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.

Adapting the Breast Cancer Surgery Decision Quality Instrument for Lower Socioeconomic Status: Improving Readability, Acceptability, and Relevance.

Introduction. Breast cancer is the second most common malignancy in women. The Decision Quality Instrument (DQI) measures the extent to which patients are informed and involved in breast surgery decisions and receive treatment that aligns with their preferences. There are limited data on the performance of the DQI in women of lower socioeconomic status (SES). Our aims were to 1) examine (and if necessary adapt) the readability, usability, and acceptability of the DQI and 2) explore whether it captures factors important to breast cancer surgery decisions among women of lower SES (relevance). Methods. We conducted semistructured cognitive interviews with women of lower SES (based on insurance status, income, and education) who had completed early-stage breast cancer treatments at three cancer centers. We used a two-step thematic analysis with dual independent coding. The study team (including Patient Partners and a Community Advisory Board) reviewed and refined suggested changes. The revised DQI was presented in two focus groups of breast cancer survivors. Results. We conducted 39 interviews. Participants found most parts of the DQI to be helpful and easy to understand. We made the following suggested changes: 1) added a glossary of key terms, 2) added two answer choices and an open text question in the goals and concerns subscale, 3) reworded the treatment intention question, and 4) revised the knowledge subscale instructions since several women disliked the wording and were unsure of what was expected. Discussion. The readability, usability, acceptability, and relevance of a measure that was primarily developed and validated in women of higher SES required adaptation for optimal use by women of lower SES. Further research will test these adaptations in lower SES populations.

Access Is Necessary but Not Sufficient: Factors Influencing Delay and Avoidance of Health Care Services.

Background: Despite recently expanded access to health insurance, consumers still face barriers to using their coverage to obtain needed health care. Objective: To examine the characteristics of those who delay or avoid health care due to costs. Methods: Participants were recruited via Amazon MTurk and completed a survey assessing demographic characteristics, financial toxicity, health care minimizer-maximizer tendencies, health insurance knowledge, numeracy, delaying/avoiding any care, and delaying/avoiding six common health care services (three preventive and three nonpreventive services). Validated measures were used when available. Delay/avoidance behaviors were categorized into delaying/avoiding any care, preventive care, and nonpreventive care. Logistic regression models examined 1) financial toxicity, 2) minimizer-maximizer tendencies, 3) numeracy, 4) health insurance knowledge, and 5) knowledge of preventive care coverage separately on three forms of delay/avoidance behaviors, controlling for chronic conditions, insurance status, and/or income where appropriate. Results: Of 518 respondents, 470 did not fail attention-check questions and were used in analyses. Forty-five percent of respondents reported delaying/avoiding care due to cost. Multivariable analyses found that financial toxicity was related to delaying/avoiding any care (odds ratio [OR] = 0.884, P < 0.001), preventive care (OR = 0.906, P < 0.001), and nonpreventive care (OR = 0.901, P < 0.001). A tendency to minimize seeking health care (OR = 0.734, P < 0.001) and lower subjective numeracy (OR = 0.794, P = 0.023) were related to delaying/avoiding any care. General health insurance knowledge (OR = 0.989, P = 0.023) and knowledge of preventive care coverage (OR = 0.422, P < 0.001) were related to delaying/avoiding preventive care. Conclusions: Many people delay or avoid health care due to costs, even when insured. Results suggest that there may be different reasons individuals delay or avoid preventive and nonpreventive care. Findings may inform interventions to educate consumers and support discussions about health care costs to facilitate appropriate health care utilization.

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.

Dissemination of a Web-Based Tool for Supporting Health Insurance Plan Decisions (Show Me Health Plans): Cross-Sectional Observational Study.

BACKGROUND: The rate of uninsured people has decreased dramatically since the Affordable Care Act was passed. To make an informed decision, consumers need assistance to understand the advantages and disadvantages of health insurance plans. The Show Me Health Plans Web-based decision support tool was developed to improve the quality of health insurance selection. In response to the promising effectiveness of Show Me Health Plans in a randomized controlled trial (RCT) and the growing need for Web-based health insurance decision support, the study team used expert recommendations for dissemination and implementation, engaged external stakeholders, and made the Show Me Health Plans tool available to the public. OBJECTIVE: The purpose of this study was to implement the public dissemination of the Show Me Health Plans tool in the state of Missouri and to evaluate its impact compared to the RCT. METHODS: This study used a cross-sectional observational design. Dissemination phase users were compared with users in the RCT study across the same outcome measures. Time spent using the Show Me Health Plans tool, knowledge, importance rating of 9 health insurance features, and intended plan choice match with algorithm predictions were examined. RESULTS: During the dissemination phase (November 2016 to January 2017), 10,180 individuals visited the SMHP website, and the 1069 users who stayed on the tool for more than one second were included in our analyses. Dissemination phase users were more likely to live outside St. Louis City or County (P<.001), were less likely to be below the federal poverty level (P<.001), and had a higher income (P=.03). Overall, Show Me Health Plans users from St. Louis City or County spent more time on the Show Me Health Plans tool than those from other Missouri counties (P=.04); this association was not observed in the RCT. Total time spent on the tool was not correlated with knowledge scores, which were associated with lower poverty levels (P=.009). The users from the RCT phase were more likely to select an insurance plan that matched the tool's recommendations (P<.001) compared with the dissemination phase users. CONCLUSIONS: The study suggests that a higher income population may be more likely to seek information and online help when making a health insurance plan decision. We found that Show Me Health Plans users in the dissemination phase were more selective in the information they reviewed. This study illustrates one way of disseminating and implementing an empirically tested Web-based decision aid tool. Distributing Web-based tools is feasible and may attract a large number of potential users, educate them on basic health insurance information, and make recommendations based on personal information and preference. However, using Web-based tools may differ according to the demographics of the general public compared to research study participants.

