Role of Cancer History and Gender in Major Health Insurance Transitions: A Longitudinal Nationally Representative Study

Gender, Women’s Health Care Concerns and Other Social Factors in Health and Health Care

ISBN: 978-1-78756-176-2, eISBN: 978-1-78756-175-5

ISSN: 0275-4959

Publication date: 18 September 2018

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Citation

Virgo, K., Lin, C., Davidoff, A., Guy, G., de Moor, J., Ekwueme, D., Kent, E., Chawla, N. and Yabroff, K. (2018), "Role of Cancer History and Gender in Major Health Insurance Transitions: A Longitudinal Nationally Representative Study", Gender, Women’s Health Care Concerns and Other Social Factors in Health and Health Care (Research in the Sociology of Health Care, Vol. 36), Emerald Publishing Limited, pp. 59-84. https://doi.org/10.1108/S0275-495920180000036003

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Emerald Publishing Limited

Copyright © 2018 Emerald Publishing Limited


Introduction

In 2012, prior to the rollout of the major Affordable Care Act (ACA) provisions, 48 million individuals were uninsured in the United States, representing 15.4% of the population (DeNavas-Walt, Proctor, & Smith, 2013). Approximately 25% of the population experienced at least one month without health insurance coverage. Those without insurance coverage face limited access to health care services (Chen, Rizzo, & Rodriguez, 2011; O’Hara & Caswell, 2013) and are less likely to be up-to-date with recommended preventive health services (Institute of Medicine, 2002; Yabroff et al., 2013). Furthermore, the uninsured are less likely to receive recommended treatment, more likely to have poor health outcomes, and are at increased risk of death (Yabroff et al., 2013). This is especially problematic for patients at risk of new or recurrent disease and those with chronic debilitating conditions. Cancer survivors are particularly at risk of losing insurance and access to care, especially during the potentially long survival period after initial treatment ends and management of late and long-term effects of treatment begins. Even if insurance is maintained after initial treatment, high cost sharing and lifetime benefit limits may leave patients with insurmountable debt (Schwartz, Claxton, Martin, & Schmidt, 2009).

Health insurance is often employment-based for the US working age population, whether through one’s own or a spouse’s place of employment, though temporary, part-time, or low-wage jobs may provide no employment-related health benefits (Gabel, Pickreign, Whitmore, & Schoen, 2001). Typically, individuals become uninsured during job transitions or periods of inability to work.

Among the general population, the literature suggests that employment, higher income, higher educational levels, and non-Hispanic white race/ethnicity are associated with gaining and maintaining insurance coverage. Younger age (19–24 years), male gender, losing or changing jobs, low income, lower educational levels, and fair or poor health status are associated with losing insurance coverage (Bennefield, 1998; Cohen, 2014a; Crimmel, 2004; Cutler & Gelber, 2009; Czajka & Mabli, 2009; Fairlie & London, 2005; Jerant, Fiscella, & Franks, 2012; Ku & Ross, 2002; Short, 1998; Short & Graefe, 2003; Short, Graefe, & Schoen, 2003; Short, Graefe, Swartz, & Uberoi, 2012; Swartz, Marcotte, & McBride, 1993a, 1993b; Zuckerman, Kenney, Dubay, Haley, & Holahan, 2001). Number of months without coverage varies widely by time interval examined and how uninsured is defined, but predictors of longer spells without health insurance are similar to predictors of losing insurance coverage. Short and Graefe (2003) found that most of the uninsured remained so for more than 12 months over a four-year period. Burstin, Swartz, O’Neil, Orav, and Brennan (1998) examined implications of being uninsured. Patients who lost or changed insurance in the past year were more likely to have no usual source of care and delay seeking care within four months after an emergency room visit than those with no change in coverage (Burstin et al., 1998).

Little research has explored major insurance transitions (becoming insured [insurance gain] and becoming uninsured [insurance loss]) and the predictors thereof among cancer survivors (Parsons et al., 2014), though the impact of insurance status (e.g., Medicaid insured, Medicare insured, privately insured, uninsured) on cancer diagnosis, treatment, and survival is well known (e.g., Grant et al., 2015; Zeng et al., 2015). It is important to understand whether cancer survivors are more likely to undergo a major health insurance transition than those with no cancer history and, if so, which factors are associated with these transitions. Similarly, information is lacking on whether gender plays an important role in major insurance transitions. This study is novel in exploring associations between cancer history and health insurance transitions, both gains and losses, by gender in a national non-elderly adult sample, and also in examining associations between major insurance transitions and access to care by cancer history and gender.

Methods

Data Source

Longitudinal data from the 2008–2013 Medical Expenditure Panel Survey (MEPS) (panels 12–17) Household Component (HC) were pooled to examine associations between cancer history and major health insurance transitions (gain and loss) over the two-year observation periods with specific sub-analyses for females. The HC is drawn annually from a nationally representative subsample of the prior year’s National Health Interview Survey (NHIS) household participants. Each panel is followed for two years through five rounds of in-person interviews. One individual typically responds for all household family members. Response rates ranged from 53.5% to 59.3% (Agency for Healthcare Research and Quality, 2014a). Available data for the current study includes socio-demographic characteristics, health status, smoking status, insurance characteristics, and access to care. More detail on survey design is available elsewhere (Agency for Healthcare Research and Quality, 2014b). For analyses of publicly available datasets, Institutional Review Board approval was not required.

