Public health surveillance

Short description: Collection, analysis and interpretation of health-related data

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 infectious diseases. Many large institutions, such as the WHO and the Centers for Disease Control and Prevention (CDC), have created databases and modern computer systems (public health informatics) that can track and monitor emerging outbreaks of illnesses such as influenza, SARS, HIV, and even bioterrorism, such as the 2001 anthrax attacks in the United States.[4]

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.[5]

Other illnesses such as one-time events like stroke and chronic conditions such as diabetes, as well as social problems such as domestic violence, are increasingly being integrated into epidemiologic databases called disease registries. A cost-benefit analysis is conducted on these registries to determine governmental funding for research and prevention.

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.[6] These systems intersect with the field of medical informatics, and are rapidly becoming adopted by hospitals and endorsed by institutions that oversee healthcare providers (such as JCAHO in the United States). Issues in regard to healthcare improvement are evolving around the surveillance of medication errors within institutions.[7]

## Contents

* 1 Syndromic surveillance
* 1.1 Digital methods
* 1.1.1 Search aggregates
* 1.1.2 Social media
* 1.1.3 Surveillance sites
* 2 Laboratory-based surveillance
* 3 See also
* 4 References

## Syndromic surveillance

See also: Medicine:Disease surveillance

Syndromic surveillance is the analysis of medical data to detect or anticipate disease outbreaks. According to a CDC definition, "the term 'syndromic surveillance' applies to surveillance using health-related data that precede diagnosis and signal a sufficient probability of a case or an outbreak to warrant further public health response. Though historically syndromic surveillance has been utilized to target investigation of potential cases, its utility for detecting outbreaks associated with bioterrorism is increasingly being explored by public health officials."[8]

The first indications of disease outbreak or bioterrorist attack may not be the definitive diagnosis of a physician or a lab.[9]

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 epidemiologists and public health professionals are alerted that there may be an issue.[10]

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.[11]

A syndromic surveillance system based on search queries was first proposed by Gunther Eysenbach, who began work on such a system in 2004.[12] Inspired by these early, encouraging experiences, Google launched Google Flu Trends[13] in 2008. More flu-related searches are taken to indicate higher flu activity. The results, which were published in Nature, closely matched CDC data, and led it by 1–2 weeks.[14] However, it has been shown that the original approach behind Google Flu Trends had various modelling deficiencies leading to significant errors in its estimates.[15] More recently, a series of more advanced linear and nonlinear approaches to influenza modeling from Google search queries have been proposed.[16] Extending Google's work researchers from the Intelligent Systems Laboratory (University of Bristol, UK) created Flu Detector;[17] an online tool which based on Information Retrieval and Statistical Analysis methods uses the content of Twitter to nowcast flu rates in the UK.[18]

### 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. Search trends provide indirect data on public health, while the latter two methods provide direct data.[19]

#### Search aggregates

Search aggregates have been most frequently used to track and model influenza. A popular example is Google Flu Trends,[20] which was first released in 2008.[19] 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.[21] During the COVID-19 pandemic a new methodology has been developed to model COVID-19 prevalence based on web search activity.[22] This methodology has also been used by Public Health England in the United Kingdom as one of their syndromic surveillance endpoints.

#### Social media

Examples of social media public health surveillance include HealthTweets, which gathers data from Twitter.[21] 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.[19] 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.[23]

#### 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.[19]

## Laboratory-based surveillance

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 NIH-funded program called the Vermedx Diabetes Information System[24] maintained a registry of laboratory values of diabetic adults in Vermont and northern New York State in the US with several years of laboratory results on thousands of patients.[25] The data included measures of blood sugar control (glycated hemoglobin A1c), cholesterol, and kidney function (serum creatinine and urine protein), and were used to monitor the quality of care at the patient, practice, and population levels. Since the data contained each patient's name and address, the system was also used to communicate directly with patients when the laboratory data indicated the need for attention. Out of control test results generated a letter to the patient suggesting they take action with their medical provider. Tests that were overdue generated reminders to have testing performed. The system also generated reminders and alerts with guideline-based advice for the practice as well as a periodic roster of each provider's patients and a report card summarizing the health status of the population. Clinical and economic evaluations of the system, including a large randomized clinical trial, demonstrated improvements in adherence to practice guidelines and reductions in the need for emergency department and hospital services as well as total costs per patient.[26][27][28] The system has been commercialized and distributed to physicians, insurers, employers and others responsible for the care of chronically ill patients. It is now being expanded to other conditions such as chronic kidney disease. A similar system, The New York City A1C Registry,[29] 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,[30] a formal evaluation has not yet been done.

In May 2008, the City Council of San Antonio, Texas approved the deployment of an A1C registry for Bexar County. Authorized by the Texas legislature and the state Health Department, the San Antonio Metropolitan Health District[31] implemented the registry which drew results from all the major clinical laboratories in San Antonio. The program was discontinued in 2010 due to lack of funds.

