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1.  Comparative effectiveness of dynamic treatment regimes: an application of the parametric g-formula 
Statistics in biosciences  2011;3(1):119-143.
Ideally, randomized trials would be used to compare the long-term effectiveness of dynamic treatment regimes on clinically relevant outcomes. However, because randomized trials are not always feasible or timely, we often must rely on observational data to compare dynamic treatment regimes. An example of a dynamic treatment regime is “start combined antiretroviral therapy (cART) within 6 months of CD4 cell count first dropping below x cells/mm3 or diagnosis of an AIDS-defining illness, whichever happens first” where x can take values between 200 and 500. Recently, Cain et al (2011) used inverse probability (IP) weighting of dynamic marginal structural models to find the x that minimizes 5-year mortality risk under similar dynamic regimes using observational data. Unlike standard methods, IP weighting can appropriately adjust for measured time-varying confounders (e.g., CD4 cell count, viral load) that are affected by prior treatment. Here we describe an alternative method to IP weighting for comparing the effectiveness of dynamic cART regimes: the parametric g-formula. The parametric g-formula naturally handles dynamic regimes and, like IP weighting, can appropriately adjust for measured time-varying confounders. However, estimators based on the parametric g-formula are more efficient than IP weighted estimators. This is often at the expense of more parametric assumptions. Here we describe how to use the parametric g-formula to estimate risk by the end of a user-specified follow-up period under dynamic treatment regimes. We describe an application of this method to answer the “when to start” question using data from the HIV-CAUSAL Collaboration.
doi:10.1007/s12561-011-9040-7
PMCID: PMC3769803  PMID: 24039638
2.  Causal Effect Models for Realistic Individualized Treatment and Intention to Treat Rules* 
Marginal structural models (MSM) are an important class of models in causal inference. Given a longitudinal data structure observed on a sample of n independent and identically distributed experimental units, MSM model the counterfactual outcome distribution corresponding with a static treatment intervention, conditional on user-supplied baseline covariates. Identification of a static treatment regimen-specific outcome distribution based on observational data requires, beyond the standard sequential randomization assumption, the assumption that each experimental unit has positive probability of following the static treatment regimen. The latter assumption is called the experimental treatment assignment (ETA) assumption, and is parameter-specific. In many studies the ETA is violated because some of the static treatment interventions to be compared cannot be followed by all experimental units, due either to baseline characteristics or to the occurrence of certain events over time. For example, the development of adverse effects or contraindications can force a subject to stop an assigned treatment regimen.
In this article we propose causal effect models for a user-supplied set of realistic individualized treatment rules. Realistic individualized treatment rules are defined as treatment rules which always map into the set of possible treatment options. Thus, causal effect models for realistic treatment rules do not rely on the ETA assumption and are fully identifiable from the data. Further, these models can be chosen to generalize marginal structural models for static treatment interventions. The estimating function methodology of Robins and Rotnitzky (1992) (analogue to its application in Murphy, et. al. (2001) for a single treatment rule) provides us with the corresponding locally efficient double robust inverse probability of treatment weighted estimator.
In addition, we define causal effect models for “intention-to-treat” regimens. The proposed intention-to-treat interventions enforce a static intervention until the time point at which the next treatment does not belong to the set of possible treatment options, at which point the intervention is stopped. We provide locally efficient estimators of such intention-to-treat causal effects.
PMCID: PMC2613338  PMID: 19122793
counterfactual; causal effect; causal inference; double robust estimating function; dynamic treatment regimen; estimating function; individualized stopped treatment regimen; individualized treatment rule; inverse probability of treatment weighted estimating functions; locally efficient estimation; static treatment intervention
3.  HIV Treatment as Prevention: Systematic Comparison of Mathematical Models of the Potential Impact of Antiretroviral Therapy on HIV Incidence in South Africa 
PLoS Medicine  2012;9(7):e1001245.
Background
Many mathematical models have investigated the impact of expanding access to antiretroviral therapy (ART) on new HIV infections. Comparing results and conclusions across models is challenging because models have addressed slightly different questions and have reported different outcome metrics. This study compares the predictions of several mathematical models simulating the same ART intervention programmes to determine the extent to which models agree about the epidemiological impact of expanded ART.
Methods and Findings
Twelve independent mathematical models evaluated a set of standardised ART intervention scenarios in South Africa and reported a common set of outputs. Intervention scenarios systematically varied the CD4 count threshold for treatment eligibility, access to treatment, and programme retention. For a scenario in which 80% of HIV-infected individuals start treatment on average 1 y after their CD4 count drops below 350 cells/µl and 85% remain on treatment after 3 y, the models projected that HIV incidence would be 35% to 54% lower 8 y after the introduction of ART, compared to a counterfactual scenario in which there is no ART. More variation existed in the estimated long-term (38 y) reductions in incidence. The impact of optimistic interventions including immediate ART initiation varied widely across models, maintaining substantial uncertainty about the theoretical prospect for elimination of HIV from the population using ART alone over the next four decades. The number of person-years of ART per infection averted over 8 y ranged between 5.8 and 18.7. Considering the actual scale-up of ART in South Africa, seven models estimated that current HIV incidence is 17% to 32% lower than it would have been in the absence of ART. Differences between model assumptions about CD4 decline and HIV transmissibility over the course of infection explained only a modest amount of the variation in model results.
Conclusions
Mathematical models evaluating the impact of ART vary substantially in structure, complexity, and parameter choices, but all suggest that ART, at high levels of access and with high adherence, has the potential to substantially reduce new HIV infections. There was broad agreement regarding the short-term epidemiologic impact of ambitious treatment scale-up, but more variation in longer term projections and in the efficiency with which treatment can reduce new infections. Differences between model predictions could not be explained by differences in model structure or parameterization that were hypothesized to affect intervention impact.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Following the first reported case of AIDS in 1981, the number of people infected with HIV, the virus that causes AIDS, increased rapidly. In recent years, the number of people becoming newly infected has declined slightly, but the virus continues to spread at unacceptably high levels. In 2010 alone, 2.7 million people became HIV-positive. HIV, which is usually transmitted through unprotected sex, destroys CD4 lymphocytes and other immune system cells, leaving infected individuals susceptible to other infections. Early in the AIDS epidemic, half of HIV-infected people died within eleven years of infection. Then, in 1996, antiretroviral therapy (ART) became available, and, for people living in affluent countries, HIV/AIDS gradually became considered a chronic condition. But because ART was expensive, for people living in developing countries HIV/AIDS remained a fatal condition. Roll-out of ART in developing countries first started in the early 2000s. In 2006, the international community set a target of achieving universal ART coverage by 2010. Although this target has still not been reached, by the end of 2010, 6.6 million of the estimated 15 million people in need of ART in developing countries were receiving ART.
Why Was This Study Done?
Several studies suggest that ART, in addition to reducing illness and death among HIV-positive people, reduces HIV transmission. Consequently, there is interest in expanding the provision of ART as a strategy for reducing the spread of HIV (“HIV treatment as prevention"), particularly in sub-Saharan Africa, where one in 20 adults is HIV-positive. It is important to understand exactly how ART might contribute to averting HIV transmission. Several mathematical models that simulate HIV infection and disease progression have been developed to investigate the impact of expanding access to ART on the incidence of HIV (the number of new infections occurring in a population over a year). But, although all these models predict that increased ART coverage will have epidemiologic (population) benefits, they vary widely in their estimates of the magnitude of these benefits. In this study, the researchers systematically compare the predictions of 12 mathematical models of the HIV epidemic in South Africa, simulating the same ART intervention programs to determine the extent to which different models agree about the impact of expanded ART.
What Did the Researchers Do and Find?
The researchers invited groups who had previously developed mathematical models of the epidemiological impact of expanded access to ART in South Africa to participate in a systematic comparison exercise in which their models were used to simulate ART scale-up scenarios in which the CD4 count threshold for treatment eligibility, access to treatment, and retention on treatment were systematically varied. To exclude variation resulting from different model assumptions about the past and current ART program, it was assumed that ART is introduced into the population in the year 2012, with no treatment provision prior to this, and interventions were evaluated in comparison to an artificial counterfactual scenario in which no treatment is provided. A standard scenario based on the World Health Organization's recommended threshold for initiation of ART, although unrepresentative of current provision in South Africa, was used to compare the models. In this scenario, 80% of HIV-infected individuals received treatment, they started treatment on average a year after their CD4 count dropped below 350 cells per microliter of blood, and 85% remained on treatment after three years. The models predicted that, with a start point of 2012, the HIV incidence would be 35%–54% lower in 2020 and 32%–74% lower in 2050 compared to a counterfactual scenario where there was no ART. Estimates of the number of person-years of ART needed per infection averted (the efficiency with which ART reduced new infections) ranged from 6.3–18.7 and from 4.5–20.2 over the periods 2012–2020 and 2012–2050, respectively. Finally, estimates of the impact of ambitious interventions (for example, immediate treatment of all HIV-positive individuals) varied widely across the models.
What Do These Findings Mean?
Although the mathematical models used in this study had different characteristics, all 12 predict that ART, at high levels of access and adherence, has the potential to reduce new HIV infections. However, although the models broadly agree about the short-term epidemiologic impact of treatment scale-up, their longer-term projections (including whether ART alone can eliminate HIV infection) and their estimates of the efficiency with which ART can reduce new infections vary widely. Importantly, it is possible that all these predictions will be wrong—all the models may have excluded some aspect of HIV transmission that will be found in the future to be crucial. Finally, these findings do not aim to indicate which specific ART interventions should be used to reduce the incidence of HIV. Rather, by comparing the models that are being used to investigate the feasibility of “HIV treatment as prevention," these findings should help modelers and policy-makers think critically about how the assumptions underlying these models affect the models' predictions.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001245.
