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. Author manuscript; available in PMC: 2010 Aug 13.
Published in final edited form as: Arch Intern Med. 2010 Aug 9;170(15):1347–1354. doi: 10.1001/archinternmed.2010.249

Comparative Effectiveness of HIV Testing and Treatment in Highly Endemic Regions

Eran Bendavid 1,2, Margaret L Brandeau 3, Robin Wood 4, Douglas K Owens 5,2
PMCID: PMC2921232  NIHMSID: NIHMS201527  PMID: 20696960

Abstract

Context

Universal testing and treatment holds promise for reducing the burden of HIV in sub-Saharan Africa, but linkage from testing to treatment sites and retention in care are inadequate.

Objective

To compare the mortality and epidemiologic implications of linkage to care and loss to follow-up when considering universal HIV testing and treatment.

Design, Settings, and Patients

We developed a simulation of the HIV epidemic and HIV disease progression in South Africa to compare the outcomes of the present scale up (Status Quo) with four strategies to increase access to antiretroviral therapy: (1) universal testing and treatment without changes in linkage to care and loss to follow-up; (2) universal testing and treatment with improved linkage to care; (3) universal testing and treatment with reduced loss to follow-up; and (4) comprehensive HIV care with universal testing and treatment, improved linkage to care, and reduced loss to follow-up.

Main Outcome Measures

Survival benefits, new HIV infections, and HIV prevalence.

Results

Compared to the Status Quo, universal testing and treatment (1) was associated with a life expectancy gain of 12.0 (11.3–12.2) months of life, and 35.3% (32.7%–37.5%) fewer infections over a 10-year time horizon. Improved linkage to care (2), prevention of loss to follow-up (3), and comprehensive HIV care (4) provided substantial additional benefits: life expectancy gains compared to the Status Quo were 16.1, 18.6, and 22.2 months, and new infections were 55.5%, 51.4%, and 73.2% lower, respectively. In sensitivity analysis, comprehensive HIV care reduced new infections by 69.7%–76.7% under a broad set of assumptions.

Conclusions

Universal testing and treatment with current levels of linkage to care and loss to follow-up could substantially reduce the HIV death toll and new HIV infections. However, scaling up linkage to care and preventing loss to follow-up provides nearly twice the benefits of universal testing and treatment alone.

Introduction

The HIV epidemic in many sub-Saharan African countries has stabilized in the past few years, with a few countries reporting reductions in incidence, prevalence, and mortality.1–3 However, the epidemic is still an unsustainable and disproportionate challenge to southern Africa, responsible for more than 20% of adult mortality in some countries, an increasing number of orphans, and possible reversals in economic growth.1, 4 Reducing the burden of the epidemic is a major goal of HIV testing and treatment programs, but difficulties in linking infected individuals to treatment sites and retaining them in care, low testing rates, and resource constraints challenge the capacity to achieve universal access.5–9

Recent studies suggest that universal testing and treatment may decrease HIV prevalence in highly endemic regions through reduced incidence (infected patients who receive treatment are less likely to infect others), while at the same time markedly reducing HIV mortality.10 This strategy has significant appeal, and clinical trials looking at the effectiveness of early antiretroviral therapy (ART) for HIV prevention are currently underway.11 However, previous estimates of the benefits of universal testing and treatment did not take into account the poor linkage between testing and treatment sites, and the high rates of attrition from care after treatment initiation. Among patients who receive a diagnosis of HIV infection in South Africa, one-third to two-thirds never return for follow-up care.12–13 In addition, many clinics report high rates of loss to follow-up (LTFU), 4–39% in a recent systematic review (after accounting for mortality following ART initiation).14 These factors are increasingly recognized as central barriers to scale-up of ART programs in sub-Saharan Africa.

Given the limited resources for scaling up HIV testing and treatment in Africa, assessing the role of improving linkage to care and reducing loss to follow-up is critical. In this study we assess the epidemiologic and health effects of four strategies to increase access to antiretroviral therapy: universal testing and treatment without substantial changes in rates of linkage to care and LTFU; universal testing and treatment with improved linkage to care; universal testing and treatment with reduced LTFU; and comprehensive HIV care with universal testing and treatment, improved linkage to care, and reduced LTFU.

