Trends of HIV Seroconversion
Young Adults in the US Army,
1985 to 1989
Among
John G. McNeil, MD; John F. Brundage, MD; Lytt I. Gardner, PhD; Z. Frank Wann, MS; Philip O. Renzullo, MPH;
Robert R. Redfield, MD; Donald S. Burke, MD; Richard N. Miller, MD; and the US Army Retrovirus Research Group
Because soldiers in the US Army are recurrently tested for the presence of
antibody to the human immunodeficiency virus (HIV), HIV seroconversion rates
can be directly measured. From November 1985 through October 1989,429 HIV
seroconversions were detected among 718 780 soldiers who contributed
1 088 447 person-years of follow-up time (HIV seroconversion rate, 0.39 per
1000 person-years). Period-specific seroconversion rates declined significantly,
from 0.49 per 1000 person-years (November 1985 through October 1987) to
0.33 per 1000 person-years (November 1987 through October 1988) to 0.29 per
1000 person-years (November 1988 through October 1989). The HIV seroconversion risk among active-duty soldiers was significantly associated with race/
ethnic group, age, gender, and marital status. Based on these trends, we
estimate that approximately 220 soldiers (95% confidence interval, 160 to 297
soldiers) were infected with HIV during 1989 and 1990, with potentially fewer in
future years.
(JAMA. 1991;265:1709-1714)
SINCE 1981, surveillance of the
ac¬
quired immunodeficiency syndrome
(AIDS) has been a keystone of the na¬
tional effort to monitor the human im¬
munodeficiency virus (HIV) epidemic in
the United States and to provide nearterm forecasts of future AIDS cases.1,2
However, over the past decade it is
quite likely that the rate and, potential¬
ly, the determinants of HIV infection
have changed. Documenting and track¬
ing an evolving infection epidemic has
proved difficult because of reliance on a
surveillance end point (ie, AIDS) repre¬
senting the clinical end stage of HIV
infections that occurred over a long and
variable period in the past. The tempo¬
ral trend of AIDS incidence reflects his¬
torical changes in HIV epidemic dynam¬
ics, and, through methods of backcalculation, AIDS case reports have
been used to estimate the occurrence of
HIV infection at specific times in the
past.3"0 However, these methods pro¬
vide insufficient information about the
From the Divisions of Preventive Medicine (Drs
McNeil, Brundage, Gardner, and Miller and Mr Ren-
zullo) and Retrovirology (Drs Redfield and Burke), Walter Reed Army Institute of Research, Washington, DC;
and SRA Technologies, Alexandria, Va (Mr Wann).
The opinions or assertions contained herein are the
private views of the authors and are not to be construed
as official or as reflecting the views of the Department of
the Army or the Department of Defense.
Reprint requests to Division of Preventive Medicine,
Department of Epidemiology, Walter Reed Army Institute of Research, Washington, DC 20307-5100 (Dr
McNeil)
occurrence of new HIV infections over
the last 2 to 3 years, a period of critical
importance for evaluating changes in
the epidemic related to current inter¬
ventions and for making more accurate
projections of future HIV-associated
morbidity.
Direct measurement of the incidence
of new HIV infection greatly enhances
the ability to track the evolving HIV
infection epidemic and substantially im¬
proves the accuracy of epidemic fore¬
casting. Unfortunately, HIV infection
incidence is difficult to directly measure
and can only be observed in groups that
undergo repeated testing for antibody
to HIV (HIV Ab). Currently, these
groups include aging cohorts of volun¬
teers recruited because of increased
risk of HIV infection,6^ repeat blood do¬
nors,9 and US military personnel.1011
The incidence of HIV seroconversion in
the US Army and Navy has been re¬
ported for the first 2 years of routine
HIV Ab testing programs.1011 The pre¬
sent report analyzes the temporal trend
and demographic correlates of incident
HIV seroconversion among young
adults in the US Army during the period
from November 1985 to October 1989.
METHODS
The US Army is composed of approxi¬
mately 760 000 volunteers, mostly
young adults (mean age, 24.5 years;
99.8% are between 17 and 50 years),
about 160 000 of whom
are new
to the
Army each year. Soldiers continuously
enter the Army from every state, the
District of Columbia, several overseas
territories, and virtually every US
county. Table 1 compares demographic
characteristics of soldiers in the Army
vs 17- to 50-year-olds in the general US
population. Compared with the US pop¬
ulation at large, the Army is composed
of substantially greater proportions of
males, 17- to 30-year-olds, and persons
of minority race/ethnicity.
