The Gerontologist
cite as: Gerontologist, 2016, Vol. 56, No. 1, 104–114
doi:10.1093/geront/gnv041
Advance Access publication July 28, 2015
Special Issue: Veterans Aging: Research Article
The Longevity Legacy of World War II: The Intersection of
GI Status and Mortality
Melissa A. Hardy, PhD* and Adriana M. Reyes, MA
Department of Sociology, The Pennsylvania State University, University Park.
*Address correspondence to Melissa Hardy, PhD, The Pennsylvania State University, University Park, 502 Oswald Tower, PA 16802. E-mail:
[email protected]
Received January 20, 2015; Accepted March 7, 2015
Decision Editor: Suzanne Meeks, PhD
Abstract
Purpose of the Study: We examine hypotheses involving the potential health advantages of selection into military service
and the potential health disadvantages associated with the experience of military service by comparing later-life mortality
rates for veterans and nonveterans as well as among veterans based on their cohort of reentry into civilian life.
Design and Methods: We use data on 3,453 men, including 1,496 veterans from the older men cohort of the National
Longitudinal Surveys to estimate Cox proportional hazard mortality models. We distinguish between veterans and nonveterans and further classify veterans by age at exit while incorporating measures associated with military selection, health
behaviors, and socioeconomic status.
Results: Veterans who were discharged from the military at older ages have a mortality advantage relative to veterans
discharged at younger ages. For the 1914–1921 birth cohorts, the mortality advantage for veterans who exited around age
30 is apparent for deaths before age 65, but rates equalize across all groups when deaths at older ages are included. These
findings are robust to the inclusion of background characteristics, education, occupation, body mass index, smoking, marital status, and proxies for service deferments.
Implications: Rather than focusing on a general health effect of military service, per se, future research should distinguish
among individual traits; the nature, timing, and duration of exposures relative to life course stage; and the sociohistorical
context of military service to expand our understanding of the differential health consequences of military service.
Key words: Analysis, Survival analysis, Health, Demography, Veterans
Even as it fades from the cache of lived memory, World
War II (WWII) remains the defining event of the 20th century. Having reshaped the lives of those born in the first
part of the century, the war arguably had its strongest
effect on those called up to military service. In today’s volunteer armed forces, military service has become a career
choice for young men and women, drawing disproportionately from groups with lower socioeconomic status (SES).
However, for most of the 20th century, the military relied
heavily on conscripts to provide the necessary numbers.
The manpower needs for WWII were so high, the military
drew from all social strata and across a relatively wide
age range to build a military force that would exceed 16
million.
For men born in the first third of the 20th century,
military service was a pivotal experience, one seldom
included in research that argues the importance of earlier life experiences for later-life outcomes. Being in the
military often uprooted people from their families and
jobs, distanced them from their homes and communities,
and asked them to respond to challenging circumstances
in ways they never imagined. War mobilization provided
a shared experience for the U.S. population, but military
service fundamentally reshaped the life course of the
© The Author 2015. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved.
For permissions, please e-mail: [email protected].
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The Gerontologist, 2016, Vol. 56, No. 1
subset of men who volunteered or were drafted. These
men comprised from one-quarter to three-quarters of the
men whose birth cohorts placed them in the specified age
range and who were deemed eligible for service. Almost
one-in-four men in the current U.S. populations are veterans, and for those aged 65 and older, the proportion
exceeds one-half (http://www.gallup.com/poll/158729/
men-women-veterans.aspx).
In this article, we take up the issue of mortality differentials among WWII veterans and between veterans and
their age counterparts who were not in the military. Using
data from the original cohort of older men in the National
Longitudinal Surveys (NLS) and recently matched data
from the National Death Index and the Social Security
Death Index, we estimate Cox proportional hazards models to determine whether and how the later-life mortality
rates for WWII veterans may have been affected by the
cohort and life course features of their military service.
We further limit our sample to white men, and distinguish
among WWII veterans by their age at exit from the military.
The current research literature lacks consensus on
whether or how military service shapes health and mortality. Arguments as to the negative health consequences of
military service, particularly for those serving in or after
Vietnam, are counterbalanced by claims of a salutary effect
of the training, comradery, and confidence gained through
the experience. A health advantage for veterans is also consistent with the epidemiological argument that the military
selection criteria initially sort healthier men into the service,
but over time, this health advantage will lessen and eventually the groups will converge (Seltzer & Jablon, 1974).
