Because the costs associated with EID in Africa are high relative to antibody-based strategies for HIV diagnosis ($1 to $2 per test [11
]), program planners and policy makers must evaluate whether the benefits of EID programs are 'worth' their greater costs. Cost-effectiveness analysis is a formal methodology used to address this question, incorporating not only costs, but also clinical outcomes, of alternative health interventions. By convention, both current and future costs and savings are included. Similarly, both short-term and long-term clinical benefits are quantified, most often in either years of life saved (YLS) or quality-adjusted years of life saved (QALY, which value each year lived in imperfect health less than each year in perfect health) [92
Using the cost and effectiveness outcomes for two alternative healthcare strategies, one can calculate an incremental cost-effectiveness ratio (ICER). The difference in costs between the competing strategies is the numerator, and the difference in effectiveness, or the incremental health benefit, comprises the denominator. ICERs are conventionally reported in $/YLS or $/QALY. Because these units are not specific to any single health condition, they can help inform decisions among a variety of health interventions for a given population. In addition, ICERs in $/QALY can be compared to international standards of cost effectiveness, such as the WHO-supported Commission on Macroeconomics and Health recommendation that an intervention with an ICER less than the per capita
GDP of a country be considered 'very cost effective' in that setting [93
]. It is important to note that cost-effectiveness results are not intended to be the sole factor in health-related decision-making; issues of fairness, feasibility, and affordability may be of equal or greater importance.
The calculation of health benefits in YLS requires detailed information regarding both short-term and long-term survival associated with key possible health outcomes from the program under evaluation. For example, in an EID program, one would need estimates of the life expectancy of: (1) an HIV-infected child diagnosed and linked to HIV care through an EID program, (2) an HIV-infected child diagnosed and linked to care through a comparator program (for example, antibody testing at 15-18 months of age), (3) an HIV-exposed uninfected child, and (4) an HIV-unexposed child. Such life expectancy estimates, which may require long-term clinical studies or detailed computer simulation models to determine, are rarely available for resource-limited settings [94
]. As a result, many cost-effectiveness analyses related to pediatric HIV prevention [96
] or diagnosis [66
] have used a cost-effectiveness outcome of 'cost/case of pediatric HIV prevented' or 'cost/case of pediatric HIV diagnosed'. Although there are no international cost-effectiveness standards denominated in these units, if several cost-effectiveness analyses use the same outcome measure, the 'cost/case diagnosed' of a newly examined testing strategy can be compared to the 'cost/case diagnosed' of strategies previously described or currently in use.
Only one published study has examined the cost effectiveness of EID programs in resource-limited settings: a population of known HIV-exposed children in Uganda [66
]. Using both a computer model and program data, the authors examined DBS-based strategies of DNA PCR for all infants ('PCR') and RHT followed by DNA PCR if RHT results were reactive ('RHT→PCR'). In all scenarios, RHT→PCR was less expensive; it was also less effective in most. Among known HIV-exposed infants in a 'poor compliance' scenario (with a 43% index of participation that is similar to many programs, Table [4
]), the ICER of the PCR algorithm compared to RHT→PCR was $539 per 'infant correctly diagnosed and informed of result'. If RHT→PCR were the current practice, adopting the PCR algorithm would be economically efficient if policymakers were willing to pay $539 to correctly diagnose one infant and inform him/her of test results. Because ICERs in such units are not directly comparable to other published cost-effectiveness outcomes, it remains challenging to answer the question, is the PCR algorithm cost effective in Uganda compared to the RHT→PCR algorithm? However, this detailed analysis demonstrates that the RHT→PCR algorithm may lead to nearly equivalent clinical outcomes and lower costs, and may become economically preferred in specific settings, for example where retention in care is high, breastfeeding is common, or HIV prevalence is low.