This study is part of the July 2012 PLoS Medicine Collection, Investigating the Impact of Treatment on New HIV Infections
Information is available from the US National Institute of Allergy and Infectious Diseases on HIV infection and AIDS
NAM/aidsmap provides basic information about HIV/AIDS and summaries of recent research findings on HIV care and treatment
Information is available from Avert, an international AIDS charity on many aspects of HIV/AIDS, including information on HIV/AIDS treatment and care, on HIV treatment as prevention, and on HIV/AIDS in South Africa (in English and Spanish)
The World Health Organization provides information about universal access to AIDS treatment (in English, French, and Spanish); its 2010 ART guidelines can be downloaded
The HIV Modelling Consortium aims to improve scientific support for decision-making by coordinating mathematical modeling of the HIV epidemic
Patient stories about living with HIV/AIDS are available through Avert; the charity website Healthtalkonline also provides personal stories about living with HIV, including stories about taking anti-HIV drugs and the challenges of anti-HIV drugs
doi:10.1371/journal.pmed.1001245
PMCID: PMC3393664  PMID: 22802730
4.  Semiparametric Estimation of Treatment Effect in a Pretest–Posttest Study with Missing Data 
The pretest–posttest study is commonplace in numerous applications. Typically, subjects are randomized to two treatments, and response is measured at baseline, prior to intervention with the randomized treatment (pretest), and at prespecified follow-up time (posttest). Interest focuses on the effect of treatments on the change between mean baseline and follow-up response. Missing posttest response for some subjects is routine, and disregarding missing cases can lead to invalid inference. Despite the popularity of this design, a consensus on an appropriate analysis when no data are missing, let alone for taking into account missing follow-up, does not exist. Under a semiparametric perspective on the pretest–posttest model, in which limited distributional assumptions on pretest or posttest response are made, we show how the theory of Robins, Rotnitzky and Zhao may be used to characterize a class of consistent treatment effect estimators and to identify the efficient estimator in the class. We then describe how the theoretical results translate into practice. The development not only shows how a unified framework for inference in this setting emerges from the Robins, Rotnitzky and Zhao theory, but also provides a review and demonstration of the key aspects of this theory in a familiar context. The results are also relevant to the problem of comparing two treatment means with adjustment for baseline covariates.
doi:10.1214/088342305000000151
PMCID: PMC2600547  PMID: 19081743
Analysis of covariance; covariate adjustment; influence function; inverse probability weighting; missing at random
5.  Estimating Optimal Dynamic Regimes: Correcting Bias under the Null 
A dynamic regime provides a sequence of treatments that are tailored to patient-specific characteristics and outcomes. In 2004 James Robins proposed g-estimation using structural nested mean models for making inference about the optimal dynamic regime in a multi-interval trial. The method provides clear advantages over traditional parametric approaches. Robins’ g-estimation method always yields consistent estimators, but these can be asymptotically biased under a given structural nested mean model for certain longitudinal distributions of the treatments and covariates, termed exceptional laws. In fact, under the null hypothesis of no treatment effect, every distribution constitutes an exceptional law under structural nested mean models which allow for interaction of current treatment with past treatments or covariates. This paper provides an explanation of exceptional laws and describes a new approach to g-estimation which we call Zeroing Instead of Plugging In (ZIPI). ZIPI provides nearly identical estimators to recursive g-estimators at non-exceptional laws while providing substantial reduction in the bias at an exceptional law when decision rule parameters are not shared across intervals.
doi:10.1111/j.1467-9469.2009.00661.x
PMCID: PMC2880540  PMID: 20526433
adaptive treatment strategies; asymptotic bias; dynamic treatment regimes; g-estimation; optimal structural nested mean models; pre-test estimators
6.  Individualized treatment rules: Generating candidate clinical trials 
Statistics in medicine  2007;26(25):4578-4601.
SUMMARY
Individualized treatment rules, or rules for altering treatments over time in response to changes in individual covariates, are of primary importance in the practice of clinical medicine. Several statistical methods aim to estimate the rule, termed an optimal dynamic treatment regime, which will result in the best expected outcome in a population. In this article, we discuss estimation of an alternative type of dynamic regime—the statically optimal treatment rule. History-adjusted marginal structural models (HA-MSM) estimate individualized treatment rules that assign, at each time point, the first action of the future static treatment plan that optimizes expected outcome given a patient’s covariates. However, as we discuss here, HA-MSM-derived rules can depend on the way in which treatment was assigned in the data from which the rules were derived. We discuss the conditions sufficient for treatment rules identified by HA-MSM to be statically optimal, or in other words, to select the optimal future static treatment plan at each time point, regardless of the way in which past treatment was assigned. The resulting treatment rules form appropriate candidates for evaluation using randomized controlled trials. We demonstrate that a history-adjusted individualized treatment rule is statically optimal if it depends on a set of covariates that are sufficient to control for confounding of the effect of past treatment history on outcome. Methods and results are illustrated using an example drawn from the antiretroviral treatment of patients infected with HIV. Specifically, we focus on rules for deciding when to modify the treatment of patients infected with resistant virus.
doi:10.1002/sim.2888
PMCID: PMC2442037  PMID: 17450501
causal inference; longitudinal data; dynamic treatment regime; adaptive treatment strategy; history-adjusted marginal structural model; human immunodeficiency virus
7.  Elimination of HIV in South Africa through Expanded Access to Antiretroviral Therapy: A Model Comparison Study 
PLoS Medicine  2013;10(10):e1001534.
Using nine structurally different models, Jan Hontelez and colleagues investigate timeframes for HIV elimination in South Africa using a universal test and treat strategy.
Please see later in the article for the Editors' Summary
Background
Expanded access to antiretroviral therapy (ART) using universal test and treat (UTT) has been suggested as a strategy to eliminate HIV in South Africa within 7 y based on an influential mathematical modeling study. However, the underlying deterministic model was criticized widely, and other modeling studies did not always confirm the study's finding. The objective of our study is to better understand the implications of different model structures and assumptions, so as to arrive at the best possible predictions of the long-term impact of UTT and the possibility of elimination of HIV.
Methods and Findings
We developed nine structurally different mathematical models of the South African HIV epidemic in a stepwise approach of increasing complexity and realism. The simplest model resembles the initial deterministic model, while the most comprehensive model is the stochastic microsimulation model STDSIM, which includes sexual networks and HIV stages with different degrees of infectiousness. We defined UTT as annual screening and immediate ART for all HIV-infected adults, starting at 13% in January 2012 and scaled up to 90% coverage by January 2019. All models predict elimination, yet those that capture more processes underlying the HIV transmission dynamics predict elimination at a later point in time, after 20 to 25 y. Importantly, the most comprehensive model predicts that the current strategy of ART at CD4 count ≤350 cells/µl will also lead to elimination, albeit 10 y later compared to UTT. Still, UTT remains cost-effective, as many additional life-years would be saved. The study's major limitations are that elimination was defined as incidence below 1/1,000 person-years rather than 0% prevalence, and drug resistance was not modeled.
Conclusions
Our results confirm previous predictions that the HIV epidemic in South Africa can be eliminated through universal testing and immediate treatment at 90% coverage. However, more realistic models show that elimination is likely to occur at a much later point in time than the initial model suggested. Also, UTT is a cost-effective intervention, but less cost-effective than previously predicted because the current South African ART treatment policy alone could already drive HIV into elimination.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
About 34 million people (mostly in low- and middle-income countries) are currently infected with HIV, the virus that causes AIDS, and every year another 2.5 million people become infected. HIV, which is usually transmitted through unprotected sex with an infected partner, gradually destroys CD4 lymphocytes and other immune system cells, leaving infected individuals susceptible to other infections. Early in the AIDS epidemic, people infected with HIV often died within ten years of infection. Then, in 1996, antiretroviral therapy (ART) became available, and, for people living in affluent countries, HIV/AIDS became a chronic condition. However, ART was expensive, so HIV/AIDS remained a fatal condition for people living in resource-limited countries. In 2006, the international community set a target of achieving universal ART coverage by 2010, and ART programs were initiated in many resource-limited countries. Although universal ART coverage has still not been achieved in South Africa, where nearly 6 million people are HIV-positive, 80% of people in need of ART were receiving a World Health Organization–recommended ART regimen by October 2012.
Why Was This Study Done?
ART is usually started when a person's CD4 count falls below 350 cells/µl blood, but it is thought that treatment of all HIV-positive individuals, regardless of their CD4 count, could reduce HIV transmission by reducing the infectiousness of HIV-positive individuals (“treatment as prevention”). Might it be possible, therefore, to eliminate HIV by screening everyone annually for infection and treating all HIV-positive individuals immediately? In 2009, a mathematical modeling study suggested that seven years of universal test and treat (UTT) could eliminate HIV in South Africa. The deterministic (nonrandom) model used in that study has been widely criticized, however, and some subsequent modeling studies have reached different conclusions, probably because of differences in the models' structures and in the assumptions built into them. A better understanding of the reasons for the discrepancies between models would help policy-makers decide whether to introduce UTT, so, here, the researchers developed several increasingly complex and realistic models of the South African HIV epidemic and used these models to predict the long-term impact of UTT in South Africa.
What Did the Researchers Do and Find?
The researchers developed nine structurally different mathematical models of the South African HIV epidemic based on the STDSIM framework, a stochastic microsimulation model that simulates the life course of individuals in a dynamic network of sexual contacts and in which events such as HIV infection are random processes. The simplest model, which resembled the original deterministic model, was extended by sequentially adding in factors such as different HIV transmission rates at different stages of HIV infection and up-to-date assumptions regarding the ability of ART to reduce HIV infectiousness. All the models replicated the prevalence of HIV in South Africa (the proportion of the population that was HIV-positive) between 1990 and 2010, and all predicted that UTT (defined as annual screening of individuals age 15+ years and immediate ART for all HIV-infected adults starting in 2012 and scaled up to 90% coverage by 2019) would result in HIV elimination (less than one new infection per 1,000 person-years). However, whereas the simplest model predicted that UTT would eliminate HIV after seven years, the more complex, realistic models predicted elimination at much later time points. Importantly, the most comprehensive model predicted that, although elimination would be reached after about 17 years of UTT, the current strategy of ART initiation for HIV-positive individuals at a CD4 cell count at or below 350 cells/µl would also lead to HIV elimination, albeit ten years later than UTT.
