Skip to main content
NCBI home page
American Journal of Men's Health logoLink to American Journal of Men's Health
. 2020 Jul 21;14(4):1557988320943359. doi: 10.1177/1557988320943359

Confidence in Understanding Health Insurance and Challenges Paying Medical Bills Among Men in the United States

Caress A Dean 1, Jacqueline Wiltshire 2, Echu Liu 3, M Ahinee Amamoo 4, Edlin Garcia Colato 2, Keith Elder 4,✉
PMCID: PMC7376301  PMID: 32693654

Abstract

Although the number of men with health insurance has increased, men are less likely to utilize health services than females, and experience difficulty in paying medical bills. Understanding the details of health insurance can be challenging and the lack of understanding can have financial consequences. This study, guided by Andersen’s model of health-care utilization, assessed the relationship between confidence level in understanding health insurance terms and difficulty in paying medical bills among American men. Data were drawn from the Health Reform Monitoring Survey, 2015–2016. The study included 6,643 men aged between 18 and 64. Descriptive statistics examined participants’ difficulty in paying medical bills by predisposing, enabling, and need characteristics, and by confidence in understanding health insurance terms. A modified Poisson regression analysis examined the association between difficulty in paying medical bills, confidence in understanding health insurance terms score, and predisposing, enabling, and need characteristics. An increase in confidence in understanding health insurance terms score was associated with significantly lower reported difficulty in paying medical bills (PR = .98; 95% CI = [.97–.99]; p = .002). Participants with a college degree or higher were less likely to report difficulty in paying their medical bills compared to participants with less than a high school degree (PR = .72; 95% CI = [.56–.92]; p = .009). A better understanding of health insurance might prevent men from experiencing difficulties in paying medical bills. Additional research should be performed to understand the relationship between the level of confidence in understanding health insurance, knowledge level of health insurance terms, use of health insurance, and their impact on difficulty in paying medical bills.

Keywords: health insurance literacy, health insurance, medical debt, confidence

Men’s health is a relatively new research area; however, substantial strides have been made to advance the science in terms of health behaviors, health status, and interaction with the health-care system (Elder et al., 2015; Gilbert et al., 2015; Griffith, Bergner, et al., 2018; Griffith, Cornish, et al., 2018; Mui et al., 2018; Sohn et al., 2017; Thorpe et al., 2017; Valdez et al., 2019). Overall, the life expectancy for men in the United States has shown positive trends since 1980 increasing by 6 years to 76.4 (National Center for Health Statistics, 2018). Furthermore, fewer men report being in fair or poor health compared to women, and men are more likely to engage in physical activity compared to women (National Center for Health Statistics, 2017a, 2017b). Although substantial progress has been made, research shows men are less likely than women to utilize preventive services (Raghupathi & Raghupathi, 2018) and have a place to go for care when they are sick or need medical care (Kaiser’s Men’s Health Survey, 2015). A growing body of literature suggests that men’s lack of health-care utilization might be attributed to their health insurance status, difficulty in paying medical bills, and health insurance literacy level (Edward et al., 2019; Kaiser’s Men’s Health Survey, 2015). The literature aligns with Andersen’s model of health-care utilization (Andersen, 1995), which suggests that health insurance status, difficulty in paying medical bills, and health insurance literacy level are enabling characteristics that can significantly impact men’s behaviors such as the use of health services (Andersen, 1995).

Health insurance is very prominent in the fabric of the United States, with 91% of Americans covered by private (56%) or public health insurance (35%; Kaiser Family Foundation, 2018). The uninsured rate has declined since 2010 for nonelderly women and men from 19% to 24%, respectively; however, the uninsured rate for men (14%) remains higher compared to women (11%) (Kaiser Family Foundation, 2020). This is of some importance as health insurance is a predictor of health status and the health of men tends to be poor (Elder & Griffith, 2016; Elder et al., 2015; Gilbert et al., 2016). Even with the decrease in uninsured rates among men, one in five men aged 18–64 years experience difficulty in paying medical bills (Kaiser’s Men’s Health Survey, 2015). Addressing difficulties in paying medical bills has significant implications on one’s debt status (Wiltshire, Elder, Kiefe, et al., 2016). Specifically, the outcomes of a 2019 study by Himmelstein et al. found medical-related costs were associated with 66.5% of all bankruptcies, even after the passage of the Affordable Care Act (Himmelstein et al., 2014).

