Date: Mon, 7 Jun 2004 08:50:31 EDT
Subject: NATAP: Efficient Sexual HIV Transmission in Acute Infection
NATAP - www.natap.org
Brief but Efficient: Acute HIV Infection and the Sexual Transmission
of HIV
Journal of Infectious Diseases 2004;189:1785-1792
Christopher D. Pilcher,1 Hsiao Chuan Tien,2 Joseph J. Eron, Jr.,1
Pietro L. Vernazza,3 Szu-Yun Leu,2 Paul W. Stewart,2 Li-Ean Goh,4 and
Myron S. Cohen,1 for the Quest Study and the Duke-UNC-Emory Acute HIV
Consortiuma
Departments of 1Medicine and 2Biostatistics, University of North
Carolina at Chapel Hill; 3University Hospital, St. Gallen,
Switzerland; 4Clinical Development Medical Affairs, GlaxoSmithKline,
Greenford, United Kingdom
“………The present study has provided empiricalrical evidence that men
with acute HIV-1 infection are biologically hyperinfectious because of
increased genital shedding of HIV-1. In addition, the present study
has provided evidence that, during acute infection, HIV-1 load
increases and decreases in semen in approximate parallel with changes
occurring in blood, which have been well described. Our present model
of viral dynamics in semen suggests that, on average, individuals are
hyperinfectious beginning before the onset of the acute retroviral
syndrome and continuing for about 6 weeks therafter……..Depenpending on
the frequency of coitus, men with average semen HIV-1 loads and
without sexually transmitted diseases (STDs) would be expected to
infect 7%*24% of suusceptible female sex partners during the first 2
months of infection. The predicted infection rate would be much higher
when either partner has an STD…..
ABSTRACT
Background. We examined whether viral dynamics in the genital
tract during the natural history of acute human immunodeficiency virus
type 1 (HIV-1) infection could explain efficient heterosexual
transmission of HIV.
Methods. We measured HIV-1 concentration in blood and semen
samples from patients with acute and long-term HIV-1 infection. We
explored the effect of changes in viral dynamics in semen on the
probability of transmission per coital act, using a probabilistic
model published elsewhere.
Results. Considered over time from infection, semen HIV-1
concentrations, in men with acute infection, increase and decrease in
approximate parallel with changes occurring in blood. Modeling
suggests that these acute dynamics alone are sufficient to increase
probability of heterosexual transmission by 8*10-fold between peak
(day 20 after infection, based on the model) and virologic set points
(day 54 and later after infection). Depending on the frequency of
coitus, men with average semen HIV-1 loads and without sexually
transmitted diseases (STDs) would be expected to infect 7%*24% of
susceptible feemale sex partners during the first 2 months of
infection. The predicted infection rate would be much higher when
either partner has an STD.
Conclusions. Empirical biological data strongly support the
hypothesis that sexual transmission by acutely infected individuals
has a disproportionate effect on the spread of HIV-1 infection. Acute
hyperinfectiousness may, in part, explain the current pandemic in
heterosexual individuals.
BACKGROUN/INTRODUCTION
The average probability of male-female transmission of HIV-1 per
unprotected coital act has been estimated, in a large number of
observational studies, to be .0005*.0026 (1/2000*1 transmission
event/384 coital acts)ts) during established (i.e., nonacute) HIV-1
infection. In a study using survey-based data on sexual behaviors in
the United States, Pinkerton et al. calculated that these
probabilities of transmission per coital act would result in low rates
of lifetime transmission (0.19*0.40 infected partners/man;
0.09 "0.18 infected partners/woman), which, by themselves, could not
sustain an epidemic. These estimates have been recently cited as
evidence for the putative importance of iatrogenic spread in areas
where the pandemic is growing the fastest. However, most estimates of
probability of transmission per coital act used for such calculations
do not take into consideration biological factors that might increase
or decrease the probability of transmission of HIV-1. Because the
studies from which probabilities of transmission are derived enroll
only HIV-1*discordaant couples, it is important to note that the
overall estimates generated from such studies generally reflect
transmission by individuals with long-term infection-necessarily
underestimating the potential influence of traansmission by acutely
infected individuals with peak HIV-1 loads. Transiently high viremia
could translate to heightened probability of transmission during acute
infection. Indeed, in a recent study of HIV-1*serodiscordant couples
in Uganda, Gray ett al. observed a strong relationship between blood
HIV-1 load and probability of heterosexual transmission.
