Syndromic Surveillance

Assessing health disparities and access to healthcare has been an important issue for emergency preparedness and response efforts in the Denver metropolitan area. There have been several high profile MJ-related illness outbreaks in the US over the past 2 years. The legalization and retail sale of recreational MJ in Colorado necessitates enhanced surveillance for adverse effects from MJ use. TCHD and DPH coordinated to use syndromic surveillance data to provide situational awareness and timely outbreak detection related to MJ, including health disparities and overall impacts on population health.

Objective

Adverse health effects related to marijuana (MJ) use may disproportionately impact populations based on age or gender. To explore whether disparities exist among persons seeking emergency department (ED) care related to MJ use, Tri-County Health Department (TCHD) and Denver Public Health (DPH) developed MJ use case definitions, described patient demographics, mapped patients’ geographic distribution relative to marijuana dispensary locations, evaluated access to healthcare, and investigated the potential impact of MJ on pediatric health.

Community health assessments are a foundation of public health practice and a prerequisite to achieving public health accreditation. Best practice dictates that CHAs must incorporate qualitative and quantitative data and utilize a number of indicators to create a detailed picture of a community’s health. Metrics may describe demographics, social and economic factors, health behaviors, health outcomes, and healthcare access and utilization. Commonly used indicators facilitate cross-jurisdiction comparisons and simplify decisionmaking. However, while many readily available indicators exist on a county level, few have been made available on the sub-county level. Syndromic surveillance messages, typically emergency room visit records, contain sub-county level data on patient residence, such as zip code or municipality. As hospitals progress towards meeting Stage 2 Meaningful Use requirements, transmission of syndromic surveillance data to public health entities will become standard. Analysis of emergency room visit data, either in aggregate or by specific syndromes, may be a valuable sub-county level indicator of community health status and access to care that can be standardized across jurisdictions.

Objective

To identify geographic clustering of elevated emergency room (ER) usage rates for incorporation into community health assessments (CHA) in suburban Cook County and to validate this metric as a potential sub-county level community health indicator.

The outbreak of the Ebola Virus Disease (EVD) in Africa in 2014 presented a major threat and concern across the world, spreading to two other continents (Europe and North America). Though the epidemic is on a downward trend, there is a need to evaluate the performance of the systems in place to detect and control such outbreaks and determine the need for improvement in countries affected.

With its first traceable case reported to have been in Guinea, the outbreak spread to Nigeria through an air traveler from Liberia which led to an outbreak in the country that luckily, was quickly contained. This imported case was initially managed at a private health facility (PHF) eventually leading to 20 cases and eight deaths, four of which were health workers from the initial managing PHF. Despite effort to contact the authorities about the suspected imported case by the PHF, it reportedly took some time before the health authorities could be reached and action at control instituted. This might suggest an inefficiency of the IDSR system which was previously adopted by Nigeria as a means of implementing the International Health Regulation (IHR) of 1969. The IHR is a set of regulations that the World Health Assembly uses to implement its constitutional responsibility to prevent the international spread of diseases.

Hemorrhagic fevers like EVD ought to be reported immediately upon suspicion to the health authorities but the delay despite effort suggests this system is not efficient. This is important as PHFs are noted to attend to over 60% of the Nigerian population. Thus, it is important to carry out an assessment of the IDSR system in PHFs to forestall a repeat episode and limit the impact of outbreak of infectious diseases in future.

Objective

To investigate the compliance of private health facilities to the integrated disease surveillance and response (IDSR) system in Nigeria.

Syndromic surveillance has been used by state agencies to collect real-time information on disease outbreaks but has not been used to collect data in the occupational setting. Therefore, OHS staff has begun evaluating a real-time surveillance tool to track a variety of occupationally-related emergency room visits throughout the state via EpiCenter, the NJDOH’s existing real-time surveillance system. This proposal applies established epidemiologic techniques to a different set of circumstances than they have been applied to in the past. Incorporating Syndromic Surveillance data with hospital discharge data will enhance the ability to classify and capture work-related nonfatal injuries and improve efforts of prevention. By employing a realtime, independent data source such as EpiCenter, the classification of work-related injuries and illnesses could be greatly enhanced, leading to a better understanding of the burden of non-fatal work-related injuries and illnesses, and allowing for quicker intervention.

Objective

The New Jersey Department of Health (NJDOH), Occupational Health Surveillance (OHS) Unit staff proposes to evaluate a realtime surveillance tool to track a variety of occupationally-related emergency room visits throughout the state via EpiCenter, the NJDOH’s existing real-time surveillance system.

