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Polyakov P, Souty C, Böelle PY, Breban R. Classification of Spatiotemporal Data for Epidemic Alert Systems: Monitoring Influenza-Like Illness in France. Am J Epidemiol 2019; 188:724-733. [PMID: 30576414 DOI: 10.1093/aje/kwy254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Revised: 11/10/2018] [Accepted: 11/12/2018] [Indexed: 11/13/2022] Open
Abstract
Surveillance data used by epidemic alert systems are typically fully aggregated in space at the national level. However, epidemics may be spatially heterogeneous, undergoing distinct dynamics in distinct regions of the surveillance area. We unveiled this in retrospective analyses by classifying incidence time series. We used Pearson correlation to quantify the similarity between local time series and then classified them using modularity maximization. The surveillance area was thus divided into regions with different incidence patterns. We analyzed 31 years (1985-2016) of data on influenza-like illness from the French Sentinelles system and found spatial heterogeneity in 19 of 31 influenza seasons. However, distinct epidemic regions could be identified only 4-5 weeks after a nationwide alert. The impact of spatial heterogeneity on influenza epidemiology was complex. First, when a nationwide alert was triggered, 32%-41% of the administrative regions of France were experiencing an epidemic, while the others were not. Second, the nationwide alert was timely for the whole surveillance area, but subsequently regions experienced distinct epidemic dynamics. Third, the epidemic dynamics were homogeneous in space. Spatial heterogeneity analyses can provide information on the timing of the peak and end of the epidemic, in various regions, for use in tailoring disease monitoring and control.
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Affiliation(s)
- Pavel Polyakov
- Unité d’Epidémiologie des Maladies Emergentes, Institut Pasteur, Paris, France
| | - Cécile Souty
- Institut Pierre Louis d’Epidémiologie et de Santé Publique, Sorbonne Université, Paris, France
- Unité 1136, Institut National de la Santé et de la Recherche Médicale, Paris, France
| | - Pierre-Yves Böelle
- Institut Pierre Louis d’Epidémiologie et de Santé Publique, Sorbonne Université, Paris, France
- Unité 1136, Institut National de la Santé et de la Recherche Médicale, Paris, France
- Hôpital Saint-Antoine, Assistance Publique–Hôpitaux de Paris, Paris, France
| | - Romulus Breban
- Unité d’Epidémiologie des Maladies Emergentes, Institut Pasteur, Paris, France
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Al-Tawfiq JA, Zumla A, Gautret P, Gray GC, Hui DS, Al-Rabeeah AA, Memish ZA. Surveillance for emerging respiratory viruses. THE LANCET. INFECTIOUS DISEASES 2014; 14:992-1000. [PMID: 25189347 PMCID: PMC7106459 DOI: 10.1016/s1473-3099(14)70840-0] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Several new viral respiratory tract infectious diseases with epidemic potential that threaten global health security have emerged in the past 15 years. In 2003, WHO issued a worldwide alert for an unknown emerging illness, later named severe acute respiratory syndrome (SARS). The disease caused by a novel coronavirus (SARS-CoV) rapidly spread worldwide, causing more than 8000 cases and 800 deaths in more than 30 countries with a substantial economic impact. Since then, we have witnessed the emergence of several other viral respiratory pathogens including influenza viruses (avian influenza H5N1, H7N9, and H10N8; variant influenza A H3N2 virus), human adenovirus-14, and Middle East respiratory syndrome coronavirus (MERS-CoV). In response, various surveillance systems have been developed to monitor the emergence of respiratory-tract infections. These include systems based on identification of syndromes, web-based systems, systems that gather health data from health facilities (such as emergency departments and family doctors), and systems that rely on self-reporting by patients. More effective national, regional, and international surveillance systems are required to enable rapid identification of emerging respiratory epidemics, diseases with epidemic potential, their specific microbial cause, origin, mode of acquisition, and transmission dynamics.
