Impact of Cigarette Smoking and Smoking Cessation on Life Expectancy Among People With HIV: A US-Based Modeling Study
Impact of Cigarette Smoking and Smoking Cessation on Life Expectancy Among People With HIV: A US-Based Modeling Study
Krishna P. Reddy () 0 7 8 15 16
Robert A. Parker 0 6 8 13 15 16
Elena Losina 0 4 8 14 15 16
Travis P. Baggett 0 2 6 15 16
A. David Paltiel 0 1 16
Nancy A. Rigotti 0 6 11 12 15 16
Milton C. Weinstein 0 3 16
Kenneth A. Freedberg 0 3 5 6 8 10 15 16
Rochelle P. Walensky 0 6 8 9 10 15 16
0 Received 24 May 2016; accepted 3 August 2016. Presented in part: Meetings at Massachusetts General Hospital and Harvard Medical School , Boston , Massachusetts. Hospital , 50 Staniford St, 9th Fl, Boston, MA 02114
1 Yale School of Public Health , New Haven, Connecticut
2 Boston Health Care for the Homeless Program
3 Department of Health Policy and Management, Harvard T. H. Chan School of Public Health , Boston, Massachusetts
4 Department of Biostatistics
5 Department of Epidemiology, Boston University School of Public Health
6 Division of General Internal Medicine
7 Division of Pulmonary and Critical Care Medicine
8 Medical Practice Evaluation Center
9 Division of Infectious Diseases, Brigham and Women's Hospital
10 Division of Infectious Diseases, Massachusetts General Hospital
11 Mongan Institute for Health Policy
12 Tobacco Research and Treatment Center
13 Biostatistics Center
14 Department of Orthopedic Surgery
15 Harvard Medical School
16 Smoking , HIV, and Life Expectancy
Background. In the United States, >40% of people infected with human immunodeficiency virus (HIV) smoke cigarettes. Methods. We used a computer simulation of HIV disease and treatment to project the life expectancy of HIV-infected persons, based on smoking status. We used age- and sex-specific data on mortality, stratified by smoking status. The ratio of the non-AIDSrelated mortality risk for current smokers versus that for never smokers was 2.8, and the ratio for former smokers versus never smokers was 1.0-1.8, depending on cessation age. Projected survival was based on smoking status, sex, and initial age. We also estimated the total potential life-years gained if a proportion of the approximately 248 000 HIV-infected US smokers quit smoking. Results. Men and women entering HIV care at age 40 years (mean CD4+ T-cell count, 360 cells/µL) who continued to smoke lost 6.7 years and 6.3 years of life expectancy, respectively, compared with never smokers; those who quit smoking upon entering care regained 5.7 years and 4.6 years, respectively. Factors associated with greater benefits from smoking cessation included younger age, higher initial CD4+ T-cell count, and complete adherence to antiretroviral therapy. Smoking cessation by 10%-25% of HIV-infected smokers could save approximately 106 000-265 000 years of life. Conclusions. HIV-infected US smokers aged 40 years lose >6 years of life expectancy from smoking, possibly outweighing the loss from HIV infection itself. Smoking cessation should become a priority in HIV treatment programs.
Cigarette smoking is the leading preventable cause of death in
the United States . Although the prevalence of smoking
among US adults decreased from 42% in 1965 to 17% in 2014
[2, 3], smoking still accounts for >480 000 deaths in the United
States annually . Smokers lose about a decade of life
compared with nonsmokers in the general population, but they
can regain much of this through smoking cessation .
Among HIV-infected people, smoking is also a major cause
of morbidity and mortality [5–7]. Over 40% of HIV-infected
people in care in the United States smoke; an additional 20%
are former smokers . Moreover, HIV-infected smokers
appear to face higher rates of cardiovascular disease, chronic
obstructive pulmonary disease, and numerous primary cancers
compared with rates expected from smoking itself [9–11].
While antiretroviral therapy (ART) has improved their life
expectancy [12, 13], HIV-infected people are now living long
enough to develop smoking-associated diseases. Unfortunately,
smoking cessation interventions have not been widely
implemented in HIV care.
We sought to estimate the impact of smoking on the survival
of HIV-infected people. We used a simulation model to
compare the life expectancies of smokers and nonsmokers among
HIV-infected people in the United States and the potential
gain in life expectancy from smoking cessation.
