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Case identification is an ongoing issue for the COVID-19 epidemic, in particular for outpatient care where physicians must decide which patients to prioritise for further testing. This paper reports tools to classify patients based on symptom profiles based on 236 severe acute respiratory syndrome coronavirus 2 positive cases and 564 controls, accounting for the time course of illness using generalised multivariate logistic regression. Significant symptoms included abdominal pain, cough, diarrhoea, fever, headache, muscle ache, runny nose, sore throat, temperature between 37.5 and 37.9 °C and temperature above 38 °C, but their importance varied by day of illness at assessment. With a high percentile threshold for specificity at 0.95, the baseline model had reasonable sensitivity at 0.67. To further evaluate accuracy of model predictions, leave-one-out cross-validation confirmed high classification accuracy with an area under the receiver operating characteristic curve of 0.92. For the baseline model, sensitivity decreased to 0.56. External validation datasets reported similar result. Our study provides a tool to discern COVID-19 patients from controls using symptoms and day from illness onset with good predictive performance. It could be considered as a framework to complement laboratory testing in order to differentiate COVID-19 from other patients presenting with acute symptoms in outpatient care.

Case identification is an ongoing issue for the COVID-19 epidemic, in particular for outpatient care where physicians must decide which patients to prioritise for further testing. This paper reports tools to classify patients based on symptom profiles based on 236 SARS-CoV-2 positive cases and 564 controls, accounting for the time course of illness at point of assessment. Clinical differentiators of cases and controls were used to derive model-based risk scores. Significant symptoms included abdominal pain, cough, diarrhea, fever, headache, muscle ache, runny nose, sore throat, temperature between 37.5°C and 37.9°C, and temperature above 38°C, but their importance varied by day of illness at assessment. With a high percentile threshold for specificity at 0.95, the baseline model had reasonable sensitivity at 0.67. To further evaluate accuracy of model predictions, we firstly used leave-one-out cross-validation, which confirmed high classification accuracy with an area under the receiver operating characteristic curve of 0.92. For the baseline model, sensitivity decreased to 0.56. Secondly, in a separate ongoing prospective study of 237 COVID-19 and 346 primary care patients presenting with symptoms of acute respiratory infection, the baseline model had a sensitivity of 0.57 and specificity of 0.89, and in retrospective notes review of 100 COVID-19 cases diagnosed in primary care, sensitivity was 0.56. A web-app based tool has been developed for easy implementation as an adjunct to laboratory testing to differentiate COVID-19 positive cases among patients presenting in outpatient settings.

Background The World Health Organization has highlighted the paucity of research into the oral health needs of older adults. In Singapore, until recently, publically funded/subsidized oral health care for adults has been limited to basic primary care at government-funded polyclinics. Access to a more comprehensive range of subsidized care in the private sector was widened through the government-funded Community Health Assistance Scheme (CHAS) in 2012 and Pioneer Generation (PG) scheme in 2015. Little is known about the attitude to dental service utilization among older adults in Singapore since then. Methods We conducted semi-structured individual interviews with 25 participants above 65 years of age who were eligible for subsidized dental care plans. Participants were recruited from a public teaching hospital and a public primary care clinic in Singapore. The duration of each interview was 15–30 min. Interviews were transcribed verbatim and the transcripts were analyzed thematically using a phenomenological approach. Results Pertinent themes emerged related to four major areas: (a) general awareness towards oral health, (b) life course perspective of oral health, (c) barriers to visit the dentist, (d) shaping dental service utilisation behaviours through provision of financial subsidies for dental care. Most participants perceived a strong relationship between oral health and systemic health. However, there were erroneous traditional beliefs such as oral health is not part of physical health and edentulous participants did not need to visit a dentist. Fear, anxiety, previous negative experience and lack of knowledge were barriers to visiting the dentist. Trust and convenience were considerations for patients when deciding whether to switch from public to private dental services where CHAS/PG were only available. Conclusion Our study provided important insights regarding oral health perceptions and beliefs of older people residing in the community which may affect their dental service utilization. This further highlights the importance of understanding the concerns of this group when implementing healthcare policies for elderly in Singapore. The findings of our study will serve as a baseline for future studies in Singapore and inform studies in other countries that implement targeted schemes for older adults.