We test hypotheses linked to arguments for both advantages and disadvantages between veterans and nonveterans
and among veterans that persist to later life. If the epidemiological argument that positive health selection creates
an initial mortality advantage is correct, then we should
see a mortality advantage for veterans in earlier deaths, but
not necessarily for all deaths. In contrast, if the argument
of the higher stress and physiological consequences of war
holds, we should see a higher rate of mortality for veterans
at older ages. Further, if men who served at older ages were
at a particular disadvantage, we should see a mortality
advantage for those whose service ended in their early 20s
rather than 30s among veterans. The selection argument is
particularly relevant if we find a positive effect of military
service, so if the effect of veteran’s status is displaced by
controls for military selection, then the advantage is consistent with the initial sorting process rather than the experience. Finally, we will assess whether smoking, body mass
index (BMI), alcohol consumption, and SES mediate any
relationship between veteran status and mortality risk.
Conceptual Framework
Studying mortality from a life course perspective directs
our attention to people’s exposure to specific experiences
105
and events within the context of cohort membership
(Elder, 1974; Ryder, 1965; Settersten, 2003). The social
meaning of these experiences can be shaped by broader
historical and cultural contexts, and the consequences
of these exposures can differ by one’s structural circumstances within the cohort (Mannheim, 1928). Although
cohorts are defined most often relative to year of birth,
more generally cohorts organize people relative to when
exposures begin or end.
Within the context of WWII, birth cohorts organize men
according to whether and when they were called up for
military service. Those called up at the same time also comprise cohorts of entrants, and those who were discharged
from military service at the same time comprise cohorts
of reentrants into civilian life. Although cohort structures
relative to these three timing dimensions overlap, they also
place military service within different temporal contexts.
And while all age-eligible men registered within a common
time frame, once quotas were filled the enlistment process,
which proceeded unevenly across regions and started with
January birthdays, was suspended until the next round;
therefore, the exact age of entry into service depended on
birth month, day, and year, quotas, and region.
If we distinguish age at entry from age at exit, we focus
on different life course stages and different critical transitions (George, 1993). Men who were older at induction
were more likely to have finished their education, but were
also more likely to have started their families and careers.
For these men, service came after they transitioned to adulthood and were in the process of establishing themselves.
Men whose service started at younger ages were more
likely to be in the process of transitioning to adulthood.
On the other hand, men who were older at enlistment were
more likely to be older at discharge; they were returning
to families and to employers who welcomed them back,
whereas those who reentered civilian life at younger ages
were likely returning to less structured circumstances. One
positive feature of that timing was that they were in a better position to gain additional education, taking advantage
of the opportunities for a college education. In addressing
the longer term consequences of WWII military service,
we conjecture that the timing of service matters, although
we will be unable to distinguish whether the reason timing matters is because of the level of maturity and adult
experience when the service was initiated, the duration and
nature of exposure, the family and employment context
into which they reentered, or any number of other possible
mechanisms.
Answering these questions raises a number of additional difficulties. First, the study of veterans is necessarily
the study of survivors, those who stayed alive until they
were discharged as well as those whose discharge was not
motivated by physical or mental condition. Second, any
health effects of military service are likely to depend on the
nature of the military experience. Those experiences differ
according to whether they served in peace or during war,
106
where they were stationed (Elder, Clipp, Brown, Martin,
& Friedman, 2009), the war they were fighting (Angrist
& Krueger, 1994; Teachman, 2004; Teachman & Call,
1996; Wilmoth, London, & Parker, 2010), the ages when
they served (MacLean & Elder, 2007), and the conditions
to which they returned. These differences underscore the
importance of making as many distinctions as possible
among veterans. Third, studies of WWII veterans have limited data resources on which to draw. Longitudinal data
projects were rare, and the few being conducted involved
special populations defined by things like age, gender, IQ,
state of residence, or other features that limit generalizability. When longitudinal studies became more prominent, samples were necessarily drawn from survivors, and
information from the past had to be collected retrospectively. Fourth, selection into the military has not been a
random process. Until recently, the military has consisted
of both volunteers and conscripts, and both groups had to
pass physical and mental exams to determine eligibility. In
addition, some men deemed fit to serve were nevertheless
granted exemptions on other criteria. These difficulties are
reflected in the set of existing findings. Subsequently, we
review the relevance of these issues for this study.
Military Service and Selection
The Selective Service Act of 1940 initiated the first prewar
draft with fewer than 20,000 inductees that same year;
however, the next year the number of inductees increased
more than 50-fold, to near 1 million. During 1942 and
1943 (when voluntary enlistment was suspended and the
minimum age of entry dropped to 18) more than 6 million
inductees were added to the rosters. By 1947 (when the
draft ended), 50 million aged 18–45 were registered; 36
million were classified, and more than 10 million men were
inducted (Selective Service Administration, 2015).
The first group of conscripts drew from the 1914–1919
birth cohorts, who were aged 21–26 in 1940. What was initially a 1-year service commitment was extended by a year in
August 1941; after Pearl Harbor in December of 1941, a goal
of 200,000 men per month was set, and all inductees were in
for the duration of the war plus 6 months (Selective Service
Administration, 2015). More than 16 million (out of a total
population of 131 million) served in the military; the number of U.S. deaths exceeded 400,000, and more than 670,000
were wounded (Selective Service Administration, 2015).