We used the Cost-Effectiveness of Preventing AIDS Complications–
US model, a validated, widely published Monte Carlo
microsimulation of HIV natural history and treatment [14–17], to
project the life expectancy of HIV-infected US people, based
on smoking status (current, former, or never), sex, and age at
entry to HIV care. We defined life expectancy as the mean
age at death, for ease of comparison across sensitivity analyses.
We calculated it by adding the age at entry to the
modelgenerated mean remaining years of life from the time of
entry, to provide the age at death. We simulated cohorts of 1
million individuals to achieve stable per-person estimates.
To estimate the number of years of life lost from smoking, we
compared the model-generated life expectancies of current and
never smokers, with both groups entering HIV care at the same
age and with the same distributions of CD4+ T-cell count and
viral load. We compared years of life lost from smoking with
years of life lost from HIV infection (with the latter calculated
as the life expectancy of HIV-negative smokers minus the life
expectancy of HIV-positive smokers). To derive the potential
number of years of life gained from smoking cessation, we
compared the model-generated life expectancies of former and
current smokers. We further estimated the cumulative potential
years of life gained if 10%–25% of HIV-infected current
smokers in the United States quit smoking.
HIV-infected, ART-naive individuals enter the model upon
initial linkage to HIV care and are followed until death. Following
current guidelines, all simulated individuals are eligible to
initiate ART (dolutegravir/abacavir/lamivudine or a similarly
efficacious regimen) immediately upon entering care, regardless of
CD4+ T-cell count ; thus, the age at entry to care is
equivalent to the age at ART initiation. For each simulated individual,
the model draws randomly from user-defined initial
distributions of CD4+ T-cell count and viral load and then tracks
clinical outcomes as the individual transitions monthly through
various states of disease progression and treatment.
Probabilities of transition between health states are defined by CD4+
T-cell count, viral load, history of opportunistic disease, and
ART use. Treatment efficacy (ie, the probability of viral
suppression) is positively correlated with an individual’s adherence; the
latter is negatively correlated with the probability of loss to
follow-up. Without effective ART, the CD4+ T-cell count falls,
raising mortality risks related to opportunistic diseases and
chronic AIDS. The model also allows the user to specify non–
AIDS-related mortality by age and sex.
Smoking Status and Non–AIDS-Related Mortality
Within each simulation, all individuals had the same smoking
status. We set mortality probabilities for current and former
smokers equal to those for never smokers until age 40 years,
assuming that few deaths prior to that age are smoking related [4,
19]. Starting at age 40 years, we used different age- and
sexspecific monthly probabilities of non–AIDS-related death,
based on smoking status.
We assumed, in the base case, that never and current smokers
remained in their respective smoking status until death. In each
cohort of former smokers, all individuals stopped smoking at
the same age (upon entering HIV care, unless otherwise
specified) and remained abstinent; we relaxed this continuous
abstinence assumption in a sensitivity analysis. We did not evaluate
those who had quit smoking prior to entering care (except in a
sensitivity analysis of years since cessation) because we sought
to estimate the potential survival benefit if a smoking cessation
intervention were provided as part of HIV care. Because the full
benefits of smoking cessation do not manifest immediately, we
conservatively assumed that former smokers had the same
mortality risk as current smokers until 5 years after cessation; this
reflected studies where former smokers were those who had
not smoked in the previous 5 years and data from large US
cohort studies indicating that the all-cause mortality risk in men
who quit smoking does not fall below that of current smokers
until 5 years after cessation [4, 20].
Our base case analysis included imperfect ART adherence
and retention in care, which are important modifiers of survival.
To compare smoking-associated and HIV-associated life
expectancy losses and to validate our model with data from published
studies, we additionally simulated, stratified by smoking status,
(1) HIV-uninfected people and (2) HIV-infected people with
perfect ART adherence and retention in care [4, 21, 22].
