Syndromic surveillance for influenza in the emergency department-A systematic review

Utilizing Wearable Device Data for Syndromic Surveillance: A Fever Detection Approach

Commercially available wearable devices (wearables) show promise for continuous physiological monitoring. Previous works have demonstrated that wearables can be used to detect the onset of acute infectious diseases, particularly those characterized by fever. We aimed to evaluate whether these devices could be used for the more general task of syndromic surveillance. We obtained wearable device data (Oura Ring) from 63,153 participants. We constructed a dataset using participants' wearable device data and participants' responses to daily online questionnaires. We included days from the participants if they (1) completed the questionnaire, (2) reported not experiencing fever and reported a self-collected body temperature below 38 °C (negative class), or reported experiencing fever and reported a self-collected body temperature at or above 38 °C (positive class), and (3) wore the wearable device the nights before and after that day. We used wearable device data (i.e., skin temperature, heart rate, and sleep) from the nights before and after participants' fever day to train a tree-based classifier to detect self-reported fevers. We evaluated the performance of our model using a five-fold cross-validation scheme. Sixteen thousand, seven hundred, and ninety-four participants provided at least one valid ground truth day; there were a total of 724 fever days (positive class examples) from 463 participants and 342,430 non-fever days (negative class examples) from 16,687 participants. Our model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.85 and an average precision (AP) of 0.25. At a sensitivity of 0.50, our calibrated model had a false positive rate of 0.8%. Our results suggest that it might be possible to leverage data from these devices at a public health level for live fever surveillance. Implementing these models could increase our ability to detect disease prevalence and spread in real-time during infectious disease outbreaks.

The Use and Structure of Emergency Nurses' Triage Narrative Data: Scoping Review

BACKGROUND

Emergency departments use triage to ensure that patients with the highest level of acuity receive care quickly and safely. Triage is typically a nursing process that is documented as structured and unstructured (free text) data. Free-text triage narratives have been studied for specific conditions but never reviewed in a comprehensive manner.

OBJECTIVE

The objective of this paper was to identify and map the academic literature that examines triage narratives. The paper described the types of research conducted, identified gaps in the research, and determined where additional review may be warranted.

METHODS

We conducted a scoping review of unstructured triage narratives. We mapped the literature, described the use of triage narrative data, examined the information available on the form and structure of narratives, highlighted similarities among publications, and identified opportunities for future research.

RESULTS

We screened 18,074 studies published between 1990 and 2022 in CINAHL, MEDLINE, Embase, Cochrane, and ProQuest Central. We identified 0.53% (96/18,074) of studies that directly examined the use of triage nurses' narratives. More than 12 million visits were made to 2438 emergency departments included in the review. In total, 82% (79/96) of these studies were conducted in the United States (43/96, 45%), Australia (31/96, 32%), or Canada (5/96, 5%). Triage narratives were used for research and case identification, as input variables for predictive modeling, and for quality improvement. Overall, 31% (30/96) of the studies offered a description of the triage narrative, including a list of the keywords used (27/96, 28%) or more fulsome descriptions (such as word counts, character counts, abbreviation, etc; 7/96, 7%). We found limited use of reporting guidelines (8/96, 8%).

CONCLUSIONS

The breadth of the identified studies suggests that there is widespread routine collection and research use of triage narrative data. Despite the use of triage narratives as a source of data in studies, the narratives and nurses who generate them are poorly described in the literature, and data reporting is inconsistent. Additional research is needed to describe the structure of triage narratives, determine the best use of triage narratives, and improve the consistent use of triage-specific data reporting guidelines.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1136/bmjopen-2021-055132.

Detecting early signals of COVID-19 outbreaks in 2020 in small areas by monitoring healthcare utilisation databases: first lessons learned from the Italian Alert_CoV project

BackgroundDuring the COVID-19 pandemic, large-scale diagnostic testing and contact tracing have proven insufficient to promptly monitor the spread of infections.AimTo develop and retrospectively evaluate a system identifying aberrations in the use of selected healthcare services to timely detect COVID-19 outbreaks in small areas.MethodsData were retrieved from the healthcare utilisation (HCU) databases of the Lombardy Region, Italy. We identified eight services suggesting a respiratory infection (syndromic proxies). Count time series reporting the weekly occurrence of each proxy from 2015 to 2020 were generated considering small administrative areas (i.e. census units of Cremona and Mantua provinces). The ability to uncover aberrations during 2020 was tested for two algorithms: the improved Farrington algorithm and the generalised likelihood ratio-based procedure for negative binomial counts. To evaluate these algorithms' performance in detecting outbreaks earlier than the standard surveillance, confirmed outbreaks, defined according to the weekly number of confirmed COVID-19 cases, were used as reference. Performances were assessed separately for the first and second semester of the year. Proxies positively impacting performance were identified.ResultsWe estimated that 70% of outbreaks could be detected early using the proposed approach, with a corresponding false positive rate of ca 20%. Performance did not substantially differ either between algorithms or semesters. The best proxies included emergency calls for respiratory or infectious disease causes and emergency room visits.ConclusionImplementing HCU-based monitoring systems in small areas deserves further investigations as it could facilitate the containment of COVID-19 and other unknown infectious diseases in the future.

Development and evaluation of an interoperable natural language processing system for identifying pneumonia across clinical settings of care and institutions

Objective

To evaluate the feasibility, accuracy, and interoperability of a natural language processing (NLP) system that extracts diagnostic assertions of pneumonia in different clinical notes and institutions.

Materials and Methods

A rule-based NLP system was designed to identify assertions of pneumonia in 3 types of clinical notes from electronic health records (EHRs): emergency department notes, radiology reports, and discharge summaries. The lexicon and classification logic were tailored for each note type. The system was first developed and evaluated using annotated notes from the Department of Veterans Affairs (VA). Interoperability was assessed using data from the University of Utah (UU).

Results

The NLP system was comprised of 782 rules and achieved moderate-to-high performance in all 3 note types in VA (precision/recall/f1: emergency = 88.1/86.0/87.1; radiology = 71.4/96.2/82.0; discharge = 88.3/93.0/90.1). When applied to UU data, performance was maintained in emergency and radiology but decreased in discharge summaries (emergency = 84.7/94.3/89.3; radiology = 79.7/100.0/87.9; discharge = 65.5/92.7/76.8). Customization with 34 additional rules increased performance for all note types (emergency = 89.3/94.3/91.7; radiology = 87.0/100.0/93.1; discharge = 75.0/95.1/83.4).

Conclusion

NLP can be used to accurately identify the diagnosis of pneumonia across different clinical settings and institutions. A limited amount of customization to account for differences in lexicon, clinical definition of pneumonia, and EHR structure can achieve high accuracy without substantial modification.

Forecasting Hospital Visits Due to Influenza Based on Emergency Department Visits for Fever: A Feasibility Study on Emergency Department-Based Syndromic Surveillance

This study evaluated the use of chief complaint data from emergency departments (EDs) to detect the increment of influenza cases identified from the nationwide medical service usage and developed a forecast model to predict the number of patients with influenza using the daily number of ED visits due to fever. The National Health Insurance Service (NHIS) and the National Emergency Department Information System (NEDIS) databases from 2015 to 2019 were used. The definition of fever included having an initial body temperature ≥ 38.0 °C at an ED department or having a report of fever as a patient's chief complaint. The moving average number of visits to the ED due to fever for the previous seven days was used. Patients in the NHIS with the International Classification of Diseases-10 codes of J09, J10, or J11 were classified as influenza cases, with a window duration of 100 days, assuming the claims were from the same season. We developed a forecast model according to an autoregressive integrated moving average (ARIMA) method using the data from 2015 to 2017 and validated it using the data from 2018 to 2019. Of the 29,142,229 ED visits from 2015 to 2019, 39.9% reported either a fever as a chief complaint or a ≥38.0 °C initial body temperature at the ED. ARIMA (1,1,1) (0,0,1)₇ was the most appropriate model for predicting ED visits due to fever. The mean absolute percentage error (MAPE) value showed the prediction accuracy of the model. The correlation coefficient between the number of ED visits and the number of patients with influenza in the NHIS up to 14 days before the forecast, with the exceptions of the eighth, ninth, and twelfth days, was higher than 0.70 (p-value = 0.001). ED-based syndromic surveillances of fever were feasible for the early detection of hospital visits due to influenza.

Surveillance of emerging infectious diseases for biosecurity

Emerging infectious diseases, such as COVID-19, continue to pose significant threats to human beings and their surroundings. In addition, biological warfare, bioterrorism, biological accidents, and harmful consequences arising from dual-use biotechnology also pose a challenge for global biosecurity. Improving the early surveillance capabilities is necessary for building a common biosecurity shield for the global community of health for all. Furthermore, surveillance could provide early warning and situational awareness of biosecurity risks. However, current surveillance systems face enormous challenges, including technical shortages, fragmented management, and limited international cooperation. Detecting emerging biological risks caused by unknown or novel pathogens is of particular concern. Surveillance systems must be enhanced to effectively mitigate biosecurity risks. Thus, a global strategy of meaningful cooperation based on efficient integration of surveillance at all levels, including interdisciplinary integration of techniques and interdepartmental integration for effective management, is urgently needed. In this paper, we review the biosecurity risks by analyzing potential factors at all levels globally. In addition to describing biosecurity risks and their impact on global security, we also focus on analyzing the challenges to traditional surveillance and propose suggestions on how to integrate current technologies and resources to conduct effective global surveillance.

