MilCohort Early Mortality Popn Health Metrics June2010

Hooper et al. Population Health Metrics 2010, 8:15
http://www.pophealthmetrics.com/content/8/1/15

Open Access

RESEARCH

Early mortality experience in a large military cohort
and a comparison of mortality data sources
Research

Tomoko I Hooper*1, Gary D Gackstetter2, Cynthia A LeardMann3, Edward J Boyko4, Lisa A Pearse5, Besa Smith3,
Paul J Amoroso6, Tyler C Smith3 for the Millennium Cohort Study Team

Abstract
Background: Complete and accurate ascertainment of mortality is critically important in any longitudinal study.
Tracking of mortality is particularly essential among US military members because of unique occupational exposures
(e.g., worldwide deployments as well as combat experiences). Our study objectives were to describe the early mortality
experience of Panel 1 of the Millennium Cohort, consisting of participants in a 21-year prospective study of US military
service members, and to assess data sources used to ascertain mortality.
Methods: A population-based random sample (n = 256,400) of all US military service members on service rosters as of
October 1, 2000, was selected for study recruitment. Among this original sample, 214,388 had valid mailing addresses,
were not in the pilot study, and comprised the group referred to in this study as the invited sample. Panel 1 participants
were enrolled from 2001 to 2003, represented all armed service branches, and included active-duty, Reserve, and
National Guard members. Crude death rates, as well as age- and sex-adjusted overall and age-adjusted, categoryspecific death rates were calculated and compared for participants (n = 77,047) and non-participants (n = 137,341)
based on data from the Social Security Administration Death Master File, Department of Veterans Affairs (VA) files, and
the Department of Defense Medical Mortality Registry, 2001-2006. Numbers of deaths identified by these three data
sources, as well as the National Death Index, were compared for 2001-2004.
Results: There were 341 deaths among the participants for a crude death rate of 80.7 per 100,000 person-years (95%
confidence interval [CI]: 72.2,89.3) compared to 820 deaths and a crude death rate of 113.2 per 100,000 person-years
(95% CI: 105.4, 120.9) for non-participants. Age-adjusted, category-specific death rates highlighted consistently higher
rates among study non-participants. Although there were advantages and disadvantages for each data source, the VA
mortality files identified the largest number of deaths (97%).
Conclusions: The difference in crude and adjusted death rates between Panel 1 participants and non-participants may
reflect healthier segments of the military having the opportunity and choosing to participate. In our study population,
mortality information was best captured using multiple data sources.
Background
Complete and accurate ascertainment of mortality is critically important in any longitudinal study. Death is an
objective outcome measure that is captured in multiple
medical and administrative data sources and provides an
assessment of the overall health of a population, as well as
patterns and trends related to specific causes of excess or
reduced mortality. This paper describes the early mortality experience of the Millennium Cohort Study, a large
* Correspondence: [email protected]
1

Department of Preventive Medicine and Biometrics, Uniformed Services
University of the Health Sciences, Bethesda, Maryland, USA

prospective cohort study of individuals who are serving
or have served in the US military [1], and compares data
sources used for mortality ascertainment.
The Millennium Cohort Study was launched in 2001 in
response to a recommendation by the Institute of Medicine that a prospective cohort study be undertaken to
better understand any health effects related to military
service [1,2]. Following three enrollment phases, 150,597
individuals consented and enrolled as Cohort members
and participated in at least one Web-based or postal survey of physical and mental health status, health risk
behaviors, and military deployment experience. This is

Full list of author information is available at the end of the article
© 2010 Hooper et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.

Hooper et al. Population Health Metrics 2010, 8:15
http://www.pophealthmetrics.com/content/8/1/15

the first large, population-based study to collect longitudinal data on military personnel from all service branches
and components. Self-reported survey data is collected
every three years (continuing through 2022), even after
separation from active service, to evaluate possible health
outcomes related to deployment, military occupations
and exposures, and military service in general. Detailed
descriptions of Millennium Cohort Study methodology
have been published elsewhere [1,3]. Previous investigations have found no determinants for enrollment bias
based on differences in health care utilization [4]; established the reliability of self-reported occupation [5] and
self-reported vaccinations [6,7]; and indicated reliable
reporting with respect to test/retest with high internal
consistency for the standardized instruments included
within the questionnaires [8].
The four data sources used to ascertain mortality
within the Millennium Cohort were: 1) the National Center for Health Statistics National Death Index (NDI), 2)
the Social Security Administration Death Master File
(SSA-DMF), 3) the Department of Veterans Affairs (VA)
data files, and 4) the Department of Defense Medical
Mortality Registry (DoD-MMR). We describe the early
mortality experience of the first enrollment panel (Panel
1) of the Cohort from July 2001 to December 2006 using
three of these data sources (NDI was excluded because of
the long lag time in posting deaths) and compare crude
and adjusted death rates as well as frequency distributions between the participating and the non-participating
members of the invited sample to assess any differences.
In order to examine the utility of all four data sources for
ongoing mortality ascertainment in our study population,
we compared the number of deaths identified by each
source from 2001 to 2004.
With the United States currently engaged in two major
conflicts, there is great public interest in the health
effects of military service, and in particular, any latent
adverse effects of combat on the men and women who
serve their country. Early tracking of mortality is important in this occupational group because of unique exposures (e.g., deployment to many regions of the world, as
well as combat experiences) to establish a baseline for allcause and cause-specific mortality over time. Additionally, because the Cohort includes a substantial proportion
of Reserve and National Guard members and increasing
numbers of individuals who separate from active military
service and re-enter civilian life, this study population
encompasses more than the narrowly defined active-duty
military community. The study participants represent
diverse socioeconomic strata and race/ethnicities and
come from many different geographic regions of the
country. Oversampling of women and Reserve/Guard
members by design gives us insight into groups of particular interest to military leaders and the general public.

Page 2 of 13

Finally, describing the relative utility of available data
sources for mortality ascertainment in our study population would benefit other researchers leveraging the rich
array of electronic data from among the largest health
maintenance organizations (DoD and VA) in the country.