ART and Other Model Details
Details regarding ART efficacy, adherence, loss to follow-up,
and other model specifications are described in the
Supplementary Materials and elsewhere [17, 23]. Model figures, flow charts,
state definitions, protocols for data compilation, user guides,
and other details are available at: http://www.massgeneral.org/
We simulated HIV-infected people reflective of those initiating
care in the United States [20, 24–32] (Table 1). Parameters were
derived from North American AIDS Cohort Collaboration on
Research and Design data, where the mean CD4+ T-cell count
(±SD) at entry to care was 360 ± 280 cells/µL, and the median
age at presentation was 43 years . In the base case, we assumed
the same CD4+ T-cell count distribution regardless of age at entry
to care; we relaxed this assumption in a 2-way sensitivity analysis.
Smoking Status and Mortality
For never smokers, we applied non–AIDS-related mortality
probabilities based on published all-cause mortality rates for
never smokers in the US general population (Supplementary
Materials and Supplementary Table 1) [4, 20]. For current
smokers, starting at age 40 years, we set non–AIDS-related
mortality probabilities equal to those of never smokers, multiplied
by a published multivariable-adjusted risk ratio (RR) of 2.8 .
For former smokers, starting at age 40 years (and at least 5 years
after cessation), we set non–AIDS-related mortality
probabilities equal to those of never smokers, multiplied by a published
multivariable-adjusted RR ranging from 1.0 to 1.8, depending
on age at cessation (Table 1) . We assumed that future
mortality probabilities for those who quit smoking before age 35
years were equal to those of never smokers .
HIV- and ART-related parameter
CD4+ T-cell count at entry to care, cells/µL
Mean ± SD
Received first-line suppressive ARTb and had <50 copies/mL at 48 wk, %
Adherence rate, %, mean ± SD
Virologic failure rate for patients who received suppressive ART,b % per mo
Loss to follow-up rate per 100 person-years, by adherence ratec
Return to care rate, per 100 person-years
Smoking- and non–AIDS-related parameter
Unadjusted monthly non–AIDS-related mortality probability, ×10−4, among never smokers, by aged
Mortality risk ratio vs never smokers (starting at age 40 y)
Former smokers, by age at cessationf,g,h
SMR, adjusted for mortality risk factors associated with both HIV risk behavior group and smoking
Men who have sex with men
Women who have sex with men
Men who have sex with women only
b Defined as dolutegravir/abacavir/lamivudine.
Although non–AIDS-related mortality risks may be greater
in HIV-infected versus HIV-uninfected persons, owing to
factors besides smoking (such as other behavioral factors and
depression), we limited the base case to changes in only smoking
because of inherent limitations in the available data to estimate
risks attributable to other behavioral factors [32, 33]. In
sensitivity analysis, we accounted for mortality differences by
behavioral risk group, incorporating these data.
Efficacy of ART (viral suppression to below detection at 48
weeks) was 93%. Details regarding ART and other input
parameters are in Table 1 and Supplementary Text.
We internally validated our model by comparing projections
with results reported in the US general population study from
which base case non–AIDS-related mortality rates were derived
. We externally validated model projections by comparing
them with studies of smoking-associated survival reduction in
HIV-infected people in (1) a Kaiser Permanente US cohort
 and (2) a largely European cohort .
We performed sensitivity analyses to examine the robustness
of the results with alternative assumptions and estimates
(Supplementary Text). These included (1) HIV risk behaviors,
using standardized mortality ratios, to account for potentially
higher non–HIV-related and non–smoking-related mortality
risks among HIV-infected people, compared with the general
population (Supplementary Table 2) ; (2) alternative
smoking-associated mortality risks (eg, mortality RR of 1.9–
4.0 for HIV-infected current vs never smokers ), to
account for a potential differential impact of smoking on
HIV-infected people versus the general population; (3) raw
mortality rates for current and former smokers [4, 20], instead
of RRs, to account for different smoking-associated mortality
risks across ages; (4) delay of smoking cessation after entering
HIV care, as some current smokers will quit after their initial
entry into HIV care; (5) years since cessation for former
smokers , since residual smoking-related risks likely
depend not only on age at cessation, but also time since
cessation; (6) immediate mortality reduction upon smoking
cessation, in light of studies showing relatively rapid decreases
in cardiovascular disease risk following cessation [34, 35]; (7)
anticipated smoking relapse after loss to follow-up, assuming
that those who had quit smoking would experience relapse
upon being lost to follow-up and that some (25%) would
quit smoking again upon returning to care; (8) perfect ART
adherence and no loss to follow-up, as the impact of smoking
and cessation might be greater when competing mortality
risks from HIV infection are lower; (9) CD4+ T-cell count at
presentation (range, 50–500 cells/µL), wherein competing
mortality risks from HIV infection are varied; and 10) age
and CD4+ T-cell count at presentation in a 2-way sensitivity
Using Centers for Disease Control and Prevention data [8, 36]
and model results, we estimated the cumulative potential years
of life gained if 10%–25% of HIV-infected current smokers quit
smoking. We assumed that there were 743 420 HIV-diagnosed
people aged 30–64 years in the United States, with 77.5% in care
 and a smoking prevalence of 38.9%–46.5%, depending on
age, yielding an overall estimate of 247 586 HIV-infected
smokers (Supplementary Text).
