Public health surveillance
Public health surveillance (also epidemiological surveillance, clinical surveillance or syndromic surveillance) is, according to the World Health Organization (WHO), "the continuous, systematic collection, analysis and interpretation of health-related data needed for the planning, implementation, and evaluation of public health practice."[1] Public health surveillance may be used to track emerging health-related issues at an early stage and find active solutions in a timely manner.[1] Surveillance systems are generally called upon to provide information regarding when and where health problems are occurring and who is affected.[2]
Public health surveillance systems can be passive or active. A passive surveillance system consists of the regular, ongoing reporting of diseases and conditions by all health facilities in a given territory. An active surveillance system is one where health facilities are visited and health care providers and medical records are reviewed in order to identify a specific disease or condition.[3] Passive surveillance systems are less time-consuming and less expensive to run but risk under-reporting of some diseases. Active surveillance systems are most appropriate for epidemics or where a disease has been targeted for elimination.[3]
Techniques of public health surveillance have been used in particular to study
Many regions and countries have their own cancer registry, which is monitors the incidence of cancers to determine the prevalence and possible causes of these illnesses.[4]
Other illnesses such as one-time events like
Systems that can automate the process of identifying adverse drug events, are currently being used, and are being compared to traditional written reports of such events.
Syndromic surveillance
Syndromic surveillance is the analysis of medical data to detect or anticipate
The first indications of disease outbreak or
Using a normal influenza outbreak as an example, once the outbreak begins to affect the population, some people may call in sick for work/school, others may visit their drug store and purchase medicine over the counter, others will visit their doctor's office and other's may have symptoms severe enough that they call the emergency telephone number or go to an emergency department.[citation needed]
Syndromic surveillance systems monitor data from school absenteeism logs, emergency call systems, hospitals' over-the-counter drug sale records, Internet searches, and other data sources to detect unusual patterns. When a spike in activity is seen in any of the monitored systems disease
An early awareness and response to a bioterrorist attack could save many lives and potentially stop or slow the spread of the outbreak. The most effective syndromic surveillance systems automatically monitor these systems in real-time, do not require individuals to enter separate information (secondary data entry), include advanced analytical tools, aggregate data from multiple systems, across geo-political boundaries and include an automated alerting process.[9]
A syndromic surveillance system based on search queries was first proposed by Gunther Eysenbach, who began work on such a system in 2004.[10] Inspired by these early, encouraging experiences,
Digital methods
Digital surveillance of public health largely relies on a number of methods. The most important ones being the use of search-based trends on sites like Google and Wikipedia, social media posts on platforms like Facebook and Twitter, and participatory surveillance websites such as Flu Near You and Influenzanet. However the range of potential data sources suitable for disease surveillance has increased as different areas have become digitized; today school attendance records, hospital emergency admissions data and even sales data, can be used for syndromic surveillance purposes.[17] Search trends provide indirect data on public health, while the latter two methods provide direct data.[18]
Search aggregates
Search aggregates have been most frequently used to track and model influenza. A popular example is Google Flu Trends,[19] which was first released in 2008.[18] Wikipedia has also been used, though it is potentially prone to "noise", as it is a popular source of health information whether a user is ill or not.[20] During the COVID-19 pandemic a new methodology has been developed to model COVID-19 prevalence based on web search activity.[21] This methodology has also been used by Public Health England in the United Kingdom as one of their syndromic surveillance endpoints.[citation needed]
Social media
Examples of social media public health surveillance include HealthTweets, which gathers data from Twitter.[20] Twitter data is considered highly useful for public health research, as its data policies allow public access to 1% samples of raw tweets. Tweets can also be geolocated, which can be used to model the spread of contagious disease. It is the most used social media platform for public health surveillance.[18] During the COVID-19 pandemic, Facebook used aggregated, anonymized data collected from its platforms to provide human movement information to disease models. It also offered users a chance to participate in a disease symptom survey through Carnegie Mellon University.[22]
Surveillance sites
Flu Near You and Influenzanet are two examples of crowd-sourced digital surveillance systems. Both sites recruit users to participate in surveys about influenza symptoms. Influenzanet was established in 2009, and operates in ten countries in Europe. Its predecessor was Grote Griepmeting, which was a Dutch/Belgian platform launched in 2003 and 2004. Flu Near You is used in the US. Another example of a surveillance sites is Dengue na Web, used to survey for dengue fever in Bahia, Brazil.[18]
Laboratory-based surveillance
Some conditions, especially chronic diseases such as
Laboratory registries allow for the analysis of the incidence and prevalence of the target condition as well as trends in the level of control. For instance, an
A similar system, The New York City A1C Registry,[28] is in used to monitor the estimated 600,000 diabetic patients in New York City, although unlike the Vermont Diabetes Information System, there are no provisions for patients to have their data excluded from the NYC database. The NYC Department of Health and Mental Hygiene has linked additional patient services to the registry such as health information and improved access to health care services. As of early 2012, the registry contains over 10 million test results on 3.6 million individuals. Although intended to improve health outcomes and reduce the incidence of the complications of diabetes,[29] a formal evaluation has not yet been done.
In May 2008, the City Council of
Laboratory surveillance differs from population-wide surveillance because it can only monitor patients who are already receiving medical treatment and therefore having lab tests done. For this reason, it does not identify patients who have never been tested. Therefore, it is more suitable for quality management and care improvement than for epidemiological monitoring of an entire population or catchment area.[citation needed]
See also
References
- ^ a b Public health surveillance, World Health Organization (accessed January 14, 2016).
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- ^ a b World Health Organization. "Surveillance for Vaccine Preventable Diseases". World Health Organization: Immunization, Vaccines and Biologicals. Archived from the original on April 1, 2014. Retrieved 19 October 2016.
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- ^ "Syndromic Surveillance: an Applied Approach to Outbreak Detection". United States Centers for Disease Control and Prevention. 13 January 2006. Archived from the original on 20 January 2007.
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- ^ "Google Flu Trends". Google.org. Retrieved 2014-04-18.
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- ^ "Flu Detector – Tracking Epidemics on Twitter". GeoPatterns.enm.bris.ac.uk. Retrieved 2014-04-18.
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- ^ Walker, Mark David (2023). Digital Epidemiology: an introduction to disease surveillance using digital data. Sheffield, UK.: Sicklebrook publishing. ASIN 1470920360.
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- ^ Newton C (6 April 2020). "Facebook begins sharing more location data with COVID-19 researchers and asks users to self-report symptoms". The Verge. Retrieved 2 September 2020.
- ^ "Vermedx Diabetes Information System". vermedx.com. Archived from the original on 2011-02-02. Retrieved 2014-04-18.
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- ^ "Diabetes Prevention and Control". The New York City A1C Registry. The City of New York. Archived from the original on June 9, 2007.
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- ^ "Metropolitan Health District". Sanantonio.gov. Retrieved 2014-04-18.