Methods
Study population

The study population, Panel 1 of the Millennium Cohort,
consists of 77,047 consenting participants enrolled
between July 1, 2001, and June 30, 2003. An overview of
the baseline Panel 1 Cohort was published in 2007 [3].
The population-based random sample of US service
members selected to participate in the study (n =
256,400) represented approximately 11.3% of 2.2 million
service members on service rosters as of October 1, 2000.
Members of the Reserve Force and National Guard
(Reserve/Guard), women, and those deployed to Southwest Asia, Bosnia, or Kosovo between 1998 and 2000
were oversampled to ensure adequate statistical power to
assess rare outcomes in these smaller subgroups. Of the
256,400 in the original sample, 214,388 had valid mailing
addresses and were not in the pilot study and comprised
the group we refer to as the invited sample (n = 214,388).
The participation rate for the baseline Cohort was 36%
for those eligible and able to be contacted in the aftermath of challenges arising from the September 11, 2001,
terrorist attack and anthrax mail threat [3]. Non-participants had the opportunity to enroll in the study, but
either declined (n = 4,796), chose not to respond (n =
129,887), or were ineligible for other reasons (n = 2,658).
Panel 1 participants completing the baseline survey were
previously reported to be generally similar to the US military as a whole, but slightly more likely to be older, married, more educated, and officers [3].
Data sources
National Death Index

The NDI is a central repository of automated US death
records maintained by the National Center for Health
Statistics (NCHS) and has been described as a comprehensive source of accurate and complete mortality data
for research purposes [9-18]. It contains death certificate
information reported by each state's vital statistics office
since 1979, as well as capturing deaths occurring in the
District of Columbia, Puerto Rico, and the Virgin Islands
[18]. Sensitivity (correctly identifying deaths) has been
reported to range between 87.0% and 97.9% [17] and is
dependent on the type and quality of identifying information available for use in matching algorithms, with the
Social Security number as the most important factor [1013,17]. Some variability in sensitivity has been reported
by race and gender, with men more likely to be identified
as deceased than women, and Caucasians more likely to

Hooper et al. Population Health Metrics 2010, 8:15
http://www.pophealthmetrics.com/content/8/1/15

be identified than those belonging to other racial or ethnic groups [10,11,13,17]. Although NDI has been cited as
the "gold standard" [17,19] for death ascertainment, it is
not without its limitations, the most important of which
is the considerable lag time (12 to 24 months) in posting
deaths to this registry [19]. In addition, deaths occurring
outside the United States and its territories are not captured, and fees related to the application process and data
searches are a disadvantage, particularly for large studies
[18,19]. However, one distinct advantage of this data
source is the option to obtain cause-of-death information
using NDI Plus [17].
To ascertain deaths among Panel 1 Cohort members
from January 2001 through December 2004, the following
information was sent to NCHS: Social Security number;
first, middle, and last name; gender; and date of birth.
Data received from NCHS in November 2006 included
identifiers listed above, as well as state of death, death
certificate number, and other matching variables.
Social Security Administration Death Master File

The SSA-DMF is another widely used source of vital status information on individuals enrolled in the US Social
Security program and includes deaths since 1936 based
on notification by family members, funeral directors,
financial institutions, US Postal Service, and other federal
and state agencies [17,19-21]. This database was established in 1988 as a public use resource with mortality
information extracted from the SSA master database of
all enrolled individuals (beneficiaries and non-beneficiaries) [9,10,20]. Monthly updates to SSA-DMF are made
based on information contained within other SSA databases [19,21]. Sensitivity has been reported to range
between 83% and 95% [19], with more complete identification of deaths among older individuals (93-96% of
those aged 65 years and older) [20]. Completeness of
mortality information depends on reporting to the SSA,
with potentially less incentive for individuals ineligible to
receive benefits (requires 10 years of work in the United
States), such as younger and foreign-born decedents [21].
Compared with NDI, advantages include free Internetbased search capability, frequent updates to the database,
and the inclusion of deaths occurring outside the United
States and its territories.
For the Millennium Cohort Study, the SSA-DMF is
accessed through the Defense Manpower Data Center
(DMDC), Seaside, Calif., under an interagency agreement. Beginning October 1, 2000, DMDC has provided
SSA-DMF mortality data (first, middle, and last name;
Social Security number; date of birth; and date of death)
on a monthly basis for all service members invited to participate in the Millennium Cohort Study whose Social
Security number matched a record in the SSA-DMF.
SSA-DMF data used for this study were acquired in Janu-

Page 3 of 13

ary 2007 and included deaths through December 31,
2006.
Department of Veterans Affairs mortality datasets

The VA is another important national resource for mortality information since military veterans comprise a large
proportion of the US population (approximately 12.7%
aged 18 years and older based on the 2000 US census)
[22]. The VA uses multiple data sources, in-house and
from other agencies, to ascertain mortality in its veteran
population [15,17,19,23-25]. The Beneficiary Identification and Records Locator Subsystem (BIRLS) is a key VA
data source containing records of all beneficiaries,
including veterans whose family members have applied
for death benefits. The BIRLS Death File is a subset of
this database and, despite primary reliance on survivor
reporting, sensitivity has been reported to range from
80% to 96.5% [11,15,23] in veteran populations. The
BIRLS Death File is supplemented by the Medical SAS
Inpatient Datasets (MSID) [19], formerly the Patient
Treatment Files, which include deaths occurring in or
shortly after discharge from Veterans Health Administration hospitals.
The VA also routinely obtains mortality information
from SSA-DMF and the Medicare Vital Status File for
Medicare-enrolled veterans (since 1999). Accuracy and
completeness in mortality ascertainment based on multiple databases have proved comparable to NDI [19]. For
this study, mortality ascertainment at the VA was based
on BIRLS, MSID, and SSA-DMF.
VA mortality data for the invited sample, matched
using Social Security numbers, were received for this
study in January 2007 and consisted of all deaths that
occurred prior to the end of December 2006. The VA
provided date of birth, date of death, and Social Security
number for each decedent.
Department of Defense Medical Mortality Registry

The DoD-MMR is maintained in the Mortality Surveillance Division of the Armed Forces Medical Examiner
System (AFMES), currently part of the Armed Forces
Institute of Pathology (AFIP). The DoD-MMR contains
detailed information on all US military active-duty
deaths, including Reserve and National Guard members
in an activated status, regardless of where the death
occurred [26]. Data collection began in 1995, with near
complete capture since 1998. There are two main
strengths of this source: It is extremely current, with a
direct connection to military casualty systems; and,
unlike NDI and SSA-DMF, which are limited to death
certificate information, it is a rich source of data, including information from autopsy reports, medical records,
and police or investigative reports. The main limitation of
the DoD-MMR is that deaths among veterans who have
been separated for more than 120 days, reservists
between activations, and military retirees are not cap-

Hooper et al. Population Health Metrics 2010, 8:15
http://www.pophealthmetrics.com/content/8/1/15

tured. Because many veterans have also served as contractors in the current conflicts, records of autopsies
performed by the AFMES on civilians and contractors
killed overseas were also reviewed, and data were
included when a match was confirmed.
Data from DoD-MMR, matched using Social Security
numbers, were received for this study in April 2007 and
included deaths that occurred among invited participants
prior to April 15, 2007. AFIP provided date of birth; date
of death; Social Security number; first, middle, and last
name; cause of death; and military-specific variables.
Millennium Cohort mortality ascertainment