Since November 1985, soldiers on ac¬
tive duty in the US Army have been
routinely tested for the presence of HI V
Ab. Also since that time, all applicants
for military service have been required
to have test results negative for HIV Ab
prior to induction into a uniformed ser¬
vice. Soldiers who remain on active duty
in the Army are required to undergo
retesting for HIV Ab at least every 2
years. During the past 2 years most
soldiers have been retested, with a
mean time from the previous test of
about 16 months. Since the inception of
the Department of Defense HIV
screening programs, the US Army HIV
Data System and retrovirus reference
laboratory, located at the Walter Reed
Army Institute of Research, Washing¬
ton, DC, have received, processed, and
cataloged all HIV antibody test results
and serological specimens from US
Army soldiers and civilian applicants
for military service. Descriptions of the
US Army's testing program, proce¬
dures, screening algorithms, and as¬
sessments of screening test perfor¬
mance have been reported in detail
elsewhere.12"14
During the period we analyzed, No¬
vember 1, 1985, through October 31,
1989, four laboratories conducted all
HIV Ab testing: Damon Laboratory,
Irving, Tex; the US Army 10th Medical
Laboratory, Landstuhl, Federal Re¬
public of Germany; Tripler US Army
Medical Center, Honolulu, Hawaii; and
the US Army Retrovirus Reference
Laboratory, Rockville, Md. AU sera
were initially tested by a commercially
available enzyme-linked immunosor-
Downloaded from jama.ama-assn.org at Florida State University on November 14, 2011
Table 1. Demographic Characteristics of US Pop¬
ulation (17 to 50 Years)* and US Army Population
Table 2. —Interval-Specific Human
Immunodeficiency Virus Seroconversion Rates
—
US
_(n
Age, y
US Population
= 126 612
000),%
17-20
12
21-30
31-40
41-50
Gender
M
F
33
32
23
Army
Population
757
(n
000),%
=
18
53
23
5
49
51
91
W
84
62
B
12
4
30
Race
Other
*1980 US Census data
9
8
projected to
1989.
bent assay (ELISA).lD Reactive sera
immediately retested in duplicate
by ELISA, and those found repeatedly
reactive were further tested using the
Western blot method.1617 Diagnostic
Western blots were routinely confirmed
by Western blot of a second indepen¬
dent serum specimen. Quality control of
testing was assured by stringent per¬
formance requirements.1819 Test results
were entered into an electronic data¬
base, transferred to tape, and forward¬
ed to the US Army HIV Data System
for analysis and archiving. Negative
test results were defined by nonreactive ELISA or reactive ELISA and
nondiagnostic Western blot results.
Positive test results were defined by
ELISA reactivity and a diagnostic
Western blot (in duplicate) and by clini¬
cal confirmation and disease staging.20
Laboratory procedures and diagnostic
criteria for test positivity were consis¬
tently applied throughout the survey
period, except for the definition of a
diagnostic Western blot, which changed
in May 1987 from the presence of anti¬
bodies to HIV-encoded proteins p24 and
p55 and/or gp41 to the presence of at
least two of the following bands:
gpl20/160, gp41, or p24. This change in
blot interpretive criteria rarely affected
the final diagnosis.21
The HIV Ab testing programs in the
Army are considered either "routine" or
"adjunct." Routine programs include
screening of applicants for military ser¬
vice, periodic birth month screening of
all soldiers on active duty (at least every
2 years), and screening during periodic
physical examinations (every 2 to 5
years). Adjunct testing is usually asso¬
ciated with medical evaluation and care
and includes such programs as the test¬
ing of patients undergoing evaluation
for sexually transmitted disease, test¬
ing of soldiers enrolled in drug rehabili¬
tation programs, testing of sex partners
of HlV-infected persons, testing per¬
formed because HIV infection is sus¬
pected based on clinical signs, and test¬
ing specifically requested by a soldier
were
Interval
Dates
No. of
Seroconverters
Incidence Rate,
Person-Years
of Follow-up
per 1000
Person-Years
Relative Rate*
(95% Confidence
Interval)
1_11/85-10/87_239_489 322_049_1.00
2_11/87-10/88_140_428 962_033_0.67 (0.54-0.82)
3
11/88-10/89
50
170 163
0.29
0.60(0.44-0.82)
*Rate relative to interval 1
who perceives his or her risk of HIV
infection to be high.