Military Service and Health
The Effect of Health on Military Service
Attempts to determine whether veteran status is associated
with a health advantage or a health disadvantage are complicated by the processes that select men into the military. The
World War I draft ended in 1918; until 1940, the military
relied on volunteers to maintain a standing force of 330,000.
Thereafter, the military relied on two volunteers for every
The Gerontologist, 2016, Vol. 56, No. 1
three draftees (Selective Service Administration, 2015). All
inductees had to be classified as 1-A, and because of the
urgent need, exemptions were limited. Even so, exemptions
were granted to conscientious objectors, farmers and those
in war industries, and for unusual hardship; those in college
could defer only until the end of the academic year. The primary sorting mechanism was the examination for physical
and mental fitness. Among white men, about one-third of
all those examined were rejected, with about twice as many
rejected due to physical issues as for other reasons. Based on
figures for 1943, 22% were rejected because of poor eyesight, ear, nose, and throat problems; 28% for cardiovascular and musculoskeletal problems; and 15% due to hernia,
feet, or gastrointestinal problems (Statistical Review: WWII,
United States Army Service Forces, 1946). Mental fitness was
evaluated with the Army General Classification Test. Those
below a threshold score also were exempted from service.
The Effect of Military Service on Health
Although relatively healthy men were being selected into
the military, the experience of military service was challenging. From 1941–1945, men in the armed forces served for
an average of 33 months, with almost three-quarters going
overseas for an average of 16 months. Noncombat jobs in
administration, support, or manual labor were assigned to
nearly 40% of enlisted personnel. Of every 1,000 men in
combat positions, 8.6 were killed in action, 3 died from
other causes, and 17.7 received nonfatal combat wounds
(Selective Service Administration, 2015).
Most injuries were inflicted by artillery and bombs. The
deployment of field medics made quick treatment more likely
and reduced the rate of infection; evacuation of the wounded
to field hospitals where they could be moved into surgery
within hours increased overall survival rates to 50% (Covey,
2002). About 70% of the war wounds were musculoskeletal,
with traumatic amputations occurring in the most serious
cases (Covey, 2002). More soldiers died from disease than
injury. Men stationed in the South Pacific were particularly
vulnerable to malaria and yellow fever (Joy, 1999).
Arguments that favor negative health consequences draw
on medical research showing that combat (and the persistent stress and hardship associated with combat) can lead to
“vital exhaustion” (Appels & Mulder, 1988), which can lead
to a higher risk of cardiovascular disease; a weakening of
the immune system (Adams, 1994; Lipton & Shaffer, 1986)
that can lower disease resistance; the development of arterial lesions, which may increase the risk of cancer (Adams,
1994); and war neuroses and posttraumatic stress disorder,
which have also been linked to cardiovascular disease (Lynch
& Smith, 2005). Delayed effects of war injuries or illnesses
could also be deleterious. Finally, military service has been
linked to unhealthy habits such as smoking and drinking,
both of which can have long term consequences. During the
war, tobacco companies distributed free cigarettes to members of the U.S. military and encouraged people to send
The Gerontologist, 2016, Vol. 56, No. 1
cigarettes to show their support. After successful lobbying
efforts, cigarettes were routinely included in soldiers’ rations
(Brandt, 2007; Goodman, 2005; Smith & Malone, 2009).
Counter arguments claim that military selection on
physical and mental health should persist after discharge
and translate into veterans who are healthier than their
counterparts (Seltzer & Jablon, 1974). The development
of health-promoting behaviors such as fitness, hygiene, and
exercise (LaVerda, Vessey, & Waters, 2006); the promotion
of self-discipline; the development of skills in leadership
and team work (Gade et al., 1991); and increasing personal
health responsibility (Hibbard et al., 2007) can all have a
positive influence on later life health. Finally, to the extent
that military experience enhances human capital, allowing
veterans to step into a career path with higher rewards, the
positive impact of more education and better jobs could
also produce better health. Due to the GI (more formally
known as the Servicemen’s Readjustment Act of 1944) Bill,
which provided tuition and stipend support, the possibility of changing career paths and gaining additional education was available to virtually all WWII veterans. Although
college attendance was on the rise before the war, WWII
military service and the GI Bill fueled substantial gains in
college attainment for veterans (Bound & Turner, 2002).
Research Studies
Relatively recent interest in potential longer term health
vulnerabilities has increased the number of studies investigating the consequences of military service. Investigations
of the relationship between military service and subsequent
health outcomes contrasts veterans with nonveterans; in a
few cases, distinctions among veterans with regard to the
timing of their service and the characteristics of their military experiences are also possible. Different data sources,
units of analysis, research designs, control variables, birth
cohorts, and eras of service, however, make comparisons of
results across studies more difficult.
