Modelling the utility of body temperature readings from primary care consults for SARS surveillance in an army medical centre

<p><b>INTRODUCTION</b>There is interest in surveillance systems for outbreak detection at stages where clinical presentation would still be undifferentiated. Such systems focus on detecting clusters of syndromes in excess of baseline levels, which may indicate an outbreak. We model the detection limits of a potential system based on primary care consults for the detection of an outbreak of severe acute respiratory syndrome (SARS).</p><p><b>MATERIALS AND METHODS</b>Data from an averaged-sized medical centre were extracted from the Patient Care Enhancement System (PACES) [the electronic medical records system serving the Singapore Armed Forces (SAF)]. Thresholds were set to 3 or more cases presenting with particular syndromes and a temperature reading of >or=38oC (T >or=38). Monte Carlo simulation was used to insert simulated SARS outbreaks of various sizes onto the background incidence of febrile cases, accounting for distribution of SARS incubation period, delay from onset to first consult, and likelihood of presenting with T >or=38 to the SAF medical centre.</p><p><b>RESULTS</b>Valid temperature data was available for 2,012 out of 2,305 eligible syndromic consults (87.2%). T >or=38 was observed in 166 consults (8.3%). Simulated outbreaks would peak 7 days after exposure, but, on average, signals at their peak would consist of 10.9% of entire outbreak size. Under baseline assumptions, the system has a higher than 90% chance of detecting an outbreak only with 20 or more cases.</p><p><b>CONCLUSIONS</b>Surveillance based on clusters of cases with T >or=38 helps reduce background noise in primary care data, but the major limitation of such systems is that they are still only able to confidently detect large outbreaks.</p>

The outbreak of SARS at Tan Tock Seng Hospital--relating epidemiology to control

<p><b>INTRODUCTION</b>The outbreak of severe acute respiratory syndrome (SARS) began after the index case was admitted on 1 March 2003. We profile the cases suspected to have acquired the infection in Tan Tock Seng Hospital (TTSH), focussing on major transmission foci, and also describe and discuss the impact of our outbreak control measures.</p><p><b>MATERIALS AND METHODS</b>Using the World Health Organization (WHO) case definitions for probable SARS adapted to the local context, we studied all cases documented to have passed through TTSH less than 10 days prior to the onset of fever. Key data were collected in liaison with clinicians and through a team of onsite epidemiologists.</p><p><b>RESULTS</b>There were 105 secondary cases in TTSH. Healthcare staff (57.1%) formed the majority, followed by visitors (30.5%) and inpatients (12.4%). The earliest case had onset of fever on 4 March 2003, and the last case, on 5 April 2003. Eighty-nine per cent had exposures to 7 wards which had cases of SARS that were not isolated on admission. In 3 of these wards, major outbreaks resulted, each with more than 20 secondary cases. Attack rates amongst ward-based staff ranged from 0% to 32.5%. Of 13 inpatients infected, only 4 (30.8%) had been in the same room or cubicle as the index case for the ward.</p><p><b>CONCLUSIONS</b>The outbreak of SARS at TTSH showed the challenges of dealing with an emerging infectious disease with efficient nosocomial spread. Super-spreading events and initial delays in outbreak response led to widespread dissemination of the outbreak to multiple wards.</p>