Methods

We developed a stochastic HIV disease and transmission model in an adult population similar to that in South Africa where HIV transmission is predominantly heterosexual. We used the model to evaluate the relative effectiveness of different strategies for scaling up access to ART through expanded testing, improved linkage to care, earlier treatment initiation, and reduced rates of loss to follow-up. The model follows groups of uninfected and HIV-infected individuals over time, and aggregates individual health outcomes as well as epidemiologic measures of HIV burden such as incidence and prevalence.

We designed the model to reflect the current pace of scale up in South Africa, including the rate of HIV testing, rate of linkage to care, treatment initiation thresholds, and rates of loss to follow-up. Below we describe the model structure, key assumptions, and the scale-up strategies.

Model Structure

The model follows groups of 10,000 individuals representative of the population of South Africa by age, gender, HIV status, circumcision status, and number of sexual partners.15–18 Individuals enter the population at age 15, and leave the population when they die from HIV or other causes. Baseline demographic parameters are shown in Table 1. The population is followed in 1-month intervals over a period of 10 years, and the health status of individuals is evaluated monthly based on their age, gender, HIV status, and HIV testing and treatment history. Risk factors for infection and disease progression are presented in Table 1 and the Supplement (available on our website and upon request).

Table 1.

Parameter Estimates and Data Sources

Variable Base Estimate Range Source
Demographics
Age distribution (% of population) US Census Bureau International Database 15
 15–24 33.7% ±5%
 25–34 22.3% ±5%
 35–44 16.5% ±5%
 >44 29.5% ±5%
Age-specific population mortality rate From WHO life tables WHO29
Life expectancy if HIV- uninfected (years) US Census Bureau International Database15
 Male 62.8 57.8–67.8
 Female 65.3 60.3–70.3
Partnership distribution (# of annual sexual partners) DHS20
 0 partners 36% ±10%
 1 partner 56% ±10%
 2 partners 7% ±1%
 3 partners 1% ±0.5%
Proportion circumcised 34% 17%–68% Connolly33
HIV Transmission Parameters
Age-specific prevalence (mean for males and females) South African Medical Research Council27
 15–24 12.4% ±4%
 25–34 32.7% ±4%
 35–44 25.6% ±4%
 >44 8.4% ±4%
Risk of infection per sex act, by log viral load Wawer34, Gray35, Quinn36
 <2.7 0.0001 0–4x
 2.7–3.48 0.0011 0.5–2x
 3.49–4 0.0012 0.5–2x
 4.01–4.48 0.0014 0.5–2x
 >4.48 0.0023 0.5–2x
 Late stage AIDS 0.0043 0.5–4x
 Acute infection 0.0081 0.5–4x
Circumcision risk reduction 55% 40%–70% Auvert,37 Bailey,38 Gray39
% reduction in sexual activity with HIV counseling 20% Kamb40
HIV Disease Parameters
CD4 count in HIV-infected, untreated population, cells/μl Williams41, Auvert42
 0–50 10.4% ±4%
 51–200 27.2% ±4%
 201–350 26.1% ±4%
 351–500 17.7% ±4%
 >500 18.6% ±4%
CD4 count in HIV-uninfected population, cells/μl 1,000 Williams41
Mean and standard deviation viral load in untreated (log10) 4.8 (0.85) Badri43–44
Probability of changing to 2nd line ART Orrell45
 At 12 months 5.4% 5%–20%
 At 24 months 8.6% 8–30%
Monthly probability of developing opportunistic diseases (%), by CD4 <50 cells/μl 51–200 cells/μl 201–350 cells/μl >350 cells/μl Holmes46
  Oral candidiasis 3.50% 2.04% 1.26% 0.57%
  Chronic diarrhea 2.00% 0.49% 0.18% 0.00%
  Esophageal candidiasis 1.46% 0.34% 0.09% 0.06%
  Wasting syndrome 1.29% 0.23% 0.02% 0.00%
  Severe bacterial infection 1.15% 0.04% 0.03% 0.00%
  Pulmonary TB 1.15% 0.71% 0.47% 0.11%
  Extrapulmonary TB 0.98% 0.47% 0.18% 0.05%
  PCP 0.67% 0.05% 0.02% 0.00%
  CMV 0.52% 0.07% 0.02% 0.00%
  Cryptococcal meningitis 0.52% 0.05% 0.00% 0.00%
Monthly HIV mortality without ART, by CD4 count Badri47
  0–49 4.8% 0.5–2x
  50–99 1.9% 0.5–2x
  100–199 1.5% 0.5–2x
  200–299 1.2% 0.5–2x
  300–399 1.0% 0.5–2x
  400–499 0.8% 0.5–2x
  ≥500 0.5% 0.5–2x
Monthly HIV mortality with ART, by CD4 count Lawn48
  0–49 3.2% 0.5–2x
  50–99 1.1% 0.5–2x
  100–199 0.4% 0.5–2x
  200–299 0.2% 0.5–2x
  300–399 0.2% 0.5–2x
  400–499 0.2% 0.5–2x
  ≥500 0.1% 0.5–2x
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ARV – antiretroviral therapy. CMV – cytomegalovirus. TB – tuberculosis. PCP – pneumocystis carinii pneumonia.