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Affiliation(s)
- Jaffar A Al-Tawfiq
- Johns Hopkins Aramco Healthcare, Dhahran, Saudi Arabia; Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Alimuddin Zumla
- Division of Infection and Immunity, University College London, London, UK; NIHR Biomedical Research Centre, University College London Hospitals, London, UK; Global Center for Mass Gatherings Medicine, Ministry of Health, Riyadh, Saudi Arabia
| | - Philippe Gautret
- Assistance Publique Hôpitaux de Marseille, CHU Nord, Pôle Infectieux, Institut Hospitalo-Universitaire Méditerranée Infection & Aix Marseille Université, Unité de Recherche en Maladies Infectieuses et Tropicales Emergentes (URMITE), Marseille, France
| | - Gregory C Gray
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida
| | - David S Hui
- Division of Respiratory Medicine and Stanley Ho Center for emerging Infectious Diseases, The Chinese University of Hong Kong, Prince of Wales Hospital, New Territories, Hong Kong
| | - Abdullah A Al-Rabeeah
- Global Center for Mass Gatherings Medicine, Ministry of Health, Riyadh, Saudi Arabia
| | - Ziad A Memish
- Global Center for Mass Gatherings Medicine, Ministry of Health, Riyadh, Saudi Arabia; Al-Faisal University, Riyadh, Saudi Arabia.
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Rosenkötter N, Ziemann A, Riesgo LGC, Gillet JB, Vergeiner G, Krafft T, Brand H. Validity and timeliness of syndromic influenza surveillance during the autumn/winter wave of A (H1N1) influenza 2009: results of emergency medical dispatch, ambulance and emergency department data from three European regions. BMC Public Health 2013; 13:905. [PMID: 24083852 PMCID: PMC3852468 DOI: 10.1186/1471-2458-13-905] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2012] [Accepted: 09/24/2013] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Emergency medical service (EMS) data, particularly from the emergency department (ED), is a common source of information for syndromic surveillance. However, the entire EMS chain, consists of both out-of-hospital and in-hospital services. Differences in validity and timeliness across these data sources so far have not been studied. Neither have the differences in validity and timeliness of this data from different European countries. In this paper we examine the validity and timeliness of the entire chain of EMS data sources from three European regions for common syndromic influenza surveillance during the A(H1N1) influenza pandemic in 2009. METHODS We gathered local, regional, or national information on influenza-like illness (ILI) or respiratory syndrome from an Austrian Emergency Medical Dispatch Service (EMD-AT), an Austrian and Belgian ambulance services (EP-AT, EP-BE) and from a Belgian and Spanish emergency department (ED-BE, ED-ES). We examined the timeliness of the EMS data in identifying the beginning of the autumn/winter wave of pandemic A(H1N1) influenza as compared to the reference data. Additionally, we determined the sensitivity and specificity of an aberration detection algorithm (Poisson CUSUM) in EMS data sources for detecting the autumn/winter wave of the A(H1N1) influenza pandemic. RESULTS The ED-ES data demonstrated the most favourable validity, followed by the ED-BE data. The beginning of the autumn/winter wave of pandemic A(H1N1) influenza was identified eight days in advance in ED-BE data. The EP data performed stronger in data sets for large catchment areas (EP-BE) and identified the beginning of the autumn/winter wave almost at the same time as the reference data (time lag +2 days). EMD data exhibited timely identification of the autumn/winter wave of A(H1N1) but demonstrated weak validity measures. CONCLUSIONS In this study ED data exhibited the most favourable performance in terms of validity and timeliness for syndromic influenza surveillance, along with EP data for large catchment areas. For the other data sources performance assessment delivered no clear results. The study shows that routinely collected data from EMS providers can augment and enhance public health surveillance of influenza by providing information during health crises in which such information must be both timely and readily obtainable.