Study Population

Cancer survivors and individuals with no cancer history ages 18–63 years upon MEPS entry during 2008–2013 were selected for study inclusion. Individuals older than 63 years at survey entry were not included due to impending Medicare eligibility during the two-year observation period. Cancer survivors were identified by the question: “Ever been told by a health professional that you had cancer or a malignancy of any kind?” Individuals with no cancer history were the comparison group. Of 2,408 excluded subjects, the majority either did not complete all five survey rounds (N = 1,718) or had non-melanoma skin cancer or skin cancer not otherwise classified only (N = 663). The remaining 27 had either multiple months of missing insurance data or no employment information. The final unweighted sample size was 52,915 (51% female): 2,223 cancer survivors (72% female) and 50,692 individuals with no cancer history (53% female). Excluded individuals were more likely to be initially uninsured, 55–63 years of age, male (56%), employed non-continuously during the survey years, high school educated or less, in fair or poor health, and smokers (exclusion table available upon request).

Covariates

Socio-demographic characteristics measured at MEPS entry included age (18–34, 35–44, 45–54, 55–63 years), gender, marital status, self-reported race/ethnicity (non-Hispanic white, other), education (≤high school graduate, >high school graduate), and family income as a percentage of the federal poverty level (FPL) (poor or near poor: <125%, low income: 125% to <200%, middle-to-high income: ≥200%). Race/ethnicity was included because of the anticipated significant effect on insurance transitions even after controlling for socioeconomic indicators (income, employment, education). Employment status (always employed (full- or part-time), always unemployed, non-continuously employed) was based on self-report at each survey round. Health-related covariates included health status at survey entry (excellent or very good, good, fair, or poor) and smoking status in round two (yes or no). Risk aversion, also from round two, was defined as whether health insurance was worth the cost (Cohen, 2014b). The original 5-point response scale (1 = disagree strongly to 5 = agree strongly) was recoded: disagree (strongly or somewhat), uncertain, and agree (strongly or somewhat). Survey panel controlled for secular trends over time. Cancer-specific variables included number of cancers (single or multiple) and years since cancer diagnosis. Cancer site was not separately included due to small sample size for most sites.

Outcomes

Insurance Status

For each of the 24 months observed, all individuals (cancer survivors and those with no cancer history) were classified as insured (any private or solely public) or uninsured. Once the first insurance status was identified, initially insured or uninsured, for each individual, each successive month was examined to identify the first month when an insurance transition occurred. Among the initially uninsured, the intent was to identify whether insurance was ever gained. Among the initially insured, the goal was to identify whether insurance was ever lost.

Access to Care

Two dichotomous (yes or no) access-to-care measures were included: inability or delay in obtaining needed medical care or prescription medications and having a usual source of care. Individuals with insurance gains or losses occurring after MEPS round four (when access questions were fielded) were excluded from this analysis only (3.9% of 52,915 individuals).

Statistical Analyses

Descriptive statistics (weighted) were used to characterize study participants by cancer history and gender according to their initial insurance status as well as to analyze months spent in each insurance status. We assessed the relationship between access to care and insurance gain or loss similarly.

To examine associations between cancer history, gender, and insurance transitions for the initially uninsured and insured, two separate multivariable logistic regression analyses were conducted (Hosmer & Lemeshow, 1989). Due to the likely interaction of marital status with gender and health insurance, gender and marital status were combined in the models (female married, female not married, male married, male not married). Other model inclusion candidates were age, race/ethnicity, family income, employment status, education, perceived health status, smoking status, and risk aversion. Parsimonious models were derived separately for insurance gain and loss using a model entry cut point of p < 0.20, among the initially uninsured and initially insured, respectively. All models included MEPS panel to control for secular trend. Results are presented as predicted marginal risk ratios (RRs), which directly standardize each group outcome to the covariate distribution of the population (Graubard & Korn, 1999). RRs were used because odds ratios are biased estimators when the outcome is common (Bieler, Brown, Williams, & Brogan, 2010; Lumley, Kronmal, & Ma, 2006). Weighted analyses accounted for the MEPS complex survey design (SAS, 2016; SUDAAN, 2012). All tests were two-tailed and alpha was set at p < 0.05.

Results

Baseline Characteristics

The study sample included 16,123 initially uninsured individuals, of whom 417 (3.0%) were cancer survivors, and 36,792 initially insured individuals, of whom 1,806 (5.2%) were cancer survivors. Cancer survivors were more likely to be older (45–63 years), married (46.9%), non-Hispanic white, and unemployed during both years observed compared to individuals with no cancer history irrespective of initial insurance status (Tables 1 and 2). Cancer survivors were also more likely to perceive their health as fair or poor and to be smokers.

Table 1.

Characteristics of 16,123 Initially Uninsured Individuals by Cancer History and Gender.a