\However, that genital fluids (not blood) are the principal vehicle
for sexual transmission of HIV presents a particular problem for
modeling the likelihood of HIV transmission during acute HIV-1
infection on the basis of blood data. This is because acute HIV-1
infection represents the period of initial establishment of anatomic
HIV-1 reservoirs; therefore, the viral dynamics in blood, which have
been well described for acute HIV-1 infection, cannot be assumed to
apply to the genital tract. If HIV-1 load were to increase more
rapidly in genital fluids than in the systemic compartment, for
instance, the probability of transmission during acute HIV-1 infection
would be greater than that predicted on the basis of concurrent blood
HIV-1 load; if, on the other hand, semen HIV-1 load were to increase
slowly, relative to blood HIV-1 load, it is possible that no peak in
probability of transmission would occur at all, despite elevated blood
HIV-1 load during acute HIV-1 infection. Previous human studies
examining the excretion of HIV-1 in semen during acute HIV-1 infection
have been limited by an inability to obtain semen samples over time
from untreated patients with acute HIV-1 infection. In a study
involving experimental infection of macaques with HIV type 2GB122 or
simian/HIV89.6p, however, Pullium et al. demonstrated that virus load
peaked and subsequently declined over a similar time frame and with
similar kinetics in both semen and blood. These data led us to
hypothesize that both semen and blood HIV-1 concentrations, within an
individual with acute infection, are related by a constant ratio-thaat
is, by a constant log difference-as they change over time.
In this report, we examine the relationship between blood and semen
HIV-1 concentrations observed in a cohort of men with acute HIV-1
infection. We then use the observed data to develop a predictive model
of the excretion of HIV-1 in semen during acute HIV-1 infection and to
explore the effect of these dynamics on the efficiency of heterosexual
transmission of HIV-1.
AUTHOR DISCUSSION
The present study has provided empirical evidence that men with acute
HIV-1 infection are biologically hyperinfectious because of increased
genital shedding of HIV-1. In addition, the present study has provided
evidence that, during acute infection, HIV-1 load increases and
decreases in semen in approximate parallel with changes occurring in
blood, which have been well described. Our present model of viral
dynamics in semen suggests that, on average, individuals are
hyperinfectious beginning before the onset of the acute retroviral
syndrome and continuing for about 6 weeks thereafter.
In a study of discordant couples in Uganda, Gray et al. provided
compelling evidence that blood HIV-1 load influences the probability
of heterosexual transmission; Chakraborty et al. developed a model to
predict the transmission of HIV-1 from an infected man to his female
partner on the basis of semen HIV-1 concentration, and the predictions
of that model were in close agreement with the empirical results from
the study of couples in Uganda. In the present study, we used this
probabilistic model to examine the effect of excretion of HIV-1 in
semen during acute HIV-1 infection on sexual transmission, for a given
population. We focused specifically on a population of men with
clade-B HIV-1 infection who had no evidence of other STDs. For an
average individual in this population, the results suggested a very
high average probability of heterosexual transmission (0.0047 or 1
transmission event/213 coital acts), about 20 days into acute
infection. These men would be expected to transmit virus to 2%*6% of
female sex partners during the first 2 months of infection. These data
contrast with very low rates projected for this population on the
basis of probabilities seen in established infection [3]. Of
importance, men in sub-Saharan Africa with clade-C HIV-1 infection
have 3*4-fold higher semen HIV-11 loads [21], even without STD
coinfection. Assuming no STDs for either the index patient or the
partner, an average man with acute HIV-1 infection in sub-Saharan
Africa would, conservatively, infect 7%*24% of female sex partners
during tthe first 2 months of infection. In partnerships in which
either partner had an STD, this rate could exceed 50%.
