Kimberly Nucifora

Influence of Alternative Thresholds for Initiating HIV Treatment on Quality-Adjusted Life Expectancy: A Decision Model

Context Trials have not answered questions about optimal timing of HIV therapy, and many benefits and harms of therapy occur over time horizons that are longer than the trials. Early treatment may postpone AIDS, but it may also increase resistance and unnecessarily subject patients to the costs and toxicity of drugs. Contribution This computer simulation estimated that starting treatment earlier (CD4 count threshold of 500 cells/mm3) would provide greater unadjusted and quality-adjusted life expectancy than starting it later (at a CD4 count of 350 or 200 cells/mm3) for all patients age 30 years and for patients age 40 years who have a viral load greater than 30000 copies/mL. Caution Increases in life expectancy were small, and the analysis did not consider costs. The Editors The optimal timing for initiating HIV treatment has long been controversial. Soon after combination antiretroviral therapies became available, the prevailing treatment ethos was hit early, hit hard, and patients often began receiving therapy immediately after HIV infection was diagnosed (1). However, as it became clear that toxicity and side effects from combination antiretroviral therapy were substantial (25) and that early treatment may hasten genotypic resistance (68), treatment guidelines increasingly have favored postponing therapy initiation (9, 10). Yet, postponing therapy has disadvantages because it exposes HIV-infected patients to a greater risk for AIDS than is otherwise necessary (915) and may impair immune reconstitution (16). Guidelines once again may be in flux, trending toward earlier treatment initiation (17). The controversy about when to initiate HIV treatment has persisted because it has been difficult to systematically and quantitatively weigh the benefits and harms of earlier treatment (17). Furthermore, because benefits and harms may be delayed (for example, the accrual of genotypic resistance) (8), they must be estimated over time horizons that exceed usual follow-up periods for clinical trials. Mathematical models offer the possibility of quantitatively weighing harms and benefits over long time horizons, and therefore have theoretical promise for informing this question. However, published mathematical models addressing this question have not considered toxicity, side effects, and accrual of resistance mutations, the primary risks of earlier treatment initiation (1821). Furthermore, they have not considered how the balance of risks to benefits may vary with advancing age because of changes in antiretroviral toxicity or immune reconstitution. For these reasons, we incorporated toxicity and side effects of combination antiretroviral therapy into our previously validated HIV computer simulation, which considers resistance mutations and their likelihood of causing premature antiretroviral failure (2224). We used the simulation to evaluate how these tradeoffs influence the optimal time to initiate therapy. Methods We first describe how we quantified the potentially harmful consequences of combination antiretroviral therapy and then discuss how we incorporated these estimates into our HIV simulation. Estimating the Harmful Consequences of Combination Antiretroviral Therapy We partitioned the harmful effect of combination antiretroviral therapy into 2 quantifiable categories: 1) a harmful effect on quantity of life due to toxicity and amplified risks for common comorbid events (for example, myocardial infarction) (25), manifested by higher nonHIV-related mortality and 2) a harmful effect on quality of life due to side effects, manifested by decreased scores on a preference-based quality-of-life measure (25). Estimating the Effect of Combination Antiretroviral Therapy on NonHIV-Related Mortality It is not possible to directly estimate the effect of combination antiretroviral therapy on nonHIV-related mortality because there has been no randomized, controlled trial with requisite statistical power. Large observational cohorts of HIV-infected patients have ample statistical power to address this question by comparing nonHIV-related mortality among individuals receiving with those not receiving combination antiretroviral therapy. However, confounding by treatment assignment may bias these results substantially, and the direction of this bias may be unclear. We therefore conducted our own analysis of mortality among all HIV-infected individuals receiving care within the Veterans Health Administration nationwide, together with a sample of uninfected Veterans Health Administration controls, matched 2:1 by age, race, and site. This virtual cohort includes inpatients as well as outpatients and patients receiving subspecialty care as well as those receiving primary care. It was created by integrating data from pharmacy, laboratory, and administrative sources from 1997 through 2004. The creation of this virtual cohort is described in detail elsewhere (26) and has been shown to identify HIV-infected veterans with a sensitivity of 90% and a specificity of 99.9% (26). We limited our analysis to HIV-infected individuals who were receiving combination antiretroviral therapy (3 drugs) and were at exceptionally low risk for HIV-related death. We then compared the mortality of HIV-infected patients who met these inclusion criteria with that of their matched uninfected controls. Our rationale in making this comparison was that excess mortality observed in the HIV-infected group could be viewed as an upper limit for the nonHIV-related mortality attributable to combination antiretroviral therapy because it would reflect nonHIV-related mortality as well as other factors (for example, residual mortality attributable to HIV). We decided not to analyze only HIV-infected patients because we did not think that we could fully control for intention-to-treat bias and because we would not know the direction of this bias (that is, we would not know that the result was accurate or that it bounded the true value in a particular direction). We selected individuals at low risk for death related to HIV by specifying a minimum threshold for their time-updated CD4 count (that is, individuals could contribute a particular interval of observation to the analysis only if they remained above the CD4 count threshold during that interval). We used a threshold of 500 cells/mm3 because it is uncommon for individuals with higher CD4 counts to die of an AIDS-related cause (27). We also explored alternative analyses using thresholds based on minimum CD4 count rather than time-varying CD4 count (9), but this did not affect our results substantially. We performed Cox proportional hazards models to compare the mortality of the HIV-infected patients with that of their matched uninfected controls, including the covariates age, sex, race, site of care, and presence or absence of the most common serious comorbid conditions in this sample (coronary artery disease, congestive heart failure, diabetes mellitus, hepatitis C, pancreatitis, peripheral vascular disease, pulmonary disease, stroke, pneumonia, and non-AIDS cancer). Diagnoses were identified by using a previously validated algorithm based on International Classification of Diseases, Ninth Revision, administrative codes in the inpatient and outpatient settings (26). To avoid overadjusting for comorbid conditions, we required comorbidity diagnoses to have been made before combination antiretroviral therapy was initiated. We did not stratify combination antiretroviral therapy by type (for example, regimens with protease inhibitors versus regimens with nonnucleoside reverse transcriptase inhibitors) because this would have greatly limited our statistical power. Because there appeared to be a substantially higher mortality hazard in the short term after the start of combination antiretroviral therapy (during the first 3 months) than over the long term, we considered this short-term hazard separately. We judged this high short-term hazard as probably due to confounding (that is, attributable to an acute clinical syndrome that may have prompted the initiation of care) rather than to combination antiretroviral therapy toxicity, and therefore we did not incorporate it into the simulations base-case analysis. However, we also explored alternative analyses in which we did attribute this high short-term hazard to toxicity, and this did not affect our results substantially. Among 33420 HIV-infected patients in the virtual cohort, 9633 (29%) had infrequent or missing CD4 counts (>1 year between successive CD4 measurements), and an additional 18045 did not meet inclusion criteria (no combination antiretroviral therapy, or CD4 count always <500 cells/mm3), leaving 5742 HIV-infected patients along with 11484 controls (17226 patients in total) in our analysis. The mean age of patients and controls was 45.7 years, 98.0% were men, and 68.9% were nonwhite. We found that combination antiretroviral therapy was associated with a 3.4-fold increased long-term hazard of nonHIV-related mortality in unadjusted analyses (95% CI, 2.8-fold to 4.1-fold) and a 3.8-fold increased hazard in adjusted analyses (CI, 3.1-fold to 4.6-fold) (Table 1). Because this association is likely to encompass factors other than toxicity of combination antiretroviral therapy, it is an upper-bound estimatethat is, the true magnitude of the effect of combination antiretroviral therapy is likely to be lower. We included this estimate in the simulation model with the understanding that it biases the model in a known, conservative direction (against showing a benefit from earlier treatment initiation). Table 1. Analysis to Estimate Upper Bound for Mortality from Therapy-Related Toxicity: Predictors of Mortality among HIV-Infected Veterans and Matched Uninfected Controls Nationwide in the Veterans Affairs Health System* Estimating the Effect of Combination Antiretroviral Therapy on Quality of Life We estimated the effect of combination antiretroviral therapy on quality of life on