Cancer History No History of Cancer Cancer History vs No Cancer History
Male Female Total Male vs Female Male Female Total Male vs Female
(N = 84) (N = 333) (N = 417) p-value (N = 8,051) (N = 7,655) (N = 15,706) p-value p-value
Baseline insurance N/A N/A N/A
   Uninsured 100 100 100 100 100 100
Insurance transition 0.830 <0.001 0.0012
   Gained private insurance 29.79 26.69 27.44 22.44 24.03 23.15
   Gained public insurance 18.92 17.67 17.98 7.78 14.68 10.88
   Gained insurance (total)b 48.71 44.36 45.42 30.21 38.71 34.03
   No change in insurance 51.29 55.64 54.58 69.79 61.29 65.97
Age 0.002 <0.001 <0.001
   18–34 13.39 24.95 22.14 50.10 44.91 47.77
   35–44 11.04 23.64 20.57 20.65 21.67 21.11
   45–54 33.32 25.08 27.08 18.74 21.15 19.82
   55–63 42.26 26.34 30.21 10.51 12.26 11.3
Marital status 0.754 <0.001 0.0183
   Married 48.62 46.33 46.89 36.92 42.09 39.24
   Not married 51.38 53.67 53.11 63.08 57.91 60.76
Race/ethnicity 0.660 0.633 <0.001
   Non-Hispanic Whites 47.14 43.42 44.33 32.46 31.99 32.25
   Others 52.86 56.58 55.67 67.54 68.01 67.75
Family income based on poverty level 0.745 <0.001 0.7132
   Poor 27.98 32.53 31.43 29.10 36.96 32.63
   Low income 23.89 20.68 21.46 22.62 23.42 22.98
   Middle and high income 48.12 46.78 47.11 48.28 39.63 44.39
Employed 0.098 <0.001 <0.001
   Always employed both years 28.77 39.48 36.88 54.04 41.18 48.27
   Not employed both years 34.49 22.96 25.77 11.07 24.06 16.91
   Employed sometime 36.73 37.56 37.36 34.89 34.76 34.83
Education 0.823 0.004 0.4163
   High school graduate or less 19.88 18.81 19.07 21.86 19.50 20.80
   Some college or more 54.80 59.41 58.29 58.97 61.91 60.29
   Missing 25.32 21.79 22.65 19.18 18.59 18.91
Perceived health status 0.435 <0.001 <0.001
   Excellent/very good 22.03 28.44 26.88 59.33 53.96 56.92
   Good 31.19 32.55 32.22 27.81 29.82 28.71
   Fair/poor 46.78 39.01 40.90 12.86 16.22 14.37
Smoking status 0.537 <0.001 <0.001
   Smoker 44.60 37.36 39.12 30.78 22.21 26.93
   Non-smoker 48.85 53.33 52.24 61.06 71.20 65.61
   Missing 6.55 9.31 8.64 8.16 6.59 7.46
Risk aversion: health insurance not worth cost 0.629 <0.001 0.1449
   Disagree strongly/somewhat 58.63 53.12 54.46 46.40 50.80 48.38
   Uncertain 13.12 18.53 17.21 19.37 18.82 19.12
   Agree strongly/somewhat 28.25 28.35 28.32 34.23 30.38 32.50
Number of cancers 0.024 N/A N/A
   Single or unknownc 94.64 85.20 87.50 N/A N/A N/A
   Multiple 5.36 14.80 12.50 N/A N/A N/A
Time since cancer diagnosisd 0.466 N/A N/A
   <2 years 18.23 13.63 14.77 N/A N/A N/A
   2–5 years 12.54 16.50 15.52 N/A N/A N/A
   6–10 years 16.43 14.57 15.03 N/A N/A N/A
   >10 years 20.26 31.61 28.81 N/A N/A N/A
   Missing 32.54 23.69 25.88 N/A N/A N/A
Panel 0.690 0.5 0.3048
   12 10.91 12.87 12.39 16.70 16.26 16.50
   13 10.61 15.42 14.25 16.62 16.36 16.50
   14 21.58 14.77 16.43 16.32 15.64 16.02
   15 16.83 18.05 17.75 15.85 16.93 16.34
   16 15.40 18.61 17.83 16.59 16.96 16.75
   17 24.68 20.28 21.35 17.92 17.85 17.89

Source: Medical Expenditure Panel Survey 2008–2013 longitudinal data (panels 12–17).

Notes: aValues represent weighted percentages. bAmong all study participants (Tables 1 and 2 combined), 10,787/52,915 (19.2% weighted) had at least one insurance transition (gain or loss) during the 24 months. Compared to all cancer survivors (403/2,223), a higher percentage of all individuals with no cancer history (10,384/50,692) had at least one insurance transition (15.9% and 19.3% weighted, respectively, p < 0.001). The division between public and private is provided in Tables 1 and 2 only as the numbers were too small to model gains and losses of public insurance separately from those of private insurance. cLess than 3% were unsure whether they had more than one cancer. dTime since cancer diagnosis was only available for panels 13–17.

Table 2.

Characteristics of 36,792 Initially Insured Individuals by Cancer History and Gender.a