It has been suggested that health insurance literacy level may be a predictor of difficulty in paying medical bills (Tolbert & Young, 2016). Health insurance literacy encompasses consumers’ confidence, knowledge, and skills related to the selection and utilization of health insurance (Kim et al., 2013; Paez & Mallery, 2014; Quincy & Child, 2012), and influences the utilization of health care (Tipirneni et al., 2018). Many Americans do not understand health insurance terms, concepts, and costs, including calculating out-of-pocket costs (Loewenstein et al., 2013; Norton et al., 2014). Furthermore, racial/ethnic minorities, lower-income households, and recently uninsured are less likely to understand health insurance terms (Long & Goin, 2014; Norton et al., 2014). This has implications since out-of-network services and out-of-pocket costs could result in being responsible for thousands of dollars (Levitt, 2015). Hamel et al. found 75% of those with health insurance experienced difficulties paying their medical bills due to the inability to afford copays, deductibles, or coinsurance (Hamel et al., 2016). As important, lower health insurance literacy may be associated with more avoidance of needed health care due to cost (Tipirneni et al., 2018). Men have been found to have lower confidence in using health insurance than women (Edward et al., 2019). However, little is known about men’s confidence in understanding health insurance terms.

A more comprehensive assessment of confidence in understanding insurance terms might have implications for their use of health insurance and health-care services. Therefore, the goal of this study, guided by Andersen’s model of health-care utilization, was to examine the relationship between confidence level in understanding health insurance terms and difficulty in paying medical bills among men using nationally representative data from the Health Reform Monitoring Survey (HRMS). This is the first study to examine this important topic.

Methodology

Guiding Theoretical Framework

Andersen’s model of health-care utilization guided this study’s methodological approach. This model delineates predisposing, enabling, and need characteristics as predictors of health-care utilization (Andersen, 1995). Predisposing characteristics are the demographic or biological (e.g., age and gender) factors that indicate the likelihood of needing services. Enabling characteristics are the resources or means (e.g., income and insurance) that must be available in order to use health-care services. Need characteristics are people’s perceived and professional evaluated health status and their need for medical care. This theoretical framework has been utilized in previous research to examine health services use among men (Griffin-Tomas et al., 2019; Hammond et al., 2011).

Participants

Data were drawn from the 2015–2016 HRMS to meet the objectives of this cross-sectional study. The HRMS, conducted by the Urban Institute, surveys nonelderly adults (aged 18–64 years) from the KnowledgePanel to gain insights on the impact of the Affordable Care Act, individuals’ understanding and use of health insurance and health services, and their health outcomes (Urban Institute, 2019). The KnowledgePanel is a probability-based internet panel consisting of approximately 55,000 individuals from households with and without Internet access. Members of the panel are emailed a link to the survey to participate. Additional details on the operationalization of the HRMS can be found on the Urban Institute’s Website (Urban Institute, 2019). In 2015 and 2016, HRMS was collected semiannually (every March and September) (Urban Institute, 2019). To complete the objectives of this study, HRMS data collected during the second data collection time point (every September) for years 2015 and 2016 were included because questions on variables of interest (e.g., confidence in understanding health insurance terms, availability of medical services, etc.) were included during these survey periods. Female participants were excluded from the analysis, yielding 6,643 participants.

Measures

Dependent Variable

Difficulty in paying medical bills was the dependent variable of interest. Participants provided a yes/no response to this question “Did you or anyone in your family have problems paying or were unable to pay any medical bills?”.

Independent Variables

The main independent variable of interest was participants’ score for confidence in understanding health insurance terms. Participants’ responses to their level of confidence (very, somewhat, not too confident, and not at all confident) in understanding seven health insurance terms—premium, deductible, copayments, coinsurance, maximum annual out-of-pocket spending, provider network, and covered services—were reverse-scored and combined to create the row sum of the aforementioned Likert-type response variables. The row sum was calculated to adhere to the literature’s recommendations of not placing undefined number values on Likert-type responses (Sullivan & Artino Jr, 2013). The score ranged from 7 to 28, with a score of 7 meaning not at all confident in understanding all seven terms to a score of 28 denoting very confident in understanding the seven terms.

Andersen Model Factors
Predisposing

Participants’ age in years (18–29, 30–44, 45–59, or 60+), race/ethnicity (White, Black, Hispanic, two or more races, or Other), and education level (less than high school, high school, some college, or bachelor’s degree or higher) were included. For marital status (married, widowed, divorced, separated, never married, or living with partner), two categories (widowed and separated) were collapsed to “other” to address low responses.

Enabling

Employment status (paid employee, self-employed, temporarily out of work, looking for employment, retired, disabled, or other reasons for not working) was included as an enabling characteristic. Additional characteristics included were insurance status (no insurance, employer-based, marketplace, Medicare, Medicaid, military health care, Indian health services, and other), which was created by combining participants’ responses to questions are you currently covered by insurance: through a current or former employer; purchased from an insurance company; through Medicare; by any kind of state or government-sponsored assistance plan (e.g., Medicaid); through TRICARE or other military health care; through Indian Health Service; and through any other type of health insurance. Each of the aforementioned questions had the response options of covered, not covered, or not sure. Usual source of care (I have one place, I have multiple places, and I do not have a place), time when survey was conducted (September 2015 and September 2016), and participants’ rating of the availability of places to get medical care in their neighborhood (excellent, very good, good, fair, or poor) were also included.