Unique identifiers for the entire baseline invited sample
were submitted to three data sources (DMDC/SSA-DMF,
VA, and AFIP/DoD-MMR) to ascertain deaths through
December 31, 2006. Unique identifiers were also submitted to NCHS/NDI for enrolled Cohort members only,
and NCHS provided information on deaths through
December 31, 2004. In addition to describing the early
mortality experience of enrolled Cohort members, the
purpose of obtaining death information was threefold: 1)
to remove decedents from the ongoing contact list of
Cohort members; 2) to assess representativeness of the
Cohort compared to the invited sample; and 3) to compare the utility of these specific mortality data sources.
For purposes of this study, a positive match between
Cohort data and information provided by each mortality
data source was defined as exact agreement on two of
three personal identifiers consisting of Social Security
number, first and last name, and full date of birth. NDI
was not used as a gold standard because it only captures
deaths that occur within the US and its territories, and
our study population consisted of many service members
stationed overseas.
Analysis

Using three of the data sources, excluding NDI, crude
death rates and 95% confidence intervals were calculated
for Panel 1 participants and non-participants, as well as
the invited sample (participants plus non-participants)
from July 1, 2001, through December 31, 2006. Using person-year denominators, individuals were censored at
time of death or the end of the observation period, and
confidence intervals for rate estimates were based on a
Poisson distribution. In order to more fully delineate any
differences in the death rates for Panel 1 participants and
non-participants, we compared age- and sex-adjusted
(direct standardization) overall death rates and also
examined the distribution of mortality by demographic
and military service variables, categorized as follows:
gender (male, female); age (based on birth year: pre-1960,
1960-1969, 1970-1979, 1980 or later); race/ethnicity
(white non-Hispanic, black non-Hispanic, other); marital
status (not married, married, divorced); education (high

Page 4 of 13

school or less, some college, bachelor's degree, advanced
degree); service component (Reserve/National Guard,
active duty); branch of service (Army, Air Force, Navy/
Coast Guard, Marine Corps); military pay grade (enlisted,
officer); occupational category (combat specialists, health
care specialists, service supply and functional support,
other); deployment experience (pre-2001, 2001-2006,
pre-2001 and 2001-2006, none); and separation from military service (no, yes). Finally, we calculated age-adjusted,
category-specific mortality rates per 100,000 personyears using direct standardization for Panel 1 participants
and non-participants. We did not adjust for sex because
of small numbers (<5) in several strata.
To assess the utility of the four sources of mortality
data, deaths ascertained among Panel 1 participants were
compared by source. Because of the lag time associated
with NDI, deaths identified only through December 31,
2004, could be compared. We again examined the distribution of mortality by demographic and military service
characteristics and graphically displayed counts and
overlap by data source using a Venn diagram.
This study was reviewed and approved by institutional
review boards at the participating organizations and conducted in accordance with federal and institutional regulations pertaining to research involving human
participants (Protocol NHRC.2000.007).

Results
Among the 214,388 individuals in the invited sample,
209,146 (97.6%) were presumed alive as of June 30, 2001,
and had complete demographic data. Of these 209,146
invited service members, 76,960 enrolled in the study
between July 1, 2001, and June 30, 2003, and the remaining 132,186 were non-participants.
Using data from three sources (SSA-DMF, VA mortality
data sets, and DoD-MMR), there were 341 deaths identified among Panel 1 participants between July 2001 and
December 2006, resulting in a crude death rate of 80.7
per 100,000 person-years (95% CI: 72.2,89.3). By comparison, 820 deaths were identified among Panel 1 non-participants for a crude death rate of 113.2 per 100,000
person-years (95% CI: 105.4, 120.9) over the same time
period. The crude death rate for the invited sample was
101.2 per 100,000 person-years (95% CI: 95.4, 107.0).
Applying direct standardization methods, the age- and
sex-adjusted overall death rate for Panel 1 participants
was 78.0 deaths per 100,000 person-years (95% CI: 68.3,
87.7) compared to 121.4 deaths per 100,000 person-years
(95% CI: 112.9, 129.8) for Panel 1 non-participants.
The distribution of mortality through December 2006
among Panel 1 participants and non-participants is presented in Table 1. Men, older individuals (birth year prior
to 1960), non-Hispanic whites, divorced, high school
education or less, Reserve/National Guard members,

Hooper et al. Population Health Metrics 2010, 8:15
http://www.pophealthmetrics.com/content/8/1/15

Page 5 of 13

Table 1: Distribution of Mortality by Demographic and Military Characteristics Comparing Panel 1 Participants and NonParticipants for the Millennium Cohort Study, United States, July 1, 2001-December 31, 2006
Baseline Characteristics

Panel 1 Participantsa

Panel 1 Non-Participantsb

Total Sample
n = 76,960
%d

Deceasedc
n = 341
%d

Total Sample
n = 132,186
%d

Deceasedc
n = 820
%d

Male

73.2

80.1

78.0

88.5

Female

26.8

19.9

22.0

11.5

Pre-1960

21.6

43.1

13.2

27.3

1960-1969

37.9

30.8

29.3

22.9

1970-1979

34.6

21.1

46.7

39.3

1980 or later

5.9

5.0

10.9

10.5

White non-Hispanic

69.6

74.8

63.9

65.0

Black non-Hispanic

13.8

11.4

20.9

20.2

Other

16.6

13.8

15.2

14.8

Not married

30.1

26.1

43.1

42.2

Married

63.1

65.4

51.3

49.6

Divorced

6.9

8.5

5.7

8.2

High school or less

48.9

49.3

61.0

66.0

Some college

25.5

25.8

24.6

19.9

Bachelor's degree

16.5

17.3

10.1

10.1

Advanced degree

9.1

7.6

4.3

4.0

Reserve/National Guard

43.1

57.2

43.4

47.3

Active duty

56.9

42.8

56.6

52.7

47.4

50.7

43.5

51.8

Gender

Birth year

Race/ethnicity

Marital status

Education

Service component

Branch of service
Army
Air Force

29.1

29.9

29.8

22.4

Navy/Coast Guard

18.5

14.7

19.9

18.4

Marine Corps

5.1

4.7

6.8

7.3

Military pay grade
Enlisted

77.0

76.8

87.8

88.7

Officer

23.0

23.2

12.2

11.3

Combat specialists

20.0

26.1

21.3

27.7

Health care specialists

10.4

9.1

7.6

6.3

Service supply and
functional support

28.7

27.6

26.6

23.4

Other

40.9

37.2

44.5

42.6

Occupational category

Deployment

experiencee

Hooper et al. Population Health Metrics 2010, 8:15
http://www.pophealthmetrics.com/content/8/1/15

Page 6 of 13

Table 1: Distribution of Mortality by Demographic and Military Characteristics Comparing Panel 1 Participants and NonParticipants for the Millennium Cohort Study, United States, July 1, 2001-December 31, 2006 (Continued)
Pre-2001

18.9

22.3

18.3

23.2

2001-2006

21.7

16.4

21.5

13.5

Pre-2001 and 2001-2006

18.6

11.4

16.3

11.5

None

40.8

49.9

43.9

51.8

Separated from military
servicef
No

76.5

84.5

73.8

83.2

Yes

23.5

15.5

26.2

16.8

a Includes

Panel 1 Millennium Cohort participants who enrolled in the study between July 1, 2001, and June 30, 2003, and had complete
demographic data.
b Includes service members who had complete demographic data, were presumed alive as of June 30, 2001, were able to be contacted by
mail and were invited to enroll in the first panel of the Millennium Cohort Study, but either declined, did not respond, or were ineligible.
c Data sources include Armed Forces Institute of Pathology, Department of Veterans Affairs, and Defense Manpower Data Center.
d Percents may not sum to 100 due to rounding.
e Deployment category "Pre-2001" refers to individuals deployed to the 1991 Gulf War or to Bosnia, Kosovo, or Southwest Asia between
January 1, 1998, and September 30, 2000; the category "2001-2006" includes individuals deployed in support of the military operations in Iraq
and Afghanistan between September 1, 2001, and December 31, 2006.
f Separation (retirement or early separation) from military service as of December 31, 2006 (excludes death as a reason for separation).