During the period we studied, inclu¬
sion in the incidence analysis required
documentation of an initial negative
HIV Ab test result from any routine or
adjunct program occurring on or after
November 1, 1985, and followed by at
least one subsequent HIV Ab test prior
to October 31, 1989. Follow-up time
commenced at the date of an individual's
earliest negative test result and accrued
until an end point was reached. For this
analysis, an end point was defined as
either detection of HIV Ab seroconver¬
sion or the date of the latest HIV Ab
negative test result to occur before Oc¬
tober 31, 1989. The actual date of HIV
Ab seroconversion was estimated to be
the midpoint date between an individ¬
ual's latest negative and earliest diag¬
nostic HIV Ab test result. The HIV
seroconversion rates were calculated
for three time intervals: November 1,
1985, to October 31, 1987 (interval 1);
November 1,1987, to October 31, 1988,
(interval 2); and November 1, 1988, to
October 31, 1989 (interval 3). Interval 1
spans a 2-year period because relatively
few HIV Ab tests were conducted be¬
tween November 1985 and October
1986. Follow-up time during the initial
2-year interval closely approximates
observed follow-up times for subse¬
quent 1-year periods. Seroconversion
rates are not presented for November
1985 through October 1986 because
they are based on very short follow-up
times and yield highly unstable
estimates.
STATISTICAL ANALYSES
Seroconversion rates were computed
as interval-specific incidence densities
(IDs) among soldiers who contributed
person-time during an interval, condi¬
tional on an initial negative result of an
HIV Ab test performed at least 60 days
prior to an end point test date. This 60day requirement was established to
minimize misclassification as initially
negative because of specimen labeling
and handling error or data management
error; these errors were generally rec¬
ognized and corrected within a few
weeks of occurrence. All rates are re¬
ported as the number of HIV Ab seroconversions occurring per 1000 person-
years of follow-up within each interval.
The Incidence density ratios (IDRs)
were calculated by the formula IDR
ID/ID2, where the subscript numbers
represent various demographic and pe¬
riod-specific strata. Poisson regression
was used to calculate the independent
summary relative risk estimates for
variables that contributed to the under¬
lying hazard rate. Using the Poisson
method and a completely categorical
model, the expected number of HIV
seroconversions per stratum of covariate was weighted by the stratum-spe¬
cific follow-up time (in person-years). A
single regression model was fitted to the
entire data set, producing summary rel¬
ative risk estimates adjusted for all oth¬
er variables in the model. The final con¬
tribution of each stratum of covariate
was modeled with a log link. Actual
model fits were carried out with the
EGRET statistical package.22 This mod¬
el produced adjusted stratum-specific
relative risks (RRs) expressed as
RRk exp(ßk), where k indicates the
stratum of the covariate.23 Confidence
intervals were calculated using SEs of
the estimate of ßk.
=
=
RESULTS
Between November 1, 1985, and Oc¬
tober 31, 1989, 429 HIV seroconver¬
sions were detected among 718 780 per¬
sons who were tested two or more times
for the presence of HIV Ab. These
718 780 soldiers contributed 1088447
person-years of follow-up time, with a
resulting overall crude rate of 0.39 (95%
confidence interval, 0.36 to 0.43) HIV
seroconversions per 1000 person-years
of follow-up during the first 4 years of
testing within the US Army.
Table 2 summarizes interval-specific
seroconversion rates over the 4 years
analyzed. Relative rates in Table 2 refer
to the HIV seroconversion rate in an
interval relative to interval 1. A signifi¬
cant rate reduction occurred from inter¬
val 1 to interval 2 (P<.001) but not be¬
tween interval 2 and interval 3
(P= .453), indicating a slowing deceler¬
ation of the epidemic curve.