Delayed correlation between the incidence rate of indigenous murine typhus in humans and the seropositive rate of Rickettsia typhi infection in small mammals in Taiwan from 2007–2019

Murine typhus is a flea-borne zoonotic disease with acute febrile illness caused by Rickettsia typhi and is distributed widely throughout the world, particularly in port cities and coastal regions. We observed that murine typhus was an endemic disease (number of annual indigenous cases = 29.23±8.76) with a low incidence rate (0.13±2.03*10⁻⁴ per 100,000 person-years) in Taiwan from 2007–2019. Most (45.79%, 174/380) indigenous infections were reported in May, June, and July. The incidence rates in both May and June were statistically higher than those in other months (p<0.05). Correspondingly, sera collected from small mammals (rodents and shrews) trapped in airports and harbors demonstrated anti-R. typhi antibody responses (seropositive rate = 8.24±0.33%). Interestingly, the ports with the highest seropositivity rates in small mammals are all inside/near the areas with the highest incidence rates of indigenous murine typhus. In addition, incidence rates in humans were positively correlated with the 1-month and 2-month prior seropositive rates in small mammals (R = 0.31 and 0.37, respectively). As early treatment with appropriate antibiotics for murine typhus could effectively shorten the duration of illness and reduce the risk of hospitalization and fatality, flea-related exposure experience should be considered in clinics during peak seasons and the months after a rise in seropositivity rates in small mammals. Surveillance in small mammals might be helpful for the development of real-time reporting or even early reminders for physicians of sporadic murine typhus cases based on the delayed correlation observed in this study.

Murine typhus is a flea-borne zoonotic disease with acute febrile illness caused by Rickettsia typhi and is distributed widely throughout the world, particularly in port cities and coastal regions. Early treatment with appropriate antibiotics for murine typhus could effectively shorten the duration of illness and reduce the risk of hospitalization and fatality. However, it presents with nonspecific symptoms and is oftentimes misdiagnosed. In Taiwan, murine typhus has been designated a notifiable disease since 2007. Meanwhile, surveillance of R. typhi infection of small mammals was also launched at 25 international airports and harbors. Since then, we observed that indigenous murine typhus patients have been detected in Taiwan annually and sera collected from small mammals trapped in ports also demonstrated anti-R. typhi antibody responses. Correspondingly, the ports with the highest seropositivity are all inside/near the areas with the highest incidence rate of indigenous murine typhus in Taiwan. We further found that incidence rates in humans were positively correlated with the 1-month and 2-month prior seropositive rates in small mammals. Surveillance in small mammals might be helpful for the development of real-time reporting or even early reminders of sporadic murine typhus cases based on the delayed correlation observed in this study.

Emergency nurses' triage narrative data, their uses and structure: a scoping review protocol

INTRODUCTION

The first clinical interaction most patients have in the emergency department occurs during triage. An unstructured narrative is generated during triage and is the first source of in-hospital documentation. These narratives capture the patient's reported reason for the visit and the initial assessment and offer significantly more nuanced descriptions of the patient's complaints than fixed field data. Previous research demonstrated these data are useful for predicting important clinical outcomes. Previous reviews examined these narratives in combination or isolation with other free-text sources, but used restricted searches and are becoming outdated. Furthermore, there are no reviews focused solely on nurses' (the primary collectors of these data) narratives.

METHODS AND ANALYSIS

Using the Arksey and O'Malley scoping review framework and PRISMA-ScR reporting guidelines, we will perform structured searches of CINAHL, Ovid MEDLINE, ProQuest Central, Ovid Embase and Cochrane Library (via Wiley). Additionally, we will forward citation searches of all included studies. No geographical or study design exclusion criteria will be used. Studies examining disaster triage, published before 1990, and non-English language literature will be excluded. Data will be managed using online management tools; extracted data will be independently confirmed by a separate reviewer using prepiloted extraction forms. Cohen's kappa will be used to examine inter-rater agreement on pilot and final screening. Quantitative data will be expressed using measures of range and central tendency, counts, proportions and percentages, as appropriate. Qualitative data will be narrative summaries of the authors' primary findings.

PATIENT AND PUBLIC INVOLVEMENT

No patients involved.

ETHICS AND DISSEMINATION

No ethics approval is required. Findings will be submitted to peer-reviewed conferences and journals. Results will be disseminated using individual and institutional social media platforms.

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.

An Evaluation of Syndromic Surveillance-Related Practices Among Selected State and Local Health Agencies

CONTEXT

Syndromic surveillance consists of the systematic collection and use of near real-time data about health-related events for situational awareness and public health action. As syndromic surveillance programs continue to adopt new technologies and expand, it is valuable to evaluate these syndromic surveillance systems and practices to ensure that they meet public health needs.

OBJECTIVE

This assessment's aim is to provide recent information about syndromic surveillance systems and practice characteristics among a group of state and local health departments.

DESIGN/SETTING

Information was obtained between November 2017 and June 2018 through a telephone survey using an Office of Management and Budget-approved standardized data collection tool. Participants were syndromic surveillance staff from each of 31 state and local health departments participating in the National Syndromic Surveillance Program funded by the Centers for Disease Control and Prevention. Questions included jurisdictional experience, data sources and analysis systems used, syndromic system data processing characteristics, data quality verification procedures, and surveillance activities conducted with syndromic data.

MEASURES

Practice-specific information such as types of systems and data sources used for syndromic surveillance, data quality monitoring, and uses of data for public health situational awareness (eg, investigating occurrences of or trends in diseases).

RESULTS

The survey analysis revealed a wide range of experiences with syndromic surveillance. Participants reported the receipt of data daily or more frequently. Emergency department data were the primary data source; however, other data sources are being integrated into these systems. All health departments routinely monitored data quality. Syndromes of highest priority across the respondents for health events monitoring were influenza-like illness and drug-related syndromes. However, a wide variety of syndromes were reported as priorities across the health departments.

CONCLUSION

Overall, syndromic surveillance was relevantly integrated into the public health surveillance infrastructure. The near real-time nature of the data and its flexibility to monitor different types of health-related issues make it especially useful for public health practitioners. Despite these advances, syndromic surveillance capacity, locally and nationally, must continue to evolve and progress should be monitored to ensure that syndromic surveillance systems and data are optimally able to meet jurisdictional needs.

Influenza Virus: Tracking, Predicting, and Forecasting

Influenza is a common respiratory infection that causes considerable morbidity and mortality worldwide each year. In recent years, along with the improvement in computational resources, there have been a number of important developments in the science of influenza surveillance and forecasting. Influenza surveillance systems have been improved by synthesizing multiple sources of information. Influenza forecasting has developed into an active field, with annual challenges in the United States that have stimulated improved methodologies. Work continues on the optimal approaches to assimilating surveillance data and information on relevant driving factors to improve estimates of the current situation (nowcasting) and to forecast future dynamics.

Impact of Viral Seasonal Outbreaks on Crowding and Health Care Quality in Pediatric Emergency Departments

ABSTRACT

In pediatric emergency departments (PEDs), seasonal viral outbreaks are believed to be associated with an increase of workload, but no quantification of this impact has been published. A retrospective cross-sectional study aimed to measure this impact on crowding and health care quality in PED. The study was performed in 1 PED for 3 years. Visits related to bronchiolitis, influenza, and gastroenteritis were defined using discharge diagnoses. The daily epidemic load (DEL) was the proportion of visits related to one of these diagnoses. The daily mean of 8 crowding indicators (selected in a published Delphi study) was used. A total of 93,976 children were admitted (bronchiolitis, 2253; influenza, 1277; gastroenteritis, 7678). The mean DEL was 10.4% (maximum, 33.6%). The correlation between the DEL and each indicator was significant. The correlation was stronger for bronchiolitis (Pearson R from 0.171 for number of hospitalization to 0.358 for length of stay). Between the first and fourth quartiles of the DEL, a significant increase, between 50% (patients left without being seen) and 8% (patient physician ratio), of all the indicators was observed. In conclusion, seasonal viral outbreaks have a strong impact on crowding and quality of care. The evolution of "patients left without being seen" between the first and fourth quartiles of DEL could be used as an indicator reflecting the capacity of adaptation of an emergency department to outbreaks.

Syndromic surveillance to detect disease outbreaks using time between emergency department presentations

OBJECTIVE

Early warning of disease outbreaks is paramount for health jurisdictions. The objective of the present study was to develop syndromic surveillance monitoring plans from routinely collected ED data with application to detecting disease outbreaks.

