Results for first-line treatments were robust to univariate sensitivity analyses except if it was assumed that ART has no side effects, leading to no reductions in quality of life, then the TLC strategy produces more QALYs than the CD4 strategy. This occurs because ART slows disease progression, but in this particular sensitivity analysis the downside of adverse drug effects from a premature ART start has been assumed away. Sensitivity tests to a discount rate of 6% did not alter the basic pattern of results shown in Fig. 1.
The cost effectiveness of second-line treatment was sensitive to the assumptions about the efficacy of both first- and second-line treatment. When first-line treatment was assumed to cause CD4 cell increments and viral load decrements that were half as large as baseline, this led to higher utilization of second-line treatment. In the baseline model, each patient could expect to spend 0.3, 0.4, 2.8, and 3.6 years on second-line medication per decade under ART ONLY, TLC, CD4, and VL algorithms respectively. When first-line treatment was assumed to be half as effective as baseline, the corresponding expected time spent on second-line treatment per patient was 0.3, 0.4, 4.8, and 6.7 years, respectively. In the CD4 algorithm, use of second-line treatment added only 0.15 additional discounted QALYs per person while costing an additional $1612 per person, yielding an ICER of $10 730 per discounted life year. If it was further assumed that second-line treatment is as effective as first line at improving CD4 cell counts, then the gain per patient rose to 0.24 life years, yielding an ICER of $7225 per discounted life year. This assumed a $900 annual cost of second-line drugs. The ICER declined linearly such that the ICER is $940 lower for every $100 reduction in the price of second-line drugs. For example, if the cost of second-line treatment declined to $400 per year, the ICER would be $3465 per discounted life year.
The estimated cost effectiveness of first-line treatment with no monitoring in our study at $626–630/QALY gained is close to a prior estimate of $547 per disability-adjusted life year averted , despite substantial differences in methodology. Another recent study estimated a cost of $620 per life year gained obtained from ART plus prophylaxis without CD4 cell count testing compared with prophylaxis alone in a model calibrated to data from a cohort in Côte d'Ivoire, where first-line treatment was only 51% effective at achieving virological suppression . For purposes of comparison, our model predicts a cost of $380 per discounted life year gained comparing the ART ONLY model with no second-line treatment with NO ART. Important differences are that in our model first-line treatment is 74% effective at achieving virological suppression at 1 year and our comparison group did not receive prophylaxis against opportunistic infections. The Côte d'Ivoire model suggested much higher benefits from introducing second-line therapy, with an average gain of 10 months of survival for every patient; this contrasts to 1.8 months per patient gained from second-line treatments in our model. This difference may be explained by both poorer effectiveness of first-line treatment in the Ivoirian model and the different assumptions about the efficiency with which failing patients are detected and switched to second-line treatment. Survival outcomes data emerging from current studies in low-resource countries should clarify the relative value of second-line treatment. Further tests of the validity of our model are discussed in detail in the technical appendix.
An important limitation of our model that is shared by some other recent models is the failure to consider disease transmission. ART could have opposing effects on transmission by lowering infectivity but prolonging the survival of potentially infective hosts. In addition, the quality of preventive counseling, laboratory monitoring, and consistency of patient adherence could alter the spread of resistance [14,15].
As expected, health improvements gained by introducing a basic ART package are large , outweighing the incremental benefits gained from refining the treatment process by introducing laboratory monitoring and second-line regimens. As shown in Table 3, introduction of a basic ART regimen for a population of 10 000 would save 6500 lives and stop the loss of 23 000 discounted QALYs over one decade. Each subsequent refinement to laboratory monitoring and to the availability of second-line drugs saves no more than 50–150 lives and saves only 250–850 QALY. However, these refinements do offer real benefits, and they are already being introduced progressively in resource-limited countries. A major potential benefit from laboratory monitoring and second-line regimens is improved capability to stem the emergence and spread of drug-resistant HIV strains in affected populations by rapidly identifying and treating patients with virological failure. Emerging data on the scope of this problem will likely indicate that control of drug resistance is essential if ART programs are to succeed.
