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The objective of this paper is to describe the evolution of human immunodeficiency virus/acquired immunodeficiency syndrome surveillance in mainland China, with a focus on reviewing the sources of data being used for improved surveillance of HIV/AIDS. We review the development of HIV/AIDS surveillance and its multiple data sources to monitor the dynamics of HIV/AIDS in China. The surveillance system for HIV/AIDS in China was initiated in 1986. It has evolved in three stages: (1) passive surveillance, (2) HIV sentinel surveillance with coexisting active surveillance and passive surveillance, and (3) comprehensive surveillance. In parallel with the evolution of the surveillance system itself, the HIV epidemic in China has gone through increasing stages of complexity, through an Introduction Phase, a Spreading Phase, and a Rapidy Spreading Phase. More reliable data from improved surveillance suggest that the HIV/AIDS epidemic is expanding in China. HIV infections among 2005 estimates remain concentrated among injection drug users (IDUs), those buying and selling sex, and men who have sex with men. Better HIV/AIDS surveillance synthesizes multiple data sources to provide a more accurate picture of the dynamics of specific HIV/AIDS circumstances in different areas of China. Improved surveillance is meaningful insofar as data are used to implement more effective HIV prevention programs in China. Support for surveillance and strategic analyses can enable policy decision makers to make more effective program choices and mobilize adequate resources to contain HIV.

Background

In recent years a wide variety of epidemiological surveillance systems have been developed to provide early identification of outbreaks of infectious disease. Each system has had its own strengths and weaknesses. In 2002 a Working Group of the Centers for Disease Control and Prevention (CDC) produced a framework for evaluation, which proved suitable for many public health surveillance systems. However this did not easily adapt to the military setting, where by necessity a variety of different parameters are assessed, different constraints placed on the systems, and different objectives required. This paper describes a proposed framework for evaluation of military syndromic surveillance systems designed to detect outbreaks of disease on operational deployments.

Methods

The new framework described in this paper was developed from the cumulative experience of British and French military syndromic surveillance systems. The methods included a general assessment framework (CDC), followed by more specific methods of conducting evaluation. These included Knowledge/Attitude/Practice surveys (KAP surveys), technical audits, ergonomic studies, simulations and multi-national exercises. A variety of military constraints required integration into the evaluation. Examples of these include the variability of geographical conditions in the field, deployment to areas without prior knowledge of naturally-occurring disease patterns, the differences in field sanitation between locations and over the length of deployment, the mobility of military forces, turnover of personnel, continuity of surveillance across different locations, integration with surveillance systems from other nations working alongside each other, compatibility with non-medical information systems, and security.

Results

A framework for evaluation has been developed that can be used for military surveillance systems in a staged manner consisting of initial, intermediate and final evaluations. For each stage of the process parameters for assessment have been defined and methods identified.

Conclusion

The combined experiences of French and British syndromic surveillance systems developed for use in deployed military forces has allowed the development of a specific evaluation framework. The tool is suitable for use by all nations who wish to evaluate syndromic surveillance in their own military forces. It could also be useful for civilian mobile systems or for national security surveillance systems.

Background

Visceral leishmaniasis (VL) is still an important public health problem in China. In recent years endemic regions spread, prevalence increased, and even an outbreak of the disease occurred in China due to global warming and population movement. It is essential to elucidate the current epidemic situation and epidemiological characteristics of VL for designing control policy. In the present study we describe the current epidemiological profile and characteristics of VL in China based on retrospectively reviewing of VL cases reported between 2005 and 2010 by a passive surveillance system.

Methods

The present study was a retrospective review of VL cases notified between 2005 and 2010 based on the passive surveillance data. The data were tabulated, diagrammatized and analyzed through descriptive statistics in a Microsoft Excel spreadsheet.

Results

A total of 2450 VL cases were notified, with a mean of 408 cases per year. 61 counties were identified as endemic area with 2224 autochthonous cases, and the other 118 counties as non-endemic areas with 226 imported cases. 97.71% of cases were concentrated in Xinjiang, Gansu and Sichuan Provinces. 9 major counties reported a mean of > 10 cases per year, with a total of 1759 cases reported. Different types of VL revealed distinct epidemiological characteristics.

