The Reversal of Fortunes: Trends in County
Mortality and Cross-County Mortality Disparities
in the United States
Majid Ezzati1,2*, Ari B. Friedman2
, Sandeep C. Kulkarni2,3, Christopher J. L. Murray1,2,4
1 Harvard School of Public Health, Boston, Massachusetts, United States of America, 2 Initiative for Global Health, Harvard University, Cambridge, Massachusetts, United
States of America, 3 University of California, San Francisco, California, United States of America, 4 Institute for Health Metrics and Evaluation, University of Washington,
Seattle, Washington, United States of America
Funding: This research was
supported by a cooperative
agreement, awarded by the Centers
for Disease Control and Prevention
and the Association of Schools of
Public Health (grant U36/
CCU300430–23). The funders had no
role in study design, data collection
and analysis, decision to publish, or
preparation of the manuscript. The
contents of this article are solely the
responsibility of the authors and do
not necessarily represent the official
views of the Centers for Disease
Control and Prevention or the
Association of Schools of Public
Competing Interests: The authors
have declared that no competing
Academic Editor: Thomas Novotny,
Center for Tobacco Control Research
and Education, United States of
Citation: Ezzati M, Friedman AB,
Kulkarni SC, Murray CJL (2008) The
reversal of fortunes: Trends in
county mortality and cross-county
mortality disparities in the United
States. PLoS Med 5(4): e66. doi:10.
Received: July 2, 2007
Accepted: January 28, 2008
Published: April 22, 2008
Copyright: 2008 Ezzati et al. This
is an open-access article distributed
under the terms of the Creative
Commons Attribution License, which
permits unrestricted use,
distribution, and reproduction in any
medium, provided the original
author and source are credited.
Abbreviations: COPD, chronic
obstructive pulmonary disease;
NCHS, National Center for Health
Statistics; SD, standard deviation
* To whom correspondence should
be addressed. E-mail: [email protected]
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 longterm 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 crosscounty 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.
The Editors’ Summary of this article follows the references.
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Average life expectancy in the United States has increased
steadily in the past few decades, rising by more than 7 y for
men and more than 6 y for women between 1960 and 2000.
Parallel to this aggregate improvement, there are large
disparities in health and mortality across population subgroups defined by race, income, geography, social class,
education, and community deprivation indices [1–18]. Furthermore, there is evidence that health and mortality
disparities have persisted or even increased over time in
both relative and absolute terms [2,10,11,15,19,20], indicating
that the observed aggregate health gains may not have been
Counties are an important unit of analysis in understanding trends in mortality disparities in the United States.
First, county-level analysis helps assess health disparities in
relation to place of residence, and therefore complements
analysis by race, income, and other socioeconomic factors.
Second, counties are the smallest measurable unit for which
mortality data are routinely available, and county-level data
allow analyses for small subgroups of the US population. For
example, analysis of the US mortality statistics for 1997–2001
(pooled over 5 y to increase the number of observations in
small counties) demonstrates that the highest and lowest
county life expectancies in the United States were 18.2 and
12.7 y apart for males and females, respectively, compared to
6.7- and 4.9-y gaps between whites and blacks as a whole.
Further, county definition is relatively invariant over time,
allowing consistent and comparable long-term analysis of
trends in health disparities. This consistency is an important
advantage because even analysis by race may be affected by
changes in self-reported race in census figures over time
[21,22]. Finally, county-level socioeconomic data are available
from the census, and cause-specific mortality data are
available from the vital statistics. These two data sources
allow analysis of trends in all-cause mortality as well as
mortality from specific diseases, in relation to both the
location of county of residence and its environmental and
socioeconomic characteristics (see also Singh [10,20]).
We used data on all-cause mortality to analyze trends in
mortality and mortality disparities in US counties for a
period of approximately four decades (1961–1999), one of the
longest trend analyses of mortality disparities in the United
States to our knowledge. We also grouped counties on the
basis of whether their mortality changed favorably or
unfavorably relative to the national average, and identified
those counties with mortality stagnation and increase. Finally,
we examined the epidemiological (disease-specific mortality)
and selected socioeconomic characteristics of counties with
below- or above-average mortality performance.
We arranged the 3,141 US counties into 2,068 units, each
consisting of one or multiple individual counties. There were
two reasons for forming merged county units: (1) to avoid
unstable death rates, smaller counties were merged with
adjacent counties to form units with a total population of at
least 10,000 males and 10,000 females in 1990 ; and (2) to
account for changes in county definitions and lines, such as
formation of new counties and reversion to non-county
status. This grouping of counties created a consistent set of
2,068 individual or merged county units that represent the
same physical land areas from 1959 through the present.
Because borough-specific death statistics were not available
prior to 1982 in New York City, its five separate counties were
merged into a single unit. For each county unit, we calculated
annual sex-specific life expectancies. Table 1 provides
summary information on the sociodemographic characteristics of counties. We also calculated probabilities of death
from all causes as well as from specific diseases and disease
clusters in the following age groups: 0–4, 5–14, 15–44, 45–64,
65–74, and 75–84 y.
We report the standard deviation (SD) of life expectancies
of the 2,068 county units in the United States, as well as life
expectancy for counties that make up the 2.5% of the US
population with the highest and lowest county life expectancies in each year, by sex. We also report changes in
mortality from specific diseases for six groups of counties,
defined on the basis of how their life expectancy changed in
relation to the national sex-specific change as follows: group
1, counties whose life expectancy increased at a level
(statistically) significantly higher than the national sexspecific mean; group 2, counties whose life expectancy
increased at a level significantly higher than zero but not
significantly distinguishable from the national sex-specific
mean; group 3, counties whose life expectancy increased at a
level significantly higher than zero but significantly less than
the national sex-specific mean; group 4, counties whose life
expectancy change was statistically indistinguishable from
zero and from the national sex-specific mean; group 5,
counties whose life expectancy change was statistically
indistinguishable from zero and was significantly less than
the national sex-specific mean; and group 6, counties whose
life expectancy had a statistically significant decline. All
statistical significance was assessed at 90%.
Mortality statistics, including county of residence and cause
of death certified and coded according to the International
Classification of Diseases (ICD) system, were obtained from
the National Center for Health Statistics (NCHS). Standard
public-use mortality files do not include geographic identifiers for deaths in counties with fewer than 100,000 people.