Need

The perceived health variable was recoded from “excellent, very good, good, fair, or poor” to “excellent, very good, good, fair/poor” to address limited responses across response options.

Statistical Analyses

Descriptive statistics were performed to examine male participants’ difficulty in paying medical bills by predisposing, enabling, and need characteristics. Descriptive statistics also examined confidence in understanding health insurance terms by men’s difficulty to pay medical bills. The data were weighted based upon the HRMS recommendations to obtain national estimates (Holahan & Long, 2016). A modified Poisson regression analysis (Zou, 2004) examined the association between difficulty in paying medical bills, score for confidence in understanding health insurance terms, and predisposing, enabling, and need characteristics. Descriptive statistics were conducted using STATA MP14 (Stata, 2015) and the modified Poisson regression was performed using R 4.0.0 (Fox et al., 2017).

Results

Table 1 summarizes the characteristics of the study sample of men by their difficulty to pay medical bills, with 15.73% reporting difficulty in paying medical bills. The majority of the participants were White (71.58%), married (59.36%), and reported having employer-based insurance (56.04%). In comparing men who reported difficulty in paying medical bills by race/ethnicity, a greater percentage of African American (18.32%) and Hispanic men (19.67%) reported difficulties versus White men (15.16%). These percentages coincide with those reported among their racial/ethnic counterparts who reported no difficulties paying their medical bills (81.68%, 80.33%, and 84.84%, respectively). Similarly, men who reported their health as fair/poor (36.09%) were the largest percentage to report difficulty in paying medical bills. In contrast, men who did not report difficulties paying medical bills were least likely to report a fair/poor health status (63.91%).

Table 1.

Unweighted Predisposing, Enabling, and Need Characteristics of Male Participants, by Difficulty in Paying Medical Bills.

Difficulty in Paying Medical Bills
No
 Yes
 Total
n = 5,598 (84.27) n = 1,045 (15.73) 6,643
Characteristics n (%) n (%)
Predisposing
Age in years
 18–29 850 (83.01) 174 (16.99) 1,024
 30–44 1,667 (85.01) 294 (14.99) 1,961
 45–59 2,222 (83.16) 450 (16.84) 2,672
 60+ 859 (87.12) 127 (12.88) 986
Race/ethnicity
 White 4,034 (84.84) 721 (15.16) 4,755
 African American 419 (81.68) 94 (18.32) 513
 Other 244 (92.42) 20 (7.58) 264
 Hispanic 739 (80.33) 181 (19.67) 920
 2+ races, non-Hispanic 162 (84.82) 29 (15.18) 191
Education level
 Less than high school 373 (75.66) 120 (24.34) 493
 High school 1,347 (80.51) 326 (19.49) 1,673
 Some college 1,658 (82.12) 361 (17.88) 2,019
 Bachelor’s degree or 2,220 (90.32) 238 (9.68) 2,458
Marital status
 Married 3,332 (84.5) 611 (15.50) 3,943
 Widowed 53 (88.33) 7 (11.67) 60
 Divorced 497 (81.61) 112 (18.39) 609
 Separated 81 (79.41) 21 (20.59) 102
 Never married 1,315 (85.95) 215 (14.05) 1,530
 Living with partner 320 (80.20) 79 (19.80) 399
Enabling
Insurance status
 No insurance 470 (73.78) 167 (26.22) 637
 Employer-based 3,295 (88.50) 428 (11.50) 3,723
 Marketplace 488 (80.93) 115 (19.07) 603
 Medicare 144 (68.25) 67 (31.75) 211
 Medicaid 578 (80.50) 140 (19.50) 718
 Military health care 309 (88.03) 42 (11.97) 351
 Indian health service 25 (89.29) 3 (10.71) 28
 Other type of insurance 289 (77.69) 83 (22.31) 372
Employment status
 Paid employee 3,869 (85.79) 641 (14.21) 4,510
 Self-employed 487 (85.89) 80 (14.11) 567
 Not working 319 (79.35) 83 (20.65) 402
 Retired 385 (90.38) 41 (9.62) 426
 Disabled 347 (69.12) 155 (30.88) 502
 Other reason not working 191 (80.93) 45 (19.07) 236
Need
Perceived health status
 Excellent 634 (86.73) 97 (13.27) 731
 Very good 2,225 (85.68) 372 (14.32) 2,597
 Good 1,857 (74.94) 621 (25.06) 2,478
 Fair/poor 680 (63.91) 384 (36.09) 1,064
Open in a new tab

Men’s confidence level in understanding health terms by difficulty in paying medical bills is depicted in Table 2. Men were more likely to report being very confident in understanding all seven health insurance terms, except for coinsurance, where the largest number of men reported being somewhat confident. Compared to men who self-reported no difficulty in paying medical bills, men who reported difficulty in paying medical bills were more likely to state that they were not at all confident or not too confident in their understanding of the seven health insurance terms.