Most studies report higher rates of illness and mortality
and steeper health declines for veterans compared to nonveterans, regardless of the era of service. However, most of
these studies do not rely on national samples to focus on
WWII veterans with individual data. Instead they draw from
the experiences of different subsets of veterans to demonstrate these effects. In part because of data availability, veterans from more recent conflicts have been studied more than
those from earlier wars. For example, veterans of the allvolunteer armed forces report worse health in mid-life than
their counterparts (Teachman, 2007, 2011). Vietnam veterans have higher than expected rates of deaths from accidents
and suicides (Dobkin & Shabani, 2009; Hearst, Newman,
& Hully, 1986). Veterans from Korea and the later cohorts
(1920–1929) who served in WWII seem to have higher rates
of lung cancer than nonveterans (Bedard & Deschenes,
2006). Analysis based on Health and Retirement Study
(HRS) data indicates that veterans from WWII, Korea, and
107
Vietnam (birth cohorts 1890–1953) are as healthy as their
nonveteran counterparts at age 65, but experience steeper
declines as they move into older age. Comparisons among
veterans of different wars also suggest that WWII veterans
have better age-adjusted health outcomes than veterans from
Korea or Vietnam (Wilmoth et al., 2010).
Empirical studies that focus on WWII veterans, in particular, are few and report conflicting results. An epidemiological study that used mortality data collected for a large
sample of army veterans compared the number of observed
deaths for veterans to expected deaths based on age-specific
national death rates (Seltzer & Jablon, 1974). Mortality rates
for returning veterans were substantially lower in the first
5 years after discharge. By the end of the observation period
(1947–1969), however, the mortality gap between veterans
and nonveterans had narrowed from 57% to less than 14%
(Seltzer & Jablon, 1974).
Studies based on the Stanford–Tremin data provide the
most nuanced view of differences among veterans, but at the
cost of a relatively small and selective sample of 856 white
men born 1900–1920, who were college educated with high
childhood IQs. Using interviews from 1945 to 1950, veterans could be sorted into those who were sent overseas, those
who saw combat, and those deployed to the Pacific Theater
(Elder et al., 2009). Although the age at induction made no
difference in mortality rates among veterans, those in combat—especially those in the South Pacific—had higher rates
of early mortality (Elder et al., 2009).
Design and Method
Data
The older men cohort of the NLS was interviewed from 1966
to 1990. This national sample of 5,020 older men aged 45–59
in 1966 represents survivors from 1906 to 1921 birth cohorts.
The initial wave collects information about their early life and
current circumstances, focusing on schooling, work, income,
family, and wealth. Subsequent surveys updated information
in these areas and included additional modules of questions
as these cohorts aged. The mortality information on these
respondents was collected in three waves. When possible,
interviewers recorded life status and age at death for men who
dropped out of the sample. After the 1990 interview, Census
obtained certificates for deaths occurring in 1966–1990 from
state vital records departments or collected reports from widows or next of kin. As of 1990, 2,693 deaths were recorded,
and the mortality observed coincided closely with estimates of
mortality from the U.S. Vital Statistics and the Social Security
Administration (Hayward & Gorman, 2004).
In 2009, we initiated a second round of linkages through
the Demographic Survey Division (DSD) of the United States
Census Bureau (USCB) and the National Center on Health
Statistics (NCHS). We followed the established protocol
for the National Longitudinal Mortality Study that has a
long history of mortality matching with the National Death
Index (NDI). NDI uses an algorithm to match respondent
The Gerontologist, 2016, Vol. 56, No. 1
108
information with death certificate data using five criteria
to evaluate matches. Results of the matching algorithm are
classified into 5 classes: (1) exact match on SSN, first name,
middle initial, last name, sex, state of birth, birth month, and
birth year; (2) SSN matches on at least seven digits (one or
more of the items from Class 1 may not match); (3) SSN
unknown; eight or more of first name, middle initial, last
name, birth day, birth month, birth year, sex, race, marital
status, or state of birth match; (4) same as Class 3 but less
than eight items match; (5) no NDI match.). Of the 5,020
men, 4,814 provided valid Social Security numbers; remaining cases had to be matched using the other criteria. Through
this process, we added 2,109 deaths, for a total of 4,802
deaths through 2008. We then searched for remaining cases
in the Social Security Death Index and assigned 346 additional deaths. By 2012, less than 5% of the initial sample had
survived, and average age of survivors was 94.5.
Birth Cohorts
Members of our sample reached age 25 during 1932–1946
time span, and all had passed their 18th birthday when the
United States entered the war in 1941. Although all were age
eligible for service during the war, the initial draft targeted the
1914–1919 cohorts, and those born in 1920–1921 were eligible once the minimum age was reduced to 18. For this reason,
we estimate our models for the full sample, but also narrow
our scope to those born in or after 1914 to address the cohorts
with the highest rates of military participation in our sample.
