Significant Disparities Exist in Consumer Health Insurance Literacy: Implications for Health Care Reform
Bibliographic record
Abstract
Background: Health insurance literacy (HIL) is defined as a person's ability to seek, obtain, and understand health insurance plans, and once enrolled use their insurance to seek appropriate health care services. Objective: The objectives of this study were to assess sociodemographic disparities in HIL, including knowledge of health insurance terms and costs, and confidence in using insurance to access health care in a nationally representative adult sample. Methods: We conducted a secondary data analysis of the Health Reform Monitoring Survey, which included 15,168 adults age 18 years and older who responded to surveys in the third quarter of 2015 and first quarter of 2016. Rao-Scott chi-square tests and weighted logistic regression were used for analysis. Key Results: The majority of our sample (51%) reported having inadequate HIL as measured by knowledge of basic insurance terms, and close to one-half (48%) had low confidence in using their insurance to access health care. Logistic regression analysis indicated significant disparities in HIL, with multiple groups identified as being at higher risk for having inadequate HIL (as measured by both knowledge and use of health insurance). These included young adults, women, those with Hispanic ethnicity, those who were not U.S. citizens, and those who were currently unmarried. Also identified to be at risk were those who are unemployed, uninsured, and enrolled in public health insurance plans, and those with lower levels of education and income. Most had inadequate knowledge of their annual out-of-pocket costs and insurance plan's deductible amounts. Conclusions: One-half of U.S. adults rate themselves as having inadequate HIL. Sociodemographic disparities in self-reported HIL underscore the need for increased consumer education, as well as efforts to simplify the health care system by promoting value-based care, supporting delivery system reforms, and designing services to be responsive to consumer HIL needs and abilities. [ HLRP: Health Literacy Research and Practice . 2019;3(4):e250–e258.] Plain Language Summary: In a nationally representative sample of 15,168 adults, the majority had low knowledge about basic health insurance terms and had difficulty using health insurance to access needed health care services. These findings indicate that health insurance literacy is a major concern in our community that disproportionately affects some underserved groups more than others, including young adults, groups with low-income, and people who are uninsured.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".