What Do These Findings Mean?
These findings confirm previous predictions that UTT could eliminate HIV in South Africa, but the development of more realistic models than those used in the past suggests that HIV elimination would occur substantially later than originally predicted. Importantly, the most comprehensive model suggests that HIV could be eliminated in South Africa using the current strategy for ART treatment alone. As with all modeling studies, the accuracy of these findings depends on the assumptions built into the models and on the structure of the models. Thus, although these findings support the use of UTT as an intervention to eliminate HIV, more research with comprehensive models that incorporate factors such as data from ongoing trials of treatment as prevention is needed to determine the population-level impact and overall cost-effectiveness of UTT.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001534.
This study is further discussed in a PLOS Medicine Perspective by Ford and Hirnschall
Information is available from the US National Institute of Allergy and Infectious Diseases on HIV infection and AIDS
NAM/aidsmap provides basic information about HIV/AIDS and summaries of recent research findings on HIV care and treatment
Information is available from Avert, an international AIDS charity, on many aspects of HIV/AIDS, including information on HIV and AIDS in South Africa, on HIV treatment as prevention and the possibility of HIV elimination (in English and Spanish)
The 2012 UNAIDS World AIDS Day Report provides up-to-date information about the AIDS epidemic and efforts to halt it
The World Health Organization provides information about universal access to AIDS treatment (in several languages); its 2010 ART guidelines can be downloaded
The PLOS Medicine Collection Investigating the Impact of Treatment on New HIV Infections provides more information about HIV treatment as prevention
Personal stories about living with HIV/AIDS are available through Avert, through NAM/aidsmap, and through the charity website Healthtalkonline
doi:10.1371/journal.pmed.1001534
PMCID: PMC3805487  PMID: 24167449
8.  Life Expectancies of South African Adults Starting Antiretroviral Treatment: Collaborative Analysis of Cohort Studies 
PLoS Medicine  2013;10(4):e1001418.
Leigh Johnson and colleagues estimate the life expectancies of HIV positive South African adults who are taking antiretroviral therapy by using information from 6 programmes between 2001 and 2010.
Background
Few estimates exist of the life expectancy of HIV-positive adults receiving antiretroviral treatment (ART) in low- and middle-income countries. We aimed to estimate the life expectancy of patients starting ART in South Africa and compare it with that of HIV-negative adults.
Methods and Findings
Data were collected from six South African ART cohorts. Analysis was restricted to 37,740 HIV-positive adults starting ART for the first time. Estimates of mortality were obtained by linking patient records to the national population register. Relative survival models were used to estimate the excess mortality attributable to HIV by age, for different baseline CD4 categories and different durations. Non-HIV mortality was estimated using a South African demographic model. The average life expectancy of men starting ART varied between 27.6 y (95% CI: 25.2–30.2) at age 20 y and 10.1 y (95% CI: 9.3–10.8) at age 60 y, while estimates for women at the same ages were substantially higher, at 36.8 y (95% CI: 34.0–39.7) and 14.4 y (95% CI: 13.3–15.3), respectively. The life expectancy of a 20-y-old woman was 43.1 y (95% CI: 40.1–46.0) if her baseline CD4 count was ≥200 cells/µl, compared to 29.5 y (95% CI: 26.2–33.0) if her baseline CD4 count was <50 cells/µl. Life expectancies of patients with baseline CD4 counts ≥200 cells/µl were between 70% and 86% of those in HIV-negative adults of the same age and sex, and life expectancies were increased by 15%–20% in patients who had survived 2 y after starting ART. However, the analysis was limited by a lack of mortality data at longer durations.
Conclusions
South African HIV-positive adults can have a near-normal life expectancy, provided that they start ART before their CD4 count drops below 200 cells/µl. These findings demonstrate that the near-normal life expectancies of HIV-positive individuals receiving ART in high-income countries can apply to low- and middle-income countries as well.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
According to the latest figures, more than 34 million people worldwide currently live with HIV/AIDS. In 2011, an estimated 2.5 million people were newly infected with HIV, and in the same year 1.7 million people died from AIDS. Since the beginning of the epidemic in the 1980s, more than 60 million people have contracted HIV and nearly 30 million have died of HIV-related causes. Despite the stark statistics, the life expectancy for people infected with the AIDS virus has dramatically improved over the past decade since the introduction of an effective combination of antiretroviral drugs. In high-income countries, people who are HIV-positive can expect a near-normal life expectancy if they take these drugs (as antiretroviral treatment—ART) throughout their life.
Why Was This Study Done?
Recent studies investigating the life expectancy of people living with HIV have mostly focused on the situation in high-income settings. The situation in low- and middle-income countries is vastly different. People who are diagnosed with HIV are often late in starting treatment, treatments regimes are sometimes interrupted, and a large proportion of patients are lost to follow-up. It is important to gain a realistic estimate of life expectancy in low- and middle-income countries so patients can be given the best information. So in this study the researchers used a model to estimate the life expectancy of patients starting ART in South Africa, using data from several ART programs.
What Did the Researchers Do and Find?
The researchers used data collected from six programs in South Africa based in Western Cape, Gauteng, and KwaZulu-Natal between 2001 and 2010. The researchers calculated the observation time from the time of ART initiation to the date of death or to the end of the study. Then the researchers used a relative survival approach to model the excess mortality attributable to HIV, relative to non-HIV mortality rates in South Africa, over different periods from ART initiation.
Using these methods, the researchers found that over the time period, 37,740 adults started ART and 2,066 deaths were recorded in patient record systems. Of the 16,250 patients who were lost to follow-up, the researchers identified 2,947 further deaths in the population register. When they inputted these figures into their model, the researchers estimated that the mortality rate was 83.2 per 1,000 person-years of observation (PYO), and was higher in males (99.8 per 1,000 PYO) than in females (72.6 per 1,000 PYO). The researchers also found that the most significant factor determining the life expectancy of treated patients was their age at ART initiation: the average life expectancy of men starting ART varied between 27.6 years at age 20 and 10.1 years at age 60, while corresponding estimates in women were 36.8 and 14.4, respectively. Life expectancies were also significantly influenced by baseline CD4 counts; life expectancies in patients with baseline CD4 counts ≥200 cells/µl were between 70% and 86% of those of HIV-negative adults of the same age and sex, while patients starting ART with CD4 counts of <50 cells/µl had life expectancies that were between 48% and 61% of those of HIV-negative adults. Importantly, the researchers found that life expectancies were also 15%–20% higher in patients who survived their first 24 months after starting ART than in patients of the same age who had just started therapy.
What Do These Findings Mean?
These findings suggest that in South Africa, patients starting ART have life expectancies around 80% of normal life expectancy, provided that they start treatment before their CD4 count drops below 200 cells/µl. Although these results are encouraging, this study highlights that health services must overcome major challenges, such as dealing with late diagnosis, low uptake of CD4 testing, loss from pre-ART care, and delayed ART initiation, if near-normal life expectancies are to be achieved for the majority of HIV-positive South Africans. With the anticipated increase in the fraction of patients starting ART at higher CD4 counts in the future, long-term survival can be expected to increase even further. It is therefore critical that appropriate funding systems and innovative ways to reduce costs are put in place, to ensure the long-term sustainability of ART delivery in low- and middle-income countries.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001418.
The International Epidemiologic Databases to Evaluate AIDS has more statistical information from world regions
amfAR, the Foundation for AIDS Research, works with health care workers and AIDS organizations in developing countries to create and implement effective HIV research, treatment, prevention, and education strategies
doi:10.1371/journal.pmed.1001418
PMCID: PMC3621664  PMID: 23585736
9.  Optimal CD4 Count for Initiating HIV Treatment 
Epidemiology (Cambridge, Mass.)  2014;25(2):194-202.
Supplemental Digital Content is available in the text.
Background:
In HIV infection, dynamic marginal structural models have estimated the optimal CD4 for treatment initiation to minimize AIDS/death. The impact of CD4 observation frequency and grace periods (permitted delay to initiation) on the optimal regimen has not been investigated nor has the performance of dynamic marginal structural models in moderately sized data sets—two issues that are relevant to many applications.
Methods:
To determine optimal regimens, we simulated 31,000,000 HIV-infected persons randomized at CD4 500–550 cells/mm3 to regimens “initiate treatment within a grace period following observed CD4 first
Results:
Decreasing the frequency of CD4 measurements from monthly to every 3, 6, and 12 months increased the optimal regimen from a CD4 level of 350 (10-year AIDS-free survival, 0.8657) to 410 (0.8650), 460 (0.8634), and 490 (0.8564), respectively. Under a regimen defined by x = 350 with annual CD4s, 10-year AIDS-free survival dropped to 0.8304. Extending the grace period from 1 to 3 or 6 months, with 3-monthly CD4s, maintained the optimal regimen at 410 for 3 months and increased it to 460 for 6 months. In observational studies with 3-monthly CD4s, the mean (SE) estimated optimal regimen was 402 (76), 424 (66), and 430 (63) with 1-, 3-, and 6-month grace periods; 24%, 15%, and 14% of estimated optimal regimens resulted in >0.5% lower AIDS-free survival compared with the true optimal regimen.
