Methodology
Data Sources
Georgetown Center for Children and Families uses the U.S. Census Bureau American Community Survey (ACS), an annual survey of approximately 3.5 million individuals, to analyze national, state, and local trends in health insurance coverage. Unless otherwise noted, the data in this report come from the Public Use Microdata Sample (PUMS), a two-thirds sample of the full ACS data file. This sample allows for disaggregation by detailed Latino origin. Files are downloaded from census.gov FTP platform.
Other analyses and reports from CCF, including kidshealthcarereport.ccf.georgetown.edu and “Children’s Uninsured Rate Rises by Largest Annual Jump in More than a Decade” (October 2020) use the American Community Survey detailed tables, published on data.census.gov. The detailed tables are based on the full sample of ACS results, but do not allow for detailed disaggregation by age.
Margin of Error and Data Suppression
Following the instructions given in the “Calculating Margins of Error the ACS Way Using Replicate Methodology to Calculate Uncertainty” webinar (February 2020), standard error and coefficients of variation are computed using successive differences replication (SDR) in STATA statistical software. Margin of error calculations are not published in this report but are available upon request.
To ensure accuracy and consistency, Georgetown CCF calculates the coefficient of variation (CV; also known as the relative standard error) for each estimate. CCF follows the instructions included in the Census Bureau’s publication, “Understanding and Using American Community Survey Data: What All Data Users Need to Know” (September 2020). CVs produce a comparable indicator of the error size by dividing the standard error of an estimate by the estimate itself. The lower the CV, the more reliable the estimate. Estimates with CVs greater than 25 percent are not presented in this analysis. Applying this rule results in the suppression of several states when disaggregating estimates by race and ethnicity.
Statistical Significance and “X Times More Likely” Estimates
Statistical significance is determined using the U.S. Census Bureau “Statistical Testing Tool” with a confidence interval of 90 percent. In other words, when the difference between two values is marked as significant, there is a 90 percent likelihood that the difference is not due to chance or sampling error. Margins of error are a critical part of determining statistical significance. Two estimates with high levels of uncertainty, or high margins of error, indicate that the difference could be due to chance or sampling error. Consequently, they are less likely to “pass” the significance test. Statistical significance indicates change over time and a difference between two groups/estimates (ie, expansion and non-expansion or the national average and a state rate.)
To calculate “X Times More Likely,” CCF divides the group in question by the comparator (the non-expansion state by the expansion state). The resulting multiplier is rounded to the nearest .5. For example, a multiplier of 2.11 is reported as “more than 2x” while a multiplier of 2.88 is reported as “almost 3x.” Multipliers less than 1.25 are suppressed. These ratios should not be interpreted as statistical significance tests or as a difference-in-difference estimate.
Demographic Characteristics
Women of childbearing age refer to women between the ages of 18 and 44. While women younger than 18 can have children, they are still eligible for Medicaid and CHIP in the child category and thus are not included in this analysis. To maintain accuracy, CCF uses the term “women” when referencing American Community Survey data; the Survey only offers two options for gender identity and consequently does not capture information about pregnant people and people who give birth who do not identify as women.
The American Community Survey allows respondents to self-identify with the following races: White alone, Black/African-American, American Indian/Alaska Native, Asian, Native Hawaiian/Pacific Islander, “Some other race,” and “Two or more races.” To report on an individual’s race, we merge the data for “Asian alone” and “Native Hawaiian or other Pacific Islander alone.” In addition, we report the ACS category “some other race alone” and “two or more races” as “other.” Except for “other,” all racial categories refer to respondents who indicated belonging to only one race.
The Census Bureau distinguishes between race and Hispanic origin/Latino ethnicity. For the purposes of this analysis, “Latina” refers to all those who self-identified as that they were of Hispanic or Latino and as a woman on the ACS. “Latinx” can also be used to respect various gender identities and expressions. “Non-Latina” refers to all those who indicated that they were not of Hispanic or Latino origin on question five of the ACS. Latina and Non-Latina individuals may be of any race.
Health Coverage
ACS data is collected over the course of a year and represents a “point-in-time” estimate of a person’s insurance status. That is, the survey collects information on if the respondent is insured at the moment they complete the form, not if they have been insured/uninsured at any point during the year. The US Census Bureau does not consider Indian Health Service (IHS) access a comprehensive form of coverage. Consequently, those who indicate that IHS is their only source of coverage are designated as uninsured.
Poverty Status
The Census Bureau determines an individual’s poverty status by comparing their estimated income to the Census Poverty Thresholds. Though the overall Census Poverty Thresholds are similar to the Department of Health and Human Services Federal Poverty Level Guidelines, there are significant differences for Alaska and Hawaii. Further, the Census does not adhere to the same modified adjusted gross income (MAGI) formula for computing income that state Medicaid and CHIP programs use when determining income-based eligibility. For example, while a grandmother living in a family home would count towards the household size in the Census calculation of the poverty level, she may not count as a household member according to the MAGI formula. For these reasons, the Census Poverty Threshold is a rough proxy for income-based eligibility.
Medicaid Expansion Analysis
This report relies on ACS data collected in 2019. For this reason, expansion status is determined based on if a state had implemented expansion for the majority of 2019. The expansion states include: Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, District of Columbia, Hawaii, Illinois, Indiana, Iowa, Kentucky, Louisiana, Maryland, Massachusetts, Michigan, Minnesota, Montana, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Dakota, Ohio, Oregon, Pennsylvania, Rhode Island, Washington, West Virginia, and Vermont. The following states are categorized as non-expansion: Alabama, Florida, Georgia, Idaho (implemented in November 2019), Kansas, Mississippi, Missouri (plans to implement in 2021), Nebraska (implemented in 2020), North Carolina, Oklahoma (plans to implement in 2021), South Carolina, South Dakota, Tennessee, Texas, Utah (implemented in 2020), Wisconsin, and Wyoming.