Conclusions

The number of VL cases and endemic counties both increased in the period 2005-2010 in China. Different type or sub-type of VL revealed distinct epidemiological characteristics. Therefore, differential control measures must be taken in different endemic areas against incidence increase and endemic area spread.

Background

With international concern over emerging infectious diseases (EID) and bioterrorist attacks, public health is being required to have early outbreak detection systems. A disease surveillance team was organized to establish a hospital emergency department-based syndromic surveillance system (ED-SSS) capable of automatically transmitting patient data electronically from the hospitals responsible for emergency care throughout the country to the Centers for Disease Control in Taiwan (Taiwan-CDC) starting March, 2004. This report describes the challenges and steps involved in developing ED-SSS and the timely information it provides to improve in public health decision-making.

Methods

Between June 2003 and March 2004, after comparing various surveillance systems used around the world and consulting with ED physicians, pediatricians and internal medicine physicians involved in infectious disease control, the Syndromic Surveillance Research Team in Taiwan worked with the Real-time Outbreak and Disease Surveillance (RODS) Laboratory at the University of Pittsburgh to create Taiwan's ED-SSS. The system was evaluated by analyzing daily electronic ED data received in real-time from the 189 hospitals participating in this system between April 1, 2004 and March 31, 2005.

Results

Taiwan's ED-SSS identified winter and summer spikes in two syndrome groups: influenza-like illnesses and respiratory syndrome illnesses, while total numbers of ED visits were significantly higher on weekends, national holidays and the days of Chinese lunar new year than weekdays (p < 0.001). It also identified increases in the upper, lower, and total gastrointestinal (GI) syndrome groups starting in November 2004 and two clear spikes in enterovirus-like infections coinciding with the two school semesters. Using ED-SSS for surveillance of influenza-like illnesses and enteroviruses-related infections has improved Taiwan's pandemic flu preparedness and disease control capabilities.

Conclusion

Taiwan's ED-SSS represents the first nationwide real-time syndromic surveillance system ever established in Asia. The experiences reported herein can encourage other countries to develop their own surveillance systems. The system can be adapted to other cultural and language environments for better global surveillance of infectious diseases and international collaboration.

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.

Syndromic surveillance systems can detect potential disease outbreaks quickly and can provide useful tools to assist in outbreak investigation. The steps used to investigate diseases detected through these newer methods are not that different from traditional investigative measures, but the differences and limitations of the systems must be understood. With syndromic surveillance systems, there is often readily available electronic demographic information that can help define the epidemic and direct disease control measures. The diagnosis needs to be confirmed as quickly as possible, however, as specific diagnostic information will be missing with early detection from nonspecific data. It is also important not to disregard smaller, nonsevere rises in disease incidence as they might be a harbinger of a worsening outbreak. The rapidity of most syndromic surveillance systems also requires an equally rapid response, and planning must be done to prioritize alert categories and the response sequence to best utilize the information available in these new systems.

This system enabled detection of the onset and peak of an epidemic.

In 2007, a surveillance system for influenza-like illness (ILI) and virologic data was established in Beijing, China. The system tracked ILI and laboratory-confirmed influenza in 153 general hospitals from September 1, 2007, through April 30, 2008. To analyze the ILI surveillance data (weekly ILI rates and counts) and the effectiveness of the system, we used the US Centers for Disease Control and Prevention Early Aberration Reporting System. The data indicated that the highest rate of influenza isolation and the highest ILI count occurred in the first week of 2008. The system enabled us to detect the onset and peak of an epidemic.

Background

A dengue fever outbreak occured in French Guiana in 2006. The objectives were to study the value of a syndromic surveillance system set up within the armed forces, compared to the traditional clinical surveillance system during this outbreak, to highlight issues involved in comparing military and civilian surveillance systems and to discuss the interest of syndromic surveillance for public health response.

Methods

Military syndromic surveillance allows the surveillance of suspected dengue fever cases among the 3,000 armed forces personnel. Within the same population, clinical surveillance uses several definition criteria for dengue fever cases, depending on the epidemiological situation. Civilian laboratory surveillance allows the surveillance of biologically confirmed cases, within the 200,000 inhabitants.

