1.23 (1.10-1.38) [<.001]	1.17 (1.00-1.36) [.05]
Never married	0.89 (0.78-1.01) [.08]	0.89 (0.73-1.08) [.24]
Region of residence at time of interview
Northeast	1 [Reference]	1 [Reference]
Midwest	1.16 (1.01-1.33) [.03]	1.00 (0.85-1.24) [.79]
South	1.04 (0.92-1.18) [.57]	0.99 (0.83-1.17) [.87]
West	1.31 (1.15-1.50) [<.001]	1.28 (1.06-1.56) [.01]
Education level
<High school graduate	1 [Reference]	1 [Reference]
High school graduate or GED equivalent	0.98 (0.84-1.14) [.77]	0.92 (0.75-1.13) [.42]
Some college or an associate’s degree	1.03 (0.88-1.21) [.70]	0.91 (0.74-1.13) [.39]
Bachelor’s or master’s degree	0.77 (0.66-0.90) [<.001]	0.70 (0.57-0.87) [.001]
Doctoral or professional degree	0.66 (0.51-0.87) [.003]	0.71 (0.47-1.08) [.11]
Someone in the household has employment with wages (family variable)
Yes	1.35 (1.22-1.50) [<.001]	1.66 (1.44-1.92) [<.001]
No	1 [Reference]	1 [Reference]
Financial worries
Worried about paying medical bills
Yes	1.13 (1.03-1.23) [.008]	1.17 (1.03-1.33) [.01]
No	1 [Reference]	1 [Reference]
Having problems paying medical bills
Yes	2.13 (1.90-2.38) [<.001]	2.32 (2.02-2.67) [<.001]
No	1 [Reference]	1 [Reference]
Body mass index, kg/m2
Underweight (<18.5)	1.27 (0.95-1.70) [.10]	1.26 (0.85-1.87) [.25]
Normal weight (18.5-24.9)	1 [Reference]	1 [Reference]
Overweight (25.0-29.9)	1.19 (1.07-1.33) [.001]	1.01 (0.87-1.16) [.92]
Obese (≥30.0)	1.63 (1.47-1.80) [<.001]	1.39 (1.20-1.60) [<.001]
No. of chronic health conditions d
0	1 [Reference]	1 [Reference]
1	1.65 (1.49-1.83) [<.001]	1.81 (1.55-2.11) [<.001]
≥2	2.75 (2.45-3.09) [<.001]	3.42 (2.89-4.05) [<.001]
Poverty index ratio e
<100% FPL	1.57 (1.36-1.82) [<.001]	1.98 (1.65-2.38) [<.001]
100%-199% FPL	1.17 (1.04-1.32) [.01]	1.23 (1.06-1.43) [.005]
200%-399% FPL	1 [Reference]	1 [Reference]
≥400% FPL	0.81 (0.73-0.90) [<.001]	0.73 (0.62-0.85) [<.001]

Abbreviations: FPL, federal poverty level; GED, General Educational Development.

^aData source: National Center for Health Statistics. ³ All values are adjusted odds ratio (95% CI) [P value]. P values determined by multivariate logistic regression; P < .05 considered significant.

^bChronic pain was defined as responses of “most days” or “every day” to the question, “In the past 3 months, how often did you have pain? Would you say never, some days, most days, or every day?” Missing responses were included in the denominator to calculate prevalence.

^cHigh-impact chronic pain was defined as responses, among respondents who reported chronic pain, of “most days” or “every day” to the question, “In the past 3 months, how often did your pain limit your life or work activities? Would you say never, some days, most days, or every day?” Missing responses were included in the denominator to calculate prevalence.

^dResponse options included chronic obstructive pulmonary disease (COPD), diabetes, heart disease, hypertension, stroke, and cancer. The question on COPD had 50 missing responses, and the question on stroke had 54 missing responses.

^ePoverty index ratio was defined as the ratio of family income to the FPL.

Adults who reported being divorced or separated had an increased risk of reporting chronic pain (aOR =1.2; 95% CI, 1.1-1.3) compared with adults who were married or living with a partner (Table 2). Adults who held a college degree or a professional/graduate degree had a reduced risk of chronic pain compared with adults who did not graduate from high school (doctoral or professional degree: aOR = 0.66 [95% CI, 0.51-0.87]; college: aOR = 0.77 [95% CI, 0.66-0.90]). Financial worries about paying medical bills was associated with an increased likelihood of chronic pain (aOR = 1.1; 95% CI, 1.0-1.2) and HICP (aOR = 1.0; 95% CI, 1.0-1.3). Financial worries about being unable to pay medical bills was also associated with chronic pain (aOR = 2.1; 95% CI, 1.9-2.3) and HICP (aOR = 2.3; 95% CI, 2.0-2.6). Respondents stating that no one in their family, including themselves, was employed with wages had an increased risk of pain and HICP compared with respondents who were employed with wages or had a family member employed with wages (chronic pain: aOR = 1.3 [95% CI, 1.2-1.5]; HICP: aOR = 1.6 [95% CI, 1.4-1.9]). Preliminary models that included depression and anxiety as covariates showed that financial worries and no family member employed with wages were significantly and positively associated with chronic pain and HICP.