Table 2.

Unweighted Confidence in Understanding Health Insurance Terms by Difficulty in Paying Medical Bills.

Confidence in understanding health insurance terms Difficulty in Paying Medical Bills
No Yes Total
Premium
 Not at all confident 229 (76.33) 71 (23.67) 300
 Not too confident 634 (77.98) 179 (22.02) 813
 Somewhat confident 1,700 (83.74) 330 (16.26) 2,030
 Very confident 3,035 (86.71) 465 (13.29) 3,500
Deductible
 Not at all confident 217 (75.87) 69 (24.13) 286
 Not too confident 602 (78.18) 168 (21.82) 770
 Somewhat confident 1,764 (83.52) 348 (16.48) 2,112
 Very confident 3,015 (86.76) 460 (13.24) 3,475
Copayments
 Not at all confident 202 (75.94) 64 (24.06) 266
 Not too confident 579 (76.79) 175 (23.21) 754
 Somewhat confident 1,765 (84.01) 336 (15.99) 2,101
 Very confident 3,052 (86.66) 470 (13.34) 3,522
Coinsurance
 Not at all confident 424 (76.26) 132 (23.74) 556
 Not too confident 1,397 (80.57) 337 (19.43) 1,734
 Somewhat confident 1,921 (86.03) 312 (13.97) 2,233
 Very confident 1,856 (87.55) 264 (12.45) 2,120
Maximum annual out-of-pocket spending
 Not at all confident 338 (66.93) 167 (33.07) 505
 Not too confident 917 (72.89) 341 (27.11) 1,258
 Somewhat confident 1,867 (79.92) 469 (20.08) 2,336
 Very confident 2,280 (82.01) 500 (17.99) 2,780
Provider network
 Not at all confident 284 (73.58) 102 (26.42) 386
 Not too confident 737 (77.74) 211 (22.26) 948
 Somewhat confident 1,896 (84.98) 335 (15.02) 2,231
 Very confident 2,681 (87.10) 397 (12.90) 3,078
Covered services
 Not at all confident 272 (71.58) 108 (28.42) 380
 Not too confident 776 (76.91) 233 (23.09) 1,009
 Somewhat confident 2,033 (85.31) 350 (14.69) 2,383
 Very confident 2,517 (87.67) 354 (12.33) 2,871
Open in a new tab

Table 3 shows the results from the modified Poisson regression model examining participants’ reporting difficulty in paying medical bills. An increase in confidence in understanding health insurance terms score (more confidence in understanding health insurance terms) was associated with significantly lower reported difficulty in paying medical bills (PR = .98; 95% CI = [.97–.99]; p = .002). Among predisposing characteristics, participants with a college degree or higher were less likely to report difficulty in paying their medical bills compared to participants with less than a high school degree (PR = .72; 95% CI = [.56–.92]; p = .009). Compared to participants with no insurance, those with employer-based (PR = .60; 95% CI = [.48–.74]; p = .000), Medicaid (PR = .73; 95% CI = [.57–.93]; p = .01) and military health-care insurance (PR = .50; 95% CI = [.35–.71]; p = .000) were less likely to report difficulty in paying their medical bills.

Table 3.

Estimated Association Between Difficulty in Paying Medical Bills Confidence Score, Predisposing, Enabling, and Need Characteristics Among Men.

Characteristics Prevalence Ratio 95% Confidence Interval p
Confidence understanding health insurance terms 0.98 [0.97–0.99] .03
Predisposing
Age
 18–29 (reference)
  30–44 0.79 [0.65–0.95] .01
  45–59 0.76 [0.63-0.93] .01
  60+ 0.60 [0.47–0.78] .00
Race/ethnicity
 White (reference)
  Black, non-Hispanic 1.06 [0.86–1.31] .60
  Other, non-Hispanic 0.51 [0.33–0.80] .00
  Hispanic 1.00 [0.84–1.19] .99
  2+ races, non-Hispanic 0.91 [0.63–1.33] .63
Education level
 Less than high school (reference)
  High school 1.09 [0.88–1.34] .43
  Some college 1.06 [0.86–1.32] .57
  Bachelor’s degree or higher 0.72 [0.56–0.92] .01
Marital status
 Married (reference)
  Widowed 0.74 [0.37–1.48] .40
  Divorced 0.92 [0.75–1.12] .41
  Separated 0.96 [0.64–1.45] .85
  Never married 0.64 [0.53–0.77] .00
  Living with partner 0.81 [0.64–1.02] .07
Enabling
Insurance status
 No insurance (reference)
  Employer-based 0.60 [0.48–0.74] .00
  Marketplace 1.04 [0.81–1.33] .77
  Medicare 1.13 [0.83–1.53] .43
  Medicaid 0.73 [0.57–0.93] .01
  Military health care 0.50 [0.35–0.71] .00
  Indian health service 0.53 [0.17–1.66] .27
  Other type of insurance 1.02 [0.78–1.33] .90
Employment status
 Paid employee (reference)
  Self-employed 0.79 [0.62–1.02] .07
  Not working 0.99 [0.78–1.26] .93
  Retired 0.70 [0.51–0.96] .03
  Disabled 1.18 [0.93–1.51] .17
  Other reason not working 1.15 [0.85–1.56] .35
 Usual source of care
 I have one place (reference)
  I have multiple places 1.15 [0.99–1.35] .07
 I do not have a place 0.87 [0.75–1.02] .08
Time
 September 2015 (reference)
  September 2016 0.96 [0.85–1.08] .50
Rating of availability of medical care
 Excellent (reference)
  Very good 0.95 [0.79–1.16] .62
  Good 1.13 [0.93–1.36] .23
  Fair 1.26 [1.01–1.57] .05
  Poor 1.77 [1.36–2.29] .00
Need
Perceived health status
 Excellent (reference)
  Very good 1.03 [0.79–1.34] .82
  Good 1.43 [1.11–1.86] .01
  Fair/poor 2.10 [1.60–2.76] .00
Open in a new tab

