16.2.10 Expected Payer/ Public Assistance and Means-Tested Programs

Expected Payer is a factor that may be a proxy for other identifiers. For example, employment-based payers may provide information about a person’s job status. Other types of payers, such as Medicaid, may provide information about a person’s income. There is demographic data that indicates populations by income level in the public domain as published by the U.S. Census Bureau, as well as public information related to various jobs and employment status through labor agencies. Given the opportunities to use information about the payer in combination with other public information, this variable is given a risk score.

It's important to be aware of the potential risks associated with this data and to ensure its security and protection. For instance, eligibility for benefits in the Medi-Cal program may be determined based on income, property, and assets. Similarly, self-pay data, often associated with the uninsured, can also indicate low income, highlighting the need for data security.

Table 23: Expected Payer/ Public Assistance and Means-Tested Programs Scoring

Size of program enrollment	Score
Enrollment > 10,000,000 people	+0
Enrollment > 4,000,001 – 10,000,000	+1
Enrollment 300,001 – 4,000,000	+2
Enrollment 100,001 – 300,000	+3
Enrollment 20,001 – 100,000	+5
Enrollment ≤20,000	+7

It is important to note that Medi-Cal is a Means-Tested Program, and overall enrollment is managed at the statewide level. In 2024, approximately 14 million Californians were enrolled in the Medi-Cal program, therefore, the Medi-Cal category has been assigned a +0 DDG score. Also, more than 6 million California residents had Medicare coverage, therefore, the Medicare category has been assigned a +1 DDG score. There are approximately 5.5 million patients captured in the HCAI Health Care Payments Database who have private insurance and thus assigned as +1 score. The uninsured population, who we presume would self-pay seems to be less than 4 million (2,752,067 based on the 2022 5-year American Community Survey estimate for California), has been assigned a +2 score.

In addition to Medi-Cal, CalHHS administers and collects data on a variety of public assistance programs or means-tested programs. In these programs, a granting entity uses information on an individual’s income and resources to determine eligibility for the program. Knowing that an individual is on a means-tested program or a public assistance program can reveal sensitive information about their income, employment status, or other personal characteristic related to eligibility. For example, recipients of SNAP are typically at 130% of the Federal Poverty Level. To qualify for TANF, assistance units must be at 100% of the Federal Poverty Level. Major federal programs include Medicaid (Medi-Cal in California), the Earned Income Tax Credit, the Supplemental Nutrition Assistance Program (CalFresh), Supplemental Security Income (SSI), and Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). Additionally, there are several state-funded public assistance programs, including state anti-poverty tax credits, housing and homelessness programs, and programs such as the Cash Assistance Program for Immigrants (CAPI) and the California Food Assistance Program (CFAP).

The size of these programs ranges widely; as noted in the previous section, 14 million Californians are enrolled in Medi-Cal. Roughly 5.5 million Californians participated in CalFresh in 2024. CalWORKs served a little over 900,000 Californians in 2023. In the Housing Support Program, fewer than 13,000 applications were approved. Risk scores should be assigned to data related to (or breaking out) participation by program enrollment. To prevent the inadvertent disclosure of an individual’s participation in a means-tested or public assistance program, the following risk scores can be assigned to data that includes information on program participation, based on the size of the program (note, these numbers are derived from Table 2) and Means-Tested Programs, and Geography.