## See also

* Contact tracing
* Gamification
* Public health informatics

## References

1. ↑ 1.0 1.1 Public health surveillance, World Health Organization (accessed January 14, 2016).
2. ↑ Encyclopedia of the City. Routledge. 2004. pp. 548. ISBN 978-0-415-25225-6. https://archive.org/details/encyclopediacity00cave.
3. ↑ 3.0 3.1 World Health Organization. "Surveillance for Vaccine Preventable Diseases". https://www.who.int/immunization/monitoring_surveillance/burden/VPDs/en/.
4. ↑ Morgan, O. W.; Pinner, R. W. (2010). Surveillance of Infectious Diseases. Academic Press. doi:10.1016/B978-012373944-5.00209-1.
5. ↑ White, Mary C.; Babcock, Frances; Hayes, Nikki S.; Mariotto, Angela B.; Wong, Faye L.; Kohler, Betsy A.; Weir, Hannah K. (2017-12-15). "The History and Use of Cancer Registry Data by Public Health Cancer Control Programs in the United States". Cancer 123 (Suppl 24): 4969–4976. doi:10.1002/cncr.30905. ISSN 0008-543X. PMID 29205307.
6. ↑ "Automated surveillance for adverse drug events at a community hospital and an academic medical center". Journal of the American Medical Informatics Association 13 (4): 372–7. Jul–Aug 2006. doi:10.1197/jamia.M2069. PMID 16622159.
7. ↑ disa.mil PDF
8. ↑ "webcitation.org". webcitation.org. https://www.cdc.gov/EPO/dphsi/syndromic.htm.
9. ↑ "What is syndromic surveillance?". MMWR Supplements 53: 5–11. September 2004. PMID 15714620. https://www.cdc.gov/mmwr/preview/mmwrhtml/su5301a3.htm.
10. ↑ Walker, Mark (2023). Digital Epidemiology: An introduction to disease surveillance using digital data. Sheffield, UK: Sicklebrook publishing. ISBN 978-1-4709-2036-4.
11. ↑ "Syndromic Surveillance: an Applied Approach to Outbreak Detection". United States Centers for Disease Control and Prevention. 13 January 2006. https://www.cdc.gov/epo/dphsi/syndromic.htm.
12. ↑ "Infodemiology: tracking flu-related searches on the web for syndromic surveillance". AMIA ... Annual Symposium Proceedings. AMIA Symposium 2006: 244–8. 2006. PMID 17238340.
13. ↑ "Google Flu Trends". Google.org. http://www.google.org/flutrends/.
14. ↑ "Detecting influenza epidemics using search engine query data". Nature 457 (7232): 1012–4. February 2009. doi:10.1038/nature07634. PMID 19020500. Bibcode: 2009Natur.457.1012G.
15. ↑ "The Parable of Google Flu: Traps in Big Data Analysis". Science 343 (6176): 1203–1205. 2014. doi:10.1126/science.1248506. PMID 24626916. Bibcode: 2014Sci...343.1203L. http://nrs.harvard.edu/urn-3:HUL.InstRepos:12016836.
16. ↑ "Advances in nowcasting influenza-like illness rates using search query logs". Scientific Reports 5. August 2015. doi:10.1038/srep12760. PMID 26234783. Bibcode: 2015NatSR...512760L.
17. ↑ "Flu Detector – Tracking Epidemics on Twitter". GeoPatterns.enm.bris.ac.uk. http://geopatterns.enm.bris.ac.uk/epidemics.
18. ↑ "Flu Detector – Tracking Epidemics on Twitter". ECML PKDD. 2010. pp. 599–602. doi:10.1007/978-3-642-15939-8_42.
19. ↑ 19.0 19.1 19.2 19.3 "Social Media- and Internet-Based Disease Surveillance for Public Health". Annual Review of Public Health 41: 101–118. April 2020. doi:10.1146/annurev-publhealth-040119-094402. PMID 31905322.
20. ↑ Brownstein, John S.; Freifeld, Clark C.; Madoff, Lawrence C. (2009-05-21). "Digital Disease Detection — Harnessing the Web for Public Health Surveillance" (in en). New England Journal of Medicine 360 (21): 2153–2157. doi:10.1056/NEJMp0900702. ISSN 0028-4793. PMID 19423867.
21. ↑ 21.0 21.1 "Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis". JMIR Public Health and Surveillance 2 (2): e161. October 2016. doi:10.2196/publichealth.5901. PMID 27765731.
22. ↑ "Tracking COVID-19 using online search". npj Digital Medicine 4 (17): 17. 2021. doi:10.1038/s41746-021-00384-w. PMID 33558607.
23. ↑ Newton, Casey (6 April 2020). "Facebook begins sharing more location data with COVID-19 researchers and asks users to self-report symptoms". https://www.theverge.com/facebook/2020/4/6/21209733/facebook-disease-prevention-maps-covid-19-symptom-reports-privacy.
24. ↑ "Vermedx Diabetes Information System". vermedx.com. http://www.vermedx.com.
25. ↑ "Diabetes decision support: initial experience with the Vermont diabetes information system". American Journal of Public Health (Ajph.org) 96 (4): 593–5. April 2006. doi:10.2105/AJPH.2005.065391. PMID 16507723.
26. ↑ "The Vermedx Diabetes Information System reduces healthcare utilization". The American Journal of Managed Care 15 (3): 166–70. March 2009. PMID 19298097. http://www.ajmc.com/issue/managed-care/2009/2009-02-vol15-n3/AJMC_09Mar_Littenberg166to170.
27. ↑ "The Vermont diabetes information system: a cluster randomized trial of a population based decision support system". Journal of General Internal Medicine 24 (12): 1303–10. December 2009. doi:10.1007/s11606-009-1147-x. PMID 19862578.
28. ↑ "The effect of the Vermont Diabetes Information System on inpatient and emergency room use: results from a randomized trial". Health Outcomes Research in Medicine 1 (1): e61–e66. July 2010. doi:10.1016/j.ehrm.2010.03.002. PMID 20975923.
29. ↑ "Diabetes Prevention and Control". The New York City A1C Registry. The City of New York. http://www.nyc.gov/html/doh/html/diabetes/diabetes-nycar.shtml.
30. ↑ "New York City's diabetes reporting system helps patients and physicians". American Journal of Public Health (Ajph.aphapublications.org) 98 (9): 1543–4; author reply 1544. September 2008. doi:10.2105/AJPH.2008.142026. PMID 18633070.
31. ↑ "Metropolitan Health District". Sanantonio.gov. http://www.sanantonio.gov/health/.

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