Army, combat specialists, those not deployed or deployed
prior to 2001, and those not separated from military service were overrepresented among the decedents of both
Panel 1 participants and non-participants.
Age-adjusted, category-specific rates are presented in
Table 2 to compare differences between Panel 1 participants and non-participants. These rates are standardized
to the total military population in October 2000 and cannot be used other than to directly compare the two population subgroups. In all categories, the rates are
consistently higher among study non-participants.
Based on all four data sources, a total of 202 deaths
were identified between July 1, 2001, and December 31,
2004. Table 3 shows the proportion of total deaths contributed by each source. The VA identified the largest
number of deaths, 97.0% (196/202). The SSA-DMF, separately accessed through DMDC, identified 85.6% (173/
202). The NDI identified 81.2% (164/202), while the
DoD-MMR accounted for less than half, 46.5% (94/202),
of total deaths.
All 202 deaths were accounted for by at least one of
three sources: NDI, VA files, and DoD-MMR. To illustrate the overlap between these three data sources, as well
as deaths uniquely contributed by each data source, a
Venn diagram was constructed (Figure 1). There was one
death in the SSA-DMF (accessed through DMDC) that
was not included in the VA files; however, it was recorded
in NDI. The VA files missed six deaths, five identified by
the NDI and one by the DoD-MMR.
The distribution of demographic and military service
characteristics among decedents stratified by data source
is shown in Table 4. Because the DoD-MMR is the most

restrictive in terms of the source population for mortality
ascertainment (active-duty military service members or
Reserve/National Guard members on activated status), it
is least similar to the other data sources. In contrast, the
VA mortality file is most similar to the composite picture
of all the data sources combined because it encompasses
the SSA-DMF. The largest contrast can be seen between
the DoD-MMR and NDI, with deaths identified by DoDMMR more likely to include young, single individuals and
those deployed since the onset of the conflicts in Iraq and
Afghanistan and less likely to capture deaths among
Reserve/National Guard members and those separated
from military service.

Discussion
In a recently published overview of the baseline Cohort
(Panel 1) [3], proportional differences in demographic
and military service characteristics between the Cohort
and the probability-based original invited sample (n =
256,400) were considered small; therefore, Cohort study
findings were expected to be generalizable to the military
population as a whole. In this mortality study, the crude
death rate for Panel 1 Cohort members was 80.7 per
100,000 person-years (95% CI: 72.2, 89.3). To put this into
context, age-specific death rates for the US civilian population over the same time period (2001-2006) were all
above 90 per 100,000 for age 19 years and above [27]. The
lower crude death rate for our study population is consistent with a healthy worker effect since individuals entering military service must undergo extensive health
screening, and accession standards ensure that only fit
and healthy individuals from the general population are

Hooper et al. Population Health Metrics 2010, 8:15
http://www.pophealthmetrics.com/content/8/1/15

Page 7 of 13

Table 2: Age-Adjusted, Category-Specific Death Ratesa Comparing Panel 1 Participants and Non-Participants for the
Millennium Cohort Study, United States, July 1, 2001-December 31, 2006
Baseline Characteristics

Panel 1 Participantsb
n = 76,960
No. of deaths (per 100,000 person-years)

Panel 1 Non-Participantsc
n = 132,186
No. of deaths (per 100,000 person-years)

Male

82.2

132.1

Female

56.4

62.8

Gender

Race/ethnicity
White non-Hispanic

77.2

117.6

Black non-Hispanic

61.7

115.4

Other

66.8

111.2

Not married

84.2

125.0

Married

75.9

97.4

Divorced

57.0

110.0

High school or less

85.3

135.5

Some college

66.6

87.9

Bachelor's degree

65.2

92.1

Advanced degree

35.2

56.2

Marital status

Education

Service component
Reserve/National Guard

80.3

112.1

Active duty

63.6

111.4

Army

78.7

137.8

Air Force

64.9

81.5

Navy/Coast Guard

60.4

110.2

Marine Corps

73.2

167.2

Enlisted

77.4

121.4

Officer

64.0

81.5

Combat specialists

104.5

150.7

Health care specialists

60.0

88.7

Service supply and functional support

65.8

98.3

Other

69.7

115.7

Pre-2001

73.7

147.4

2001-2006

61.7

71.5

Pre-2001 and 2001-2006

46.3

92.7

None

77.8

130.9

No

80.2

129.3

Yes

51.8

77.2

Branch of service

Military pay grade

Occupational category

Deployment experienced

Separated from military servicee

a Number

of deaths per 100,000 person years between Jul 1, 2001 and Dec 31, 2006, adjusted for birth year, standardized to the total US
military as of October 1, 2000. Data sources include Armed Forces Institute of Pathology, Department of Veterans Affairs, and Defense
Manpower Data Center.
b Includes Panel 1 Millennium Cohort participants who enrolled in the study between July 1, 2001, and June 30, 2003, and had complete
demographic data.
c Includes service members who had complete demographic data, were presumed alive as of June 30, 2001, were able to be contacted by
mail and were invited to enroll in the first panel of the Millennium Cohort Study, but either declined, did not respond, or were ineligible.
d Deployment category "Pre-2001" refers to individuals deployed to the 1991 Gulf War or to Bosnia, Kosovo, or Southwest Asia between
January 1, 1998, and September 30, 2000; the category "2001-2006" includes individuals deployed in support of the military operations in Iraq
and Afghanistan between September 1, 2001, and December 31, 2006.
e Separation (retirement or early separation) from military service as of December 31, 2006 (excludes death as a reason for separation).