The HIV seroconversion rates were
consistently higher when the end point
test source was an adjunct program vs a
routine testing program. The HIV sero¬
conversions were most efficiently de-
Downloaded from jama.ama-assn.org at Florida State University on November 14, 2011
tected in testing programs oriented to¬
ward individuals at presumed high risk
(clinical suspicion of infection or dis¬
ease, sex partner of infected persons,
soldier-initiated testing), followed by
testing of populations at presumed high
risk (sexually transmitted disease clin¬
ics, drug and alcohol treatment pro¬
grams), followed by routine testing pro¬
grams, such as birth month testing.
Between November 1988 and October
1989, adjunct tests accounted for 5% of
all HIV Ab tests performed in the
Army, while accounting for nearly 20%
of detected seroconversions. The pro¬
portion of all HIV Ab test samples sub¬
mitted from adjunct programs re¬
mained relatively constant from year to
year. Adjunct test-associated serocon¬
version rates declined significantly,
from 1.03 per 1000 person-years in in¬
terval 1 to 0.61 per 1000 person-years in
interval 2 (P<.01), increasing to 0.78
per 1000 person-years in interval 3
(P>.05). Routine test-associated sero¬
conversion rates fell from 0.44 per 1000
person-years in interval 1 to 0.29 per
1000 person-years in interval 2
(P<.001), declining again to 0.22 per
¡2«
ftif
1.25
1.00
0.75
0.50
5*
0.25
8§
Interval 1
Interval 2
D Interval 3
S 0.00
Hispanic
Black
White
Fig 1 .—Temporal trend of human immunodeficien¬
cy virus seroconversion by race/ethnic group.
Fig 2.—Temporal
trend of human
1000 person-years in interval 3 (P>.05).
Figures 1 through 4 depict temporal
trends of HIV seroconversion for vari¬
ous demographic groups. Trend ana¬
lyses for Hispanic soldiers were limited,
because relatively small numbers pro¬
duced unstable rate estimates. Time-
dependent demographic characteris¬
tics, such as age, duration of Army
service, and marital status, were based
each person's status in
interval.
on
Age
Unadjusted infection incidence rates
did not vary substantially by age, ex¬
cept for a significantly lower rate among
soldiers 35 years of age
3).
immunodeficiency virus
Interval-specific
seroconversion
by
2.0
older (Table
seroconversion
or
Fig 3.—Temporal
race and gender.
of HIV seroconversion occurred only
among 25- to 29-year-olds. Among black
teenage soldiers, HIV incidence in¬
creased over twofold from interval 1 to
interval 3. During the three intervals,
regardless of race/ethnicity, soldiers
who were 25 through 29 years of age had
the highest infection rate but also had
the greatest rate reduction over time.
On the other hand, soldiers with the
lowest overall infection rate (eg, &35
years old) had a higher rate of HIV sero¬
conversion during interval 3 than dur¬
ing interval 1.
Gender
During the first 4 years of HIV
screening in the Army, 407 males (0.42
per 1000 person-years) and 22 females
(0.15 per 1000 person-years) were de¬
tected as newly infected with HIV. The
trend of human
immunodeficiency virus seroconversion by
Interval 1
Interval 2
D Interval 3
0 Interval
D
given
Race or Ethnic Group
Figure 1 illustrates interval-specific
HIV seroconversion rates for white,
black, and Hispanic soldiers. For each
race/ethnic group, seroconversion rates
were significantly greater during inter¬
val 1 than interval 2. Among white and
Hispanic soldiers, the trend of reduc¬
tion in HIV seroconversion rates con¬
tinued between the last two intervals.
Such a reduction was not observed
among black soldiers, and seroconver¬
sion rates remained stable at about 0.73
per 1000 person-years during the last 2
years. During the most recent period
surveyed, November 1988 through Oc¬
tober 1989, black soldiers had over a
sixfold increased crude rate of new HIV
infection compared with all other race/
ethnic groups in the US Army. This
extremely elevated relative infection
rate among blacks was adjusted down¬
ward in the multivariate analysis from
6.08 to 3.80.
age and race.