Perceived health status was significantly associated with difficulty in paying medical bills, with men who reported their health status as fair/poor (PR = 1.77; 95% CI = [1.36–2.29]; p = .000) more likely to report difficulty in paying medical bills.

Discussion

The major finding of this study was men with higher confidence in understanding health insurance terms were significantly less likely to report difficulty in paying their medical bills. This finding suggests men’s understanding of health insurance terms might be important in understanding the financial consequences of the health-care encounter. Even though this is the first study to find this, it closely aligns with the literature on health literacy, health insurance literacy, having unpaid medical bills, and financial literacy (Mattina, 2017; National Academies of Sciences, Engineering, and Medicine, 2017). Previous research shows individuals have some knowledge of health insurance terms, but experience difficulties performing cost-sharing calculations (Levitt, 2015; National Academies of Sciences, Engineering, and Medicine, 2017; Paez & Mallery, 2014), which can result in unexpected medical costs that they are unable to pay. The health-care encounter can have financial ramifications that lead to medical debt and hardship (Dickman et al., 2017; Wiltshire, Elder & Allison, 2016; Wiltshire et al., 2016; Yabroff et al., 2019), which continues to burden Americans (Ekwueme et al., 2019; Kaiser’s Men’s Health Survey, 2015). More than 130 million reported financial hardship in the previous year, according to a study by Yabroff et al. (2019). Increasing awareness of health insurance concepts has been strongly recommended to combat medical debt (Mattina, 2017). The outcomes of this study illustrate the potential benefits of targeting men when promoting awareness of health insurance terms.

Among predisposing characteristics, a significant relationship was identified between age and difficulty in paying medical bills. Men aged 30–44, 45–59, and 60 years and older were less likely to report challenges paying medical bills compared to those aged 18–29 years. This finding aligns with those reported by Batty and Colleagues in a 2018 study on the number of Americans whose medical bills were sent to a collections agency (Batty et al., 2018). This cross-sectional study showed medical bill collections decreased with age, and that most medical debt collections occur among those 27 years of age (Batty et al., 2018). Additionally, men with a bachelor’s degree or higher were less likely to report difficulty in paying their medical bills compared to men with less than a high school diploma, which is in line with previous research (Yabroff et al., 2019). Using secondary data from the National Health Interview Survey, Yabroff et al’s. findings show lower educational attainment as a robust predictor of medical financial hardship (Yabroff et al., 2019).

Per enabling characteristics, men with employer-based, Medicaid, and military health care were less likely to report difficulty in paying medical bills, which is consistent with the literature that the uninsured experience challenges paying their medical bills/debt compared to the insured (Hamel et al., 2016; Rustgi et al., 2008; Yabroff et al., 2019).

Although previous research has not been performed on the association between difficulty in paying medical bills and perceived availability of medical services in a neighborhood, the literature indicates a higher percentage of low socioeconomic status individuals reside in neighborhoods with limited resources (Khullar & Chokshi, 2018; Robinette et al., 2017). Further research should be performed to examine the relationship between an individual’s medical bill status, their neighborhood’s socioeconomic status, and access to medical services.