Hooper et al. Population Health Metrics 2010, 8:15
http://www.pophealthmetrics.com/content/8/1/15

selected. Furthermore, periodic health examinations and
fitness standards that must be met for continued active
service plus universal access to medical care at military
treatment facilities worldwide result in a healthy activeduty work force. When Kang and Bullman previously
investigated mortality in veterans of the 1991 Gulf War
era, they found that cause-specific standardized mortality
ratios for both deployed and non-deployed veterans were
significantly lower compared with the general US population [28].
The crude death rate for Panel 1 Cohort members is
also in line with the crude death rate of 72.9 per 100,000
person-years reported for the active-duty component of
the military between 1990 and 2008 by the Armed Forces
Health Surveillance Center [29]. The higher crude death
rate for the Millennium Cohort likely reflects that our
study population includes individuals separated from
military service, some potentially due to serious illnesses.
We initially compared the Panel 1 Cohort's crude death
rate with that of the invited sample and found a statistically significant difference (80.7 per 100,000 personyears, 95% CI: 72.2, 89.3, vs. 101.2 per 100,000 personyears; 95% CI: 95.4, 107.0). To more fully investigate differences between the two independent groups comprising the invited sample, we compared crude death rates as
well as age- and sex-adjusted overall death rates for study
participants and non-participants. We also examined the
distribution of mortality by demographic and military
service characteristics, and finally, compared ageadjusted, category-specific death rates for these two population subgroups.
Despite higher proportions of survey respondents in
older birth cohorts, the lower crude death rate for participants compared with non-participants was consistent

Page 8 of 13

with healthier segments of the military population having
the opportunity and consenting to participate in a survey.
Even more apparent was the difference between age- and
sex-adjusted overall death rate for Panel 1 participants
and non-participants--78.0 deaths per 100,000 personyears (95% CI: 68.3, 87.7) vs. 121.4 deaths per 100,000
person-years (95% CI: 112.9, 129.8), respectively.
In addition to healthy individuals having the opportunity to participate, more health-conscious individuals
(e.g., higher educational level, married) might have
greater interest in and, thus, motivation to join a healthrelated study. To further assess the representativeness of
Panel 1 Cohort members, we compared mortality distribution by demographic and military service characteristics in Panel 1 participants and non-participants. The
distribution of mortality by measured demographic factors was generally similar in the two groups. As expected,
older persons and men were proportionally higher among
decedents. Caucasians were overrepresented and those
"not married" somewhat underrepresented among decedents in the Cohort. Among non-participants, this distribution was less pronounced but trending in the same
direction. Educational level of "high school or less" was
overrepresented among decedents in the non-participant
group and consistent with the association between lower
educational achievement and higher mortality described
in the published literature [30], but this was less evident
among the participants.
In addition to demographics, we examined military service characteristics to compare their distribution among
Panel 1 participants and non-participants. Disproportionately higher numbers of deaths occurred in the following categories: Reserve/National Guard, Army,
combat specialists, pre-2001 or no deployment experi-

Table 3: Mortality Ascertainment among Millennium Cohort Study Participants by Mortality Data Source, July 1, 2001December 31, 2004
Mortality Data Source

Deceaseda

Proportion of Total Deceased (%)

(n = 202)
Department of Defense Medical Mortality Registryb

94

46.5

National Center for Health Statistics National Death Indexc

164

81.2

173

85.6

196

97.0

Social Security Administration Death Master

Filed

Department of Veterans Affairs Mortality Data Filese
a Participants

who enrolled in the study between July 1, 2001, and June 30, 2003, were counted among the deceased if two of the following
personal identifiers were an exact match between Millennium Cohort and mortality data sources: Social Security number, last and first name,
date of birth. Deceased counts are not mutually exclusive.
b Data received from the Armed Force Institute of Pathology.
c Data request received by National Death Index on November 9, 2006, for death ascertainment through December 31, 2004.
d Data obtained from the Defense Manpower Data Center.
e Includes mortality data from the Beneficiary Identification and Records Locator Subsystem Death File, the Medical SAS Inpatient Datasets,
formerly known as the Patient Treatment Files, and the Social Security Administration Death Master File.

Hooper et al. Population Health Metrics 2010, 8:15
http://www.pophealthmetrics.com/content/8/1/15

NDI+VA+
DoD-MMR
n = 58
VA+NDI
n = 101

Page 9 of 13

VA
n=2
VA+
DoD-MMR
n = 35

NDI
n=5

DoD-MMR
n=1

N = 202

Figure 1 Mortality Ascertainment based on the Department of
Defense Medical Mortality Registry (DoD-MMR, n = 94), the National Death Index (NDI, n = 164), and the Department of Veterans Affairs Mortality Files (VA, n = 196), July 2001-December
2004.

ence, and not separated from active service. Army personnel and combat specialists were overrepresented
among decedents, consistent with current combat operations in Iraq and Afghanistan, and may reflect the population at highest risk. Disproportionately higher numbers
of deaths in Reserve/National Guard members and those
with no deployment history may represent older age or
certain medical conditions rendering one unfit for
deployment and at the same time at higher risk of death.
A similar effect, although less pronounced, was seen in
the subgroup with deployment experience prior to 2001.
This group included 1991 Gulf War veterans as well as
veterans of conflicts in Bosnia, Kosovo, and Southwest
Asia. Again, patterns of mortality by military service
characteristics were generally similar when comparing
Panel 1 participants and non-participants. Pay grade
(officer vs. enlisted) was not an influential variable, but
mortality was differentially distributed by continuing service or separation from military service. There were disproportionately fewer decedents among those separated
from military service among both the participants and
non-participants, which may be explained by factors such
as age, service branch, and occupation. For example, a
substantial proportion of young service members separate from military service after one tour of duty, and these
individuals are less likely to die from disease (as opposed
to external causes). Army and combat specialists were
overrepresented among decedents, and since death was
excluded as a reason for separation, this could contribute

to disproportionately lower mortality in the separated
group.
After adjustment for age, a comparison of categoryspecific death rates for Panel 1 participants and non-participants showed consistently higher death rates for those
not participating in the study, although substantial variability in rate estimates for some categories might be the
case due to small numbers. As in other health-related
studies, non-participants may have less opportunity to
participate due to serious illness or injury, institutionalization, or other life circumstances. Individuals who
engage in unhealthy behaviors may also decide not to
participate. These age-adjusted rate comparisons highlight some important differences between the two groups
that warrant further investigation, notably in the categories of race/ethnicity, marital status, branch of service,
and deployment experience. Next steps would include the
examination of causes of death to better understand rate
differences. Over time, any continued meaningful divergence in the mortality experience between the participants and non-participants should become more
apparent as greater numbers accrue. Adjustment for
additional covariates (survival analysis) and a focus on
cause-specific rates, as well as selective subgroup analyses would be very informative.
Our final study objective was to examine the utility of
selected data sources for ongoing mortality ascertainment in the Millennium Cohort. Out of the 202 total
deaths identified by at least one of the four data sources,
the VA databases identified the largest number (196 or
97.0%), presumably because the VA dataset also includes
the SSA-DMF. In a recent study of mortality data in the
VA, the BIRLS Death File alone was reported to be accurate but not complete, but with the SSA-DMF, it was
viewed as the best choice if just one source had to be
selected for mortality ascertainment in veteran populations [19]. The VA data files would seem to be highly
suited to our study population, with all potentially eligible
for death benefits from the VA and presumably all
accounted for in SSA databases. If deaths were related to
combat deployment, families of decedents may be more
likely to claim VA death benefits, including burial at
national cemeteries. Notably, the VA dataset identified
two deaths not included in any of the other three data
sources.
The number of deaths captured by NDI was 164 of 202
(81.2%). While we expected NDI to capture a larger proportion of the Cohort deaths, we did consider it possible
that many of the deaths not captured by NDI may have
occurred outside the United States and its territories
(e.g., combat deaths in theaters of operations or other
overseas assignments). In fact, using information from
the DoD-MMR, as well as electronic military deployment
data, 31 of the 38 deaths (82%) not identified by NDI were

