Working Age YPLL

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.

But since we were focused on examining the relationship between health and economics, I wondered if it were possible to slice the data so that we could look more specifically at the health of the working age population as opposed to the population as a whole.

A couple of years ago, I bumped into Gordon Freymann, head of the Office of Health Indicators for Planning at the Georgia Department of Public Health, at a conference and asked him that question.  Would it be possible, for instance, to look at premature death rates for the working-age population (which I call YPLL 65).  Yes, he said.  In fact, there is a lesser-known metric known as Years of Productive Life Lost that measures premature death among 18-to-64-year-olds.

It takes a few additional keystrokes, but that data too can be pulled from OASIS, with interesting and at least mildly surprising results.  Surprising, at least to me, because my hunch going in was that the premature death rate for the working age population would be better than for the population as a whole.  Not so.  Freymann’s OASIS system now has 20 years of data (covering the period 1994-2013) and one of the first things it reveals is that, statewide, premature death rates have been consistently better for the population as a whole than for the working age population.  Here’s how the statewide data graphs:

(Note: As an admittedly imprecise bit of shorthand, we refer to the premature death rates for the working age population as the YPLL 65 Rate, both in the graph above and the tables below.  Actually, it’s YPLL 75 Rate for Georgians between the age of 18 and 65, but, again, we needed a shorter way to say that for the graph and the tables.)

At first glance, it appears the two lines have moved along largely parallel paths throughout the 20-year period, but a closer look finds that they’ve been separating throughout the period.  Between 1994 and 2000, the difference between the two rates floated up and down between 6.7 and 8.0 percent.  But starting in 2001, the gap began to widen at an accelerating pace; the difference peaked at 14.1 percent in 2012 and then closed up slightly in 2013.

In other words, the difference between the premature death rates for our population as a whole and our working age population has basically doubled (from less than seven percent to right at 13 percent) in the 20 years for which we have data, with the working age population on the short end of that stick.

The fact that the Working Age YPLL Rate isn’t improving on the same pace as the overall YPLL 75 Rate gives rise to an obvious question: Where are the gains being made?  To answer that question, I pulled YPLL 75 data for Georgians under the age of 18 and between 65 and 75 (which I called Young YPLL and Old YPLL, respectively).

It turns out both those groups have fared very well over the 1994-2013 period.  The really good news is that Georgia has cut the premature death for young people by 38.1 percent during that period.  Also impressive is that the premature death rate for older Georgians has improved 24.6 percent.  But those gains were offset by the lagging performance among working age Georgians, who make up the largest part of the population and are, of course, shouldering the state’s economic load.

Regional Differences

Those generally parallel statewide lines mask major differences between and among Georgia’s various regions.  Our 12-county Metro Atlanta region, which has seen far and away the most economic growth over the past two decades, also saw the most improvement in both its YPLL 75 and YPLL 65 rates, and it was in fact the only region where YPLL 65 gains came close to keeping pace with the YPLL 75 improvements.  The two tables below summarize our findings so far.

Change in YPLL 75 Rates by Region: 1994-2013

Change in YPLL 65 Rates by Region: 1994-2013

Again, Metro Atlanta is the only region where the YPLL 65 Rate came close to matching improvements in the YPLL 75 Rate, and the region easily outpaced the rest of the state in both categories.  That absolutely squares with the fact that Metro Atlanta has been outperforming the remainder of the state economically by widening margins for several decades now – as does the fact that our 56-county South Georgia region pretty much brings up the rear in both economic performance and YPLL gains.

(We’ll take a deeper dive into regional economic performance in a future blog.)

But the data also offers up some puzzles.  Among the five regions, Middle Georgia, for instance, has made the second-strongest gains in both YPLL 75 and YPLL 65 rates, but its economic performance is only marginally superior to South Georgia’s.  And North Georgia, whose economic performance is stronger than both Middle and South Georgia, trailed Middle Georgia in both YPLL 75 and YPLL 65 gains, and barely outperformed South Georgia.

My working theory to explain Middle Georgia’s surprisingly good performance is that it owes at least partially the presence of three military bases in the region – Fort Benning outside Columbus, Warner-Robins AFB in Houston County just south of Macon, and Fort Gordon in Richmond County – and the fact that military personnel have better access to healthcare than many other Georgians in the region.  But that hypothesis needs further study and testing.  The reasons for North Georgia’s relatively lackluster performance are less apparent.

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