HIV testing criteria to reduce testing volume and increase positivity in Botswana. (1st October 2021)

• Record Type: Journal Article
• Authors: Rowlinson, Emily; Mawandia, Shreshth; Ledikwe, Jenny; Bakae, Odirile; Tau, Lenna; Grande, Matias; Seckel, Laura; Mogomotsi, Goabaone Pankie; Mmatli, Esther; Ngombo, Modise; Norman, Tebogo; Golden, Matthew R.
• Is Part Of: AIDS. Volume 35:Number 12(2021)
• Publication Date: 2021-10-01
• Subjects: Botswana -- HIV infections -- HIV seropositivity -- HIV testing -- risk factors; AIDS (Disease) -- Periodicals; Acquired Immunodeficiency Syndrome; AIDS (Disease); Periodicals; Periodicals; 616.9792005
• DOI: 10.1097/QAD.0000000000002997
• Languages: English
• ISSNs: 0269-9370

Abstract

Abstract : 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.