Hooper et al. Population Health Metrics 2010, 8:15
http://www.pophealthmetrics.com/content/8/1/15

Page 10 of 13

Table 4: Demographic and Military Characteristics of Millennium Cohort Study Decedents by Mortality Data Source, July
1, 2001-December 31, 2004
Baseline
Characteristic

All Sources
(n = 202)
%b

Department of
Defense Medical
Mortality Registry
(n = 94) %b

National Center for
Health Statistics
National Death Index
(n = 164) %b

Social Security
Administration
Death Master File
(n = 173) %b

Department of
Veterans Affairs
Mortality Data Files
(n = 196) %b

Gender
Male

79.7

80.9

76.2

79.8

80.1

Female

20.3

19.2

23.8

20.2

19.9

Pre-1960

43.1

22.3

48.8

48.6

43.9

1960-1969

28.7

36.2

29.9

29.5

28.6

1970-1979

21.8

31.9

15.2

17.3

20.9

1980 or later

6.4

9.6

6.1

4.6

6.6

White nonHispanic

74.8

69.2

74.4

75.7

75.5

Black nonHispanic

10.4

10.6

11.0

9.3

9.7

Other

14.9

20.2

14.6

15.0

14.8

Not married

22.8

29.8

18.9

19.7

23.5

Married

68.8

67.0

72.0

71.1

67.9

Divorced

8.4

3.2

9.2

9.3

8.7

High school or
less

50.5

48.9

50.0

48.6

51.0

Some college

24.8

22.3

26.8

25.4

24.0

Bachelor's
degree

15.8

17.0

13.4

16.8

16.3

Advanced
degree

8.9

11.7

9.8

9.3

8.7

Reserve/National
Guard

55.5

31.9

61.0

56.7

56.6

Active duty

44.6

68.1

39.0

43.4

43.4

Birth year

Race/ethnicity

Marital status

Education

Service component

Branch of service
Army

48.5

46.8

47.6

47.4

48.5

Air Force

30.7

24.5

31.7

33.0

30.6

Navy/Coast
Guard

15.8

20.2

18.3

15.6

15.8

Marine Corps

5.0

8.5

2.4

4.1

5.1

Military pay grade
Enlisted

75.7

73.4

76.8

75.1

76.0

Officer

24.3

26.6

23.2

24.9

24.0

22.3

26.6

18.3

21.4

22.5

Occupational
category
Combat
specialists

Hooper et al. Population Health Metrics 2010, 8:15
http://www.pophealthmetrics.com/content/8/1/15

Page 11 of 13

Table 4: Demographic and Military Characteristics of Millennium Cohort Study Decedents by Mortality Data Source, July
1, 2001-December 31, 2004 (Continued)
Health care
specialists

9.4

8.5

11.0

11.0

9.7

Service supply
and functional
support

32.2

27.7

37.8

32.4

32.1

Other

36.1

37.2

32.9

35.3

35.7

Deployment
experiencec
Pre-2001

23.3

21.3

26.2

24.9

24.0

2001-2006

15.4

25.5

7.3

13.9

15.3

Pre-2001 and
2001-2006

9.4

16.0

5.5

9.8

9.2

None

52.0

37.2

61.0

51.5

51.5

85.6

89.4

82.9

85.6

86.2

14.4

10.6

17.1

14.5

13.8

Separated from
military serviced
No
Yes
a Includes

Millennium Cohort participants who enrolled in the study between July 1, 2001, and June 30, 2003, and had complete demographic

data.
b Percents may not sum to 100 due to rounding.
c Deployment category "Pre-2001" refers to individuals deployed to the 1991 Gulf War or to Bosnia, Kosovo, or Southwest Asia between January
1, 1998, and September 30, 2000; the category "2001-2006" includes individuals deployed in support of the military operations in Iraq and
Afghanistan between September 1, 2001, and December 31, 2006.
d Separation (retirement or early separation) from military service as of December 31, 2006 (excludes death as a reason for separation).

confirmed to have occurred overseas. The other seven
(18%) unidentified deaths may be a result of non-matches
due to inconsistencies in death certificate information
and possibly longer-than-expected lag time in posting
deaths. Despite missing a considerable number of Cohort
deaths, NDI did identify four deaths not captured by any
of the other three data sources.
The SSA-DMF was examined separately from the combined VA dataset because it is currently the main source
of updates to mortality information for the Cohort
(accessed through DMDC). It is also more generally
accessible for mortality ascertainment than VA files. One
SSA-DMF death was, in fact, not identified by the VA
dataset that includes the SSA-DMF. Because mortality
ascertainment is more complete in older age groups, the
utility of the SSA-DMF is expected to increase over time.
However, inclusion in the SSA-DMF is more likely among
eligible beneficiaries either claiming death benefits or
reporting a death to discontinue Social Security benefits
to the deceased; thus, deaths may be incompletely captured in some population subgroups [21].
Potential sources of bias in recording or reporting of
deaths by data source need to be understood and monitored over time. Additionally, changes in relative importance of currently used data sources are likely to occur as
the Cohort ages and a greater proportion of individuals

separate from military service, and other sources may
gain importance, such as the Centers for Medicare &
Medicaid Services. Finally, the success of long-term follow-up, including mortality ascertainment, is often
affected by time since last follow-up and the type and
amount of information available for use [10]. Thus, ongoing efforts to improve retention in the Millennium
Cohort, including regular contact with participants
through biennial postcards [31], provide multiple opportunities to maintain contact and correct any inconsistencies in personal identifying information.
There are several study limitations. First, due to the
short period of observation (2001-2006), the total number of deaths in this relatively young, healthy occupational cohort is small (n = 341 through 2006), based on
three frequently updated data sources (excluding NDI).
When NDI was included, mortality could only be
assessed through 2004, thus reducing the number of
deaths available for the comparison of all data sources to
202. On the other hand, the large size of this Cohort with
oversampling of Reserve/National Guard personnel, as
well as the current involvement of US troops in combat
theaters of operation, make a strong case for early assessment of mortality as an important health outcome. Over
time, the number of deaths will increase, providing statistical power for cause-specific and other analyses. A sec-