1-5H
a
rates for each age group, further strati¬
fied for blacks and whites, are shown in
Fig 2. Age-specific seroconversion
rates were significantly higher among
black soldiers of all ages than among
white counterparts. Among white sol¬
diers, there was continuous reduction in
interval-specific seroconversion rates
at all ages, except for soldiers 35 years
of age or older. The rate of HIV serocon¬
version declined most significantly
among 25- to 29-year-old white soldiers,
from 0.43 per 1000 person-years during
interval 1 (25 seroconversions) to 0.14
per 1000 person-years during interval 2
(eight seroconversions) to no new infec¬
tions during 58 290 person-years of fol¬
low-up in interval 3. White soldiers who
were 35 years of age or older experi¬
enced consecutive increases in their
rate of seroconversion from interval 1
through interval 3. Among black sol¬
diers, substantial reduction in the rate
1
Interval 2
Interval 3
s. 10-i
8
g
||
0.5
0.0
wmkJHWl
<20 20-24 25-29 30-34 >35
Age, y
White Soldiers
>—i—r-
<20 20-24 25-29 30-34 >35
Age, y
Black Soldiers
Black
Overall White
Male Soldiers
K%1.H,n,ftl,
Black
Overall White
Female Soldiers
Downloaded from jama.ama-assn.org at Florida State University on November 14, 2011
Table 3.—Age-Specific Human
October 1989
Interval 1
Interval 2
Interval 3
Immunodeficiency Virus Seroconversion Rates for November 1985 Through
Incidence Rate
No. of
Age
Group, y
Person-Years
of Follow-up
Seroconverters
<20
20-24
25-29
per 1000 Person-Years
0.36 (0.26-0.47)
0.43 (0.37-0.50)
143 577
30-34
Unmarried
(95% Confidence Interval),
239 099
152 703
60
0.46
0.39
0.22
(0.38-0.56)
(0.30-0.51)
(0.16-0.31)
Married
Table 4. -Poisson
Ratios
Fig 4.—Temporal trend of human immunodeficien¬
cy virus seroconversion by marital status.
Regression
Model of
Adjusted Human Immunodeficiency Virus Seroconversion Rate
Adjusted Rate Ratio
(95% Confidence Interval)
Variable
Age, y (vs <25 y)
overall crude male-female IDR was 2.8.
The adjusted male-female IDR in¬
creased to 3.47 in the multivariate anal¬
ysis. Among black soldiers, males (0.90
per 1000 person-years) were at signifi¬
cantly higher risk for acquiring HIV in¬
fection than females (0.24 per 1000 per¬
son-years); the male-female IDR was
3.7 (95% confidence interval, 2.1 to 6.5).
Among white soldiers, the male sero¬
conversion rate (0.23 per 1000 personyears) was also significantly higher than
the female rate (0.08 per 1000 personyears); the male-female IDR was 2.9
(95% confidence interval, 1.4 to 4.8).
Among males, the black-white IDR was
3.9, while, among women, the blackwhite IDR was 3.0. These crude rate
ratios were similar to comparable ratios
reported for prevalent HIV infections
in the US
Seroconversion trends are summa¬
rized in Fig 3. Among male soldiers,
seroconversion rates declined consecu¬
tively overall and among whites but de¬
clined among blacks only from interval 1
to interval 2. Among female soldiers,
regardless of race, the rate of new HIV
infection during interval 3 did not differ
significantly from that during interval
1. During the overall period, the rate
among black women (0.24 per 1000 per¬
son-years) was similar to that among
white men (0.23 per 1000 person-years).
Army.24
Marital Status
Overall, soldiers who were not mar¬
ried had a significantly higher crude
rate of new infection (0.59 per 1000 per¬
son-years) than married soldiers (0.27
per 1000 person-years); the IDR was 2.2
(95% confidence interval, 1.8 to 2.6).
Figure 4 indicates seroconversion
trends for married and unmarried sol¬
diers. Among unmarried soldiers, infec¬
tion rates declined from interval 1 to
interval 3, while, among married sol¬
diers, rates remained relatively stable.
During the most recent interval, the
rates among married and unmarried
soldiers did not significantly differ.