All these estimates of the probability of transmission during acute
infection should be considered as minimal estimates, for several
additional reasons. First, average fitted curves for populations tend
to blunt individual peak values when individual curves are
asynchronous-which is almost certaainly the case in patients in our
study and, hence, in our final constructed model. Second, overall,
genital fluid HIV-1 inoculum is only 1 potentially important
biological determinant of individual infectiousness. A given inoculum
of HIV-1 may be expected to have higher infectious potential during
acute infection, since early variants (typically R5) are very
homogeneous in env and are closely related to successfully transmitted
strains. The absence of antibodies to HIV-1 in infectious body fluids
early during acute infection might also increase the potential of
transmission. Third, other factors related to increased susceptibility
of the partner (e.g., frequent genital ulceration or absence of
acquired mucosal immunity) or high rates of partner change and riskier
modes of sexual interaction would be expected to amplify the effect of
increased infectiousness during acute HIV-1 infection on probability
transmission and spread within sexual networks.
A number of lines of epidemiologic evidence suggest that acute
infection fuels heterosexual spread of HIV-1. Both look-back studies
examining transmission rates and case series documenting rapid
secondary transmission have suggested an elevated risk of transmission
per coital act, relative to chronic infection. In addition,
mathematical modeling studies and evidence of extensive
case-clustering among acutely infected patients have demonstrated that
acute HIV-1 infection may play an important role in the pandemic.
The present study used cross-sectional data and cannot account for
possible selection bias with regard to the time samples were obtained
from patients. It is also not possible to completely evaluate the
influence of potential confounders, such as STDs or differences in
viral subtype or phenotype, in the study population. In addition,
patients with symptomatic, acute HIV-1 infection may have higher than
average blood HIV-1 loads; however, in the present study, higher blood
HIV-1 load values would not necessarily lead to overestimation of the
probability of transmission, given the way that our particular model
was constructed. Moreover, the present study's weaknesses are
counterbalanced by important strengths. By pooling longitudinally
collected blood data from multiple studies, we were able to develop a
model of viral dynamics for a population, with precision and
predictive power. The hypothesis-driven statistical approach that we
used to assess viral dynamics in semen in the present study may have
promise in assessing other clinical situations and difficult-to-sample
tissue compartments, such as the female genital tract or the central
nervous system. Finally, the present study directly relates measured
changes in genital HIV-1 shedding to probability of transmission, by
use of the empirically derived model of Chakraborty et al. The
projections derived from the model of Chakraborty et al., on the basis
of our estimates of viral dynamics in semen, cannot account for the
influence of variations in the susceptibility of female partners;
nonetheless, the ability to estimate the effect of measurable
biological phenomena on the probability of HIV-1 transmission is a
powerful tool that could be useful in modeling HIV-1 prevention
strategies. In particular, the observation that, in the absence of
STDs, viral dynamics in blood during acute HIV infection may
accurately reflect viral dynamics in the genital tract allows
more-confident exploration of the effects of interventions early
during HIV disease on transmission in populations.
Only a small number of the estimated 40,000 acute HIV-1 infections
that occur annually in the United States are diagnosed, and there are
few specific public health systems in place to facilitate
identification or management of the syndrome. It has been assumed
that, because of the brevity of acute infection, individuals with
acute infection would make a minor contribution to the epidemic.
However, the efficiency of transmission predicted by the present study
forces reconsideration of such assumptions and underscores acute HIV-1
infection as a unique public health opportunity. This may be
particularly true in sub-Saharan Africa. Public health interventions
directed at acutely infected individuals, including safe-sex
counseling, condom promotion, and antiretroviral therapy, can decrease
sexual infectivity to the extent that sexual practices are altered
and/or HIV-1 load is reduced over time. These effects are maximized
when introduced early during infection and at a time of high
transmission potential. The potential clinical benefits of acute
antiretroviral treatment for individual patients who are diagnosed
during antibody-negative acute infection further emphasize the need
for improved and early identification of cases of HIV-1 infection.
Treatment considerations for recent sex contacts should include both
prospective screening for acute and chronic HIV infection and
appropriate screening for other sexually transmitted pathogens. An
estimated average probability of sexual transmission of 1 transmission
event/213 coital acts-a risk of HIV-1 acquisition similar to that
associated with percutaneous blood exposures in the setting of chronic
HIV-1 infection -may further warrrant rapid identification and
notification of potentially exposed partners, with possible provision
of post-sexual-exposure prophylaxis.
The benefits potentially associated with diagnosis of acute HIV-1
infection are likely to be greatest if cases of infection are
identified around the period of peak viremia, a circumstance that is,
at present, rare. Public health strategies that may aid in the
identification of early acute infections include incorporating pooling
and nucleic acid screening into routine HIV-1 testing and developing
more-effective acute HIV-infection referral networks. These efforts
may make it possible to implement a proactive response to prevention
of transmission by patients with acute HIV-1 infection and to
facilitate these patients' early entry into care.
