Peachcare and Young YPLL
In a recent post, we began to explore premature death rates within Georgia’s working-age population, men and women between the ages of 18 and 65. We were initially surprised to learn that improvements in the so-called YPLL 75 Rate for this segment of the state’s population lagged gains for the population as a whole. That led us to drill down a bit and look at premature death trends in the younger and older age groups – specifically, Georgians under the age of 18 and between the ages of 65 and 75.
Both groups saw significantly stronger gains in their premature death rates than did working-age Georgians. The question was why; what factors were driving premature death gains for younger and older Georgians that were somehow not impacting working-age Georgians?
The 24.6 percent improvement in premature death rates for older Georgians (versus 14.1 percent for working age Georgians) is easy enough to understand. It’s widely understood that American lifespans have been increasing for some time. Premature death calculations (formally known as YPLL 75 Rates, for Years of Potential Life Lost before Age 75) don’t include people over the age of 75. So as more and more people live beyond that age, their death, whenever it comes, is no longer premature. Hence the number of older people dying by 75 is shrinking, and the premature death rate for that 65-to-75 cohort improves accordingly.
The 38.1 percent gain among younger Georgians was more perplexing. After reporting this number in the initial Working Age YPLL blog, it hit me that one likely factor in that improvement is Peachcare, Georgia’s version of the federal government’s Children’s Health Insurance Program. Under this program, children in families with income of no more than 247 percent of the federal poverty level are eligible for low-cost access to healthcare.
After a raucous legislative fight, the Georgia General Assembly in 1998 approved a proposal by Governor Zell Miller to join the federal program, but it was a couple of years before the program really got off the ground. In its most recent annual report, the Georgia Department of Community Health includes Peachcare enrollment figures from 2000 through 2014; the Georgia Department of Public Health’s OASIS site includes YPLL 75 data from 1994 through 2013.
Overlay data for the years the two datasets have in common (2000 through 2013) and it would be hard to avoid a judgment that Peachcare had not helped improve premature death rates among Georgia’s children and young people.
As Peachcare enrollment began to climb starting in 2002, YPLL 75 rates for the 0-18 population showed steady progress and then improved even more dramatically after Peachcare enrollment peaked at 273,659 in 2007. This obviously makes intuitive sense. With the onset of the Great Recession in 2007, Peachcare enrollment declined significantly, and after a couple of years the YPLL 75 Rate line flattened out and then ticked up in 2013.
But that may be a little misleading. Because the Public Health OASIS system has YPLL 75 data going back to 1994, we had an opportunity to examine the pre-Peachcare trend line. And here, the picture gets a little murkier.
The truth is, Georgia had been making pretty good YPLL 75 Rate gains with its 0-18 population even before Peachcare came along. Between 1994 and 2000, annual improvement averaged 2.8 percent. By my calculations, if Georgia had sustained that average annual rate through 2013, the YPLL 75 Rate for young Georgians would have been pretty much the same as it ended up, maybe even a hair better.
So did Peachcare not have a beneficial impact? Looking at either chart, it’s hard to avoid seeing that the most dramatic gains in the YPLL 75 Rate for young Georgians coincided (allowing for a lag effect of a year or two) with the implementation of Peachcare, and that those gains seemed to have been mitigated once Peachcare enrollment fell off during the economic downturn starting in ’07.
At the same time, it’s hard to avoid thinking that the picture is more complicated than this, and that part of the explanation is still missing. We’ll come back to this topic in a future blog. We’ll also try to subject this data to some actual statistical analysis.
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