In cost-effectiveness terms, many of the monitoring options offer similar ICER values to that obtained when first introducing ART. From the solid line in Fig. 1, one sees that ART ONLY, TLC, and CD4 strategies in the absence of second-line treatment are all of similar total cost ($15–35 million), but that of these three the CD4 strategy in the absence of second-line treatment registers the highest gain to health. By offering second-line treatment, CD4 and VL strategies in the model will magnify the health gains from this decision, but will increase costs from under $17 million to $25–32 million. The model predicts that without CD4 cell and viral load testing, the second-line treatments will seldom be used; in this case, neither their benefits to health nor their high costs would be realized. Finally, according to the model, if second-line treatments are not available, the costs of CD4 cell count tests would be offset by savings from fewer inappropriately treated patients. The cost offsets from CD4 cell count testing are notably larger in populations with less-advanced disease (see Technical appendix, Fig. 10a).
Ongoing cohort trials of ART will shed additional light on the effects and costs of various strategies. The sensitivity analyses in this model predict that the absolute costs and gains will not generalize across populations with different disease severity. However, our extensive sensitivity analysis predicts that CD4, TLC, and ART ONLY strategies will have relatively similar costs to each other in the absence of second-line treatment under a wide variety of assumptions and settings.
Changing prices of drugs and laboratory tests will alter the cost effectiveness of the strategies in the model, which can be updated easily to reflect these changes. In addition the model can be readily altered to accommodate populations with less advanced or more advanced disease.
Simulation models of HIV have been helpful in guiding policies and informing research strategies [17,18]. Ultimately, policies are directed by the objectives of the people affected and the decision-makers serving them. There are many considerations other than cost effectiveness that would bear upon the choice of an optimal strategy for scaling up an ART program in a resource-constrained setting. The existing infrastructure and availability of facilities, trained personnel, logistics, and availability of funds would be foremost considerations. Many other important consequences stem from these decisions that are not captured by the model. The occurrence and spread of resistant strains of virus would be influenced by the availability of second-line treatment.
It would be unusual if cost effectiveness became the only deciding factor in making health policy. If cost effectiveness were the primary concern, this model indicates that the ART ONLY program is sufficiently cost effective that it would be unwise to postpone ART scale-up until second-line drugs or better laboratory facilities became available. In the absence of second-line treatments, investments in CD4 cell count testing capability would offset their costs by restraining the use of therapy in patients who do not need it. In the presence of costly second-line treatments, CD4 cell count and viral load tests would enhance the appropriate selection of cases for second-line treatment, resulting in higher utilization of second-line treatment, more lives saved, and substantially higher costs unless the cost of second-line treatment falls dramatically. Ultimately, the choices between these options will rest with payers and policymakers.
DB and AC had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Helpful comments were received from John Bartlett, Richard Chaisson, Steven Chapman, Ricardo Diaz, Ronald Gray, Sabina Haberlen, Andrew Kambugu, Keith McAdam, Carolyn Mohan, David Thomas, Thomas Quinn, Steven Reynolds, Jeanne Brosnan and seminar participants at the Infectious Diseases Institute in Kampala and Rakai Health Sciences Program in Kalisizo, Uganda. We thank the MACS/WIHS study personnel and Lisa Jacobson for permitting us access to these data to compute the covariance structure of levels and changes in log viral load and the fourth root of CD4.
This study was conceived jointly by BD Biosciences and the investigators. BD Biosciences funded and participated in the study through a grant from Becton, Dickinson and Company through its BD Biosciences Segment to Johns Hopkins Bloomberg School of Public Health. David Bishai has been a consultant to BD Biosciences; David Durack is an employee of BD Biosciences.
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Keywords:© 2007 Lippincott Williams & Wilkins, Inc.
cost effectiveness; HIV; AIDS; CD4 lymphocytes; viral load; antiretroviral treatment; resource-limited settings; laboratory testing; developing countries