Testing every 3 months rather than every 6 months was associated with modest increases in life expectancy and significant increases in lifetime costs. For equivalent strategies, testing every 3 months was associated with a life expectancy gain of 2 to 19 days. Monitoring of CD4 counts reduced net costs compared with symptom-based management because it reduced expensive hospitalizations for opportunistic infections.
In parts of southern Africa, hospitals are unavailable and medical care may be elementary. To assess the importance of inpatient care, we varied the cost of an inpatient day over the range reported for southern Africa. The cost-effectiveness of viral load monitoring was sensitive to the cost of testing and virologic failure Figure 3. In addition, high rates of virologic failure, which might occur when adherence is low or rates of resistance are high, increased the importance of viral load monitoring.
We evaluated changes in the rates of treatment discontinuation because of the cost of HAART, rates of CD4 counts change, rates of virologic suppression, and medication toxicities in sensitivity analyses. Within the range of variability we examined Table 1 , none of these sensitivity analyses changed our results substantively. We evaluated the relative merits of alternative HIV monitoring strategies in resource-limited settings using data from southern Africa. We found that CD4 count monitoring could substantially increase life expectancy and reduce total costs relative to the symptom-based approaches currently practiced in many regions, especially outside of major urban areas.
These gains in life expectancy are substantial. In South Africa, and perhaps in several other countries, much of this gain in life expectancy could be obtained while reducing total expenditures for HIV care by averting expensive hospitalizations because of opportunistic diseases, which outweighed the higher costs of HAART and CD4 testing.
The reduction in total costs is large in South Africa, in part because of the relatively high cost of inpatient care, and our analysis suggests that CD4 count monitoring may also reduce costs of HIV care in Botswana, Swaziland, and Namibia, where the quality of the epidemic is similar and inpatient care costs are relatively high. Our analysis highlights that the sizeable worldwide investments that would make HAART available could be strongly leveraged by using CD4 count monitoring to initiate treatment before onset of serious opportunistic diseases and severe immunocompromise.
Recent evidence shows that, in resource-limited settings, where HAART is commonly started at low CD4 counts or with opportunistic diseases, mortality after treatment initiation is much higher than in Europe and North America, especially in the first few months of treatment.
Addition of viral load monitoring resulted in an additional increase in life expectancy of 2 months relative to use of only CD4 count monitoring. Two months is an important additional benefit. However, this gain in effectiveness came at a less favorable incremental cost-effectiveness ratio than did CD4 count monitoring because viral load testing is substantially more expensive and provides about a fourth of the benefit of CD4 testing. If the price of viral load testing were substantially reduced, the cost-effectiveness would improve markedly.
In developed countries, where cost-effectiveness acceptability thresholds are substantially higher, viral load monitoring is considered a cost-effective intervention. Viral load monitoring has other benefits, such as reduced transmission by limiting the number of persons with nonsuppressed HIV replication, and fewer accumulated resistance mutations.
Because we did not include these potential benefits, we may have underestimated the overall benefits of viral load testing. Why has CD4 count monitoring not been universally adopted in resource-limited settings? The initial investment in CD4 technology and infrastructure is expensive. The cost of CD4 flow cytometers, which require highly trained personnel and laboratories with refrigeration, is high, and ministries of health and public health programs may be unable or unwilling to make the investment.
In addition, the cost of an individual CD4 test, while modest in comparison with the cost of HAART or viral load monitoring, may limit access to testing and treatment. These challenges are increasingly surmountable. Recent advances in CD4 enumeration technology enable lower per-test cost, as well as smaller machines that require relatively little infrastructure, maintenance, and technical expertise.
Both the reductions in technical challenges and our finding that CD4 count monitoring is cost-effective or cost saving support expanding CD4 count monitoring as a valuable tool in improving treatment in southern Africa. Use of CD4 count monitoring to determine treatment initiation and initiating HAART early will benefit a substantial percentage of individuals in whom treatment would be otherwise delayed until life-threatening symptoms develop.