METHODS

The study involved secondary data analysis of ED presentations to major public hospitals in Queensland and South Australia spanning 2017-2020. Monitoring plans were developed for all major Queensland and South Australian public hospitals using an adaptation of Exponentially Weighted Moving Averages - a process control method used in detecting anomalies in industrial production processes. The methods rely on setting a threshold (control limit) relating to the time between an event of interest (e.g. flu outbreak) using ED presentations as a signal to monitor. An outbreak is flagged as this time gets significantly smaller, and each event offers a decision point on whether an outbreak has occurred. The models incorporate differing levels of temporal memory to cover outbreaks of different sizes.

RESULTS

The novel approach to real-time outbreak detection indicates outbreaks for individual hospitals coinciding with the first wave of the COVID-19 outbreak in Queensland and South Australia as well as the large 2017 and 2019 influenza seasons.

CONCLUSION

Outbreak detection models demonstrate the ability to quickly flag an outbreak based on clinician-assigned ED diagnoses. An implemented syndromic surveillance approach can pick up geographic outbreaks quickly so they can be contained. Such capability can help with surveillance related to the current COVID-19 pandemic and potential future pandemics.

Development and validation of an automated emergency department-based syndromic surveillance system to enhance public health surveillance in Yukon: a lower-resourced and remote setting

BACKGROUND

Automated Emergency Department syndromic surveillance systems (ED-SyS) are useful tools in routine surveillance activities and during mass gathering events to rapidly detect public health threats. To improve the existing surveillance infrastructure in a lower-resourced rural/remote setting and enhance monitoring during an upcoming mass gathering event, an automated low-cost and low-resources ED-SyS was developed and validated in Yukon, Canada.

METHODS

Syndromes of interest were identified in consultation with the local public health authorities. For each syndrome, case definitions were developed using published resources and expert elicitation. Natural language processing algorithms were then written using Stata LP 15.1 (Texas, USA) to detect syndromic cases from three different fields (e.g., triage notes; chief complaint; discharge diagnosis), comprising of free-text and standardized codes. Validation was conducted using data from 19,082 visits between October 1, 2018 to April 30, 2019. The National Ambulatory Care Reporting System (NACRS) records were used as a reference for the inclusion of International Classification of Disease, 10th edition (ICD-10) diagnosis codes. The automatic identification of cases was then manually validated by two raters and results were used to calculate positive predicted values for each syndrome and identify improvements to the detection algorithms.

RESULTS

A daily secure file transfer of Yukon's Meditech ED-Tracker system data and an aberration detection plan was set up. A total of six syndromes were originally identified for the syndromic surveillance system (e.g., Gastrointestinal, Influenza-like-Illness, Mumps, Neurological Infections, Rash, Respiratory), with an additional syndrome added to assist in detecting potential cases of COVID-19. The positive predictive value for the automated detection of each syndrome ranged from 48.8-89.5% to 62.5-94.1% after implementing improvements identified during validation. As expected, no records were flagged for COVID-19 from our validation dataset.

CONCLUSIONS

The development and validation of automated ED-SyS in lower-resourced settings can be achieved without sophisticated platforms, intensive resources, time or costs. Validation is an important step for measuring the accuracy of syndromic surveillance, and ensuring it performs adequately in a local context. The use of three different fields and integration of both free-text and structured fields improved case detection.

Development and assessment of a hospital admissions-based syndromic surveillance system for COVID-19 in Ontario, Canada: ACES Pandemic Tracker

BACKGROUND

The COVID-19 pandemic has continued to pose a major global public health risk. The importance of public health surveillance systems to monitor the spread and impact of COVID-19 has been well demonstrated. The purpose of this study was to describe the development and effectiveness of a real-time public health syndromic surveillance system (ACES Pandemic Tracker) as an early warning system and to provide situational awareness in response to the COVID-19 pandemic in Ontario, Canada.

METHODS

We used hospital admissions data from the Acute Care Enhanced Surveillance (ACES) system to collect data on pre-defined groupings of symptoms (syndromes of interest; SOI) that may be related to COVID-19 from 131 hospitals across Ontario. To evaluate which SOI for suspected COVID-19 admissions were best correlated with laboratory confirmed admissions, laboratory confirmed COVID-19 hospital admissions data were collected from the Ontario Ministry of Health. Correlations and time-series lag analysis between suspected and confirmed COVID-19 hospital admissions were calculated. Data used for analyses covered the period between March 1, 2020 and September 21, 2020.

RESULTS

Between March 1, 2020 and September 21, 2020, ACES Pandemic Tracker identified 22,075 suspected COVID-19 hospital admissions (150 per 100,000 population) in Ontario. After correlation analysis, we found laboratory-confirmed hospital admissions for COVID-19 were strongly and significantly correlated with suspected COVID-19 hospital admissions when SOI were included (Spearman's rho = 0.617) and suspected COVID-19 admissions when SOI were excluded (Spearman's rho = 0.867). Weak to moderate significant correlations were found among individual SOI. Laboratory confirmed COVID-19 hospital admissions lagged in reporting by 3 days compared with suspected COVID-19 admissions when SOI were excluded.

CONCLUSIONS

Our results demonstrate the utility of a hospital admissions syndromic surveillance system to monitor and identify potential surges in severe COVID-19 infection within the community in a timely manner and provide situational awareness to inform preventive and preparatory health interventions.

Characteristics of emergency patients admitted to hospital with asthma: A population-based cohort study in Queensland, Australia

OBJECTIVE

Patient characteristics with exacerbation of asthma accessing care in the ED who are at risk of hospital admission have not been determined in subtropical climates. The objective of the study was to investigate the spatiotemporal burden of asthma hospital admissions across Queensland (QLD) and model risk factors for asthma hospital admission following an ED visit.

METHODS

Six years of routinely collected data (2012-2017) from 28 QLD public hospitals were extracted from Queensland Health's Emergency Data Collection. The dataset contained individual, episode-level ED presentations having asthma-like diagnoses, and an indicator of hospital admission, including to short-stay unit (SSU). A generalised additive model was used to examine the risk of asthma hospital admission.

RESULTS

Asthma hospital admissions increased from a weekly median of 79 (interquartile range [IQR] 66-99) in 2012 to 104 (IQR 81-135) in 2017. A higher incidence of asthma hospital admission was observed among males (median age 9, IQR 5-32) in childhood and females in adulthood (median age 32, IQR 11-51). Compared to the state capital Brisbane, the odds of asthma hospital admission ranged from 0.48 (95% CI 0.42-0.54) to 1.34 (95%CI 1.21-1.48) in other regions of QLD.

CONCLUSION

Asthma hospital admissions appear to be increasing in QLD, largely driven by utilisation of the SSU admissions for asthma. With large variation in both incidence and proportion admitted across different regions, routinely collected data can in part be used to understand risk factors for asthma-related hospital admission following an ED presentation and further inform public health policy development.

A Unifying Framework and Comparative Evaluation of Statistical and Machine Learning Approaches to Non-Specific Syndromic Surveillance

Monitoring the development of infectious diseases is of great importance for the prevention of major outbreaks. Syndromic surveillance aims at developing algorithms which can detect outbreaks as early as possible by monitoring data sources which allow to capture the occurrences of a certain disease. Recent research mainly concentrates on the surveillance of specific, known diseases, putting the focus on the definition of the disease pattern under surveillance. Until now, only little effort has been devoted to what we call non-specific syndromic surveillance, i.e., the use of all available data for detecting any kind of infectious disease outbreaks. In this work, we give an overview of non-specific syndromic surveillance from the perspective of machine learning and propose a unified framework based on global and local modeling techniques. We also present a set of statistical modeling techniques which have not been used in a local modeling context before and can serve as benchmarks for the more elaborate machine learning approaches. In an experimental comparison of different approaches to non-specific syndromic surveillance we found that these simple statistical techniques already achieve competitive results and sometimes even outperform more elaborate approaches. In particular, applying common syndromic surveillance methods in a non-specific setting seems to be promising.

Quantifying asymptomatic infection and transmission of COVID-19 in New York City using observed cases, serology, and testing capacity

The contributions of asymptomatic infections to herd immunity and community transmission are key to the resurgence and control of COVID-19, but are difficult to estimate using current models that ignore changes in testing capacity. Using a model that incorporates daily testing information fit to the case and serology data from New York City, we show that the proportion of symptomatic cases is low, ranging from 13 to 18%, and that the reproductive number may be larger than often assumed. Asymptomatic infections contribute substantially to herd immunity, and to community transmission together with presymptomatic ones. If asymptomatic infections transmit at similar rates as symptomatic ones, the overall reproductive number across all classes is larger than often assumed, with estimates ranging from 3.2 to 4.4. If they transmit poorly, then symptomatic cases have a larger reproductive number ranging from 3.9 to 8.1. Even in this regime, presymptomatic and asymptomatic cases together comprise at least 50% of the force of infection at the outbreak peak. We find no regimes in which all infection subpopulations have reproductive numbers lower than three. These findings elucidate the uncertainty that current case and serology data cannot resolve, despite consideration of different model structures. They also emphasize how temporal data on testing can reduce and better define this uncertainty, as we move forward through longer surveillance and second epidemic waves. Complementary information is required to determine the transmissibility of asymptomatic cases, which we discuss. Regardless, current assumptions about the basic reproductive number of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) should be reconsidered.

