The AJC is up today with an excellent and hugely important story by Lynne Anderson about the state’s rural hospitals bailing out of baby business. This is the bow wave in the slow-motion disaster that is rural healthcare in Georgia in the 21st century.
One of several money grafs:
Our recent research on premature death rates in Georgia produced a couple of unexpected revelations, and we decided to loop back for a closer look. The revelations involve two of our five regions, North Georgia and Middle Georgia. (For a quick primer on that earlier work, click here, here and here.)
For our purposes, North Georgia is made up of 41 counties that lie outside Metro Atlanta and above the gnat line (see map below and click on the map for a larger view). As a region, it has clearly benefited from its proximity to Metro Atlanta; in recent decades, it has posted far and away the second-strongest population and economic growth in the state, outpacing not only Middle and South Georgia, but the Coastal region as well. Read more
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?
Throughout the Partner Up! for Public Health campaign, when we were conducting the research and analysis that enabled us to “connect the dots” between community health and economic vitality at a local level, one of the key health metrics we relied upon was premature death – better known as Years of Potential Life Lost before Age 75, or YPLL 75. YPLL 75 is generally regarded as the Dow Jones Industrial Average of a community’s (or a state’s, or a nation’s) health. If you want to look at one metric and get a sense of a community’s health, look at its YPLL 75 rate.
YPLL 75 is part of the formula the Robert Wood Johnson Foundation uses to calculate a county’s overall Health Outcomes Rankings, and it’s easy enough to pull from the State of Georgia’s excellent online public health data system, OASIS (for Online Analytical Statistical Information System). So it was a natural data point to work with. Read more
Over the past several weeks, I’ve been working on a presentation I’ll be delivering next month to the Kentucky Public Health Association. I was invited to speak to the group after one of its leaders saw me deliver an early version of the “Connecting the Dots: Community Health & Economic Vitality” presentation we developed as part of the Partner Up! for Public Health campaign that officially concluded last year.
It was literally yesterday afternoon that I finished double-checking data and proofing maps I’ll be using to demonstrate the overlap between good health and strong economies in the Bluegrass State – and this morning The New York Times gave me a major assist by publishing a front-page story built around a first-ever look at county-level smoking rates.
Here at the Partner Up! for Public Health campaign, we’re always looking for ways to illustrate the high cost of poor health. Recently we began to study the mountains of public health and economic data we’ve collected to see if we could develop a reasonable methodology for putting a price tag on premature death in Georgia.
This is not a new idea, but most of the studies and reports we’ve found rely on complex formulas that most of us can’t understand and produce results that people have trouble relating to. Truth is, those are easy traps to fall into; it’s difficult to deal with data without dealing with data. One formula we found looked like this: Read more