During public health crises, people living with HIV (PLWH) may become disengaged from care. The goal of this study was to understand the impact of the COVID-19 pandemic and recent flooding disasters on HIV care delivery in western Kenya. We conducted ten individual in-depth interviews with HIV providers across four health facilities. We used an iterative and integrated inductive and deductive data analysis approach to generate four themes. First, increased structural interruptions created exacerbating strain on health facilities. Second, there was increased physical and psychosocial burnout among providers. Third, patient uptake of services along the HIV continuum decreased, particularly among vulnerable patients. Finally, existing community-based programs and teleconsultations could be adapted to provide differentiated HIV care. Community-centric care programs, with an emphasis on overcoming the social, economic, and structural barriers will be crucial to ensure optimal care and limit the impact of public health disruptions on HIV care globally.
Background: Participants of health research studies such as cancer screening trials usually have better health than the target population. Data-enabled recruitment strategies might be used to help minimise healthy volunteer effects on study power and improve equity. Methods: A computer algorithm was developed to help target trial invitations. It assumes participants are recruited from distinct sites (such as different physical locations or periods in time) that are served by clusters (such as general practitioners in England, or geographical areas), and the population may be split into defined groups (such as age and sex bands). The problem is to decide the number of people to invite from each group, such that all recruitment slots are filled, healthy volunteer effects are accounted for, and equity is achieved through representation in sufficient numbers of all major societal and ethnic groups. A linear programme was formulated for this problem. Results: The optimisation problem was solved dynamically for invitations to the NHS-Galleri trial (ISRCTN91431511). This multi-cancer screening trial aimed to recruit 140,000 participants from areas in England over 10 months. Public data sources were used for objective function weights, and constraints. Invitations were sent by sampling according to lists generated by the algorithm. To help achieve equity the algorithm tilts the invitation sampling distribution towards groups that are less likely to join. To mitigate healthy volunteer effects, it requires a minimum expected event rate of the primary outcome in the trial. Conclusion: Our invitation algorithm is a novel data-enabled approach to recruitment that is designed to address healthy volunteer effects and inequity in health research studies. It could be adapted for use in other trials or research studies.
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