Human temperatures for syndromic surveillance in the emergency department: data from the autumn wave of the 2009 swine flu (H1N1) pandemic and a seasonal influenza outbreak

Syndromic Surveillance Systems for Mass Gatherings: A Scoping Review

Syndromic surveillance involves the near-real-time collection of data from a potential multitude of sources to detect outbreaks of disease or adverse health events earlier than traditional forms of public health surveillance. The purpose of the present study is to elucidate the role of syndromic surveillance during mass gathering scenarios. In the present review, the use of syndromic surveillance for mass gathering scenarios is described, including characteristics such as methodologies of data collection and analysis, degree of preparation and collaboration, and the degree to which prior surveillance infrastructure is utilized. Nineteen publications were included for data extraction. The most common data source for the included syndromic surveillance systems was emergency departments, with first aid stations and event-based clinics also present. Data were often collected using custom reporting forms. While syndromic surveillance can potentially serve as a method of informing public health policy regarding specific mass gatherings based on the profile of syndromes ascertained, the present review does not indicate that this form of surveillance is a reliable method of detecting potentially critical public health events during mass gathering scenarios.

Fever Incidence Is Much Lower in the Morning than the Evening: Boston and US National Triage Data

INTRODUCTION

In this observational study, we evaluated time-of-day variation in the incidence of fever that is seen at triage. The observed incidence of fever could change greatly over the day because body temperatures generally rise and fall in a daily cycle, yet fever is identified using a temperature threshold that is unchanging, such as ≥38.0° Celsius (C) (≥100.4° Fahrenheit [F]).

METHODS

We analyzed 93,225 triage temperature measurements from a Boston emergency department (ED) (2009-2012) and 264,617 triage temperature measurements from the National Hospital Ambulatory Medical Care Survey (NHAMCS, 2002-2010), making this the largest study of body temperature since the mid-1800s. Boston data were investigated exploratorily, while NHAMCS was used to corroborate Boston findings and check whether they generalized. NHAMCS results are nationally representative of United States EDs. Analyses focused on adults.

RESULTS

In the Boston ED, the proportion of patients with triage temperatures in the fever range (≥38.0°C, ≥100.4°F) increased 2.5-fold from morning to evening (7:00-8:59 PM vs 7:00-8:59 AM: risk ratio [RR] 2.5, 95% confidence interval [CI], 2.0-3.3). Similar time-of-day changes were observed when investigating alternative definitions of fever: temperatures ≥39.0°C (≥102.2°F) and ≥40.0°C (≥104.0°F) increased 2.4- and 3.6-fold from morning to evening (7:00-8:59 PM vs 7:00-8:59 AM: RRs [95% CIs] 2.4 [1.5-4.3] and 3.6 [1.5-17.7], respectively). Analyses of adult NHAMCS patients provided confirmation, showing mostly similar increases for the same fever definitions and times of day (RRs [95% CIs] 1.8 [1.6-2.1], 1.9 [1.4-2.5], and 2.8 [0.8-9.3], respectively), including after adjusting for 12 potential confounders using multivariable regression (adjusted RRs [95% CIs] 1.8 [1.5-2.1], 1.8 [1.3-2.4], and 2.7 [0.8-9.2], respectively), in age-group analyses (18-64 vs 65+ years), and in several sensitivity analyses. The patterns observed for fever mirror the circadian rhythm of body temperature, which reaches its highest and lowest points at similar times.

CONCLUSION

Fever incidence is lower at morning triages than at evening triages. High fevers are especially rare at morning triage and may warrant special consideration for this reason. Studies should examine whether fever-causing diseases are missed or underappreciated during mornings, especially for sepsis cases and during screenings for infectious disease outbreaks. The daily cycling of fever incidence may result from the circadian rhythm.