The statistics in Table 1 allow us to connect enlistment
percentages for white men to our sample. By 1940, our
cohorts spanned the age range of early adulthood, from 19
to 34. Some were recent high school graduates, while others were establishing their careers and beginning their families. By 1942, when the draft cohorts tripled in size, all were
in the target age range. Therefore, our sample includes the
early waves of inductees, but not the 1922–1929 cohorts.
The percentage of veterans in our sample closely matches
those reported from the Census (1984).
Variables
Military Service
In 1967, respondents were asked whether they had ever
served in the U.S. Armed Forces (1,962 served; 2,741 did
not). Those who served also were asked when they served
(1,822 WWII; 33 WWII and Korea; 27 Korea only; 90 during peacetime). Restricting our sample to white men who
served in WWII yields a sample of 3,453 men (1,496 veterans and 1,760 nonveterans) (During WWII, many African
Americans also served in the military; however, they
receiveddifferent treatment during and after their service.
They also served in segregated units, since integration of the
military did not occur until 1948.). This additional information allowed us to calculate the age their service began,
age at discharge, and duration of service. After conducting
sensitivity analyses for different functional specifications,
we classified age at discharge into four categories: 19–23,
24–28, 29–31, 32, and older. We also include a dummy
variable to denote veteran status and code nonveterans 0
on these variables. Information on age at entry, duration of
service, and age at exit is linearly dependent. As the terms
of enlistment were routinely until the end of the war plus
6 months, our primary choice was between age of entry
and age of exit. Bayesian fit statistics favored age at exit.
Controls for Exemptions
We have no direct information on whether respondents
were drafted or volunteered, nor do we know which
respondents may have been given exemptions. Draft
boards called men up on the basis of birthdays, with men
born in January called first and men born in December
called last if the quota had not been met (Selective Service
Administration, 2015). As exemptions were based on certain types of work, physical health, and mental acuity, we
include three variables linked to these deferment criteria.
First, we include a dummy variable for whether respondent’s father was in farming, because working on a family
farm might have exempted respondents from service. To
proxy those who may not have been deemed mentally fit,
we include two dummy variables for less than a high school
education: 0–7 and 8–11 years of schooling. Last, in 1966
respondents were asked whether they had a health limitation that prevented work or limited the kind or amount
of work they could perform. Those who responded in
the affirmative were asked when the limitation began.
Based on this information, we include a dummy variable
to indicate men who reportedly having a work-limiting
health condition that began before 1940. While this variable likely identifies those with more serious (and activity
Table 1. Proportion of Men Serving in the Military by Birth
Cohorts
Census 1980a
NLS Older Men
Year of
birth
% in
Service
% War
Service
%
Veterans
Age in
1942
1906–
1910
1911–
1915
1916–
1920
1921–
1925
24.3
22.0
23.8
32–36
35.6
34.0
32.7
31–35
57.3
56.5
62.8
22–26
74.2
73.5
72b
21b
Notes: U.S. Veterans Administration, Veterans in the United States: A Statistical
Portrait from the 1980 Census. Washington, DC: Office of Information
Management and Statistics, 1984.
a
As these national statistics were based on the 1980 Census, these proportions
reflect those aged 50–59 (for the 1906–1910 cohorts) and those aged 35–44
(for the 1911–1925 cohorts).
b
Reflects those born in 1921 only.
The Gerontologist, 2016, Vol. 56, No. 1
limiting) health conditions, it does not capture all those
who may have received medical deferments.
Health Behaviors
We control for two important health behaviors that
are related to mortality, smoking and BMI. Studies have
reported that the provision of cigarettes for those in military service increased rates of smoking and may have
had delayed negative health consequences (Bedard &
Deschenes, 2006). Therefore, we control for any history of
smoking. The variable “smoker” combines responses to the
question “Did you ever smoke cigarettes?” asked of surviving respondents in 1990 and “Did he ever smoke cigarettes”
asked of surviving relatives in 1990. “Smoker” was coded
“1” for respondents who ever smoked cigarettes and “0”
otherwise. BMI is calculated from self-reported height and
weight in 1973 (respondents are aged 52–67). We then use
Center for Disease Control and Prevention guidelines for
constructing BMI classifications as “Normal” (BMI < 25),
“Overweight” (BMI = 25–29.9), and “Obese” (BMI ≥ 30).
SES and Demographics
We use respondent’s longest job to measure occupation,
which serves as one indicator of SES. We classified occupation into three categories, white collar or skilled, farming,
and a combination of semiskilled, unskilled, and service
professions. We also include a third indicator of education for respondents who attended college. We include it
here with the SES measures because it was not a basis of
exemption, but as an indicator of higher SES, it has been
shown to correlate with mortality (Montez et al., 2010).
