Your ZIP Code May Influence Your Death as Much as Your Genes

2749

Where you live may determine how you die, and how long you live.

A first of its kind study published Tuesday in JAMA analyzes all 80 million deaths in the U.S. between 1980 and 2014. It plots them by color-coded cause and overall mortality on a map of the nation.

“Among counties within the United States, relatively little is known about geographic patterns and inequalities in mortality by underlying cause of death,” JAMA reported in a news release. “Information about variation in cause-specific mortality could provide important insights into geographic inequalities and divergent trends in life expectancy.”

The research by Dr. Christopher J. L. Murray and colleagues of the Institute for Health Metrics and Evaluation at the University of Washington in Seattle looked at death registration data from the National Vital Statistics System. It broke the data down into county-level causes of death for 21 different conditions.

Credit: Institute for Health Metrics and Evaluation
Credit: Institute for Health Metrics and Evaluation

For an interactive version of the map, click here.

For approximately one in four deaths, researchers did not have sufficient evidence to determine a cause of death. Among the 3,110 counties or clusters of counties analyzed, causes of death varied widely.  But they did observe some trends.

“Cardiovascular disease mortality tended to be highest among the southern half of the Mississippi River, while mortality rates from self-harm and interpersonal violence were elevated in southwestern counties, and mortality rates from chronic respiratory disease were highest in Eastern Kentucky and western West Virginia,” according to the JAMA news release.

It may not be surprising to find a link between the south (and its love for certain fatty foods) and cardiovascular disease. Nor is it a shocker to see that chronic respiratory disease is more prevalent in the tobacco belt than in other parts of the U.S.

“Geographic patterns differed significantly across causes, underscoring the importance of considering cause-specific mortality in addition to measures of all-cause mortality such as life expectancy,” the authors wrote. “For some causes (e.g. cardiovascular disease) counties in the south and Appalachia had elevated mortality, while counties in Western states had mortality much lower than average, a pattern that, broadly speaking, has also been documented in maps of life expectancy as well as maps of risk factors such as smoking, physical activity, and obesity.”

So, what can public health officials do with all this data?

“There are a number of potential uses for these estimates: state and county health departments could use county-level mortality estimates to identify pressing local needs and to tailor policies and programs accordingly; physicians could use these estimates to better understand the health concerns of the populations they serve; researchers could identify counties that have done unexpectedly well or poorly with regard to a particular cause of death and that warrant additional study to identify factors driving these trends; and communities can use these estimates as evidence when advocating for change,” the authors wrote. “Further, for causes of death for which effective treatments are available, variation in mortality rates can highlight where access to treatment or quality of care is a pressing problem.”