Conclusions:
The optimal regimen is strongly influenced by CD4 frequency and less by grace period length. Dynamic marginal structural models lack precision at moderate sample sizes.
doi:10.1097/EDE.0000000000000043
PMCID: PMC3914951  PMID: 24487204
We report that multi-stable perception operates in a consistent, dynamical regime, balancing the conflicting goals of stability and sensitivity. When a multi-stable visual display is viewed continuously, its phenomenal appearance reverses spontaneously at irregular intervals. We characterized the perceptual dynamics of individual observers in terms of four statistical measures: the distribution of dominance times (mean and variance) and the novel, subtle dependence on prior history (correlation and time-constant). The dynamics of multi-stable perception is known to reflect several stabilizing and destabilizing factors. Phenomenologically, its main aspects are captured by a simplistic computational model with competition, adaptation, and noise. We identified small parameter volumes (~3% of the possible volume) in which the model reproduced both dominance distribution and history-dependence of each observer. For 21 of 24 data sets, the identified volumes clustered tightly (~15% of the possible volume), revealing a consistent “operating regime” of multi-stable perception. The “operating regime” turned out to be marginally stable or, equivalently, near the brink of an oscillatory instability. The chance probability of the observed clustering was <0.02. To understand the functional significance of this empirical “operating regime,” we compared it to the theoretical “sweet spot” of the model. We computed this “sweet spot” as the intersection of the parameter volumes in which the model produced stable perceptual outcomes and in which it was sensitive to input modulations. Remarkably, the empirical “operating regime” proved to be largely coextensive with the theoretical “sweet spot.” This demonstrated that perceptual dynamics was not merely consistent but also functionally optimized (in that it balances stability with sensitivity). Our results imply that multi-stable perception is not a laboratory curiosity, but reflects a functional optimization of perceptual dynamics for visual inference.
doi:10.3389/fncom.2013.00017
PMCID: PMC3602966  PMID: 23518509
multi-stability; binocular rivalry; adaptation; model; exploitation-exploration dilemma
Biometrics  2012;68(4):1010-1018.
Summary
A treatment regime is a rule that assigns a treatment, among a set of possible treatments, to a patient as a function of his/her observed characteristics, hence “personalizing” treatment to the patient. The goal is to identify the optimal treatment regime that, if followed by the entire population of patients, would lead to the best outcome on average. Given data from a clinical trial or observational study, for a single treatment decision, the optimal regime can be found by assuming a regression model for the expected outcome conditional on treatment and covariates, where, for a given set of covariates, the optimal treatment is the one that yields the most favorable expected outcome. However, treatment assignment via such a regime is suspect if the regression model is incorrectly specified. Recognizing that, even if misspecified, such a regression model defines a class of regimes, we instead consider finding the optimal regime within such a class by finding the regime the optimizes an estimator of overall population mean outcome. To take into account possible confounding in an observational study and to increase precision, we use a doubly robust augmented inverse probability weighted estimator for this purpose. Simulations and application to data from a breast cancer clinical trial demonstrate the performance of the method.
doi:10.1111/j.1541-0420.2012.01763.x
PMCID: PMC3556998  PMID: 22550953
Doubly robust estimator; Inverse probability weighting; Outcome regression; Personalized medicine; Potential outcomes; Propensity score
PLoS Medicine  2011;8(5):e1001029.
Molly Franke, Megan Murray, and colleagues report that early cART reduces mortality among HIV-infected adults with tuberculosis and improves retention in care, regardless of CD4 count.
Background
Randomized clinical trials examining the optimal time to initiate combination antiretroviral therapy (cART) in HIV-infected adults with sputum smear-positive tuberculosis (TB) disease have demonstrated improved survival among those who initiate cART earlier during TB treatment. Since these trials incorporated rigorous diagnostic criteria, it is unclear whether these results are generalizable to the vast majority of HIV-infected patients with TB, for whom standard diagnostic tools are unavailable. We aimed to examine whether early cART initiation improved survival among HIV-infected adults who were diagnosed with TB in a clinical setting.
Methods and Findings
We retrospectively reviewed charts for 308 HIV-infected adults in Rwanda with a CD4 count≤350 cells/µl and a TB diagnosis. We estimated the effect of cART on survival using marginal structural models and simulated 2-y survival curves for the cohort under different cART strategies:start cART 15, 30, 60, or 180 d after TB treatment or never start cART. We conducted secondary analyses with composite endpoints of (1) death, default, or lost to follow-up and (2) death, hospitalization, or serious opportunistic infection. Early cART initiation led to a survival benefit that was most marked for individuals with low CD4 counts. For individuals with CD4 counts of 50 or 100 cells/µl, cART initiation at day 15 yielded 2-y survival probabilities of 0.82 (95% confidence interval: [0.76, 0.89]) and 0.86 (95% confidence interval: [0.80, 0.92]), respectively. These were significantly higher than the probabilities computed under later start times. Results were similar for the endpoint of death, hospitalization, or serious opportunistic infection. cART initiation at day 15 versus later times was protective against death, default, or loss to follow-up, regardless of CD4 count. As with any observational study, the validity of these findings assumes that biases from residual confounding by unmeasured factors and from model misspecification are small.
Conclusions
Early cART reduced mortality among individuals with low CD4 counts and improved retention in care, regardless of CD4 count.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
HIV infection has exacerbated the global tuberculosis (TB) epidemic, especially in sub-Saharan Africa, in which in some countries, 70% of people with TB are currently also HIV positive—a condition commonly described as HIV/TB co-infection. The management of patients with HIV/TB co-infection is a major public health concern.
There is relatively little good evidence on the best time to initiate combination antiretroviral therapy (cART) in adults with HIV/TB co-infection. Clinicians sometimes defer cART in individuals initiating TB treatment because of concerns about complications (such as immune reconstitution inflammatory syndrome) and the risk of reduced adherence if patients have to remember to take two sets of pills. However, starting cART later in those patients who are infected with both HIV and TB can result in potentially avoidable deaths during therapy.
Why Was This Study Done?
Several randomized control trials (RCTs) have been carried out, and the results of three of these studies suggest that, among individuals with severe immune suppression, early initiation of cART (two to four weeks after the start of TB treatment) leads to better survival than later ART initiation (two to three months after the start of TB treatment). These results were reported in abstract form, but the full papers have not yet been published. One problem with RCTs is that they are carried out under controlled conditions that might not represent well the conditions in varied settings around the world. Therefore, observational studies that examine how effective a treatment is in routine clinical conditions can provide information that complements that obtained during clinical trials. In this study, the researchers aimed to confirm the results from RCTs among a cohort of adult patients with HIV/TB co-infection in Rwanda, diagnosed under routine program conditions and using routinely collected clinical data. The researchers also wanted to investigate whether early cART initiation reduced the risk of other adverse outcomes, including treatment default and loss to follow-up.
What Did the Researchers Do and Find?
The researchers retrospectively reviewed the charts and other program records of 308 patients with HIV, who had CD4 counts≤350 cells/µl, were aged 15 years or more, had never previously taken cART, and received their first TB treatment at one of five cART sites (two urban, three rural) in Rwanda between January 2004 and February 2007. Using this method, the researchers collected baseline demographic and clinical variables and relevant clinical follow-up data. They then used this data to estimate the effect of cART on survival by using sophisticated statistical models that calculated the effects of initiating cART at 15, 30, 60, or 180 d after the start of TB treatment or not at all.
The researchers then conducted a further analysis to assess combined outcomes of (1) death, default, lost to follow-up, and (2) death, hospitalization due to any cause, or occurrence of severe opportunistic infections, such as Kaposi's sarcoma. The researchers used the resulting multivariable model to estimate survival probabilities for each individual, based on his/her baseline characteristics.
The researchers found that when they set their model to first CD4 cell counts of 50 and 100 cells/µl, and starting cART at day 15, mean survival probabilities at two years were 0.82 and 0.86, respectively, statistically significantly higher than the survival probabilities calculated for each of the other treatment strategies, where cART was started later. They observed a similar pattern for the combined outcome of death, hospitalization, or serious opportunistic infection In addition, two-year outcomes for death or lost to follow-up were also improved with early cART, regardless of CD4 count at treatment initiation.
What Do These Findings Mean?
These findings show that in a real world program setting, starting cART 15 d after the start of TB treatment is more beneficial (measured by differences in survival probabilities) among patients with HIV/TB co-infection who have CD4 cell counts≤100 cells/µl than starting later. Early cART initiation may also increase retention in care for all individuals with CD4 cell counts≤350 cells/µl.
As the outcomes of this modeling study are based on data from a retrospective observational study, the biases associated with use of these data must be carefully addressed. However, the results support the recommendation of cART initiation after 15 d of TB treatment for patients with CD4 cell counts≤100 cells/µl and can be used as an advocacy base for TB treatment to be used as an opportunity to refer and retain HIV-infected individuals in care, regardless of CD4 cell count.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001029.
Information is available on HIV/TB co-infection from the World Health Organization, the US Centers for Disease Control and Prevention, and the International AIDS Society
doi:10.1371/journal.pmed.1001029
PMCID: PMC3086874  PMID: 21559327
PLoS Medicine  2013;10(11):e1001555.
Michael Schomaker and colleagues estimate the mortality associated with starting ART at different CD4 thresholds among children aged 2–5 years using observational data collected in cohort studies in Southern Africa.
Please see later in the article for the Editors' Summary
Background
There is limited evidence on the optimal timing of antiretroviral therapy (ART) initiation in children 2–5 y of age. We conducted a causal modelling analysis using the International Epidemiologic Databases to Evaluate AIDS–Southern Africa (IeDEA-SA) collaborative dataset to determine the difference in mortality when starting ART in children aged 2–5 y immediately (irrespective of CD4 criteria), as recommended in the World Health Organization (WHO) 2013 guidelines, compared to deferring to lower CD4 thresholds, for example, the WHO 2010 recommended threshold of CD4 count <750 cells/mm3 or CD4 percentage (CD4%) <25%.