In addition, because the GI bill increased access to high
education for veterans, it could also be a positive outcome linked to veteran status. As a third measure of SES,
we include the log of total assets accumulated by age 50.
Finally, we include age in 1966 as a continuous measure
to capture differences in the mortality hazard by age, and
add indicators of marital status at first interview, classifying respondents as married, never married, or previously
married but not currently married.
Data on basic demographic and SES measures were
missing in less than 1% of the cases. In contrast, because
health behaviors were addressed toward the end of the survey, approximately 20% of cases were missing BMI and
smoking data. Data specific to veterans were missing most
often for the duration of military service (about 15%). We
use multivariate normal regression in Stata’s mi command
to impute missing data for all independent variables using
all covariates in the fully specified model.
Approach
We estimate Cox proportional hazards models (Cox,
1972) using Stata 13, which take the following form:
h(t | xj ) = h0 (t)exp(xj β x ),
109
where h(t|xj) is the hazard rate (or force of mortality) at age
t for a man with xj characteristics. This model assumes that
all hazard rates are proportional to a baseline hazard h0(t),
which describes variation by age in the mortality transition
rate for an average person in this sample. We estimate four
models to test our hypotheses. We begin with a model that
includes veteran status, controlling for age at interview.
The second model differentiates among veterans by their
age at discharge; these variables are analogous to interaction terms and estimate differences in mortality risk by age
at military exit among the subset of veterans. The third
model adds controls for selection criteria—if respondent’s
father was a farmer (agriculture exemption); less than a
high school education, because AGCT scores correlate .73
with years of schooling (Personnel Research Section, AGO,
1945), and a pre-1940 work limiting health condition. The
fourth and final model adds measures of later life SES, BMI,
and smoking behavior. Given the late collection of some
of our key variables and other sources of missing data, we
use multiple imputation using Stata’s mi commands to utilize all cases (Rubin, 1987; Royston, 2009). The estimates
presented are based on results from 20 imputed data sets.
Results
We begin with an overview of the full sample, reported on
the left side of Table 2, and the subsample of those born
from 1914 to 1921, reported on the right side of Table 2.
Both the full sample and subsample are further divided into
those who served in the armed forces and those who did not.
Of the 3,453 men, we found no recorded deaths for 171
through March 2012, for an overall mortality rate of 95%;
79% of our sample survived to age 65. Of the deceased, the
average age at death was 74.6. The mortality rate for veterans is 4% less than that of civilians, but the age at death is
not significantly different for the two groups. These mortality rates are not adjusted for age, and since the age at first
interview for veterans is younger, on average, than nonveterans, this difference may reflect the different age distributions.
Of those who served, almost half were discharged when
aged 24–28; very few got out at younger ages, but more
than one-quarter were not discharged until age 32 or older.
Nonveterans were more likely to have grown up on a farm,
were more likely to have had a career in farming, and were
more likely to have left school before the 8th grade. In contrast, more than one-quarter of veterans had some postsecondary education, and two-thirds worked in white-collar or
skilled craftsman positions. Veterans also were significantly
more likely to have been smokers, but the two groups had
very similar distributions on BMI and marital status, with 9
of 10 respondents spending most of their lives as husbands.
The only difference in distributions between the full sample
and those born from 1914 to 1921 involves veteran status
itself. In the full sample, 43% had been in the military; for
the smaller set of cohorts, 60% were veterans. The age at
discharge also reflects the exclusion of the older cohorts,
The Gerontologist, 2016, Vol. 56, No. 1
110
with the proportion leaving the military in their mid-30s
much smaller.
We organize our discussion of the hazard models into
two sections. First we estimate mortality models for any
deaths occurring after first interview for our full range of
birth cohorts. Then we narrow our focus to the 1914–1921
birth cohorts, whose members were more heavily recruited
and more heavily drafted for military service (as noted
above). For the 1914–21 birth cohort we estimate models
only on deaths that occur before age 65 to see whether
we find evidence that any difference between veterans and
nonveterans weakens as survival time is extended.
Later-Life Mortality for the 1906–1921 Birth
Cohorts
Results for these models are reported in Table 3. In comparing
estimates from models 1 and 2, we see no global difference in
mortality rates relative to veteran status. Once we disaggregate veterans relative to their age when discharged, however,
it appears that veterans who exited at the youngest ages have
higher mortality rates than those discharged at older ages,
but not significantly different from nonveterans (evidenced
by the nonsignificant coefficient for veteran status). In contrast, veterans who exited at older ages have lower mortality
rates, particularly those who age out in their mid-20s and
those who age out when aged 32 or older.
