HIV testing criteria to reduce testing volume and increase positivity in Botswana.

Objective: We used data from a routine HIV testing program to develop risk scores to identify patients with undiagnosed HIV infection while reducing the number of total tests performed.
Design: Multivariate logistic regression.
Methods: We included demographic factors from HIV testing data collected in 134 Botswana Ministry of Health & Wellness facilities during two periods (1 October 2018- 19 August 2019 and 1 December 2019 to 30 March 2020). In period 2, the program collected additional demographic and risk factors. We randomly split each period into prediction/validation datasets and used multivariate logistic regression to identify factors associated with positivity; factors with adjusted odds ratios at least 1.5 were included in the risk score with weights equal to their coefficient. We applied a range of risk score cutoffs to validation datasets to determine tests averted, test positivity, positives missed, and costs averted.
Results: In period 1, three factors were significantly associated with HIV positivity (coefficients range 0.44-0.87). In period 2, 12 such factors were identified (coefficients range 0.44-1.37). In period 1, application of risk score cutoff at least 1.0 would result in 50% fewer tests performed and capture 61% of positives. In period 2, a cutoff at least 1.0 would result in 13% fewer tests and capture 96% of positives; a cutoff at least 2.0 would result in 40% fewer tests and capture 83% of positives. Costs averted ranged from 12.1 to 52.3%.
Conclusion: Botswana's testing program could decrease testing volume but may delay diagnosis of some positive patients. Whether this trade-off is worthwhile depends on operational considerations, impact of testing volume on program costs, and implications of delayed diagnoses.
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