Hooper et al. Population Health Metrics 2010, 8:15
http://www.pophealthmetrics.com/content/8/1/15

ond limitation is that slightly different methods were
used to obtain death data because mortality ascertainment occurs on a regular basis, including prior to each
survey administration, to avoid contacting family members of decedents. However, the same criteria were
applied to count deaths (exact match on two of three personal identifiers). Finally, because our Cohort is composed of relatively young, healthy individuals subject to
unique occupational experiences or exposures, it may be
difficult to generalize our findings to other cohort studies. For example, higher likelihood of combat deaths
overseas among relatively young individuals affects the
degree of capture in various national mortality data
sources.
Strengths of our study include using four data sources
to conduct a baseline assessment of all-cause mortality in
this large, population-based cohort of active and former
military service members to be followed through 2022 in
order to monitor any trends over time. We were able to
link deployment, demographic, and separation data to
the mortality data, enabling us to examine category-specific differences in the mortality experience between the
participants and non-participants, as well as the four data
sources. The Millennium Cohort is of particular interest
to policymakers and the public because of ongoing military deployments to Iraq and Afghanistan. Many questions are being raised about potential short-term as well
as long-term health effects of military service, particularly deployment. As statistical power increases, any
effect of deployment or other military service-related
exposures on all-cause and cause-specific mortality can
be evaluated with adjustment for important covariates.

Conclusions
In summary, early assessment of the mortality experience
of the Millennium Cohort between 2001 and 2006
showed a statistically significant difference between the
crude death rates of the participants and non-participants of the invited sample. Although the distribution of
mortality by demographic and military service characteristics did not differ substantially between these two
groups, a comparison of age-adjusted, category-specific
rates highlighted some potentially important differences
in early mortality experience. These findings will serve as
a baseline for comparisons over time and inform future
analyses focusing on cause-specific death rates to better
understand mortality within our study population.
Finally, available resources for mortality ascertainment
were also assessed, including advantages and disadvantages. For this US military cohort, timely queries to
DMDC (SSA-DMF) and AFIP (DoD-MMR) were efficient means of regularly updating vital status information
at no cost. The VA was also an excellent resource, available to investigators engaging in collaborative research

Page 12 of 13

with VA investigators. Although restricted to the activeduty subgroup, DoD-MMR is a rich source of data on
manner and circumstances of death that is not available
through other sources. This additional information may
be especially useful in evaluating risk factors for causespecific mortality in order to develop prevention strategies in the active-duty component. However, NDI Plus
remains the only source of information on cause-specific
mortality for the entire Cohort. The advantages and disadvantages of each data source support the continued use
of multiple sources for long-term follow-up, along with
periodic assessments of their utility since completeness in
the capture of deaths by data source is likely to change
over time as Cohort members age and separate from military service.
Abbreviations
AFIP: Armed Forces Institute of Pathology; BIRLS: Beneficiary Identification and
Records Locator Subsystem; DMDC: Defense Manpower Data Center; DMF:
Death Master File; DoD: Department of Defense; MMR: Medical Mortality Registry; MSID: Medical SAS Inpatient Datasets; NCHS: National Center for Health
Statistics; NDI: National Death Index; SSA: Social Security Administration; VA:
Department of Veterans Affairs.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
All authors contributed to study concept and design. TH helped direct study
implementation, interpreted analytic results, and prepared major portions of
the draft manuscript, as well as editing and integrating co-author comments in
subsequent drafts. GG helped direct study implementation, contributed to
interpretation of results, and did substantial editing for content. CL was the
major contributor to study implementation, performed all analyses, drafted a
portion of methods, and edited the manuscript for content. EB drafted a section of the discussion and edited the manuscript for content. LP drafted a section of methods and edited for content. BS assisted with study implementation
and edited the manuscript for content. PA helped interpret results and edited
the manuscript for content. Finally, TS helped oversee analytic activities and
edited the manuscript for content. All authors read and approved the final
manuscript.
Acknowledgements
In addition to the authors, the Millennium Cohort Study Team includes Margaret A. K. Ryan, MD, MPH, Naval Hospital Camp Pendleton, California; Timothy S.
Wells, DVM, PhD, MPH, Air Force Research Laboratory, Wright-Patterson Air
Force Base, Dayton, Ohio; Gregory C. Gray, MD, MPH, Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, Iowa; James R. Riddle, DVM, MPH, Air Force Research Laboratory, Wright-Patterson Air Force Base,
Ohio; Gina Creaven; James Davies; Lacy Farnell; Nisara Granado, MPH, PhD; Gia
Gumbs, MPH; Isabel Jacobson, MPH; Kelly Jones, MPH; Molly Kelton, MS; Travis
Leleu; Jamie McGrew; Donald Sandweiss, MD, MPH; Amber Seelig, MPH; Beverly Sheppard; Katherine Snell; Steven Speigle; Kari Sausedo, MA; Martin White,
MPH; James Whitmer; and Charlene Wong, MPH, from the Department of
Deployment Health Research, Naval Health Research Center, San Diego, California.
We thank Scott L. Seggerman and Greg D. Boyd from the Management Information Division, Defense Manpower Data Center, Seaside, California. Additionally, we thank Michelle Stoia from the Naval Health Research Center. We also
thank all the professionals from the US Army Medical Research and Materiel
Command, especially those from the Military Operational Medicine Research
Program, Fort Detrick, Maryland. We appreciate the support of the Henry M.
Jackson Foundation for the Advancement of Military Medicine, Rockville, Maryland. We are indebted to the Millennium Cohort Study participants, without
whom these analyses would not be possible.

Hooper et al. Population Health Metrics 2010, 8:15
http://www.pophealthmetrics.com/content/8/1/15

This represents Naval Health Research Center report 09-09, supported by the
US Department of Defense, under work unit no. 60002. The views expressed in
this article are those of the authors and do not reflect the official policy or position of the US Department of the Navy, US Department of the Army, US
Department of the Air Force, US Department of Defense, US Department of
Veterans Affairs, or the US Government. The Millennium Cohort Study is
funded through the Military Operational Medicine Research Program of the US
Army Medical Research and Materiel Command, Fort Dietrick, Maryland. The
funding organization had no role in the design and conduct of the study; collection, preparation, analysis, or interpretation of data; or preparation, review,
or approval of the manuscript.
Author Details
of Preventive Medicine and Biometrics, Uniformed Services
University of the Health Sciences, Bethesda, Maryland, USA, 2Analytic Services
Inc., Arlington, Virginia, USA, 3Department of Defense Center for Deployment
Health Research, Naval Health Research Center, San Diego, California, USA,
4Seattle Epidemiologic Research and Information Center, Department of
Veterans Affairs Puget Sound Health Care System, Seattle, Washington, USA,
5Mortality Surveillance Division, Armed Forces Medical Examiner System,
Rockville, Maryland, USA and 6Madigan Army Medical Center, Fort Lewis,
Washington, USA
1Department

Received: 27 August 2009 Accepted: 24 May 2010
Published: 24 May 2010
©
This
Population
2010
is
article
an
Hooper
Open
Health
is available
Access
et Metrics
al; licensee
from:
article
2010,
http://www.pophealthmetrics.com/content/8/1/15
distributed
BioMed
8:15 Central
under
Ltd.
the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