Methods and Findings
ART-naïve children enrolling in HIV care at IeDEA-SA sites who were between 24 and 59 mo of age at first visit and with ≥1 visit prior to ART initiation and ≥1 follow-up visit were included. We estimated mortality for ART initiation at different CD4 thresholds for up to 3 y using g-computation, adjusting for measured time-dependent confounding of CD4 percent, CD4 count, and weight-for-age z-score. Confidence intervals were constructed using bootstrapping.
The median (first; third quartile) age at first visit of 2,934 children (51% male) included in the analysis was 3.3 y (2.6; 4.1), with a median (first; third quartile) CD4 count of 592 cells/mm3 (356; 895) and median (first; third quartile) CD4% of 16% (10%; 23%). The estimated cumulative mortality after 3 y for ART initiation at different CD4 thresholds ranged from 3.4% (95% CI: 2.1–6.5) (no ART) to 2.1% (95% CI: 1.3%–3.5%) (ART irrespective of CD4 value). Estimated mortality was overall higher when initiating ART at lower CD4 values or not at all. There was no mortality difference between starting ART immediately, irrespective of CD4 value, and ART initiation at the WHO 2010 recommended threshold of CD4 count <750 cells/mm3 or CD4% <25%, with mortality estimates of 2.1% (95% CI: 1.3%–3.5%) and 2.2% (95% CI: 1.4%–3.5%) after 3 y, respectively. The analysis was limited by loss to follow-up and the unavailability of WHO staging data.
Conclusions
The results indicate no mortality difference for up to 3 y between ART initiation irrespective of CD4 value and ART initiation at a threshold of CD4 count <750 cells/mm3 or CD4% <25%, but there are overall higher point estimates for mortality when ART is initiated at lower CD4 values.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Infection with HIV, the virus that causes AIDS, contributes substantially to the burden of disease in children. Worldwide, more than 3 million children younger than 15 years old (90% of whom live in sub-Saharan Africa) are HIV-positive, and every year around 330,000 more children are infected with HIV. Children usually acquire HIV from their mother during pregnancy, birth, or breastfeeding. The virus gradually destroys CD4 lymphocytes and other immune system cells, leaving infected children susceptible to other potentially life-threatening infections. HIV infection can be kept in check, with antiretroviral therapy (ART)—cocktails of drugs that have to be taken daily throughout life. ART is very effective in children but is expensive, and despite concerted international efforts over the past decade to provide universal access to ART, in 2011, less than a third of children who needed ART were receiving it.
Why Was This Study Done?
For children diagnosed as HIV-positive between the ages of two and five years, the 2010 World Health Organization (WHO) guidelines for the treatment of HIV infection recommended that ART be initiated when the CD4 count dropped below 750 cells/mm3 blood or when CD4 cells represented less than 25% of the total lymphocyte population (CD4 percent). Since June 2013, however, WHO has recommended that all HIV-positive children in this age group begin ART immediately, irrespective of their CD4 values. Earlier ART initiation might reduce mortality (death) and morbidity (illness), but it could also increase the risk of toxicity and of earlier development of drug resistance. In this causal modeling analysis, the researchers estimate the mortality associated with starting ART at different CD4 thresholds among children aged 2–5 years using observational data collected in cohort studies of ART undertaken in southern Africa. Specifically, they compared the estimated mortality associated with the WHO 2010 and WHO 2013 guidelines. Observational studies compare the outcomes of groups (cohorts) with different interventions (here, the timing of ART initiation). Data from such studies are affected by time-dependent confounding: CD4 count, for example, varies with time and is a predictor of both ART initiation and the probability of death. Causal modeling techniques take time-dependent confounding into account and enable the estimation of the causal effect of an intervention on an outcome from observational data.
What Did the Researchers Do and Find?
The researchers used g-computation (a type of causal modeling) adjusting for time-dependent confounding of CD4 percent, CD4 count, and weight-for-age z-score (a measure of whether a child is underweight for their age that provides a proxy indicator of the clinical stage of HIV infection) to estimate mortality for ART initiation at different CD4 thresholds in 2,934 ART-naïve, HIV-positive children aged 2–5 years old at their first visit to one of eight study sites in southern Africa. The average initial CD4 values of these children were a CD4 count of 592 cells/mm3 and a CD4 percent of 16%. The estimated cumulative mortality after three years was 3.4% in all children if ART was never started. If all children had started ART immediately after diagnosis irrespective of CD4 value or if the 2010 WHO-recommended threshold of a CD4 count below 750 cells/mm3 or a CD4 percent below 25% was followed, the estimated cumulative mortalities after three years were 2.1% and 2.2%, respectively (a statistically non-significant difference).
What Do These Findings Mean?
These findings suggest that, among southern African children aged 2–5 years at HIV diagnosis, there is no difference in mortality for up to three years between children in whom ART is initiated immediately and those in whom ART initiation is deferred until their CD4 value falls below a CD4 count of 750 cells/mm3 or a CD4 percent of 25%. Although causal modeling was used in this analysis, the accuracy of these results may be affected by residual confounding. For example, the researchers were unable to adjust for the clinical stage of HIV disease at HIV diagnosis and instead had to use weight-for-age z-scores as a proxy indicator of disease severity. Other limitations of the study include the large number of children lost to follow-up and a possible lack of generalizability—most of the study participants were from urban settings in South Africa. Importantly, however, these findings suggest that the recent change in the WHO guidelines for ART initiation in young children is unlikely to increase or reduce mortality, with the proviso that the long-term effects of earlier ART initiation such as toxicity and the development of resistance to ART need to be explored further.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001555
Information is available from the US National Institute of Allergy and Infectious Diseases on HIV infection and AIDS
NAM/aidsmap provides basic information about HIV/AIDS and summaries of recent research findings on HIV care and treatment
Information is available from Avert, an international AIDS charity, on many aspects of HIV/AIDS, including information on HIV and AIDS in Africa and on children and HIV/AIDS (in English and Spanish)
The UNAIDS World AIDS Day Report 2012 provides up-to-date information about the AIDS epidemic and efforts to halt it; the 2013 Progress Report on the Global Plan provides information on progress towards eliminating new HIV infections among children by 2015
The World Health Organization provides information about universal access to AIDS treatment (in several languages); its 2010 guidelines for ART in infants and children and its 2013 consolidated guidelines on the use of ART can be downloaded
The researchers involved in this study are part of the International Epidemiologic Databases to Evaluate AIDSSouthern Africa collaboration, which develops and implements methodology to generate the large datasets needed to address high-priority research questions related to HIV/AIDS
Personal stories about living with HIV/AIDS, including stories from young people infected with HIV, are available through Avert, through NAM/aidsmap, and through the charity website Healthtalkonline
doi:10.1371/journal.pmed.1001555
PMCID: PMC3833834  PMID: 24260029
Biometrika  2013;100(3):10.1093/biomet/ast014.
Summary
A dynamic treatment regime is a list of sequential decision rules for assigning treatment based on a patient’s history. Q- and A-learning are two main approaches for estimating the optimal regime, i.e., that yielding the most beneficial outcome in the patient population, using data from a clinical trial or observational study. Q-learning requires postulated regression models for the outcome, while A-learning involves models for that part of the outcome regression representing treatment contrasts and for treatment assignment. We propose an alternative to Q- and A-learning that maximizes a doubly robust augmented inverse probability weighted estimator for population mean outcome over a restricted class of regimes. Simulations demonstrate the method’s performance and robustness to model misspecification, which is a key concern.
doi:10.1093/biomet/ast014
PMCID: PMC3843953  PMID: 24302771
A-learning; Double robustness; Outcome regression; Propensity score; Q-learning
PLoS Medicine  2012;9(4):e1001207.
Luis Montaner and colleagues retrospectively apply a potential capacity-saving CD4 count prediction tool to a cohort of HIV patients on antiretroviral therapy.
Background
Global programs of anti-HIV treatment depend on sustained laboratory capacity to assess treatment initiation thresholds and treatment response over time. Currently, there is no valid alternative to CD4 count testing for monitoring immunologic responses to treatment, but laboratory cost and capacity limit access to CD4 testing in resource-constrained settings. Thus, methods to prioritize patients for CD4 count testing could improve treatment monitoring by optimizing resource allocation.
Methods and Findings
Using a prospective cohort of HIV-infected patients (n = 1,956) monitored upon antiretroviral therapy initiation in seven clinical sites with distinct geographical and socio-economic settings, we retrospectively apply a novel prediction-based classification (PBC) modeling method. The model uses repeatedly measured biomarkers (white blood cell count and lymphocyte percent) to predict CD4+ T cell outcome through first-stage modeling and subsequent classification based on clinically relevant thresholds (CD4+ T cell count of 200 or 350 cells/µl). The algorithm correctly classified 90% (cross-validation estimate = 91.5%, standard deviation [SD] = 4.5%) of CD4 count measurements <200 cells/µl in the first year of follow-up; if laboratory testing is applied only to patients predicted to be below the 200-cells/µl threshold, we estimate a potential savings of 54.3% (SD = 4.2%) in CD4 testing capacity. A capacity savings of 34% (SD = 3.9%) is predicted using a CD4 threshold of 350 cells/µl. Similar results were obtained over the 3 y of follow-up available (n = 619). Limitations include a need for future economic healthcare outcome analysis, a need for assessment of extensibility beyond the 3-y observation time, and the need to assign a false positive threshold.