Data on Patients
Patients with acute HIV-1 infection.
For preliminary descriptive modeling of compartmental viral dynamics,
both blood and semen HIV-1 concentrations were available for men with
known dates of HIV-1 infection or with known dates of onset of an
acute retroviral syndrome, who had been enrolled in the Duke-UNC-Emory
Acute HIV Consortium and the GlaxoSmithKline-sponsored Quest study
cohorts. Included data from these cohorts were obtained at single time
points before antiretroviral therapy. Semen data were not included for
patients with evidence of concurrent sexually transmitted diseases
(STDs). To develop a more precise model, viral dynamics in blood were
assessed by combining blood data from these same cohorts with
additional data on blood HIV-1 concentrations from individuals with
well-characterized acute HIV infection that had been directly
abstracted from the published literature. When the date of infection
for an individual patient was not known, the date of infection was
estimated assuming a 14-day incubation period, on the basis of
previously published data. For all data on patients with acute
infection, blood and semen HIV-1 concentrations were obtained and
recorded before antiretroviral therapy, up to 1 year after HIV-1
infection. Informed consent was obtained from all participants;
human-experimentation guidelines of the US Department of Health and
Human Services and/or those of all participating authors' institutions
were followed in the conduct of this research.
Patients with long-term HIV-1 infection.
Developing the constructed model of viral dynamics in semen required
us to estimate the distribution of semen HIV-1 concentrations for
patients with chronic HIV-1 infection at virologic set point; for this
purpose, data on semen HIV-1 load were collected from patients with
chronic HIV-1 infection enrolled in previously published studies at
the University of North Carolina at Chapel Hill and University
Hospital in St. Gallen, Switzerland. Data were included for patients
who were HIV-1 antibody positive, had CD4+ cell counts > 200
cells/mm3, and had no documented concurrent STDs.
HIV-1 RNA measurements.
HIV-1 RNA concentrations in semen plasma were determined by use of
NucliSens HIV-1 QT (lower limit of detection, <400 copies/mL; NASBA;
bioMerieux), by use of a modification of the Roche Ultrasensitive
reverse-transcriptase polymerase chain reaction (lower limit of
detection, <200 copies/mL), or by use of both assays. Roche PCR was
performed as follows: 500 uL of seminal plasma was mixed with 500 uL
of normal human plasma and was centrifuged for 90 min at 50,000 g. The
upper 900 uL of the supernatant was discarded, and the pellet was
resuspended in the remaining 100 uL and used in the Roche PCR Amplicor
kit, according to the manufacturer's descriptions. NASBA results were
used for analysis when results of both assays were available; results
for the 2 assays were similar for the subset of specimens on which
both assays were run (P.L.V., unpublished data). HIV-1 RNA
concentrations in blood plasma were determined by use of various
commercially available assays.
Study Design
This retrospective cohort study approached the problem of the
probability of transmission of acute HIV-1 infection as follows:
1. The relationship between HIV-1 concentrations and time was
assessed in both semen and blood compartments, for patients donating
both types of samples;
2. A precise model of viral dynamics in semen was constructed on the
basis of longitudinally collected blood data and a hypothesized
relationship between blood and semen compartments (i.e., parallel
compartmental dynamics)â€"the prediction accuracy of the constructed
model was then tested for agreement with observed data on semen HIV-1
concentration; and
3. The effect of predicted changes in viral dynamics in semen on
the probability of transmission per coital act over time during acute
HIV-1 infection was explored by combining the constructed model of
viral dynamics in semen with a probabilistic model published
elsewhere.
Statistical Methods
HIV-1 RNA measurements. Results for either compartment (blood or
semen) that were undetectable were assigned a value that was one-half
the absolute lower limit of detection for the assay that was used and
for that compartment. All HIV-1 RNA data were then log-transformed
before analysis.
Descriptive viral dynamics in semen. For patients with concurrent
blood and semen HIV-1 load values, individual observations were
plotted versus the time from infection. Correlation of blood and semen
HIV-1 concentrations and trends in semen HIV-1 RNA concentration over
time were assessed by use of Pearson's correlation. Descriptive
regression models on the observed semen data were compared by use of
the likelihood ratio test.