Averting HIV Infections in New York City: A Modeling Approach Estimating the Future Impact of Additional Behavioral and Biomedical HIV Prevention Strategies

Background New York City (NYC) remains an epicenter of the HIV epidemic in the United States. Given the variety of evidence-based HIV prevention strategies available and the significant resources required to implement each of them, comparative studies are needed to identify how to maximize the number of HIV cases prevented most economically. Methods A new model of HIV disease transmission was developed integrating information from a previously validated micro-simulation HIV disease progression model. Specification and parameterization of the model and its inputs, including the intervention portfolio, intervention effects and costs were conducted through a collaborative process between the academic modeling team and the NYC Department of Health and Mental Hygiene. The model projects the impact of different prevention strategies, or portfolios of prevention strategies, on the HIV epidemic in NYC. Results Ten unique interventions were able to provide a prevention benefit at an annual program cost of less than

Cost-effectiveness of different strategies to monitor adults on antiretroviral treatment: a combined analysis of three mathematical models

360,000, the threshold for consideration as a cost-saving intervention (because of offsets by future HIV treatment costs averted). An optimized portfolio of these specific interventions could result in up to a 34% reduction in new HIV infections over the next 20 years. The cost-per-infection averted of the portfolio was estimated to be

Alternative antiretroviral monitoring strategies for HIV-infected patients in east Africa: opportunities to save more lives?

