A growing proportion of AIDS cases involves late diagnosis of HIV infection. Persons who are unaware of their HIV infection cannot benefit from antiretroviral therapy and, hence, early diagnosis would strengthen the impact of such therapy and so reduce AIDS incidence.
The use of digital vending machines (VMs) to delivery HIV self-testing (HIVST) could expand HIV testing in priority populations. We surveyed primarily Black African (BA) participants and other minority ethnicities, to identify acceptability, preferences, and concerns of using VMs for HIVST dispensing. A structured survey was developed with Black African and Caribbean, Latin American and other Minorities (BLAM) communities, and distributed between September 2018 and January 2019. Participants were recruited using mobile tablet surveys distributed by outreach volunteers, and online through BLAM communities’ websites, workshops, and language-specific messages on social media. Descriptive analyses were undertaken stratified by ethnic groups. One hundred and twenty-eight (67.0%) participants identified as BAs, 31 (16.2%) Black Caribbeans (BCs), 22 (11.5%) Latin Americans (LAs), and 10 (5.2%) other non-white ethnicities (ONWEs). Rates of willingness to use the HIVST were high in all groups except BCs (BAs 77.9%, BCs 53.6%, LAs 81.8%, ONWEs 80.0%). Most participants favoured healthcare-associated venues for VM placement, but there were differences in community venues favoured by different ethnic groups and concerns reported. HIVST is acceptable in many BLAM communities and increases understanding of the concerns and how to address them in the design of VMs for HIVST, to expand HIV testing in these priority communities.
Quality indices in clustering are used not only to assess the quality of the partitions but also to determine the number of clusters in the final result. When these indices are evaluated in a case study, real data conditions or different clustering algorithms are seldom taken into account. Here, some of the standard indices used in the literature are compared using more realistic databases that include outliers or noisy dimensions, which is more like a real problem-solving approach. Besides, three different clustering methods are used in an attempt to identify different behaviours. Also, the performance of the quality index-clustering algorithm tandem is compared to random grouping, with the aim of running an additional check. The indices are ranked, and index-based conclusions are drawn for all the scenarios.
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