Life expectancy for men entering HIV care at age 40 years was
65.2 years, 70.9 years, and 71.9 years for current, former, and
never smokers; for women, it was 68.1 years, 72.7 years, and
74.4 years, respectively (Figure 1; the depicted life expectancy
was greater for those entering care at older ages, because of a
survivor effect – their years of life remaining were fewer than
those for younger people). Men and women who quit
smoking upon entering HIV care at age 40 years gained 5.7 years
and 4.6 years of life expectancy, respectively, compared with
those who continued to smoke. Smoking cessation at younger
ages yielded survival that more closely resembled that of
never smokers (Figure 2 and Supplementary Figure 1).
Gains from smoking cessation decreased with age, but even
smokers who quit upon entering care at age 60 years had a
substantial gain in life expectancy (2.5 years and 2.4
years for men and women, respectively; Supplementary
Life Expectancy Losses From Smoking and From HIV Infection
Simulated 40-year-old, HIV-uninfected current smokers had a
life expectancy of 72.1 years ( for men) and 76.4 years ( for
women), losing 10.7 years and 10.6 years of life expectancy,
respectively, compared with HIV-uninfected never smokers. We
compared the life expectancy losses from smoking and from
HIV infection among 40-year-old HIV-infected current smokers
(with imperfect ART adherence and retention in care, as per the
base case [Supplementary Text]): the losses associated with
smoking and with HIV infection were 6.7 years and 6.9 years,
respectively, for men, and 6.3 years and 8.3 years, respectively, for
women. When we simulated HIV-infected current smokers with
perfect ART adherence and retention in care, life expectancy was
68.6 years (for men) and 72.1 years (for women); the life
expectancy losses associated with smoking and HIV infection were 8.6
years and 3.5 years, respectively, for men, and 8.2 years and 4.3
years, respectively, for women (Supplementary Table 4).
The model-generated life expectancy gain from smoking
cessation among 40 year-old HIV-uninfected people was similar to
that reported in a prior US study (7.7–9.0 years [depending on
sex] in the model vs 9 years in the prior study) .
Modelgenerated life expectancy losses from smoking and from HIV
infection were similar to those reported in a Kaiser Permanente
cohort  and in a largely European cohort in which loss to
follow-up was low  (complete validation comparisons are
available in Supplementary Table 5).
We report sensitivity analyses where input data were most
uncertain and/or where variation in the underlying parameter
materially impacted our findings. Details, as well as results of other
sensitivity analyses that did not differ substantially from those
of the base case (eg, anticipated smoking relapse after loss to
follow-up), are in the Supplementary Text and Table 6.
HIV Risk Behaviors
Differences between standardized mortality ratio–adjusted and
base case analyses were <1.0 years regarding life expectancy
losses from smoking and gains from cessation. In standardized
mortality ratio–adjusted analyses, male and female current
smokers entering HIV care at age 40 years lost 7.2 years and 7.0
years, respectively, compared with never smokers; those who
quit upon entering care regained 5.9 years and 4.9 years,
respectively, compared with those who continued to smoke
(Supplementary Table 7).
Alternative Smoking-Associated Mortality Risks
When applying alternative smoking-associated mortality RRs
from general population studies, the life expectancy loss
associated with current (continued) smoking, compared with never
smoking, for those entering HIV care at age 40 years ranged
from 6.4 years to 6.9 years for men and from 6.1 years to 6.6
years for women, compared with base case losses of 6.7 years
and 6.3 years, respectively (Supplementary Table 8).