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Affiliation(s)
- Nicole Rosenkötter
- Department of International Health, CAPHRI School for Public Health and Primary Care, Faculty for Health, Medicine and Life Sciences, Maastricht University, Duboisdomein 30, Maastricht 6229 GT, The Netherlands
| | - Alexandra Ziemann
- Department of International Health, CAPHRI School for Public Health and Primary Care, Faculty for Health, Medicine and Life Sciences, Maastricht University, Duboisdomein 30, Maastricht 6229 GT, The Netherlands
| | | | - Jean Bernard Gillet
- Department of Emergency Medicine, University Hospital Leuven, Leuven, Belgium
| | | | - Thomas Krafft
- Department of International Health, CAPHRI School for Public Health and Primary Care, Faculty for Health, Medicine and Life Sciences, Maastricht University, Duboisdomein 30, Maastricht 6229 GT, The Netherlands
| | - Helmut Brand
- Department of International Health, CAPHRI School for Public Health and Primary Care, Faculty for Health, Medicine and Life Sciences, Maastricht University, Duboisdomein 30, Maastricht 6229 GT, The Netherlands
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Hiller KM, Stoneking L, Min A, Rhodes SM. Syndromic surveillance for influenza in the emergency department-A systematic review. PLoS One 2013; 8:e73832. [PMID: 24058494 PMCID: PMC3772865 DOI: 10.1371/journal.pone.0073832] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2013] [Accepted: 07/25/2013] [Indexed: 11/23/2022] Open
Abstract
The science of surveillance is rapidly evolving due to changes in public health information and preparedness as national security issues, new information technologies and health reform. As the Emergency Department has become a much more utilized venue for acute care, it has also become a more attractive data source for disease surveillance. In recent years, influenza surveillance from the Emergency Department has increased in scope and breadth and has resulted in innovative and increasingly accepted methods of surveillance for influenza and influenza-like-illness (ILI). We undertook a systematic review of published Emergency Department-based influenza and ILI syndromic surveillance systems. A PubMed search using the keywords "syndromic", "surveillance", "influenza" and "emergency" was performed. Manuscripts were included in the analysis if they described (1) data from an Emergency Department (2) surveillance of influenza or ILI and (3) syndromic or clinical data. Meeting abstracts were excluded. The references of included manuscripts were examined for additional studies. A total of 38 manuscripts met the inclusion criteria, describing 24 discrete syndromic surveillance systems. Emergency Department-based influenza syndromic surveillance has been described worldwide. A wide variety of clinical data was used for surveillance, including chief complaint/presentation, preliminary or discharge diagnosis, free text analysis of the entire medical record, Google flu trends, calls to teletriage and help lines, ambulance dispatch calls, case reports of H1N1 in the media, markers of ED crowding, admission and Left Without Being Seen rates. Syndromes used to capture influenza rates were nearly always related to ILI (i.e. fever +/- a respiratory or constitutional complaint), however, other syndromes used for surveillance included fever alone, "respiratory complaint" and seizure. Two very large surveillance networks, the North American DiSTRIBuTE network and the European Triple S system have collected large-scale Emergency Department-based influenza and ILI syndromic surveillance data. Syndromic surveillance for influenza and ILI from the Emergency Department is becoming more prevalent as a measure of yearly influenza outbreaks.
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Affiliation(s)
- Katherine M. Hiller
- Department of Emergency Medicine, University of Arizona, Tucson, Arizona, United States of America
| | - Lisa Stoneking
- Department of Emergency Medicine, University of Arizona, Tucson, Arizona, United States of America
| | - Alice Min
- Department of Emergency Medicine, University of Arizona, Tucson, Arizona, United States of America
| | - Suzanne Michelle Rhodes
- Department of Emergency Medicine, University of Arizona, Tucson, Arizona, United States of America
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Yan W, Palm L, Lu X, Nie S, Xu B, Zhao Q, Tao T, Cheng L, Tan L, Dong H, Diwan VK. ISS--an electronic syndromic surveillance system for infectious disease in rural China. PLoS One 2013; 8:e62749. [PMID: 23626853 PMCID: PMC3633833 DOI: 10.1371/journal.pone.0062749] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2012] [Accepted: 03/29/2013] [Indexed: 12/04/2022] Open
Abstract
Background syndromic surveillance system has great advantages in promoting the early detection of epidemics and reducing the necessities of disease confirmation, and it is especially effective for surveillance in resource poor settings. However, most current syndromic surveillance systems are established in developed countries, and there are very few reports on the development of an electronic syndromic surveillance system in resource-constrained settings. Objective this study describes the design and pilot implementation of an electronic surveillance system (ISS) for the early detection of infectious disease epidemics in rural China, complementing the conventional case report surveillance system. Methods ISS was developed based on an existing platform ‘Crisis Information Sharing Platform’ (CRISP), combining with modern communication and GIS technology. ISS has four interconnected functions: 1) work group and communication group; 2) data source and collection; 3) data visualization; and 4) outbreak detection and alerting. Results As of Jan. 31st 2012, ISS has been installed and pilot tested for six months in four counties in rural China. 95 health facilities, 14 pharmacies and 24 primary schools participated in the pilot study, entering respectively 74256, 79701, and 2330 daily records into the central database. More than 90% of surveillance units at the study sites are able to send daily information into the system. In the paper, we also presented the pilot data from health facilities in the two counties, which showed the ISS system had the potential to identify the change of disease patterns at the community level. Conclusions The ISS platform may facilitate the early detection of infectious disease epidemic as it provides near real-time syndromic data collection, interactive visualization, and automated aberration detection. However, several constraints and challenges were encountered during the pilot implementation of ISS in rural China.
