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18 Mar, 2014 22:39

​Tale of two counties: Poverty linked to lower life expectancy in US

​Tale of two counties: Poverty linked to lower life expectancy in US

Separated by only 350 miles, affluent Fairfax County, Virginia and hardscrabble McDowell County, West Virginia represent a life expectancy gap in the United States that many see expanding as income inequality grows.

The New York Times analyzed economic factors in the two very different counties and, while income’s correlation with health and longevity can be difficult to unequivocally prove, indicators suggest the high unemployment and stagnant opportunities in McDowell County have led to significantly more drug abuse, less access to health care, and an overall lower life expectancy than Fairfax County.

Fairfax County – buoyed by high median household income as a result of the Washington, DC area’s government-fueled economy – has one of the longest life expectancy rates in the US. Men live an average of 82 years, and women for 85 years, similar to Sweden, the Times noted.

In McDowell County, where the once-mighty coal industry is vanishing, men and women live an average of 64 and 73 years, respectively. These numbers resemble those in Iraq.

The life expectancy gap has grown between the two locales in recent decades. Since 1985, McDowell’s females have seen a two-year drop in longevity, while Fairfax females’ expectancy went up five years.

This divergence holds true throughout post-recession America, researchers have found. Men in the upper half of income earners live about six years longer than they did in the late 1970s, as opposed to 1.3 years longer for men in the lower half.

Fairfax unemployment is a low 3.6 percent, while the McDowell rate is 8.8 percent, down from over 13 percent during the height of the recession. The national unemployment average is 6.7 percent.

Yet the current McDowell figure is only that low since many have stopped looking for work altogether. Half of the income in McDowell is government assistance.

Lower income areas of the US have less outlets available for fitness, recreation, or decent health care. There are fewer options for decent food, and higher rates of smoking and obesity. Plus, the often unseen stress and hypertension associated with poverty take a toll, cutting lives short.

“Poverty is a thief,” said Michael Reisch, a professor of social justice at the University of Maryland, in a recent Senate testimony on the issue. “Poverty not only diminishes a person’s life chances, it steals years from one’s life.”

Public health researchers and the like are focusing more on the obvious links between income and life expectancy at a time when policymakers across the nation discuss minimum wage levels, unemployment insurance, upping the retirement age, and adjusting aspects of Social Security.

“The gaps continue to widen between the communities with the highest life expectancy and the lowest,” Christopher Murray, director of the Institute for Health Metrics and Evaluation in Seattle, told the Times. “There is nothing in sight that suggests that the 25-year trend is going to stop.”

“Would that be different if the income inequality were reduced?” he said. “If you took a 30-year view, then yes. There does seem to be that long-run relationship between community income and these life-expectancy outcomes.”

Despite all signs pointing to a clear correlation between income and life expectancy, researchers caution that accurate information on a county level can be tough to come by, and the fact that people move to new areas, say from McDowell to Fairfax, further confuses the issue. For instance, since the 1970s, McDowell’s population has dropped by half while Fairfax’s has doubled. How does this accurately reflect on overall life expectancy?

“These things are not nearly as clear as they seem, or as clear as epidemiologists seem to think,” said Princeton economist Angus Deaton.

Getting a raise does not mean a definite boost in health, for example.

“The statistical term is the ecological fallacy,” said David Kindig, emeritus professor at the University of Wisconsin School of Medicine. “We can’t apply aggregate data to an individual, and that’s underappreciated when you’re looking at these numbers.”

But, “having said that, I still think that the averages and the variation across counties tells us a lot,” he said. “We don’t want to let the perfect be the enemy of the good here.”