Reports of Insurance-Based Discrimination in Health Care and Its Association With Access to Care (2024)

Objectives. We examined reports of insurance-based discrimination and its association with insurance type and access to care in the early years of the Patient Protection and Affordable Care Act.

Methods. We used data from the 2013 Minnesota Health Access Survey to identify 4123 Minnesota adults aged 18 to 64 years who reported about their experiences of insurance-based discrimination. We modeled the association between discrimination and insurance type and predicted odds of having reduced access to care among those reporting discrimination, controlling for sociodemographic factors. Data were weighted to represent the state’s population.

Results. Reports of insurance-based discrimination were higher among uninsured (25%) and publicly insured (21%) adults than among privately insured adults (3%), which held in the regression analysis. Those reporting discrimination had higher odds of lacking a usual source of care, lacking confidence in getting care, forgoing care because of cost, and experiencing provider-level barriers than those who did not.

Conclusions. Further research and policy interventions are needed to address insurance-based discrimination in health care settings.

Study Population

Our study population was first restricted to 7201 nonelderly targets aged 18 to 64 years, excluding 2734 children younger than 18 years whose interviews were completed by adults and 1843 adults older than 64 years who were mostly covered by Medicare and likely to have different experiences with health care and access to health insurance than the nonelderly adults. We further limited the sample to 4222 nonelderly adult respondents who answered the survey themselves to ensure measurement of personal experiences (we excluded 2979 targets providing proxy reports for other household members). Finally, we excluded 99 responses of “don’t know” or “refused” to the insurance-based discrimination question, resulting in a total of 4123 respondents in the analytical sample. We compared the restricted study population with the total population on a range of demographic characteristics and found that the restricted population tends to be older and had higher income and education levels than the total population.

Measures

Statistical Analysis

We first performed the Pearson χ² test to examine variation in reports of insurance-based discrimination among uninsured, publicly insured, and privately insured adults and to compare the percentage distribution of reduced access to care for those who reported insurance-based discrimination and those who did not. We then ran 2 multivariate logistic regression models. The first model explored the adjusted associations between insurance-based discrimination and insurance type, in which insurance-based discrimination acted as the outcome of interest. The second model investigated whether insurance-based discrimination was associated with 4 measures of access to care (i.e., lack of usual source of care, lack of confidence in getting needed care, any care forgone because of cost, and any provider-level barriers), in which insurance-based discrimination acted as a main predictor in each access model. We also included the measure of insurance type in the access models to see whether insurance type was a mediator of these relationships.

We performed sensitivity analyses using Poisson regression with count indicators of care forgone because of cost and provider-level barriers; the results were consistent across methods (data not shown; detailed results are available on request). We performed all multivariate analyses controlling for respondents’ demographic characteristics and health status. Characteristics that were significantly related to insurance-based discrimination in bivariate analyses were included in the regression models.

Data were weighted to adjust for nonresponse bias and ensure that estimates were representative of the state’s noninstitutionalized population, based on a set of population controls from the Census Bureau.30 We report percentages and standard errors from the χ² test and odds ratios (ORs) and 95% confidence intervals (CIs) from the regression models. The level of significance was set at P values of .05, .01, and .001 using the 2-tailed test. We performed all analyses with Stata version 13.0 (StataCorp LP, College Station, TX), accounting for the complex survey design.

TABLE 1—

Reports of Insurance-Based Discrimination by Insurance Type and Selected Characteristics: 2013 Minnesota Health Access Survey

TABLE 2—

Comparison of Access to Care Between Insurance-Based Discrimination and No Insurance-Based Discrimination: 2013 Minnesota Health Access Survey

Reports of Insurance-Based Discrimination and Insurance Type

Regression analysis demonstrated a strong relationship between reports of insurance-based discrimination and insurance type, controlling for demographics and health status differences between adults who did and did not report discrimination. As shown in Table 3, the odds of reporting insurance-based discrimination were 5.75 times higher (95% CI = 3.19, 10.37) among uninsured adults and 4.40 times higher (95% CI = 2.58, 7.52) among publicly insured adults than among privately insured adults.

Access to Care and Insurance-Based Discrimination

Insurance-based discrimination was associated with reduced access to care in the regression models, adjusting for adults’ health status and demographics related to insurance-based discrimination (Table 4). Compared with adults who did not report insurance-based discrimination, those reporting discrimination had 1.79 times higher odds (95% CI = 1.09, 2.95) of lacking a usual source of care, 2.79 times higher odds (95% CI = 1.44, 5.39) of lacking confidence in getting needed care, 4.68 times higher odds (95% CI = 3.02, 7.24) of forgoing needed care because of cost, and 2.63 times higher odds (95% CI = 1.62, 4.25) of experiencing provider-level barriers.

Notably, regression models of the 4 access measures also indicated that uninsured adults had significantly higher odds of lacking a usual source of care (OR = 4.40; 95% CI = 2.89, 6.71), lacking confidence in getting needed care (OR = 7.13; 95% CI = 3.67, 13.86), and forgoing needed care because of cost (OR = 3.42; 95% CI = 2.30, 5.08) than did privately insured adults. Adults with public insurance had higher odds of provider-level barriers (OR = 1.69; 95% CI = 1.12, 2.57). However, insurance-based discrimination maintained strong associations with reduced access to care.

