Sentinel Surveillance

In December 2009, Taiwan’s CDC stopped its sentinel physician surveillance system. Currently, infectious disease surveillance systems in Taiwan rely on not only the national notifiable disease surveillance system but also real-time outbreak and disease surveillance (RODS) from emergency rooms, and the outpatient and hospitalization surveillance system from National Health Insurance data. However, the timeliness of data exchange and the number of monitored syndromic groups are limited. The spatial resolution of monitoring units is also too coarse, at the city level. Those systems can capture the epidemic situation at the nationwide level, but have difficulty reflecting the real epidemic situation in communities in a timely manner. Based on past epidemic experience, daily and small area surveillance can detect early aberrations. In addition, emerging infectious diseases do not have typical symptoms at the early stage of an epidemic. Traditional disease-based reporting systems cannot capture this kind of signal. Therefore, we have set up a clinic-based surveillance system to monitor 23 kinds of syndromic groups. Through longitudinal surveillance and sensitive statistical models, the system can automatically remind medical practitioners of the epidemic situation of different syndromic groups, and will help them remain vigilant to susceptible patients. Local health departments can take action based on aberrations to prevent an epidemic from getting worse and to reduce the severity of the infected cases.

Objective: Sentinel physician surveillance in the communities has played an important role in detecting early aberrations in epidemics. The traditional approach is to ask primary care physicians to actively report some diseases such as influenza-like illness (ILI), and hand, foot, and mouth disease (HFMD) to health authorities on a weekly basis. However, this is labor-intensive and time-consuming work. In this study, we try to set up an automatic sentinel surveillance system to detect 23 syndromic groups in the communites.

Sentinel surveillance, where selected jurisdictions follow standardized protocols to collect and report enhanced public health data not available through other routine surveillance efforts, is a key part of national surveillance of sexually transmitted diseases (STDs). Although four STDs are nationally notifiable conditions (chlamydia, gonorrhea, syphilis and chancroid), the burden of these conditions (over 2.3 million cases were reported in 2017) limits the amount of detailed clinical and demographic data available for all cases. Sentinel surveillance in clinical settings serving at-risk populations, such as STD clinics, provides an opportunity to collect enhanced data elements on persons seeking STD-related services, such as sex of sex partners and anatomic site of infection. However, there are challenges in combining data across jurisdictions as estimated effect measures may vary by jurisdiction (e.g., some may have higher observed burden of disease among certain populations) and the amount of data contributed by jurisdiction may vary; combined this could lead to biased estimates if heterogeneity is not taken into account.

Objective: To identify best practices for combining public health data for multi-jurisdiction surveillance projects.

Much progress has been made on the development of novel systems for influenza surveillance, or explored the choices of algorithms for detecting the start of a peak season. The use of multiple streams of surveillance data has been shown to improve performance but few studies have explored its use in situational awareness to quantify level or trend of disease activity. In this study we propose a multivariate statistical approach which describes overall influenza activity and handles interrupted or drop-in surveillance systems.

Objective

This paper describes the use of multiple influenza surveillance data for situational awareness of influenza activity.

The New York State (NYS) Medicaid Program provides healthcare for 34% of the population in New York City (NYC) and 4%-20% in each of the 57 county populations up-state. Prescription data are collected through the sub-mission of claims forms to the Medicaid Program and transmitted daily to the NYS Syndromic Surveillance Program as summary counts by drug category and patient’s ZIP Code, age category, and sex. One of the 18 drug categories is influenza agents, which in-cludes rimantadine, oseltamivir, and zanamivir.

For surveillance of influenza-like illness (ILI) activity, the NYS and NYC Sentinel Physician Influenza Surveillance Program collects from sentinel physicians weekly reports of the total number of patients seen and the number of patients presenting with ILI (defined as temperature > 100 degrees F, presence of cough or sore throat, and absence of other known cause of these symptoms). Not all counties in NYS have sentinel physicians: in the 2003-2004 flu surveillance season (Week 40, in early October, 2003, to Week 20, in late May, 2004), 37 of 57 upstate counties and all 5 counties of NYC had sentinel physicians.

