The remarkable adaptability of syndromic surveillance to meet public health needs

Recent Advances in the Use of the Mortality Syndromic Surveillance System-New York City, 2015-2020

OBJECTIVE

New York City's automated mortality syndromic surveillance system monitors temporal and spatial patterns in mortality. To describe the use of the syndromic surveillance system, we used the system to find mortality patterns for the 15 leading causes of death and for deaths from rare and reportable diseases in New York City from February 2015 through June 2020. We used results to find aberrations that indicate threats to public health.

METHODS

We used unobserved components models to analyze time series of mortality counts for leading causes of death, historical limits methods for rare and reportable diseases, and SaTScan for temporal-spatial cluster analysis. We obtained data on the number of deaths from the electronic death registry system maintained by the city's Bureau of Vital Statistics.

RESULTS

The mortality syndromic surveillance system detected an increase in the number of deaths from heart disease by April 1, 2020, when 75.0 deaths occurred on March 24, 2020, instead of an expected 45.8 deaths (95% upper prediction limit of 61.0) and an increase in the number of deaths from all causes on March 20, 2020, when 194.0 deaths were observed while 150.1 deaths were expected (95% upper prediction limit of 178.0). The number of deaths from all causes returned to normal the week beginning June 14, 2020, when 990.0 deaths were observed and 998.8 deaths were expected.

PRACTICE IMPLICATIONS

When compared with efforts from New York City to provide yearly vital statistics, the automated mortality syndromic surveillance system can provide timely mortality data with fewer resources and raise the capacity to detect anomalous increases in mortality.

Members' experiences and perceptions of participating in an Australian Regional One Health Network

BACKGROUND

The One Health framework is intended to optimise the interdependent health of humans, animals and ecosystems. It relies on effective collaborations across disciplines, sectors and communities. One Health networks have become increasingly important platforms for encouraging, creating and supporting collaborations. Their success is usually judged by evaluations of their outputs. However, there is also a need to understand member experiences and perceptions of the networks in which they participate. To that end, we undertook the first membership survey of a One Health network that was established in Australia in 2005.

METHODS

An online membership survey was created, comprising closed and open-ended questions.

RESULTS

Around one third of the Regional One Health Partnership ('the Network') participated in the study (33 members). Participants contributed a combined total of 170 years of experience in the Network and 414 years of combined experience working in/on One Health. The Network has provided excellent opportunities for cross sectoral collaboration that would otherwise not have been possible. Findings also highlighted the intangible benefits of membership such as the creation of a collaborative support group for emerging and established One Health practitioners.

CONCLUSIONS

The Network plays an important role in One Health collaborations in New South Wales and further afield. Commensurate with the literature on One Health collaborations globally, we identified a need for greater diversity amongst members, especially from First Nations people, local communities, non-government organisations and wildlife/environment experts, as well as concerted attempts to identify policy implications. Our membership survey tool could be adapted for future One Health Network membership surveys in Australia and internationally.

Stakeholder opinion-led study to identify canine priority diseases for surveillance and control in the UK

BACKGROUND

Many pathogens cause disease in dogs; however, meaningful surveillance in small companion animals is often only possible for the most impactful diseases. We describe the first stakeholder opinion-led approach to identify which canine infectious diseases should be prioritised for inclusion in surveillance and control strategies in the UK.

METHODS

Participants were identified through a stakeholder analysis. A multicriteria decision analysis was undertaken to establish and weight epidemiological criteria for evaluating diseases, and a Delphi technique was employed to achieve a consensus among participants on the top-priority canine diseases.

RESULTS

Nineteen stakeholders from multiple backgrounds participated in this study. Leptospirosis and parvovirus were identified as the top two endemic diseases of concern, while leishmaniosis and babesiosis were the top two exotic diseases. Respiratory and gastrointestinal diseases were identified as the top two syndromes of concern.

LIMITATIONS

Due to the COVID-19 pandemic, the number of participants was reduced. Despite this, a representative multidisciplinary sample of relevant stakeholders contributed to the present study.

CONCLUSIONS

Findings from this study are being used to inform the development of a future UK-wide epidemic response strategy. This methodology could provide a blueprint for other countries.

[Epidemiological surveillance system for the acute febrile syndrome in Villeta, Colombia]

OBJECTIVE

To generate a preliminary proposal of an integral surveillance system for the acute febrile syndrome in Villeta municipality, Cundinamarca department, with the goal to establish a collection processes and data capture in the presentation of human and animal cases.

MATERIALS AND METHODS

A database was generated from a cross-sectional retrospective study of 40 sheets of mandatory reporting for suspected dengue patients collected during October 2011 and March 2013 from Hospital Salazar of Villeta. These data were analyzed by the Epiinfo 7 program. Also, because of the evidence of leptospirosis and rickettsial circulation in this municipality and whereas these etiologies are zoonotic, an adaptation was made regarding the event in dogs. These results allowed to design the proposed system, including operational surveillance case definitions for febrile etiologies, action algorithms and reporting tools.

RESULTS

Sixty percent of people who consulted for febrile syndrome belonged to the county seat. 30% of patients were under 10 years. Fever (98%), myalgia (85%), headache (75%) and arthralgia (65%) were the symptoms reported with more frequency. The largest number of cases occurred in September, 2012. The proposed system of syndromic surveillance will strengthen surveillance considering four components: human, animal, community and environmental by facilitating the opportune identification and treatment of cases of acute febrile illnesses.

CONCLUSION

The syndromic surveillance system allows to relate comprehensively febrile illnesses with common signs, making the reporting process more efficient.

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.