This study has some limitations. The main outcome variable, difficulty in paying medical bills, was self-reported and recall bias must be considered. Additionally, the survey asked if men or their immediate family members experienced difficulties paying medical bills; therefore, difficulty reported may not be direct bills of male participants. Nonetheless, the literature shows men and women experience difficulty in paying medical bills at a similar rate (Cha & Cohen, 2020), so awareness by men of this issue might be robust. Furthermore, the survey inquires about the difficulty experienced by the immediate family members, which narrows the scope of awareness of difficulty in paying medical bills the men need to recall. Variables of interests were asked during the second data collection time point; therefore, this surveyal point for 2015 and 2016 was included, which limits the sample size of this study. The confidence in understanding health insurance terms score was constructed from these data and has not been used previously. Two advantages of composite scores are the questions are related, and it aids in simplyfing the information (Agency for Healthcare Research and Quality, 2019; Carman, 2006). Lastly, this is a cross-sectional study, which cannot assess causality.

This study also has several strengths. No previous estimates have been derived on the relationship between confidence in understanding health insurance terms and difficulty in paying medical bills among a nationally representative sample of American men. Moreover, the inclusion of seven health insurance terms provides a comprehensive perspective on their confidence in their understanding of health insurance. Finally, the results of this study are consistent with previous research on health insurance literacy and medical debt (Mattina, 2017).

Conclusion

The findings of this study highlight that men who experience difficulty in paying their medical bills had lower confidence in their understanding of health insurance. Their lower confidence may negatively impact their use of health insurance and health-care services. Previous research has discussed how low health insurance literacy influences the use of health insurance (Grob et al., 2014; Long et al., 2014). Moreover, the inability to accurately calculate cost-sharing (e.g., calculate deductible and copay for services; Levitt, 2015; Paez & Mallery, 2014) may result in unexpected and unaffordable medical bills that may impact confidence in understanding health insurance terms. Further research should be performed to understand the relationship between confidence in understanding health insurance, knowledge level of health insurance terms, use of health insurance, and their impact on difficulty in paying medical bills. The outcomes of this study also highlight predisposing, enabling, and need factors that should be taken into consideration in efforts to aid men in paying their medical bills.

Footnotes

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) received no financial support for the research, authorship, and/or publication of this article.

