Social Media and Black Maternal Health: The Role of Health Literacy and eHealth Literacy.

BACKGROUND: Black women experience greater maternal mortality and morbidity than White women. Although there are many causes of this disparity, providing more and better maternal health information to this population may be beneficial. Social media offers a way to easily and quickly disseminate information to empower and educate Black women about health during pregnancy. OBJECTIVE: This study sought to identify social media use patterns to determine what sources Black women used to obtain information about pregnancy and to explore whether health literacy/eHealth literacy influence those patterns. METHODS: This cross-sectional, nationally representative survey panel included 404 Black women. Health literacy was measured by the Single Item Literacy Screener, and eHEALS was used to measure eHealth literacy. We examined participants' social media activity, social media use, social media use for support, and sharing of pregnancy-related health information. Relationships between health literacy, eHealth literacy, and social media use were assessed. KEY RESULTS: Overall, 67.5% of participants had high health literacy, and the average eHealth literacy score was high (34.5). Most women (71.6%) reported using more than three social media accounts as a source for pregnancy information. Women with low health literacy searched social media for general and specific pregnancy health information, reported more social media use during pregnancy in general (p < .001), and more use of social media for giving and getting support (p = .003). Women with higher eHealth literacy were more likely to report more social media use (r = 0.107, p = .039) and often used social media to give and get support (r = 0.197, p = .0001). Women with high health literacy more often reported sharing the pregnancy information they found on social media with their nurse (χ2 = 7.068, p = .029), doula (χ2 = 6.878, p = .032), and childbirth educator (χ2 = 10.289, p = .006). Women who reported higher eHealth literacy also reported more often sharing the pregnancy information they found on social media with their doctor (r = 0.115, p = .030), nurse (r = 0.139, p = .001), coworkers (r = 0.160, p = .004), and family or friends (r = 0.201, p = .0001). CONCLUSION: Substantial numbers of Black women use social media to find pregnancy health information. Future studies should elicit more detailed information on why and how Black women use social media to obtain pregnancy information and support as well as what role health literacy and eHealth literacy may have on birth outcomes. [HLRP: Health Literacy Research and Practice. 2023;7(3):e119-e129.].

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.

Improving Cancer Patients' Insurance Choices (I Can PIC): A Randomized Trial of a Personalized Health Insurance Decision Aid.

BACKGROUND: Many cancer survivors struggle to choose a health insurance plan that meets their needs because of high costs, limited health insurance literacy, and lack of decision support. We developed a web-based decision aid, Improving Cancer Patients' Insurance Choices (I Can PIC), and evaluated it in a randomized trial. MATERIALS AND METHODS: Eligible individuals (18-64 years, diagnosed with cancer for ≤5 years, English-speaking, not Medicaid or Medicare eligible) were randomized to I Can PIC or an attention control health insurance worksheet. Primary outcomes included health insurance knowledge, decisional conflict, and decision self-efficacy after completing I Can PIC or the control. Secondary outcomes included knowledge, decisional conflict, decision self-efficacy, health insurance literacy, financial toxicity, and delayed care at a 3-6-month follow-up. RESULTS: A total of 263 of 335 eligible participants (79%) consented and were randomized; 206 (73%) completed the initial survey (106 in I Can PIC; 100 in the control), and 180 (87%) completed a 3-6 month follow-up. After viewing I Can PIC or the control, health insurance knowledge and a health insurance literacy item assessing confidence understanding health insurance were higher in the I Can PIC group. At follow-up, the I Can PIC group retained higher knowledge than the control; confidence understanding health insurance was not reassessed. There were no significant differences between groups in other outcomes. Results did not change when controlling for health literacy and employment. Both groups reported having limited health insurance options. CONCLUSION: I Can PIC can improve cancer survivors' health insurance knowledge and confidence using health insurance. System-level interventions are needed to lower financial toxicity and help patients manage care costs. IMPLICATIONS FOR PRACTICE: Inadequate health insurance compromises cancer treatment and impacts overall and cancer-specific mortality. Uninsured or underinsured survivors report fewer recommended cancer screenings and may delay or avoid needed follow-up cancer care because of costs. Even those with adequate insurance report difficulty managing care costs. Health insurance decision support and resources to help manage care costs are thus paramount to cancer survivors' health and care management. We developed a web-based decision aid, Improving Cancer Patients' Insurance Choices (I Can PIC), and evaluated it in a randomized trial. I Can PIC provides health insurance information, supports patients through managing care costs, offers a list of financial and emotional support resources, and provides a personalized cost estimate of annual health care expenses across plan types.