Construction of model of viral dynamics in semen. Because
descriptive models were based on relatively few semen samples obtained
from study subjects before the initiation of antiretroviral therapy,
we constructed a model of viral dynamics in semen starting with
more-abundant data on blood HIV-1 concentrations in samples obtained
from untreated patients with acute HIV-1 infection: a piecewise
polynomial linear mixed model with a covariance structure based on
random coefficients was first used to obtain the population average
curve for blood data. Knots for the piecewise regression were chosen
on the basis of a grid search for maximum likelihood estimators, with
peak viremia estimated to occur on day 20, and the period of chronic
infection estimated to begin on day 54. The mean HIV-1 RNA load was
assumed to be -6.00 log copies/mL on day 0, for consistency with
previous reports. A predictive average semen HIV-1 curve was made by
adjusting the model of viral dynamics in blood so that the predicted
set point matched the mean semen HIV-1 concentration for a population
of 42 patients with chronic HIV infection and CD4 cell counts >200
cells/mm3. The prediction accuracy of the final constructed model was
assessed in terms of agreement between predicted and observed values,
as measured by the number of observed data points falling within
prediction bands around the predicted population curve in the final
constructed model.
Estimation of probability of transmission for hypothetical
partnerships during acute HIV-1 infection.
To estimate the effect of changes in semen HIV-1 load on an
individual's infectiousness during acute infection, probabilities of
male-female transmission per coital act were calculated from predicted
semen HIV-1 RNA load values, for hypothetical partnerships, by use of
a probabilistic model published elsewhere. As put forth by Chakraborty
et al., this model based estimates of probability of transmission per
coital act on rates observed among HIV-serodiscordant couples and on
an assumption that, for any individual partnership, this probability
was primarily determined by the absolute R5 HIV-1 count per ejaculate,
for the male partner, and by the CCR5+ cervicovaginal receptor-cell
density, for the susceptible female partner. By reconciling
distributions for these parameters, which were observed in clinical
studies, with observed transmission rates, Chakraborty et al. were
able to estimate the effects of varying either parameter on the
probability of transmission within hypothetical partnerships. To do
the same, using the present data, we estimated inputs to the model of
Chakraborty et al., assuming 100% R5 HIV-1 in semen during acute
infection, 70% R5 in semen for patients with chronic infection, a
median ejaculate volume of 2.30 mL, and a median cervicovaginal
receptor-cell density of 184.8 CCR5+ cells/mm3. Predicted semen HIV-1
curves were generated by adjusting the curve from the constructed
model on viral dynamics in semen to match (at day 54) R5 HIV-1 counts
in semen for representative men at set point. The estimated
probability of transmission for partnerships including these men were
then plotted versus the time from infection. Probabilities of
transmission within partnerships in which the partners had different
frequencies of unprotected coitus during the period from day 0 to day
54 were calculated assuming regular coitus at evenly spaced intervals,
beginning on day 0.
RESULTS
Excretion of HIV-1 in semen during acute infection.
To assess the dynamics of the excretion of HIV-1 in semen during acute
HIV-1 infection, we examined concurrent blood and semen HIV-1 RNA
concentrations in samples from 30 men with acute HIV-1 infection
participating in 2 large, acute-infection cohorts. All semen samples
from subjects with acute infection were donated 14â€"84 days (median,
38 days) after the estimated date of infection. Semen HIV-1 RNA
concentrations were similar between cohorts. Overall, in samples from
men with acute infection, semen HIV-1 RNA concentrations (mean ± SD,
4.1 ± 1.14 log copies/mL) were significantly higher than those in
samples from a comparison group of 42 antiretroviral treatment*naive
men with chronic HIV-1 infection (meaan ± SD, 3.49 ± 1.28 log
copies/mL) (P = .04). Semen HIV-1 RNA concentrations determined close
to the time of the onset of symptoms were significantly higher than
those determined later during acute infection (P < .01). The fitted
cubic regression curve for the semen data closely resembled the form
of previously published models [6*9] describing viral dynamics in
blood; this curvve had a statistically significantly better fit than
did the alternate quadratic regression curve (P < .0001). In addition,
we found that acute-infection blood and semen HIV-1 concentrations
were significantly correlated over time, despite a likely rapid flux
in these measurements within individuals (Pearson's correlation, 0.37;
P = .04).
