Assessing the public health impact of HIV interventions: the critical role of demographics
Dobromir Dimitrov1,4, Yang Kuang2,5, Benoît R. Mâsse3
1 Vaccine & Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
2 School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, USA
3 CHU Sainte-Justine Research Centre, University of Montreal, Montreal, Quebec, Canada
4 Department of Applied Mathematics, University of Washington, Seattle, WA, USA
5 Department of Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Human immunodeficiency virus (HIV) continues to spread and take the lives of millions
around the world. Advances in antiretroviral therapy (ART) have substantially extended
survivorship without completely removing the risk of transmission. Some preventive
interventions have demonstrated efficacy in clinical trials [1,2] but their effectiveness remains to
be confirmed at community levels where the optimized level of implementation may not be
In a recent analysis, Kato and colleagues estimated the potential impact of expanding
ART and combination prevention in Vietnam . Other mathematical models have also been
used to project the public-health impact of HIV prevention into sexually-active populations [4-
10]. Presented results in these modeling studies necessarily depend on various assumptions
regarding population demographics, sexual behavior and HIV transmission. With focus on the
impact of the interventions, the primary interest falls on the ability of the proposed preventive
method to reduce transmission, its acceptability by the targeted groups, the willingness
ofpotential users to follow prescribed regimen and the public health risks associated with the new
product (such as the spread of drug resistance associated with antiretroviral products). However,
insufficient evidence is presented to justify the way processes of: i) population recruitment,
including immigration and sexual maturation, and ii) population departures, including
emigration, age- and gender-specific mortality, infection-induced fatalities, and sexual inactivity
are modeled. In this commentary, we discuss the merits of the demographic assumptions
employed in Kato et al. , among other commonly used, and outline the need for their
Most of the published models on intervention impact assume that the number of individuals
joining the population per year is constant (constant recruitment) [3,6-8,10-12] or proportional to
the total population size (proportional recruitment) [4,5,9,13,14]. In sexually-active population
with no immigration, constant recruitment implies that the same number of people reach sexual
maturity annually. That may be an acceptable approximation over short periods of time, but
becomes troublesome when the simulation period increases. The progression of HIV infection
from acquisition to full-blown AIDS and death is incredibly slow and delayed even further by
ART. Therefore, a meaningful impact of prevention intervention should be expected over several
decades which may explain why simulation periods of 20-50 years are used in mathematical
models. Projections, presented in Kato et al , are over 40 years. If the population growth rate
is 1% as currently estimated for Vietnam  the cumulative growth over 40 years will be 49%.
In comparison, the population in Sub-Saharan Africa, which is impacted the most by the HIV
epidemic, is growing at a rate of 2.2% per year  and is expected to more than double by
2050 . It is not realistic to expect that the number of 15-year olds in these populations will
remain the same over several decades. Proportional recruitment seems to address this issue but
has the deficiency of connecting the current population size directly to the number of new
recruits, i.e., any change in the population size affects the cohort that joins the community
instantaneously. However, the cohort of 15-year-olds is more likely to depend on the population
size when they were conceived, i.e., 16 years prior to their sexual maturation. A delayed
proportional mechanism, which is certainly more appropriate when sexually-active populations
are simulated, adds computational complexity and limits the analytical tools available to study
the model behavior (see comparison between some recruitment mechanisms in Fig.1A).
Another popular modeling decision is to assume that the cohort joining the population
consists entirely of susceptible individuals [3-12,14]. In sexually-active populations, the
newcomers may be adolescents reaching sexual maturity or adults migrating into the community.
We may agree that the prevalence of HIV among sexually inactive teenagers could be negligible
due to the success of worldwide prevention strategies in mother-to-child transmission. However,
that may not be the case for the migrating individuals. This is particularly important when
analyzing populations with steady influx of people, such as the men-who-have-sex-with-men
community in San Francisco or large metropolitan areas in Sub-Saharan Africa. Recent data
from King County, Washington (including Seattle area) show that over a 5-year period, three
times more HIV-positive individuals have moved in than left the county. Ignoring the
immigration of infected individuals, one may draw an imprecise picture of the drivers of the HIV
epidemic or overestimate the projected impact of prevention interventions which target
uninfected young adults, by agglomerating all newcomers into that group (see Fig.1B).