References

  1. Agency for Healthcare Research and Quality. (2019). Combining healthcare quality measures into composites or summary scores. https://www.ahrq.gov/talkingquality/translate/scores/combine-measures.html#1
  2. Andersen R. M. (1995). Revisiting the behavioral model and access to medical care: Does it matter? Journal of Health and Social Behavior, 36(1), 1–10. [PubMed] [Google Scholar]
  3. Batty M., Gibbs C., Ippolito B. (2018). Unlike medical spending, medical bills in collections decrease with patients’ age. Health affairs, 37(8), 1257–1264. [DOI] [PubMed] [Google Scholar]
  4. Carman K. (2006). Improving quality information in a consumer-driven era: Showing differences is crucial to informed consumer choice. [Conference session] Paper presented at the 10th National CAHPS User Group Meeting, Baltimore, MD. [Google Scholar]
  5. Cha A. E., Cohen R. A. (2020). Problems paying medical bills, 2018 (NCHS Data Brief No. 357). [PubMed] [Google Scholar]
  6. Dickman S. L., Himmelstein D. U., Woolhandler S. (2017). Inequality and the health-care system in the USA. Lancet, 389(10077), 1431–1441. doi: 10.1016/S0140-6736(17)30398-7 [DOI] [PubMed] [Google Scholar]
  7. Edward J., Wiggins A., Young M. H., Rayens M. K. (2019). Significant disparities exist in consumer health insurance literacy: Implications for health care reform. HLRP: Health Literacy Research and Practice, 3(4), e250–e258. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Ekwueme D. U., Zhao J., Rim S. H., de Moor J. S., Zheng Z., Khushalani J. S., Han X., Kent E. E., Yabroff K. R. (2019). Annual out-of-pocket expenditures and financial hardship among cancer survivors aged 18–64 years—United States, 2011–2016. Morbidity and Mortality Weekly Report, 68(22), 494. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Elder K., Griffith D. M. (2016). Men’s health: Beyond masculinity. American Journal of Public Health, 106(7), 1157. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Elder K., Meret-Hanke L., Dean C., Wiltshire J., Gilbert K. L., Wang J., Shacham E., Barnidge E., Baker E., Wray R., Rice S., Johns M. (2015). How do African American men rate their health care? An analysis of the consumer assessment of health plans 2003–2006. American Journal of Men’s Health, 9(3), 178–185. [DOI] [PubMed] [Google Scholar]
  11. Fox J., Weisberg S., Friendly M., Hong J. (2017). R package version 4.0-0. [Google Scholar]
  12. Gilbert K. L., Elder K., Lyons S., Kaphingst K., Blanchard M., Goodman M. (2015). Racial composition over the life course: Examining separate and unequal environments and the risk for heart disease for African American men. Ethnicity & Disease, 25(3), 295. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Gilbert K. L., Ray R., Siddiqi A., Shetty S., Baker E. A., Elder K., Griffith D. M. (2016). Visible and invisible trends in black men’s health: Pitfalls and promises for addressing racial, ethnic, and gender inequities in health. Annual Review of Public Health, 37(1), 295–311. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Griffin-Tomas M., Cahill S., Kapadia F., Halkitis P. N. (2019). Access to health services among young adult gay men in New York City. American Journal of Men’s Health, 13(1), 1557988318818683. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Griffith D. M., Bergner E. M., Cornish E. K., McQueen C. M. (2018). Physical activity interventions with African American or Latino men: A systematic review. American Journal of Men’s Health, 12(4), 1102–1117. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Griffith D. M., Cornish E. K., Bergner E. M., Bruce M. A., Beech B. M. (2018). “Health is the ability to manage yourself without help”: How older African American men define health and successful aging. The Journals of Gerontology: Series B, 73(2), 240–247. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Grob R., Schlesinger M., Pollitz K. (2014). Taking stock and taking steps: a report from the field after the first year of Marketplace consumer assistance under the ACA. Henry J. Kaiser Family Foundation. [Google Scholar]
  18. Hamel L., Norton M., Pollitz K., Levitt L., Claxton G., Brodie M. (2016). The burden of medical debt: Results from the Kaiser Family Foundation/New York Times medical bills survey. Kaiser Family Foundation. [Google Scholar]
  19. Hammond W. P., Mohottige D., Chantala K., Hastings J. F., Neighbors H. W., Snowden L. (2011). Determinants of usual source of care disparities among African American and Caribbean Black men: Findings from the National Survey of American Life. Journal of Health Care for the Poor and Underserved, 22(1), 157. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Himmelstein D. U., Jun M., Busse R., Chevreul K., Geissler A., Jeurissen P., Thomson S., Vinet M.-A., Woolhandler S. (2014). A comparison of hospital administrative costs in eight nations: US costs exceed all others by far. Health Affairs, 33(9), 1586–1594. [DOI] [PubMed] [Google Scholar]
  21. Holahan J., Long S. (2016). Health reform monitoring survey third quarter, United States, 2016, ICPSR 36842 Codebook. https://www.icpsr.umich.edu/icpsrweb/HMCA/studies/36842/versions/V2/publications
  22. Kaiser Family Foundation. (2018). Health Insurance Coverage of the Total Population. https://www.kff.org/other/state-indicator/total-population/?activeTab=graph&currentTimeframe=0&startTimeframe=9&sortModel=%7B%22colId%22:%22Location%22,%22sort%22:%22asc%22%7D
  23. Kaiser Family Foundation. (2020). Women’s Health Insurance Coverage. https://www.kff.org/womens-health-policy/fact-sheet/womens-health-insurance-coverage-fact-sheet/
  24. Kaiser’s Men’s Health Survey. (2015). 2013 Kaiser’s Men’s Health Survey. http://files.kff.org/attachment/slides-gender-differences-in-health-care-status-and-use-spotlight-on-mens-health
  25. Khullar D., Chokshi D. A. (2018). Health, income, & poverty: Where we are & what could help. Health affairs. https://www.healthaffairs.org/do/10.1377/hpb20180817.901935/full/HPB_2017_RWJF_05_W.pdf
  26. Kim J., Braun B., Williams A. D. (2013). Understanding health insurance literacy: A literature review. Family and Consumer Sciences Research Journal, 42(1), 3–13. [Google Scholar]
  27. Levitt L. (2015). Why health insurance literacy matters. JAMA, 313(6), 555–556. [DOI] [PubMed] [Google Scholar]