Evaluating disease outbreaks with syndromic surveillance using medical student clinical rotation patient encounter logs

Context

While the data generated by medical students at schools that require electronic patient encounter logs is primarily used to monitor their training progress, it can also be a great source of public health data. Specifically, it can be used for syndromic surveillance, a method used to analyze instantaneous health data for early detection of disease outbreaks.

Objective

To analyze how the International Classification of Diseases, 10th Revision (ICD-10) codes input by medical students at the Edward Via College of Osteopathic Medicine into the Clinical Rotation Evaluation and Documentation Organizer (CREDO) patient encounter logging system could act as a new syndromic surveillance tool.

Methods

A CREDO database query was conducted for ICD-10 codes entered between November 1, 2019 and March 13, 2020 using the World Health Organization's 2011 revised case definitions for Influenza Like Illness (ILI). During that period, medical students had an approximated mean of 3,000 patient encounters per day from over 1,500 clinical sites. A cumulative sum technique was applied to the data to generate alert thresholds. Breast cancer, a disease with a stable incidence during the specified timeframe, was used as a control.

Results

Total ILI daily ICD-10 counts that exceeded alert thresholds represented unusual levels of disease occurred 11 times from November 20, 2020 through February 28, 2020. This analysis is consistent with the COVID-19 pandemic timeline. The first statistically significant ILI increase occurred nine days prior to the first laboratory confirmed case in the country.

Conclusion

Syndromic surveillance can be timelier than traditional surveillance methods, which require laboratory testing to confirm disease. As a result of this study, we are installing a real-time alert for ILI into CREDO, so rates can be monitored continuously as an indicator of possible future new infectious disease outbreaks.

Outbreak of Severe Vomiting in Dogs Associated with a Canine Enteric Coronavirus, United Kingdom

The lack of population health surveillance for companion animal populations leaves them vulnerable to the effects of novel diseases without means of early detection. We present evidence on the effectiveness of a system that enabled early detection and rapid response a canine gastroenteritis outbreak in the United Kingdom. In January 2020, prolific vomiting among dogs was sporadically reported in the United Kingdom. Electronic health records from a nationwide sentinel network of veterinary practices confirmed a significant increase in dogs with signs of gastroenteric disease. Male dogs and dogs living with other vomiting dogs were more likely to be affected. Diet and vaccination status were not associated with the disease; however, a canine enteric coronavirus was significantly associated with illness. The system we describe potentially fills a gap in surveillance in neglected populations and could provide a blueprint for other countries.

Emergency department syndromic surveillance systems: a systematic review

BACKGROUND

Syndromic surveillance provides public health intelligence to aid in early warning and monitoring of public health impacts (e.g. seasonal influenza), or reassurance when an impact has not occurred. Using information collected during routine patient care, syndromic surveillance can be based on signs/symptoms/preliminary diagnoses. This approach makes syndromic surveillance much timelier than surveillance requiring laboratory confirmed diagnoses. The provision of healthcare services and patient access to them varies globally. However, emergency departments (EDs) exist worldwide, providing unscheduled urgent care to people in acute need. This provision of care makes ED syndromic surveillance (EDSyS) a potentially valuable tool for public health surveillance internationally. The objective of this study was to identify and describe the key characteristics of EDSyS systems that have been established and used globally.

METHODS

We systematically reviewed studies published in peer review journals and presented at International Society of Infectious Disease Surveillance conferences (up to and including 2017) to identify EDSyS systems which have been created and used for public health purposes. Search criteria developed to identify "emergency department" and "syndromic surveillance" were applied to NICE healthcare, Global Health and Scopus databases.

RESULTS

In total, 559 studies were identified as eligible for inclusion in the review, comprising 136 journal articles and 423 conference abstracts/papers. From these studies we identified 115 EDSyS systems in 15 different countries/territories across North America, Europe, Asia and Australasia. Systems ranged from local surveillance based on a single ED, to comprehensive national systems. National EDSyS systems were identified in 8 countries/territories: 2 reported inclusion of ≥85% of ED visits nationally (France and Taiwan).

CONCLUSIONS

EDSyS provides a valuable tool for the identification and monitoring of trends in severe illness. Technological advances, particularly in the emergency care patient record, have enabled the evolution of EDSyS over time. EDSyS reporting has become closer to 'real-time', with automated, secure electronic extraction and analysis possible on a daily, or more frequent basis. The dissemination of methods employed and evidence of successful application to public health practice should be encouraged to support learning from best practice, enabling future improvement, harmonisation and collaboration between systems in future.

PROSPERO NUMBER

CRD42017069150 .

Analysis of Emergency Department Visits and Hospital Activity during Influenza Season, COVID-19 Epidemic, and Lockdown Periods in View of Managing a Future Disaster Risk: A Multicenter Observational Study

ED-visits and through-ED admissions to medical/surgical wards (MSW) and intensive care unit (ICU) during influenza, COVID-19 and lockdown periods were evaluated in a four-hospital prospective observational study from November 2018 to March 2020. ED visit characteristics and main diagnostic categories were assessed. Analysis of 368,262 ED-visits highlighted a significantly increasing trend in ED-visits during influenza followed by a significantly decreasing trend after lockdown. For MSW-admissions, a pattern of growth during influenza was followed by a fall that began during COVID-19 pandemic and intensified during the lockdown. For ICU-admissions, a significant rise during the COVID-19 pandemic was followed by diminution during the lockdown period. During lockdown, significantly diminishing trends were shown for all diagnostic categories (between -40.8% and -73.6%), except influenza-like illness/COVID cases (+31.6%), Pulmonary embolism/deep vein thrombosis (+33.5%) and frequent users (+188.0%). The present study confirms an increase in demand during the influenza epidemic and during the initial phase of the COVID-19 epidemic, but a drop in activity during the lockdown, mainly related to non-COVID conditions. Syndromic surveillance of ILI cases in ED is a tool for monitoring influenza and COVID-19, and it can predict ED activity and the need for MSW and ICU beds.

Increase in emergency department visits related to cannabis reported using syndromic surveillance system

BACKGROUND

Cannabis is illegal in France but, as in many countries, legalization is under debate. In the United States, an increase of emergency department (ED) visits related to cannabis exposure (CE) in infants and adults was reported. In France, a retrospective observational study also suggested an increase of CE in children under 6 years old. This study only included toddlers and the data sources used did not allow repeated analysis for monitoring.

METHODS

Our study aimed to evaluate the trend in visits for CE in ED in patients younger than 27 years old in Southern France. A cross-sectional study using the Electronic Emergency Department Abstracts (EEDA) included in the national Syndromic Surveillance System. CE visits were defined using International Classification of Disease (ICD-10).

RESULTS

From 2009 to 2014, 16 EDs consistently reported EEDA with <5% missing diagnosis code. Seven hundred and ninety seven patients were admitted for CE including 49 (4.1%) children under 8 years old. From 2009-11 to 2012-14, the rate of CE visits increased significantly across all age groups. The highest increase was in the 8-14 years old (+144%; 1.85-4.51, P < 0.001) and was also significant in children under 8 (0.53-1.06; P = 0.02). Among children under 8, hospitalization rate (75.5% vs. 16.8%; P < 0.001) and intensive care unit admissions (4.1% vs. 0.1%; P < 0.001) were higher compared with patients older than 8 years.

CONCLUSION

These trends occurred despite cannabis remaining illegal. EEDA could be useful for monitoring CE in EDs.

Triage in the Time of Diphtheria

Introduction

A diphtheria outbreak occurred in 2017 in Jakarta, Indonesia, during which our hospital was appointed as a referral hospital where patients with upper respiratory tract symptoms were sent for confirmation of the diagnosis and medical intervention. In this study we review the implementation of the emergency department (ED) triage process and patient flow management during the diphtheria outbreak. No previous study in Indonesia has provided a detailed report on the triage process during infectious disease outbreaks.

Method

We modified our pre-existing hospital triage method according to the “identify, isolate, and inform” principle. We developed novel criteria for triage to identify triage-suspected cases and also a diphtheria package to simplify the diagnostic process. Four separate rooms were modified to isolation spaces to enable medical staff to observe these patients. We obtained data from the ED outbreak registry and electronic health records.

Results

Of 60 cases of triage-suspected diphtheria, six were classified as suspected diphtheria. The mean time from “identify” to “isolate” was 3.5 minutes, and from “isolate” to “inform” was 10 minutes. Mean ED length of stay for probable diphtheria was 24.46 hours. No medical personnel in the ED showed any signs of diphtheria 30 days after the outbreak had abated.

Conclusion

The modified criteria can help triage officers detect suspected diphtheria cases and measure the triage response time. Use of the diphtheria package and four separate rooms in the ED could act as an infection control procedure and facilitate the improvement of the diagnostic process.