Cancer History No History of Cancer Cancer History vs No Cancer History
Male Female Total Male vs Female Male Female Total Male vs Female
(N = 541) (N = 1,265) (N = 1,806) p-value (N = 15,841) (N = 19,145) (N = 34,986) p-value p-value
Baseline insurance <0.001 <0.001 <0.001
   Private insurance 85.59 79.04 81.23 89.56 84.44 86.89
   Public insurance 14.41 20.96 18.77 10.44 15.56 13.11
Insurance transition 0.021 <0.001 <0.001
   Lost private insurance 6.53 7.79 7.37 11.98 10.19 11.05
   Lost public insurance 1.89 3.68 3.08 2.54 4.33 3.47
   Lost insurance (total)b 8.42 11.47 10.45 14.52 14.52 14.52
   No change in insurance 91.58 88.53 89.55 85.48 85.48 85.48
Age <0.001 <0.001 <0.001
   18–34 5.68 10.66 8.99 33.11 35.40 34.30
   35–44 9.25 16.75 14.24 23.33 22.43 22.86
   45–54 31.15 30.88 30.97 25.52 24.09 24.78
   55–63 53.92 41.71 45.79 18.03 18.08 18.06
Marital status 0.080 0.045 0.0078
   Married 66.09 60.47 62.35 59.05 57.86 58.43
   Not married 33.91 39.53 37.65 40.95 42.14 41.57
Race/ethnicity 0.719 <0.001 <0.001
   Non-Hispanic Whites 53.86 52.86 53.20 47.56 45.50 46.49
   Others 46.14 47.14 46.80 52.44 54.50 53.51
Family income based on poverty level 0.023 <0.001 0.0044
   Poor 11.89 16.76 15.13 9.87 13.92 11.98
   Low income 9.91 10.48 10.29 9.50 11.26 10.42
   Middle and high income 78.20 72.77 74.58 80.63 74.82 77.61
Employed 0.002 <0.001 <0.001
   Always employed both years 61.72 52.04 55.28 73.15 62.01 67.35
   Not employed both years 22.02 29.85 27.24 9.88 17.79 14.00
   Employed sometime 16.26 18.11 17.49 16.98 20.20 18.66
Education 0.940 0.090 0.2419
   High school graduate or less 8.62 9.09 8.93 8.16 7.47 7.80
   Some college or more 74.47 73.89 74.08 74.98 75.59 75.30
   Missing 16.90 17.03 16.98 16.85 16.94 16.90
Perceived health status 0.653 0.001 <0.001
   Excellent/very good 43.79 42.18 42.72 66.51 64.78 65.61
   Good 30.34 29.37 29.69 23.14 23.43 23.29
   Fair/poor 25.87 28.45 27.59 10.34 11.79 11.09
Smoking status 0.054 <0.001 0.0041
   Smoker 21.76 20.85 21.16 18.83 15.32 17.00
   Non-smoker 69.59 73.70 72.33 73.72 78.98 76.46
   Missing 8.65 5.45 6.52 7.45 5.70 6.54
Risk aversion: health insurance not worth cost 0.918 <0.001 <0.001
   Disagree strongly/somewhat 70.66 71.58 71.27 64.79 68.76 66.86
   Uncertain 9.25 8.59 8.81 11.15 11.11 11.13
   Agree strongly/somewhat 20.09 19.83 19.91 24.06 20.13 22.01
Number of cancers 0.002 N/A N/A
   Single or unknownc 94.20 89.42 91.02 N/A N/A N/A
   Multiple 5.80 10.58 8.98 N/A N/A N/A
Time since cancer diagnosisd <0.001 N/A N/A
   <2 years 26.21 17.86 20.58 N/A N/A N/A
   2–5 years 23.24 17.29 19.23 N/A N/A N/A
   6–10 years 10.34 14.88 13.40 N/A N/A N/A
   >10 years 18.21 28.20 24.95 N/A N/A N/A
   Missing 22.01 21.76 21.84 N/A N/A N/A
Panel 0.522 0.953 0.8769
   12 18.68 15.36 16.47 16.15 16.33 16.24
   13 17.28 17.93 17.72 16.66 16.38 16.51
   14 14.37 17.29 16.32 16.91 16.79 16.85
   15 14.84 16.57 15.99 16.99 16.90 16.95
   16 17.92 15.95 16.61 16.80 16.97 16.89
   17 16.90 16.89 16.90 16.50 16.63 16.56

Source: Medical Expenditure Panel Survey 2008–2013 longitudinal data (panels 12–17).

aValues represent weighted percentages. bAmong all study participants (Tables 1 and 2 combined), 10,787/52,915 (19.2% weighted) had at least one insurance transition (gain or loss) during the 24 months. Compared to all cancer survivors (403/2,223), a higher percentage of all individuals with no cancer history (10,384/50,692) had at least one insurance transition (15.9% and 19.3% weighted, respectively, p < 0.001). The division between public and private is provided in Tables 1 and 2 only as the numbers were too small to model gains and losses of public insurance separately from those of private insurance. cLess than 3% were unsure whether they had more than one cancer. dTime since cancer diagnosis was only available for panels 13–17.

Among initially uninsured cancer survivors (Table 1), only two gender-specific differences were identified. Females were significantly younger (<45 years of age) than males and were more likely to report having multiple cancers. In contrast, initially uninsured females with no cancer history were older (45 +  years of age) than their male counterparts, more likely to be married, poor based on family income, unemployed in both study years, have completed at least some college, have rated their health status as good or fair/poor, be non-smokers, and disagree strongly or somewhat with the statement that health insurance is not worth the cost.

In addition to the previously described differences between cancer survivors and those with no cancer history that were common to both initial insurance statuses, initially insured cancer survivors (Table 2) were also more likely than those with no cancer history to be publicly insured at baseline, poor based on family income, and to strongly or somewhat disagree with the statement that “health insurance is not worth the cost.” Several gender-specific differences were also identified. Among initially insured cancer survivors, females were more likely than males to have public insurance at baseline, were younger (<45 years of age), with a family income classified as poor or low, were unemployed or only occasionally employed, and were more likely to report having multiple cancers and a longer elapsed time since cancer diagnosis (six or more years). In contrast, initially insured females with no cancer history were significantly more likely than their male counterparts to have public insurance at baseline, were younger (<35 years of age), unmarried, of other race/ethnicity, with a family income classified as poor or low, were unemployed or only occasionally employed, to have rated their health status as fair/poor, were non-smokers, and disagreed strongly or somewhat that health insurance is not worth the cost.

Though, for obvious reasons, no comparisons were possible between those with and without a cancer history for the two cancer-specific variables, comparisons were possible between initially uninsured and initially insured cancer survivors. Among all 2,223 cancer survivors, the initially uninsured reported greater elapsed time (six or more years) since cancer diagnosis compared to the initially insured (43.84% and 38.35%, respectively, p = 0.0271).