Results

It was shown that syndromic surveillance detected the dengue fever outbreak several weeks before clinical surveillance, allowing quick and effective enhancement of vector control within the armed forces. Syndromic surveillance was also found to have detected the outbreak before civilian laboratory surveillance.

Conclusion

Military syndromic surveillance allowed an early warning for this outbreak to be issued, enabling a quicker public health response by the armed forces. Civilian surveillance system has since introduced syndromic surveillance as part of its surveillance strategy. This should enable quicker public health responses in the future.

Syndromic surveillance refers to methods relying on detection of individual and population health indicators that are discernible before confirmed diagnoses are made. In particular, prior to the laboratory confirmation of an infectious disease, ill persons may exhibit behavioral patterns, symptoms, signs, or laboratory findings that can be tracked through a variety of data sources. Syndromic surveillance systems are being developed locally, regionally, and nationally. The efforts have been largely directed at facilitating the early detection of a covert bioterrorist attack, but the technology may also be useful for general public health, clinical medicine, quality improvement, patient safety, and research. This report, authored by developers and methodologists involved in the design and deployment of the first wave of syndromic surveillance systems, is intended to serve as a guide for informaticians, public health managers, and practitioners who are currently planning deployment of such systems in their regions.

Event-based biosurveillance is a recognized approach to early warning and situational awareness of emerging health threats. In this study, we build upon previous human and animal health work to develop a new approach to plant pest and pathogen surveillance. We show that monitoring public domain electronic media for indications and warning of epidemics and associated social disruption can provide information about the emergence and progression of plant pest infestation or disease outbreak. The approach is illustrated using a case study, which describes a plant pest and pathogen epidemic in China and Vietnam from February 2006 to December 2007, and the role of ducks in contributing to zoonotic virus spread in birds and humans. This approach could be used as a complementary method to traditional plant pest and pathogen surveillance to aid global and national plant protection officials and political leaders in early detection and timely response to significant biological threats to plant health, economic vitality, and social stability. This study documents the inter-relatedness of health in human, animal, and plant populations and emphasizes the importance of plant health surveillance.

Background

Outbreak detection algorithms play an important role in effective automated surveillance. Although many algorithms have been designed to improve the performance of outbreak detection, few published studies have examined how epidemic features of infectious disease impact on the detection performance of algorithms. This study compared the performance of three outbreak detection algorithms stratified by epidemic features of infectious disease and examined the relationship between epidemic features and performance of outbreak detection algorithms.

Methods

Exponentially weighted moving average (EWMA), cumulative sum (CUSUM) and moving percentile method (MPM) algorithms were applied. We inserted simulated outbreaks into notifiable infectious disease data in China Infectious Disease Automated-alert and Response System (CIDARS), and compared the performance of the three algorithms with optimized parameters at a fixed false alarm rate of 5% classified by epidemic features of infectious disease. Multiple linear regression was adopted to analyse the relationship of the algorithms’ sensitivity and timeliness with the epidemic features of infectious diseases.

Results

The MPM had better detection performance than EWMA and CUSUM through all simulated outbreaks, with or without stratification by epidemic features (incubation period, baseline counts and outbreak magnitude). The epidemic features were associated with both sensitivity and timeliness. Compared with long incubation, short incubation had lower probability (β* = −0.13, P < 0.001) but needed shorter time to detect outbreaks (β* = −0.57, P < 0.001). Lower baseline counts were associated with higher probability (β* = −0.20, P < 0.001) and longer time (β* = 0.14, P < 0.001). The larger outbreak magnitude was correlated with higher probability (β* = 0.55, P < 0.001) and shorter time (β* = −0.23, P < 0.001).

Conclusions

The results of this study suggest that the MPM is a prior algorithm for outbreak detection and differences of epidemic features in detection performance should be considered in automatic surveillance practice.

Background

All countries need effective disease surveillance systems for early detection of outbreaks. The revised International Health Regulations [IHR], which entered into force for all 194 World Health Organization member states in 2007, have expanded traditional infectious disease notification to include surveillance for public health events of potential international importance, even if the causative agent is not yet known. However, there are no clearly established guidelines for how countries should conduct this surveillance, which types of emerging disease syndromes should be reported, nor any means for enforcement.