Utility of emergency call centre, dispatch and ambulance data for syndromic surveillance of infectious diseases: a scoping review

BACKGROUND

Syndromic surveillance can supplement conventional health surveillance by analyzing less-specific, near-real-time data for an indication of disease occurrence. Emergency medical call centre dispatch and ambulance data are examples of routinely and efficiently collected syndromic data that might assist in infectious disease surveillance. Scientific literature on the subject is scarce and an overview of results is lacking.

METHODS

A scoping review including (i) review of the peer-reviewed literature, (ii) review of grey literature and (iii) interviews with key informants.

RESULTS

Forty-four records were selected: 20 peer reviewed and 24 grey publications describing 44 studies and systems. Most publications focused on detecting respiratory illnesses or on outbreak detection at mass gatherings. Most used retrospective data; some described outcomes of temporary systems; only two described continuously active dispatch- and ambulance-based syndromic surveillance. Key informants interviewed valued dispatch- and ambulance-based syndromic surveillance as a potentially useful addition to infectious disease surveillance. Perceived benefits were its potential timeliness, standardization of data and clinical value of the data.

CONCLUSIONS

Various dispatch- and ambulance-based syndromic surveillance systems for infectious diseases have been reported, although only roughly half are documented in peer-reviewed literature and most concerned retrospective research instead of continuously active surveillance systems. Dispatch- and ambulance-based syndromic data were mostly assessed in relation to respiratory illnesses; reported use for other infectious disease syndromes is limited. They are perceived by experts in the field of emergency surveillance to achieve time gains in detection of infectious disease outbreaks and to provide a useful addition to traditional surveillance efforts.

Electronic Health Record-Based Surveillance for Community Transmitted COVID-19 in the Emergency Department

Introduction

SARS-CoV-2, a novel coronavirus, manifests as a respiratory syndrome (COVID-19) and is the cause of an ongoing pandemic. The response to COVID-19 in the United States has been hampered by an overall lack of diagnostic testing capacity. To address uncertainty about ongoing levels of SARS-CoV-2 community transmission early in the pandemic, we aimed to develop a surveillance tool using readily available emergency department (ED) operations data extracted from the electronic health record (EHR). This involved optimizing the identification of acute respiratory infection (ARI)-related encounters and then comparing metrics for these encounters before and after the confirmation of SARS-CoV-2 community transmission.

Methods

We performed an observational study using operational EHR data from two Midwest EDs with a combined annual census of over 80,000. Data were collected three weeks before and after the first confirmed case of local SARS-CoV-2 community transmission. To optimize capture of ARI cases, we compared various metrics including chief complaint, discharge diagnoses, and ARI-related orders. Operational metrics for ARI cases, including volume, pathogen identification, and illness severity, were compared between the preand post-community transmission timeframes using chi-square tests of independence.

Results

Compared to our combined definition of ARI, chief complaint, discharge diagnoses, and isolation orders individually identified less than half of the cases. Respiratory pathogen testing was the top performing individual ARI definition but still only identified 72.2% of cases. From the pre to post periods, we observed significant increases in ED volumes due to ARI and ARI cases without identified pathogen.

Conclusion

Certain methods for identifying ARI cases in the ED may be inadequate and multiple criteria should be used to optimize capture. In the absence of widely available SARS-CoV-2 testing, operational metrics for ARI-related encounters, especially the proportion of cases involving negative pathogen testing, are useful indicators for active surveillance of potential COVID-19 related ED visits.

Ambulance dispatch calls attributable to influenza A and other common respiratory viruses in the Netherlands (2014-2016)

BACKGROUND

Ambulance dispatches could be useful for syndromic surveillance of severe respiratory infections. We evaluated whether ambulance dispatch calls of highest urgency reflect the circulation of influenza A virus, influenza B virus, respiratory syncytial virus (RSV), rhinovirus, adenovirus, coronavirus, parainfluenzavirus and human metapneumovirus (hMPV).

METHODS

We analysed calls from four ambulance call centres serving 25% of the population in the Netherlands (2014-2016). The chief symptom and urgency level is recorded during triage; we restricted our analysis to calls with the highest urgency and identified those compatible with a respiratory syndrome. We modelled the relation between respiratory syndrome calls (RSC) and respiratory virus trends using binomial regression with identity link function.

RESULTS

We included 211 739 calls, of which 15 385 (7.3%) were RSC. Proportion of RSC showed periodicity with winter peaks and smaller interseasonal increases. Overall, 15% of RSC were attributable to respiratory viruses (20% in out-of-office hour calls). There was large variation by age group: in <15 years, only RSV was associated and explained 11% of RSC; in 15-64 years, only influenza A (explained 3% of RSC); and in ≥65 years adenovirus explained 9% of RSC, distributed throughout the year, and hMPV (4%) and influenza A (1%) mainly during the winter peaks. Additionally, rhinovirus was associated with total RSC.

CONCLUSION

High urgency ambulance dispatches reflect the burden of different respiratory viruses and might be useful to monitor the respiratory season overall. Influenza plays a smaller role than other viruses: RSV is important in children while adenovirus and hMPV are the biggest contributors to emergency calls in the elderly.

Supportive care for patient with respiratory diseases: an umbrella review

Background

Supportive treatment is an important and effective part of the management for patients with life-threatening diseases. This study aims to identify and evaluate the forms of supportive care for patients with respiratory diseases.

Methods

An umbrella review of supportive care for patient with respiratory diseases was undertaken. We comprehensively searched the following databases: Medline, EMBASE, Web of Science, CNKI (China National Knowledge Infrastructure), Wanfang Data and CBM (SinoMed) from their inception to 31 March 2020, and other sources to identify systematic reviews and meta-analyses related to supportive treatments for patient with respiratory diseases including Coronavirus Disease 2019 (COVID-19), severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS) and influenza. We assessed the methodological quality using the AMSTAR score and the quality of the evidence for the primary outcomes of each included systematic review and meta-analysis.

Results

We included 18 systematic reviews and meta-analyses in this study. Most studies focused on the respiratory and circulatory support. Ten studies were of high methodological quality, five studies of medium quality, and three studies of low quality. According to four studies extracorporeal membrane oxygenation did not reduce mortality in adults [odds ratio/relative risk (OR/RR) ranging from 0.71 to 1.28], but two studies reported significantly lower mortality in patients receiving venovenous extracorporeal membrane oxygenation than in the control group (OR/RR ranging from 0.38 to 0.73). Besides, monitoring of vital signs and increasing the number of medical staff may also reduce the mortality in patients with respiratory diseases.

Conclusions

Our overview suggests that supportive care may reduce the mortality of patients with respiratory diseases to some extent. However, the quality of evidence for the primary outcomes in the included studies was low to moderate. Further systematic reviews and meta-analyses are needed to address the evidence gap regarding the supportive care for SARS, MERS and COVID-19.

Hospital management preparedness tools in biological events: A scoping review

INTRODUCTION

The objective of the present study was to systematically review the current research knowledge on hospital preparedness tools used in biological events and factors affecting hospital preparedness in such incidents in using a scoping review methodology.

MATERIALS AND METHODS

The review process was conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guideline. Online databases (PubMed, Scopus, Web of Science, and Google Scholar) were used to identify papers published that evaluated instruments or tools for hospital preparedness in biological disasters (such as influenza, Ebola, and bioterrorism events). The search, article selection, and data extraction were carried out by two researchers independently.

RESULTS

A total of 3440 articles were screened, with 20 articles identified for final analysis. The majority of research studies identified were conducted in the United States (45%) and were focused on CBRN incident (20%), Ebola, infectious disease and bioterrorism events (15%), mass casualty incidents and influenza pandemic (10%), public health emergency, SARS, and biological events (5%). Factors that were identified in the study to hospitals preparedness in biological events classified in seven areas including planning, surge capacity, communication, training and education, medical management, surveillance and standard operation process.

CONCLUSIONS

Published evidences of hospital preparedness on biological events as well as the overall quality of the psychometric properties of most studies were limited. The results of the current scoping review could be used as a basis for designing and developing a standard assessment tool for hospital preparedness in biological events, and it can also be used as a clear vision for the healthcare managers and policymakers in their future plans to confront the challenges identified by healthcare institutes in biologic events.

Evaluation of Syndromic Surveillance Systems in 6 US State and Local Health Departments

OBJECTIVE

Evaluating public health surveillance systems is critical to ensuring that conditions of public health importance are appropriately monitored. Our objectives were to qualitatively evaluate 6 state and local health departments that were early adopters of syndromic surveillance in order to (1) understand the characteristics and current uses, (2) identify the most and least useful syndromes to monitor, (3) gauge the utility for early warning and outbreak detection, and (4) assess how syndromic surveillance impacted their daily decision making.

DESIGN

We adapted evaluation guidelines from the Centers for Disease Control and Prevention and gathered input from the Centers for Disease Control and Prevention subject matter experts in public health surveillance to develop a questionnaire.

PARTICIPANTS

We interviewed staff members from a convenience sample of 6 local and state health departments with syndromic surveillance programs that had been in operation for more than 10 years.