Health Insurance Transition Frequency

Initially uninsured cancer survivors were more likely to gain insurance (primarily private insurance) than individuals with no cancer history (45.4% and 34.0%, respectively, p < 0.01; Table 1). Initially uninsured females with no cancer history were also significantly more likely to gain insurance (with the largest difference for public insurance gain) than initially uninsured males with no cancer history (p < 0.001).

Initially insured cancer survivors were significantly less likely to lose insurance when compared with individuals with no cancer history (10.5% and 14.5%, respectively, p < 0.001; Table 2). However, the subset of initially insured female cancer survivors was more likely than initially insured male cancer survivors to have lost insurance in general. Similarly, among those with no cancer history, initially insured females were significantly more likely than their male counterparts to have lost public insurance (p < 0.001).

Health Insurance Status Duration in Months

For the 10,787 (19.2%) who eventually had an insurance transition, the average durations of the initial and second insurance statuses (insured or uninsured) within the 24-month data period were 11.4 and 9.4 months, respectively (table available upon request). The initially insured spent 1.1 months longer in their initial insurance status than the initially uninsured did (11.9 and 10.8 months, p < 0.001). Thus, time to an insurance transition was slightly longer for the initially insured (becoming uninsured) than for the initially uninsured (becoming insured). The reverse was, therefore, true for subsequent insurance status (7.9 and 11.4 months, respectively, p < 0.001). Insurance status duration did not differ by cancer history. Thus, sub-analyses were not conducted by gender.

Health Insurance Gain or Loss

In unadjusted analyses, cancer survivors were more likely to gain insurance (RR: 1.33; 95% CI: 1.16–1.54; Table 3) and less likely to lose insurance (RR: 0.72: 95% CI: 0.61–0.85; Table 4) than those with no cancer history. In adjusted analyses, cancer history only remained significant for gaining insurance (RR: 1.25; 95% CI: 1.08–1.44; Table 3). The risk of losing insurance was similar between cancer survivors and individuals with no cancer history (RR: 0.88; 95% CI: 0.75–1.02; Table 4). Other factors positively associated with insurance gain among the initially uninsured in the adjusted analyses were younger age (18–34 years), female gender (independent of marital status), being non-Hispanic white, employed at least sometime during both years observed, and disagreeing strongly or somewhat that “health insurance is not worth the cost.” Factors negatively associated with insurance gain among the initially uninsured were being an unmarried male, poor or low income status, and having no education beyond high school. Sensitivity analyses using Cox proportional hazards regressions were conducted modeling time to insurance change rather than change (yes, no). The results were essentially unchanged.

Table 3.

Weighted Unadjusted and Adjusted Relative Risk (RR) of Health Insurance Gain among Initially Uninsured Non-elderly Adults MEPS Panels 12–17, N = 16,123.

Unadjusted RR Adjusted RRa
(95% CI) p-value (95% CI) p-value
Cancer history
   Yes 1.33 (1.16–1.54) <0.001 1.25 (1.08–1.44) 0.0045
   No 1.00 (referent) 1.00 (referent)
Age
   18–34 1.14 (1.04–1.25) 0.0052 1.24 (1.12–1.37) <0.001
   35–44 0.98 (0.87–1.10) 0.7028 1.08 (0.96–1.21) 0.1807
   45–54 0.98 (0.88–1.09) 0.7218 1.04 (0.93–1.16) 0.5011
   55–63 1.00 (referent) 1.00 (referent)
Gender/marital status
   Male, not married 0.91 (0.82–1.01) 0.0883 0.83 (0.75–0.92) <0.001
   Male, married 1.00 (referent) 1.00 (referent)
   Female, not married 1.26 (1.15–1.39) <0.001 1.16 (1.06–1.28) 0.0017
   Female, married 1.14 (1.06–1.21) <0.001 1.09 (1.02–1.16) 0.0101
Race/ethnicity
   Non-Hispanic Whites 1.28 (1.17–1.40) <0.001 1.30 (1.18–1.43) <0.001
   Other 1.00 (referent) 1.00 (referent)
Family income based on poverty level
   Poor 0.79 (0.72–0.86) <0.001 0.81 (0.76–0.88) <0.001
   Low income 0.82 (0.75–0.89) <0.001 0.85 (0.79–0.92) <0.001
   Middle and high income 1.00 (referent) 1.00 (referent)
Employedb
   Always employed both years 1.07 (0.97–1.17) 0.1649 0.99 (0.90–1.08) 0.8276
   Employed sometime 1.29 (1.19–1.41) <0.001 1.22 (1.12–1.32) <0.001
   Not employed both years 1.00 (referent) 1.00 (referent)
Education
   High school graduate or less 0.61 (0.56–0.67) <0.001 0.71 (0.65–0.77) <0.001
   Some college or more 1.00 (referent) 1.00 (referent)
Non-smoker
   Yes 1.07 (0.99–1.16) 0.0669 1.06 (0.99–1.14) 0.1016
   No 1.00 (referent) 1.00 (referent)
Risk aversion: health insurance not worth cost
   Disagree strongly/somewhat 1.27 (1.19–1.36) <0.001 1.26 (1.18–1.34) <0.001
   Uncertain 0.94 (0.85–1.04) 0.2212 0.99 (0.90–1.08) 0.7666
   Agree strongly/somewhat 1.00 (referent) 1.00 (referent)
Panelc
   17 0.88 (0.78–0.99) 0.0277 1.29 (1.15–1.45) <0.001
   16 0.86 (0.76–0.96) 0.011 1.01 (0.89–1.13) 0.9154
   15 0.92 (0.83–1.03) 0.15 0.94 (0.84–1.05) 0.264
   14 0.76 (0.67–0.87) <0.001 0.78 (0.69–0.88) <0.001
   13 0.84 (0.75–0.94) 0.0027 0.86 (0.77–0.97) 0.0116
   12 1.00 (referent) 1.00 (referent)

Source: Medical Expenditure Panel Survey 2008–2013 longitudinal data (panels 12–17).

aMultivariate logistic regression models examined the relationship between cancer history and health insurance gain adjusting for all other factors displayed above. Results are reported as predicted marginal risk ratios. Gains of public insurance are not modeled separately from gains in private insurance due to the small N among cancer survivors. bSensitivity analyses excluding employment from the models did not significantly change the results. Additional sensitivity analyses using Cox proportional hazards regressions were conducted modeling time to insurance gain rather than gain (yes or no). The results were basically the same. cPanel was included in the model solely to control for secular trend. The results are only displayed for completeness of reporting. No attempt is made to derive any specific meaning from the related estimates.