We obtained county identifiers for all deaths for years 1959
through 2001 through a special request to the NCHS. County
identifiers for years after 2001 were not provided to us by the
NCHS. County population by age for 1960 and all years
between 1970 and 2001 are publicly available through the US
Census Bureau; data prior to 1990 were accessed through the
US Census Bureau and for 1990 and later through the NCHS
. We estimated intercensal county population for 1959
and 1961–1969 using the 1960 and 1970 censual estimates and
an exponential growth model.
We used the following data sources for county-level
sociodemographic characteristics, and for cross-county migration: (1) proportion of population by race and urban/rural
place of residence, US Census via American FactFinder
(http://factfinder.census.gov/) (for the 1990 and 2000 censuses)
and via the Inter-University Consortium for Political and
Social Research (ICPSR) (http://www.icpsr.umich.edu) (for the
1960, 1970, and 1980 censuses); (2) education, US Census via
the National Historical Geographic Information System
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Mortality Trends in US Counties
(http://www.nhgis.org/) (1970 and 1980) and via American
FactFinder (1990 and 2000). Linear interpolation was used to
estimate values for intermediate years; (3) per capita income,
US Census (years 1979, 1989, and 1999) and County Databooks based on the US Census (years 1969, 1972, 1975, 1981,
1983, and 1988), both via ICPSR. Per-capita income for other
years was interpolated using an exponential growth model.
All income estimates were adjusted for inflation with 2000 as
the base year; and (4) cross-county migration, IRS External
Data Product ‘‘County-to-County Migration Flows’’ (see
http://www.irs.gov/pub/irs-soi/prodserv.pdf), which contains
tabulations of the number of individuals moving from each
county to every other county, and their mean and median
income, by matching the Taxpayer Identification Number
and comparing zip codes of filing addresses from one year to
the next. Detailed data to quantify cross-county migration for
all counties were available for 1993–1999.
Statistical Methods for the Analysis of Mortality Data
We estimated life expectancy and probabilities of death
between specific ages, from all causes combined as well as
from specific diseases, using standard life table techniques
. Life tables for each county-year were constructed using
age-specific death and population data in 5-y age groups.
Following standard life table techniques , those surviving
to 85 y of age were assigned a life expectancy equal to the
inverse of their observed mortality rate. For each year in each
county unit, we calculated life expectancy and probabilities of
death by pooling death and population data over 5 y (the year
of analysis and two years on each side) to reduce sensitivity to
small numbers (e.g., life expectancy in 1999 used death and
population data from 1997 to 2001). Therefore, data from
1959 to 2001 yielded life expectancy estimates for 1961–1999,
presented in Dataset S2 for all county-years. National-level
life expectancy was not affected by small number of deaths
and was calculated using data from individual years.
We estimated uncertainty in county life expectancy, and in
change in life expectancy over time, using a binomial
simulation. In brief, the uncertainty in the age-specific death
rate in each county depends on the number of deaths
(numerator) (n) and population (denominator) (N), and can
be characterized with a binomial parameter with an expected
value of p ¼ n/N and a variance of p 3 (1 p)/N. We simulated
1,000 draws from this distribution for every age-sex combination in 1961, 1983, and 1999, leading to 1,000 life
expectancies for each county-year in these 3 y. The
distribution of the 1,000 differences in the randomly drawn
life expectancies for 1961–1983 and for 1983–1999 was then
used to calculate confidence intervals and establish the
statistical significance for life expectancy change. Owing to
computational constraints, we used 100 draws for estimating
the confidence intervals for absolute disparity between
counties at the extremes of mortality advantage and
disadvantage. The total number of simulated life tables was
40,946,400, calculated from 737,035,200 death rates.
Effects of Cross-County Migration on Life Expectancy
We simulated the effect of cross-county migration on each
county’s life expectancy using a time-based simulation model.
This analysis was only done for 1993–1999, for which
sufficiently detailed cross-county migration data were available. For each year after 1993, simulated life expectancy in
each of the 2,068 county units was calculated as the weighted
sum of its own life expectancy from the previous year and
those of immigrants from all other counties, with weights
being equal to the proportion of the new population from
each county (i.e., population mixing based on a 2,068 3 2,068
migration matrix). This analysis was done for both sexes
combined, because sex-specific migration data were not
available. We repeated this analysis using three alternative
assumptions: (1) emigrants had the same life expectancy as
those who stay in the county of origin; (2) the life expectancy
of all emigrants was 1 y higher than the life expectancy of
those who stayed in the country of origin; and (3) the life
expectancy of the emigrants to counties with higher life
expectancy was 1 y higher than those who stayed in the
country of origin or migrated to counties of lower life
expectancy. The last two scenarios were based on some
evidence that migrants may be healthier than those who do
not move [24,25]. In both scenarios, the life expectancy of the
remaining population was adjusted downwards so that overall
life expectancy was correctly calculated.
All analyses were done using Stata version 9.2 and ESRI
ArcGIS version 9.2.
Between 1961 and 1999, average life expectancy in the
United States increased from 66.9 to 74.1 y for men and from
Table 1. Summary Statistics for Socioeconomic and Demographic Characteristics of the 2,068 County Units Used in the Analysis in the
Sociodemographic Variable Mean SD Median Interquartile Range Minimum Maximum
Population 134,930 381,880 45,790 70,990 18,780 9,437,290
Average income ($) 18,530 4,070 17,850 4,280 7,220 45,930
Percent completing high school 81.3 7.0 82.5 9.8 42.0 95.5
Within-county Gini coefficienta 38.3 3.8 38.0 5.0 28.7 56.5
Male life expectancy (y)b 73.4 2.3 73.6 3.2 62.0 80.2
Female life expectancy (y)b 79.2 1.7 79.2 2.4 71.8 84.5
5-y in-migration (%) 21.0 6.4 20.0 7.9 7.4 56.9
Summarized across all counties in the United States for the year 2000 because figures were only available for individual counties and not for merged county units in our analysis (http://
Pooled over 1997–2001.