HIV Infection

HIV status in the initial time period of the model is determined by current estimates of age- and gender-specific HIV prevalence in South Africa. Individuals who are uninfected may become infected if they have at least one HIV-infected sexual partner. The number of sexual partnerships at any point in time is determined by estimates of concurrency in South Africa.19–20 The risk of infection per month is calculated based on the gender of the individual, the number of infected partners, and the viral load of any infected partners. The viral load of infected partners may be either suppressed or elevated, based on the distribution of viral loads in the population. Uninfected partners of individuals who recently seroconverted have a much higher risk of acquiring HIV infection. Risk of infection is reduced for men who are circumcised and for individuals who received HIV counseling. The magnitude of risk reduction is shown in Table 1.

HIV Testing and Treatment

Infected individuals are identified through HIV testing or when they develop a severe opportunistic disease. Individuals who develop a severe opportunistic disease are linked to care and initiate treatment, even if they were lost to care between testing and treatment or lost to follow-up. We assumed that those who are identified through testing are referred to an HIV treatment facility, where they are followed prior to and after starting ART. The imperfect linkage between testing and treatment sites, the treatment initiation criteria, and the loss to follow-up after treatment initiation are shown in Table 2. We assumed that individuals were monitored on average every 6 months for clinical symptoms of disease progression and with CD4 counts.

Table 2.

HIV Testing and Treatment Strategies

Scale-up Strategy Rate of testing Likelihood of connection to care Criteria for treatment initiation Rate of loss to follow-up
1. Status Quo 10% of the population tested per year, with infected individuals twice as likely to seek testing as uninfected individuals; retesting no sooner than every 3 years. 67% of individuals who test HIV+ are monitored for treatment initiation. CD4 cell count of 200 cells/μl or an AIDS-defining illness. 20% of individuals who start treatment are lost to follow-up in the first year of treatment, and treatment stops 3 months after loss.
2. Test and Treat (Test & Treat) 90% of the population tested in the first 2 years, and retested every 2 years on average. Same as Status Quo. Within 6 months after HIV+ diagnosis. Same as Status Quo.
3. Test, Improve Linkage, and Treat (Test & Link & Treat) 90% of the population tested in the first 2 years, and retested every 2 years on average. 100% of individuals who test HIV+ are monitored for treatment eligibility. Within 6 months after HIV+ diagnosis. Same as Status Quo.
4. Test, Treat, and Prevent Loss to Follow-Up (Test & Treat & LTFU) 90% of the population tested in the first 2 years, and retested every 2 years on average. Same as Status Quo. Within 6 months after HIV+ diagnosis. No living patients are lost to follow-up in the first year after treatment initiation.
5. Comprehensive Scale Up 90% of the population tested in the first 2 years, and retested every 2 years on average. 100% of individuals who test HIV+ are monitored for treatment eligibility. Within 6 months after HIV+ diagnosis. No living patients are lost to follow-up in the first year after treatment initiation.
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All individuals in our population are eligible to be tested. In the Status Quo strategy, testing rates reflect the current mix of voluntary testing and counseling or provider-initiated testing, while the universal testing and treatment strategies assume 90% of the population is tested, and individuals undergo testing every 2 years on average. In the Status Quo we assumed that 67% of individuals who tested HIV-positive are linked with an HIV treatment facility.21 Without targeting linkage, we assume that this rate of presentation to treatment sites remains unchanged.