106,378; the total cost was in excess of

Evaluating the impact of prioritization of antiretroviral pre-exposure prophylaxis in New York.

2 billion (over the 20 year period, or approximately

Impact of Interventions Targeting Unhealthy Alcohol Use in Kenya on HIV Transmission and AIDS‐Related Deaths

100 million per year, on average). The cost-savings of prevented infections was estimated at more than

Do Benefits of Earlier Antiretroviral Treatment Initiation Outweigh Harms for Individuals at Risk for Poor Adherence

5 billion (or approximately

How do different eligibility guidelines for antiretroviral therapy affect the cost-effectiveness of routine viral load testing in sub-Saharan Africa?

250 million per year, on average). Conclusions Optimal implementation of a portfolio of evidence-based interventions can have a substantial, favorable impact on the ongoing HIV epidemic in NYC and provide future cost-saving despite significant initial costs.

Can Broader Diffusion of Value-Based Insurance Design Increase Benefits from US Health Care without Increasing Costs? Evidence from a Computer Simulation Model

BACKGROUND WHOs 2013 revisions to its Consolidated Guidelines on antiretroviral drugs recommend routine viral load monitoring, rather than clinical or immunological monitoring, as the preferred monitoring approach on the basis of clinical evidence. However, HIV programmes in resource-limited settings require guidance on the most cost-effective use of resources in view of other competing priorities such as expansion of antiretroviral therapy coverage. We assessed the cost-effectiveness of alternative patient monitoring strategies. METHODS We evaluated a range of monitoring strategies, including clinical, CD4 cell count, and viral load monitoring, alone and together, at different frequencies and with different criteria for switching to second-line therapies. We used three independently constructed and validated models simultaneously. We estimated costs on the basis of resource use projected in the models and associated unit costs; we quantified impact as disability-adjusted life years (DALYs) averted. We compared alternatives using incremental cost-effectiveness analysis. FINDINGS All models show that clinical monitoring delivers significant benefit compared with a hypothetical baseline scenario with no monitoring or switching. Regular CD4 cell count monitoring confers a benefit over clinical monitoring alone, at an incremental cost that makes it affordable in more settings than viral load monitoring, which is currently more expensive. Viral load monitoring without CD4 cell count every 6-12 months provides the greatest reductions in morbidity and mortality, but incurs a high cost per DALY averted, resulting in lost opportunities to generate health gains if implemented instead of increasing antiretroviral therapy coverage or expanding antiretroviral therapy eligibility. INTERPRETATION The priority for HIV programmes should be to expand antiretroviral therapy coverage, firstly at CD4 cell count lower than 350 cells per μL, and then at a CD4 cell count lower than 500 cells per μL, using lower-cost clinical or CD4 monitoring. At current costs, viral load monitoring should be considered only after high antiretroviral therapy coverage has been achieved. Point-of-care technologies and other factors reducing costs might make viral load monitoring more affordable in future. FUNDING Bill & Melinda Gates Foundation, WHO.

Using mechanistic models to simulate comparative effectiveness trials of therapy and to estimate long-term outcomes in HIV care.

BackgroundUpdated World Health Organization guidelines have amplified debate about how resource constraints should impact monitoring strategies for HIV-infected persons on combination antiretroviral therapy (cART). We estimated the incremental benefit and cost effectiveness of alternative monitoring strategies for east Africans with known HIV infection.MethodsUsing a validated HIV computer simulation based on resource-limited data (USAID and AMPATH) and circumstances (east Africa), we compared alternative monitoring strategies for HIV-infected persons newly started on cART. We evaluated clinical, immunologic and virologic monitoring strategies, including combinations and conditional logic (e.g., only perform virologic testing if immunologic testing is positive). We calculated incremental cost-effectiveness ratios (ICER) in units of cost per quality-adjusted life year (QALY), using a societal perspective and a lifetime horizon. Costs were measured in 2008 US dollars, and costs and benefits were discounted at 3%. We compared the ICER of monitoring strategies with those of other resource-constrained decisions, in particular earlier cART initiation (at CD4 counts of 350 cells/mm3 rather than 200 cells/mm3).ResultsMonitoring strategies employing routine CD4 testing without virologic testing never maximized health benefits, regardless of budget or societal willingness to pay for additional health benefits. Monitoring strategies employing virologic testing conditional upon particular CD4 results delivered the most benefit at willingness-to-pay levels similar to the cost of earlier cART initiation (approximately