SARS in Singapore--predictors of disease severity

<p><b>INTRODUCTION</b>Severe acute respiratory syndrome (SARS) affected 8096 individuals in 29 countries, with 774 deaths. In Singapore, there were 238 cases of SARS with 33 deaths. A retrospective analysis was performed to identify predictors of poor outcome in patients with SARS locally.</p><p><b>MATERIALS AND METHODS</b>Clinical, laboratory and outcome data of 234 patients admitted to Tan Tock Seng Hospital and Singapore General Hospital were collected and analysed. Only data collected at the time of admission were used in the analysis for predictors of poor outcome. Adverse events were defined as admission to the intensive care unit or death.</p><p><b>RESULTS</b>Clinical (temperature, FiO2) and laboratory [leukocyte, lymphocyte, neutrophil, platelet, lactate dehydrogenase (LDH), albumin] trends in groups with and without an adversarial event were presented. Fifty patients experienced an adverse event. On univariate analysis, male gender, advanced age, presence of comorbidities, neutrophilia, lymphopaenia, hyponatraemia, hypoalbuminaemia, transaminitis and elevated LDH or C-reactive protein were found to be significant predictors. On multivariate analysis, predictors of poor outcome were increased age [odds ratio (OR) 1.73 for every 10-year increase; 95% CI, 1.35 to 2.21], neutrophilia (OR 1.06 for every 1 x 10(9)/L increase; 95% CI, 1.02 to 1.11) and high LDH (OR 1.17 for every 100 U/L increase; 95% CI, 1.02 to 1.34). None of the 12 paediatric patients had an adverse event.</p><p><b>CONCLUSION</b>Advanced age, neutrophilia and high LDH predict poor outcomes in patients with SARS.</p>

Clinical and laboratory findings of SARS in Singapore

<p><b>INTRODUCTION</b>Singapore was one of 29 countries worldwide affected by severe acute respiratory syndrome (SARS) in 2003.</p><p><b>MATERIALS AND METHODS</b>There were 238 cases identified during the outbreak. We performed a retrospective analysis of the clinical and laboratory data of 234 patients admitted to Tan Tock Seng Hospital and Singapore General Hospital.</p><p><b>RESULTS</b>The mean age of patients was 21 years, 31.6% of patients were males and 41.8% were healthcare workers. At presentation, the common symptoms were fever, myalgia, cough and headache; rhinorrhoea was uncommon. On admission, 21% had leukopenia, 18% had thrombocytopaenia, 29% had hyponatraemia, 31% had hypokalaemia, 21% had transaminitis. Polymerase chain reaction (PCR) testing of respiratory and stool samples provided the best yield at the end of the first week of illness. Thirty-two patients were initially not recognised as probable SARS and were reclassified when the serology test results were available. The chief reasons for not identifying these patients early were persistently normal chest X-rays (68.8%), very mild presentation (43.8%) and the presence of a concomitant illness (12.5%). Overall, 12% of the patients were probable SARS with atypical presentations. Overall mortality was 11.8%.</p><p><b>CONCLUSION</b>Patients infected with the SARS coronavirus had a wide clinical presentation with non-specific symptoms.</p>

Understanding the super-spreading events of SARS in Singapore

<p><b>INTRODUCTION</b>It has been noted that SARS transmission is characterised by a few super-spreading events (SSEs) giving rise to a disproportionate number of secondary cases. Clinical and environmental features surrounding the index cases involved were compared with cases in non- SSEs.</p><p><b>MATERIALS AND METHODS</b>Data on 231 cases of probable SARS admitted to Tan Tock Seng Hospital (TTSH) were used. Index cases directly causing 10 or more secondary cases were classified as having been involved in SSEs; all others were defined as non-SSEs.</p><p><b>RESULTS</b>Only 5 cases were involved in SSEs; all 5 were isolated on day 5 of illness or later, and spent at least a brief period in a non-isolation ward; in contrast, amongst the 226 non-SSE cases, only 40.7% and 4.0% were isolated late and admitted to non-isolation wards respectively, and only 3.1% had both these environmental features present; the differences were highly significant (P = 0.012, P <0.001 and P <0.001 by Fisher's Exact test). When compared to 7 non-SSE cases with delayed isolation and an admission to non-isolation wards, SSEs were more likely to have co-morbid disease or require ICU care at time of isolation (P = 0.045 for both factors).</p><p><b>CONCLUSION</b>SSEs were likely due to a conglomeration of environmental factors of delayed isolation and admission to a non-isolation ward, coupled with severe disease stage at time of isolation.</p>