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Mortality Trends in US Counties
73.5 to 79.6 y for women. The spread of male life expectancy
across US counties, as measured by SD, rose slowly in the
1960s, then declined steeply until 1983 (1.9 y), when it began
to rise again to 2.3 in 1999; the rate of increase declined in
the 1990s (Figure 1). For women, cross-county life expectancy
SD declined between 1961 and 1983 (from 2.0 y to 1.4 y), but
rose steadily to 1.7 y in 1999. Cross-county life expectancy SD
was always larger for men than for women.
In the early 1980s, the absolute disparity between counties
at the extremes of mortality advantage and disadvantage also
began to increase. For example, the difference between life
expectancies of the counties that make up the 2.5% of the US
population with the lowest and highest mortality in each year
rose from 9.0 y in 1983 to 11.0 y in 1999 for men, and from 6.7
y to 7.5 y for women (Figure 2B). This widening gap was
caused by stagnating improvements in life expectancy among
the worst off, while the best off experienced consistent
mortality decline (Figure 2A). Between 1961 and 1999, male
life expectancy in counties with the lowest mortality rose
from 70.5 to 78.7 y; the corresponding rise for females was
from 76.9 to 83.0 y. Trends in the worst-off counties were
more punctuated: Life expectancy increased in the 1960s and
1970s for females and in the 1970s for males. Starting in the
early 1980s, life expectancy of the worst-off females remained
relatively stable (68.7 y in 1961, 74.5 in 1983, and only 75.5 in
1999); that of the worst-off men had a period of decline,
rising again in the 1990s. The stagnation of mortality among
the worst off was primarily caused by a slowdown or halt of
the earlier decline of cardiovascular mortality, coupled with a
moderate rise in a number of other chronic diseases, for both
sexes as well as HIV/AIDS and homicide for men.
Figure 3 groups counties by change in life expectancy,
separately for 1961–1983 and 1983–1999, in relation to the
sex-specific national average life expectancy change over the
same period. Figure 4 shows the epidemiological characteristics of below- or above-average mortality reduction observed in Figure 3. Dataset S2 and Movies S1 and S2 show the
estimated life expectancy for individual counties and for all
years between 1961 and 1999. Figures S1 and S2 show the
absolute change in county life expectancy for 1961–1983 and
1983–1999. Between 1961 and 1983, counties whose life
expectancy increase was (statistically) significantly larger than
the national average were primarily in the rural Deep South
and the Eastern Seaboard, in the West from the Mexican
border into the Rocky Mountains for both sexes, in Alaska,
California, and Hawaii for men, and in the Dakotas and along
the Mississippi River for women. During this same period, life
expectancy improvements were significantly below the national average in the Midwest and Southern California for
both sexes, in the Mississippi Delta for men, and in parts of
the West Coast for women. In this period, the best-performing counties had the lowest average starting life expectancy of
all groups in Figure 3 (66.1 y for men and 72.0 for women;
Table 2), and the worst-performing ones had the highest
starting life expectancy (68.9 for men and 77.6 y for women).
This negative association between starting life expectancy
and change in life expectancy supports the finding of
shrinking cross-county mortality disparities. In 1961–1983,
no counties had a statistically significant decline in sexspecific life expectancy at the 90% confidence level. The
broad improvement of life expectancy observed during this
period was primarily caused by major reductions in cardiovascular disease mortality for both sexes, compensating and
surpassing the rise in cancers and chronic obstructive
pulmonary disease (COPD). The distinction between those
counties that performed better or worse than the national
average in this period was primarily the rate of decline in
cardiovascular diseases, with secondary effects from injuries
and other noncommunicable diseases.
Between 1983 and 1999, male and female life expectancies
had statistically significant decline in 11 and 180 counties,
respectively (0.5% and 3.0% of the male and female
populations); average decline in these counties was 1.3 y for
both men and women. Another 48 and 783 counties had
nonsignificant life expectancy decline for men and women
(0.4% and 8.8% of the male and female populations),
respectively. The average life expectancy decline in these
counties was 0.5 y for women and 0.4 y for men, but these
were not statistically significant because these counties were
relatively small. Of the counties with statistically significant
life expectancy decline, all for males and all but seven for
females were in the Deep South, along the Mississippi River,
and in Appalachia, extending into the southern portion of
the Midwest and into Texas. There were also a number of
counties with significant female life expectancy decline in the
Rocky Mountain area and the Four Corners region, and one
in Maine. Between 1983 and 1999, above-average mortality
gain also became geographically more concentrated, and
shifted to the Northeastern and Pacific Coast counties.
The decline in female life expectancy after 1983 was caused
by a rise in mortality from lung cancer, COPD, diabetes, and a
range of other noncommunicable diseases in the older ages
(Figure 4; detailed numerical results available in Dataset S1).
Female mortality from lung cancer, COPD, and even diabetes
had also risen in 1961–1983, but this rise was surpassed by the
decline in cardiovascular disease mortality. The rise in
mortality for these causes in 1983–1999 was no longer
compensated by the decline in cardiovascular mortality
because cardiovascular decline became substantially smaller
than it was in 1961–1983 (women in the worst-performing
group, group 6, actually experienced a rise in cardiovascular
mortality in the oldest age group). In 1983–1999, the rise in
Figure 1. SD of Life Expectancies of the 2,068 County Units in the United
States by Sex
Inequality in family income (e.g., as measured by the Gini coefficient)
declined in the United States between the 1920s and 1970s, and has
increased after that period [49,50].
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Mortality Trends in US Counties
HIV/AIDS and homicide deaths in young and middle-aged
men was a major contributor to male, but not female, life
expectancy decline. Mortality from diabetes, cancers, and
COPD in the older ages also worsened in men but these
continued to be countered by relatively large reductions in
male cardiovascular mortality.
Between 1961 and 1983, counties with life expectancy
improvement above and below the national average had
relatively similar income levels; average county income was
lower in those counties whose life expectancy change was
below average and indistinguishable from zero (group 5), but
these represented ,1% of the female population (Table 2).
Black women formed a larger proportion of the population
in counties with above-average life expectancy improvement
than in those counties with below-average life expectancy
change; the pattern was reversed for men. After 1983, gain in
life expectancy was positively associated with county income.