25-34
=35
Race/ethnicity (vs white)
Black
Hispanic
Male gender (vs female)
Unmarried (vs married)
Duration of active-duty service, y (vs
4-9
210
Interval
1
s3 y)
(vs interval 2)
3
Multivariate Analysis (Poisson
Regression)
To control for the simultaneous ef¬
fects of potentially confounding demo¬
graphic factors, a Poisson regression
model22 was constructed that included
the variables listed in Table 4. Table 4
provides adjusted IDRs and 95% confi¬
dence intervals for each adjusted rate
ratio. Significant independent determi¬
nants of HIV seroconversion included
minority race/ethnicity, age 25 through
34 years, male sex, and unmarried. In¬
dependent of all other factors in the
model, the seroconversion rate was sig¬
nificantly higher between November 1,
1985, and October 31, 1987, than in ei¬
ther subsequent period. Rates during
intervals 2 and 3 were not significantly
different from each other.
COMMENT
Because soldiers are periodically
screened for HIV Ab, it is possible to
directly measure and track the inci¬
dence of HIV seroconversion in this
large, well-characterized population.
Over the surveyed period, HIV inci¬
dence rates declined significantly over¬
all and for many demographic groups,
but, disturbingly, remained largely un¬
changed or increased among black men
and women in many age groups. Most
notably, the seroconversion risk nearly
doubled among black teenagers during
the 4 years analyzed.
1.40
0.76
(1.10-1.80)
(0.49-1.17)
3.80
1.85
<.001
3.47
2.64
(3.05-4.64)
(1.27-2.71)
(2.23-5.40)
(2.11-3.31)
1.18
0.94
(0.90-1.56)
(0.69-1.29)
.218
1.57
(1.27-1.94)
(0.68-1.25)
0.88
.210
.001
<001
.453
Independent of other demographic
factors, 25- to 34-year-old soldiers were
at significantly higher risk for acquiring
HIV infection than other age groups.
This finding reflects HIV risk behaviors
(eg, risky sex, drug, or partner selec¬
tion practices) that were more likely to
occur in this age group. On the other
hand, the most dramatic downward
trend in HIV seroconversion rates was
among 25- to 29-year-old soldiers.
Behavioral determinants of HIV ser¬
oconversion have been under intensive
investigation by collaborators from the
US Army, the Centers for Disease Con¬
trol, and state and territorial health
agencies since 1989. Preliminary results
of this ongoing case-control study indi¬
cate that most men on active duty ac¬
quired HIV infection via sex with other
men and that nearly 75% of these men
also had sex with women (who were not
known to be at increased risk for HIV
infection). In this study ofincident seroconverters, intravenous drug use, al¬
though reported by uninfected controls,
has not been reported by any seroconverter to date. The HIV seroconverter
case-patients were also much more like¬
ly to report female sex partners at
known increased risk for HIV infection
(usually because of intravenous drug
use) and anonymous female partners
who had nonmenstrual bleeding during
sex or from whom they acquired a sexu¬
ally transmitted disease.25
The fact that HIV incidence rates fell
Downloaded from jama.ama-assn.org at Florida State University on November 14, 2011
among
some
soldiers but not others
might indicate that programs of educa¬
tion and risk reduction had selective ef¬
fects or simply that outmigration pat¬
terns varied among different groups.
Over the period analyzed, demographic
characteristics of soldiers who left the
Army did not change. However, be¬
cause HIV seroconversion is relatively
rare among soldiers, demographic char¬
acteristics of those who leave are proba¬
bly not a sensitive indicator of selective
outmigration of high-risk individuals.
An in-depth qualitative assessment of
the potential impact of outmigration
patterns on observed incidence trends
has recently been completed. This anal¬
ysis involved evaluation of medical in¬
formation for over 750 000 soldiers who
left the Army during the survey period.
The analysis revealed that soldiers who
left the Army were no more likely than
demographically comparable soldiers
who remained on active duty to experi¬
ence morbidity strongly correlated with
subsequent HIV infection diagnosis
(McNeil et al26 and P.O.R., J.G.M., and
F.W., unpublished results, October
1990). This finding suggests that the
observed incidence trends were not
heavily influenced by selective outmi¬
gration practices during the survey
period.
Decelerating infection incidence over
time might be explained by a military
induction system that effectively and
progressively halted inmigration of
high-risk persons into the Army popula¬
tion. Figure 5 depicts age-adjusted inci¬
dence rate trends for soldiers with 3
years or less of continuous active-duty
service compared with those with more
than 3 years of service. Three years was
chosen as a cutoff point because it was
assumed that most soldiers with 3 years
ofservice or less entered the Army after
it was widely recognized that HIV Ab
screening was routinely performed dur¬
ing the preinduction medical examina¬
tion and within the Army. The figure
demonstrates that absolute values and
trends of seroconversion were nearly
identical for the two groups, and, there¬
fore, selective inmigration of lower-risk
individuals does not appear to account
for the decelerating incidence trend we
report.