References
1. Gray GC, Chesbrough KB, Ryan MA, Amoroso P, Boyko EJ, Gackstetter GD,
Hooper TI, Riddle JR, Millennium Cohort Study Group: The Millennium
Cohort Study: a 21-year prospective cohort study of 140,000 military
personnel. Mil Med 2002, 167(6):483-488.
2. Committee on Measuring the Health of Gulf War Veterans (Institute of
Medicine): Gulf War veterans: measuring health Washington, DC: National
Academies Press; 1999.
3. Ryan MA, Smith TC, Smith B, Amoroso P, Boyko EJ, Gray GC, Gackstetter
GD, Riddle JR, Wells TS, Gumbs G, Corbeil TE, Hooper TI: Millennium
Cohort: enrollment begins a 21-year contribution to understanding
the impact of military service. J Clin Epidemiol 2007, 60:181-191.
4. Wells TS, Jacobson IG, Smith TC, Spooner CN, Smith B, Reed RJ, Amoroso
PJ, Ryan MA, Millennium Cohort Study Team: Prior health care utilization
as a potential determinant of enrollment in a 21-year prospective
study, the Millennium Cohort Study. Eur J Epidemiol 2008, 23(2):79-87.
5. Smith TC, Jacobson IG, Smith B, Hooper TI, Ryan MA, Team FT: The
occupational role of women in military service: validation of
occupation and prevalence of exposures in the Millennium Cohort
Study. Int J Environ Health Res 2007, 17(4):271-284.
6. Smith B, Leard CA, Smith TC, Reed RJ, Ryan MA: Anthrax vaccination in
the Millennium Cohort: validation and measures of health. Am J Prev
Med 2007, 32(4):347-353.
7. LeardMann CA, Smith B, Smith TC, Wells TS, Ryan MAK, Millennium Cohort
Study Team: Smallpox vaccination: comparison of self-reported and
electronic vaccine records in the Millennium Cohort Study. Hum Vaccin
2007, 3(6):245-251.
8. Smith TC, Smith B, Jacobson IG, Corbeil TE, Ryan MA: Reliability of
standard health assessment instruments in a large, population-based
cohort study. Ann Epidemiol 2007, 17(7):525-532.
9. Wentworth DN, Neaton JD, Rasmussen WL: An evaluation of the Social
Security Administration master beneficiary record file and the National
Death Index in the ascertainment of vital status. Am J Public Health
1983, 73(11):1270-1274.
10. Curb JD, Ford CE, Pressel S, Palmer M, Babcock C, Hawkins CM:
Ascertainment of vital status through the National Death Index and
the Social Security Administration. Am J Epidemiol 1985, 121(5):754-766.
11. Boyle CA, Decoufle P: National sources of vital status information:
extent of coverage and possible selectivity in reporting. Am J Epidemiol
1990, 131(1):160-168. Comment in: Am J Epidemiol. 1990;132(6):11961197
12. Williams BC, Demitrack LB, Fries BE: The accuracy of the National Death
Index when personal identifiers other than Social Security number are
used. Am J Public Health 1992, 82(8):1145-1447.

Page 13 of 13

13. Calle EE, Terrell DD: Utility of the National Death Index for
ascertainment of mortality among Cancer Prevention Study II
participants. Am J Epidemiol 1993, 137:235-241.
14. Rich-Edwards JW, Corsano KA, Stampfer MJ: Test of the National Death
Index and Equifax Nationwide Death Search. Am J Epidemiol 1994,
140(11):1016-1019.
15. Fisher SG, Weber L, Goldberg J, Davis F: Mortality ascertainment in the
veteran population: alternatives to the National Death Index. Am J
Epidemiol 1995, 141(3):242-250.
16. Lash TL, Silliman RA: A comparison of the National Death Index and
Social Security Administration databases to ascertain vital status.
Epidemiology 2001, 12(2):259-261.
17. Cowper DC, Kubal JD, Maynard C, Hynes DM: A primer and comparative
review of major US mortality databases. Ann Epidemiol 2002,
12(7):462-468.
18. Sesso HD, Paffenbarger RS, Lee IM: Comparison of National Death Index
and World Wide Web death searches. Am J Epidemiol 2000,
152(2):107-111. Comment in: Am J Epidemiol. 2001;153(7):719
19. Sohn MW, Arnold N, Maynard C, Hynes DM: Accuracy and completeness
of mortality data in the Department of Veterans Affairs [electronic
article]. Popul Health Metr 2006, 4:e2.
20. Hill ME, Rosenwaife I: The Social Security Administration's Death Master
File: the completeness of death reporting at older ages. Soc Secur Bull
2001, 64(1):45-51.
21. Schisterman EF, Whitcomb BW: Use of the Social Security
Administration Death Master File for ascertainment of mortality status
[electronic article]. Popul Health Metr 2004, 2(1):e2.
22. Richardson C, Waldrop J: Veterans 2000. Census 2000 Brief Washington, DC:
U.S. Census Bureau; 2003.
23. Page WF, Mahan CM, Kang HK: Vital status ascertainment through the
files of the Department of Veterans Affairs and the Social Security
Administration. Ann Epidemiol 1996, 6(2):102-109.
24. Dominitz JA, Maynard C, Boyko EJ: Assessment of vital status in
Department of Veterans Affairs national databases. comparison with
state death certificates. Ann Epidemiol 2001, 11(5):286-291.
25. Lorentz KA, Asch SM, Yano EM, Wang M, Rubenstein LV: Comparing
strategies for United States veterans' mortality ascertainment
[electronic article]. Popul Health Metr 2005, 3(1):e2.
26. Gardner JW, Cozzini CB, Kelley PW, Kark JA, Peterson MR, Gackstetter GD,
Spencer JD: The Department of Defense Medical Mortality Registry. Mil
Med 2000, 165(7 suppl 2):57-61.
27. Human Mortality Database 2009 [http://www.mortality.org]. University
of California, Berkeley (USA), and Max Planck Institute for Demographic
Research (Germany)
28. Kang HK, Bullman TA: Mortality among US veterans of the Persian Gulf
War. N Engl J Med 1996, 335(20):1498-1504.
29. Medical Surveillance Monthly Report 2009, 16(52-5 [http://
www.afhsc.mil/msmrToc]. Armed Forces Health Surveillance Center
30. Jemal A, Thun MJ, Ward EE, Henley SJ, Cokkinides VE, Murray TE: Mortality
from leading causes by education and race in the United States, 2001.
Am J Prev Med 2008, 34(1):1-8.
31. Welch KE, LeardMann CA, Jacobson IG, Spiegle SJ, Smith B, Smith TC, Ryan
MA: Postcards encourage participant updates. Epidemiology 2009,
20(2):313-314.
doi: 10.1186/1478-7954-8-15
Cite this article as: Hooper et al., Early mortality experience in a large military cohort and a comparison of mortality data sources Population Health
Metrics 2010, 8:15