Clinical surveillance

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Clinical surveillance (or Syndromic surveillance) refers to the surveillance (systematic collection, analysis, and interpretation) 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.

Contents 1 Clinical surveillance 2 Syndromic surveillance 3 Laboratory-based surveillance 4 See also 5 Sources and notes

[edit] 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 the statistics, like Quality-adjusted life years and Disability Adjusted Life Years, involve quantifying the worth of human lives or years lived according to highly subjective concepts such as survival, quality of life, and productivity measures. 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]

[edit] 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]

[edit] Laboratory-based surveillance

Some conditions, especially chronic diseases such as diabetes mellitus, are 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 usefully 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, 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. 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 use to monitor the estimated 600,000 diabetic patients in New York City. The NYC Department of Health plans to link additional patient services to the registry such as health information and improved access to health care services.

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.

[edit] See also

• Disease surveillance
• Infection control
• List of notifiable diseases
• Mass surveillance
• STD testing
• UK statutory notification system
• 1985 World Health Organization AIDS surveillance case definition
• 1994 expanded World Health Organization AIDS case definition
• AIDS defining clinical condition
• Council of State and Territorial Epidemiologists
• Early Warning and Response System EWRS
• GIDEON-Global Infectious Disease Epidemiology Network
• Global spread of H5N1 (a strain of influenza)
• Workplace health surveillance

[edit] Sources and notes

• University of Washington, Dept of Epidemiology, online course, Introduction to Epidemiologic Methods [1]
• University of Washington, Dept of Epidemiology, online course, Cost & Outcomes Research [2]
• JAMIA: Implementing Syndromic Surveillance: A Practical Guide Informed by the Early Experience [3]
• JAMIA: Automated Syndromic Surveillance for the 2002 Winter Olympics [4]
• Healthcare IT Collaboration in Massachusetts. First published July 27, 2005 as JAMIA PrePrint; doi:10.1197/jamia.M1866 [5]
• James L Gale, MD, MS. Introduction to Public Health Surveillance, Northwest Center for Public Health Practice, University of Washington [6]
• Ivan J Gotham, Perry F Smith, Guthrie S Birkhead, et al. Policy Issues in Developing Information Systems for Public Health Surveillance of Communicable Diseases. In Patrick W O'Carroll, William A Yasnoff, M Elizabeth Ward, et al, eds. Public Health Informatics and Information Systems. New York: Springer, 2003. p 537-573. [7]
• 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. [8]