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This article presents the findings of a study conducted in Singapore that examined the use of oral antivirals (OAVs) among older adults with COVID-19 in primary care during the Omicron waves. The study revealed that a small percentage of older adults received OAVs, with nirmatrelvir-ritonavir being the most commonly prescribed medication. The study also identified factors such as primary care characteristics and socioeconomic status (SES) indicators that influenced the likelihood of receiving OAVs. The authors emphasize the importance of addressing disparities in OAV access among older adults and socioeconomically disadvantaged groups. They recommend targeted efforts to improve access for individuals with lower SES. [Extracted from the article]

Background: The COVID-19 pandemic has changed the epidemiology of upper respiratory tract infections (URTI) and the disease profile of patients attending the emergency department (ED). Hence, we sought to explore the changes in ED physicians' attitudes and behaviours in four EDs in Singapore. Methods: We employed a sequential mixed-methods approach (quantitative survey followed by in-depth interviews). Principal component analysis was performed to derive latent factors, followed by multivariable logistic regression to explore the independent factors associated with high antibiotic prescribing. Interviews were analysed using the deductive-inductive-deductive framework. We derive five meta-inferences by integrating the quantitative and qualitative findings with an explanatory bidirectional framework. Results: We obtained 560 (65.9%) valid responses from the survey and interviewed 50 physicians from various work experiences. ED physicians were twice as likely to report high antibiotic prescribing rates pre-COVID-19 pandemic than during the pandemic (AOR = 2.12, 95% CI 1.32 to 3.41, p = 0.002). Five meta-inferences were made by integrating the data: (1) Less pressure to prescribe antibiotics due to reduced patient demand and more patient education opportunities; (2) A higher proportion of ED physicians self-reported lower antibiotic prescribing rates during the COVID-19 pandemic but their perception of the overall outlook on antibiotic prescribing rates varied; (3) Physicians who were high antibiotic prescribers during the COVID-19 pandemic made less effort for prudent antibiotic prescribing as they were less concerned about antimicrobial resistance; (4) the COVID-19 pandemic did not change the factors that lowered the threshold for antibiotic prescribing; (5) the COVID-19 pandemic did not change the perception that the public's knowledge of antibiotics is poor. Conclusions: Self-reported antibiotic prescribing rates decreased in the ED during the COVID-19 pandemic due to less pressure to prescribe antibiotics. The lessons and experiences learnt from the COVID-19 pandemic can be incorporated into public and medical education in the war against antimicrobial resistance going forward. Antibiotic use should also be monitored post-pandemic to assess if the changes are sustained. [ABSTRACT FROM AUTHOR]

Objectives: Pre-COVID-19 pandemic, patients who attended the emergency department (ED) for upper respiratory tract infection (URTI) were more likely to receive antibiotics if they expected them. These expectations could have changed with the change in health-seeking behaviour during the pandemic. We assessed the factors associated with antibiotics expectation and receipt for uncomplicated URTI patients in four Singapore EDs during the COVID-19 pandemic., Methods: We conducted a cross-sectional study on adult patients with URTI from March 2021 to March 2022 in four Singapore EDs and assessed the determinants of antibiotics expectation and receipt using multivariable logistic regression models. We also assessed the reasons patients expect antibiotics during their ED visit., Results: Among 681 patients, 31.0% expected antibiotics while 8.7% received antibiotics during their ED visit. Factors (adjusted odds ratio [95% confidence interval]) that significantly influenced expectation for antibiotics include: 1) prior consultation for current illness with (6.56 [3.30-13.11]) or without (1.50 [1.01-2.23]) antibiotics prescribed; 2) anticipation for COVID-19 test (1.56 [1.01-2.41]); and 3) poor (2.16 [1.26-3.68]) to moderate (2.26 [1.33-3.84]) knowledge on antibiotics use and resistance. Patients expecting antibiotics were 10.6 times (10.64 [5.34-21.17]) more likely to receive antibiotics. Those with tertiary education were twice (2.20 [1.09-4.43]) as likely to receive antibiotics., Conclusion: In conclusion, patients with URTI who expected antibiotics to be prescribed remained more likely to receive it during the COVID-19 pandemic. This highlights the need for more public education on the non-necessity for antibiotics for URTI and COVID-19 to address the problem of antibiotic resistance., Competing Interests: Competing interests The authors declare that they have no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Deep learning is a novel machine learning technique that has been shown to be as effective as human graders in detecting diabetic retinopathy from fundus photographs. We used a cost-minimisation analysis to evaluate the potential savings of two deep learning approaches as compared with the current human assessment: a semi-automated deep learning model as a triage filter before secondary human assessment; and a fully automated deep learning model without human assessment., Methods: In this economic analysis modelling study, using 39 006 consecutive patients with diabetes in a national diabetic retinopathy screening programme in Singapore in 2015, we used a decision tree model and TreeAge Pro to compare the actual cost of screening this cohort with human graders against the simulated cost for semi-automated and fully automated screening models. Model parameters included diabetic retinopathy prevalence rates, diabetic retinopathy screening costs under each screening model, cost of medical consultation, and diagnostic performance (ie, sensitivity and specificity). The primary outcome was total cost for each screening model. Deterministic sensitivity analyses were done to gauge the sensitivity of the results to key model assumptions., Findings: From the health system perspective, the semi-automated screening model was the least expensive of the three models, at US$62 per patient per year. The fully automated model was $66 per patient per year, and the human assessment model was $77 per patient per year. The savings to the Singapore health system associated with switching to the semi-automated model are estimated to be $489 000, which is roughly 20% of the current annual screening cost. By 2050, Singapore is projected to have 1 million people with diabetes; at this time, the estimated annual savings would be $15 million., Interpretation: This study provides a strong economic rationale for using deep learning systems as an assistive tool to screen for diabetic retinopathy., Funding: Ministry of Health, Singapore., (Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)