The proportion of blacks was higher in counties with life
expectancy decline, especially for men, but there were no
detectable patterns of sociodemographic factors across other
county groups in Figure 3.
If cross-county migration had been the only factor
affecting any county’s mortality, the SD of life expectancy
in US counties would have declined from 1.89 y in 1993 to
1.71 in 1999 if emigrants had the same life expectancy as
those who stayed in the county or origin; in practice it
increased to 1.99 (Table 3). The SD in 1999 would have been
1.71 if emigrants had a life expectancy that was 1 y higher
than those who stayed in the country of origin, and 1.78 if
only those migrating to counties of higher life expectancy had
a life expectancy 1 y higher than those who stayed in the
country of origin or migrated to counties of lower life
expectancy. In fact, only in extremely polarized migration
scenarios—when migrants to counties with higher life
expectancy have a 2.3-y advantage over those who stay in
the county of origin—does the net effect of migration
Figure 2. Annual Absolute Life Expectancy Disparity between the Counties with the Highest and Lowest Life Expectancies
(A) Life expectancy for counties that make up the 2.5% of the US population with the highest and lowest county life expectancies in each year.
(B) Difference between highest and lowest life expectancies in (A). Data for each year show the combined life expectancy for counties that make up
2.5% of the US population and have the highest/lowest life expectancy for that year. The mean number of years that each county was in the highest/
lowest life expectancy group was 7.9/10.5 y for men and 8.6/8.6 y for women.
The vertical lines show the 90% confidence interval (CI) for the estimated life expectancy or life expectancy difference. In each year, the 5% and 95%
confidence limits correspond to the 5th and 95th percentiles of the distribution of life tables, calculated using the population-weighted average of
death rates of constituent counties in 100 independent draws (see Methods).
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Mortality Trends in US Counties
Figure 3. Change in County Life Expectancy in 1961–1983 and 1983–1999
Counties are categorized into six groups on the basis of how their life expectancy changed in relation to national sex-specific change in life expectancy
(4.1 y for men and 4.8 y for women in 1961–1983; 3.1 y for men and 1.3 y for women in 1983–1999). Actual life expectancies are shown in Figure S1, and
absolute changes in life expectancy are shown in Figure S2.
Group 1, life expectancy increased at a level significantly higher than the national sex-specific mean; group 2, life expectancy increased at a level
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Mortality Trends in US Counties
become an increase, rather than a decrease, in cross-county
life expectancy SD (for comparison, the change in national
life expectancy in the United States was 2.3 y between 1982
and 1999). In 1993, individuals migrating to counties with
lower life expectancy came from counties with an average life
expectancy of 76.9 y; those migrating to counties with higher
life expectancy came from counties with an average life
expectancy of 75.2 y (with similar ordering for 1994–1999).
Therefore, at the county level, migration seems to have
worked to dampen the rising cross-country mortality inequality.
Our analysis of county-level mortality demonstrates that
the 1980s and 1990s marked an era of increased inequalities
in mortality in the United States, measured both as the
distribution of life expectancy in the US counties, and as the
difference between the best-off and worst-off counties. Our
finding on the decline and subsequent rise of mortality
disparities across US counties would be the same using other
metrics of mortality disparity, such as the interquartile range.
Equally important is the finding that the higher disparity
partly resulted from stagnation or increase in mortality
among the worst-off segment of the population, with life
expectancy for approximately 4% of the male population and
19% of the female population having had either statistically
significant decline or stagnation. This stagnation and reversal
of mortality decline, although affecting a minority of the
nation’s population, is particularly troubling because an oftstated 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/
Analyses of life expectancy of individual counties over
these four decades showed worsening of life expectancy in a
large number of counties after the early 1980s, especially
among women. The majority of these counties were in the
Deep South, along the Mississippi River, and in Appalachia,
extending into the southern portion of the Midwest and into
Texas. The rise in all-cause mortality was caused by an
increase in cancers, diabetes, COPD, and a reduction in the
rate of decline of cardiovascular diseases. There was also
important influence of HIV/AIDS and homicide among men.
Harper et al.  found a widening gap in life expectancy
between whites and blacks between 1983 and 1993, followed
by a narrowing. To examine if the observed cross-county
mortality distribution in Figure 1 is caused by a clustering of
black population, we estimated the SD of county life
expectancies separately for white and black populations of
US counties in 1983–1999 (the race-specific analysis included
all counties for whites and those counties with sufficient
population and deaths to allow life expectancy calculation for
blacks). Race-specific cross-county SD for white males and
females and for black males mirrored the combined pattern
seen in Figure 1; it remained relatively unchanged for black
females. In other words, the observed trends in geographical
mortality disparities were applicable even for the same race.
This study has a number of limitations. First, our analysis
did not incorporate data after 2001 (leading to estimates up
to 1999), because these data were not provided to us by the
NCHS. However, the post-2001 data are particularly important for continued monitoring of mortality and mortality
disparities in US counties, for example for the purpose of
monitoring progress towards Healthy People 2010 goals.
Second, although counties provide the smallest unit of
analysis for mortality and causes of death and are ideal for
comparable analysis over time, they do not allow considering
disparities within individual counties, especially in relation to
sociodemographic factors. In the absence of detailed data on
within-county mortality disparities, the cross-county SD does
not directly measure total mortality inequalities in the United
States (this applies to all analyses of population subgroups;
see Gakidou et al.  for a discussion of within- and
between-group health disparities). Third, analysis of cause-ofdeath data may be affected by regional variability in cause-ofdeath coding, which may also change over time . The
relatively broad disease categories (e.g., all cardiovascular
diseases and clusters of cancers) used in our analysis have
likely limited this effect. Fourth, the estimated uncertainty in
life expectancy accounts for statistical uncertainty in death
rates caused by (finite) population size and number of deaths.
However, it was not possible to formally incorporate nonsampling error in death and population numbers. For
example, population figures in the US censuses are not
adjusted for under- and over-counts; the extent of under- and
over-count may itself vary across counties or over time.