The overall declining incidence of
HIV infection among soldiers may indi¬
cate that education and prevention ef¬
forts in the Army have produced reduc¬
tions in HIV-associated risk behavior.
At a minimum, every soldier on active
duty is required to receive at least 4
hours of HIV prevention-oriented edu¬
cation each year. The presentation for¬
mat may vary from didactic instruction
to interactive video and role playing.
Educator proficiency can range from
someone who is knowledgeable, charis¬
matic, and experienced to someone who
is a relative novice. Most instructional
programs focus on health awareness
and strategies appropriate for preven¬
tion of sexually transmitted diseases in
general. Concurrent with the observed
reduction in HIV incidence, substantial
reductions in the rates of many other
sexually transmitted diseases have
been reported among large populations
of active-duty soldiers in the United
States and the Republic of Korea (Wil¬
liam Ryan, PhD, Walter Reed Army
Institute of Research, Washington,
DC, oral communication, January 8,
1991).
As with virtually all surveillance
data, the incidence data we report are
potentially limited by sources of bias
that require careful consideration.
Point estimates of seroconversion risk
might be invalid because of selective
misclassification, the effects of self-de¬
ferral from testing, or imprecision asso¬
ciated with small numbers and relative¬
ly rare events. Incidence trends could
be biased by inconsistent screening test
performance, by self-deferral practices
that change over time, and by precision
constraints.
It is highly unlikely that poor or in¬
consistent program performance signif¬
icantly biased the data presented here¬
in, because, from the inception, Army
screening programs have operated with
strict quality assurances and with ex¬
tremely high specificity.19 Test sensitiv¬
ity has been more difficult to assess, but
operating characteristics of the screen¬
ing test panel should not have changed
remarkably over the period we
surveyed.
Seroconversion rates and trends
indistinguishable between sol¬
diers with a single negative test result
before a positive or negative end point
vs soldiers with multiple negative test
results before a positive or negative end
point. It is highly unlikely that a nega¬
tive HIV Ab test result confirmed by
more than one subsequent negative test
result was initially misclassified. Thus,
the similarity of results for soldiers ini¬
tially classified as HIV-negative based
on a single test result compared with
soldiers who were persistently HIVnegative is reassuring.
Data reported in this analysis would
be biased if soldiers at high risk for HIV
infection were effectively avoiding test¬
ing or if the frequency of self-deferral
were changing over time. Since nearly
all routine HIV Ab tests in the Army
are conducted as administrative ac¬
tions, a soldier could conceivably post¬
pone testing by simply ignoring the
were
1.00
e
5> 0.75
25
Time
on
Active
Duty
-ei-<3y
- ->3y
s e
¿%°
0.25
S.
0.00
i
i
Interval 1
Interval 2
i
Interval 3
Fig 5.—Temporal trend of age-adjusted human im¬
munodeficiency virus seroconversion by time on
active duty.
computer-generated reminder sent ev¬
ery other year during the birth month
calling for a current HIV Ab test. Even
though it is fairly easy to temporarily
self-defer from testing, over the past
2 V2 years, a relatively constant propor¬
tion of the Army population (30% to
32%) has been out of compliance with
current testing policy (ie, >24 months
since the last HIV Ab test). Moreover,
among soldiers who had gone longer
than 24 months since their most recent
negative test result, seroconversion
rates and temporal trends were indis¬
tinguishable from those of soldiers who
were last tested within 24 months. This
similarity persisted in an analysis strat¬
ified by age, race/ethnicity, gender, and
marital status. Among 15 657 soldiers
on active duty when HIV Ab screening
began in the Army who were not initial¬
ly tested until after November 1, 1988,
HIV infection prevalences were virtual¬
ly identical to demographically similar
soldiers who were initially tested be¬
tween November 1, 1985, and October
31, 1988. Although these data do not
disprove that soldiers at high risk for
HIV infection avoid screening, they at
least suggest that self-deferral was not
an overwhelming source of bias in this
analysis.