Evaluating Adaptation of a Cancer Clinical Trial Decision Aid for Rural Cancer Patients: A Mixed-Methods Approach.

Rural-residing cancer patients often do not participate in clinical trials. Many patients misunderstand cancer clinical trials and their rights as participant. The purpose of this study is to modify a previously developed cancer clinical trials decision aid (DA), incorporating the unique needs of rural populations, and test its impact on knowledge and decision outcomes. The study was conducted in two phases. Phase I recruited 15 rural-residing cancer survivors in a qualitative usability study. Participants navigated the original DA and provided feedback regarding usability and implementation in rural settings. Phase II recruited 31 newly diagnosed rural-residing cancer patients. Patients completed a survey before and after using the revised DA, R-CHOICES. Primary outcomes included decisional conflict, decision self-efficacy, knowledge, communication self-efficacy, and attitudes towards and willingness to consider joining a trial. In phase I, the DA was viewed positively by rural-residing cancer survivors. Participants provided important feedback about factors rural-residing patients consider when thinking about trial participation. In phase II, after using R-CHOICES, participants had higher certainty about their choice (mean post-test = 3.10 vs. pre-test = 2.67; P = 0.025) and higher trial knowledge (mean percentage correct at post-test = 73.58 vs. pre-test = 57.77; P < 0.001). There was no significant change in decision self-efficacy, communication self-efficacy, and attitudes towards or willingness to join trials. The R-CHOICES improved rural-residing patients' knowledge of cancer clinical trials and reduced conflict about making a trial decision. More research is needed on ways to further support decisions about trial participation among this population.

A Patient Decision Support Tool for Hepatitis C Virus and CKD Treatment.

RATIONALE & OBJECTIVE: Patient education and decision support tools could facilitate decisions around the timing of antiviral therapy in patients living with both hepatitis C virus (HCV) infection and chronic kidney disease (CKD). We previously developed a tool through the HELP (Helping Empower Liver and Kidney Patients) study. This article evaluates the preliminary efficacy and usability of the tool among participants with both HCV infection and CKD. STUDY DESIGN: Pre-post study pilot evaluation. SETTING & PARTICIPANTS: Participants were at least 18 years old, were English speaking, and had a diagnosis of chronic HCV infection and CKD; they were seen in CKD clinics, dialysis units, and/or hepatology and liver transplantation clinics. INTERVENTION: Electronic patient decision support tool. OUTCOMES: Participants' change in knowledge, certainty about choice, decision self-efficacy, patients' treatment preferences, and tool usability. RESULTS: 70 participants were recruited; 56 of 70 (80.0%) completed study procedures. Nearly all (51/56; 91.1%) requested paper-based study procedures despite the electronic design of the tool. Participants reported that they were most worried about the following treatment factors: (1) cost of drugs to treat HCV infection, (2) how their HCV infection affected their CKD, and (3) wait times for a kidney transplant. After using the decision tool, participants had significantly higher HCV infection and CKD knowledge (mean posttest percent of questions answered correctly = 65.74% vs pretest percent of questions answered correctly = 53.44%; P < 0.001) and more certainty about choice (mean posttest = 3.13 vs pretest = 2.65; P = 0.05). There were no significant changes in decision self-efficacy (mean posttest = 86.62 vs pretest = 84.68; P = 0.48). LIMITATIONS: Single-site pilot study to explore preliminary tool efficacy and usability. CONCLUSIONS: This study suggests that a decision tool may support informed patient-centered choices among patients with HCV infection and CKD. Future studies should evaluate ways to improve care decisions in a larger sample using both paper-based and electronic materials. FUNDING: Merck & Co, Inc, Kenilworth, NJ. TRIAL REGISTRATION: Registered at clinicaltrials.gov with study number NCT03426787.

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.