When using mortality RRs from a study of HIV-infected
people, life expectancy losses from smoking and gains from
cessation had wide ranges, but point estimates were similar to those
of the base case (Supplementary Table 9). The mean (range)
loss for current smokers entering care at age 40 years, compared
with never smokers, was 6.2 years (range, 4.1–9.1 years) for
men, compared with 6.7 years in the base case, and 5.9 years
(range, 3.8–8.7 years) for women, compared with 6.3 years in
the base case.
Delay of Smoking Cessation
Smoking cessation 5 or 10 years after entering HIV care still
resulted in substantial life expectancy gains (Table 2). Compared
with those who continued to smoke, men who entered care at
age 40 years and quit smoking 0, 5, and 10 years later gained 5.7
years (base case), 3.3 years, and 2.9 years, respectively; women
gained 4.6 years (base case), 3.1 years, and 2.8 years,
Age and CD4+ T-Cell Count at Presentation: 2-Way Sensitivity
Results were sensitive to both age and CD4+ T-cell count at
entry to care; smoking cessation improved life expectancy across
strata of both variables. Younger individuals with higher initial
CD4+ T-cell counts gained the most from cessation (Figure 3).
Population-Level Impact of Smoking Cessation
Among HIV-infected people in care in the United States,
smoking cessation and sustained abstinence by 10%–25% of current
smokers aged 30–64 years would result in 106 000–265 000
expected years of life gained (Supplementary Tables 10 and 11).
Using a simulation model of HIV disease, we found that
smoking substantially reduces the life expectancy of HIV-infected
people in the United States who are linked to care and that
smoking cessation could have a major impact on survival. For
men initially linked to care, the life expectancy loss associated
with smoking is similar to the loss associated with HIV. For
both men and women who are adherent to ART and remain
in care, the loss from smoking is about double that from HIV.
The survival benefit for an HIV-infected individual who quits
YLG, Compared With
YLG, Compared With
smoking upon entering care at age 40 years exceeds that of other
common interventions (Figure 4) [38–44]. Further, at the
population level, smoking cessation could lead to a substantial
number of years of life gained, given the very high prevalence
of smoking among HIV-infected people.
Unfortunately, to this point, HIV-infected US people have
been less likely to quit smoking, compared with HIV-uninfected
people, a pattern consistent across age groups: of those who
have ever smoked, 32% of HIV-infected people and 52% of
other US adults quit smoking . These strikingly low cessation
rates underpin the need for novel cessation strategies. The rate
of desire to quit is similar between HIV-infected and
HIVuninfected people . Few studies have examined smoking
cessation interventions in HIV-infected people; they have
generally shown low abstinence rates . It is unclear whether the
success of cessation interventions differs between HIV-infected
people and others.
ART has transformed the course of HIV infection, such that
virally suppressed persons now have a life expectancy
approaching that of uninfected persons . However, life expectancy
gaps persist , due partly to smoking-associated
comorbidities that HIV-infected people are increasingly experiencing.
Studies of HIV-infected people in the ART era consistently
show higher mortality among smokers, compared with
nonsmokers [5–7, 21, 47, 48]. Recent cohort studies of HIV-infected
people in Denmark  and Western Europe and North
America , in which accessibility and adherence to ART were high,
found that smoking was associated with more years of life lost
than HIV infection (only approximately 5% of the subjects in
the latter study were in North America).
Our model accounts for nonadherence to ART and loss to
follow-up, which occur more frequently in the United States
than in European cohorts and which increase the risk of
AIDS-related death, thereby potentially dampening the impact
of smoking and the benefit of cessation. Nonetheless, even in
this simulated US population, the life expectancy loss from
continued smoking among HIV-infected people entering care at
age 40 years is extremely high, at >6 years per person.
We used smoking-stratified mortality rates from the US
general population in our simulations of non–AIDS-related
mortality risks. Smoking might be more harmful in HIV-infected as
compared with HIV-uninfected persons, particularly regarding
the risk of myocardial infarction , so the actual impact of
smoking and cessation on the life expectancy of HIV-infected
persons may be even greater than we have shown in the base
case. However, 2 large studies did not detect a mortality
interaction between smoking and HIV infection (ie, the mortality
RR associated with smoking was similar for HIV-infected and
HIV-uninfected people)  or an interaction between smoking
and CD4+ T-cell count (ie, the mortality RR associated with
smoking was similar in those with high and low baseline
CD4+ T-cell counts) .