Discussion

The commonly established concept of syndromic surveillance in developed regions encompasses the use of pre-diagnostic information in a near real time fashion for further investigation for public health action. Syndromic surveillance is widely used in North America and Europe, and is typically thought of as a highly complex, technology driven automated tool for early detection of outbreaks. Nonetheless, low technology applications of syndromic surveillance are being used worldwide to augment traditional surveillance.

Summary

In this paper, we review examples of these novel applications in the detection of vector-borne diseases, foodborne illness, and sexually transmitted infections. We hope to demonstrate that syndromic surveillance in its basic version is a feasible and effective tool for surveillance in developing countries and may facilitate compliance with the new IHR guidelines.

SYNOPSIS

Objective

Integrated infectious disease surveillance information systems have the potential to provide important new surveillance capacities and business efficiencies for local health departments. We conducted a case study at a large city health department of the primary computer-based infectious disease surveillance information systems during a 10-year period to identify the major challenges for information integration across the systems.

Methods

The assessment included key informant interviews and evaluations of the computer-based surveillance information systems used for acute communicable diseases, human immunodeficiency virus/acquired immunodeficiency syndrome, sexually transmitted diseases, and tuberculosis. Assessments were conducted in 1998 with a follow-up in 2008. Assessments specifically identified and described the primary computer-based surveillance information system, any duplicative information systems, and selected variables collected.

Results

Persistent challenges to information integration across the information systems included the existence of duplicative data systems, differences in the variables used to collect similar information, and differences in basic architecture.

Conclusions

The assessments identified a number of challenges for information integration across the infectious disease surveillance information systems at this city health department. The results suggest that local disease control programs use computer-based surveillance information systems that were not designed for data integration. To the extent that integration provides important new surveillance capacities and business efficiencies, we recommend that patient-centric information systems be designed that provide all the epidemiologic, clinical, and research needs in one system. In addition, the systems should include a standard system of elements and fields across similar surveillance systems.

Early detection of disease outbreaks in human and animal populations is crucial to the effective surveillance of emerging infectious diseases. However, there are marked geographical disparities in capacity for early detection of outbreaks, which limit the effectiveness of global surveillance strategies. Linking surveillance approaches for emerging and neglected endemic zoonoses, with a renewed focus on existing disease problems in developing countries, has the potential to overcome several limitations and to achieve additional health benefits. Poor reporting is a major constraint to the surveillance of both emerging and endemic zoonoses, and several important barriers to reporting can be identified: (i) a lack of tangible benefits when reports are made; (ii) a lack of capacity to enforce regulations; (iii) poor communication among communities, institutions and sectors; and (iv) complexities of the international regulatory environment. Redirecting surveillance efforts to focus on endemic zoonoses in developing countries offers a pragmatic approach that overcomes some of these barriers and provides support in regions where surveillance capacity is currently weakest. In addition, this approach addresses immediate health and development problems, and provides an equitable and sustainable mechanism for building the culture of surveillance and the core capacities that are needed for all zoonotic pathogens, including emerging disease threats.

Surveillance systems tracking health patterns in animals have potential for early warning of infectious disease in humans, yet there are many challenges that remain before this can be realized. Specifically, there remains the challenge of detecting early warning signals for diseases that are not known or are not part of routine surveillance for named diseases. This paper reports on the development of a hidden Markov model for analysis of frontline veterinary sentinel surveillance data from Sri Lanka. Field veterinarians collected data on syndromes and diagnoses using mobile phones. A model for submission patterns accounts for both sentinel-related and disease-related variability. Models for commonly reported cattle diagnoses were estimated separately. Region-specific weekly average prevalence was estimated for each diagnoses and partitioned into normal and abnormal periods. Visualization of state probabilities was used to indicate areas and times of unusual disease prevalence. The analysis suggests that hidden Markov modelling is a useful approach for surveillance datasets from novel populations and/or having little historical baselines.

Clinical observations will allow early detection of emerging diseases in animal to enhance response time and capabilities.