Real-time surveillance of severe acute respiratory infections in Scottish hospitals: an electronic register-based approach, 2017-2022

OBJECTIVES

The COVID-19 pandemic highlighted the importance of routine syndromic surveillance of respiratory infections, specifically new cases of severe acute respiratory infection (SARI). This surveillance often relies on questionnaires carried out by research nurses or transcriptions of doctor's notes, but existing, routinely collected electronic healthcare data sets are increasingly being used for such surveillance. We investigated how patient diagnosis codes, recorded within such data sets, could be used to capture SARI trends in Scotland.

STUDY DESIGN

We conducted a retrospective observational study using electronic healthcare data sets between 2017 and 2022.

METHODS

Sensitive, specific and timely case definition (CDs) based on patient diagnosis codes contained within national registers in Scotland were proposed to identify SARI cases. Representativeness and sensitivity analyses were performed to assess how well SARI cases captured by each definition matched trends in historic influenza and SARS-CoV-2 data.

RESULTS

All CDs accurately captured the peaks seen in laboratory-confirmed positive influenza and SARS-CoV-2 data, although the completeness of patient diagnosis records was discovered to vary widely. The timely CD provided the earliest detection of changes in SARI activity, whilst the sensitive CD provided insight into the burden and severity of SARI infections.

CONCLUSIONS

A universal SARI surveillance system has been developed and demonstrated to accurately capture seasonal SARI trends. It can be used as an indicator of emerging secondary care burden of emerging SARI outbreaks. The system further strengthens Scotland's existing strategies for respiratory surveillance, and the methods described here can be applied within any country with suitable electronic patient records.

Syndromic Surveillance as a Tool for Case-Based Varicella Reporting in Georgia, 2016-2019

OBJECTIVES

Syndromic surveillance can be used to enhance notifiable disease case-based surveillance. We analyzed features of varicella reported in Georgia to evaluate case detection through syndromic surveillance and to compare varicella reported through syndromic surveillance with varicella reported from all other sources.

METHODS

Syndromic surveillance was incorporated into case-based varicella surveillance by the Georgia Department of Public Health (GDPH) in May 2016. A cross-sectional study design evaluated syndromic and nonsyndromic varicella reported to GDPH from May 1, 2016, through December 31, 2019. Varicella was reported by nonsyndromic sources including health care providers, schools, and laboratories. We identified syndromic varicella cases from urgent care and emergency department visit data with discharge diagnoses containing the terms "varicella" or "chickenpox."

RESULTS

Syndromic notifications accounted for 589 of 2665 (22.1%) suspected varicella reports investigated by GDPH. The positive predictive value was 33.1% for syndromic notifications and 31.3% for nonsyndromic notifications. Mean days from rash onset to GDPH notification was 3.2 days fewer (P < .001) among patients identified through syndromic notification than among patients identified through nonsyndromic notification. The odds of varicella identified by syndromic notification being outbreak-associated were 0.18 (95% CI, 0.09-0.36) times those of varicella identified through nonsyndromic notification.

PRACTICE IMPLICATIONS

Syndromic notifications were an effective, timely means for varicella case detection. Syndromic patients were significantly less likely than nonsyndromic patients to be outbreak-associated, possibly because of early detection. Syndromic surveillance enhanced case-based reporting for varicella in Georgia and was a useful tool to improve notifiable disease surveillance.

The Utility of Ambulance Dispatch Call Syndromic Surveillance for Detecting and Assessing the Health Impact of Extreme Weather Events in England

Extreme weather events present significant global threats to health. The National Ambulance Syndromic Surveillance System collects data on 18 syndromes through chief presenting complaint (CPC) codes. We aimed to determine the utility of ambulance data to monitor extreme temperature events for action. Daily total calls were observed between 01/01/2018−30/04/2019. Median daily ’Heat/Cold’ CPC calls during “known extreme temperature” (identified a priori), “extreme temperature”; (within 5th or 95th temperature percentiles for central England) and meteorological alert periods were compared to all other days using Wilcoxon signed-rank test. During the study period, 12,585,084 calls were recorded. In 2018, median daily “Heat/Cold” calls were higher during periods of known extreme temperature: heatwave (16/day, 736 total) and extreme cold weather events (28/day, 339 total) compared to all other days in 2018 (6/day, 1672 total). Median daily “Heat/Cold” calls during extreme temperature periods (16/day) were significantly higher than non-extreme temperature periods (5/day, p < 0.001). Ambulance data can be used to identify adverse impacts during periods of extreme temperature. Ambulance data are a low resource, rapid and flexible option providing real-time data on a range of indicators. We recommend ambulance data are used for the surveillance of presentations to healthcare related to extreme temperature events.

Public health risks threatening health of people participating in mass gatherings: A qualitative study

Background

Mass gatherings (MGs) have made many problems for the health system and potentially threaten the health of those participated in these gatherings. MGs account for a range of public health risks including communicable diseases, waterborne diseases, water treatment, and outbreaks.

Objectives

The present study aimed to identify public health risks threatening the health of people participating in MGs in Iran.

Methods

A qualitative study was designed using a conventional qualitative content analysis approach. Participants were selected using a purposive sampling method among the managers and staff who have experiences in the field of health in MGs in Iran. Subjects were interviewed through individual in-depth interviews after obtaining informed consent. Semi-structured interviews were used to collect the data from December 2018 to February 2019. To analyze the data, the content of recorded interviews was written verbatim. Preliminary data were repeatedly and simultaneously retrieved to give a general understanding of them.

Results

The main theme emerged from data analysis on 16 interviews was related to public health risks involving the following categories: food hygiene deficiencies, communicable diseases risks, defects in health supervision, and the risk of environmental contamination.

Conclusion

It is of paramount importance to identify the risks threatening the health of people participating in MGs. In the present study, the main public health risks were detected.