Conclusions
Our results support the use of PBC modeling as a triage point at the laboratory, lessening the need for laboratory-based CD4+ T cell count testing; implementation of this tool could help optimize the use of laboratory resources, directing CD4 testing towards higher-risk patients. However, further prospective studies and economic analyses are needed to demonstrate that the PBC model can be effectively applied in clinical settings.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
AIDS has killed nearly 30 million people since 1981, and about 34 million people (most of them living in low- and middle-income countries) are now infected with HIV, the virus that causes AIDS. HIV destroys immune system cells (including CD4 cells, a type of lymphocyte and one of the body's white blood cell types), leaving infected individuals susceptible to other infections. Early in the AIDS epidemic, most HIV-infected people died within ten years of infection. Then, in 1996, antiretroviral therapy (ART) became available, and for people living in affluent countries, HIV/AIDS became a chronic condition. However, ART was expensive, and for people living in developing countries, HIV/AIDS remained a fatal illness. In 2003, HIV was declared a global health emergency, and in 2006, the international community set itself the target of achieving universal access to ART by 2010. By the end of 2010, only 6.6 million of the estimated 15 million people in need of ART in developing countries were receiving ART.
Why Was This Study Done?
One factor that has impeded progress towards universal ART coverage has been the limited availability of trained personnel and laboratory facilities in many developing countries. These resources are needed to determine when individuals should start ART—the World Health Organization currently recommends that people start ART when their CD4 count drops below 350 cells/µl—and to monitor treatment responses over time so that viral resistance to ART is quickly detected. Although a total lymphocyte count can be used as a surrogate measure to decide when to start treatment, repeated CD4 cell counts are the only way to monitor immunologic responses to treatment, a level of monitoring that is rarely sustainable in resource-constrained settings. A method that optimizes resource allocation by prioritizing who gets tested might be one way to improve treatment monitoring. In this study, the researchers applied a new tool for prioritizing laboratory-based CD4 cell count testing in resource-constrained settings to patient data that had been previously collected.
What Did the Researchers Do and Find?
The researchers fitted a mixed-effects statistical model to repeated CD4 count measurements from HIV-infected individuals from seven sites around the world (including some resource-limited sites). They then used model-derived estimates to apply a mathematical tool for predicting—from a CD4 count taken at the start of treatment, and white blood cell counts and lymphocyte percentage measurements taken later—whether CD4 counts would be above 200 cells/µl (the original threshold recommended for ART initiation) and 350 cells/µl (the current recommended threshold) for up to three years after ART initiation. The tool correctly classified 91.5% of the CD4 cell counts that were below 200 cells/µl in the first year of ART. With this threshold, the potential savings in CD4 testing capacity was 54.3%. With a CD4 count threshold of 350 cells/µl, the potential savings in testing capacity was 34%. The results over a three-year follow-up were similar. When applied to six representative HIV-positive individuals, the tool correctly predicted all the CD4 counts above 200 cells/µl, although some individuals who had a predicted CD4 count of less than 200 cells/µl actually had a CD4 count above this threshold. Thus, none of these individuals would have been exposed to an undetected dangerous CD4 count, but the application of the tool would have saved 57% of the CD4 laboratory tests done during the first year of ART.
What Do These Findings Mean?
These findings support the use of this new tool—the prediction-based classification (PBC) algorithm—for predicting a drop in CD4 count below a clinically meaningful threshold in HIV-infected individuals receiving ART. Further studies are now needed to demonstrate the feasibility, clinical effectiveness, and cost-effectiveness of this approach, to find out whether the tool can be used over extended periods of time, and to investigate whether the accuracy of its predictions can be improved by, for example, adding in periodic CD4 testing. Provided these studies confirm its early promise, the researchers suggest that the PBC algorithm could be used as a “triage” tool to direct available laboratory testing capacity to high-priority individuals (those likely to have a dangerously low CD4 count). By optimizing the use of limited laboratory resources in this and other ways, the PBC algorithm could therefore help to maintain and expand ART programs in low- and middle-income countries.
Additional Information
Please access these web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001207.
Information is available from the US National Institute of Allergy and Infectious Diseases on HIV infection and AIDS
NAM/aidsmap provides basic information about HIV/AIDS and summaries of recent research findings on HIV care and treatment
Information is available from Avert, an international AIDS charity, on many aspects of HIV/AIDS, including information on HIV/AIDS treatment and care and on universal access to AIDS treatment (in English and Spanish)
The World Health Organization provides information about universal access to AIDS treatment (in several languages)
More information about universal access to HIV treatment, prevention, care, and support is available from UNAIDS
Patient stories about living with HIV/AIDS are available through Avert and through the charity website Healthtalkonline
doi:10.1371/journal.pmed.1001207
PMCID: PMC3328436  PMID: 22529752
PLoS Medicine  2011;8(3):e1000423.
A cost-effectiveness study by Sabina Alistar and colleagues evaluates the effectiveness and cost effectiveness of different levels of investment in methadone, ART, or both, in the mixed HIV epidemic in Ukraine.
Background
Injection drug use (IDU) and heterosexual virus transmission both contribute to the growing mixed HIV epidemics in Eastern Europe and Central Asia. In Ukraine—chosen in this study as a representative country—IDU-related risk behaviors cause half of new infections, but few injection drug users (IDUs) receive methadone substitution therapy. Only 10% of eligible individuals receive antiretroviral therapy (ART). The appropriate resource allocation between these programs has not been studied. We estimated the effectiveness and cost-effectiveness of strategies for expanding methadone substitution therapy programs and ART in mixed HIV epidemics, using Ukraine as a case study.
Methods and Findings
We developed a dynamic compartmental model of the HIV epidemic in a population of non-IDUs, IDUs using opiates, and IDUs on methadone substitution therapy, stratified by HIV status, and populated it with data from the Ukraine. We considered interventions expanding methadone substitution therapy, increasing access to ART, or both. We measured health care costs, quality-adjusted life years (QALYs), HIV prevalence, infections averted, and incremental cost-effectiveness. Without incremental interventions, HIV prevalence reached 67.2% (IDUs) and 0.88% (non-IDUs) after 20 years. Offering methadone substitution therapy to 25% of IDUs reduced prevalence most effectively (to 53.1% IDUs, 0.80% non-IDUs), and was most cost-effective, averting 4,700 infections and adding 76,000 QALYs compared with no intervention at US$530/QALY gained. Expanding both ART (80% coverage of those eligible for ART according to WHO criteria) and methadone substitution therapy (25% coverage) was the next most cost-effective strategy, adding 105,000 QALYs at US$1,120/QALY gained versus the methadone substitution therapy-only strategy and averting 8,300 infections versus no intervention. Expanding only ART (80% coverage) added 38,000 QALYs at US$2,240/QALY gained versus the methadone substitution therapy-only strategy, and averted 4,080 infections versus no intervention. Offering ART to 80% of non-IDUs eligible for treatment by WHO criteria, but only 10% of IDUs, averted only 1,800 infections versus no intervention and was not cost effective.
Conclusions
Methadone substitution therapy is a highly cost-effective option for the growing mixed HIV epidemic in Ukraine. A strategy that expands both methadone substitution therapy and ART to high levels is the most effective intervention, and is very cost effective by WHO criteria. When expanding ART, access to methadone substitution therapy provides additional benefit in infections averted. Our findings are potentially relevant to other settings with mixed HIV epidemics.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
HIV epidemics in Eastern Europe and Central Asia are mainly driven by increasing use of injection drugs combined with heterosexual transmission. In the Ukraine, in 2007, there were 82,000 officially registered people living with HIV—three times the number registered in 1999—and an estimated 395,000 HIV infected adults. The epidemic in Ukraine, like other countries in the region, is concentrated in at-risk populations, particularly people who inject drugs: in 2007, an estimated 390,000 Ukrainians were injecting drugs, an increase in drug use over the previous decade, not only in Ukraine, but in other former USSR states, owing to the easy availability of precursors for injection drugs in a climate of economic collapse.
The common practices of people who inject drugs in Ukraine and in other countries in the region, such as social injecting, syringe sharing, and using common containers, increase the risk of transmitting HIV. Public health interventions such as needle exchange can limit these risk factors and have been gradually implemented in these countries. In 2007, Ukraine approved the use of methadone substitution therapy and the current target is for 11,000 people who inject drugs to be enrolled in substitution therapy by 2011. Furthermore, since treatment for HIV-infected individuals is also necessary, national HIV control plans included a target of 90% antiretroviral therapy (ART) coverage by 2010 but in 2007 less than 10% of the 91,000 eligible people received treatment. Although the number of people who inject drugs and who receive ART is unknown, physicians are often reluctant to treat people who inject drugs using ART owing to alleged poor compliance.
Why Was This Study Done?
As resources for HIV interventions in the region are limited, it is important to investigate the appropriate balance between investments in methadone substitution therapy and ART in order to maximize benefits to public health. Several studies have analyzed the cost effectiveness of methadone substitution therapy in similar settings but have not considered tradeoffs between ART and methadone substitution therapy. Therefore, to provide insights into the appropriate public health investment in methadone substitution therapy and ART in Ukraine, the researchers evaluated the public health effectiveness and cost effectiveness of different strategies for scaling up methadone substitution therapy and/or expanding ART.
What Did the Researchers Do and Find?
The researchers developed a model to accommodate different population groups: people who inject drugs on substitution therapy with methadone; people who inject opiates and do not take any substitution therapy; and people who do not inject any drugs, hence do not need substitution therapy. The researchers inputted Ukraine country-level data into this model and used current HIV trends in Ukraine to make rational assumptions on possible future trends and scenarios. They considered scenarios expanding methadone substitution therapy availability, increasing acces to ART, or both. Then, the researchers measured health care costs, quality-adjusted life years (QALYs), HIV prevalence, infections averted, and incremental cost effectiveness for the different scenarios. They found that after 20 years, HIV prevalence reached 67.2% in people who inject drugs and 0.88% in people who do not inject drugs without further interventions. Offering methadone substitution therapy to 25% of people who inject drugs was the most effective strategy in reducing prevalence of HIV and was also the most cost effective, averting 4,700 infections and adding 75,700 QALYs versus the status quo at $530/QALY gained. Expanding both methadone substitution therapy and ART was also a highly cost effective option, adding 105,000 QALYs at US$1,120/QALY gained versus the methadone substitution therapy-only strategy. Offering ART to 80% of eligible people who did not inject drugs, and 10% of people who injected drugs averted only 1,800 infections, and added 76,400 QALYs at $1,330/QALY gained.