RESULTS

Three of the 6 interviewees provided an example of using syndromic surveillance to identify an outbreak (ie, cluster of foodborne illness in 1 jurisdiction) or detect a surge in cases for seasonal conditions (eg, influenza in 2 jurisdictions) prior to traditional, disease-specific systems. Although all interviewees noted that syndromic surveillance has not been routinely useful or efficient for early outbreak detection or case finding in their jurisdictions, all agreed that the information can be used to improve their understanding of dynamic disease control environments and conditions (eg, situational awareness) in their communities.

CONCLUSION

In the jurisdictions studied, syndromic surveillance may be useful for monitoring the spread and intensity of large outbreaks of disease, especially influenza; enhancing public health awareness of mass gatherings and natural disasters; and assessing new, otherwise unmonitored conditions when real-time alternatives are unavailable. Future studies should explore opportunities to strengthen syndromic surveillance by including broader access to and enhanced analysis of text-related data from electronic health records. Health departments may accelerate the development and use of syndromic surveillance systems, including the improvement of the predictive value and strengthening the early outbreak detection capability of these systems. These efforts support getting the right information to the right people at the right time, which is the overarching goal of CDC's Surveillance Strategy.

Methods for detecting seasonal influenza epidemics using a school absenteeism surveillance system

Background

School absenteeism data have been collected daily by the public health unit in Wellington-Dufferin-Guelph, Ontario since 2008. To date, a threshold-based approach has been implemented to raise alerts for community-wide and within-school illness outbreaks. We investigate several statistical modelling approaches to using school absenteeism for influenza surveillance at the regional level, and compare their performances using two metrics.

Methods

Daily absenteeism percentages from elementary and secondary schools, and report dates for influenza cases, were obtained from Wellington-Dufferin-Guelph Public Health. Several absenteeism data aggregations were explored, including using the average across all schools or only using schools of one type. A 10% absence threshold, exponentially weighted moving average model, logistic regression with and without seasonality terms, day of week indicators, and random intercepts for school year, and generalized estimating equations were used as epidemic detection methods for seasonal influenza. In the regression models, absenteeism data with various lags were used as predictor variables, and missing values in the datasets used for parameter estimation were handled either by deletion or linear interpolation. The epidemic detection methods were compared using a false alarm rate (FAR) as well as a metric for alarm timeliness.

Results

All model-based epidemic detection methods were found to decrease the FAR when compared to the 10% absence threshold. Regression models outperformed the exponentially weighted moving average model and including seasonality terms and a random intercept for school year generally resulted in fewer false alarms. The best-performing model, a seasonal logistic regression model with random intercept for school year and a day of week indicator where parameters were estimated using absenteeism data that had missing values linearly interpolated, produced a FAR of 0.299, compared to the pre-existing threshold method which at best gave a FAR of 0.827.

Conclusions

School absenteeism can be a useful tool for alerting public health to upcoming influenza epidemics in Wellington-Dufferin-Guelph. Logistic regression with seasonality terms and a random intercept for school year was effective at maximizing true alarms while minimizing false alarms on historical data from this region.

Valuing Health Surveillance as an Information System: Interdisciplinary Insights

The economic evaluation of health surveillance systems and of health information is a methodological challenge, as for information systems in general. Main present threads are considering cost-effectiveness solutions, minimizing costs for a given technically required output, or cost-benefit analysis, balancing costs with economic benefits of duly informed public interventions. The latter option, following a linear command-and-control perspective, implies considering a main causal link between information, decision, action, and health benefits. Yet, valuing information, taking into account its nature and multiple sources, the modalities of its processing cycle, from production to diffusion, decentralized use and gradual building of a shared information capital, constitutes a promising challenge. This work proposes an interdisciplinary insight on the value of health surveillance to get a renewed theoretical framework integrating information and informatics theory and information economics. The reflection is based on a typological approach of value, basically distinguishing between use and non-use values. Through this structured discussion, the main idea is to expand the boundaries of surveillance evaluation, to focus on changes and trends, on the dynamic and networked structure of information systems, on the contribution of diverse data, and on the added value of combining qualitative and quantitative information. Distancing itself from the command-and-control model, this reflection considers the behavioral fundaments of many health risks, as well as the decentralized, progressive and deliberative dimension of decision-making in risk management. The framework also draws on lessons learnt from recent applications within and outside of health sector, as in surveillance of antimicrobial resistance, inter-laboratory networks, the use of big data or web sources, the diffusion of technological products and large-scale financial risks. Finally, the paper poses the bases to think the challenge of a workable approach to economic evaluation of health surveillance through a better understanding of health information value. It aims to avoid over-simplifying the range of health information benefits across society while keeping evaluation within the boundaries of what may be ascribed to the assessed information system.

Syndromic Surveillance of Respiratory Disease in Free-Living Chimpanzees

Disease surveillance in wildlife is rapidly expanding in scope and methodology, emphasizing the need for formal evaluations of system performance. We examined a syndromic surveillance system for respiratory disease detection in Gombe National Park, Tanzania, from 2004 to 2012, with respect to data quality, disease trends, and respiratory disease detection. Data quality was assessed by examining community coverage, completeness, and consistency. The data were examined for baseline trends; signs of respiratory disease occurred at a mean frequency of less than 1 case per week, with most weeks containing zero observations of abnormalities. Seasonal and secular (i.e., over a period of years) trends in respiratory disease frequency were not identified. These baselines were used to develop algorithms for outbreak detection using both weekly counts and weekly prevalence thresholds and then compared retrospectively on the detection of 13 respiratory disease clusters from 2005 to 2012. Prospective application of outbreak detection algorithms to real-time syndromic data would be useful in triggering a rapid outbreak response, such as targeted diagnostic sampling, enhanced surveillance, or mitigation.

Cross Disciplinary Consultancy to Bridge Public Health Technical Needs and Analytic Developers: Negation Detection Use Case

This paper describes a continuing initiative of the International Society for Disease Surveillance designed to bring together public health practitioners and analytics solution developers from both academia and industry. Funded by the Defense Threat Reduction Agency, a series of consultancies have been conducted on a range of topics of pressing concern to public health (e.g. developing methods to enhance prediction of asthma exacerbation, developing tools for asyndromic surveillance from chief complaints). The topic of this final consultancy, conducted at the University of Utah in January 2017, is focused on defining a roadmap for the development of algorithms, tools, and datasets for improving the capabilities of text processing algorithms to identify negated terms (i.e. negation detection) in free-text chief complaints and triage reports.

Choosing the best algorithm for event detection based on the intended application: A conceptual framework for syndromic surveillance

There is an extensive list of methods available for the early detection of an epidemic signal in syndromic surveillance data. However, there is no commonly accepted classification system for the statistical methods used for event detection in syndromic surveillance. Comparing and choosing appropriate event detection algorithms is an increasingly challenging task. Although lists of selection criteria, and statistical methods used for signal detection have been reported, selection criteria are rarely linked to a specific set of appropriate statistical methods. The paper presents a practical approach for guiding surveillance practitioners to make an informed choice from among the most popular event detection algorithms based on the intended application of the algorithm. We developed selection criteria by mapping the assumptions and performance characteristics of event detection algorithms directly to important characteristics of the time series used in syndromic surveillance. We also considered types of epidemics that may be expected and other characteristics of the surveillance system. These guidelines will provide decisions makers, data analysts, public health practitioners, and researchers with a comprehensive but practical overview of the domain, which may reduce the technical barriers to the development and implementation of syndromic surveillance systems in animal and human health. The classification scheme was restricted to univariate and temporal methods because they are the most commonly used algorithms in syndromic surveillance.

Poor outcomes among elderly patients hospitalized for influenza-like illness

BACKGROUND AND OBJECTIVE

Global Influenza Hospital Surveillance Network is a worldwide initiative that aims to document the burden of influenza infections among acute admissions and vaccine effectiveness in particular countries. As a partner of this platform, we aimed to determine the frequency of influenza infections among acute admissions with influenza-like illness and the outcomes of enrolled patients during the 2015-2016 influenza season in selected hospitals in Turkey.

PATIENTS AND METHODS

The investigators screened the hospital admission registries, chart review or available records, and screened all patients hospitalized in the previous 24-48 hours or overnight in the predefined wards or emergency room. A total of 1351 patients were screened for enrollment in five tertiary care referral hospitals in Ankara and 774 patients (57.3% of the initial screened population) were eligible for swabbing. All of the eligible patients who consented were swabbed and tested for influenza with real-time polymerase chain reaction (PCR) based methods.

RESULTS

Overall, influenza positivity was detected in 142 patients (18.4%). The predominant influenza strain was A H1N1pdm09. Outcomes were worse among elderly patients, regardless of the presence of the influenza virus. Half of the patients over 65 years of age were admitted to the intensive care unit, while one third required any mode of mechanical ventilation and one fourth died in the hospital in that particular episode.

CONCLUSION

These findings can guide hospitals to plan and prepare for the influenza season. Effective influenza vaccination strategies, particularly aimed at the elderly and adults with chronic diseases, can provide an opportunity for prevention of deaths due to influenza-like illness.

