Clinical surveillance

Clinical surveillance (or Syndromic surveillance) refers to the surveillance of health data about a clinical syndrome that has a significant impact on public health, which is then used to drive decisions about health policy and health education. This is distinct from active surveillance, which applies to individuals.

Clinical surveillance

Techniques of clinical surveillance have been used in particular to study infectious diseases. Many large institutions, such as the WHO and the 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 on federal agencies in the United States.

Many regions and countries have their own cancer registry, one function of which is to monitor the incidence of cancers to determine the prevalence and possible causes of these illnesses.

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 that are being used in cost-benefit analysis in determining governmental funding for research and prevention.

Many see this health outcomes data as greatly beneficial, but this kind of work is often controversial because many of measures such as quality-adjusted life years and disability-adjusted life years, which involve quantifying benefit according to subjective concepts such as survival, quality of life, and productivity measures. In addition, civil-libertarians believe that without disclosure or provisions to enable a way of opting out of such registries is a violation of both personal civil liberties and the doctor-patient privilege. Population-based healthcare is being promoted as registries are integrated, and health outcomes are increasingly being monitored.

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.^[1] These systems intersect with the field of medical informatics, and are rapidly becoming adapted by hospitals and endorsed by institutions that oversee healthcare providers (such as JCAHO in the United States). Issues in regards to healthcare improvement are evolving around the surveillance of medication errors within institutions.^[2]

Syndromic surveillance

See also: 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."^[3]

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

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 room.

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 may be an issue.

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.^[4]

A syndromic surveillance system based on search queries was first proposed by Gunther Eysenbach, who began work on such a system in 2004 ^[5]. Inspired by these early, encouraging experiences, Google launched Google Flu Trends in 2008. More flu-related searches are taken to indicate higher flu activity. The results closely match CDC data, and lead it by - 1–2 weeks. The results appeared in Nature.^[6] Extending Google's work researchers from the Intelligent Systems Laboratory (University of Bristol, UK) created Flu Detector; an online tool which based on Information Retrieval and Statistical Analysis methods uses the content of Twitter to nowcast flu rates in the UK.^[7]

Influenzanet

Influenzanet is a syndromic surveillance system based on voluntary reports of symptoms via the internet. Residents of the participant countries are invited to provide regularly information regarding the presence or absence of flu related symptoms. The system runs since 2003, in The Netherlands and Belgium. The success of this first initiative, led to the implementation, in 2005, of Gripenet in Portugal, followed by Italy in 2008, and Brasil, Mexico and United Kingdom, in 2009.

Laboratory-based surveillance

Some conditions, especially chronic diseases such as diabetes mellitus, are supposed to be routinely managed with frequent laboratory measurements. Since many laboratory results, at least in Europe and the US, are automatically processed by computerized laboratory information systems, the results are relatively easy to inexpensively collate in special purpose databases or disease registries. Unlike most syndromic surveillance systems, in which each record is assumed to be independent of the others, laboratory data in chronic conditions can be theoretically linked together at the individual patient level. If patient identifiers can be matched, a chronological record of each patient's laboratory results can be analyzed as well as aggregated to the population level.

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 maintains a registry of laboratory values of diabetic adults in Vermont and northern New York State in the US that contains many years of laboratory results on thousands of patients.^[8] The data include measures of blood sugar control (glycosolated hemoglobin A1C), cholesterol, and kidney function (serum creatinine and urine protein), and have been used to monitor the quality of care at the patient, practice, and population levels. Since the data contain each patient's name and address, the system has also been used to communicate directly with patients when the laboratory data indicate the need for attention. Out of control test results generate a letter to the patient suggesting they take action with their medical provider. Tests that are overdue generate reminders to have testing performed. The system also generates 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.

A similar system, The New York City A1C Registry, 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 aims to link additional patient services to the registry such as health information and improved access to health care services. The NYC Health Department promised that the registry would help to reduce the risk of blindness, kidney failure, leg amputations and early death among people with diabetes^[9], but as of 2010, the department has provided little (if any) solid evidence to validate these assertions.

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 has begun implementation of the registry which will draw results from the major clinical laboratories in San Antonio. If successful, the registry may be expanded to the rest of Texas.

The real reason laboratory surveillance differs from population-wide surveillance in the case of diabetes is that in that it can only monitor glycemic control of patients who are already receiving medical treatment and therefore having these lab tests done, while simultaneously ignoring patients who never have the tests done, the segments who are often most at-risk. This limits the ability of public health officials to implement interventions that are effective on a population-wide basis.

References

1. ^ Kilbridge PM, Campbell UC, Cozart HB, Mojarrad MG (2006 Jul–Aug). "Automated Surveillance for Adverse Drug Events at a Community Hospital and an Academic Medical Center". J Am Med Inform Assoc 13 (4): 372–7. doi:10.1197/jamia.M2069. PMC 1513675. PMID 16622159. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1513675. 
2. ^ disa.mil PDF
3. ^ webcitation.org
4. ^ United States Centers for Disease Control and Prevention (WebCited/cached version)
5. ^ Eysenbach G (2006). "Infodemiology: Tracking Flu-Related Searches on the Web for Syndromic Surveillance". AMIA Annu Symp Proc 2006: 244–8. PMC 1839505. PMID 17238340. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1839505. 
6. ^ Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L (2009). "Detecting influenza epidemics using search engine query data". Nature 457 (7232): 1012–4. doi:10.1038/nature07634. PMID 19020500. 
7. ^ Lampos V, De Bie T, Cristianini N (2010). "Flu Detector - Tracking Epidemics on Twitter". ECML PKDD. pp. 599–602. doi:10.1007/978-3-642-15939-8_42. 
8. ^ MacLean CD, Littenberg B, Gagnon M. Diabetes Decision Support: Initial Experience with the Vermont Diabetes Information System in Community Primary Care. Am J Pub Health 2006; 96:593-595. http://www.ajph.org/cgi/content/abstract/96/4/593
9. ^ Frieden, Thomas R; "Letter: New York City's Diabetes Reporting System Helps Patients and their Physicians"; American Journal of Public Health; July 16, 2006.