We used descriptive statistics to summarize participant characteristics at study enrolment. Continuous variables were described using medians and interquartile ranges (IQR) where appropriate. Categorical variables were described using frequencies and percentages. We accounted for missing data by including a “missing” category where more than 5% of the data were missing. Modified Poisson regression with robust standard errors was used to evaluate associations between baseline characteristics and the primary motivator for HIV testing. The Poisson model estimate adjusted prevalence ratio recommended for cross-sectional studies assessing binary outcomes with a prevalence greater than 10%.[26,27] Factors identified with a univariate P value <.1 and a priori variables of importance such as sex and age were included in the adjusted model. Adjusted prevalence ratios (aPRs) with 95% confidence intervals (CIs) are presented.

Data analysis was conducted using STATA version 14 (StataCorp, College Station, TX).

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