Monitoring Interest in Herpes Zoster Vaccination: Analysis of Google Search Data

Background

A new recombinant subunit vaccine for herpes zoster (HZ or shingles) was approved by the United States Food and Drug Administration on October 20, 2017 and is expected to replace the previous live attenuated vaccine. There have been low coverage rates with the live attenuated vaccine (Zostavax), ranging from 12-32% of eligible patients receiving the HZ vaccine.

Objective

This study aimed to provide insight into trends and potential reasons for interest in HZ vaccination.

Methods

Internet search data were queried from the Google Health application programming interface from 2004-2017. Seasonality of normalized search volume was analyzed using wavelets and Fisher’s g test.

Results

The search terms “shingles vaccine,” “zoster vaccine,” and “zostavax” all exhibited significant periodicity in the fall months (P<.001), with sharp increases after recommendations for vaccination by public health-related organizations. Although the terms “shingles blisters,” “shingles itch,” “shingles rash,” “skin rash,” and “shingles medicine” exhibited statistically significant periodicities with a seasonal peak in the summer (P<.001), the terms “shingles contagious,” “shingles pain,” “shingles treatment,” and “shingles symptoms” did not reveal an annual trend.

Conclusions

There may be increased interest in HZ vaccination during the fall and after public health organization recommendations are broadcast. This finding points to the possibility that increased awareness of the vaccine through public health announcements could be evaluated as a potential intervention for increasing vaccine coverage.

Identifying Geographic Disparities in Diabetes Prevalence Among Adults and Children Using Emergency Claims Data

Geographic surveillance can identify hotspots of disease and reveal associations between health and the environment. Our study used emergency department surveillance to investigate geographic disparities in type 1 and type 2 diabetes prevalence among adults and children. Using all-payer emergency claims data from 2009 to 2013, we identified unique New York City residents with diabetes and geocoded their location using home addresses. Geospatial analysis was performed to estimate diabetes prevalence by New York City Census tract. We also used multivariable regression to identify neighborhood-level factors associated with higher diabetes prevalence. We estimated type 1 and type 2 diabetes prevalence at 0.23% and 10.5%, respectively, among adults and 0.20% and 0.11%, respectively, among children in New York City. Pediatric type 1 diabetes was associated with higher income (P = 0.001), whereas adult type 2 diabetes was associated with lower income (P < 0.001). Areas with a higher proportion of nearby restaurants categorized as fast food had a higher prevalence of all types of diabetes (P < 0.001) except for pediatric type 2 diabetes. Type 2 diabetes among children was only higher in neighborhoods with higher proportions of African American residents (P < 0.001). Our findings identify geographic disparities in diabetes prevalence that may require special attention to address the specific needs of adults and children living in these areas. Our results suggest that the food environment may be associated with higher type 1 diabetes prevalence. However, our analysis did not find a robust association with the food environment and pediatric type 2 diabetes, which was predominantly focused in African American neighborhoods.

Extreme precipitation and emergency room visits for influenza in Massachusetts: a case-crossover analysis

BACKGROUND

Influenza peaks during the wintertime in temperate regions and during the annual rainy season in tropical regions - however reasons for the observed differences in disease ecology are poorly understood. We hypothesize that episodes of extreme precipitation also result in increased influenza in the Northeastern United States, but this association is not readily apparent, as no defined 'rainy season' occurs. Our objective was to evaluate the association between extreme precipitation (≥ 99th percentile) events and risk of emergency room (ER) visit for influenza in Massachusetts during 2002-2008.

METHODS

A case-crossover analysis of extreme precipitation events and influenza ER visits was conducted using hospital administrative data including patient town of residence, date of visit, age, sex, and associated diagnostic codes. Daily precipitation estimates were generated for each town based upon data from the National Oceanic and Atmospheric Administration. Odds ratio (OR) and 95% confidence intervals (CI) for associations between extreme precipitation and ER visits for influenza were estimated using conditional logistic regression.

RESULTS

Extreme precipitation events were associated with an OR = 1.23 (95%CI: 1.16, 1.30) for ER visits for influenza at lag days 0-6. There was significant effect modification by race, with the strongest association observed among Blacks (OR = 1.48 (1.30, 1.68)).

CONCLUSIONS

We observed a positive association between extreme precipitation events and ER visits for influenza, particularly among Blacks. Our results suggest that influenza is associated with extreme precipitation in a temperate area; this association could be a result of disease ecology, behavioral changes such as indoor crowding, or both. Extreme precipitation events are expected to increase in the Northeastern United States as climate change progresses. Additional research exploring the basis of this association can inform potential interventions for extreme weather events and influenza transmission.

Outbreak detection and evaluation of a school-based influenza-like-illness syndromic surveillance in Tianjin, China

School-based influenza-like-illness (ILI) syndromic surveillance can be an important part of influenza community surveillance by providing early warnings for outbreaks and leading to a fast response. From September 2012 to December 2014, syndromic surveillance of ILI was carried out in 4 county-level schools. The cumulative sum methods(CUSUM) was used to detect abnormal signals. A susceptible-exposed-infectious/asymptomatic-recovered (SEIAR) model was fit to the influenza outbreak without control measures and compared with the actual influenza outbreak to evaluate the effectiveness of early control efforts. The ILI incidence rates in 2014 (14.51%) was higher than the incidence in 2013 (5.27%) and 2012 (3.59%). Ten school influenza outbreaks were detected by CUSUM. Each outbreak had high transmissibility with a median R_unc of 4.62. The interventions in each outbreak had high effectiveness and all R_con were 0. The early intervention had high effectiveness within the school-based ILI syndromic surveillance. Syndromic surveillance within schools can play an important role in controlling influenza outbreaks.

Integrated Detection and Prediction of Influenza Activity for Real-Time Surveillance: Algorithm Design

Background

Influenza is a viral respiratory disease capable of causing epidemics that represent a threat to communities worldwide. The rapidly growing availability of electronic “big data” from diagnostic and prediagnostic sources in health care and public health settings permits advance of a new generation of methods for local detection and prediction of winter influenza seasons and influenza pandemics.

Objective

The aim of this study was to present a method for integrated detection and prediction of influenza virus activity in local settings using electronically available surveillance data and to evaluate its performance by retrospective application on authentic data from a Swedish county.

Methods

An integrated detection and prediction method was formally defined based on a design rationale for influenza detection and prediction methods adapted for local surveillance. The novel method was retrospectively applied on data from the winter influenza season 2008-09 in a Swedish county (population 445,000). Outcome data represented individuals who met a clinical case definition for influenza (based on International Classification of Diseases version 10 [ICD-10] codes) from an electronic health data repository. Information from calls to a telenursing service in the county was used as syndromic data source.

Results

The novel integrated detection and prediction method is based on nonmechanistic statistical models and is designed for integration in local health information systems. The method is divided into separate modules for detection and prediction of local influenza virus activity. The function of the detection module is to alert for an upcoming period of increased load of influenza cases on local health care (using influenza-diagnosis data), whereas the function of the prediction module is to predict the timing of the activity peak (using syndromic data) and its intensity (using influenza-diagnosis data). For detection modeling, exponential regression was used based on the assumption that the beginning of a winter influenza season has an exponential growth of infected individuals. For prediction modeling, linear regression was applied on 7-day periods at the time in order to find the peak timing, whereas a derivate of a normal distribution density function was used to find the peak intensity. We found that the integrated detection and prediction method detected the 2008-09 winter influenza season on its starting day (optimal timeliness 0 days), whereas the predicted peak was estimated to occur 7 days ahead of the factual peak and the predicted peak intensity was estimated to be 26% lower than the factual intensity (6.3 compared with 8.5 influenza-diagnosis cases/100,000).

Conclusions

Our detection and prediction method is one of the first integrated methods specifically designed for local application on influenza data electronically available for surveillance. The performance of the method in a retrospective study indicates that further prospective evaluations of the methods are justified.

Analysis of Influenza and RSV dynamics in the community using a 'Local Transmission Zone' approach

Understanding the dynamics of pathogen spread within urban areas is critical for the effective prevention and containment of communicable diseases. At these relatively small geographic scales, short-distance interactions and tightly knit sub-networks dominate the dynamics of pathogen transmission; yet, the effective boundaries of these micro-scale groups are generally not known and often ignored. Using clinical test results from hospital admitted patients we analyze the spatio-temporal distribution of Influenza Like Illness (ILI) in the city of Jerusalem over a period of three winter seasons. We demonstrate that this urban area is not a single, perfectly mixed ecology, but is in fact comprised of a set of more basic, relatively independent pathogen transmission units, which we term here Local Transmission Zones, LTZs. By identifying these LTZs, and using the dynamic pathogen-content information contained within them, we are able to differentiate between disease-causes at the individual patient level often with near-perfect predictive accuracy.

