Barriers to and facilitators of populational adherence to prevention and control measures of COVID-19 and other respiratory infectious diseases: a qualitative evidence synthesis.

AIMS: To summarise the evidence on barriers to and facilitators of population adherence to prevention and control measures for coronavirus disease 2019 (COVID-19) and other respiratory infectious diseases. METHODS: A qualitative synthesis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis and the Cochrane Effective Practice and Organization of Care: Qualitative Evidence Synthesis. We performed an electronic search on MEDLINE, Embase and PsycINFO from their inception to March 2023. RESULTS: We included 71 studies regarding COVID-19, pneumonia, tuberculosis, influenza, pertussis and H1N1, representing 5966 participants. The measures reported were vaccinations, physical distancing, stay-at-home policy, quarantine, self-isolation, facemasks, hand hygiene, contact investigation, lockdown, infection prevention and control guidelines, and treatment. Tuberculosis-related measures were access to care, diagnosis and treatment completion. Analysis of the included studies yielded 37 barriers and 23 facilitators. CONCLUSIONS: This review suggests that financial and social support, assertive communication, trust in political authorities and greater regulation of social media enhance adherence to prevention and control measures for COVID-19 and infectious respiratory diseases. Designing and implementing effective educational public health interventions targeting the findings of barriers and facilitators highlighted in this review are key to reducing the impact of infectious respiratory diseases at the population level.

Image dataset of urine test results on petri dishes for deep learning classification.

Recent advancements in image analysis and interpretation technologies using computer vision techniques have shown potential for novel applications in clinical microbiology laboratories to support task automation aiming for faster and more reliable diagnostics. Deep learning models can be a valuable tool in the screening process, helping technicians spend less time classifying no-growth results and quickly separating the categories of tests that deserve further analysis. In this context, creating datasets with correctly classified images is fundamental for developing and improving such models. Therefore, a dataset of urine test Petri dishes images was collected following a standardized process, with controlled conditions of positioning and lighting. Image acquisition was conducted by applying a hardware chamber equipped with a led lightning source and a smartphone camera with 12 MP resolution. A software application was developed to support image classification and handling. Experienced microbiologists classified the images according to the positive, negative, and uncertain test results. The resulting dataset contains a total of 1500 images and can support the development of deep learning algorithms to classify urine exams according to their microbial growth.