A recent report published by the Institute of Health Metrics and Evaluation found that nearly one in four rural residents live in a county where female life expectancy is declining.
The report found that, overall, life expectancy in the United States is falling behind most other industrialized countries. People in the U.S. are not living as long as those in many other countries — and in a great number of rural counties, the average age at death is declining.
It appears there is no relationship between living in a rural places and longevity. A full explanation is below, but I wanted to make this clear from the beginning. Look at the chart above. It divides all counties according to how rural or urban they are and then divides them into five groups. The average ages at death for men and women in those groups are then shown in the bars.
As you can see, the longest living people can be found in the most rural and the most urban counties.
So, if it’s not ruralness, what, if anything, can explain differences in life expectancy? Is there something peculiar to rural places that is causing this decline? Is it a lack of insurance? Obesity? Access to doctors?
We decided to try to find what factors caused this troubling trend in life expectancy.
First, we picked a series of indicators that we thought might affect longevity. All of these indicators could be measured in each county. To show how rural a county was, we used a sliding scale developed at Purdue University that measures degrees of “rurality.”
All data is for the year 2007 except primary care providers (using 2006 data) and level of rurality (using data from 2000).
The indicators we used included:
• The Purdue index of relative rurality ranging from 0 (very urban) to 100 (very rural);
• The percent of people 20 years and over with obesity;
• The percent of people 20 years and over with diabetes;
• The percent of people under 65 years without health insurance;
• The primary care providers available per 10,000 residents;
• Median household income.
Using these six indicators, we can explain 70 percent of the variation in life expectancy among U.S. counties.
We then compared life expectancy to these factors. The vertical axis in the graphs with this story shows life expectancy (in years) for both men and women. (Men are the shorter of the two bars, of course.)
The horizontal axis shows five equal groups of counties for each of the indicators (rurality, diabetes, etc.) The averages of male and female life expectancy for each of those county groups are shown in the bars. The bottom 20% of the county groups (the least rural, the lowest rates of diabetes, the fewest primary care doctors) is shown to the left. The graph then steps up in fifths to the top 20% (most rural, most diabetes, most physicians) on the right.
What did we find?
First, there is no clear relationship between rural/urban and life expectancy. You can see in the chart at the top of this page that there is no real trend. In fact, life expectancy is the greatest for the most urban AND the most rural counties.
If there is one lesson, it’s that life expectancy is shortest in the suburbs or exurbs.
The relationship between obesity and life expectancy is clearly shown in the graph below.
As the percent of people 20 years and older with obesity increases (from left to right on the graph), life expectancy decreases. For example, male life expectancy for those counties in the top 20% based on obesity rates was of 71.8 compared to 76 years for those counties in the bottom 20%. That’s a difference of almost 5 years!
A similar relationship, though a bit stronger, is shown between diabetes rates and life expectancy (see graph below). As diabetes rates increase, life expectancy decreases. Counties in the top 20% based on diabetes rates had a life expectancy of 71.3 years for males and 77.8 for females, about five years less than those living in counties in the bottom 20%.
There appears to be no relationship between the percent of a county’s population that is uninsured and life expectancy.
Though this finding is somewhat unexpected, a potential explanation may be that the timing of our measures is off. In other words, since both percent uninsured and life expectancy data are for the same year (2007), perhaps a 3 or 5-year lag between percent uninsured (say 2002 or 2005) and life expectancy (2007) may show a different pattern.
For this study, however, health insurance doesn’t seem to play a role in longevity.
Primary Care Physicians
As you can see in the chart below, there is a positive relationship between primary care physicians and life expectancy. As the ratio of family care doctors increases in a county, so does life expectancy.
Not surprisingly, as household income goes up, so does life expectancy.
The top fifth of counties according to median household income had an average life expectancy of almost 81 years for females and 76 for males. Men in richer counties live nearly five years longer than men in the counties with the lowest median income.
It’s interesting to note that the relationship between median household income and gender varies, being stronger among males than females.
In summary, high rates of obesity and diabetes push longevity down while the presence of primary care providers and high median household income tends to increase life expectancy.
The relationship between life expectancy and level of rurality and percent uninsured is not clear.
Taken together, these six indicators explain almost 70% in the variance of life expectancy.
Roberto Gallardo is a research associate at the Southern Rural Development Center at Mississippi State University and a regular contributor to the Daily Yonder.