What Do These Findings Mean?
The results show that methadone substitution-focused therapeutic scenarios are the most cost effective, and that benefits increase with the scale of the project, even among people who do not inject drugs. This makes a methadone substitution strategy a highly cost-effective option for addressing the growing HIV epidemic in Ukraine. Therefore, if it is not feasible to invest in large-scale methadone substitution programs for any reason, political circumstances for example, providing as much methadone substitution as is acceptable is still desirable. While substitution therapy appears to avert the most HIV infections, expanded ART provides the largest total increase in QALYs. Thus, methadone substitution therapy and ART offer complementary benefits. Because the HIV epidemic in Ukraine is representative of the HIV epidemic in Eastern Europe and Central Asia, the cost-effective strategies that the researchers have identified may help inform all decision makers faced with a mixed HIV epidemic.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000423.
Alliance provides information on its work supporting community action on AIDS in Ukraine
USAID provides an HIV/AIDS Health Profile for Ukraine
UNICEF provides information about its activities to help Ukraine fight rising HIV/AIDS infection rates
International Harm Reduction Association provides information about the status of harm reduction interventions such as methadone substitution therapy around the world
doi:10.1371/journal.pmed.1000423
PMCID: PMC3046988  PMID: 21390264
Given causal graph assumptions, intervention-specific counterfactual distributions of the data can be defined by the so called G-computation formula, which is obtained by carrying out these interventions on the likelihood of the data factorized according to the causal graph. The obtained G-computation formula represents the counterfactual distribution the data would have had if this intervention would have been enforced on the system generating the data. A causal effect of interest can now be defined as some difference between these counterfactual distributions indexed by different interventions. For example, the interventions can represent static treatment regimens or individualized treatment rules that assign treatment in response to time-dependent covariates, and the causal effects could be defined in terms of features of the mean of the treatment-regimen specific counterfactual outcome of interest as a function of the corresponding treatment regimens. Such features could be defined nonparametrically in terms of so called (nonparametric) marginal structural models for static or individualized treatment rules, whose parameters can be thought of as (smooth) summary measures of differences between the treatment regimen specific counterfactual distributions.
In this article, we develop a particular targeted maximum likelihood estimator of causal effects of multiple time point interventions. This involves the use of loss-based super-learning to obtain an initial estimate of the unknown factors of the G-computation formula, and subsequently, applying a target-parameter specific optimal fluctuation function (least favorable parametric submodel) to each estimated factor, estimating the fluctuation parameter(s) with maximum likelihood estimation, and iterating this updating step of the initial factor till convergence. This iterative targeted maximum likelihood updating step makes the resulting estimator of the causal effect double robust in the sense that it is consistent if either the initial estimator is consistent, or the estimator of the optimal fluctuation function is consistent. The optimal fluctuation function is correctly specified if the conditional distributions of the nodes in the causal graph one intervenes upon are correctly specified. The latter conditional distributions often comprise the so called treatment and censoring mechanism. Selection among different targeted maximum likelihood estimators (e.g., indexed by different initial estimators) can be based on loss-based cross-validation such as likelihood based cross-validation or cross-validation based on another appropriate loss function for the distribution of the data. Some specific loss functions are mentioned in this article.
Subsequently, a variety of interesting observations about this targeted maximum likelihood estimation procedure are made. This article provides the basis for the subsequent companion Part II-article in which concrete demonstrations for the implementation of the targeted MLE in complex causal effect estimation problems are provided.
doi:10.2202/1557-4679.1211
PMCID: PMC3126670  PMID: 20737021
causal effect; causal graph; censored data; cross-validation; collaborative double robust; double robust; dynamic treatment regimens; efficient influence curve; estimating function; estimator selection; locally efficient; loss function; marginal structural models for dynamic treatments; maximum likelihood estimation; model selection; pathwise derivative; randomized controlled trials; sieve; super-learning; targeted maximum likelihood estimation
BMC Systems Biology  2012;6(Suppl 1):S10.
Background
The accumulation of deleterious mutations of a population directly contributes to the fate as to how long the population would exist, a process often described as Muller's ratchet with the absorbing phenomenon. The key to understand this absorbing phenomenon is to characterize the decaying time of the fittest class of the population. Adaptive landscape introduced by Wright, a re-emerging powerful concept in systems biology, is used as a tool to describe biological processes. To our knowledge, the dynamical behaviors for Muller's ratchet over the full parameter regimes are not studied from the point of the adaptive landscape. And the characterization of the absorbing phenomenon is not yet quantitatively obtained without extraneous assumptions as well.
Methods
We describe how Muller's ratchet can be mapped to the classical Wright-Fisher process in both discrete and continuous manners. Furthermore, we construct the adaptive landscape for the system analytically from the general diffusion equation. The constructed adaptive landscape is independent of the existence and normalization of the stationary distribution. We derive the formula of the single click time in finite and infinite potential barrier for all parameters regimes by mean first passage time.
Results
We describe the dynamical behavior of the population exposed to Muller's ratchet in all parameters regimes by adaptive landscape. The adaptive landscape has rich structures such as finite and infinite potential, real and imaginary fixed points. We give the formula about the single click time with finite and infinite potential. And we find the single click time increases with selection rates and population size increasing, decreases with mutation rates increasing. These results provide a new understanding of infinite potential. We analytically demonstrate the adaptive and unadaptive states for the whole parameters regimes. Interesting issues about the parameters regions with the imaginary fixed points is demonstrated. Most importantly, we find that the absorbing phenomenon is characterized by the adaptive landscape and the single click time without any extraneous assumptions. These results suggest a graphical and quantitative framework to study the absorbing phenomenon.
doi:10.1186/1752-0509-6-S1-S10
PMCID: PMC3403021  PMID: 23046686
A dynamic treatment regime consists of a sequence of decision rules, one per stage of intervention, that dictate how to individualize treatments to patients based on evolving treatment and covariate history. These regimes are particularly useful for managing chronic disorders, and fit well into the larger paradigm of personalized medicine. They provide one way to operationalize a clinical decision support system. Statistics plays a key role in the construction of evidence-based dynamic treatment regimes – informing best study design as well as efficient estimation and valid inference. Due to the many novel methodological challenges it offers, this area has been growing in popularity among statisticians in recent years. In this article, we review the key developments in this exciting field of research. In particular, we discuss the sequential multiple assignment randomized trial designs, estimation techniques like Q-learning and marginal structural models, and several inference techniques designed to address the associated non-standard asymptotics. We reference software, whenever available. We also outline some important future directions.
doi:10.1146/annurev-statistics-022513-115553
PMCID: PMC4231831  PMID: 25401119
dynamic treatment regime; reinforcement learning; sequential randomization; non-regularity; Q-learning
Summary
Acute lung injury (ALI) is a condition characterized by acute onset of severe hypoxemia and bilateral pulmonary infiltrates. ALI patients typically require mechanical ventilation in an intensive care unit. Low tidal volume ventilation (LTVV), a time-varying dynamic treatment regime, has been recommended as an effective ventilation strategy. This recommendation was based on the results of the ARMA study, a randomized clinical trial designed to compare low vs. high tidal volume strategies (The Acute Respiratory Distress Syndrome Network, 2000) . After publication of the trial, some critics focused on the high non-adherence rates in the LTVV arm suggesting that non-adherence occurred because treating physicians felt that deviating from the prescribed regime would improve patient outcomes. In this paper, we seek to address this controversy by estimating the survival distribution in the counterfactual setting where all patients assigned to LTVV followed the regime. Inference is based on a fully Bayesian implementation of Robins’ (1986) G-computation formula. In addition to re-analyzing data from the ARMA trial, we also apply our methodology to data from a subsequent trial (ALVEOLI), which implemented the LTVV regime in both of its study arms and also suffered from non-adherence.
doi:10.1111/j.1467-9876.2010.00757.x
PMCID: PMC3197806  PMID: 22025809
Bayesian inference; Causal inference; Dynamic treatment regime; G-computation formula
PLoS ONE  2011;6(3):e17661.
The effect of spatial structure has been proved very relevant in repeated games. In this work we propose an agent based model where a fixed finite population of tagged agents play iteratively the Nash demand game in a regular lattice. The model extends the multiagent bargaining model by Axtell, Epstein and Young [1] modifying the assumption of global interaction. Each agent is endowed with a memory and plays the best reply against the opponent's most frequent demand. We focus our analysis on the transient dynamics of the system, studying by computer simulation the set of states in which the system spends a considerable fraction of the time. The results show that all the possible persistent regimes in the global interaction model can also be observed in this spatial version. We also find that the mesoscopic properties of the interaction networks that the spatial distribution induces in the model have a significant impact on the diffusion of strategies, and can lead to new persistent regimes different from those found in previous research. In particular, community structure in the intratype interaction networks may cause that communities reach different persistent regimes as a consequence of the hindering diffusion effect of fluctuating agents at their borders.
doi:10.1371/journal.pone.0017661
PMCID: PMC3052375  PMID: 21408019
Statistics in medicine  2012;31(18):2000-2009.