Predicting emerging diseases is among the most difficult challenges facing researchers and health managers. We present available approaches and tools to detect emerging diseases in animals based on clinical observations of farm animals by veterinarians. Three information systems are described and discussed: Veterinary Practitioner Aided Disease Surveillance in New Zealand, the Rapid Syndrome Validation Project—Animal in the United States, and "émergences" in France. These systems are based on syndromic surveillance with the notification of every case or of specific clinical syndromes or on the notification of atypical clinical cases. Data are entered by field veterinarians into forms available through Internet-accessible devices. Beyond challenges of implementing new information systems, minimizing economic and health effects from emerging diseases in animals requires strong synergies across a group of field partners, in research, and in international animal and public health customs and practices.

Fragile states are home to a sixth of the world's population, and their populations are particularly vulnerable to infectious disease outbreaks. Timely surveillance and control are essential to minimise the impact of these outbreaks, but little evidence is published about the effectiveness of existing surveillance systems. We did a systematic review of the circumstances (mode) of detection of outbreaks occurring in 22 fragile states in the decade 2000-2010 (i.e. all states consistently meeting fragility criteria during the timeframe of the review), as well as time lags from onset to detection of these outbreaks, and from detection to further events in their timeline. The aim of this review was to enhance the evidence base for implementing infectious disease surveillance in these complex, resource-constrained settings, and to assess the relative importance of different routes whereby outbreak detection occurs.

We identified 61 reports concerning 38 outbreaks. Twenty of these were detected by existing surveillance systems, but 10 detections occurred following formal notifications by participating health facilities rather than data analysis. A further 15 outbreaks were detected by informal notifications, including rumours.

There were long delays from onset to detection (median 29 days) and from detection to further events (investigation, confirmation, declaration, control). Existing surveillance systems yielded the shortest detection delays when linked to reduced barriers to health care and frequent analysis and reporting of incidence data.

Epidemic surveillance and control appear to be insufficiently timely in fragile states, and need to be strengthened. Greater reliance on formal and informal notifications is warranted. Outbreak reports should be more standardised and enable monitoring of surveillance systems' effectiveness.

To facilitate rapid detection of a future bioterrorist attack, an increasing number of public health departments are investing in new surveillance systems that target the early manifestations of bioterrorism-related disease. Whether this approach is likely to detect an epidemic sooner than reporting by alert clinicians remains unknown. The detection of a bioterrorism-related epidemic will depend on population characteristics, availability and use of health services, the nature of an attack, epidemiologic features of individual diseases, surveillance methods, and the capacity of health departments to respond to alerts. Predicting how these factors will combine in a bioterrorism attack may be impossible. Nevertheless, understanding their likely effect on epidemic detection should help define the usefulness of syndromic surveillance and identify approaches to increasing the likelihood that clinicians recognize and report an epidemic.

Conclusions

The promise of syndromic surveillance extends beyond early warning for bioterrorist attacks. Even if bioterrorism is first detected by an astute clinician, syndromic surveillance can help delineate the size, location, and tempo of the epidemic or provide reassurance that a large outbreak is not occurring when a single case or a small, localized cluster of an unusual illness is detected. More broadly, however, as public health and medicine proceed in our information age, the use of existing electronic data for public health surveillance will not appear to be an untested experiment for long. The challenge is to allow these systems to flower without burdening them with unrealistic expectations, centralized control, and unbalanced funding. To help syndromic surveillance systems reach their full potential, we need data standards, guidance to the developers of clinical information systems that will ensure data flow and interoperability, evaluations of best practices, links to improved laboratory diagnostics, regulations that protect privacy and data security, and reliable sustained funding for public health infrastructure to ensure the capacity to respond when the alarm sounds.