  28. Loewenstein G., Friedman J. Y., McGill B., Ahmad S., Linck S., Sinkula S., Beshears J., Choi J. J., Kolstad J., Laibson D., Madrian B. C., List J. A., Volpp K. G. (2013). Consumers’ misunderstanding of health insurance. Journal of Health Economics, 32(5), 850–862. doi: 10.1016/j.jhealeco.2013.04.004 [DOI] [PubMed] [Google Scholar]
  29. Long S. K., Goin D. (2014). Large racial and ethnic differences in health insurance literacy signal need for targeted education and outreach. Health, 73(12.4), 14.16. [Google Scholar]
  30. Long S. K., Shartzer A., Politi M. (2014). Low levels of self-reported literacy and numeracy create barriers to obtaining and using health insurance coverage. Literacy, 9, 20.21. [Google Scholar]
  31. Mattina C. (2017). Past-Due Medical Debt, Partially Linked to Financial Literacy, Has Declined. https://www.ajmc.com/newsroom/past-due-medical-debt-partially-linked-to-financial-knowledge-has-declined
  32. Mui P., Hill S. E., Thorpe Jr., R. J (2018). Overweight and obesity differences across ethnically diverse subgroups of Asian American men. American Journal of Men’s Health, 12(6), 1958–1965. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. National Academies of Sciences, Engineering, and Medicine. (2017). Health insurance and insights from health literacy: Helping consumers understand: Proceedings of a workshop. National Academies Press. [PubMed] [Google Scholar]
  34. National Center for Health Statistics. (2017. a). Men’s health. https://www.cdc.gov/nchs/fastats/mens-health.htm
  35. National Center for Health Statistics. (2017. b). Women’s Health. https://ftp.cdc.gov/pub/Health_Statistics/NCHS/NHIS/SHS/2017_SHS_Table_A-11.pdf
  36. National Center for Health Statistics. (2018). Life expectancy. https://www.cdc.gov/nchs/fastats/life-expectancy.htm [Google Scholar]
  37. Norton M., Hamel L., Brodie M. (2014). Assessing Americans’ familiarity with health insurance terms and concepts. The Henry J. Kaiser Family Foundation. https://www.kff.org/health-reform/poll-finding/assessing-americans-familiarity-with-health-insurance-terms-and-concepts/
  38. Paez K. A., Mallery C. J. (2014). A little knowledge is a risky thing: Wide gap in what people think they know about health insurance and what they actually know. American Institutes for Research Issue Brief, 1–6. [Google Scholar]
  39. Quincy L., Child W. (2012). Measuring health insurance literacy: A call to action. https://advocacy.consumerreports.org/wp-content/uploads/2013/03/Health_Insurance_Literacy_Roundtable_rpt.pdf
  40. Raghupathi W., Raghupathi V. (2018). An empirical study of chronic diseases in the United States: a visual analytics approach to public health. International Journal of Environmental Research and Public Health, 15(3), 431. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Robinette J. W., Charles S. T., Gruenewald T. L. (2017). Neighborhood socioeconomic status and health: A longitudinal analysis. Journal of Community Health, 42(5), 865–871. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Rustgi S., Doty M., Collins S., Kriss J. (2008). Seeing Red: The Growing Burden of Medical Bills and Debt Faced by U.S. Families. https://www.commonwealthfund.org/publications/issue-briefs/2008/aug/seeing-red-growing-burden-medical-bills-and-debt-faced-us?redirect_source=/publications/issue-briefs/2008/aug/seeing-red–the-growing-burden-of-medical-bills-and-debt-faced-by-u-s–families [PubMed]
  43. Sohn E. K., Porch T., Hill S., Thorpe Jr., R. J (2017). Geography, race/ethnicity, and physical activity among men in the United States. American Journal of Men’s Health, 11(4), 1019–1027. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. StataCorp A. (2015). Stata Statistical Software: Release 14. College Station, TX: StataCorp LP. [Google Scholar]
  45. Sullivan G. M., Artino Jr., A. R (2013). Analyzing and interpreting data from Likert-type scales. Journal of Graduate Medical Education, 5(4), 541–542. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Thorpe Jr, R. J., Parker L. J., Cobb R. J., Dillard F., Bowie J. (2017). Association between discrimination and obesity in African-American men. Biodemography and Social Biology, 63(3), 253–261. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Tipirneni R., Politi M. C., Kullgren J. T., Kieffer E. C., Goold S. D., Scherer A. M. (2018). Association between health insurance literacy and avoidance of health care services owing to cost. JAMA Netw Open, 1(7), e184796. doi: 10.1001/jamanetworkopen.2018.4796 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Tolbert J., Young K. (2016). Paying for health coverage: The challenge of affording health insurance among marketplace enrollees. Kaiser Foundation Newsletter. [Google Scholar]
  49. Urban Institute. (2019). Health reform monitoring survey. http://hrms.urban.org/about.html
  50. Valdez L. A., Morrill K. E., Griffith D. M., Lindberg N. M., Hooker S. P., Garcia D. O. (2019). Mexican origin Hispanic men’s perspectives of physical activity–Related health behaviors. American Journal of Men’s Health, 13(1), 1557988319834112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Wiltshire J. C., Elder K., Allison J. J. (2016). Differences in problems paying medical bills between African Americans and Whites from 2007 and 2009: The underlying role of health status. Journal of Racial and Ethnic Health Disparities, 3(2), 381–388. doi: 10.1007/s40615-015-0197-5 [DOI] [PubMed] [Google Scholar]
  52. Wiltshire J. C., Elder K., Kiefe C., Allison J. J. (2016). Medical debt and related financial consequences among older African American and white adults. American Journal of Public Health, 106(6), 1086–1091. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Yabroff K. R., Zhao J., Han X., Zheng Z. (2019). Prevalence and correlates of medical financial hardship in the USA. Journal of General Internal Medicine, 34(8), 1494–1502. doi: 10.1007/s11606-019-05002-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Zou G. (2004). A modified poisson regression approach to prospective studies with binary data. American Journal of Epidemiology, 159(7), 702–706. [DOI] [PubMed] [Google Scholar]

Articles from American Journal of Men's Health are provided here courtesy of SAGE Publications

RESOURCES