Population and death figures have additional uncertainty
owing to factors such as illegal or seasonal migrants and age
misreporting. Finally, even the best-available data on migration could only provide indicative, versus conclusive, evidence of its role in trends in county mortality. The use of
Internal Revenue Service migration data required making an
assumption on the relative health status of emigrants,
compared to nonmigrants. Alternative specifications of this
assumption showed that migration still attenuated the rise in
inequality, even when migrants were assumed to have
substantial advantages in life expectancy. Therefore, although
uncertainty about the precise relationship between the health
of migrants and nonmigrants remains, it is unlikely that our
findings on rising cross-county disparity are an artifact of
migration. Although the mortality rates of cross-county
migrants are not known, analysis of income data illustrates
that, on average, family incomes of migrants were about $500
lower than nonmigrants in 1998–1999 (compared to a median
1999 national family income of $50,594). Detailed migration
data were available only for 1993–1999; pre-1993 migration
may have acted to further attenuate the true rise in mortality
if it had the same pattern as the years in our analysis. It is,
however, possible that the migration pattern differed in the
earlier years of the analysis period. Although our results
indicate that migration worked to attenuate the actual rise in
significantly higher than zero but not significantly distinguishable from the national sex-specific mean; group 3, life expectancy increased at a level
significantly higher than zero but significantly less than the national sex-specific mean; group 4, life expectancy change was statistically
indistinguishable from zero and from the national sex-specific mean; group 5, life expectancy change was statistically indistinguishable from zero and
was significantly less than the national sex-specific mean; group 6, life expectancy had a statistically significant decline. All statistical significance was
assessed at 90%.
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Mortality Trends in US Counties
Figure 4. Change in Probability of Dying in Specific Age Ranges between 1961 and 1983 and between 1983 and 1999, with Counties Grouped on the
Basis of the Level of Change in Life Expectancy as in Figure 3
The total height of each column shows the change in the probability of dying (from all causes) in the age range shown, divided into the probability of
dying from specific diseases and injuries. The change is calculated as the probability of death in the end year minus that of the initial year. Therefore, a
positive number indicates an increase in mortality, and a negative number indicates a decline in mortality (disease-specific or all-cause for the net
effects of all diseases). Group 6 for females in 1983–1999 is shown on a different scale to increase resolution for all other groups.
Notes: Results are not shown for 5–14 y because there are few deaths in these ages in the United States. Groups with less than 0.2% of the country’s
population (groups 4 and 6 for both sexes in 1961–1983, and group 4 for males in 1983–1999) have not been shown because the results are based on
too few deaths. COPD and lung cancer are presented together and changed in the same direction for all age and county group. The other
noncommunicable disease group includes diabetes, for which the direction of change in probability of death is identical to other noncommunicable
diseases exclusive of diabetes.
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Mortality Trends in US Counties
Table 2. Selected Socioeconomic and Demographic Characteristics of the Populations of the Counties Showing Significantly Above-Average, Average, and Below-Average (Including
Stagnation or Decline) Life Expectancy Change
Gender Sociodemographic Variable Year
Male County groupa Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 National Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 National
Number of counties 609 1,983 454 2 102 0 3,150 299 1,871 542 4 423 11 3,150
Beginning percent US populationb 18.94 43.83 36.27
,0.2 0.95 0.00 100 30.91 41.29 23.51
, 0.2 3.82 0.45 100
Ending percent US populationc 23.24 42.91 32.91
,0.2 0.93 0.00 100 32.73 41.55 21.99
,0.2 3.36 0.35 100
Beginning LE (y)d 66.1 67.1 66.6 N/A 68.9 N/A 66.9 71.3 71.1 70.4 N/A 70.4 67.0 71
Ending LE (y)e 71.6 71.2 69.6 N/A 69.6 N/A 71 75.5 74.1 72.5 N/A 71.0 65.7 74.1
Average income ($)f 11,907 11,405 12,770 N/A 8,717 N/A 12,122 16,964 13,502 13,946 N/A 10,695 11,600 14,563
Income change (%)g 24 23 16 N/A 31 N/A 20 52 51 46 N/A 51 47 51
Beginning average inequality index (Gini 3100)h 35.9 35.0 35.5 N/A 36.8 N/A 37.4 36.5 35.6 36.2 N/A 38.3 40.3 38.2
Ending average inequality index
36.0 35.3 37.2 N/A 37.5 N/A 38.2 41.4 38.5 39.9 N/A 40.0 44.6 42.9
Percent completing high schoolj 81 84 84 N/A 88 N/A 83 65 72 73 N/A 79 77 70
Percent blackk 10.00 8.70 16.50 N/A 9.30 N/A 11.20 12.30 9.60 17.00 N/A 16.60 49.70 12.40
Percent urbanl 57 47 82 N/A 12 N/A 62 88 62 73 N/A 30 82 72
Female County groupa Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 National Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 National
Number of counties 652 2,013 452 5 28 0 3,150 270 730 46 989 935 180 3,150
Beginning percent US populationb 24.96 41.82 32.93
,0.2 0.27 0.00 100 34.88 28.88 8.94 8.50 15.77 3.03 100
Ending percent US populationc 23.20 41.53 34.92
,0.2 0.33 0.00 100 35.39 29.74 8.94 8.10 15.12 2.71 100
Beginning LE (y)d 72.0 73.7 74.1 N/A 77.6 N/A 73.5 78.1 78.3 78.1 78.6 78.5 77.9 78.3
Ending LE (y)e 77.9 78.4 77.8 N/A 78.4 N/A 78.3 80.4 79.6 78.8 79.3 78.4 76.7 79.6
Average incomef 12,042 11,232 12,968 N/A 9,439 N/A 12,122 16,682 14,231 15,097 11,540 12,360 11,825 14,563
Income change (%)g 19 23 19 N/A 37 N/A 20 52 50 45 51 48 45 51
Beginning average inequality index (Gini 3100)h 36.8 35.4 34.4 N/A 36.4 N/A 37.4 36.9 35.1 35.8 36.6 36.3 37.5 38.2
Ending average inequality index (Gini 3100)i 37.4 35.7 35.6 N/A 35.6 N/A 38.2 41.4 38.7 40.1 38 38.9 40.4 42.9
Percent completing high schoolj 85 84 81 N/A 85 N/A 83 66 71 69 77 75 77 70
Percent blackk 16.70 9.90 11.20 N/A 12.90 N/A 11.20 13.90 11.40 16.40 9.60 12.00 18.70 12.40
Percent urbanl 68 45 79 N/A 23 N/A 62 90 71 90 31 48 49 72
The figures show averages across all counties in the group, weighted by county population. Information for groups with less than 0.2% of the US population is not shown because they are sensitive to small numbers. All figures for groups 1–6 are
weighted by county population. For comparison, national figures are also shown. LE, life expectancy; N/A, not applicable.