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Affiliation(s)
- Weirong Yan
- Division of Global Health (IHCAR), Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden.
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Sawford K, Vollman AR, Stephen C. A focused ethnographic study of Sri Lankan government field veterinarians' decision making about diagnostic laboratory submissions and perceptions of surveillance. PLoS One 2012; 7:e48035. [PMID: 23133542 PMCID: PMC3485039 DOI: 10.1371/journal.pone.0048035] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2012] [Accepted: 09/20/2012] [Indexed: 11/18/2022] Open
Abstract
The global public health community is facing the challenge of emerging infectious diseases. Historically, the majority of these diseases have arisen from animal populations at lower latitudes where many nations experience marked resource constraints. In order to minimize the impact of future events, surveillance of animal populations will need to enable prompt event detection and response. Many surveillance systems targeting animals rely on veterinarians to submit cases to a diagnostic laboratory or input clinical case data. Therefore understanding veterinarians’ decision-making process that guides laboratory case submission and their perceptions of infectious disease surveillance is foundational to interpreting disease patterns reported by laboratories and engaging veterinarians in surveillance initiatives. A focused ethnographic study was conducted with twelve field veterinary surgeons that participated in a mobile phone-based surveillance pilot project in Sri Lanka. Each participant agreed to an individual in-depth interview that was recorded and later transcribed to enable thematic analysis of the interview content. Results found that field veterinarians in Sri Lanka infrequently submit cases to laboratories – so infrequently that common case selection principles could not be described. Field veterinarians in Sri Lanka have a diagnostic process that operates independently of laboratories. Participants indicated a willingness to take part in surveillance initiatives, though they highlighted a need for incentives that satisfy a range of motivations that vary among field veterinarians. This study has implications for the future of animal health surveillance, including interpretation of disease patterns reported, system design and implementation, and engagement of data providers.
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Affiliation(s)
- Kate Sawford
- Farm Animal & Veterinary Public Health, Faculty of Veterinary Science, University of Sydney, Camden, New South Wales, Australia.
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Yan WR, Nie SF, Xu B, Dong HJ, Palm L, Diwan VK. Establishing a web-based integrated surveillance system for early detection of infectious disease epidemic in rural China: a field experimental study. BMC Med Inform Decis Mak 2012; 12:4. [PMID: 22305256 PMCID: PMC3395861 DOI: 10.1186/1472-6947-12-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2012] [Accepted: 02/03/2012] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND A crucial goal of infectious disease surveillance is the early detection of epidemics, which is essential for disease control. In China, the current surveillance system is based on confirmed case reports. In rural China, it is not practical for health units to perform laboratory tests to confirm disease and people are more likely to get 'old' and emerging infectious diseases due to poor living conditions and closer contacts with wild animals and poultry. Syndromic surveillance, which collects non-specific syndromes before diagnosis, has great advantages in promoting the early detection of epidemics and reducing the necessities of disease confirmation. It will be especially effective for surveillance in resource poor settings. METHODS/DESIGN This is a field experimental study. The experimental tool is an innovative electronic surveillance system, combining syndromic surveillance with the existing case report surveillance in four selected counties in China. In the added syndromic surveillance, three types of data are collected including patients' major symptoms from health clinics, pharmaceutical sales from pharmacies and absenteeism information from primary school. In order to evaluate the early warning capability of the new added syndromic surveillance, the timelines and validity of the alert signals will be analyzed in comparison with the traditional case reporting system. The acceptability, feasibility and economic evaluation of the whole integrated surveillance system will be conducted in a before and after study design. DISCUSSIONS Although syndromic surveillance system has mostly been established in developed areas, there are opportunities and advantages of developing it in rural China. The project will contribute to knowledge, experience and evidence on the establishment of an integrated surveillance system, which aims to provide early warning of disease epidemics in developing countries.