We used our previous work to model the progress of infected individuals.22–23 Individuals who are linked to a treatment program are monitored regularly to determine the appropriate timing for initiating ART. We assumed that individuals are monitored for clinical signs of advanced disease and CD4 cell counts to guide timing of treatment initiation. Timing of treatment initiation is a topic of substantial debate;24 our Status Quo estimates reflect current guidelines (Table 2). Finally, we tracked whether individuals remained in treatment over time. We estimated rates of loss to follow-up based on clinical experience from Cape Town, but considered the range of reported experience in southern Africa. Base-case estimates of each component of HIV care are shown in Table 2.

Strategiesfor HIV CareScale Up

We projected the course of the epidemic using the Status Quo and four additional strategies of universal testing and treatment. The strategies (Table 2) are:

  1. Status Quo – The number of people tested for HIV, accessing treatment centers, and starting treatment continues to increase, but the pace of scale up remains similar to that observed since 2007.

  2. Universal testing and treatment – A shift from the current rate of testing, where 18% of the adult population has been tested previously and about 10% of the adult population is tested per year, to a strategy where 90% of the adult population is tested in the first 2 years, and individuals seek an additional HIV test every 2 years on average. About 67% of those who test HIV-positive are linked to a treatment facility, and start treatment with first-line ART within 6 months after their diagnosis; and about 20% of individuals who start treatment are lost to follow-up in the first year of treatment. Compared with the Status Quo, this strategy illustrates the benefits of universal testing and treatment with the current approaches to patient linkage and retention in care.

  3. Universal testing and treatment with improved linkage to care – Same as Strategy 2, except that linkage to care is now 100%; that is, every individual who tests HIV-positive is linked with a treatment facility and starts treatment within 6 months of diagnosis. Rates of LTFU are unchanged.

  4. Universal testing and treatment with prevention of loss to follow-up – Same as Strategy 2, except that retention in care is 100%. As with Strategy 2, 33% of individuals who test HIV-positive are not linked with treatment facilities.

  5. Universal testing, improved linkage to care, early treatment, and prevention of loss to follow-up (Comprehensive) – This includes universal testing, improved linkage to care, early treatment, and prevention of LTFU as noted above. While perfect linkage and retention in care are not likely to be possible, this strategy is illustrative of the maximal benefits of a universal testing and treatment approach.

Outcomes Measured

We measured two primary outcomes: gains in life expectancy and reduction in new HIV infections at the end of 10 years compared to the Status Quo. The former is a measure of health benefits for individuals while the latter is an important determinant of the burden of disease. While the benefits of scaling up HIV care apply most directly to the infected population, the benefits spill over to the uninfected population through reduced HIV transmission. Thus, we measured the gains in life expectancy across the entire population. We estimated four other important epidemiologic and health outcomes: reduction in HIV-related deaths, HIV death rates, adult HIV prevalence, and population growth. Life expectancy gains are reported using a 3% discount rate.

Sensitivity Analysis

Recognizing the challenges in achieving 100% linkage and retention in care, we varied the rates from the Status Quo levels to perfect linkage and retention, and report the results in percent improvement over universal testing and treatment without changes to linkage or retention in care. For example, improving linkage from 67% to 90% may provide a 25% greater improvement in life expectancy compared with universal testing and treatment without improvements in linkage. In probabilistic sensitivity analysis we varied all parameters simultaneously (details in Supplement). We drew each input parameter from a random distribution (normal, beta, gamma, or uniform), and repeated the analysis 1,000 times. We report the results of this analysis as a 95% uncertainty bound around our estimates. These bounds are reported throughout the Results, and in the Tables and Figures.