aGroup 1, life expectancy increased at a level significantly higher than the national sex-specific mean; group 2, life expectancy increased at a level significantly higher than zero but not significantly distinguishable from the national sex-specific mean;
group 3, life expectancy increased at a level significantly higher than zero but significantly less than the national sex-specific mean; group 4, life expectancy change was statistically indistinguishable from zero and from the national sex-specific mean;
group 5, life expectancy change was statistically indistinguishable from zero and was significantly less than the national sex-specific mean; group 6, life expectancy had a statistically significant decline. All statistical significance was assessed at 90%. See
also Figure 3 for graphical presentation. Groups are defined on the basis of change over the period and therefore contain different counties in 1961–1983 than they do in 1983–1999. bPercent of US population in the first year of the period (1961 and 1983).
cPercent of US population in the last year of the period (1983 and 1999).
dLife expectancy for the first year of the period (1961 and 1983).
eLife expectancy for the last year of the period (1983 and 1999).
fIncome in the first year for which data were available in the period (1970 and 1983). Income data are for both sexes combined due to data availability.
gPercent change from the first year for which data were available in the period to the last year of the period (1970–1983 and 1983–1999).
hAverage of within-county Gini coefficients in the first year in the analysis period for which data were available without interpolation (1970 and 1980). Source: http://www.unc.edu/;nielsen/data/data.htm for 1970–1990 and http://www.socanth.uncc.
edu/smoller/ for 2000. The same methods were used in both sources and for all years. Because Gini coefficient is defined using a nonlinear relationship, national estimate would not be the same as the population-weighted average of individual
iAverage of within-county Gini coefficients for all counties in the group, in the last year in the analysis period for which data were available without interpolation (1980 and 2000). Because Gini coefficient is defined using a nonlinear relationship, national
estimate would not be the same as the population-weighted average of individual counties.
jPercent having at least completed the 12th grade in the first year of the period for which data were available (1970 and 1983). The data have not been age standardized because of the format in which they are presented in the data sources. The
implication is that cohort effects on education are not adjusted for in these estimates. The data are for both sexes combined due to data availability.
kAverage of the first and last year of the period (i.e., 1961 and 1983, 1983 and 1999). The data are for both sexes combined due to data availability.
lAverage of the first year for which data were available in the period (1970 and 1983) and the last year of the period (1983 and 1999). The data are for both sexes combined due to data availability.
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Mortality Trends in US Counties
mortality disparities, even an opposite effect (i.e., a situation
in which the rise in disparities is a result of health-selective
migration, especially in the worst-off counties) would be of
public health and policy concern. A separate implication of
cross-county migration is that, in counties with large
population growth, a larger proportion of the population
has recently immigrated from other counties. There was a
weak relationship between change in life expectancy and
population growth rate (correlation coefficients were 0.14 for
1961–1983 and 0.23 for 1983–1999).
Demographic and epidemiologic research has commonly
documented the continued rise in life expectancy in the
Western populations and the epidemiological dynamics and
transitions that occur as mortality declines [28–30]. At the
national level, the most notable examples of adult mortality
rise are those in populations affected by the HIV/AIDS
epidemic and in the former Soviet Union as a result of a rise
in chronic diseases and injuries [31–35]. In high-income
countries, there were periods of stagnation, or even slight
increases, in adult mortality in the 1950s–1970s (e.g., in
Australia, the Netherlands, and Norway), possibly as a result
very high prevalence of smoking; the effects were substantially more pronounced among men [36,37]. To the best of
our knowledge, Denmark is the only high-income country
with a recent (1990s) increase in female mortality ,
possibly because of high smoking prevalence among Danish
women . Subnationally, recent analyses in Australia and
the United Kingdom have found that an increase in mortality
disparities was not accompanied by an actual rise in mortality
in population subgroups; rather it was caused by differential
rates of mortality decline [40,41].
HIV/AIDS mortality in the United States declined after the
introduction of highly active antiretroviral therapy in the
mid 1990s [42,43]. The remaining HIV/AIDS mortality in
Figure 4 may be due to incomplete coverage and/or
continued (albeit lower) mortality among the previously
incident cases, itself due to treatment failure; the disparities
in HIV/AIDS mortality could be caused by higher incidence
or lower access and effectiveness in the worst-off groups. The
rise in chronic disease mortality for relatively large segments
of the American population, especially women, however,
defies recent trends in other high-income countries. The
epidemiological (disease-specific) patterns of female mortality rise are consistent with the geographical patterns of, and
trends in, smoking, high blood pressure, and obesity [44–47].
In particular, the sex and cohort patterns of the increase in
lung cancer and chronic respiratory disease mortality point
to an important potential role for smoking (see also Preston
and Wang ). The role of these risk factors in the reversal
of the epidemiological transition should be further investigated, and programs that increase the coverage of interventions for chronic disease and injury risk factors in the
worst-off counties, states, and regions should be established
and regularly monitored and evaluated with respect to their
local, versus aggregate only, impacts.
Dataset S1. Change in Probability of Dying in Specific Age Ranges,
with Counties Grouped on the Basis of Level of Change in Life
Expectancy, Divided by Disease (Numerical Data for Figure 4)
Found at doi:10.1371/journal.pmed.0050066.sd001 (33 KB XLS).
Dataset S2. Life Expectancy at Birth by County, 1961–1999
Found at doi:10.1371/journal.pmed.0050066.sd002 (2.5 MB ZIP).
Figure S1. County Life Expectancy in (A) 1961; (B) 1983; and (C) 1999
Found at doi:10.1371/journal.pmed.0050066.sg001 (3.5 MB PPT).
Figure S2. Absolute Change in County Life Expectancy in (A) 1961–
1983 and (B) 1983–1999
Found at doi:10.1371/journal.pmed.0050066.sg002 (2.4 MB PPT).