The effectiveness of syndromic surveillance for the early detection of waterborne outbreaks: a systematic review

Background

Waterborne outbreaks are still a risk in high-income countries, and their early detection is crucial to limit their societal consequences. Although syndromic surveillance is widely used for the purpose of detecting outbreaks days earlier than traditional surveillance systems, evidence of the effectiveness of such systems is lacking. Thus, our objective was to conduct a systematic review of the effectiveness of syndromic surveillance to detect waterborne outbreaks.

Method

We searched the Cochrane Library, Medline/PubMed, EMBASE, Scopus, and Web of Science for relevant published articles using a combination of the keywords ‘drinking water’, ‘surveillance’, and ‘waterborne disease’ for the period of 1990 to 2018. The references lists of the identified articles for full-text record assessment were screened, and searches in Google Scholar using the same key words were conducted. We assessed the risk of bias in the included articles using the ROBINS-I tool and PRECEPT for the cumulative body of evidence.

Results

From the 1959 articles identified, we reviewed 52 articles, of which 18 met the eligibility criteria. Twelve were descriptive/analytical studies, whereas six were simulation studies. There is no clear evidence for syndromic surveillance in terms of the ability to detect waterborne outbreaks (low sensitivity and high specificity). However, one simulation study implied that multiple sources of signals combined with spatial information may increase the timeliness in detecting a waterborne outbreak and reduce false alarms.

Conclusion

This review demonstrates that there is no conclusive evidence on the effectiveness of syndromic surveillance for the detection of waterborne outbreaks, thus suggesting the need to focus on primary prevention measures to reduce the risk of waterborne outbreaks. Future studies should investigate methods for combining health and environmental data with an assessment of needed financial and human resources for implementing such surveillance systems. In addition, a more critical thematic narrative synthesis on the most promising sources of data, and an assessment of the basis for arguments that joint analysis of different data or dimensions of data (e.g. spatial and temporal) might perform better, should be carried out.

Trial registration

PROSPERO: International prospective register of systematic reviews. 2019. CRD42019122332.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06387-y.

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.

Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE): Overview, Components, and Public Health Applications

Background

The Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) is a secure web-based tool that enables health care practitioners to monitor health indicators of public health importance for the detection and tracking of disease outbreaks, consequences of severe weather, and other events of concern. The ESSENCE concept began in an internally funded project at the Johns Hopkins University Applied Physics Laboratory, advanced with funding from the State of Maryland, and broadened in 1999 as a collaboration with the Walter Reed Army Institute for Research. Versions of the system have been further developed by Johns Hopkins University Applied Physics Laboratory in multiple military and civilian programs for the timely detection and tracking of health threats.

Objective

This study aims to describe the components and development of a biosurveillance system increasingly coordinating all-hazards health surveillance and infectious disease monitoring among large and small health departments, to list the key features and lessons learned in the growth of this system, and to describe the range of initiatives and accomplishments of local epidemiologists using it.

Methods

The features of ESSENCE include spatial and temporal statistical alerting, custom querying, user-defined alert notifications, geographical mapping, remote data capture, and event communications. To expedite visualization, configurable and interactive modes of data stratification and filtering, graphical and tabular customization, user preference management, and sharing features allow users to query data and view geographic representations, time series and data details pages, and reports. These features allow ESSENCE users to gather and organize the resulting wealth of information into a coherent view of population health status and communicate findings among users.

Results

The resulting broad utility, applicability, and adaptability of this system led to the adoption of ESSENCE by the Centers for Disease Control and Prevention, numerous state and local health departments, and the Department of Defense, both nationally and globally. The open-source version of Suite for Automated Global Electronic bioSurveillance is available for global, resource-limited settings. Resourceful users of the US National Syndromic Surveillance Program ESSENCE have applied it to the surveillance of infectious diseases, severe weather and natural disaster events, mass gatherings, chronic diseases and mental health, and injury and substance abuse.

Conclusions

With emerging high-consequence communicable diseases and other health conditions, the continued user requirement–driven enhancements of ESSENCE demonstrate an adaptable disease surveillance capability focused on the everyday needs of public health. The challenge of a live system for widely distributed users with multiple different data sources and high throughput requirements has driven a novel, evolving architecture design.

Exploring Vector-Borne Disease Surveillance and Response Systems in Beijing, China: A Qualitative Study from the Health System Perspective

Background: Climate change may contribute to higher incidence and wider geographic spread of vector borne diseases (VBDs). Effective monitoring and surveillance of VBDs is of paramount importance for the prevention of and timely response to outbreaks. Although international regulations exist to support this, barriers and operational challenges within countries hamper efficient monitoring. As a first step to optimise VBD surveillance and monitoring, it is important to gain a deeper understanding of system characteristics and experiences in to date non-endemic regions at risk of becoming endemic in the future. Therefore, this study qualitatively analyses the nature and flexibility of VBD surveillance and response in Beijing. Methods: In this qualitative study, eleven experts working in Beijing’s vector-borne diseases surveillance and response system were interviewed about vector-borne disease surveillance, early warning, response, and strengths and weaknesses of the current approach. Results: Vector-borne disease surveillance occurs using passive syndromic surveillance and separate vector surveillance. Public health authorities use internet reporting networks to determine vector-borne disease risk across Beijing. Response toward a vector-borne disease outbreak is uncommon in this setting due to the currently low occurrence of outbreaks. Conclusions: A robust network of centralised institutions provides the continuity and flexibility needed to adapt and manage possible vector-borne disease threats. Opportunities exist for population-based health promotion and the integration of environment and climate monitoring in vector-borne disease surveillance.

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.

