VISIT STATE HEALTH COMPARE
Blog & News
2015 ACS: Widespread State-level Declines in Uninsurance, with Inter-State Variation Persisting (Infographic)
September 13, 2016:The U.S. Census Bureau released new health insurance coverage estimates today from the American Community Survey (ACS). The new estimates show a nationwide drop in uninsurance from 11.7% in 2014 to 9.4% in 2015 (down 2.3 percentage points), translating to almost 7 million fewer uninsured Americans in 2015. This decline is consistent with the historic decrease seen between 2013 and 2014.
| Most States Saw Uninsurance Declines in 2015, As in 2014 | |
|---|---|
| • | The percentage of the population who were uninsured when they responded to the ACS fell in 47 states and the District of Columbia from 2014 to 2015. |
| • | Uninsurance was stable in three states during this time: North Dakota, South Dakota, and Wyoming. |
| Variation in Uninsurance between States Continues | |
|---|---|
| • | Texas had the highest uninsured rate in 2015, at 17.1%. |
| • | The lowest 2015 uninsured rate was 2.8% in Massachusetts. |
| • | California saw the largest decline in uninsurance from 2014 to 2015, with a 3.9 percentage-point drop, from 12.4% to 8.6%. |
| • | No state saw a statistically significant increase in uninsurance in 2015. (As indicated above, North Dakota, South Dakota, and Wyoming each saw no significant change in uninsurance from 2014 to 2015.) |
| • | The uninsurance rate for expansion states (i.e., states that expanded Medicaid as of January 1, 2015) fell from 9.6% in 2014 to 7.2 % in 2015 (a 2.4 percentage-point decline). |
| • | The uninsurance rate for non-expansion states fell from 14.4% in 2014 to 12.3% in 2015 (a 2.1 percentage-point drop) . |
| • | Among expansion states, 2015 uninsurance rates ranged from a low of 2.8% in Massachusetts to a high of 12.3% in Nevada. |
| • | Among non-expansion states, 2015 uninsurance rates ranged from a low of 5.7% in Wisconsin to a high of 17.1% in Texas. |
The ACS Estimates in Context
The findings from the 2015 ACS are consistent with findings from the Current Population Survey (CPS), which were also released today. The CPS, which is the official source for information on poverty, found that the percentage of all people who were uninsured for the entire calendar year fell 1.3 percentage points nationwide, from 10.4% in 2014 to 9.1% in 2015. An early release of 2016 findings from the National Health Interview Survey (NHIS) showed stable uninsurance rates between the first quarter of 2015 and the same period in 2016.
Census Bureau Experts Will Share Insights during September 21st SHADAC Webinar
On Wednesday, September 21st, SHADAC will host a webinar to examine the new 2015 ACS and CPS estimates, with technical insight provided by researchers from SHADAC and the U.S. Census Bureau. Speakers will discuss the new national and state estimates, and attendees will learn when to use which estimates from which survey, how to access the estimates via Census reports and American FactFinder, and how to access state-level estimates from the ACS using SHADAC tables. Attendees will also have the opportunity to hear more about CPS employer offer and take-up estimates, for which three full years of data will be available. Speakers will answer questions from attendees after the presentations.
Publication
SHADAC Newsletter - September 2016
The SHADAC newsletter contains updates on SHADAC activities, news from the states, resource updates, and blog highlights. Subscribe to our newsletter here.
Publication
Webinar Podcast: Overview of the 2014 SAHIE Estimates with Census Experts
This webinar (recorded June 9, 2016) provides an overview of the recently released 2014 Small Area Health Insurance Estimates (SAHIE) from the U.S. Census Bureau .
David Powers and Lauren Bowers of the U.S. Census Bureau presented highlights from the recent SAHIE data release, provided guidance on how researchers can access the estimates, and discussed the updated Medicaid figures to generate new SAHIE data.
SHADAC researcher Brett Fried introduced the forthcoming SHADAC analyses that use SAHIE data to characterize the geographic concentration of the uninsured across the United States.
Presentation Materials
Click here to access the webinar slides.
Click here to access the webinar transcript.
Additional recording formats: Video for iPod, MP3 128 kbps, MP3 64 kbps
About the SAHIE
The SAHIE program uses statistical modeling of survey and administrative data to create estimates of health insurance coverage for counties and states by detailed demographic and income groups. SAHIE data can be used to analyze geographic variation in health insurance coverage as well as coverage differences by age, sex, race, and income.
Why Are the SAHIE Important?
The SAHIE are the only source of single-year health insurance coverage estimates for all U.S. counties. They are an enhanced version of the American Community Survey (ACS) health insurance coverage estimates.
What's New with this Release?
The 2014 SAHIE incorporate more current Medicaid data, helping to capture Medicaid expansion under the Affordable Care Act (ACA). The Census Bureau also re-released the 2013 SAHIE, likewise using more current Medicaid data, in order to facilitate comparisons between the 2013 and 2014 data.
The latest SAHIE release provides researchers and policy analysts with the first opportunity to compare uninsured rates for all U.S. counties during the implementation of the coverage provisions of the ACA.
Additional SAHIE Resources
From SHADAC:
Data Summary: Geographic Concentration of the Uninsured in 2013 & 2014: Provides results from the 2013 and 2014 Small Area Health Insurance Estimates (SAHIE) program at the U.S. Census Bureau.