Population departures are most often modeled assuming proportional rates of population losses
per year due to sexual inactivity, HIV-related or –unrelated causes, etc. [3-10]. Problems with
such assumptions arise in populations with strong gender imbalance in HIV-unrelated deaths
(“background mortality”). Data from South Africa suggests that the mortality among 15-49-year-
old men is higher than among women even though the number of HIV-related deaths among
women is larger . The influence of this differential gender mortality on the dynamics of the
HIV epidemic is unclear but it may partially compensate for the stronger HIV toll on women.
Additionally, both HIV mortality and acquisition risk differ by age, which suggests the
importance of the age structure to the epidemic dynamics. Unfortunately, stratification by age
requires individual-based models or models with large number of compartments and
sophisticated parameterization. However, some published studies have shown that the
complexity is manageable and feasible . In depth analyses are necessary to determine when
differences in departure rates between population subgroups have to be taken into consideration.
Bridging recruitment and departures
Kato and colleagues , as well as other modelers [5,8,10,11], assume that recruitment and
departures are balanced (equal in size) before the start of the HIV epidemic. Assuming that
before the introduction of HIV the population size remains constant implies persistent population
decline when HIV-related fatalities are added to the departures and may result in substantial
population reduction over 20 to 50 years. This is inconsistent with observed demographic trends
in Vietnam and even in countries with significantly higher HIV prevalence. Alternatively,
modelers often assume that the population in presence of HIV is already at equilibrium when
new prevention is introduced [4,6,12]. This is obtained by simulating the HIV epidemic for
sufficiently long time until all compartments show negligible changes in size. Unfortunately, in
the process of achieving “equilibrium”, all epidemic conditions including transmission rates and
survival time of the infected individuals are kept unchanged for periods often longer than the
time HIV has been circulating in the population. In reality, the introduction of various treatment
options at different times has affected the transmission and mortality rates. Even if the epidemic
parameters are selected “to fit” historic epidemic data (usually HIV prevalence) they will most
likely represent the “average HIV epidemic” over the whole period toward dynamic stability
rather than the state of the epidemic at the end of the period (the current time at which point the
intervention will be initiated). However, no parameter adjustments can be made when
equilibrium is reached because that will destabilize the population and lead to a new steady state.
The way we model demographic processes may have little impact on short lasting
epidemics but shall not be neglected for simulations over extended time periods, typically used
to evaluate HIV prevention interventions. Demographic assumptions affect the projected HIV
epidemic by controlling the intake of new susceptible individuals and the removal of infected
individuals from the population. They also alter the HIV prevalence in different subgroups and
therefore play an important role in determining the forces of infection between different
compartments. The influence of the demographic assumptions on the projected effectiveness of
HIV interventions is rarely the focus of modeling analyses but deserves the attention of the
researchers who work in the field of HIV prevention, as the role of mathematical modeling to
intervention evaluations increases.
The authors are grateful to Danielle Buch, medical writer/editor at the Applied Clinical Research
Unit of the CHU Sainte-Justine Research Centre for revision and editing of the manuscript.
Figure 1. A) Effects of the choice of recruitment mechanism on the number of individuals joining the population of initial size of
100,000 assuming that 2% population growth is observed at the beginning of the simulations. B) Effects of the assumptions
related to the HIV status of the individuals joining the population on the HIV epidemic over extended periods of time. The
curves are obtained by fitting dynamic model to annual HIV prevalence data over 5-year period twice, under scenarios with 0%
and 6% infected individuals among new recruits. Graphs show perfect alignment over the fitted period but diverge afterward.
Model description is provided in the Supplemental Digital Content.
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