Modeling viral dynamics in semen.
Although these cross-sectional data suggested similar dynamics in
blood and semen, the resultant descriptive model had insufficient
prediction accuracy to allow estimation of the dynamics of the
probability of transmission. However, we had access to abundant,
longitudinally obtained blood data, which we used to develop a more
precise model of viral dynamics in semen. We reasoned that, if the
relationship between blood and semen HIV-1 concentrations were
relatively constant within each individual in a population during
acute infection, then the scatter of semen HIV-1 concentrations
plotted versus time, for a population, would parallel changes in blood
plasma viremia in the same population. We therefore constructed a
predictive model of viral dynamics in semen, from blood data, assuming
the hypothesized relationship, and then assessed the prediction
accuracy of this constructed model in terms of agreement between
predicted and observed semen HIV-1 RNA load values, following a
paradigm frequently used to study population pharmacokinetics. A blood
curve representing mean log10 HIV-1 RNA concentration was constructed
on 171 longitudinal blood HIV-1 concentration data points, for 53
patients (mean follow-up, 12.4 days; median follow-up, 1 day; range,
1*80 dayys), by use of piecewise polynomial regression. This curve
demonstrated an initial rapid increase in blood HIV-1 load, reaching
an estimated average peak HIV-1 RNA load of 5.93 log copies/mL at day
20 after infection (6 days after onset of symptoms, for patients with
an acute retroviral syndrome) and declining to 4.74 log copies/mL by
day 54 (day 40 after onset of symptoms). Assuming parallel dynamics in
blood and semen, we constructed a predictive average semen
probabilities curve by adjusting the model of viral dynamics in blood
so that the predicted set point matched the mean semen HIV-1
concentration for patients with chronic HIV-1 infection and CD4 cell
counts >200 cells/mm3. Finally, we measured the prediction accuracy of
the constructed model by calculating the number of semen HIV RNA
values from the primary infection cohorts that fell within prediction
bands around the predicted population curve. Predictions of the
constructed model of viral dynamics in semen were in excellent
agreement with the distribution of timed semen HIV-1 concentrations:
77% of semen HIV-1 load values were within 1 SD of the predicted
population mean, on the basis of the distribution of set-point
concentrations for patients with chronic HIV-1 infection. Altering the
assigned value for initial inoculum by 100-fold (i.e., by 2.0 log) in
either direction had a minimal (consistently <11%) effect on peak and
set-point HIV-1 load estimates generated by the model. Changing this
parameter had no effect on the prediction accuracy of the constructed
model. An alternative model, imposing a shift of 14 days in the time
to peak HIV RNA level between blood and semen, showed lesser agreement
with observed semen HIV-1 load values.
Dynamics of the probability of transmission.
To better understand the effect of the changes in semen HIV-1 load
predicted by our constructed model of viral dynamics in semen on an
individual's infectiousness during acute infection, we used the
previously published probabilistic model of Chakraborty et al.,
treating male-female sexual transmission of HIV-1 as a function of R5
HIV-1 count in an ejaculate and CCR5+ receptor-cell density in
cervicovaginal tissues. We explored the effect of varying semen HIV-1
concentrations on the probability of transmission in a hypothetical
partnership. By assuming the accuracy of the constructed model of
viral dynamics in semen, we generated predicted semen HIV-1 curves for
men with representative semen HIV-1 concentrations at day 54. We then
derived estimates of the dynamics of the probability of transmission,
assuming average susceptibility in a hypothetical partner. We
estimated that, at the time of peak viremia predicted by our
constructed model (day 20 after infection), the probabilities of
transmission per coital act, for individuals at the 25th, 50th, and
75th percentiles of the observed distribution of semen values for men
at set point, were 1/1099, 1/213, and 1 transmission event/53 coital
acts, respectively. For calculations of the probabilities of
transmission within hypothetical partnerships in which the partners
had different frequencies of unprotected coitus during the period from
day 0 to day 54, we assumed average susceptibility of the partner, no
change in the susceptibility of the partner during this time, and
regular coitus at evenly spaced intervals, beginning on day 0. The
probabilities of transmission within stable partnerships, assuming 8
coital acts/month over the course of 54 days of infection, were 0.6%,
3.2%, and 12.4%, respectively, for the same categories of individuals
as above.
