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Abstract Background Female sex workers (FSW) in sub-Saharan Africa are disproportionately affected by HIV and are critical to engage in HIV prevention, testing and care services. We describe the design of our evaluation of the ‘AMETHIST’ intervention, nested within a nationally-scaled programme for FSW in Zimbabwe. We hypothesise that the implementation of this intervention will result in a reduction in the risk of HIV transmission within sex work. Methods The AMETHIST intervention (Adapted Microplanning to Eliminate Transmission of HIV in Sex Transactions) is a risk-differentiated intervention for FSW, centred around the implementation of microplanning and self-help groups. It is designed to support uptake of, and adherence to, HIV prevention, testing and treatment behaviours among FSW. Twenty-two towns in Zimbabwe were randomised to receive either the Sisters programme (usual care) or the Sisters programme plus AMETHIST. The composite primary outcome is defined as the proportion of all FSW who are at risk of either HIV acquisition (HIV-negative and not fully protected by prevention interventions) or of HIV transmission (HIV-positive, not virally suppressed and not practicing consistent condom use). The outcome will be assessed after 2 years of intervention delivery in a respondent-driven sampling survey (total n = 4400; n = 200 FSW recruited at each site). Primary analysis will use the ‘RDS-II’ method to estimate cluster summaries and will adapt Hayes and Moulton’s ‘2-step’ method produce adjusted effect estimates. An in-depth process evaluation guided by our project trajectory will be undertaken. Discussion Innovative pragmatic trials are needed to generate evidence on effectiveness of combination interventions in HIV prevention and treatment in different contexts. We describe the design and analysis of such a study. Trial registration Pan African Clinical Trials Registry PACTR202007818077777 . Registered on 2 July 2020.

Abstract Background Record linkage is the process of identifying and combining records about the same individual from two or more different datasets. While there are many open source and commercial data linkage tools, the volume and complexity of currently available datasets for linkage pose a huge challenge; hence, designing an efficient linkage tool with reasonable accuracy and scalability is required. Methods We developed CIDACS-RL (Centre for Data and Knowledge Integration for Health – Record Linkage), a novel iterative deterministic record linkage algorithm based on a combination of indexing search and scoring algorithms (provided by Apache Lucene). We described how the algorithm works and compared its performance with four open source linkage tools (AtyImo, Febrl, FRIL and RecLink) in terms of sensitivity and positive predictive value using gold standard dataset. We also evaluated its accuracy and scalability using a case-study and its scalability and execution time using a simulated cohort in serial (single core) and multi-core (eight core) computation settings. Results Overall, CIDACS-RL algorithm had a superior performance: positive predictive value (99.93% versus AtyImo 99.30%, RecLink 99.5%, Febrl 98.86%, and FRIL 96.17%) and sensitivity (99.87% versus AtyImo 98.91%, RecLink 73.75%, Febrl 90.58%, and FRIL 74.66%). In the case study, using a ROC curve to choose the most appropriate cut-off value (0.896), the obtained metrics were: sensitivity = 92.5% (95% CI 92.07–92.99), specificity = 93.5% (95% CI 93.08–93.8) and area under the curve (AUC) = 97% (95% CI 96.97–97.35). The multi-core computation was about four times faster (150 seconds) than the serial setting (550 seconds) when using a dataset of 20 million records. Conclusion CIDACS-RL algorithm is an innovative linkage tool for huge datasets, with higher accuracy, improved scalability, and substantially shorter execution time compared to other existing linkage tools. In addition, CIDACS-RL can be deployed on standard computers without the need for high-speed processors and distributed infrastructures.