Stakeholders' perceptions of ways to support decisions about health insurance marketplace enrollment: a qualitative study

Accessibility of the Affordable Care Act (ACA) marketplace websites

BACKGROUND

The Affordable Care Act (ACA) mandated that creation of online health insurance websites to ease the complex process of shopping for and enrolling into coverage. Ensuring that these sites are not only available but also meet digital accessibility standards is important so that individuals with disabilities are able to access healthcare services and efficiently obtain insurance coverage.

METHOD

We evaluated each of the marketplace sites in 2020 to assess whether they are digitally accessible. We employed a custom audit tool based on a subset of the Web Content Accessibility Guidelines (WCAG) 1.0 and 2.0 AA and used content analysis to compare the site's accessibility statements with best practices.

RESULTS

Nearly all of the ACA marketplace websites have significant room to improve their digital accessibility. Notable technical problem areas include lack of text equivalents for images, difficult site navigation, and lack of optimization for mobile use, particularly on those pages that provide instructions on how to get in-person help.

CONCLUSIONS

Given that access to health insurance is a primary predictor of access to health care - sites must be easy to use and accessible to all individuals regardless of ability. Barriers to online enrollment, such as those identified in this work, may exacerbate disparities in quality of care, treatment continuity and affordability for individuals with mental and physical disabilities. Entities providing health-related online information & engagement should be aware of actionable opportunities to improve digital accessibility to optimize the enrollment process for both maintaining coverage and assisting those that remain uninsured.

Health insurance enrollment strategies during the Affordable Care Act (ACA): a scoping review on what worked and for whom

BACKGROUND

The Affordable Care Act (ACA) provided an opportunity for millions of people in the U.S. to get coverage from the publicly funded Medicaid program or private insurance from the newly established marketplace. However, enrolling millions of people for health insurance was an enormous task. The aim of this review was to examine the strategies used to enroll people for health insurance and their effectiveness after implementing the ACA's coverage expansion.

METHODS

The PRISMA Extension for Scoping Review (PRISMA-ScR) guided this review. Included studies were empirical studies that met the inclusion criteria and published between 2010 and 2020. Studies were searched mainly from two scholarly databases, CINAHL Plus and Medline (PubMed) using keyword searches. Hand searches from the references of selected journals were also performed. Content analysis was conducted by two authors in which codes were inductively developed to identify themes.

RESULTS

There were 2213 potential studies identified from the search, but 10 met the inclusion criteria. The research design of the studies varied. Two studies were randomized trials, one quasi-experimental trial, three mixed-methods, two qualitative and two quantitative. All studies focused on strategies used to inform and help people enroll for either Medicaid or private insurance from the marketplace. This review identified three key strategies used to help enroll people for coverage: 1) individual assistance; 2) community outreach; and 3) health education and promotion (HE&P).

CONCLUSION

Community-based organizations were likely to use a combination of the three strategies simultaneously to reach uninsured individuals and directly help them enroll for health insurance. Other organizations that aimed to reach a wider segment of the population used single strategies, such as community outreach or HE&P.

Predicting Future Utilization Using Self-Reported Health and Health Conditions in a Longitudinal Cohort Study: Implications for Health Insurance Decision Support

Decision support techniques and online algorithms aim to help individuals predict costs and facilitate their choice of health insurance coverage. Self-reported health status (SHS), whereby patients rate their own health, could improve cost-prediction estimates without requiring individuals to share personal health information or know about undiagnosed conditions. We compared the predictive accuracy of several models: (1) SHS only, (2) a “basic” model adding health-related variables, and (3) a “full” model adding measures of healthcare access. The Medical Expenditure Panel Survey was used to predict 2015 health expenditures from 2014 data. Relative performance was assessed by comparing adjusted-R² values and by reporting the predictive accuracy of the models for a new cohort (2015–2016 data). In the SHS-only model, those with better SHS were less likely to incur expenditures. However, after accounting for health variables, those with better SHS were more likely to incur expenses. In the full model, SHS was no longer predictive of incurring expenses. Variables indicating better access to care were associated with higher likelihood of spending and higher spending. The full model (R² = 0.290) performed slightly better than the basic model (R² = 0.240), but neither performed well at the upper tail of the cost distribution. While our SHS-based models perform well in the aggregate, predicting population-level risk well, they are not sufficiently accurate to guide individuals’ insurance shopping decisions in all cases. Policies that rely heavily on health insurance consumers making individually optimal choices cannot assume that decision tools can accurately anticipate high costs.