Results

In the Status Quo strategy, we estimate that the prevalence of HIV in South African adults will decrease from 18.0% to 17.2% over the next decade. This is consistent with current epidemiologic projections and recent trends.25 We estimate the HIV-specific mortality rate in the first year of our analysis to be 1,140 deaths per 100,000 adults over age 15, consistent with WHO estimates from vital registration data and demographic projections.26–27

Mortality Benefits of Testing and Treatment Strategies

Compared to the Status Quo, universal testing and treatment alone (Strategy 2) was associated with a per person gain in life expectancy of 12.0 (11.3–12.2) months averaged over the entire population. Universal testing and treatment with improved linkage to care (Strategy 3) and prevention of LTFU (Strategy 4) were associated with greater life expectancy gains: 16.1 and 18.6 months per person over the entire population, respectively, while Comprehensive HIV care (Strategy 5) was associated with an average life expectancy gain of 22.2 months per person. Two related events account for the gains in life years: a decrease in HIV mortality from improved case detection and care, and an increase in the size of the population due to the decreased mortality in the population of childbearing age. At the end of 10 years, we estimate that the Comprehensive strategy resulted in 61.6% fewer deaths from HIV compared to the Status Quo and an HIV death rate of 415 (318–515) per 100,000 person-years, 63.5% lower than the Status Quo. Universal testing and treatment alone (Strategy 2) yielded a 27.7% decrease in deaths from HIV and an HIV mortality rate of 802 (664–948) per 100,000 person-years. The mortality benefits are summarized in Table 3 and Figure 1.

Table 3.

Mortality Benefits of HIV Testing and Treatment Strategies

Scale-up Strategy Months of life gained, per person*,† Percentage reduction in HIV deaths*,† HIV death rate, per 1,000 PY*,**
1. Status Quo Comparator Comparator 11.4 (9.5–12.9)
2. Test & Treat 12.0 (11.3–12.2) 27.7 (24.5–28.3) 8.0 (6.6–9.5)
3. Test & Link & Treat 16.1 (15.4–16.2) 43.3 (39.5–45.0) 6.2 (5.1–7.5)
4. Test & Treat & LTFU 18.6 (17.0–18.9) 45.1 (41.2–46.7) 6.0 (4.9–7.3)
5. Comprehensive Scale Up 22.2 (21.8–22.5) 61.6 (58.0–64.8) 4.2 (3.2–5.1)
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*

Numbers in parentheses represent 95% uncertainty bounds

**

PY – person-years

†

- Gains in life expectancy and reduction in deaths among HIV-positive population are compared with the Status Quo strategy

Figure 1.

Figure 1

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Estimated deaths from HIV over 10 years in South Africa for different HIV testing and treatment strategies. A comparison of the total number of HIV-related deaths over 10 years, by strategy, scaled to South Africa. The error bars represent the 95% confidence bounds from the probabilistic sensitivity analysis.

Transmission Benefits of Testing and Treatment Strategies

While scaling up HIV care is likely to improve survival of HIV-infected individuals, the effect on HIV transmission and prevalence is less obvious. Increasing treatment coverage may reduce infectivity per coital act. However, the longer survival of HIV-infected individuals and reduction in deaths may increase the opportunity for individuals to infect others, and may increase prevalence, which is in turn a determinant of infection. We estimate the number of new infections in the adult South African population over the next ten years to be 4.5 million (3.8–5.1) in the Status Quo, and 1.2 million (0.9–1.6) in a Comprehensive strategy, a 73.2% reduction.

