Statistical analysis
REGRESSION
To quantify the effect of the intervention on sexually transmissible HIV, we will fit a logistic regression model to data from all three survey waves to estimate the odds ratio (OR) and 95% confidence interval (CI), adjusting for design factors and survey wave, acknowledging the clustering of the data through generalized estimating equations. To increase power and also handle any chance imbalance in the distribution of HIV risk factors between exposures, we will also adjust for pre-specified predictors known to be associated with HIV transmission risk in this population at cluster and individual level.To measure the uptake of the universal risk-informed HIV prevention intervention we will use the survey data collected at the end of intervention exposure periods, among survey participants who consent to linkage of their survey data with our programmatic and clinical data. We will calculate the proportion and 95% CI (acknowledging clustering) of participants who are aware of their HIV status and are either on treatment if living with HIV or have taken up PrEP if HIV negative and eligible. As a sensitivity analysis, we will also compare uptake of PrEP based on our programmatic data, AHRI’s Clinic Link system, and routine electronic health records (e.g. TIER.net), and compare this with what we find in the cross-sectional surveys.To examine the cost of transmissible HIV averted, we will measure the costs in the intervention and SOC periods including HIV screening, risk assessment, PrEP initiation and adherence on PrEP. This analysis will take the cost perspective of the provider (i.e., Department of Health, implementing partners and other agencies) and include the cost of any resources used to provide services to the client irrespective of the payer. We will use different costing methods to estimate the costs of: a) services provided prior to PrEP initiation, b) PrEP provision and c) ancillary services provided alongside PrEP provision. A top down costing approach will be used to fully capture the costs which occur prior to PrEP initiation and at an above-site level, as provision of these services is not always recorded at the level of the participant. A bottom-up micro-costing of the resources used to provide PrEP and ancilliary services that are captured in the routine medical records of the participants (e.g., PrEP and other drugs dispensed, clinic visits, pharmacy, laboratory tests performed, counselling visits) will be conducted. The total and average costs of achieving each endpoint (i.e. the cost per transmissible HIV case averted, the cost per case linked to risk differentiated biosocial HIV prevention and the cost per case linked to PrEP) for each period will be calculated and an incremental cost-effectiveness ratio determined.We will use mixed methods to explore acceptability and equity of reach; fidelity and facets of the packages that are valued and by whom (including key groups that are harder to reach e.g. mobile youth, young women who sell sex and men who have sex with men); and any unintended social harm [A significance level of 0.05 will be used for all inferential analyses unless otherwise stated; a correction for multiplicity will not be applied because the three outcomes reflect different trial domains (effectiveness, process and cost). A detailed statistical analysis plan will be completed before the end of data collection.