The Trump Effect: Postinauguration Changes in Marketplace Enrollment

CONTEXT

On January 20, 2017, President Donald Trump penned his first executive order, which aimed to "minimiz[e] the economic burden" of the Affordable Care Act, signaling his intent to make good on promises to repeal and replace the law. This executive order, along with concurrent changes in political messaging associated with the transition in power and reductions in HealthCare.gov advertising, lowered Health Insurance Marketplace enrollment at the end of the 2017 open enrollment period.

METHODS

The authors used difference-in-differences and event-study models with weekly county-level Marketplace application data from 1,476 counties in 37 states to estimate the incremental enrollment loss in the postinauguration period.

FINDINGS

Estimates indicate a population-weighted decline of over 700 applications per county-week during the final 2 weeks of the 2017 open enrollment period relative to 2016, corresponding to a nearly 30% decline in applications submitted. A more flexible event-study approach that better accounts for time shifting of enrollment across open enrollment periods found a similar decline of approximately 660 applications per county-week associated with the postinauguration period (-24%).

CONCLUSIONS

The lack of political support for the law by the incoming administration seemingly had an immediate and significant downward effect on Marketplace enrollment nationwide.

The Roles Of Assisters And Automated Decision Support Tools In Consumers' Marketplace Choices: Room For Improvement

Assisters provide in-person and phone-based support to help consumers narrow their plan options on the Affordable Care Act's health insurance Marketplaces. We elicited the perspectives of a national sample of thirty-two assisters from ten states on consumer plan selection and available Marketplace decision support tools (for example, total cost estimators and provider network look-up tools). Assisters identified several shortcomings that limited their use of decision support tools, such as nonspecific cost estimates and inaccurate provider network data. Assisters instead provided individualized cost estimates, called provider offices to verify network coverage, and found innovative strategies to help consumers access care affordably under their chosen plan. Two priorities emerged for optimizing consumers' Marketplace insurance selection process: improve the quality of data used in decision support tools and invest in assister programs. Assister strategies should be a benchmark for improving decision support tools, with lessons to be learned for future tool development.

Burden Associated With Selecting and Using Health Insurance to Manage Care Costs: Results of a Qualitative Study of Nonelderly Cancer Survivors

This qualitative study explored cancer survivors' experiences selecting and using health insurance and anticipating out-of-pocket care costs. Thirty individuals participated in semistructured interviews. On average, participants were 54 years (SD ± 8.85, range 34-80) and diagnosed with cancer about 5 years prior (range 0.5-10 years). About 57% were female, 77% were non-Hispanic White, and 53% had less than a college education. Participants struggled to access information about health insurance and costs. Lack of cost transparency made it difficult to anticipate expenses and increased anxiety. Many participants were surprised that after cancer, care that was once preventive with no out-of-pocket costs became diagnostic with associated fees. They discussed the cognitive burden of managing finances on top of treatment and overseeing communication between doctors and insurance. Interventions are needed to clearly communicate information about insurance coverage and care costs to improve cancer survivors' confidence in selecting health insurance and anticipating out-of-pocket expenses.

Association Between Health Insurance Literacy and Avoidance of Health Care Services Owing to Cost

IMPORTANCE

Navigating health insurance and health care choices requires considerable health insurance literacy. Although recommended preventive services are exempt from out-of-pocket costs under the Affordable Care Act, many people may remain unaware of this provision and its effect on their required payment. Little is known about the association between individuals' health insurance literacy and their use of preventive or nonpreventive health care services.