Objective

To evaluate the usefulness of daily counts of prescriptions for influenza agents charged to Medicaid insurance, by county of residence of the recipient, for detection of elevated ILI in NYS, currently monitored through physicians participating in the CDC Influenza Surveillance Program.

The South Carolina (SC) Department of Health and Environmental Control uses multiple surveillance systems to monitor influenza activity from October to May of each year, including participating in the U.S. Influenza Sentinel Providers Surveillance Network. A percentage of influenza-like-illness surpassing the national 2.5% baseline is considered evidence of increased influenza activity by the CDC; this baseline is historical and does not change throughout the influenza season. Though not a part of the national influenza surveillance, SC also requires health care providers in the state to report positive rapid influenza tests, by number, on a weekly basis. Currently, only a trend analysis is used on weekly reports of positive rapid influenza test data for SC. A more robust method for determining statistically significant increases in activity for these two influenza surveillance systems is needed, and would provide a more accurate assessment of the status of seasonal influenza activity in SC.

Objective

Use the Early Aberration Reporting System (EARS) to analyze influenza sentinel provider surveillance data and positive rapid influenza test reports to identify weeks where influenza activity was significantly increased in South Carolina. Demonstrate the utility of using EARS to detect increases in influenza activity using existing surveillance systems.

As public health surveillance is becoming more and more prevalent, new sources of data collection are more evident. One such data source is school absenteeism. By monitoring the symptoms of illness recorded when students are absent, health departments ideally can pinpoint potential outbreaks prior to their existence, all at little to no cost. The symptoms reported may not amount to disease, but their increase in incidence may indicate the preliminary spread of illness. This surveillance tool is also used to develop community intervention containment practices.

Objective

This paper describes the application of syndromic surveillance data from area school districts to detect influenza epidemics in a county setting.

Overseas studies showed that increases in over-the-counter (OTC) drug sales might serve as an indicator of community disease outbreaks before they are detected by conventional surveillance systems. Using data collected retrospectively from commercial drug retailers, the Department of Health of Hong Kong conducted an exploratory study to examine the potential of monitoring OTC drug sales for early detection of community disease outbreaks.

Objective

This study evaluates whether OTC drug sales can serve as an earlier indicator for detecting community disease outbreaks in Hong Kong.

In addition to monitoring Emergency Department chief complaint data and pharmacy sales as indicators of outbreaks, the New York State Department of Health (NYSDOH) Syndromic Surveillance System also monitors information from the CDC’s Early Event Detection and Situational Awareness System, BioSense. BioSense includes Department of Defense (DOD) and Veterans Affairs (VA) outpatient clinical data (ICD-9-CM diagnoses and CPT procedure codes), and LabCorp test order data. Within NYS excluding New York City, there are a total of 7 DOD and 60 VA hospitals and/or clinics reporting to the BioSense system, located across 41 of 57 counties.

BioSense includes a Sentinel Alert system, which monitors for diagnoses of CDC-classified Category A, B, and C diseases that have been reported from DOD and VA facilities. Sentinel Alerts are issued for single disease records, and can be followed up at local discretion to assess for public health significance and to determine whether the source of the disease might be intentional.

Objective

To describe the NYSDOH's experience with the monitoring of Sentinel Alerts generated for NYS within the CDC’s BioSense application, following up each alert with local health department staff to determine case resolution, and providing user-level feedback to the CDC to effect system improvements.

The interest of medication sales data in Syndromic Surveillance is well recognized. In France, where a real-time computerized surveillance system of frequent communicable diseases based on Sentinel general practitioners (SGPs) provides since 1984 a gold standard to evaluate other indicators, it has been shown that medication sales provided early alerts for influenza. Gastroenteritis surveillance relies in France on the surveillance of acute diarrhea by the SGPs in the general population, since 1991. The main objective of this study is to validate, at a national level, new indicators based on medication sales data to facilitate the detection of gastroenteritis epidemics.

Objective

This study examines how medication sales data can detect gastroenteritis epidemics in France.