Risks threatening the health of people participating in mass gatherings: A systematic review

CONTEXT

Mass gatherings (MGs) are established for different religious, social, political, cultural, and sport motivations. Many risks threaten the participants' health in MGs for different reasons. One duty of health-care system is to identify the risks and manage them to reduce the participants' injuries in MGs. This study aimed to identify and classify the risks threatening the health of participants in MGs and review related articles systematically.

SUBJECTS AND METHODS

In this study, we investigated electronic databases including Science Direct, PubMed, Ovid, ProQuest, Web of Science, Scopus, and Google Scholar. This systematic review investigated those health articles which studied MGs published after 2000. Keywords that were searched included (Mass gathering OR Crowd) AND (Assessment OR Evaluation) AND (Disaster OR Emergencies) AND (Injuries OR Stamped). The final full text articles were assessed qualitatively by STROBE article assessment checklist, and then the data extracted from the results of the articles were analyzed and reported.

RESULTS

Initially, a total of 1874 articles were found, qualitative exclusion and inclusion criteria were applied, and finally 29 full-text articles were selected for the analysis. More than forty health-related risks were recognized. In order to better understand the risks and use of the results more efficiently, the risks were classified and reported into the following five domains: environmental risks, individual risks, psychological risks, public health risks, and management risks.

CONCLUSION

Many risks threaten the health of participants in MGs. Recognizing and managing the risks are considered the primary and basic essentials of health sector for the better management of MGs.

Evaluation of the trends in the incidence of infectious diseases using the syndromic surveillance system, early warning and response unit, Mongolia, from 2009 to 2017: a retrospective descriptive multi-year analytical study

Background

In recent times, emerging and re-emerging infectious diseases are posing a public health threat in developing countries, and vigilant surveillance is necessary to prepare against these threats. Analyses of multi-year comprehensive infectious disease syndrome data are required in Mongolia, but have not been conducted till date. This study aimed to describe the trends in the incidence of infectious disease syndromes in Mongolia during 2009–2017 using a nationwide syndrome surveillance system for infectious diseases established in 2009.

Methods

We analyzed time trends using monthly data on the incidence of infectious disease syndromes such as acute fever with rash (AFR), acute fever with vesicular rash (AFVR), acute jaundice (AJ), acute watery diarrhea (AWD), acute bloody diarrhea (ABD), foodborne disease (FD) and nosocomial infection (NI) reported from January 1, 2009 to December 31, 2017. Time series forecasting models based on the data up to 2017 estimated the future trends in the incidence of syndromes up to December 2020.

Results

During the study, the overall prevalence of infectious disease syndromes was 71.8/10,000 population nationwide. The average number of reported infectious disease syndromes was 14,519 (5229-55,132) per year. The major types were AFR (38.7%), AFVR (31.7%), AJ (13.9%), ABD (10.2%), and AWD (1.8%), accounting for 96.4% of all reported syndromes. The most prevalent syndromes were AJ between 2009 and 2012 (59.5–48.7%), AFVR between 2013 and 2014 (54.5–59%), AFR between 2015 and 2016 (67.6–65.9%), and AFVR in 2017 (62.2%). There were increases in the prevalence of AFR, with the monthly number of cases being 37.7 ± 6.1 during 2015–2016; this could be related to the measles outbreak in Mongolia during that period. The AFVR incidence rate showed winter’s multiplicative seasonal fluctuations with a peak of 10.6 ± 2 cases per 10,000 population in 2017. AJ outbreaks were identified in 2010, 2011, and 2012, and these could be associated with hepatitis A outbreaks. Prospective time series forecasting showed increasing trends in the rates of AFVR and ABD.

Conclusions

The evidence-based method for infectious disease syndromes was useful in gaining an understanding of the current situation, and predicting the future trends of various infectious diseases in Mongolia.

Coping with ill-health: health care facility, chemist or medicinal plants? Health-seeking behaviour in a Kenyan wetland

BACKGROUND

Sub-Saharan African wetlands, settlement areas to growing populations, expose their users to diseases as necessary health infrastructure remains underdeveloped.

METHODS

Mixed methods were adopted to assess the health-seeking behaviour of different exposure groups (farmers, pastoralists, service sector workers) in a Kenyan wetland community. Based on a cross-sectional survey (n = 400), syndromic surveillance was linked to health-seeking event analysis. In-depth interviews with community members (n = 20) and experts (n = 8) enabled the integration of healthcare user and provider perspectives.

RESULTS

Health-seeking behaviour in the wetland was determined by physical/infrastructural, natural/environmental, financial/socioeconomic and social/demographic factors, as well as human/cultural aspects such as traditional preferences rooted in health beliefs. Community members had different strategies of coping with ill-health and few symptoms remained untreated. Whether via a health care facility admission, the visit of a chemist, or the intake of pharmaceuticals or medicinal plants: treatment was usually applied either via a healthcare service provider or by the community members themselves. An undersupply of easy-to-reach healthcare options was detected, and healthcare services were not available and accessible to all. The widely-practiced self-treatment of symptoms, e.g. by use of local medicinal plants, mirrors both potential healthcare gaps and cultural preferences of wetland communities.

CONCLUSIONS

Integrated into an overall health-promoting wetland management approach, widely accepted (cultural) realities of health-seeking behaviours could complement health sector service provision and help ensure healthy lives and promote well-being for all in wetlands.