Table 4.

Weighted Unadjusted and Adjusted Relative Risk (RR) of Health Insurance Loss among Initially Insured Non-elderly Adults MEPS Panels 12–17, N = 36,792.

Unadjusted RR Adjusted RRa
(95% CI) p-value (95% CI) p-value
Cancer history
   Yes 0.72 (0.61–0.85) <0.001 0.88 (0.75–1.02) 0.0942
   No 1.00 (referent) 1.00 (referent)
Age
   18–34 2.69 (2.40–3.02) <0.001 1.78 (1.59–1.98) <0.001
   35–44 1.47 (1.30–1.67) <0.001 1.35 (1.21–1.51) <0.001
   45–54 1.18 (1.04–1.33) 0.0082 1.16 (1.05–1.30) 0.0054
   55–63 1.00 (referent) 1.00 (referent)
Gender/marital status
   Male, not married 2.08 (1.91–2.27) <0.001 1.34 (1.23–1.46) <0.001
   Male, married 1.00 (referent) 1.00 (referent)
   Female, not married 2.01 (1.87–2.17) <0.001 1.27 (1.18–1.37) <0.001
   Female, married 1.03 (0.96–1.10) 0.4344 0.92 (0.86–0.99) 0.0239
Race/ethnicity
   Non-Hispanic Whites 0.88 (0.82–0.95) <0.001 0.92 (0.85–1.00) 0.0383
   Other 1.00 (referent) 1.00 (referent)
Family income based on poverty level
   Poor 2.32 (2.14–2.52) <0.001 1.53 (1.40–1.67) <0.001
   Low income 2.28 (2.07–2.51) <0.001 1.77 (1.62–1.95) <0.001
   Middle and high income 1.00 (referent) 1.00 (referent)
Employedb
   Always employed both years 0.77 (0.70–0.86) <0.001 0.97 (0.86–1.08) 0.5677
   Employed sometime 3.07 (2.79–3.39) <0.001 2.99 (2.70–3.32) <0.001
   Not employed both years 1.00 (referent) 1.00 (referent)
Perceived health status
   Fair/poor 1.14 (1.03–1.26) 0.01 1.09 (0.99–1.20) 0.0945
   Good 1.12 (1.05–1.20) <0.001 1.09 (1.02–1.16) 0.013
   Excellent/very good 1.00 (referent) 1.00 (referent)
Non-smoker
   Yes 0.65 (0.60–0.70) <0.001 0.81 (0.75–0.87) <0.001
   No 1.00 (referent) 1.00 (referent)
Risk aversion: health insurance not worth cost
   Disagree strongly/somewhat 0.66 (0.62–0.71) <0.001 0.70 (0.65–0.75) <0.001
   Uncertain 0.98 (0.89–1.09) 0.7147 0.83 (0.75–0.91) <0.001
   Agree strongly/somewhat 1.00 (referent) 1.00 (referent)
Panelc
   17 0.92 (0.81–1.05) 0.2198 0.84 (0.74–0.95) 0.0079
   16 0.92 (0.81–1.04) 0.1902 0.82 (0.72–0.93) 0.0023
   15 0.88 (0.77–0.99) 0.0395 0.85 (0.76–0.95) 0.0061
   14 0.96 (0.85–1.10) 0.5875 0.92 (0.82–1.03) 0.1354
   13 1.07 (0.96–1.19) 0.2331 1.03 (0.94–1.13) 0.5573
   12 1.00 (referent) 1.00 (referent)

Source: Medical Expenditure Panel Survey 2008–2013 longitudinal data (panels 12–17).

aMultivariate logistic regression models examined the relationship between cancer history and health insurance loss adjusting for all other factors displayed above. Results are reported as predicted marginal risk ratios. Losses of public insurance are not modeled separately from losses of private insurance due to the small N among cancer survivors. bSensitivity analyses excluding employment from the models did not significantly change the results. Additional sensitivity analyses using Cox proportional hazards regressions were conducted modeling time to insurance loss rather than loss (yes or no). The results were basically the same. cPanel was included in the model solely to control for secular trend. The results are only displayed for completeness of reporting. No attempt is made to derive any specific meaning from the related estimates.

Access to Care

The initially uninsured who gained insurance coverage were less likely than those remaining uninsured to be without a usual source of care (36.6% and 59.3%, respectively, p < 0.001; Fig. 1). The high rate of no usual source of care among those gaining insurance overall was striking and slightly higher among those with no cancer history who gained insurance (37.3%), but was not seen among initially uninsured cancer survivors who gained insurance. Among cancer survivors who gained insurance coverage, 21.3% reported having no usual source of care compared to 44.1% among cancer survivors who remained uninsured (p < 0.001). Cancer survivors who gained insurance were also less likely than those remaining uninsured to report inability or delay in obtaining medical care or prescription medications (18.1% and 29.6%, respectively; p < 0.05).