In models 3 and 4, we see that these differences remain
largely consistent when we introduce correlates of selection into the military, BMI, smoking, and SES. Respondents
who were raised on farms have lower mortality rates, while
those who reported having health limitations that began
before 1940 or left school without a high school diploma
have higher mortality rates. Being a smoker increases the
mortality rate more than obesity (but does not differ in its
effect for veterans vs. nonveterans); working in lower blue
Table 2. Descriptive Statistics by Veteran Status for All Birth Cohorts (1906–1921) and Cohort Subset (1914+)
All Cohorts
All
Age in 1967
Age out of service (for veterans)
19–23
24–28
29–31
32+
Farmer (father)
Education
0–7 years
8–11 years
12 years
13+ years
Health limitation before
1942
BMI
Normal
Overweight
Obese
Smoked
Longest occupation
White collar/skilled
Semiskilled/
service/
unskilled
Farm (Respondent)
Marital status (1966)
Married
Separated/
divorced/
widowed
Never married
Military service
Number of cases
Cohorts 1914+
Veterans
Nonveterans
52.84
50.99
54.40
0.35
0.04
0.47
0.21
0.28
0.28
0.14
0.39
0.26
0.21
0.02
All
Veterans
Nonveterans
49.61
48.98
50.56
0.42
0.34
0.05
0.59
0.24
0.12
0.28
0.43
0.09
0.35
0.31
0.26
0.02
0.18
0.43
0.23
0.16
0.03
0.11
0.37
0.07
0.34
0.16
0.42
0.22
0.02
0.26
0.01
0.14
0.04
0.45
0.48
0.08
0.72
0.44
0.48
0.08
0.74
0.46
0.47
0.07
0.70
0.47
0.08
0.73
0.48
0.09
0.74
0.47
0.08
0.70
0.59
0.29
0.65
0.29
0.53
0.29
0.29
0.29
0.30
0.12
0.07
0.18
0.11
0.06
0.19
0.90
0.05
0.91
0.04
0.91
0.05
0.05
0.04
0.05
0.05
0.43
3,453
0.05
0.04
0.04
0.04
1,622
1,831
0.04
0.60
1,965
1,176
789
The Gerontologist, 2016, Vol. 56, No. 1
111
collar jobs or being previously (but not currently) married
also increased mortality rates (Table 4).
Survival to Age 65 for the 1914–1921 Birth Cohort
When we focus on this subset of cohorts and deaths before
age 65, we do see a mortality advantage of almost 30%
for veterans (model 1). As before, among veterans mortality rates are highest for those who mustered out in their
late-teens or early 20s. In limiting our analysis to these
cohorts, we also changed the composition in these age-atdischarge categories. In this subgroup, a smaller proportion was discharged at age 32 or later (12% vs. 28% in
the full sample), and almost three-in-five were discharged
when aged 29–31. This latter age group has the lowest
mortality rate—half the rate of nonveterans—an advantage that persists as additional variables are included in
models 3 and 4.
As before, sons of farmers have lower mortality rates
as do those with more assets and postsecondary schooling.
Smokers, those with health problems earlier in adulthood
and less than a high school education have higher mortality
rates than their counterparts. In results not reported here, we
also looked at all deaths for these birth cohorts, but these
findings indicated no difference between veterans and nonveterans (B = .913, p = .079), no advantage for those with 13
or more years of schooling, and a disadvantage for the obese.
Discussion
No one disputes the horrors of war, nor do we question
that those who serve in the military make a considerable
sacrifice. Much of the research on those who have been
in combat has documented the immediate and longer term
consequences of that experience. Although the nature
of warfare has changed, the physical and psychological
trauma associated with its practice has not. This was our
mindset as we began our study of WWII veterans.
In contrast to other studies, we use a national sample of WWII veterans, men who not only survived the
war, but were still alive in 1966 when the NLS sample
Table 3. Results from Cox Regression Models, Birth Cohorts 1906–1921
Age
Veteran
(Age out 19–23 = ref)
Age out 24–28
Age out 29–31
Age out 32+
Farmer (father)
Education
(12 years = ref)
0–7 years
8–11 years
13 or more years
Health limit <1942
BMI
Overweight
Obese
Smoker
Log assets at age 50
R’s longest occupation
(White collar/
skilled = ref)
semi/skilled/service/
unskilled
Farm
Marital status
(Married = ref)
Separated/divorced/
widowed
Never married
Observations
(1)
(2)
(3)
(4)
1.002 (0.612)
0.942 (0.130)
1.005 (0.385)
1.264 (0.118)
1.001 (0.850)
1.284 (0.098)
0.999 (0.874)
1.251 (0.144)
0.725* (0.033)
0.875 (0.399)
0.690* (0.020)
0.740* (0.046)
0.877 (0.407)
0.706* (0.030)
0.870*** (0.000)
0.742 (0.053)
0.905 (0.536)
0.714* (0.038)
0.906* (0.024)
1.511*** (0.000)
1.263*** (0.000)
1.339*** (0.000)
1.155** (0.002)
0.921 (0.125)
1.216 (0.085)
1.257* (0.043)
1.021 (0.604)
1.212* (0.011)
1.285*** (0.000)
0.993 (0.059)
1.130** (0.004)
0.994 (0.924)
1.221* (0.012)
3,453
3,453
Notes: Exponentiated coefficients; p values in parentheses. BMI = body mass index.