The robustness of our results in the face of alternative
assumptions about smoking-associated mortality risks is notable
(Supplementary Table 6). Use of the upper and lower bounds of
RR confidence intervals from a study of HIV-infected people
 had the greatest effect on the impact of smoking and of
cessation. The confidence intervals in that study were wide because
of the small number of deaths. The smoking-associated
mortality RR in that study was slightly lower than that in a US general
population study , likely because former smokers were
combined with current smokers in generating the HIV-specific
HIV-infected people might face higher non–AIDS-related
mortality risks than HIV-uninfected people due to depression,
substance use (besides tobacco), and high-risk behaviors; these
may be more common in HIV-infected smokers compared with
HIV-uninfected smokers . In a sensitivity analysis, we
sought to account for potentially higher competing mortality
risks among smokers and among people at risk for HIV
infection, compared with the general population, by using
standardized mortality ratios. Although standardized mortality
ratio–adjusted life expectancies were lower than base case
results, the differences in the effects of smoking and cessation
on survival were small. Data used to inform US standardized
mortality ratios were limited by small sample size, short
follow-up period, and relatively young patients .
While the harmful effects of smoking depend on the
quantity of tobacco consumed (eg, pack-years), we could not
explicitly account for this in our model. Reliable and precise
clinical histories of smoking quantity are generally not
reported in cohort studies because they are challenging to elicit.
The data we used for smoking-associated RRs are based on
studies of large numbers of subjects with varying pack-year
histories; use of the average of these histories is common in
the smoking literature for both general populations and
HIVinfected people [4, 8, 21], although the average tobacco
exposure might differ between the 2 populations. We believe that
these published averages, combined with our simulations of
large numbers of individuals and our wide-ranging sensitivity
analyses, provide reasonable estimates for population-based
Similar to other model-based investigations, our analysis is
limited by uncertainties in the input parameters and by some
inevitable simplifications in our representation of risk factors
and disease. Key uncertainties and limitations include exclusion
of possible increases in AIDS-related mortality due to smoking;
assumption that those who quit smoking would remain
abstinent (relaxed in a sensitivity analysis); lack of differentiation
between current, former, and never smokers regarding initial
CD4+ T-cell count and viral load, ART adherence, and loss to
follow-up; the assumption, in the base case, that the initial
CD4+ T-cell count distribution (and, hence, distribution of
time since HIV infection) was the same regardless of age at
entry to HIV care; and population projections based on a
cross-sectional survey of HIV-infected people in care, in
which the prevalence of injection drug users was low (2%)
. Former smokers in published cohorts might have adopted
other healthy lifestyle modifications in addition to smoking
cessation; our estimates of survival gains from smoking cessation
similarly reflect these changes.
In conclusion, smokers in HIV care may now lose as much or
more life expectancy from smoking as from HIV infection, but
cessation can greatly improve survival. Providers caring for
HIV-infected people should address smoking during every
patient encounter and offer guideline-based behavioral and
pharmacologic treatments for tobacco use . Novel intervention
strategies targeting this important population are needed.
Smoking cessation should be a major priority in HIV care
Supplementary materials are available at http://jid.oxfordjournals.org.
Consisting of data provided by the author to benefit the reader, the posted
materials are not copyedited and are the sole responsibility of the author, so
questions or comments should be addressed to the author.
Acknowledgments. We thank Amy Zheng for her technical assistance.
Disclaimer. The funding sources had no role in the design, analysis, or
interpretation of the study or in the decision to submit the manuscript
for publication. The content is solely the responsibility of the authors and
does not necessarily represent the official views of the National Institutes of
Financial support. This work was supported by the National Institute
of Allergy and Infectious Diseases, NIH (grants T32 AI007433, R01
AI420006, and R37 AI093269); and Massachusetts General Hospital
(Research Scholars Award to R. P. W.).
Potential conflicts of interest. All authors: No reported conflicts. All
authors have submitted the ICMJE Form for Disclosure of Potential
Conflicts of Interest. Conflicts that the editors consider relevant to the content
of the manuscript have been disclosed.
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