RAND study on health insurance
April 8, 2014 1 Comment
- Rate of uninsured dropped from 20.5% in September 2013 to 15.8% in Mid-March 2014 (so before the surge at the end of open enrollment). The study appears to be ongoing, so there should be essentially tracking poll-like information.
- They do a good job of demonstrating movement in and out of insurance and amongst types. From September to March, 14.5 Million gained coverage, while 5.2 Million lost it.
- Net gain in coverage driven by: gains in ESI (+8.2 Million), Medicaid (+5.9 Million), and exchange based plans through mid-March (+3.9 Million)
- They note that most newly covered by Medicaid were previously uninsured, while one-third of those covered by exchange plans were (again, prior to the March 2014 surge).
- They say less than 1 Million persons had individual market plans and are now uninsured
- 80% of adults had same coverage in March that they had in September
The best news is that they say they will update soon; more data is always good.
The survey results reported here were collected through March 28, 2014, but many panelists responded earlier in the month and may have made new insurance choices since then. Respondents will be surveyed again in April and our figures will be updated when new data is available.
Further, they note the error related to surveys:
Given the strong interest in understanding the impact of the ACA, a variety of different organizations, including the Urban Institute, are conducting surveys to estimate the impact of the ACA on insurance enrollment. When making comparisons, it is important to keep in mind that there is always a margin of error. In this case, because we are extrapolating from a small survey to the entire U.S. population, the margin of error is relatively large. For example, while we estimate 9.3 million individuals become newly insured, the margin of error is 3.5 million people.Furthermore, the timing of surveys may vary. Given the surge in enrollment at the end of March, whether that period is included in the survey may dramatically affect results. Thus, it should not be surprising that our estimates may not match perfectly.