Percentage of population under age 65 without health insurance. The 2023 County Health Rankings used data from 2020 for this measure.
Lack of health insurance coverage is a significant barrier to accessing needed health care and to maintaining financial security. One key finding from the Kaiser Family Foundation report on access to healthcare is that, "Going without coverage can have serious health consequences for the uninsured because they receive less preventative care, and delayed care often results in serious illness or other health problems. Being uninsured can also have serious financial consequences, with many unable to pay their medical bills, resulting in medical debt."1
Data and methods
Small Area Health Insurance Estimates
The US Census Bureau's Small Area Health Insurance Estimates (SAHIE) program produces estimates of health insurance coverage for all states and counties. In July 2005, SAHIE released the first nationwide set of county-level estimates on the number of people without health insurance coverage for all ages and those under 18 years old. SAHIE releases estimates of health insurance coverage by age, sex, race, Hispanic origin, and income categories at the state level and by age, sex, and income categories at the county level.
Key Measure Methods
Uninsured is a percentage
Uninsured is the percentage of the population under age 65 without health insurance coverage. A person is uninsured if they are currently not covered by insurance through a current/former employer or union, purchased from an insurance company, Medicare, Medicaid, Medical Assistance, any kind of government-assistance plan for those with low incomes or disability, TRICARE or other military health care, Indian Health Services, VA, or any other health insurance or health coverage plan.
Uninsured is created using statistical modeling
Our Uninsured measure is based on one year of survey data and is created using complex statistical modeling by The Small Area Health Insurance Estimates. Modeling generates more stable estimates for places with small numbers of residents or populations.
There are also drawbacks to using modeled data. The smaller the population or sample size of a county, the more the estimates are derived from the model itself and the less they are based on the actual rates. Models make statistical assumptions about relationships that may not hold in all cases. Finally, there is no perfect model and each model generally has limitations specific to their methods.
The numerator is the number of people currently uninsured in the county under the age of 65.
The denominator is the number of people in the county under age 65.
Can This Measure Be Used to Track Progress
This measure can be used to track progress with some caveats. Data on all types of insurance plans are combined, making it difficult to drill down the data to see changes in specific types of insurance. In order to better understand and validate modeled estimates, confirming this data with additional sources of data at the local level is particularly valuable.
Finding More Data
Disaggregation means breaking data down into smaller, meaningful subgroups. Disaggregated data are often broken down by characteristics of people or where they live. Disaggregated data can reveal inequalities that are otherwise hidden. These data can be disaggregated by:
- Subcounty Area
We recommend starting with the Small Area Health Insurance Estimates website, which contains information on insurance by race, ethnicity, age, income, and gender for counties. In addition, the Community Health Needs Assessment Report has a variety of data available, including health insurance by gender, age group, race/ethnicity at the county-level, as well as a census-tract level map. These data are available by clicking “view tool,” selecting a location, selecting your indicators (insurance is under Social & Economic Factors), and clicking on reports.
1 Kaiser Family Foundation. The Uninsured: A Primer - Key Facts about Health Insurance and the Uninsured Under the Affordable Care Act. December, 2017.