SUMMARY
The parametric g-formula can be used to contrast the distribution of potential outcomes under arbitrary treatment regimes. Like g-estimation of structural nested models and inverse probability weighting of marginal structural models, the parametric g-formula can appropriately adjust for measured time-varying confounders that are affected by prior treatment. However, there have been few implementations of the parametric g-formula to date. Here, we apply the parametric g-formula to assess the impact of highly active antiretroviral therapy on time to AIDS or death in two US-based HIV cohorts including 1,498 participants. These participants contributed approximately 7,300 person-years of follow-up of which 49% was exposed to HAART and 382 events occurred; 259 participants were censored due to drop out. Using the parametric g-formula, we estimated that antiretroviral therapy substantially reduces the hazard of AIDS or death (HR=0.55; 95% confidence limits [CL]: 0.42, 0.71). This estimate was similar to one previously reported using a marginal structural model 0.54 (95% CL: 0.38, 0.78). The 6.5-year difference in risk of AIDS or death was 13% (95% CL: 8%, 18%). Results were robust to assumptions about temporal ordering, and extent of history modeled, for time-varying covariates. The parametric g-formula is a viable alternative to inverse probability weighting of marginal structural models and g-estimation of structural nested models for the analysis of complex longitudinal data.
doi:10.1002/sim.5316
PMCID: PMC3641816  PMID: 22495733
Cohort study; Confounding; g-formula; HIV/AIDS; Monte Carlo methods
Biometrics  2014;70(1):53-61.
Summary
Dynamic treatment regimes operationalize the clinical decision process as a sequence of functions, one for each clinical decision, where each function maps up-to-date patient information to a single recommended treatment. Current methods for estimating optimal dynamic treatment regimes, for example Q-learning, require the specification of a single outcome by which the ‘goodness’ of competing dynamic treatment regimes is measured. However, this is an over-simplification of the goal of clinical decision making, which aims to balance several potentially competing outcomes, e.g., symptom relief and side-effect burden. When there are competing outcomes and patients do not know or cannot communicate their preferences, formation of a single composite outcome that correctly balances the competing outcomes is not possible. This problem also occurs when patient preferences evolve over time. We propose a method for constructing dynamic treatment regimes that accommodates competing outcomes by recommending sets of treatments at each decision point. Formally, we construct a sequence of set-valued functions that take as input up-to-date patient information and give as output a recommended subset of the possible treatments. For a given patient history, the recommended set of treatments contains all treatments that produce non-inferior outcome vectors. Constructing these set-valued functions requires solving a non-trivial enumeration problem. We offer an exact enumeration algorithm by recasting the problem as a linear mixed integer program. The proposed methods are illustrated using data from the CATIE schizophrenia study.
doi:10.1111/biom.12132
PMCID: PMC3954452  PMID: 24400912
Dynamic Treatment Regimes; Personalized Medicine; Composite Outcomes; Competing Outcomes; Preference Elicitation
Biostatistics (Oxford, England)  2006;7(4):615-629.
SUMMARY
Using validation sets for outcomes can greatly improve the estimation of vaccine efficacy (VE) in the field (Halloran and Longini, 2001; Halloran and others, 2003). Most statistical methods for using validation sets rely on the assumption that outcomes on those with no cultures are missing at random (MAR). However, often the validation sets will not be chosen at random. For example, confirmational cultures are often done on people with influenza-like illness as part of routine influenza surveillance. VE estimates based on such non-MAR validation sets could be biased. Here we propose frequentist and Bayesian approaches for estimating VE in the presence of validation bias. Our work builds on the ideas of Rotnitzky and others (1998, 2001), Scharfstein and others (1999, 2003), and Robins and others (2000). Our methods require expert opinion about the nature of the validation selection bias. In a re-analysis of an influenza vaccine study, we found, using the beliefs of a flu expert, that within any plausible range of selection bias the VE estimate based on the validation sets is much higher than the point estimate using just the non-specific case definition. Our approach is generally applicable to studies with missing binary outcomes with categorical covariates.
doi:10.1093/biostatistics/kxj031
PMCID: PMC2766283  PMID: 16556610
Bayesian; Expert opinion; Identifiability; Influenza; Missing data; Selection model; Vaccine efficacy
PLoS Medicine  2011;8(11):e1001123.
Hallett et al use a mathematical model to examine the long-term impact and cost-effectiveness of different pre-exposure prophylaxis (PrEP) strategies for HIV prevention in serodiscordant couples.
Background
Antiretrovirals have substantial promise for HIV-1 prevention, either as antiretroviral treatment (ART) for HIV-1–infected persons to reduce infectiousness, or as pre-exposure prophylaxis (PrEP) for HIV-1–uninfected persons to reduce the possibility of infection with HIV-1. HIV-1 serodiscordant couples in long-term partnerships (one member is infected and the other is uninfected) are a priority for prevention interventions. Earlier ART and PrEP might both reduce HIV-1 transmission in this group, but the merits and synergies of these different approaches have not been analyzed.
Methods and Findings
We constructed a mathematical model to examine the impact and cost-effectiveness of different strategies, including earlier initiation of ART and/or PrEP, for HIV-1 prevention for serodiscordant couples. Although the cost of PrEP is high, the cost per infection averted is significantly offset by future savings in lifelong treatment, especially among couples with multiple partners, low condom use, and a high risk of transmission. In some situations, highly effective PrEP could be cost-saving overall. To keep couples alive and without a new infection, providing PrEP to the uninfected partner could be at least as cost-effective as initiating ART earlier in the infected partner, if the annual cost of PrEP is <40% of the annual cost of ART and PrEP is >70% effective.
Conclusions
Strategic use of PrEP and ART could substantially and cost-effectively reduce HIV-1 transmission in HIV-1 serodiscordant couples. New and forthcoming data on the efficacy of PrEP, the cost of delivery of ART and PrEP, and couples behaviours and preferences will be critical for optimizing the use of antiretrovirals for HIV-1 prevention.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Every year, about 2.5 million people become infected with HIV, the virus that causes AIDS. HIV is usually transmitted through unprotected sex with an HIV-infected partner. It destroys immune system cells (including CD4 cells, a type of lymphocyte), leaving infected individuals susceptible to other infections. There is no cure for AIDS, although HIV can be held in check with antiretroviral therapy (ART), and there is no vaccine that protects against HIV infection. So, to halt the AIDS epidemic, other ways of preventing the spread of HIV are needed. Antiretroviral drugs could potentially be used in two ways to reduce HIV transmission. First, ART could be given to HIV-infected people before they need it for their own health to reduce their infectiousness; the World Health Organization currently recommends that HIV-positive people initiate ART when their CD4 count drops below 350 cells/µl blood but in many African countries ART is only initiated when CD4 counts fall below 200 cells/µl. Second, ART could be given to HIV-uninfected people to reduce acquisition of the virus. This approach—preexposure prophylaxis (PrEP)—has provided protection against HIV transmission in some but not all clinical trials.
Why Was This Study Done?
Couples in long-term relationships where one partner is HIV-positive and the other is HIV-negative (HIV serodiscordant couples) are a priority group for prevention interventions. In sub-Saharan Africa, where most new HIV infections occur, 10%–20% of stable partnerships are serodiscordant and condom use is often low, not least because such couples may want children. Earlier ART or PrEP might reduce HIV transmission in this group but the merits of different approaches have not been analyzed. In this study, the researchers use a mathematical model to examine the long-term impact and cost-effectiveness of different PrEP and ART strategies for HIV prevention in serodiscordant couples.
What Did the Researchers Do and Find?
The researchers constructed a model to simulate HIV infection and disease progression among hypothetical HIV serodiscordant stable heterosexual couples. The model incorporated data from South Africa on couple characteristics, disease progression, ART use, pregnancies, frequency of sex, and contact with other sexual partners, as well as estimates of the effectiveness of PrEP from clinical trials. The researchers used the model to compare the impact on HIV transmission, survival and quality of life, and the cost-effectiveness of no PrEP with four PrEP strategies—always use PrEP after diagnosis of HIV serodiscordancy, use PrEP up to and for a year after ART initiation by the HIV-infected partner (at a CD4 count of ≤200 cells/µl or ≤350 cells/µl), use PrEP only up to ART initiation by the infected partner, and use PrEP only while trying for a baby and during pregnancy. The model predicts, for example, that the cost per infection averted of PrEP used before ART initiation will be offset by future savings in lifelong treatment, particularly among couples with multiple partners, low condom use, and a high risk of transmission. To keep couples alive without the HIV-uninfected partner becoming infected, it could be more cost-effective to provide PrEP to the uninfected partner than to initiate ART earlier in the infected partner, provided the annual cost of PrEP is less than 40% of the annual cost of ART and PrEP is more than 70% effective. Finally, if PREP is 30%–60% effective, the most cost-effective strategy for couples could be to use PrEP in the uninfected partner prior to ART initiation in the infected partner at a CD4 count ≤350 cells/µl.
What Do These Findings Mean?
These findings suggest that the strategic use of PrEP and ART could cost-effectively reduce HIV transmission in HIV serodiscordant stable heterosexual couples in sub-Saharan Africa. The accuracy of these findings depends on the assumptions included in the mathematical model and on the data fed into it. In particular, the interpretation of these results is complicated by uncertainties in the likely cost of PrEP and the “real-world” effectiveness of PrEP. Nevertheless, these findings suggest that PrEP may become a valuable addition in some settings to existing approaches for HIV prevention such as condom promotion and male circumcision programs. Moreover, additional simulations with this mathematical model using more accurate information on the costs and effectiveness of PrEP could assist in future policy making decisions.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001123.
Information is available from the US National Institute of Allergy and infectious diseases on HIV infection and AIDS
NAM/aidsmap provides basic information about HIV/AIDS, summaries of recent research findings on HIV care and treatment, and a section on PrEP
Information is available from Avert, an international AIDS charity on many aspects of HIV/AIDS, including information on all aspects of HIV prevention, and on HIV/AIDS in Africa (in English and Spanish)
AVAC Global Advocacy for HIV Prevention provides up-to-date information on all aspects of HIV prevention, including PrEP
The US Centers for Disease Control and Prevention also has information on PrEP
WHO provides information about antiretroviral therapy
Patient stories about living with HIV/AIDS are available through Avert and through the charity website Healthtalkonline
doi:10.1371/journal.pmed.1001123
PMCID: PMC3217021  PMID: 22110407

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