OBJECTIVE

To assess the association between health insurance literacy and self-reported avoidance of health care services owing to cost.

DESIGN, SETTING, AND PARTICIPANTS

In this survey study, a US national, geographically diverse, nonprobability sample of 506 US residents aged 18 years or older with current health insurance coverage was recruited to participate in an online survey between February 22 and 23, 2016.

MAIN OUTCOMES AND MEASURES

The validated 21-item Health Insurance Literacy Measure (HILM) assessed individuals' self-rated confidence in selecting and using health insurance (score range, 0-84, with higher scores indicating greater levels of health insurance literacy). Dependent variables included delayed or foregone preventive and nonpreventive services in the past 12 months owing to perceived costs, and preventive and nonpreventive use of services. Covariates included age, sex, race/ethnicity, income, educational level, high-deductible health insurance plan, health literacy, numeracy, and chronic health conditions. Analyses included descriptive statistics and bivariate and multivariable logistic regression.

RESULTS

A total of 506 of 511 participants who began the survey completed it (participation rate, 99.0%). Of the 506 participants, 339 (67.0%) were younger than 35 years (mean [SD] age, 34 [10.4] years), 228 (45.1%) were women, 406 of 504 who reported race (80.6%) were white, and 245 (48.4%) attended college for 4 or more years. A total of 228 participants (45.1%) had 1 or more chronic health condition, 361 of 500 (72.2%) who responded to the survey item had seen a physician in the outpatient setting in the past 12 months, and 446 of the 501 (89.0%) who responded to the survey item had their health insurance plan for 12 or more months. One hundred fifty respondents (29.6%) reported having delayed or foregone care because of cost. The mean (SD) HILM score was 63.5 (12.3). In multivariable logistic regression, each 12-point increase in HILM score was associated with a lower likelihood of both delayed or foregone preventive care (adjusted odds ratio [aOR], 0.61; 95% CI, 0.48-0.78) and delayed or foregone nonpreventive care (aOR, 0.71; 95% CI, 0.55-0.91).

CONCLUSIONS AND RELEVANCE

This study's findings suggest that lower health insurance literacy may be associated with greater avoidance of both preventive and nonpreventive services. It appears that to improve appropriate use of recommended health care services, including preventive health services, clinicians, health plans, and policymakers may need to communicate health insurance concepts in accessible ways regardless of individuals' health insurance literacy. Plain language communication may be able to improve patients' understanding of services exempt from out-of-pocket costs.

Variability of assister availability in health insurance marketplace in the U.S

Background

The Affordable Care Act (ACA) established the health insurance marketplaces to provide people the opportunity to obtain healthcare coverage. Assisters have worked with people who may have difficulty understanding the new system and selecting the right plan. This study aims to describe the local availability of assister programs, and examine the factors influencing assister provision.

Methods

The 2016 Small Area Health Insurance Estimates data and a database of assister programs constructed using healthcare.gov were analyzed at the county level. Bivariate analysis by assister provision was performed to determine the differences between the two groups, and the hierarchical generalized linear model was used to examine the factors predicting assister availability.

Results

The study analyzed 2260 counties nested within 35 states. Assister availability largely varied across counties and states. About half of the counties did not provide assisters at all, and the assister provision rate at state level ranged between 19 - 100%. Counties in metropolitan areas were more likely to provide assister programs than rural areas, and so were counties with higher adult uninsured rate or higher uninsured rate among the people with incomes between 138 - 400% of federal poverty level (FPL).

Conclusions

Despite the important role of in-person assistance in plan enrollment, no previous study has examined the local variability of assister program. Our study found a large geographical variation in assister availability, raising concerns about the disparity in access to assister service.