Movie S1. Life Expectancy at Birth by County, 1961–1999 (Males)
Found at doi:10.1371/journal.pmed.0050066.sv001 (14.4 MB AVI).
Movie S2. Life Expectancy at Birth by County, 1961–1999 (Females)
Found at doi:10.1371/journal.pmed.0050066.sv002 (15.8 MB AVI).
Author contributions. ME, ABF, SCK, and CJLM designed the
study. SCK collected data for the study. ME, ABF, and SCK analyzed
Table 3. Cross-County SD of Life Expectancy in a Migration Simulation
Year SD of Actual
SD of Simulated Life Expectancy
(Emigrant LE ¼ Nonemigrant LE)
SD of Simulated Life Expectancy
(Emigrant LE . Nonemigrant LE)
for All Emigrantsa,b
SD of Simulated Life
Expectancy (Emigrant LE .
Nonemigrant LE for Migrants
to Counties with Higher LE)c,d
1993 1.89 1.89 1.89 1.89
1994 1.91 1.85 1.85 1.87
1995 1.92 1.82 1.82 1.85
1996 1.93 1.79 1.79 1.83
1997 1.95 1.76 1.76 1.81
1998 1.97 1.73 1.74 1.80
1999 1.99 1.71 1.71 1.78
For each year after 1993, life expectancy (LE) is calculated by ‘‘mixing’’ life expectancies from the previous year proportional to migration between any pair of counties (see Methods). a
The life expectancy of the emigrants was 1 y higher than the average life expectancy of the county of origin. The life expectancy of the corresponding nonmigrants was adjusted
downwards proportionally so that overall life expectancy was correctly calculated.
The estimated SDs are very similar to those in the scenario with emigrant LE ¼ nonemigrant LE because migrants move to counties with higher as well as lower life expectancy. c
If the (emigrant LE ¼ county of origin LE þ 2.3) for migrants to counties with higher LE, then the cross-county SD in 1999 would be approximately 1.89; the advantage of migrants would
have to be .3 y to lead to the observed SD of 1.99. d
The life expectancy of the emigrants to higher LE counties was 1 y higher than the average life expectancy of the county of origin. The life expectancy of the remaining population
(nonmigrants and migrants to counties not of higher LE) was adjusted downwards proportionally so that overall life expectancy was correctly calculated.
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the data. ME and ABF wrote the first draft of the paper. SCK and
CJLM contributed to writing the paper.
1. Hahn RA, Eberhardt S (1995) Life expectancy in four U.S. racial/ethnic
populations: 1990. Epidemiology 6: 350–355.
2. Cooper R, Cutler J, Desvigne-Nickens P, Fortmann SP, Friedman L, et al.
(2000) Trends and disparities in coronary heart disease, stroke, and other
cardiovascular diseases in the United States: findings of the national
conference on cardiovascular disease prevention. Circulation 102: 3137–
3. Geronimus AT, Bound J, Waidmann TA, Hillemeier MM, Burns PB (1996)
Excess mortality among blacks and whites in the United States. N Engl J
Med 335: 1552–1558.
4. Geronimus AT, Bound J, Waidmann TA (1999) Poverty, time, and place:
variation in excess mortality across selected US populations, 1980–1990. J
Epidemiol Community Health 53: 325–334.
5. Hahn RA, Eaker ED, Barker ND, Teutsch SM, Sosniak WA, et al. (1996)
Poverty and death in the United States. Int J Health Serv 26: 673–690.
6. Navarro V (1990) Race or class versus race and class: mortality differentials
in the United States. Lancet 336: 1238–1240.
7. Singh GK, Miller BA (2004) Health, life expectancy, and mortality patterns
among immigrant populations in the United States. Can J Public Health 95:
8. Singh GK, Yu SM (1996) Trends and differentials in adolescent and young
adult mortality in the United States, 1950 through 1993. Am J Public Health
9. Wong MD, Shapiro MF, Boscardin WJ, Ettner SL (2002) Contribution of
major diseases to disparities in mortality. N Engl J Med 347: 1585–1592.
10. Singh GK (2003) Area deprivation and widening inequalities in US
mortality, 1969–1998. Am J Public Health 93: 1137–1143.
11. Pappas G, Queen S, Hadden W, Fisher G (1993) The increasing disparity in
mortality between socioeconomic groups in the United States, 1960 and
1986. N Engl J Med 329: 103–109.
12. Davey SG, Neaton JD, Wentworth D, Stamler R, Stamler J (1998) Mortality
differences between black and white men in the USA: contribution of
income and other risk factors among men screened for the MRFIT. MRFIT
Research Group. Multiple Risk Factor Intervention Trial. Lancet 351: 934–
13. Krieger N, Chen JT, Waterman PD, Rehkopf DH, Subramanian SV (2005)
Painting a truer picture of US socioeconomic and racial/ethnic health
inequalities: The Public Health Disparities Geocoding Project. Am J Public
Health 95: 312–323.
14. Murray CJ, Kulkarni SC, Michaud C, Tomijima N, Bulzacchelli MT, et al.
(2006) Eight Americas: investigating mortality disparities across races,
counties, and race-counties in the United States. PLoS Med 3: e260. doi:10.
15. Feldman JJ, Makuc DM, Kleinman JC, Corioni-Huntley J (1989) National
trends in educational differentials in mortality. Am J Epidemiol 129: 919–
16. Kaplan GA, Pamuk ER, Lynch JW, Cohen RD, Balfour JL (1996) Inequality
in income and mortality in the United States: analysis of mortality and
potential pathways. BMJ 312: 999–1003.
17. Steenland K, Henley J, Calle E, Thun M (2004) Individual- and area-level
socioeconomic status variables as predictors of mortality in a cohort of
179,383 persons. Am J Epidemiol 159: 1047–1056.
18. Harper S, Lynch J, Burris S, Davey Smith G (2007) Trends in the black-white
life expectancy gap in the United States, 1983–2003. JAMA 297: 1224–1232.
19. Williams JE, Massing M, Rosamond WD, Sorlie PD, Tyroler HA (1999)
Racial disparities in CHD mortality from 1968–1992 in the state economic
areas surrounding the ARIC study communities. Atherosclerosis Risk in
Communities. Ann Epidemiol 9: 472–480.