The decrease in new infections led to reductions in adult HIV prevalence compared to the Status Quo of 1.6%, 2.5%, 1.9%, and 3.4% with universal testing and treatment alone, universal testing and treatment with improved linkage, universal testing and treatment with reduced LTFU, and the Comprehensive strategy, respectively, at the end of ten years. The reduction in new infections and HIV deaths, especially among individuals of childbearing age, was associated with an increase in the size of the population. We estimated 11.5% population growth over 10 years in the Status Quo (about 1.1% per year), and 15.2% with universal testing and treatment alone (Strategy 2). Figure 2 and Table 4 show the transmission and demographic benefits.

Figure 2.

Figure 2

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Projected HIV prevalence in South Africa for different HIV testing and treatment strategies.

Table 4.

Transmission and Epidemiologic Benefits of HIV Testing and Treatment Strategies

Scale-up Strategy Reduction in new HIV infections, %*,† Final projected prevalence, %* Population growth, %*,**,†
1. Status Quo Comparator 17.2 (17.0–17.5) Comparator
2. Test & Treat 35.3 (32.7–37.5) 15.6 (15.5–15.7) 3.7 (3.6–3.9)
3. Test & Link & Treat 55.5 (51.8–58.2) 14.7 (14.6–14.8) 5.5 (5.4–5.7)
4. Test & Treat & LTFU 51.4 (48.0–54.2) 15.3 (15.2–15.4) 6.2 (6.0–6.4)
5. Comprehensive Scale Up 73.2 (69.7–76.7) 13.8 (13.7–14.0) 8.2 (7.9–8.3)
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*

Numbers in parentheses represent 95% uncertainty bounds

**

Population growth represents the growth above the population growth in the Status Quo strategy (e.g., 3.7 means that population growth under such a strategy is 3.7% greater than in the Status Quo)

†

- Reduction in new HIV infections and growth in population size are compared with the Status Quo strategy

Sensitivity Analyses

Figure 3 shows the sensitivity of our results to gradually improving linkage to care and reducing LTFU. The figure shows that even relatively modest improvements in linkage to care and prevention of LTFU provide substantial mortality and prevention benefits. A 10% higher linkage and 6% reduction in LTFU (67% to 77% and 20% to 14%, respectively) are associated with life expectancy improvements that are 30% higher than with universal testing and treatment alone. Similar improvements in linkage and prevention of LTFU are associated with 36% fewer HIV infections compared with universal testing and treatment alone. We did 2-way sensitivity analysis on rates of testing and treatment initiation threshold which show that as long as treatment is initiated early, substantial reductions in prevalence and new infections can be accomplished with testing rates as low as 30–40% of the population. These results are discussed in the Supplement.

Figure 3.

Figure 3

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Sensitivity analysis of the mortality benefits and reduction in transmission from gradual improvements in linkage to care and prevention of LTFU. Panel A shows the mortality benefits and panel B the reduction in new HIV infections. Each line represents the benefit compared with a universal testing and treatment alone. The relative benefits for improved linkage range over 67%–100% linkage, and for LTFU from 20%-0% loss. The Comprehensive strategy represents simultaneous improvements in linkage and prevention of LTFU. For example, universal testing and treatment with improved linkage to 80% results in a gain of about 2.5 months of life expectancy, or 15% improvement, over universal testing and treatment alone.

Discussion

Our comparative evaluation of the mortality and transmission benefits of scaling up HIV testing and treatment in sub-Saharan Africa provides several important insights. First, universal testing and early treatment alone have important health and epidemiologic benefits, but we estimate that they provide about half of the benefits of a comprehensive scale-up strategy that also includes improved linkage to care and prevention of loss to follow-up. Second, the mortality and transmission benefits of scaling up HIV testing and treatment have implications for population growth. Finally, we estimate that even under a strategy of Comprehensive HIV care it will take longer than a decade to substantially reduce the burden of South Africa’s widespread epidemic.

