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Median Medical Out-of-Pocket Spending

Measure Overview

While health insurance can help protected individuals from unexpected health care costs, enrollees are typically required to pay for some share of costs on their own (i.e., “out-of-pocket”). Out-of-pocket (OOP) spending can include the costs of premiums, co-pays, diagnostic tests, prescription drugs, eyeglasses, contact lenses, and medical supplies. High medical out-of-pocket spending poses an issue for insured individuals who seek care but still struggle to afford the share of costs not covered by insurance and for uninsured individuals who may bear the full cost of services.

State Health Compare provides annual, state-level estimates of median OOP spending by population (either individuals with employer-sponsored coverage or individuals in the total U.S. population). These estimates reflect data collected in the Current Population Survey.

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Notes

Family defined as a Health Insurance Unit (HIU), based on individuals who would likely be considered a family unit in determining eligibility for either private or public coverage. The margin of error (MOE) represents the uncertainty of an estimate due to sampling variability; the calculated MOE is 95%. N/A indicates that data were not available or suppressed because the number of sample cases was too small.

Data collection for the 2020 CPS (2019 estimates) was impacted by the COVID-19 pandemic, which likely caused measurable non-response bias. Users should treat 2019 estimates with caution. More information is available in Census Working Paper SEHSD WP2020-10. Data collection for the 2021 CPS (2020 estimates) was also impacted by the COVID-19 pandemic, which likely caused measurable non-response bias. More information is available in a September 2021 Census blog post. Though data collection for the 2022 CPS (2021 estimates) was not impacted by the pandemic, response rates continued to be below pre-pandemic normal, which has resulted in small, but measurable nonresponse bias. More information is available in a September 2022 Census blog post. Users should treat 2019–2021 estimates with caution.

For this analysis, we estimated OOP costs for individuals using the median as the rate of measure for each state, as using the average or mean may be distorted by a few outliers representing particularly high rates of OOP spending.

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