In recent years, the early detection of low pathogenicity avian influenza (LPAI) viruses in poultry has become increasingly important, given their potential to mutate into highly pathogenic viruses. However, evaluations of LPAI surveillance have mainly focused on prevalence and not on the ability to act as an early warning system. We used a simulation model based on data from Italian LPAI epidemics in turkeys to evaluate different surveillance strategies in terms of their performance as early warning systems. The strategies differed in terms of sample size, sampling frequency, diagnostic tests, and whether or not active surveillance (i.e., routine laboratory testing of farms) was performed, and were also tested under different epidemiological scenarios. We compared surveillance strategies by simulating within-farm outbreaks. The output measures were the proportion of infected farms that are detected and the farm reproduction number (Rh). The first one provides an indication of the sensitivity of the surveillance system to detect within-farm infections, whereas Rh reflects the effectiveness of outbreak detection (i.e., if detection occurs soon enough to bring an epidemic under control). Increasing the sampling frequency was the most effective means of improving the timeliness of detection (i.e., it occurs earlier), whereas increasing the sample size increased the likelihood of detection. Surveillance was only effective in preventing an epidemic if actions were taken within two days of sampling. The strategies were not affected by the quality of the diagnostic test, although performing both serological and virological assays increased the sensitivity of active surveillance. Early detection of LPAI outbreaks in turkeys can be achieved by increasing the sampling frequency for active surveillance, though very frequent sampling may not be sustainable in the long term. We suggest that, when no LPAI virus is circulating yet and there is a low risk of virus introduction, a less frequent sampling approach might be admitted, provided that the surveillance is intensified as soon as the first outbreak is detected.

Background

With the increase of international travels, infectious disease control is gaining a greater importance across regional borders. Adequate surveillance system function is crucial to prevent a global spread of infectious disease at the earliest stage. There have been limited reports on the characteristics of infectious disease surveillance in Asia. The authors studied the timeliness of the Korean National Notifiable Disease Surveillance System with regard to major notifiable diseases from 2001 to 2006.

Methods

Six notifiable infectious diseases reported relatively frequently were included in this study. Five diseases were selected by the criteria of reported cases > 100 per year: typhoid fever, shigellosis, mumps, scrub typhus, and hemorrhagic fever with renal syndrome. In addition, dengue fever was also included to represent an emerging disease, despite its low number of cases. The diseases were compared for the proportion notified within the recommended time limits, median time lags, and for the cumulative distribution of time lags at each surveillance step between symptom onset and date of notification to the Korea Centers for Disease Control and Prevention (KCDC).

Results

The proportion of cases reported in time was lower for disease groups with a recommended time limit of 1 day compared with 7 days (60%–70% vs. > 80%). The median time from disease onset to notification to KCDC ranged between 6 and 20 days. The median time from onset to registration at the local level ranged between 2 and 15 days. Distribution of time lags showed that main delays arose in the time from onset to diagnosis. There were variations in timeliness by disease categories and surveillance steps.

Conclusion

Time from disease onset to diagnosis generally contributed most to the delay in reporting. It is needed to promote public education and to improve clinical guidelines. Rapid reporting by doctors should be encouraged, and unification of recommended reporting time limit can be helpful. Our study also demonstrates the utility of the overall assessment of time-lag distributions for disease-specific strategies to improve surveillance.

Background

The Department of Defense Military Health System operates a syndromic surveillance system that monitors medical records at more than 450 non-combat Military Treatment Facilities (MTF) worldwide. The Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE) uses both temporal and spatial algorithms to detect disease outbreaks. This study focuses on spatial detection and attempts to improve the effectiveness of the ESSENCE implementation of the spatial scan statistic by increasing the spatial resolution of incidence data from zip codes to street address level.

Methods

Influenza-Like Illness (ILI) was used as a test syndrome to develop methods to improve the spatial accuracy of detected alerts. Simulated incident clusters of various sizes were superimposed on real ILI incidents from the 2008/2009 influenza season. Clusters were detected using the spatial scan statistic and their displacement from simulated loci was measured. Detected cluster size distributions were also evaluated for compliance with simulated cluster sizes.

Results

Relative to the ESSENCE zip code based method, clusters detected using street level incidents were displaced on average 65% less for 2 and 5 mile radius clusters and 31% less for 10 mile radius clusters. Detected cluster size distributions for the street address method were quasi normal and sizes tended to slightly exceed simulated radii. ESSENCE methods yielded fragmented distributions and had high rates of zero radius and oversized clusters.