What matters most: protocol for a randomized controlled trial of breast cancer surgery encounter decision aids across socioeconomic strata.

BACKGROUND: Breast cancer is the most commonly diagnosed malignancy in women. Mastectomy and breast-conserving surgery (BCS) have equivalent survival for early stage breast cancer. However, each surgery has different benefits and harms that women may value differently. Women of lower socioeconomic status (SES) diagnosed with early stage breast cancer are more likely to experience poorer doctor-patient communication, lower satisfaction with surgery and decision-making, and higher decision regret compared to women of higher SES. They often play a more passive role in decision-making and are less likely to undergo BCS. Our aim is to understand how best to support women of lower SES in making decisions about early stage breast cancer treatments and to reduce disparities in decision quality across socioeconomic strata. METHODS: We will conduct a three-arm, multi-site randomized controlled superiority trial with stratification by SES and clinician-level randomization. At four large cancer centers in the United States, 1100 patients (half higher SES and half lower SES) will be randomized to: (1) Option Grid, (2) Picture Option Grid, or (3) usual care. Interviews, field-notes, and observations will be used to explore strategies that promote the interventions' sustained use and dissemination. Community-Based Participatory Research will be used throughout. We will include women aged at least 18 years of age with a confirmed diagnosis of early stage breast cancer (I to IIIA) from both higher and lower SES, provided they speak English, Spanish, or Mandarin Chinese. Our primary outcome measure is the 16-item validated Decision Quality Instrument. We will use a regression framework, mediation analyses, and multiple informants analysis. Heterogeneity of treatment effects analyses for SES, age, ethnicity, race, literacy, language, and study site will be performed. DISCUSSION: Currently, women of lower SES are more likely to make treatment decisions based on incomplete or uninformed preferences, potentially leading to poorer decision quality, quality of life, and decision regret. This study hopes to identify solutions that effectively improve patient-centered care across socioeconomic strata and reduce disparities in decision and care quality. TRIAL REGISTRATION: NCT03136367 at ClinicalTrials.gov Protocol version: Manuscript based on study protocol version 2.2, 7 November 2017.

Training scholars in dissemination and implementation research for cancer prevention and control: a mentored approach.

BACKGROUND: As the field of D&I (dissemination and implementation) science grows to meet the need for more effective and timely applications of research findings in routine practice, the demand for formalized training programs has increased concurrently. The Mentored Training for Dissemination and Implementation Research in Cancer (MT-DIRC) Program aims to build capacity in the cancer control D&I research workforce, especially among early career researchers. This paper outlines the various components of the program and reports results of systematic evaluations to ascertain its effectiveness. METHODS: Essential features of the program include selection of early career fellows or more experienced investigators with a focus relevant to cancer control transitioning to a D&I research focus, a 5-day intensive training institute, ongoing peer and senior mentoring, mentored planning and work on a D&I research proposal or project, limited pilot funding, and training and ongoing improvement activities for mentors. The core faculty and staff members of the MT-DIRC program gathered baseline and ongoing evaluation data regarding D&I skill acquisition and mentoring competency through participant surveys and analyzed it by iterative collective reflection. RESULTS: A majority (79%) of fellows are female, assistant professors (55%); 59% are in allied health disciplines, and 48% focus on cancer prevention research. Forty-three D&I research competencies were assessed; all improved from baseline to 6 and 18 months. These effects were apparent across beginner, intermediate, and advanced initial D&I competency levels and across the competency domains. Mentoring competency was rated very highly by the fellows--higher than rated by the mentors themselves. The importance of different mentoring activities, as rated by the fellows, was generally congruent with their satisfaction with the activities, with the exception of relatively greater satisfaction with the degree of emotional support and relatively lower satisfaction for skill building and opportunity initially. CONCLUSIONS: These first years of MT-DIRC demonstrated the program's ability to attract, engage, and improve fellows' competencies and skills and implement a multicomponent mentoring program that was well received. This account of the program can serve as a basis for potential replication and evolution of this model in training future D&I science researchers.

Exploring Characteristics and Health Care Utilization Trends Among Individuals Who Fall in the Health Insurance Assistance Gap in a Medicaid Nonexpansion State.

States that did not expand Medicaid under the Affordable Care Act (ACA) in the United States have seen a growth in the number of individuals who fall in the assistance gap, defined as having incomes above the Medicaid eligibility limit (≥44% of the federal poverty level) but below the lower limit (<100%) to be eligible for tax credits for premium subsidies or cost-sharing reductions in the marketplace. The purpose of this article is to present findings from a secondary data analysis examining the characteristics of those who fell in the assistance gap ( n = 166) in Missouri, a Medicaid nonexpansion state, by comparing them with those who did not fall in the assistance gap ( n = 157). Participants completed online demographic questionnaires and self-reported measures of health and insurance status, health literacy, numeracy, and health insurance literacy. A select group completed a 1-year follow-up survey about health insurance enrollment and health care utilization. Compared with the nonassistance gap group, individuals in the assistance gap were more likely to have lower levels of education, have at least one chronic condition, be uninsured at baseline, and be seeking health care coverage for additional dependents. Individuals in the assistance gap had significantly lower annual incomes and higher annual premiums when compared with the nonassistance gap group and were less likely to be insured through the marketplace or other private insurance at the 1-year follow-up. Findings provide several practice and policy implications for expanding health insurance coverage, reducing costs, and improving access to care for underserved populations.