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 The county-specific findings, available online (http://bit.ly/1nUtNEw),
may illuminate areas and issues needing attention, the research team
says.
"Within any individual county, knowing how big of a problem a
condition is" can help counties know which conditions need
attention, resources and policies, said the study's lead author
Laura Dwyer-Lindgren, of the Institute for Health Metrics and
Evaluation at the University of Washington in Seattle.
Also, she told Reuters Health, knowing how causes of death change
over time in one county may help identify ways to address that same
issue in another.
For the new study, the researchers used data on more than 60 million
deaths in 3,110 U.S. counties or groups of counties from 1980
through 2014.
As reported in JAMA, rates of death, and causes of death, varied
widely. For example, death rates from cardiovascular disease tended
to be highest in counties near the southern half of the Mississippi
river.
Also, death rates from self-harm and interpersonal violence were
highest in southwestern counties, while the highest rates of death
from chronic respiratory issues were in eastern Kentucky and western
Virginia.
Death rates and causes also changed over time, according to the
researchers.
The study can't say why death rates and causes of death fluctuate
and vary so widely by county.
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"It’s probably reasonable to guess that part of the variation is to
due to variations in risk factors," said Dwyer-Lindgren, such as
obesity, social and economic status and access to healthcare.
In an editorial, Dr. Cheryl Clark of Brigham and Women's Hospital
and Harvard Medical School in Boston and David Williams of the
Harvard T.H. Chan School of Public Health write that the researchers
"have provided powerful tools with which to examine geographic
inequalities in health."
Dwyer-Lindgren hopes the data will be used by public health
officials and clinicians. Also, she said, "We’re hoping lay people
will make use of these estimates in their own communities to
possibly advocate for policies they think would be most beneficial
based on that information."
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