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Initial postRead “The Reversal of Fortunes” by Ezzati, et al., located in the Reading & Study folder in
Module/Week 3. Discuss the following points in your thread. Review the Discussion Board
Instructions before posting your thread.

In general public health measures appear to be working. Not only has US life expectancy
increased over the past half century, but mortality rates of major lifestyle-related diseases—
particularly heart disease and stroke—have decreased in both men and women. In the 1980s,
however, a disconcerting “reversal of fortunes” began to occur in some vulnerable populations in
some regions of the US. Describe what happened and give the proximal (immediate) influences
for this backward trend.
What do you think are the distal (ultimate) influences for the “reversal of fortunes” described in
the article? Suggest a comprehensive intervention plan to reverse this reversal. Upon which
theoretical framework or model would it be based? Why?
Classmate #1Between 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).1 During this same
period, 1961–1983, the differences in death rates among/across different counties fell.1 However,
beginning in the early 1980s the differences in death rates among/across different counties began
to increase.1 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.”1 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.1 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”.1
In my opinion the reason for this cause of reversal of fortune was due to the increase in
lung cancer. A CDC report in 1989 stated that deaths from lung cancer had increased to 44
percent among women.2 Although almost half of all Americans who ever smoked have quit,
more than 50 million continue to smoke.2 ”This decline has been slow in women and negligible
among persons with less than a high school education,” the report said, adding that rates were
particularly high among blue-collar workers and less educated people.2 Occupational and
environmental factors led to other lung cancer deaths.2
I think the best model for changing a population/individual behavior is the
transtheoretical model, which places a person into stages. Stage theories are useful for designing
stage-targeted and stage-matched interventions based on people’s readiness to change their
behavior.3 We must know where a person is, or a starting point, if we want to help them get to a
certain point/behavior. Knowing where someone is will help us get them to their end point/goal
(ex. smoking cessation). This model can help us address where someone is in the planning or
decision-making process in trying to quit smoking. Stage theories allow more precise targeting of
interventions relative to nonstage models for planning, implementing, and evaluating healthpromotion interventions.3
Ezzati M, Friedman AB, Kulkarni SC, Murray CJ. The reversal of fortunes: trends in county
mortality and cross-county mortality disparities in the United States [published correction
appears in PLoS Med. 2008 May 27;5(5). doi: 10.1371/journal.pmed.0050119]. PLoS Med.
2008;5(4):e66. doi:10.1371/journal.pmed.0050066.
2. Altman L. New York Times. U.S. Reports Steep Rise in 1980’s In Lung Cancer Deaths in
Women. 1989. Accessed March 31, 2019.
3. DiClemente R, Salazar L, Crosby R. Health Behavior Theory for Public Health. Second Edition.
Burlington, MA: Jones and Bartlett Learning; 2019.
Classmate #2Between the 1960s and the 1990s, life expectancy increased for both men and women by
seven and six year’s respectively1. From the early 1960s to the 1980s, the death rate decreased
for both genders. In the 1980s, there was a disconcerting reversal of fortunes in vulnerable
populations and especially in women. This means that the many countries saw an increase in
death rates due to higher levels of cardiovascular disease as well as an increase in other diseases
such as diabetes or lung cancer in both genders with a rise in homicide rates and HIV/AIDs rates
specifically in men1.
I believe that this reversal of fortunes was proximally due in part to an increase in
diseases such as lung cancer1. I believe that those who were already disadvantaged simply were
not affected by the increase in life expectancy shown in other populations1. This would be a
distal influence. Additionally, I believe a decrease in health care accessibility played a proximal
role in this trend2. One article explains that around this time health care facilities were trying to
cut down on their bed counts, but ended up counting down too much therefore disallowing
access to patients in need2.
If we were to address a specific health needed to reverse this trend, I would focus on the
increase in lung cancer. This could have been due largely in part to smoking habits. By utilizing
the Transtheoretical Model of Change, we could target that specific population to work towards
creating healthier habits. Because smoking is an individual choice and no one theory can account
for individual behaviors, I believe that this model would work well at addressing the issue. The
model works through constructs that include precontemplation, contemplation, preparation,
action, and maintenance3. During precontemplation, the individual does not really have an idea
of changing their behavior3. One could target this specific population with health education and
promotion events to bring awareness to the issue. During contemplation, the individual now
realizes the need for change and decide to make that change3. This could also be where health
education comes into play. During preparation, the individual begins to plan to make that change
soon3. One could give the individuals the tools needed to make the change during this stage.
During the action stage, the individual actively works towards changing their behavior3. Finally,
during the maintenance phase, the individual has made the change for a period of at least six
months and is actively working towards avoiding a relapse to their old behavior3. This is where
one could continue to give the individual education, tools, and alternatives to avoid their old
health behavior.
The bible states in 1 Corinthians 10:31, “So whether you eat or drink or whatever you do,
do it all for the glory of God”. Making healthy choices and avoiding unhealthy ones will allow us
to live the lives we should be living.
Word Count: 480
1. Marmot M. Social determinants of health inequalities. The Lancet. 2005;365(9464):1099-1104.
2. Mantone J. Reversal of fortune. Modern Healthcare. Published October 5, 2006. Accessed April 2, 2019.
3. DiClemente RJ, Salazar LF, Crosby RA. Health Behavior Theory for Public Health:
Principles, Foundations, and Applications. Burlington, MA: Jones & Bartlett Learning; 2019.
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
interests exist.
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.
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: Ó 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: majid_ezzati@
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
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.
PLoS Medicine |
April 2008 | Volume 5 | Issue 4 | e66
Mortality Trends in US Counties
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%.
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
distributed evenly.
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 selecte …
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