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Affiliation(s)
- Wei-rong Yan
- Division of Global Health (IHCAR), Department of Public Health Sciences, Karolinska Institutet, Nobelsvag 9, SE-17177, Stockholm, Sweden
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College of Huazhong University of Science and Technology, Hangkong Road 13#, Wuhan, 430030, Hubei, China
| | - Shao-fa Nie
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College of Huazhong University of Science and Technology, Hangkong Road 13#, Wuhan, 430030, Hubei, China
| | - Biao Xu
- Department of Epidemiology, School of Public Health, Fudan University, No 138 Yi Xue Yuan Road, Shanghai, 200032, China
| | - Heng-jin Dong
- Center for Health Policy Studies, School of Public Health, Zhejiang University School of Medicine, 866 Yuhangtang Road, Hangzhou, 310058, Zhejiang, China
| | - Lars Palm
- Future Position X (FPX), Nobelvägen 2, Box 975, SE-801 33, Gävle, Sweden
| | - Vinod K Diwan
- Division of Global Health (IHCAR), Department of Public Health Sciences, Karolinska Institutet, Nobelsvag 9, SE-17177, Stockholm, Sweden
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Moore K, Black J, Rowe S, Franklin L. Syndromic surveillance for influenza in two hospital emergency departments. Relationships between ICD-10 codes and notified cases, before and during a pandemic. BMC Public Health 2011; 11:338. [PMID: 21592398 PMCID: PMC3111580 DOI: 10.1186/1471-2458-11-338] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2011] [Accepted: 05/18/2011] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Interest in the use of emergency department (ED) data by syndromic surveillance systems to detect influenza outbreaks has been growing. Evaluations of these systems generally focus on events during influenza seasons. The aims of this study were to identify which emergency department disease codes best correlated with confirmed influenza cases and to determine if these same codes would be useful in the non-influenza season. The 2009 influenza pandemic in Victoria, Australia, provided further opportunity to examine the performance of the syndromic surveillance system during this event. METHODS We undertook a retrospective analysis of data from the Victorian Department of Health's pilot syndromic surveillance programme, 'SynSurv'. SynSurv automatically captures patient information as it is entered by ED staff. This information includes patient demographics, their presenting symptoms and a preliminary diagnosis using ICD-10 coding. To determine which codes were best correlated with influenza notifications, weekly counts for each of the ICD-10 diagnosis codes ever used in the dataset were calculated and compared with the corresponding weekly count of confirmed influenza cases. Correlations between these codes and confirmed influenza cases in the non-influenza season were then undertaken. The data covered the period from July 2001 until August 2009 and included the 2009 influenza pandemic. RESULTS There was a marked increase in weekly counts of both laboratory-confirmed influenza cases and relevant ICD-10 codes during the influenza pandemic period. The increase in laboratory confirmed cases was more than four times greater than the previous highest number reported, in 2007, even though the influenza-like-illness activity in the community was considered comparable to 2003 and 2007. We found five ICD-10 codes to be moderately and significantly correlated with influenza cases. None of these codes was correlated with laboratory confirmed influenza notifications outside the influenza season, at least in part because of the small number of influenza cases notified during that period. CONCLUSIONS This study suggests that the choice of codes made by ED staff to record a case of influenza-like illness is influenced by their perceptions of how much influenza is circulating at the time. The ability of syndromic surveillance to detect outbreaks early may be impeded because case diagnosis is influenced by what ED staff believes to be occurring in the community.
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Affiliation(s)
- Karen Moore
- Monash University, Melbourne, Victoria, Australia.
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Outpatient physician billing data for age and setting specific syndromic surveillance of influenza-like illnesses. J Biomed Inform 2011; 44:221-8. [DOI: 10.1016/j.jbi.2010.10.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2010] [Revised: 09/18/2010] [Accepted: 10/11/2010] [Indexed: 11/21/2022]
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Greene SK, Kulldorff M, Huang J, Brand RJ, Kleinman KP, Hsu J, Platt R. Timely detection of localized excess influenza activity in Northern California across patient care, prescription, and laboratory data. Stat Med 2011; 30:549-59. [PMID: 21312219 PMCID: PMC3058686 DOI: 10.1002/sim.3883] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Timely detection of clusters of localized influenza activity in excess of background seasonal levels could improve situational awareness for public health officials and health systems. However, no single data type may capture influenza activity with optimal sensitivity, specificity, and timeliness, and it is unknown which data types could be most useful for surveillance. We compared the performance of 10 types of electronic clinical data for timely detection of influenza clusters throughout the 2007/08 influenza season in northern California. Kaiser Permanente Northern California generated zip code-specific daily episode counts for: influenza-like illness (ILI) diagnoses in ambulatory care (AC) and emergency departments (ED), both with and without regard to fever; hospital admissions and discharges for pneumonia and influenza; antiviral drugs dispensed (Rx); influenza laboratory tests ordered (Tests); and tests positive for influenza type A (FluA) and type B (FluB). Four credible events of localized excess illness were identified. Prospective surveillance was mimicked within each data stream using a space-time permutation scan statistic, analyzing only data available as of each day, to evaluate the ability and timeliness to detect the credible events. AC without fever and Tests signaled during all four events and, along with Rx, had the most timely signals. FluA had less timely signals. ED, hospitalizations, and FluB did not signal reliably. When fever was included in the ILI definition, signals were either delayed or missed. Although limited to one health plan, location, and year, these results can inform the choice of data streams for public health surveillance of influenza.