Interactive Map: Provides information on the number of uninsured, the percent of the population that is uninsured, and the change in the uninsured rate from 2013 to 2014 for all counties.
Blog Entry: Discusses the 2014 SAHIE release.
From the U.S. Census Bureau:
Release Highlights of 2014 Estimates
Interactive Data Visualization and Mapping Tool
Blog & News
Validating Self-Reported Health Insurance Coverage: Preliminary Results on CPS and ACS (Cross-Post)
May 24, 2016:The following content is cross posted from the U.S. Census Bureau. It was first published on May 14, 2016.
External Authors: Joanne Pascale, U.S. Census Bureau; Angela Fertig, Medica Research Institute; and Don Oellerich, U.S. Department of Health and Human Services
Many federal, state and private surveys include questions that measure health insurance coverage. Each survey has different origins, constraints and methodologies and, as a consequence, the surveys produce different estimates of coverage. While several factors could contribute to the variation in the estimates, research points to subtle differences in the questionnaires as driving much of this variation.
Validation studies that evaluate survey responses for individuals whose coverage is known from insurance plan records are rare. In September 2014, a number of researchers and sponsors from different agencies came together to launch the Comparing Health Insurance Measurement Error study. The goal of this study is to assess reporting accuracy in two major federal surveys, the Current Population Survey Annual Social and Economic Supplement (CPS ASEC) and the American Community Survey (ACS), by comparing survey reports of coverage to enrollment records from a private health plan in Minnesota. Individuals known to be enrolled in a range of different coverage types, including employer-sponsored insurance, nongroup coverage, qualified health plans from the marketplace and public coverage, were sampled.
Phone numbers associated with these individuals were then randomly assigned to one of two survey treatments, and a split panel telephone survey was conducted in the spring of 2015. Person-level matching was then conducted between the survey data and the enrollment records, and the accuracy of reported point-in-time health insurance coverage was established. My presentation at the 2016 American Association for Public Opinion Research annual conference covers reporting accuracy for public and private coverage, in both the CPS ASEC and ACS, and future research will explore reporting the accuracy of more detailed coverage types.
The line between public and private coverage is becoming increasingly blurry. For example, some states offer public programs that charge a premium while other states offer marketplace coverage (which is considered private) that is completely subsidized. In addition, the “no wrong door” marketplace encourages individuals to explore different plans and complete one application to determine eligibility for a range of coverage types, from completely subsidized Medicaid to unsubsidized marketplace coverage. These blurry lines make it increasingly difficult to capture coverage type in surveys. Our research suggests that no single data point is sufficient for categorizing coverage type but rather several data points are needed, including general sources of coverage (employer, government, direct purchase, etc.), and whether the coverage is (1) obtained on the marketplace, (2) has a premium and (3) whether the premium is subsidized.
Judgements must also be made as to how these data points should be pieced together to categorize coverage. For example, coverage reported to be obtained through the government, on the marketplace, with a subsidized premium could be subsidized marketplace coverage (i.e., private), or it could be Children’s Health Insurance Program (CHIP), Medicaid or another public program that requires enrollees to pay a premium. As a result, an algorithm converting these data points into an estimate of coverage type is needed.
Beginning in 2014, CPS ASEC was adapted with questions about the marketplace, premiums and subsidies. In the Comparing Health Insurance Measurement Error study, two algorithms were developed for categorizing public versus private coverage. The first algorithm, V1, was strictly conceptual and based on program eligibility rules, while the other, V2, was data driven, using a machine learning approach. ACS does not yet include these questions so coverage type was categorized based on answers to a standard “laundry list” of questions on coverage type (direct purchase, Medicaid, etc.), where the direct purchase category is assumed to capture marketplace coverage.
Two different metrics are used to evaluate the agreement between survey estimates of coverage type and the actual coverage type from enrollment records. First is underreporting (see Figure 1). Among those known to be enrolled in private coverage under the CPS ASEC/V1 treatment, 93.7 percent reported private coverage. This was slightly higher than both the CPS ASEC/V2 at 92.1 percent and the ACS at 91.6 percent. There was no significant difference between the CPS ASEC/V2 and the ACS estimates. Among those known to be enrolled in public coverage under the CPS ASEC/V2 treatment, 79.8 percent reported public coverage. This was significantly higher than both the ACS at 71.8 percent and the CPS ASEC/V1 at 68.96 percent. There was no significant difference between the CPS ASEC/V1 and the ACS.
The other metric is the flip side of underreporting (see Figure 2). Among those who reported private coverage, the percent that could be validated in the enrollment records to have private coverage was highest in the CPS ASEC/V2 (92.6 percent), which was significantly higher than both the CPS ASEC/V1 at 86.2 percent, and the ACS at 81.5 percent. The 4.7 percentage point difference between the CPS ASEC/V1 and the ACS was also significant. Among those who reported public coverage, the percent that could be validated in the enrollment records to have public coverage was 92.7 percent in the CPS ASEC/V1 and 91.4 percent in both the CPS ASEC/V2 and the ACS, and no differences across surveys were significant.
Publication
Contrasting Measures of Health Insurance Literacy and their Relationship to Health Care Access
View a PDF of the presentation slides.
Presentation by Kathleen Call at the 2016 American Association for Public Opinion Research Conference (AAPOR), May 14, 2016, in Austin, TX.