Surveillance for Healthcare-Associated Influenza-Like Illness in Pediatric Clinics: Validity of Diagnosis Codes for Case Identification

Compared to chart review, a definition based on the International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis code for healthcare-associated influenza-like illness (HA-ILI) among young children in a large pediatric network demonstrated high positive and negative predictive values. This finding suggests that electronic health record-based definitions for surveillance can accurately identify medically attended outpatient HA-ILI cases for research and surveillance. Infect Control Hosp Epidemiol 2016;1-4.

Development and Application of Syndromic Surveillance for Severe Weather Events Following Hurricane Sandy

OBJECTIVE

Following Hurricane Superstorm Sandy, the New Jersey Department of Health (NJDOH) developed indicators to enhance syndromic surveillance for extreme weather events in EpiCenter, an online system that collects and analyzes real-time chief complaint emergency department (ED) data and classifies each visit by indicator or syndrome.

METHODS

These severe weather indicators were finalized by using 2 steps: (1) key word inclusion by review of chief complaints from cases where diagnostic codes met selection criteria and (2) key word exclusion by evaluating cases with key words of interest that lacked selected diagnostic codes.

RESULTS

Graphs compared 1-month, 3-month, and 1-year periods of 8 Hurricane Sandy-related severe weather event indicators against the same period in the following year. Spikes in overall ED visits were observed immediately after the hurricane for carbon monoxide (CO) poisoning, the 3 disrupted outpatient medical care indicators, asthma, and methadone-related substance use. Zip code level scan statistics indicated clusters of CO poisoning and increased medicine refill needs during the 2 weeks after Hurricane Sandy. CO poisoning clusters were identified in areas with power outages of 4 days or longer.

CONCLUSIONS

This endeavor gave the NJDOH a clearer picture of the effects of Hurricane Sandy and yielded valuable state preparation information to monitor the effects of future severe weather events. (Disaster Med Public Health Preparedness. 2016;10:463-471).

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

Background

The emergency department (ED) increasingly acts as a gateway to the evaluation and treatment of acute illnesses. Consequently, it has also become a key testing ground for systems that monitor and identify outbreaks of disease. Here, we describe a new technology that automatically collects body temperatures during triage. The technology was tested in an ED as an approach to monitoring diseases that cause fever, such as seasonal flu and some pandemics.

Methods

Temporal artery thermometers that log temperature measurements were placed in a Boston ED and used for initial triage vital signs. Time-stamped measurements were collected from the thermometers to investigate the performance a real-time system would offer. The data were summarized in terms of rates of fever (temperatures ≥100.4 °F [≥38.0 °C]) and were qualitatively compared with regional disease surveillance programs in Massachusetts.

Results

From September 2009 through August 2011, 71,865 body temperatures were collected and included in our analysis, 2073 (2.6 %) of which were fevers. The period of study included the autumn–winter wave of the 2009–2010 H1N1 (swine flu) pandemic, during which the weekly incidence of fever reached a maximum of 5.6 %, as well as the 2010–2011 seasonal flu outbreak, during which the maximum weekly incidence of fever was 6.6 %. The periods of peak fever rates corresponded with the periods of regionally elevated flu activity.

Conclusions

Temperature measurements were monitored at triage in the ED over a period of 2 years. The resulting data showed promise as a potential surveillance tool for febrile disease that could complement current disease surveillance systems. Because temperature can easily be measured by non-experts, it might also be suitable for monitoring febrile disease activity in schools, workplaces, and transportation hubs, where many traditional syndromic indicators are impractical. However, the system’s validity and generalizability should be evaluated in additional years and settings.

Electronic supplementary material

The online version of this article (doi:10.1186/s12873-016-0080-7) contains supplementary material, which is available to authorized users.

National and Regional Representativeness of Hospital Emergency Department Visit Data in the National Syndromic Surveillance Program, United States, 2014

OBJECTIVE

We examined the representativeness of the nonfederal hospital emergency department (ED) visit data in the National Syndromic Surveillance Program (NSSP).

METHODS

We used the 2012 American Hospital Association Annual Survey Database, other databases, and information from state and local health departments participating in the NSSP about which hospitals submitted data to the NSSP in October 2014. We compared ED visits for hospitals submitting data with all ED visits in all 50 states and Washington, DC.

RESULTS

Approximately 60.4 million of 134.6 million ED visits nationwide (~45%) were reported to have been submitted to the NSSP. ED visits in 5 of 10 regions and the majority of the states were substantially underrepresented in the NSSP. The NSSP ED visits were similar to national ED visits in terms of many of the characteristics of hospitals and their service areas. However, visits in hospitals with the fewest annual ED visits, in rural trauma centers, and in hospitals serving populations with high percentages of Hispanics and Asians were underrepresented.

CONCLUSIONS

NSSP nonfederal hospital ED visit data were representative for many hospital characteristics and in some geographic areas but were not very representative nationally and in many locations. Representativeness could be improved by increasing participation in more states and among specific types of hospitals. (Disaster Med Public Health Preparedness. 2016;10:562-569).

Leading Indicators and the Evaluation of the Performance of Alerts for Influenza Epidemics

BACKGROUND

Most evaluations of epidemic thresholds for influenza have been limited to internal criteria of the indicator variable. We aimed to initiate discussion on appropriate methods for evaluation and the value of cross-validation in assessing the performance of a candidate indicator for influenza activity.

METHODS

Hospital records of in-patients with a diagnosis of confirmed influenza were extracted from the Canadian Discharge Abstract Database from 2003 to 2011 and aggregated to weekly and regional levels, yielding 7 seasons and 4 regions for evaluation (excluding the 2009 pandemic period). An alert created from the weekly time-series of influenza positive laboratory tests (FluWatch, Public Health Agency of Canada) was evaluated against influenza-confirmed hospitalizations on 5 criteria: lead/lag timing; proportion of influenza hospitalizations covered by the alert period; average length of the influenza alert period; continuity of the alert period and length of the pre-peak alert period.

RESULTS

Influenza hospitalizations led laboratory positive tests an average of only 1.6 (95% CI: -1.5, 4.7) days. However, the difference in timing exceeded 1 week and was statistically significant at the significance level of 0.01 in 5 out of 28 regional seasons. An alert based primarily on 5% positivity and 15 positive tests produced an average alert period of 16.6 weeks. After allowing for a reporting delay of 2 weeks, the alert period included 80% of all influenza-confirmed hospitalizations. For 20 out of the 28 (71%) seasons, the first alert would have been signalled at least 3 weeks (in real time) prior to the week with maximum number of influenza hospitalizations.

CONCLUSIONS

Virological data collected from laboratories was a good indicator of influenza activity with the resulting alert covering most influenza hospitalizations and providing a reasonable pre-peak warning at the regional level. Though differences in timing were statistically significant, neither time-series consistently led the other.

Spatial patterns of epilepsy-related emergency department visits in california

Background

Socio-demographic factors are associated with increased emergency department (ED) use among patients with epilepsy. However, there has been limited spatial analysis of such visits.

Design and methods

California ED visit at the patient ZIP Code level were examined using Kulldorf’s spatial scan statistic to identify clusters of increased risk for epilepsy-related visits. Logistic regression was used to examine the relative importance of patient socio-demographics, Census-based and hospital measures.

Results

During 2009-2011 there were 29,715,009 ED visits at 330 hospitals, of which 139,235 (0.5%) had epilepsy (International Classification of Disease-9 345.xx) as the primary diagnosis. Three large urban clusters of high epilepsy-related ED visits were centred in the cities of Los Angeles, Oakland and Stockton and a large rural cluster centred in Kern County. No consistent pattern by age, race/ethnicity, household structure, and income was observed among all clusters. Regression found only the Los Angeles cluster significant after adjusting for other measures.

Conclusions

Geospatial analysis within a large and geographically diverse region identified a cluster within its most populous city having an increased risk of ED visits for epilepsy independent of selected socio-demographic and hospital measures. Additional research is necessary to determine whether elevated rates of ED visits represent increased prevalence of epilepsy or an inequitable system of epilepsy care.

Visualizing the quality of partially accruing data for use in decision making

Secondary use of clinical health data for near real-time public health surveillance presents challenges surrounding its utility due to data quality issues. Data used for real-time surveillance must be timely, accurate and complete if it is to be useful; if incomplete data are used for surveillance, understanding the structure of the incompleteness is necessary. Such data are commonly aggregated due to privacy concerns. The Distribute project was a near real-time influenza-like-illness (ILI) surveillance system that relied on aggregated secondary clinical health data. The goal of this work is to disseminate the data quality tools developed to gain insight into the data quality problems associated with these data. These tools apply in general to any system where aggregate data are accrued over time and were created through the end-user-as-developer paradigm. Each tool was developed during the exploratory analysis to gain insight into structural aspects of data quality. Our key finding is that data quality of partially accruing data must be studied in the context of accrual lag-the difference between the time an event occurs and the time data for that event are received, i.e. the time at which data become available to the surveillance system. Our visualization methods therefore revolve around visualizing dimensions of data quality affected by accrual lag, in particular the tradeoff between timeliness and completion, and the effects of accrual lag on accuracy. Accounting for accrual lag in partially accruing data is necessary to avoid misleading or biased conclusions about trends in indicator values and data quality.