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Affiliation(s)
- Sharon K Greene
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA, USA.
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Bellazzini MA, Minor KD. ED syndromic surveillance for novel H1N1 spring 2009. Am J Emerg Med 2010; 29:70-4. [PMID: 20825786 DOI: 10.1016/j.ajem.2009.09.009] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2009] [Revised: 08/23/2009] [Accepted: 09/11/2009] [Indexed: 11/18/2022] Open
Abstract
PURPOSE The aim of this study is to demonstrate the use of emergency department (ED) syndromic surveillance in the setting of a novel and unexpected H1N1 influenza outbreak. BASIC PROCEDURES Data collection from ED electronic medical records was used to track initial chief complaint and discharge International Classification of Diseases, Ninth Revision, codes related to influenza-like illness (ILI). An alert threshold was generated using cumulative sum sequential analysis technique. The data were retrospectively analyzed to identify alerts that correlated with novel influenza H1N1 illness. MAIN FINDINGS Our system alerted for ILI earlier than both the official national Centers for Disease Control and Prevention (CDC) press release for novel H1N1 and the first laboratory confirmed case in our county. PRINCIPAL CONCLUSIONS Emergency department syndromic surveillance can be used to detect unexpected ILI before laboratory confirmation and serve as an adjunct to traditional laboratory-guided public health alerts. Early identification may allow for more efficient laboratory testing and early implementation of respiratory isolation precautions.
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Affiliation(s)
- Marc A Bellazzini
- University of Wisconsin School of Medicine and Public Health, Madison, 53792, USA.
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Early detection of influenza outbreaks using the DC Department of Health's syndromic surveillance system. BMC Public Health 2009; 9:483. [PMID: 20028535 PMCID: PMC2807869 DOI: 10.1186/1471-2458-9-483] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2009] [Accepted: 12/22/2009] [Indexed: 12/02/2022] Open
Abstract
Background Since 2001, the District of Columbia Department of Health has been using an emergency room syndromic surveillance system to identify possible disease outbreaks. Data are received from a number of local hospital emergency rooms and analyzed daily using a variety of statistical detection algorithms. The aims of this paper are to characterize the performance of these statistical detection algorithms in rigorous yet practical terms in order to identify the optimal parameters for each and to compare the ability of two syndrome definition criteria and data from a children's hospital versus vs. other hospitals to determine the onset of seasonal influenza. Methods We first used a fine-tuning approach to improve the sensitivity of each algorithm to detecting simulated outbreaks and to identifying previously known outbreaks. Subsequently, using the fine-tuned algorithms, we examined (i) the ability of unspecified infection and respiratory syndrome categories to detect the start of the flu season and (ii) how well data from Children's National Medical Center (CNMC) did versus all the other hospitals when using unspecified infection, respiratory, and both categories together. Results Simulation studies using the data showed that over a range of situations, the multivariate CUSUM algorithm performed more effectively than the other algorithms tested. In addition, the parameters that yielded optimal performance varied for each algorithm, especially with the number of cases in the data stream. In terms of detecting the onset of seasonal influenza, only "unspecified infection," especially the counts from CNMC, clearly delineated influenza outbreaks out of the eight available syndromic classifications. In three of five years, CNMC consistently flags earlier (from 2 days up to 2 weeks earlier) than a multivariate analysis of all other DC hospitals. Conclusions When practitioners apply statistical detection algorithms to their own data, fine tuning of parameters is necessary to improve overall sensitivity. With fined tuned algorithms, our results suggest that emergency room based syndromic surveillance focusing on unspecified infection cases in children is an effective way to determine the beginning of the influenza outbreak and could serve as a trigger for more intensive surveillance efforts and initiate infection control measures in the community.
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