Syndromic survey and molecular analysis of influenza viruses at the human-swine interface in two West African cosmopolitan cities suggest the possibility of bidirectional interspecies transmission

Influenza viruses are frequently transmitted between pigs and their handlers, and among pig handlers. However, reports on socio-environmental variables as potential risk factors associated with transmission of influenza in West African swine production facilities are very scarce. Syndromic survey for influenza was therefore conducted in Ibadan, Nigeria, and Kumasi, Ghana, in order to identify and elucidate selected socio-environmental variables that may contribute to the occurrence and distribution of influenza-like illness (ILI) among swine industry workers. In addition, molecular analyses were conducted to elucidate the nature of influenza viruses circulating at the human-swine interface in these cities and better understand the dynamics of their transmission. Influenza viruses were detected by type-specific and subtype-specific RT-PCR. Sequencing and phylogenetic analyses were carried out. Socio-environmental variables were tested by both univariable and multivariable regression methods for significance at p < 0.05. Three risk factors for ILI were identified in each city. These included "frequency of visit of pig handler to pig pen or lairage" (Ibadan: risk ratio [RR] = 1.54, 95% confidence interval [CI] = 1.36-1.79, p = 0.02; Kumasi: RR = 1.28, 95% CI = 1.11-1.71, p = 0.01) and "pig handler's awareness about biosecurity measures" (Ibadan: RR = 7.09, 95% CI = 2.36-21.32, p < 0.001; Kumasi: RR = 4.84, 95% CI = 1.98-11.80, p < 0.001). Influenza A(H1N1)pdm09 viruses, with M genes closely related to those which circulated among pigs in the two cities during the same period, were detected among Nigerian and Ghanaian pig industry workers. These findings suggest the possibility of bidirectional transmission of influenza at the human-swine interface in these cities and underscore the need for more extensive molecular studies. Risk factors identified may assist in the control of human-to-human and human-to-swine transmission of influenza in the West African swine industry.

Epidemic surveillance in a low resource setting: lessons from an evaluation of the Solomon Islands syndromic surveillance system, 2017

Background

Solomon Islands is one of the least developed countries in the world. Recognising that timely detection of outbreaks is needed to enable early and effective response to disease outbreaks, the Solomon Islands government introduced a simple syndromic surveillance system in 2011. We conducted the first evaluation of the system and the first exploration of a national experience within the broader multi-country Pacific Syndromic Surveillance System to determine if it is meeting its objectives and to identify opportunities for improvement.

Methods

We used a multi-method approach involving retrospective data collection and statistical analysis, modelling, qualitative research and observational methods.

Results

We found that the system was well accepted, highly relied upon and designed to account for contextual limitations. We found the syndromic algorithm used to identify outbreaks was moderately sensitive, detecting 11.8% (IQR: 6.3–25.0%), 21.3% (IQR: 10.3–36.8%), 27.5% (IQR: 12.8–52.3%) and 40.5% (IQR: 13.5–65.7%) of outbreaks that caused small, moderate, large and very large increases in case presentations to health facilities, respectively. The false alert rate was 10.8% (IQR: 4.8–24.5%). Rural coverage of the system was poor. Limited workforce, surveillance resourcing and other ‘upstream’ health system factors constrained performance.

Conclusions

The system has made a significant contribution to public health security in Solomon Islands, but remains insufficiently sensitive to detect small-moderate sized outbreaks and hence should not be relied upon as a stand-alone surveillance strategy. Rather, the system should sit within a complementary suite of early warning surveillance activities including event-based, in-patient- and laboratory-based surveillance methods. Future investments need to find a balance between actions to address the technical and systems issues that constrain performance while maintaining simplicity and hence sustainability.

Electronic supplementary material

The online version of this article (10.1186/s12889-018-6295-7) contains supplementary material, which is available to authorized users.

Towards effective outbreak detection: a qualitative study to identify factors affecting nurses' early warning surveillance practice in Solomon Islands

Background

Intelligence generated by a surveillance system is dependent on the quality of data that are collected. We investigated the knowledge, attitudes and practices of nurses responsible for outbreak early warning surveillance data collection in Solomon Islands to identify factors that influence their ability to perform surveillance-related tasks with rigour.

Methods

We interviewed 12 purposively selected surveillance nurses and conducted inductive analysis on resulting data.

Results

Interviewees were knowledgeable and willing to contribute to the surveillance system. Constraining factors included the perception that surveillance was less important than patient care and could be ‘deferred’ during busy periods and wide variability in the application of case definitions. Motivating factors were frequent in-clinic training, formal recognition for good performance, incentives and designation of a focal point. Nurses held mixed views about the effect of mobile technologies on surveillance practice.

Conclusions

This study identified several challenges to consistent and accurate data collection and reporting. Engagement of different parts of the health system, including human resources and health facilities’ management, is needed to address these challenges.

Electronic supplementary material

The online version of this article (10.1186/s12913-018-3508-9) contains supplementary material, which is available to authorized users.

The impact of water on health and ill-health in a sub-Saharan African wetland: Exploring both sides of the coin

Wetlands are a source of water out of which humans derive their livelihoods in Sub-Saharan Africa. They are often over-utilized and expose humans to disease-causing infectious agents. This calls for an evaluation of the role of water, sanitation and hygiene (WASH) and their effects in disease prevention and transmission in wetlands. A health risk assessment based on syndromic surveillance of self-reported abdominal complaints and fever gathered from a rural wetland in semiarid Kenya is presented with symptoms serving as proxies for real health threats in wetlands. The incidence of abdominal complaints was significantly higher for those using unimproved water sources compared to improved water users (odds ratio 7.5; 95% CI 2.59-26.9; p=0.001). Drainage of stagnant water near the house (odds ratio 0.2; 95% CI 0.08-0.54; p=0.002) and sanitary hygiene (odds ratio 0.4; 95% CI 0.71-0.97; p=0.056) were associated with reduced risk of abdominal complaints. Drainage of water was also associated with reduced risk of fever (odds ratio 0.3; 95% CI 0.02-0.59; p=0.002) and so was the use of mosquito nets (odds ratio 0.6; 95% CI 0.39-0.02; p=0.063). Usage of wetlands in the afternoon, e.g. for irrigated agriculture, increased the incidence of fever (odds ratio 1.5; 95% CI 0.91-2.33; p=0.040). Overall, there appears a greater likelihood of reducing pathogen exposure in the domestic than in the occupational domain or in the proximity to the wetland. We show that WASH, environmental hygiene and human behaviour are risk factors associated with the contraction of diseases characterized by abdominal complaints (e.g. diarrhoea) and fever (e.g. malaria) in wetlands. The same factors also have the potential to promote human health in the context of wetlands. We demonstrate the applicability of syndromic approaches in surveillance-scarce areas and emphasize the importance of adopting an integrated health-based wetland management that considers WASH and incorporates strategies based on grassroots level risk assessments.