Fig. 1. 
Insurance Gains and Access to Care by Cancer History.

Fig. 1.

Insurance Gains and Access to Care by Cancer History.

The initially insured who lost insurance coverage were more likely than those remaining insured to report inability or delay in (12.7% and 6.5%, respectively, p < 0.001) accessing needed medical care or prescription medications and to be without a usual source of care (39.0% and 17.9%, respectively, p < 0.001) (Fig. 2). For no usual source of care, consistently higher rates were identified for cancer survivors (24.2% and 9.3%, respectively, p < 0.01) and those with no cancer history (39.6% and 18.4%, respectively, p < 0.001) who lost insurance compared to remaining insured, though the rates of no usual source of care for cancer survivors were one-third or one-half lower than the rates among those with no cancer history. For inability or delay obtaining medical care or prescription medications, those who lost insurance consistently reported higher rates than those who remained insured among both cancer survivors (23.7% and 12.5%, respectively, p < 0.05) and those with no cancer history (12.3% and 6.2%, respectively, p < 0.001). However, among cancer survivors, the rates were approximately double those reported among those with no cancer history.

Fig. 2. 
Insurance Losses and Access to Care by Cancer History.

Fig. 2.

Insurance Losses and Access to Care by Cancer History.

In gender-specific analyses (not factoring in cancer history due to small sample size), initially uninsured females who gained insurance reported significantly higher rates of inability or delay accessing needed medical care or prescription medications compared to initially uninsured males (13.2% and 9.1%, respectively; p < 0.001; Fig. 3). Similar results were seen for those who remained uninsured, but the rates were higher (16.3% and 10.1%, respectively; p < 0.001). When inability and delay in accessing needed medical care were examined separately, the rates were slightly less than when combined, but the general patterns were similar. Irrespective of whether initially uninsured females gained insured or not, they remained significantly more likely (p < 0.01) to report inability or delay problems than males.

Fig. 3. 
Insurance Gains and Access to Care by Gender.

Fig. 3.

Insurance Gains and Access to Care by Gender.

For no usual source of care, initially uninsured females were significantly less likely (p < 0.001) to have no usual source of care when compared to initially uninsured males, irrespective of whether they gained insurance or not. Rates of reporting no usual source of care were high, even among those who gained insurance (30.6% of females and 43.3% of males). Among those remaining uninsured, 49.9% of females and 66.3% of males reported no usual source of care.

Results for the access to care comparisons for the initially insured by gender (Fig. 4) were consistent with those for the initially uninsured. Initially insured females reported significantly (p < 0.001) higher rates of inability or delay in accessing needed medical care or prescription medications when compared to initially insured males, irrespective of whether they remained insured (7.5% and 5.3%, respectively) or lost insurance (14.9% and 10.3%, respectively). When inability and delay in accessing needed medical care were examined separately, the rates were slightly less than when combined, but the general patterns were similar. Irrespective of whether initially insured females lost insurance or not, they remained significantly more likely (p < 0.01) to report inability or delay problems than males.

Fig. 4. 
Insurance Losses and Access to Care by Gender.

Fig. 4.

Insurance Losses and Access to Care by Gender.

For no usual source of care, initially insured females were significantly less likely (p < 0.001) to have no usual source of care when compared to initially insured males, irrespective of whether they lost insurance or not. Rates of reporting no usual source of care, though lower than among the initially uninsured, were still somewhat high, even among those who remained insured (14.3% of females and 22.0% of males). Among those who lost insurance, 34.9% of females and 43.6% of males reported no usual source of care.

Discussion

Based on longitudinal nationally representative data, our findings shed new light regarding insurance transition patterns, particularly regarding gender differences among cancer survivors compared to individuals with no cancer history, and factors associated with insurance gain and loss. Within the 24-month data period, time to an insurance transition was significantly longer for the initially insured (becoming uninsured) than for the initially uninsured (becoming insured) (11.9 and 10.8 months, p < 0.001). Uninsured cancer survivors were more likely to gain insurance coverage compared to individuals with no cancer history. Females in particular, both married and unmarried, were significantly more likely to gain insurance. The risk of losing insurance was similar between cancer survivors and their counterparts with no cancer history. Potential implications of these results for promoting access to care among cancer survivors, particularly females, are encouraging.

Our finding that 10.5% of initially insured cancer survivors lost insurance and 54.6% of initially uninsured cancer survivors remained uninsured suggests further efforts are needed to prevent insurance loss and promote insurance gain among those with chronic diseases such as cancer, particularly the disadvantaged in states that opted out of the Medicaid expansion (Sommers, Graves, Swartz, & Rosenbaum, 2014). Major ACA provisions implemented in 2014 (after the study period and not reflected in our data), such as Medicaid program expansion (in most states) to adults with family incomes up to 138% of the FPL, elimination of health insurance denials for pre-existing health conditions, and affordable insurance plan availability through Health Insurance Marketplaces (Kaiser Family Foundation, 2010), have reduced the uninsured rate. Urban Institute data for non-elderly adults showed a 4 percentage point drop in the uninsured rate from 17.9% in September 2013 to 13.9% in June 2014 (Long et al., 2014). Gallup poll estimates for first quarter 2016 showed an additional drop to 11.0% (Markin, 2016). Rand estimates for September 2013 through February 2015 suggest that 22.8 million people gained insurance coverage and 5.9 million lost coverage, for a net gain of 16.9 million individuals with coverage after the major ACA provisions had taken effect (Carmen, Eibner, & Paddock, 2015).