In multivariate and bivariate analyses, overweight or obese respondents were more likely than normal weight or underweight respondents to experience chronic pain. Adults with obesity also had an increased risk for HICP compared with adults with a normal BMI (Tables 1 and 2). In bivariate analysis, respondents with ≥2 chronic health conditions were more likely than respondents with no chronic health conditions to report chronic pain (41.9% vs 12.2%) and HICP (21.3% vs 3.9%) (Table 1). Adjusted models indicated that adults who reported having ≥1 chronic health condition had an increased risk of chronic pain and HICP compared with those without any chronic health conditions (Table 2). The odds of reporting pain and HICP were greater among adults with lower incomes compared with higher incomes. Adults with the highest incomes (≥400% FPL) had lower odds of HICP compared with those in the second-highest income group (200%-399% FPL) (aOR = 0.73; 95% CI, 0.62-0.85).

In bivariate analyses that examined the relationship between having no health insurance or not having someone in the household with employment with wages and chronic pain or HICP, among adults aged <65 years, most of whom did not have access to Medicare, chronic pain and HICP were not associated with having no health insurance. The percentage of adults with no health insurance was similar by employment status. That is, 14.9% of respondents with no health insurance reported no employment with wages, and 14.8% of respondents with no health insurance reported employment with wages.

Discussion

Our findings demonstrate a strong positive association between financial worries and low income and chronic pain and HICP. These associations persisted after adjusting for chronic health conditions and increased BMI. Occupation type may partially explain the inverse relationship between income, chronic pain, or HICP. Manual labor is typically low paying and performed by adults with a high school degree or less. ²⁷ However, the NHIS sample was representative of the United States and included other occupations. Our results also demonstrated that adults in a family without an employed family member had higher odds than adults in a family with at least 1 employed family member of reporting chronic pain and HICP. Employment instability and uncertainty about wages are known stressors. ²⁸ Loneliness associated with unemployment is a mediating factor between depression and pain.^29,30 While lack of health insurance from unemployment could contribute to inadequate treatment for chronic pain and HICP, we found that having no health insurance was not associated with chronic pain and HICP among workers aged <65 years.

Health care costs, even among those with health insurance, are a common source of stress, reported by 64% of US adults. ³⁰ Financial debt is associated with increases in perceived stress, depression, and poor health. ³⁰ Increases in stress and pain observed among US workers may have their source in widening income inequalities and insecurities related to savings and retirement. ² The National Center for Health Statistics reported prevalence rates of chronic pain in the United States that are generally similar to or higher than those reported by other countries.^1,31-33 Current pain treatments in the United States may be expedient, but they do not address the social, economic, and health challenges facing US adults. ² Our findings differed from the findings of previous studies in that women were not at increased risk for chronic pain compared with men and had only marginally increased risk for HICP. ³¹ Moreover, advanced age did not necessarily correlate with chronic pain and HICP.^34,35 Widespread business closures and employment loss from COVID-19 increased financial stressors and likely increased the incidence and prevalence of chronic pain and HICP. ⁹ Our study supports treating pandemic-related financial stress as a mechanism for chronic pain and HICP. Treating pain may also help individuals return to work.^36,37 Our study also supports future research, as well as health and economic policies to reduce financial stress. Pathways between financial stressors and chronic pain or HICP have been understudied and not well understood. ³³ Intervening to reduce financial stress in the United States could improve the mental and physical health of the nation.

Strengths and Limitations

The major strength of this study is that the NHIS is a high-quality national dataset that provides the best estimates of financial worries, chronic pain, and HICP. The study also had several potential limitations. First, data were self-reported and could have been subject to recall bias. Second, the cross-sectional design did not permit us to examine temporal ordering. Therefore, it was not possible to identify whether financial problems led to increased pain or vice versa.

Conclusion

We found a strong relationship between financial worries, employment for wages, income, and self-reported chronic pain and HICP. These observed relationships were independent of poor physical health and BMI. Our findings suggest that interventions to reduce chronic pain and HICP should include those that address economic instability and financial stressors.