Counties are the smallest unit for which mortality data are routinely available, allowing consistent and comparable long-term analysis of trends in health disparities. Average life expectancy has steadily increased in the United States but there is limited information on long-term mortality trends in the US counties This study aimed to investigate trends in county mortality and cross-county mortality disparities, including the contributions of specific diseases to county level mortality trends.
Methods and Findings
We used mortality statistics (from the National Center for Health Statistics [NCHS]) and population (from the US Census) to estimate sex-specific life expectancy for US counties for every year between 1961 and 1999. Data for analyses in subsequent years were not provided to us by the NCHS. We calculated different metrics of cross-county mortality disparity, and also grouped counties on the basis of whether their mortality changed favorably or unfavorably relative to the national average. We estimated the probability of death from specific diseases for counties with above- or below-average mortality performance. We simulated the effect of cross-county migration on each county's life expectancy using a time-based simulation model. Between 1961 and 1999, the standard deviation (SD) of life expectancy across US counties was at its lowest in 1983, at 1.9 and 1.4 y for men and women, respectively. Cross-county life expectancy SD increased to 2.3 and 1.7 y in 1999. Between 1961 and 1983 no counties had a statistically significant increase in mortality; the major cause of mortality decline for both sexes was reduction in cardiovascular mortality. From 1983 to 1999, life expectancy declined significantly in 11 counties for men (by 1.3 y) and in 180 counties for women (by 1.3 y); another 48 (men) and 783 (women) counties had nonsignificant life expectancy decline. Life expectancy decline in both sexes was caused by increased mortality from lung cancer, chronic obstructive pulmonary disease (COPD), diabetes, and a range of other noncommunicable diseases, which were no longer compensated for by the decline in cardiovascular mortality. Higher HIV/AIDS and homicide deaths also contributed substantially to life expectancy decline for men, but not for women. Alternative specifications of the effects of migration showed that the rise in cross-county life expectancy SD was unlikely to be caused by migration.
There was a steady increase in mortality inequality across the US counties between 1983 and 1999, resulting from stagnation or increase in mortality among the worst-off segment of the population. Female mortality increased in a large number of counties, primarily because of chronic diseases related to smoking, overweight and obesity, and high blood pressure.
Majid Ezzati and colleagues analyze US county-level mortality data for 1961 to 1999, and find a steady increase in mortality inequality across counties between 1983 and 1999.
It has long been recognized that the number of years that distinct groups of people in the United States would be expected to live based on their current mortality patterns (“life expectancy”) varies enormously. For example, white Americans tend to live longer than black Americans, the poor tend to have shorter life expectancies than the wealthy, and women tend to outlive men. Where one lives might also be a factor that determines his or her life expectancy, because of social conditions and health programs in different parts of the country.
Why Was the Study Done?
While life expectancies have generally been rising across the United States over time, there is little information, especially over the long term, on the differences in life expectancies across different counties. The researchers therefore set out to examine whether there were different life expectancies across different US counties over the last four decades. The researchers chose to look at counties—the smallest geographic units for which data on death rates are collected in the US—because it allowed them to make comparisons between small subgroups of people that share the same administrative structure.
What Did the Researchers Do and Find?
The researchers looked at differences in death rates between all counties in US states plus the District of Columbia over four decades, from 1961 to 1999. They obtained the data on number of deaths from the National Center for Health Statistics, and they obtained data on the number of people living in each county from the US Census. The NCHS did not provide death data after 2001. They broke the death rates down by sex and by disease to assess trends over time for women and men, and for different causes of death.
Over these four decades, the researchers found that the overall US life expectancy increased from 67 to 74 years of age for men and from 74 to 80 years for women. Between 1961 and 1983 the death rate fell in both men and women, largely due to reductions in deaths from cardiovascular disease (heart disease and stroke). During this same period, 1961–1983, the differences in death rates among/across different counties fell. However, beginning in the early 1980s the differences in death rates among/across different counties began to increase. The worst-off counties no longer experienced a fall in death rates, and in a substantial number of counties, mortality actually increased, especially for women, a shift that the researchers call “the reversal of fortunes.” This stagnation in the worst-off counties was primarily caused by a slowdown or halt in the reduction of deaths from cardiovascular disease coupled with a moderate rise in a number of other diseases, such as lung cancer, chronic lung disease, and diabetes, in both men and women, and a rise in HIV/AIDS and homicide in men. The researchers' key finding, therefore, was that the differences in life expectancy across different counties initially narrowed and then widened.
What Do these Findings Mean?
The findings suggest that beginning in the early 1980s and continuing through 1999 those who were already disadvantaged did not benefit from the gains in life expectancy experienced by the advantaged, and some became even worse off. The study emphasizes how important it is to monitor health inequalities between different groups, in order to ensure that everyone—and not just the well-off—can experience gains in life expectancy. Although the “reversal of fortune” that the researchers found applied to only a minority of the population, the authors argue that their study results are troubling because an oft-stated aim of the US health system is the improvement of the health of “all people, and especially those at greater risk of health disparities” (see, for example http://www.cdc.gov/osi/goals/SIHPGPostcard.pdf).
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050066.
A study by Nancy Krieger and colleagues, published in PLoS Medicine in February 2008, documented a similar “fall and rise” in health inequities. Krieger and colleagues reported that the difference in health between rich and poor and between different racial/ethnic groups, as measured by rates of dying young and of infant deaths, shrank in the US from 1966 to 1980 then widened from 1980 to 2002
Murray and colleagues, in a 2006 PLoS Medicine article, calculated US mortality rates according to “race-county” units and divided into the “eight Americas,” and found disparities in life expectancy across them
The US Centers for Disease Control has an Office of Minority Health and Health Disparities. The office “aims to accelerate CDC's health impact in the US population and to eliminate health disparities for vulnerable populations as defined by race/ethnicity, socioeconomic status, geography, gender, age, disability status, risk status related to sex and gender, and among other populations identified to be at-risk for health disparities”
Wikipedia has a chapter on health disparities (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
In 2001 the US Agency for Healthcare Research and Quality sponsored a workshop on “strategies to reduce health disparities”