Containing COVID-19 in the Emergency Department: The Role of Improved Case Detection and Segregation of Suspect Cases.

OBJECTIVES: Patients with COVID-19 may present with respiratory syndromes indistinguishable from common viruses. This poses a challenge for early detection during triage in the emergency department (ED). Over a 3-month period, our ED aimed to minimize nosocomial transmission by using broader suspect case criteria for better detection and using appropriate personal protective equipment (PPE) for health care workers (HCWs). METHODS: All ED admissions with respiratory syndromes over a 3-month period were tested for COVID-19. The sensitivity and specificity of screening criteria in detecting COVID-19 were assessed. A risk-stratified approach was adopted for PPE usage in the ED, based on high-risk "fever areas" and lower-risk zones. When a case of COVID-19 was confirmed, surveillance was conducted for potentially exposed patients and HCWs. RESULTS: A total of 1,841 cases presenting with respiratory syndromes required admission over the study period. Among these, 70 cases of COVID-19 were subsequently confirmed. The majority (84.2%, 59/70) were detected at ED triage because they fulfilled suspect case criteria. Of these, 34 met the official screening criteria; an additional 25 were detected by the broader internal screening criteria. Over the 12-week period, the cumulative sensitivity of internal screening criteria was 84.3% (95% confidence interval [CI] = 73.6% to 91.9%), whereas the sensitivity of the official screening criteria was 48.6% (95% CI = 36.4% to 60.8%). Given the broadened internal criteria, the preexisting ED "fever area" was insufficient and had to be expanded. However, there were no cases of nosocomial transmission from intra-ED exposure, despite extensive surveillance. CONCLUSION: Frontline physicians need to be given leeway to decide on the disposition of cases based on clinical suspicion during an ongoing outbreak of COVID-19. If a broader criterion is used at ED triage, ED facilities and isolation facilities need to be readied to accommodate a surge of suspect cases. Usage of appropriate PPE is essential in minimizing nosocomial transmission.

Socio-demographic and clinical profile of admissions to community hospitals in Singapore from 1996 to 2005: a descriptive study.

INTRODUCTION: Little data is available on community hospital admissions. We examined the differences between community hospitals and the annual trends in sociodemographic characteristics of all patient admissions in Singaporean community hospitals over a 10- year period from 1996 to 2005. MATERIALS AND METHODS: Data were manually extracted from medical records of 4 community hospitals existent in Singapore from 1996 to 2005. Nineteen thousand and three hundred and sixty patient records were examined. Chisquare test was used for univariate analysis of categorical variables by type of community hospitals. For annual trends, test for linear by linear association was used. ANOVA was used to generate beta coefficients for continuous variables. RESULTS: Mean age of all patient admissions has increased from 72.8 years in 1996 to 74.8 years in 2005. The majority was Chinese (88.4%), and female (58.1%) and admissions were mainly for rehabilitation (88.0%). Almost one third had foreign domestic workers as primary caregivers and most (73.5%) were discharged to their own home. There were significant differences in socio-demographic profile of admissions between hospitals with one hospital having more patients with poor social support. Over the 10-year period, the geometric mean length of stay decreased from 29.7 days (95% CI, 6.4 to 138.0) to 26.7 days (95% CI, 7.5 to 94.2), and both mean admission and discharge Barthel Index scores increased from 41.0 (SD = 24.9) and 51.8 (SD = 30.0), respectively in 1996 to 48.4 (SD = 24.5) and 64.2 (SD = 27.3) respectively in 2005. CONCLUSION: There are significant differences in socio-demographic characteristics and clinical profile of admissions between various community hospitals and across time. Understanding these differences and trends in admission profiles may help in projecting future healthcare service needs.