*p < .05, **p < .01, ***p < .001.
3,453
1.047 (0.597)
3,453
The Gerontologist, 2016, Vol. 56, No. 1
112
was drawn. When considering the full range of birth
cohorts, we found no difference in mortality rates by
veteran status, but among veterans, those who were discharged in their 30s had higher survival rates. These findings changed when we focused on the 1914–1921 birth
cohorts and looked only at deaths before age 65. Here,
we did find a mortality advantage for veterans, and it
was located among those who left the military around
age 30, which included more than half of the veterans
in these cohorts. These results fit the selection hypothesis—that positive health selection would provide a mortality advantage that would weaken over time. However,
earlier work comparing mortality rates by veteran status
controlled only for age (Seltzer & Jablon, 1974). That
the mortality gap persisted when we controlled for a
range of other factors suggests that something other than
health selection is involved. Other possible explanations
should also be noted. For example, to the extent that age
at exit is associated with whether or not veterans were
in combat situations or whether or not they were officers, age at exit may serve as a proxy for very different
types of exposure. Although in general, officers tend to be
somewhat older, during wartime that correlation weakens, and promotions can elevate the very young and the
more mature soldier.
Study limitations must also be kept in mind. Our sample captures many of the cohorts age eligible for service,
but we are missing the 1922–1926 cohorts, who could
have been called up before the end of the war in 1945.
Therefore, our findings do not reflect the full population
of WWII veterans surviving to 1966, but only those born
before 1922. Although HRS includes all WWII cohorts,
that sample was drawn 25 years after the NLS for an
initial interview in 1992; therefore, the HRS cohorts of
WWII veterans had been subject to higher mortality selection. Second, we are also unable to distinguish among veterans in terms of their military experiences. We do not
know whether they were stationed overseas; whether an
Table 4. Results for Cox Regression Models of Deaths Before Age 65, Birth Cohorts 1914–1921
Age
Veteran
(Age out 19–23 = ref)
Age out 24–28
Age out 29–31
Age out 32+
Farmer (father)
Education
(12 years = ref)
0–7 years
8–11 years
13 or more years
Health limit <1942
BMI
(BMI ≤ xx = ref)
Overweight
Obese
Smoker
Log assets at age 50
R’s longest occupation
(White collar/
skilled = ref)
semi/skilled/service/
unskilled
Farm
Marital status
(Married = ref)
Separated/divorced/
widowed
Never married
Observations
(1)
(2)
(3)
(4)
0.987 (0.518)
0.711*** (0.001)
1.003 (0.907)
1.224 (0.499)
1.003 (0.914)
1.250 (0.457)
1.004 (0.864)
1.228 (0.509)
0.586 (0.071)
0.406** (0.010)
0.867 (0.702)
0.620 (0.109)
0.415* (0.011)
0.933 (0.854)
0.761* (0.014)
0.631 (0.138)
0.454* (0.027)
0.956 (0.907)
0.751* (0.023)
2.015*** (0.000)
1.531*** (0.000)
1.644** (0.002)
1.265* (0.045)
0.683* (0.016)
1.798* (0.014)
1.766* (0.016)
0.977 (0.829)
1.161 (0.438)
1.406* (0.012)
0.962*** (0.000)
1.047 (0.671)
1.178 (0.359)
1.326 (0.151)
1965
1965
Notes: Exponentiated coefficients. p values in parentheses. BMI = body mass index.
*p < .05, **p < .01, ***p < .001.
1965
1.141 (0.558)
1965
The Gerontologist, 2016, Vol. 56, No. 1
overseas assignment placed them in Europe or the South
Pacific; whether they were in combat roles; or whether
they were wounded. Certainly this last point could be relevant if those who exited at younger ages received medical
discharges. Being able to incorporate more detailed information about their experiences in the military would add
important layers to the story. We are continuing to investigate how their lives unfolded once they returned home
to see if we can better identify and explain how the war
changed the lives of those who served.
Finally, we have been careful to frame our questions in
terms of military service during WWII. Although certain
commonalities of service extend across time and space,
other important dimensions of military service, in general,
and the war experience, in particular, depend on which
assignment, which branch of the service, which war, or
which era is under consideration. From that standpoint,
an answer to the question—What are the health effects of
military service?—may differ by context. What should not
differ is the extent to which we provide a strong system
of care and support when we bring our men and women
home.
Acknowledgements
We are grateful for the helpful comments from Steven Haas, Suzanne
Meeks, and the anonymous reviewers.
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