Our analysis has several limitations. Although the phase and prevalence of the epidemic in South Africa is similar to that in other countries in the region, most of the data for our model are from a single region. Some opportunistic diseases, most notably tuberculosis, place a unique burden on that region and may limit the generalizability of our results.
In addition, although our estimates of the health benefits of alternative management strategies are likely applicable more broadly in Africa, the study cohorts in Cape Town received care in a setting with potential access to clinics and hospitals. In settings in which individuals with opportunistic diseases have no access to hospitals, mortality will be higher and the cost of care will be lower than we projected.
In those settings, more efforts to prevent severe opportunistic diseases may have additional mortality benefits. We also used some data from clinical trials. While clinical trials may provide the best or only source of data, events such as treatment failure and response to HAART may differ in other settings. In addition, we used a societal perspective for this analysis, in which all costs and benefits are included. However, additional perspectives may be relevant to parts of southern Africa where costs and benefits are accrued by different parts of the health care system.
For example, the perspective of a donor organization that bears costs but realizes no direct benefits may be important where donors have an important role in the health care system. Our model is not intended to restrict the use of viral load monitoring in southern Africa. Rather, we highlight the importance of CD4 count monitoring and early treatment initiation as the priority in improving health care in southern Africa.
The rapid increase in access to treatment in resource-limited regions represents major progress toward reducing HIV-related morbidity and mortality. Our analysis shows that, where HAART is available, CD4 count monitoring with earlier treatment initiation provides a substantial increase in life expectancy, which in some settings may be achievable while reducing total expenditures for HIV infection.
As the number of persons receiving HAART increases, the potential health benefit and cost savings from use of CD4 monitoring will also increase. Author Contributions: Dr Bendavid 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.
Study concept and design : Bendavid, Young, Katzenstein, and Owens.
Cost-Effectiveness of HIV Prevention in Developing Countries
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Supplement Article Open Access. Mary M. Anthony T. Dedication We would like to dedicate this paper to the late Professor Gavin Mooney whose many years of work on priority setting helped pave the way for this review. Correction has been added on 12 February , following initial online publication 25 January Tools Request permission Export citation Add to favorites Track citation. Share Give access Share full text access. Share full text access. Please review our Terms and Conditions of Use and check box below to share full-text version of article.
Methods and data sources Interventions Type of health sector interventions to be prioritised. Criteria Stated criteria upon which priorities were set. Includes ratio used e. Appraisal Was the perspective of the economic analysis specified? Perspective or viewpoint of the economic analysis. Includes society or provider. Identification and testing of uncertain parameters associated with costs and consequences.
Recognition of an explicit budget constraint. Decommissioning, disinvesting or redeploying resources from currently funded interventions. Figure 1 Open in figure viewer PowerPoint. Selection of studies flow chart. PS, priority setting; CE, cost effectiveness. Child health, reproductive health, and communicable diseases b b These are the final set of interventions identified using MCDA.
National Ghana 12 Laxminarayan et al. India or Nepal. Mental health National Nigeria. AIDS control including the provision of antiretroviral therapy. Colorectal cancer control — increasing the coverage of treatment interventions. Ranking interventions based on MCDA Childhood interventions, most interventions targeting communicable diseases and two reproductive health interventions supervised deliveries and emergency obstetric care.
See full article for details. Combined intervention strategy that simultaneously enforces multiple road safety laws e. For depression, epilepsy, and alcohol use disorders: older antidepressants, with or without proactive case management in primary care, older anticonvulsants in primary care, and random breath testing for motor vehicle drivers.
Article Author s Was the perspective of the economic analysis specified? Was allowance made for uncertainty in the estimates of costs and consequences? Was affordability assessed? Did the exercise investigate disinvestment as well as investment? Was the study embedded in the local policy and planning context? Y Y 5 Baltussen, Y provider N Y — budget impact analysis d d Budget impact was one of the criteria for priority setting.
Budget impact was not measured. N N 6 Baltussen et al.