Conclusions

Spatial detection accuracy improved notably with regard to both location and size when incidents were geocoded to street addresses rather than zip code centroids. Since street address geocoding success rates were only 73.5%, zip codes were still used for more than one quarter of ILI cases. Thus, further advances in spatial detection accuracy are dependant on systematic improvements in the collection of individual address information.

Syndromic surveillance is the gathering of data for public health purposes before laboratory or clinically confirmed information is available. Interest in syndromic surveillance has increased because of concerns about bioterrorism. In addition to bioterrorism detection, syndromic surveillance may be suited to detecting waterborne disease outbreaks. Theoretical benefits of syndromic surveillance include potential timeliness, increased response capacity, ability to establish baseline disease burdens, and ability to delineate the geographical reach of an outbreak. This review summarises the evidence gathered from retrospective, prospective, and simulation studies to assess the efficacy of syndromic surveillance for waterborne disease detection. There is little evidence that syndromic surveillance mitigates the effects of disease outbreaks through earlier detection and response. Syndromic surveillance should not be implemented at the expense of traditional disease surveillance, and should not be relied upon as a principal outbreak detection tool. The utility of syndromic surveillance is dependent on alarm thresholds that can be evaluated in practice. Syndromic data sources such as over the counter drug sales for detection of waterborne outbreaks should be further evaluated.

Background

Surveillance data allow for analysis, providing public health officials and policy-makers with a basis for long-term priorities and timely information on possible outbreaks for rapid response (data for action). In this article we describe the considerations and technology behind a newly introduced public web tool in Sweden for easy retrieval of county and national surveillance data on communicable diseases.

Methods

The web service was designed to automatically present updated surveillance statistics of some 50 statutory notifiable diseases notified to the Swedish Institute for Infectious Disease Control (SMI). The surveillance data is based on clinical notifications from the physician having treated the patient and laboratory notifications, merged into cases using a unique personal identification number issued to all Swedish residents. The web service use notification data from 1997 onwards, stored in a relational database at the SMI.

Results

The web service presents surveillance data to the user in various ways; tabulated data containing yearly and monthly disease data per county, age and sex distribution, interactive maps illustrating the total number of cases and the incidence per county and time period, graphs showing the total number of cases per week and graphs illustrating trends in the disease data. The system design encompasses the database (storing the data), the web server (holding the web service) and an in-the-middle computer (to ensure good security standards).

Conclusions

The web service has provided the health community, the media, and the public with easy access to both timely and detailed surveillance data presented in various forms. Since it was introduced in May 2003, the system has been accessed more than 1,000,000 times, by more than 10,000 different viewers (over 12.600 unique IP-numbers).

Objective

Hand, foot, and mouth disease (HFMD) has posed a great threat to the health of children and become a public health priority in China. This study aims to investigate the epidemiological characteristics, spatial-temporal patterns, and risk factors of HFMD in Guangdong Province, China, and to provide scientific information for public health responses and interventions.

Methods

HFMD surveillance data from May 2008 to December 2011were provided by the Chinese Center for Disease Control and Prevention. We firstly conducted a descriptive analysis to evaluate the epidemic characteristics of HFMD. Then, Kulldorff scan statistic based on a discrete Poisson model was used to detect spatial-temporal clusters. Finally, a spatial paneled model was applied to identify the risk factors.

Results

A total of 641,318 HFMD cases were reported in Guangdong Province during the study period (total population incidence: 17.51 per 10,000). Male incidence was higher than female incidence for all age groups, and approximately 90% of the cases were children years old. Spatial-temporal cluster analysis detected four most likely clusters and several secondary clusters (P<0.001) with the maximum cluster size 50% and 20% respectively during 2008–2011. Monthly average temperature, relative humidity, the proportion of population years, male-to-female ratio, and total sunshine were demonstrated to be the risk factors for HFMD.

Conclusion

Children years old, especially boys, were more susceptible to HFMD and we should take care of their vulnerability. Provincial capital city Guangzhou and the Pearl River Delta regions had always been the spatial-temporal clusters and future public health planning and resource allocation should be focused on these areas. Furthermore, our findings showed a strong association between HFMD and meteorological factors, which may assist in predicting HFMD incidence.