Policy Implications

Since the passage of the ACA, Minnesota has witnessed a considerable reduction in the number of uninsured people,27 and much of the decline in uninsurance is attributed to enrollment in the state’s 2 public programs, with some increase in private insurance.44 People gaining insurance are also expected to experience improved access to health care, yet our study indicates that experiencing discrimination by health care providers because of insurance type is associated with lacking a usual source of care, lacking confidence in getting needed care, forgoing needed care because of cost, and experiencing provider-level barriers when seeking care. Previous research has found insurance-based discrimination to be associated with delays in needed care and receipt of suboptimal care.16,25

These findings highlight the need for both academic and policy attention to addressing insurance-based discrimination in health care settings. One intervention would be to foster positive relationships between providers and patients45 at both the organizational and the state level. Health care sectors could establish, monitor, and enforce policies that prohibit discriminatory practices and provide ongoing cultural competency training for health care providers (at all levels within the organization) that targets their perceptions and behavior toward patients with public insurance and without insurance. Education to improve physicians’ ability to understand, communicate with, and effectively interact with Medicaid enrollees could increase their propensity to maintain consistent, respectful, and fair relationships with Medicaid patients.43 State legislators might consider enforcing mandatory provisions and adopting voluntary provisions of the National Standards for Culturally and Linguistically Appropriate Services. This blueprint, designed for individuals and organizations to promote health care equity and quality, has been recommended by the US Department of Health and Human Services’ Office of Minority Health.46 Six states have adopted these standards,47,48 and positive effects on improving patients’ overall health care experiences and quality of care have been demonstrated.49

Reducing insurance-based discrimination in health care may also require long-term support and investment from government agencies. In 2013 and 2014, all states’ Medicaid programs were required to raise primary care reimbursem*nts on par with Medicare rates to boost physicians’ participation in Medicaid,50–52 and the fee increase is federally funded. Still, it is unclear whether this temporary incentive to promote health care services effectively reduces discriminatory attitudes and behaviors toward Medicaid patients. We recommend including measures of insurance-based discrimination in the federal surveillance system (e.g., through the Consumer Assessment of Healthcare Providers and Systems program) and monitoring the impact of insurance-based discrimination on timely use and patients’ experiences of health care at the national, state, and local levels. Adopting this step may offer the opportunity to adjust policies that ensure all forms of health insurance translate into actual access to care while at the same time facilitating equity in the health care setting.

Limitations

Our study provides recent evidence of insurance-based discrimination and its association with access to care for adult residents of Minnesota. Several limitations remain, however. First, the generalizability of our results to the nation and to other states merits discussion. To extrapolate findings from this study, it would be necessary to consider the specific policy context in each state in terms of Medicaid eligibility, payment and reimbursem*nt rates, or attitudes toward Medicaid expansion. Our findings may not be fully generalizable to states with low Medicaid acceptance or reimbursem*nt rates, because reports of insurance-based discrimination in these states may possibly be much higher. Also, in states that do not expand Medicaid, public programs may be viewed as less acceptable within their health care systems, potentially exacerbating rates of discrimination. Yet, our results for Minnesota are consistent with an earlier study of insurance-based discrimination conducted in Oregon.16 Therefore, our findings may best represent those states that have similar Medicaid policies.

Second, the cross-sectional nature of the study design limits our ability to determine the causal pathway between insurance-based discrimination and access to care. However, it is plausible that insurance-based discrimination may have long-term consequences for patients’ health through its impact on access to care.

Third, similar to most survey research on health care discrimination,53 our analysis relied on a single measure to capture respondents’ experiences of insurance-based discrimination and did not specify what kind of unfair treatment respondents have encountered. This could result in an underestimation of the true prevalence of discrimination.54 Furthermore, the question does not reference a time frame (e.g., experiences at any point in time or within the past 6 or 12 months). If interpreted as “any experiences of unfair treatment,” this measure could lead to an overestimation of the prevalence of discrimination.54 Finally, the question does not specify a type of provider. However, the question was asked with direct reference to respondents’ insurance type or status and clearly distinguished insurance-based discrimination from discrimination based on other factors, ensuring the specificity and accuracy of discrimination measurement in this domain. Future research that explores these issues will improve understanding and measurement of insurance-based discrimination in health care.

Fourth, other factors, such as being a member of an underrepresented racial or ethnic community, may influence respondents to report insurance-based discrimination and their access to care. The 2013 survey included a measure of racial discrimination, using the same unfair-treatment language as the insurance-based discrimination question. Although we found a significant relationship between racial discrimination and insurance-based discrimination (data not shown), including the measure of racial discrimination in the access models did not diminish the association between access to care and insurance-based discrimination. Finally, future research might also consider linking to other data sets to control for supply-side measures such as medically underserved areas.

Conclusions

Health insurance coverage, although significant, may not be sufficient to achieve accessible and equitable health care. Insurance-based discrimination contributes to disparities in health care and may reduce people’s ability to access health care when needed. Given its negative effect on health care utilization and potential threat to longer term health outcomes, state and local governments should adopt policies targeting the reduction of insurance-based discrimination in health care. However, addressing insurance-based discrimination, especially reducing the stigma associated with public programs and poverty more generally, requires long-term commitment and joint efforts at both the government and the societal level. Because more Americans have gained coverage, the success of health care reform should not be judged simply by how many people are covered by insurance, but by how well coverage ensures access to needed health care. One important step forward in that direction includes measuring and monitoring the presence and impact of insurance-based discrimination in health care settings.

Xinxin Han received support and resources from the State Health Access Data Assistance Center (SHADAC) to conduct this study. The 2013 Minnesota Health Access Survey was funded by the Minnesota Department of Health and Department of Human Services and represents a collaboration between the Minnesota Department of Health, Health Economics Program, and SHADAC.

We also thank Szu-Yu Kao, MA, a student at the University of Minnesota, School of Public Health, Division of Health Policy and Management, and Jennifer Richard, MS, a senior research fellow at the University of Minnesota State Health Access Data Assistance Center, for providing valuable feedback on the article.

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