A Comparison Between Subjective and Objective Methods of Predicting Health Care Expenses to Support Consumers' Health Insurance Plan Choice

Objective. Numerous electronic tools help consumers select health insurance plans based on their estimated health care utilization. However, the best way to personalize these tools is unknown. The purpose of this study was to compare two common methods of personalizing health insurance plan displays: 1) quantitative healthcare utilization predictions using nationally representative Medical Expenditure Panel Survey (MEPS) data and 2) subjective-health status predictions. We also explored their relations to self-reported health care utilization. Methods. Secondary data analysis was conducted with responses from 327 adults under age 65 considering health insurance enrollment in the Affordable Care Act (ACA) marketplace. Participants were asked to report their subjective health, health conditions, and demographic information. MEPS data were used to estimate predicted annual expenditures based on age, gender, and reported health conditions. Self-reported health care utilization was obtained for 120 participants at a 1-year follow-up. Results. MEPS-based predictions and subjective-health status were related (P < 0.0001). However, MEPS-predicted ranges within subjective-health categories were large. Subjective health was a less reliable predictor of expenses among older adults (age × subjective health, P = 0.04). Neither significantly related to subsequent self-reported health care utilization (P = 0.18, P = 0.92, respectively). Conclusions. Because MEPS data are nationally representative, they may approximate utilization better than subjective health, particularly among older adults. However, approximating health care utilization is difficult, especially among newly insured. Findings have implications for health insurance decision support tools that personalize plan displays based on cost estimates.

Is the Affordable Care Act Cultivating a Cross-Class Constituency? Income, Partisanship, and a Proposal for Tracing the Contingent Nature of Positive Policy Feedback Effects

Social Security and Medicare enjoy strong political coalitions within the mass public because middle-class Americans believe they derive benefits from these programs and stand alongside lower-income beneficiaries in defending them from erosion. By pooling data from nine nationally representative surveys, this article examines whether the Affordable Care Act (ACA) is cultivating a similar cross-class constituency. The results show that middle-income Americans are less likely than low-income Americans to say the ACA has helped them personally so far. On the other hand, partisanship conditions the relationship between income and beliefs about benefits likely to be derived from the ACA in the long run. In total, the results suggest that cross-class Democratic optimism about long-run benefits may enable the ACA to reap positive beneficiary feedbacks, but a large and bipartisan cross-class constituency appears unlikely. Drawing on these results, this article also makes theoretical contributions to the policy feedback literature by underscoring the need for research on prospections' power in policy feedbacks and proposing a strategy for researchers, policy makers, and public managers to identify where partisanship intervenes in the standard policy feedback logic model, and thereby to better assess how it fragments and conditions positive feedback effects in target populations.

Show Me My Health Plans: Using a Decision Aid to Improve Decisions in the Federal Health Insurance Marketplace

Introduction: Since the Affordable Care Act was passed, more than 12 million individuals have enrolled in the health insurance marketplace. Without support, many struggle to make an informed plan choice that meets their health and financial needs. Methods: We designed and evaluated a decision aid, Show Me My Health Plans (SMHP), that provides education, preference assessment, and an annual out-of-pocket cost calculator with plan recommendations produced by a tailored, risk-adjusted algorithm incorporating age, gender, and health status. We evaluated whether SMHP compared to HealthCare.gov improved health insurance decision quality and the match between plan choice, needs, and preferences among 328 Missourians enrolling in the marketplace. Results: Participants who used SMHP had higher health insurance knowledge (LS-Mean = 78 vs. 62; P < 0.001), decision self-efficacy (LS-Mean = 83 vs. 75; P < 0.002), confidence in their choice (LS-Mean = 3.5 vs. 2.9; P < 0.001), and improved health insurance literacy (odds ratio = 2.52, P < 0.001) compared to participants using HealthCare.gov. Those using SMHP were 10.3 times more likely to select a silver- or gold-tier plan (P < 0.0001). Discussion: SMHP can improve health insurance decision quality and the odds that consumers select an insurance plan with coverage likely needed to meet their health needs. This study represents a unique context through which to apply principles of decision support to improve health insurance choices.