Implementing a public web based GIS service for feedback of surveillance data on communicable diseases in Sweden

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)

CASE: A Framework for Computer Supported Outbreak Detection

Background: In computer supported outbreak detection, a statistical method is applied to a collection of cases to detect any excess cases for a particular disease. Whether a detected aberration is a true outbreak is decided by a human expert. We present a technical framework designed and implemented at the Swedish Institute for Infectious Disease Control for computer supported outbreak detection, where a database of case reports for a large number of infectious diseases can be processed using one or more statistical methods selected by the user. Results: Based on case information, such as diagnosis and date, different statistical algorithms for detecting outbreaks can be applied, both on the disease level and the subtype level. The parameter settings for the algorithms can be configured independently for different diagnoses using the provided graphical interface. Input generators and output parsers are also provided for all supported algorithms. If an outbreak signal is detected, an email notification is sent to the persons listed as receivers for that particular disease. Conclusions: The framework is available as open source software, licensed under GNU General Public License Version 3. By making the code open source, we wish to encourage others to contribute to the future development of computer supported outbreak detection systems, and in particular to the development of the CASE framewor

From planning to practice: building the national network for the surveillance of severe maternal morbidity

Background: Improving maternal health is one of the Millennium Development Goals for 2015. Recently some progress has been achieved in reducing mortality. On the other hand, in developed regions, maternal death is a relatively rare event compared to the number of cases of morbidity; hence studying maternal morbidity has become more relevant. Electronic surveillance systems may improve research by facilitating complete data reporting and reducing the time required for data collection and analysis. Therefore the purpose of this study was to describe the methods used in elaborating and implementing the National Network for the Surveillance of Severe Maternal Morbidity in Brazil. Methods: The project consisted of a multicenter, cross-sectional study for the surveillance of severe maternal morbidity including near-miss, in Brazil. Results: Following the development of a conceptual framework, centers were selected for inclusion in the network, consensus meetings were held among the centers, an electronic data collection system was identified, specific software and hardware tools were developed, research material was prepared, and the implementation process was initiated and analyzed. Conclusion: The conceptual framework developed for this network was based on the experience acquired in various studies carried out in the area over recent years and encompasses maternal and perinatal health. It is innovative especially in the context of a developing country. The implementation of the project represents the first step towards this planned management. The system online elaborated for this surveillance network may be used in further studies in reproductive and perinatal health

An evaluation and comparison of three commonly used statistical models for automatic detection of outbreaks in epidemiological data of communicable diseases

We evaluated three established statistical models for automated ‘early warnings’ of disease outbreaks; counted data Poisson CuSums (used in New Zealand), the England and Wales model (used in England and Wales) and SPOTv2 (used in Australia). In the evaluation we used national Swedish notification data from 1992 to 2003 on campylobacteriosis, hepatitis A and tularemia. The average sensitivity and positive predictive value for CuSums were 71 and 53%, for the England and Wales model 87 and 82% and for SPOTv2 95 and 49% respectively. The England and Wales model and the SPOTv2 model were superior to CuSums in our setting. Although, it was more difficult to rank the former two, we recommend the SPOTv2 model over the England and Wales model, mainly because of a better sensitivity. However, the impact of previous outbreaks on baseline levels was less in the England and Wales model. The CuSums model did not adjust for previous outbreaks