Taking into account reporting delays in surveillance systems is not methodologically trivial. Consequently, most use the date of the reception of data, rather than the (often unknown) date of the health event itself. The main drawback of this approach is the resulting reduction in sensitivity and specificity1. Combining syndromic data from multiple data streams (most health events may leave a “signature” in multiple data sources) may be performed in a Bayesian framework where the result is presented in the form of a posterior probability for a disease2.

Objective

We apply an empirical Bayesian framework to perform change point analysis on multiple cattle mortality data streams, accounting for delayed reporting of syndromes.

The North Dakota Department of Health (NDDoH) collects outpatient ILI data through North Dakota Influenza-like Illness Network (ND ILINet), providing situational awareness regarding the percent of visits for ILI at sentinel sites across the state. Because of increased clinic staff time devoted to electronic health initiatives and an expanding population, we have found sentinel sites have been harder to maintain in recent years, and the number of participating sentinel sites has decreased. Outpatient sentinel surveillance for influenza is an important component of influenza surveillance because hospital and death surveillance does not capture the full spectrum of influenza illness. Syndromic surveillance (SyS) is another possible source of information for outpatient ILI that can be used for situational awareness during the influenza season; one benefit of SyS is that it can provide more timely information than traditional outpatient ILI surveillance [1,2]. The NDDoH collects SyS data from hospitals (emergency department and inpatient visits) and outpatient clinics, including urgent and primary care locations. Visits include chief complaint and/or diagnosis code data. This data is sent to the BioSense 2.0 SyS platform. We compared our outpatient SyS ILI with our ND ILINet and reported influenza cases, and included hospital and combined SyS ILI for comparison.

Objective

To explore how outpatient and urgent care syndromic surveillance for influenza-like illness (ILI) compare with emergency department syndromic ILI and other seasonal ILI surveillance indicators

Clinical quality measures (CQMs) are tools that help measure and track the quality of health care services. Measuring and reporting CQMs helps to ensure that our health care system is delivering effective, safe, efficient, patient-centered, equitable, and timely care. The CQM for influenza immunization measures the percentage of patients aged 6 months and older seen for a visit between October 1 and March 31 who received (or reports previous receipt of) an influenza immunization. Centers for Disease Control and Prevention recommends that everyone 6 months of age and older receive an influenza immunization every season, which can reduce influenzarelated morbidity and mortality and hospitalizations.

Objective

To explain the utility of using an automated syndromic surveillance program with advanced natural language processing (NLP) to improve clinical quality measures reporting for influenza immunization.

Recreational drug use is a major problem in the United States and around the world. Specifically, drug abuse results in heavy use of emergency department (ED) services, and is a high financial burden to society and to the hospitals due to chronic ill health and multiple injection drug use complications. Intravenous drug users are at high risk of developing sepsis and endocarditis due to the use of a dirty or infected needle that is either shared with someone else or re-used. It can also occur when a drug user repeatedly injects into an inflamed and infected site or due to the poor overall health of an injection drug user. The average cost of hospitalization for aortic valve replacement in USA is about $165,000, and in order for the valve replacement to be successful, patients must abstain from using drugs.

Objective

To describe how the state syndromic surveillance system (NC DETECT) was used to initiate near real time surveillance for endocarditis, sepsis and skin infection among drug users.

The North Dakota Department of Health (NDDoH) investigated the feasibility of using syndromic surveillance (SyS) data to identify health care visits due to electronic cigarette (e-cigarette) use. E-cigarettes have been associated with injuries and fatalities in all age groups, including young children attracted to the colorful liquid nicotine carriage packaging [1]. Previously, poison control data was the only resource available to the NDDoH for e-cigarette adverse outcomes surveillance.

Objective

To explore the use of emergency department syndromic surveillance data to identify adverse health events related to electronic cigarettes in order in enhance existing surveillance.

In New Jersey, real-time emergency department (ED) data are currently received from EDs by Health Monitoring Systems Inc.’s (HMS) EpiCenter, which collects, manages and analyzes ED registration data for syndromic surveillance, and provides alerts to state and local health departments for surveillance anomalies.

EpiCenter receives pre-diagnostic chief complaint data from 78 of 80 acute care and satellite EDs. The need for more specific information raises the possibility that other data elements from EDs such as triage notes can be of utility in detecting outbreaks without a significant delay. This study evaluates the inclusion of triage notes in EpiCenter to detect a recent increased usage of synthetic cannabinoids. At the time of this evaluation, three New Jersey hospitals were providing triage notes in their EpiCenter data.

Objective

Describe the inclusion of triage notes into a syndromic surveillance system to enhance population health surveillance activities.