The daily, weekly, and seasonal cycles of body temperature analyzed at large scale

We performed large-scale analyses of circadian and infradian cycles of human body temperature, focusing on changes over the day, week, and year. Temperatures (n= 93,225) were collected using temporal artery thermometers from a Boston emergency department during 2009-2012 and were statistically analyzed using regression with cyclic splines. The overall mean body temperature was 36.7°C (98.1°F), with a 95% confidence interval of 36.7-36.7°C (98.1-98.1°F) and a standard deviation of 0.6°C (1.1°F). Over the day, mean body temperature followed a steady cycle, reaching its minimum at 6:00-8:00 and its maximum at 18:00-20:00. Across days of the week, this diurnal cycle was essentially unchanged, even though activities and sleeping hours change substantially during the weekly cycles of human behavior. Over the year, body temperatures were slightly colder in winter than summer (~0.2°C difference), consistent with most prior studies. We propose these seasonal differences might be due to ambient effects on body temperature that are not eliminated because they fall within the tolerance range of the thermoregulatory system. Over the year, bathyphase (daily time of minimum temperature) appeared to parallel sunrise times, as expected from sunrise's zeitgeber role in circadian rhythms. However, orthophase (daily time of maximum temperature) and sunset times followed opposite seasonal patterns, with orthophase preceding nightfall in summer and following nightfall in winter. Throughout the year, bathyphase and orthophase remained separated by approximately 12 h, suggesting this interval might be conserved. Finally, although 37.0°C (98.6°F) is widely recognized as the mean or normal human body temperature, analysis showed mean temperature was <37.0°C during all times of day, days of the week, and seasons of the year, supporting prior arguments that the 37.0°C standard has no scientific basis. Overall, this large study showed robust and consistent behavior of the human circadian cycle at the population level, providing a strong example of circadian homeostasis.

Proof of concept for a syndromic surveillance system based on routine ambulance records in the South West of England, for the influenza season 2016/2017

INTRODUCTION

The introduction of electronic patient records in the ambulance service provides new opportunities to monitor the population. Approximately 36% of patients presenting to English ambulance services are discharged at scene. Ambulance records are therefore an ideal data source for syndromic early event detection systems to monitor infectious disease in the pre-hospital population. It has been previously found that tympanic temperature records can be used to detect influenza outbreaks in emergency departments. This study aimed to determine whether routine tympanic temperature readings collected by ambulance crews can be used to detect seasonal influenza.

METHODS

Here we show that temperature readings do allow the detection of seasonal influenza before methods applied to conventional data sources. The counts of pyretic patients were used to calculate a sliding case ratio as a measurement to detect seasonal influenza outbreaks. This method does not rely on conventional thresholds and can be adapted to the data.

RESULTS

The data collected correlated with seasonal influenza. The 2016/2017 outbreak was detected up to nine weeks before other surveillance programmes. The results show that ambulance records can be a useful data source for biosurveillance systems.

CONCLUSION

Temperature readings from routinely collected ambulance patient records can be used as a surveillance tool for febrile diseases.

A methodological framework for the evaluation of syndromic surveillance systems: a case study of England

BACKGROUND

Syndromic surveillance complements traditional public health surveillance by collecting and analysing health indicators in near real time. The rationale of syndromic surveillance is that it may detect health threats faster than traditional surveillance systems permitting more timely, and hence potentially more effective public health action. The effectiveness of syndromic surveillance largely relies on the methods used to detect aberrations. Very few studies have evaluated the performance of syndromic surveillance systems and consequently little is known about the types of events that such systems can and cannot detect.

METHODS

We introduce a framework for the evaluation of syndromic surveillance systems that can be used in any setting based upon the use of simulated scenarios. For a range of scenarios this allows the time and probability of detection to be determined and uncertainty is fully incorporated. In addition, we demonstrate how such a framework can model the benefits of increases in the number of centres reporting syndromic data and also determine the minimum size of outbreaks that can or cannot be detected. Here, we demonstrate its utility using simulations of national influenza outbreaks and localised outbreaks of cryptosporidiosis.

RESULTS

Influenza outbreaks are consistently detected with larger outbreaks being detected in a more timely manner. Small cryptosporidiosis outbreaks (<1000 symptomatic individuals) are unlikely to be detected. We also demonstrate the advantages of having multiple syndromic data streams (e.g. emergency attendance data, telephone helpline data, general practice consultation data) as different streams are able to detect different outbreak types with different efficacy (e.g. emergency attendance data are useful for the detection of pandemic influenza but not for outbreaks of cryptosporidiosis). We also highlight that for any one disease, the utility of data streams may vary geographically, and that the detection ability of syndromic surveillance varies seasonally (e.g. an influenza outbreak starting in July is detected sooner than one starting later in the year). We argue that our framework constitutes a useful tool for public health emergency preparedness in multiple settings.

CONCLUSIONS

The proposed framework allows the exhaustive evaluation of any syndromic surveillance system and constitutes a useful tool for emergency preparedness and response.