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.

Use of Emergency Department Data to Monitor and Respond to an Increase in Opioid Overdoses in New Hampshire, 2011-2015

OBJECTIVES

Opioid-related overdoses and deaths in New Hampshire have increased substantially in recent years, similar to increases observed across the United States. We queried emergency department (ED) data in New Hampshire to monitor opioid-related ED encounters as part of the public health response to this health problem.

METHODS

We obtained data on opioid-related ED encounters for the period January 1, 2011, through December 31, 2015, from New Hampshire's syndromic surveillance ED data system by querying for (1) chief complaint text related to the words "fentanyl," "heroin," "opiate," and "opioid" and (2) opioid-related International Classification of Diseases ( ICD) codes. We then analyzed the data to calculate frequencies of opioid-related ED encounters by age, sex, residence, chief complaint text values, and ICD codes.

RESULTS

Opioid-related ED encounters increased by 70% during the study period, from 3300 in 2011 to 5603 in 2015; the largest increases occurred in adults aged 18-29 and in males. Of 20 994 total opioid-related ED visits, we identified 18 554 (88%) using ICD code alone, 690 (3%) using chief complaint text alone, and 1750 (8%) using both chief complaint text and ICD code. For those encounters identified by ICD code only, the corresponding chief complaint text included varied and nonspecific words, with the most common being "pain" (n = 3335, 18%), "overdose" (n = 1555, 8%), "suicidal" (n = 816, 4%), "drug" (n = 803, 4%), and "detox" (n = 750, 4%). Heroin-specific encounters increased by 827%, from 4% of opioid-related encounters in 2011 to 24% of encounters in 2015.

CONCLUSIONS

Opioid-related ED encounters in New Hampshire increased substantially from 2011 to 2015. Data from New Hampshire's ED syndromic surveillance system provided timely situational awareness to public health partners to support the overall response to the opioid epidemic.

A Conceptual Architecture for National Biosurveillance: Moving Beyond Situational Awareness to Enable Digital Detection of Emerging Threats

Despite numerous calls for improvement, the US biosurveillance enterprise remains a patchwork of uncoordinated systems that fail to take advantage of the rapid progress in information processing, communication, and analytics made in the past decade. By synthesizing components from the extensive biosurveillance literature, we propose a conceptual framework for a national biosurveillance architecture and provide suggestions for implementation. The framework differs from the current federal biosurveillance development pathway in that it is not focused on systems useful for "situational awareness" but is instead focused on the long-term goal of having true warning capabilities. Therefore, a guiding design objective is the ability to digitally detect emerging threats that span jurisdictional boundaries, because attempting to solve the most challenging biosurveillance problem first provides the strongest foundation to meet simpler surveillance objectives. Core components of the vision are: (1) a whole-of-government approach to support currently disparate federal surveillance efforts that have a common data need, including those for food safety, vaccine and medical product safety, and infectious disease surveillance; (2) an information architecture that enables secure national access to electronic health records, yet does not require that data be sent to a centralized location for surveillance analysis; (3) an inference architecture that leverages advances in "big data" analytics and learning inference engines-a significant departure from the statistical process control paradigm that underpins nearly all current syndromic surveillance systems; and (4) an organizational architecture with a governance model aimed at establishing national biosurveillance as a critical part of the US national infrastructure. Although it will take many years to implement, and a national campaign of education and debate to acquire public buy-in for such a comprehensive system, the potential benefits warrant increased consideration by the US government.

Early warning epidemic surveillance in the Pacific island nations: an evaluation of the Pacific syndromic surveillance system

OBJECTIVE

The Pacific Syndromic Surveillance System (PSSS), launched in 2010, provides a simple mechanism by which 121 sentinel surveillance sites in 21 Pacific island countries and areas perform routine indicator- and event-based surveillance for the early detection of infectious disease outbreaks. This evaluation aims to assess whether the PSSS is meeting its objectives, what progress has been made since a formative evaluation of the system was conducted in 2011, and provides recommendations to enhance the PSSS's performance in the future.

METHODS

Twenty-one informant interviews were conducted with national operators of the system and regional public health agencies that use information generated by it. Historic PSSS data were analysed to assess timeliness and completeness of reporting.

RESULTS

The system is simple, acceptable and useful for public health decision-makers. The PSSS has greatly enhanced Pacific island countries' ability to undertake early warning surveillance and has contributed to efforts to meet national surveillance-related International Health Regulation (2005) capacity development obligations. Despite this, issues with timeliness and completeness of reporting, data quality and system stability persist.

CONCLUSION

A balance between maintaining the system's simplicity and technical advances will need to be found to ensure its long-term sustainability, given the low-resource context for which it is designed.

