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Abstract
Minimum accuracy of HIV diagnostic tests is considered the pillar on which testing strategies for all settings must be based. Systematic reviews and meta-analyses have shown that performance of the same test in different settings may vary according to several factors, resulting in different confidence intervals for sensitivity and specificity. Prevalence of HIV infection may influence observed test accuracy. The purpose of this article is to use the knowledge from meta-analyses of general diagnostic tests to inform the specific field of HIV diagnostic strategies. We propose the ‘Bayesian’ thinking: considering the pretest probability (i.e., prevalence, risk factors) and understanding test limitations to estimate a post-test probability of HIV diagnosis. Cost–effectiveness analysis, patient preferences and ethical issues must also be considered in HIV testing strategies.
Financial & competing interests disclosure
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
No writing assistance was utilized in the production of this manuscript.
