Health insurance literacy: How best to measure and does it matter to health care access and affordability?

BACKGROUND: Health insurance is complex, cost are continuously rising, and people are assuming more of these costs. Health insurance literacy (HIL) is related to healthcare access, yet there is no agreement about how best to measure HIL. OBJECTIVES: Contrast two HIL measures. First, evaluating their association with demographic characteristics, insurance type, and health status. Second, comparing how these distinct measures relate to access, forgone care, and financial burden of health care. METHODS: Data are from a 2017 telephone survey focused on health insurance coverage and access. Participants were randomly assigned either the 4-item likelihood of proactive use scale or a 4-item measure of confidence in use of insurance. Logistic regressions assess correlates of each HIL measure and their association with a range of access measures. RESULTS: For both measures, 25% of insured adults report high HIL. Few demographic and health status measures are associated with high HIL and they are different for each measure. For both measures, high HIL translates into reports of having a usual source of care and confidence in getting care when needed. The HIL measures behave in opposite ways for forgone care due to costs and problems paying medical bills. Adults scoring high on the likelihood measure are more likely to forgo care and report financial burden. By contrast, adults scoring high on the confidence measure are less likely to forgo care and report burdensome medical bills. CONCLUSIONS: The two measures capture different concepts and raise the question of whether reporting a likely behavior or being confident of that behavior are predictive when it is time to use health insurance. Because HIL is measured at the same time as the outcomes, we reason that the likelihood measure is capturing peoples' past experience using insurance and may result in more proactive use of insurance in the future.

Assessing DIF among small samples with separate calibration t and Mantel-Haenszel χ² statistics in the Rasch model.

The National Council Licensure Examination (NCLEX) program has evaluated differential item functioning (DIF) using the Mantel-Haenszel (M-H) chi-square statistic. Since a Rasch model is assumed, DIF implies a difference in item difficulty between a reference group, e.g., White applicants, and a focal group, e.g., African-American applicants. The National Council of State Boards of Nursing (NCSBN) is planning to change the statistic used to evaluate DIF on the NCLEX from M-H to the separate calibration t-test (t). In actuality, M-H and t should yield identical results in large samples if the assumptions of the Rasch model hold (Linacre and Wright, 1989, also see Smith, 1996). However, as is true throughout statistics, "how large is large" is undefined, so it is quite possible that systematic differences exist in relatively smaller samples. This paper compares M-H and t in four sets of computer simulations. Three simulations used a ten-item test with nine fair items and one potentially containing DIF. To address instability that may result from a ten-item test, the fourth used a 30-item test with 29 fair items and one potentially containing DIF. Depending upon the simulation, the magnitude of population DIF (0, .5, 1.0, and 1.5 z-score units), the ability difference between the focal and reference group (-1, 0, and 1 z-score units), the focal group size (0, 10, 20, 40, 50, 80, 160, and 1000), and the reference group size (500 and 1000) were varied. The results were that: (a) differences in estimated DIF between the M-H and t statistics are generally small, (b) t tends to estimate lower chance probabilities than M-H with small sample sizes, (c) neither method is likely to detect DIF, especially when it is of slight magnitude in small focal group sizes, and (d) M-H does marginally better than t at detecting DIF but this improvement is also limited to very small focal group sizes.