Our results support the notion that universal testing and treatment have significant mortality benefits in South Africa. Recent estimates from South Africa suggest that ART may prolong life expectancy of infected individuals by 12.5 years.28 We estimate that a Comprehensive HIV testing, treatment, and care strategy will increase the average life expectancy of the entire population by 22 months compared to the Status Quo. Between 1990 and 2006, life expectancy in South Africa declined by about 12 years. Scaling up HIV testing and treatment may go a significant way towards reversing that trend.29 While a Comprehensive plan with perfect linkage and full retention in care is not realistic, it provides an important bound for the possible benefits.

However, over a decade, the benefits of universal testing and treatment alone are much lower than the benefits of universal testing and treatment with improved linkage to care and prevention of loss to follow-up. This underscores the role of increasing the number of people who initiate treatment early: each individual who starts treatment early decreases the number of downstream infections by more than one. Insights from mathematical epidemiology suggest that, in the absence of ART, each individual infected with HIV transmits the infection to more than one person, on average, over his or her lifetime. Thus, the benefits of long-term effective ART are multiplied, and so are the losses from having individuals forego ART because of poor linkage to care or loss to follow-up.

Despite all these benefits, we find that even under the Comprehensive strategy of HIV care, the burden of disease over the next decade is expected to remain substantial. Some researchers suggest that it would take as long as 50 years to reduce HIV prevalence in South Africa to below 1%.10 Our estimates, which include a detailed microsimulation of HIV disease and treatment, demographic changes, and multiple transmission risk factors, agree with these estimates: we show a nearly linear decrease in prevalence of 4.2% over a decade, suggesting that it would take more than four decades at the estimated rate of decline to decrease prevalence to around 1%.

Our estimates of benefits rely on several important assumptions. Most importantly, we assumed that HIV transmission risk is reduced for individuals on ART. While much evidence supports this phenomenon, it has not been verified in a major clinical trial to date.30–31 We made several assumptions about the benefits of treatment in South Africa which affect our estimates of the longevity benefits of ART, but do not change the comparative effectiveness of the strategies we examined. We also assumed no behavioral risk modification with decreasing disease burden: for example, as HIV mortality and prevalence drop, individuals may perceive the disease as less threatening and increase risk behaviors such as multiple concurrent partnerships.32 However, we had no basis for assuming the type or extent of behavior risk modification. Finally, we assumed that the fertility rate will remain stable (i.e., the number of children per woman will not change over the next decade). A decrease in the fertility rate may slow the decline in prevalence, as the growth in population size will slow down while the number of infected individuals may not change appreciably.

Our analysis uses a detailed epidemiologic simulation model to estimate the mortality and transmission benefits of HIV testing, treatment, and care in South Africa, and quantifies the comparative effectiveness of alternative strategies for universal testing and treatment. We find that scaling up all aspects of HIV care nearly doubles the benefits of universal testing and treatment alone. An economic and operational evaluation of these strategies would further help in clarifying priorities.

Supplementary Material

Supplement

Acknowledgments

This research is supported in part by the National Institute of Allergy and Infectious Diseases (K01-AI084582), the Department of Veterans Affairs, and the National Institute on Drug Abuse (R01 DA15612). The funding agencies had no part in the design and conduct of the study; collection, management, analysis, and interpretation of the data; nor in preparation, review, or approval of the manuscript.

Footnotes

Author Contribution and Conflicts of Interest (Author Form Submitted Separately)

Eran Bendavid:

I participated in originating the concept, in conducting the data collection, construction of the model, and data analysis. I did most of the writing of the paper, including the final revision. I had full access to all of the data in the study and I take responsibility for the integrity of the data and the accuracy of the data analysis. I have no conflicts of interest.

Margaret L. Brandeau:

I participated in elucidating the project’s concept, in conceptualizing the model structure, and revising the manuscript. I have seen and approved the final version and I have no conflicts of interest.

Robin Wood:

I participated in defining the project’s concept, in clarifying issues relating to HIV testing and treatment in South Africa, and in revising the manuscript. I have seen and approved the final version and I have no conflicts of interest.

Douglas K. Owens:

I participated in defining the original concept, in conceptualizing the model, in advising on issues related to outcomes analysis, and in writing and revising the manuscript. I have seen and approved the final version, and I have no conflicts of interest.

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