Success Factors of European Syndromic Surveillance Systems: A Worked Example of Applying Qualitative Comparative Analysis

INTRODUCTION

Syndromic surveillance aims at augmenting traditional public health surveillance with timely information. To gain a head start, it mainly analyses existing data such as from web searches or patient records. Despite the setup of many syndromic surveillance systems, there is still much doubt about the benefit of the approach. There are diverse interactions between performance indicators such as timeliness and various system characteristics. This makes the performance assessment of syndromic surveillance systems a complex endeavour. We assessed if the comparison of several syndromic surveillance systems through Qualitative Comparative Analysis helps to evaluate performance and identify key success factors.

MATERIALS AND METHODS

We compiled case-based, mixed data on performance and characteristics of 19 syndromic surveillance systems in Europe from scientific and grey literature and from site visits. We identified success factors by applying crisp-set Qualitative Comparative Analysis. We focused on two main areas of syndromic surveillance application: seasonal influenza surveillance and situational awareness during different types of potentially health threatening events.

RESULTS

We found that syndromic surveillance systems might detect the onset or peak of seasonal influenza earlier if they analyse non-clinical data sources. Timely situational awareness during different types of events is supported by an automated syndromic surveillance system capable of analysing multiple syndromes. To our surprise, the analysis of multiple data sources was no key success factor for situational awareness.

CONCLUSIONS

We suggest to consider these key success factors when designing or further developing syndromic surveillance systems. Qualitative Comparative Analysis helped interpreting complex, mixed data on small-N cases and resulted in concrete and practically relevant findings.

Algorithms for detecting and predicting influenza outbreaks: metanarrative review of prospective evaluations

OBJECTIVES

Reliable monitoring of influenza seasons and pandemic outbreaks is essential for response planning, but compilations of reports on detection and prediction algorithm performance in influenza control practice are largely missing. The aim of this study is to perform a metanarrative review of prospective evaluations of influenza outbreak detection and prediction algorithms restricted settings where authentic surveillance data have been used.

DESIGN

The study was performed as a metanarrative review. An electronic literature search was performed, papers selected and qualitative and semiquantitative content analyses were conducted. For data extraction and interpretations, researcher triangulation was used for quality assurance.

RESULTS

Eight prospective evaluations were found that used authentic surveillance data: three studies evaluating detection and five studies evaluating prediction. The methodological perspectives and experiences from the evaluations were found to have been reported in narrative formats representing biodefence informatics and health policy research, respectively. The biodefence informatics narrative having an emphasis on verification of technically and mathematically sound algorithms constituted a large part of the reporting. Four evaluations were reported as health policy research narratives, thus formulated in a manner that allows the results to qualify as policy evidence.

CONCLUSIONS

Awareness of the narrative format in which results are reported is essential when interpreting algorithm evaluations from an infectious disease control practice perspective.

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).

Shared Longitudinal Health Records for Clinical and Population Health

The ability of a health information exchange to consolidate information, collected in multiple, disparate information systems, into a single, person-centric health record can provide a comprehensive and longitudinal representation of an individual’s medical history. Shared, longitudinal health records can be leveraged to enhance the delivery of individual clinical care and provide opportunities to improve health outcomes at the population level. This chapter will describe the clinical benefits imparted by the shared health record (SHR) component of the OpenHIE infrastructure. It will also characterize the potential population health benefits of the aggregate level data contained and distributed by the Health Management Information System component of OpenHIE. The chapter will further discuss the implementation of these systems.

By the end of the chapter, the reader should be able to:●

Identify and describe the differences among an electronic medical record, electronic health record, and a shared heath record.

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Explain the role of a shared health record in a health information exchange.

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List and describe the components of a shared health record.

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Discuss the role and benefits of a health management information system within a health information exchange.

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Define a population health indicator.

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Identify and describe application domains for a health management information system.

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Define a database management system.

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Compare the implications of implementing a shared health record using an electronic health record system versus a database management system.

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Discuss emerging trends likely to shape the evolution of shared health records and health management information systems.

Practical comparison of aberration detection algorithms for biosurveillance systems

National syndromic surveillance systems require optimal anomaly detection methods. For method performance comparison, we injected multi-day signals stochastically drawn from lognormal distributions into time series of aggregated daily visit counts from the U.S. Centers for Disease Control and Prevention's BioSense syndromic surveillance system. The time series corresponded to three different syndrome groups: rash, upper respiratory infection, and gastrointestinal illness. We included a sample of facilities with data reported every day and with median daily syndromic counts ⩾1 over the entire study period. We compared anomaly detection methods of five control chart adaptations, a linear regression model and a Poisson regression model. We assessed sensitivity and timeliness of these methods for detection of multi-day signals. At a daily background alert rate of 1% and 2%, the sensitivities and timeliness ranged from 24 to 77% and 3.3 to 6.1days, respectively. The overall sensitivity and timeliness increased substantially after stratification by weekday versus weekend and holiday. Adjusting the baseline syndromic count by the total number of facility visits gave consistently improved sensitivity and timeliness without stratification, but it provided better performance when combined with stratification. The daily syndrome/total-visit proportion method did not improve the performance. In general, alerting based on linear regression outperformed control chart based methods. A Poisson regression model obtained the best sensitivity in the series with high-count data.

Utility of a near real-time emergency department syndromic surveillance system to track injuries in New York City

Background

The New York City emergency department (ED) syndromic surveillance (SS) system provides near real-time data on the majority of ED visits. The utility of ED SS for injury surveillance has not been thoroughly evaluated. We created injury syndromes based on ED chief complaint information and evaluated their utility compared to administrative billing data.

Methods

Six injury syndromes were developed: traffic-related injuries to pedal cyclists, pedestrians, and motor vehicle occupants; fall-related injuries; firearm-related injuries; and assault-related stabbings. Daily injury counts were compared for ED SS and the administrative billing data for years 2008–2010. We examined characteristics of injury trends and patterns between the two systems, calculating descriptive statistics for temporal patterns and Pearson correlation coefficients (r) for temporal trends. We also calculated proportions of demographic and geospatial patterns for both systems.