The size and demographic character¬
istics of the Army population and the
method used to compute interval-spe¬
cific IDs can result in somewhat impre¬
cise rate estimates. Imprecision is espe¬
cially troublesome during interval 3,
and parameter estimates during that
period must be viewed with some cau¬
tion. Precision for interval 3 will im¬
prove dramatically when these data are
reanalyzed in 1991.
We previously reported that during
the first 2 years of screening in the US
Army, the HIV seroconversion rate
was at least 0.74 per 1000 person10
years. The rate we currently report for
that same period is substantially lower
(0.49 per 1000 person-years). In our ini¬
tial analysis, conducted in December
1987, soldiers were required to have
Downloaded from jama.ama-assn.org at Florida State University on November 14, 2011
>
been tested for HIV Ab at least two
times between November 1, 1985, and
October 31,1987. In the present report,
soldiers had to receive only a single neg¬
ative test result during interval 1 to
contribute follow-up time to that inter¬
val. Soldiers who were tested two or
more times within the first 2 years of
Army screening, a period during which
the goal was to test all soldiers only
once,
were
at
substantially higher over¬
all risk of infection than soldiers who
were tested only once during that same
period. Moreover, the different analyti¬
cal approaches taken in our original re¬
port and in the present analysis do not
account for the observed trend of decel¬
erating infection incidence we currently
report. If we apply our initial analytical
criteria to the period from November
1987 through October 1989 (ie, two or
more tests in this 2-year period), the
resulting HIV seroconversion rate is
0.41 per 1000 person-years (107 seroconverters, 258 774 person-years of fol¬
low-up time). This rate is significantly
lower (P<.001) than the rate (0.77 per
1000 person-years) we previously re¬
ported for the first 2 years of HlV Ab
screening in the Army.10
The validity of the data presented in
this analysis should not be confused
with their generalizability. Seroconver¬
sion rates and trends reported herein do
not necessarily reflect the course of the
HIV infection epidemic outside the US
Army. Institutional and self-selection
factors probably result in an Army pop¬
ulation that imperfectly represents de¬
mographically similar individuals not
serving in the Armed Forces. This may
be especially true for certain behavioral
and life-style characteristics, such as in¬
travenous drug use and homosexuality.
In addition, most soldiers live and work
in geographic locations that are some¬
what remote from epicenters of the
HIV/AIDS epidemic, so potentially
risky drug use and sex practices might
not result in the same degree of infec¬
tion risk as when they occur in hyperendemic settings.
Nonetheless, the HIV incidence data
we
currently report
are
remarkably
similar to data in other populations,
such as civilian applicants for military
service,27 and are consistent with ob¬
served reductions in the rate of HIV
incidence observed among homosexual
men.28'29
Human immunodeficiency virus sero¬
conversions among soldiers have been
detected at nearly every location that
conducts screening worldwide. Because
the active-duty population is highly mo¬
bile and relatively transient, serocon¬
versions diagnosed at a particular loca¬
tion do not necessarily imply that
infection occurred there. Seroconver¬
sion rates among soldiers with current
or
overseas duty assignments
consistently fallen within the
recent
have
range of rates observed for soldiers as¬
signed to duty locations within the con¬
tinental United States. Moreover, the
number of soldiers assigned outside the
continental United States, as well as
overall assignment patterns, remained
constant over the 4 years surveyed.
Since soldiers are not routinely tested
for HIV Ab on reassignment in the
United States, location-specific sero¬
conversion rates cannot be precisely
measured. Detailed risk assessments of
newly infected soldiers25 will better de¬
fine infection risk by location for the
Army population.
Tracking the incidence of HIV infec¬
tion over time within the US Army pre¬
sents a unique opportunity to evaluate
the epidemic dynamics of HIV infec¬
tion. Follow-up of this population will
continue for the foreseeable future.
We acknowledge the contributions of Chet Rob¬
erts, PhD (Walter Reed Army Institute of Re¬
search), for assistance with analysis and interpreta¬
tion of serological specimens; Mitchell Gail, MD, of
the National Cancer Institute, Bethesda, Md, for
advice regarding the Poisson analysis; and Bill
Ryan, PhD (Walter Reed Army Institute of Re¬
search), for providing US Army venereal disease
data.
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