20. Singh GK, Siahpush M (2006) Widening socioeconomic inequalities in US
life expectancy, 1980–2000. Int J Epidemiol 35: 969–979.
21. Hahn RA, Truman BI, Barker ND (1996) Identifying ancestry: the reliability
of ancestral identification in the United States by self, proxy, interviewer,
and funeral director. Epidemiology 7: 75–80.
22. Ingram DD, Parker JD, Schenker N, Weed JA, Hamilton B, et al. (2003)
United States Census 2000 population with bridged race categories. Vital
Health Stat 2 135: 1–55.
23. Preston SH, Heuveline P, Guillot M (2001) Demography. Malden
24. Connolly S, O’Reilly D (2007) The contribution of migration to changes in
the distribution of health over time: five-year follow-up study in Northern
Ireland. Soc Sci Med 65: 1004–1011.
25. Fox AJ, Goldblatt PO, Adelstein AM (1982) Selection and mortality
differentials. J Epidemiol Community Health 36: 69–79.
26. Murray CJ, Gakidou EE, Frenk J (1999) Health inequalities and social group
differences: what should we measure? Bull World Health Organ 77: 537–
27. Murray CJL, Kulkarni SC, Ezzati M (2006) Understanding the coronary
heart disease versus total cardiovascular mortality paradox: a method to
enhance the comparability of cardiovascular death statistics in the United
States. Circulation 113: 2071–2081.
28. Oeppen J, Vaupel J (2007) Broken limits to life expectancy. Science 296:
29. Olshansky S, Ault A (1986) The fourth stage of the epidemiologic
transition: the age of delayed degenerative diseases. Milbank Mem Fund
Q 64: 355–391.
30. Riley JC (2001) Rising life expectancy: a global history. Cambridge (United
Kingdom) and New York: Cambridge University Press.
31. National Academy Press (1997) Premature death in the new independent
states. Washington, D.C.: National Academy Press.
32. McKee M, Shkolnikov V (2001) Understanding the toll of premature death
among men in eastern Europe. BMJ 323: 1051–1055.
33. Shkolnikov V, Mesle F, Vallin J (1996) Health crisis in Russia. II. Changes in
causes of death: a comparison with France and England and Wales (1970 to
1993). Population 8: 155–189.
34. Shkolnikov V, Mesle F, Vallin J (1996) Health crisis in Russia. I. Recent
trends in life expectancy and causes of death from 1970 to 1993. Population
35. Shkolnikov V, McKee M, Leon D (2001) Changes in life expectancy in
Russia in the mid-1990s. Lancet 357: 917–921.
36. Taylor R, Lewis M, Powles J (1998) The Australian mortality decline: allcause mortality 1788–1990. Aust N Z J Public Health 22: 27–36.
37. Taylor R, Lewis M (1998) The Australian mortality decline: cause-specific
mortality 1907–1990. Aust N Z J Public Health 22: 37–44.
38. Department of Economic and Social Affairs (2005) World population
prospects the 2004 revision: population database. New York: United
Nations Population Division.
39. World Health Organization (1997) Tobacco or health: a global status
report. Geneva: World Health Organization.
40. Raleigh V, Kiri V (1997) Life expectancy in England: variations and trends
by gender, health authority, and level of deprivation. J Epidemiol
Community Health 51: 649–658.
41. Hayes L, Quine S, Taylor R (2005) New South Wales trends in mortality
differentials between small rural and urban communities over a 25-year
period, 1970–1994. Aust J Rural Health 13: 71–76.
42. Centers for Disease Control and Prevention (CDC) (2006) Epidemiology of
HIV/AIDS — United States, 1981–2005. MMWR Morb Mortal Wkly Rep 55:
43. Selik RM, Byers RH Jr., Dworkin MS (2002) Trends in diseases reported on
US death certificates that mentioned HIV infection, 1987–1999. J Acquir
Immune Defic Syndr 29: 378–387.
44. Ezzati M, Martin H, Skjold S, Vander Hoorn S, Murray CJL (2006) Trends in
national and state-level obesity in the USA after correction for self-report
bias: analysis of health surveys. J Roy Soc Med 99: 250–257.
45. Ezzati M, Oza S, Danaei G, Murray CJL (2008) Trends and cardiovascular
mortality effects of state-level blood pressure and uncontrolled hypertension in the United States. Circulation 117: 905–914.
46. Hicks LS, Fairchild DG, Cook EF, Ayanian JZ (2003) Association of region
of residence and immigrant status with hypertension, renal failure,
cardiovascular disease, and stroke, among African-American participants
in the third National Health and Nutrition Examination Survey (NHANES
III). Ethn Dis 13: 316–323.
47. Obisesan TO, Vargas CM, Gillum RF (2000) Geographic variation in stroke
risk in the United States. Region, urbanization, and hypertension in the
Third National Health and Nutrition Examination Survey. Stroke 31: 19–
48. Preston SH, Wang H (2006) Sex mortality differences in the United States:
the role of cohort smoking patterns. Demography 43: 631–646.
49. Nielsen F, Alderson AS (1997) The Kuznets Curve and the great u-turn:
income inequality in US counties, 1970 to 1990. Am Sociol Rev 62: 12–33.
50. Harrison B, Bluestone B (1988) The great u-turn: corporate restructuring
and the polarizing of America. New York: Basic Books.
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Background. 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
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
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/
Additional Information. Please access these Web sites via the online
version of this summary at http://dx.doi.org/10.1371/journal.pmed.
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
In 2001 the US Agency for Healthcare Research and Quality sponsored
a workshop on ‘‘strategies to reduce health disparities’’
PLoS Medicine | www.plosmedicine.org April 2008 | Volume 5 | Issue 4 | e66 0568
Mortality Trends in US Counties
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Each paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.Read more
Thanks to our free revisions, there is no way for you to be unsatisfied. We will work on your paper until you are completely happy with the result.Read more
Your email is safe, as we store it according to international data protection rules. Your bank details are secure, as we use only reliable payment systems.Read more
By sending us your money, you buy the service we provide. Check out our terms and conditions if you prefer business talks to be laid out in official language.Read more