Results

Although daily volume of the injuries varied between the two systems, the temporal patterns were similar (all r values for daily volume exceeded 0.65). Comparisons of injuries by time of day, day of week, and quarter of year demonstrated high agreement between the two systems—the majority had an absolute percentage point difference of 2.0 or less. Distributions of injury by sex and age group also aligned well. Distribution of injury by neighborhood of residence showed mixed results—some neighborhood comparisons showed a high level of agreement between systems, while others were less successful.

Conclusions

As evidenced by the strong positive correlation coefficients and the small absolute percentage point differences in our comparisons, we conclude that ED SS captures temporal trends and patterns of injury-related ED visits effectively. The system could be used to identify changes in injury patterns, allowing for situational awareness during emergencies, timely response, and public messaging.

Electronic supplementary material

The online version of this article (doi:10.1186/s40621-015-0044-5) contains supplementary material, which is available to authorized users.

Informatics and Autopsy Pathology

Many health care providers believe that the autopsy is no longer relevant in high-technology medicine era. This has fueled a decline in the hospital autopsy rate. Although it seems that advanced diagnostic tests answer all clinical questions, studies repeatedly demonstrate that an autopsy uncovers as many undiagnosed conditions today as in the past. The forensic autopsy rate has also declined, although not as precipitously. Pathologists are still performing a nineteenth century autopsy procedure that remains essentially unchanged. Informatics offers several potential answers that will evolve the low-tech autopsy into the high-tech autopsy.

Meeting the International Health Regulations (2005) surveillance core capacity requirements at the subnational level in Europe: the added value of syndromic surveillance

Background

The revised World Health Organization’s International Health Regulations (2005) request a timely and all-hazard approach towards surveillance, especially at the subnational level. We discuss three questions of syndromic surveillance application in the European context for assessing public health emergencies of international concern: (i) can syndromic surveillance support countries, especially the subnational level, to meet the International Health Regulations (2005) core surveillance capacity requirements, (ii) are European syndromic surveillance systems comparable to enable cross-border surveillance, and (iii) at which administrative level should syndromic surveillance best be applied?

Discussion

Despite the ongoing criticism on the usefulness of syndromic surveillance which is related to its clinically nonspecific output, we demonstrate that it was a suitable supplement for timely assessment of the impact of three different public health emergencies affecting Europe. Subnational syndromic surveillance analysis in some cases proved to be of advantage for detecting an event earlier compared to national level analysis. However, in many cases, syndromic surveillance did not detect local events with only a small number of cases.

The European Commission envisions comparability of surveillance output to enable cross-border surveillance. Evaluated against European infectious disease case definitions, syndromic surveillance can contribute to identify cases that might fulfil the clinical case definition but the approach is too unspecific to comply to complete clinical definitions. Syndromic surveillance results still seem feasible for comparable cross-border surveillance as similarly defined syndromes are analysed.

We suggest a new model of implementing syndromic surveillance at the subnational level. In this model, syndromic surveillance systems are fine-tuned to their local context and integrated into the existing subnational surveillance and reporting structure. By enhancing population coverage, events covering several jurisdictions can be identified at higher levels. However, the setup of decentralised and locally adjusted syndromic surveillance systems is more complex compared to the setup of one national or local system.

Summary

We conclude that syndromic surveillance if implemented with large population coverage at the subnational level can help detect and assess the local and regional effect of different types of public health emergencies in a timely manner as required by the International Health Regulations (2005).

An early warning system based on syndromic surveillance to detect potential health emergencies among migrants: results of a two-year experience in Italy

Profound geopolitical changes have impacted the southern and eastern Mediterranean since 2010 and defined a context of instability that is still affecting several countries today. Insecurity combined with the reduction of border controls has led to major population movements in the region and to migration surges from affected countries to southern Europe, especially to Italy. To respond to the humanitarian emergency triggered by this migration surge, Italy implemented a syndromic surveillance system in order to rapidly detect potential public health emergencies in immigrant reception centres. This system was discontinued after two years. This paper presents the results of this experience detailing its strengths and weaknesses in order to document the applicability and usefulness of syndromic surveillance in this specific context.

Temporal and spatial monitoring and prediction of epidemic outbreaks

This paper introduces a nonlinear dynamic model to study spatial and temporal dynamics of epidemics of susceptible-infected-removed type. It involves modeling the respective collections of epidemic states and syndromic observations as random finite sets. Each epidemic state consists of the number of infected individuals in an isolated population system and the corresponding partially known parameters of the epidemic model. The infectious disease could spread between population systems with known probabilities based on prior knowledge of ecological and biological features of the environment. The problem is then formulated in the context of Bayesian framework and estimated via a probability hypothesis density filter. Each population system under surveillance is assumed to be homogenous and fixed, with daily reports on the number of infected people available for monitoring and prediction. When model parameters are partially known, results of numerical studies indicate that the proposed approach can help early prediction of the epidemic in terms of peak and duration.

Non-infectious events under the International Health Regulations (2005) in Europe--a case for syndromic surveillance

The scope of the International Health Regulations of 2005 (IHR (2005)) has been expanded. The IHR (2005) are no longer limited to a specific set of infectious diseases, instead they prescribe detection and assessment of any event of potential public health concern regardless of its source or origin. We examine events of non-infectious origin that might fulfill the criteria of a potential public health emergency of international concern under the IHR (2005). These comprise predominately events related to food safety, but also events related to drug safety or of chemical or industrial origin. We argue that to identify these events and assess health effects related to them, existing disease surveillance systems should be augmented with less specific indicator-based syndromic surveillance strategies that use available routine health-related service data for monitoring purposes.