However, as our analyses of access-to-care effects of insurance transitions show, even among the uninsured who gain insurance, the percent with no usual source of care remains high for both females and males, at levels similar to insured individuals who lost insurance. This suggests that some newly insured individuals may face high deductibles, co-pays, and limited benefits, restricting access to care (Collins, Rasmussen, Beutel, & Doty, 2015). Other barriers may remain, such as transportation costs and difficulty navigating the health care system. Limited understanding of how to choose a health insurance plan may be a factor (Loewenstein et al., 2013). In a recent article, Schleicher, Mullangi, and Feeley (2016) suggest that narrow hospital networks within the ACA exchanges continue to restrict access to high quality cancer care.

Our finding, that a lower percentage of those gaining insurance versus remaining uninsured (overall: 7.6% vs 10.5%; among females: 9.1% vs 13.0%) were unable to obtain needed medical care or prescription medications, was encouraging. Somewhat surprising was the relatively small difference (overall 2.9%; females 3.9%) between groups. We suspect inability to obtain needed services among those remaining uninsured is underreported. The definition of “needed” medical care and prescription medications is likely more precise among those with a usual source of care, but this aspect is difficult to definitely assess from our data. Our preliminary analyses suggest that insurance longevity may also be important.

Though insurance transition frequency was low, this was expected due to the somewhat short (24 month) observation period. Health insurance transitions for non-elderly adults are often employment-related and permitted infrequently (i.e., due to household size, primary residence, or income changes). Even for Medicaid or Tricare enrollees, similar enrollment period restrictions generally apply. With respect to insurance status duration within the 24-month data period, the finding that cancer survivors were initially insured for a longer period than similar cancer survivors were initially uninsured is somewhat reassuring, though the duration of remaining uninsured is still excessively long.

Though longitudinal, nationally representative and utilizing rich patient-level data, our study has limitations. First, the two-year observation window for insurance transitions is relatively short as limited by the dataset. Datasets, such as the Survey of Income and Program Participation, provide longer follow-up, but inadequate cancer-related data. Second, individuals diagnosed with cancers of short survival duration are likely underrepresented in our study, due to the household-based MEPS structure (and NHIS from which it is drawn) and our exclusion of individuals not participating in all survey rounds. Third, the small percentage (4.7%) of excluded individuals differed from the study cohort in some aspects. This was not unexpected, as participation in all five rounds may be more difficult for young adults in transition and disadvantaged populations. Fourth, our cancer survivor sample was heterogeneous, with varying times since cancer diagnosis relative to the survey observation years, although those who gained and those who lost insurance had similar times since diagnosis. Fifth, panel may not be the perfect proxy for secular trend over time due to the overlapping nature of the panels. Finally, small sample size prohibited multivariable modeling of the relationship between insurance transitions and access to care by cancer history. Studies with larger samples are needed.

Further study is also needed of the impact of chronic conditions, such as cancer, on insurance gain and loss among younger populations with variable employment status who are at higher risk for losing health insurance. Cancer survivors need consistent health insurance coverage, yet coverage is inconsistent. Providing incentives for those with inconsistent access, such as younger populations, to enroll in health insurance could decrease coverage volatility and improve continuity of care. Analyses of future MEPS panels will permit examination of the major 2014 ACA provision effects on insurance transitions for comparison with the current study’s baseline results. Future MEPS panels will also permit examination of the effects on insurance transitions of either a modified ACA or whatever replacement health care legislation is eventually enacted (Kaiser Family Foundation, 2017).

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Disclaimers

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of Emory University, University of Michigan, Yale University, the American Cancer Society, Centers for Disease Control and Prevention, National Cancer Institute, or the Department of Veterans Affairs.

Conflict of Interest

The authors declare that they have no conflicts of interest.

Presentations

A preliminary version of this chapter “Impact of Health Insurance Transitions on Cancer Survivors and Those with No Cancer History,” with less years of data was presented at the 48th Annual Meeting of the American Society of Clinical Oncology, May 29–June 2, 2015, Chicago, IL, http://meetinglibrary.asco.org/content/150426-156 (Journal of Clinical Oncology 33s, 2015; abstract 6539).

Prelims
Part I Introduction to Volume
Gender, Women, and Other Social Factors in Health and Health Care
Part II Gender- and Cancer-Related Issues
Growth from Trauma: Gender Differences in the Experience of Cancer and Long-term Survivorship
Younger Women with Breast Cancer and Treatment Decision-Making: Rethinking Patient Involvement and Empowerment
Role of Cancer History and Gender in Major Health Insurance Transitions: A Longitudinal Nationally Representative Study
Part III Pregnancy and Childbirth
Dignity in Childbirth: US Women’s Perceptions of Respect and Autonomy in Hospital Births
Relationship Matters: An Examination of Educational and Racial Disparities in Unintended Pregnancy
Part IV Use of Health Care and Gender
Understanding the Impact of Gender in the Decision-Making Process to Undergo Certain Surgeries Compared to Uncertain Surgeries
Trust in Health Care: Understanding the Role of Gender and Racial Differences between Patients and Providers
Weight Loss Surgery Patients’ Gender-Differentiated Experiences of Vanity Stigma
Are There Gender Differences in the Capability to Use Facilities of Health Care? A Multilevel Analysis of 22 Countries
Part V Gender Issues Outside of the US and Europe
Gender Differences in Health Care Utilization among Older Adults in Barbados
Burundian Female Survivors of War (SOW): Views of Health Before, During, and Post Conflict
Part VI Other Gender Topics
Health and Gender: Quantifying the Unquantifiable
Handled without Care: Women’s Health Experiences in Jail
Index