Emergency Medical Service (EMS)

The North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) provides early event detection and public health situational awareness to hospital-based and public health users statewide. Authorized users are currently able to view data from emergency departments (n=110), the statewide poison control center, the statewide EMS data system, a regional wildlife center and pilot data from a college veterinary laboratory as well as select urgent care centers. While NC DETECT has over 200 registered users, there are public health officials, hospital clinicians and administrators who do not access the system on a regular basis, but still like to be kept abreast of syndromic trends in their jurisdictions. In order to accommodate this interest and reduce redundant data entry among active users, we developed a summary report that can be easily exported and distributed outside of NC DETECT.

Objective

This paper describes a user driven weekly syndromic report designed and developed to improve the efficiency of sharing syndromic information statewide.

The North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) serves public health users across NC at the local, regional and state levels, providing early event detection and situational awareness capabilities. At the state level, our primary users are in the General Communicable Disease Control Branch of the NC Division of Public Health. NC DETECT receives 10 different data feeds daily including emergency department visits, emergency medical service runs, poison center calls, veterinary laboratory test results, and wildlife treatment.

In order to fulfill our users’ needs with NC DETECT’s limited staff, business intelligence tools are utilized for the acquisition and processing of our multiple, disparate data sources as well as reporting our findings to our numerous end users. Business intelligence can be described as a broad category of application programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions.

Objective

We report here on how NC DETECT uses business intelligence tools to automate both data capture and reporting in order to run a comprehensive surveillance system with limited resources.

In 2005, three hurricanes made landfall in Florida, with Hurricane Wilma having the most severe impact on Miami-Dade County. Syndromic surveillance is typically used to detect bioterrorism or natural disease outbreaks before specific diagnoses are made. After Wilma, however, the Miami-Dade County Health Department assessed the utility of syndromic data for surveillance of hurricane-related injuries.

Objective

To determine the proportion of injuries in Miami-Dade County that could be related to the impact of Hurricane Wilma, which made landfall in Florida on October 25, 2005.

One of the emerging priorities for the use of syndromic surveillance is for the monitoring of environmental health conditions. Heat-related illness (HRI) is of growing public health importance, particularly with climate change and anticipated increased frequency of heat waves. High ambient temperatures are responsible for significant morbidity and mortality, as was demonstrated during the 2003 heat waves in Europe that resulted in an estimated 45,000 excess deaths. A syndromic surveillance system that is able to detect early indications of excess HRI may start the public health response earlier, and thus reduce associated morbidity and mortality. Our research group is exploring the potential use of 911 medical dispatch data for the surveillance of HRI in Toronto. An important step in this assessment is exploring the association between temperature and 911 dispatch calls for HRI.

Objective

This paper describes the association between 911 medical dispatch calls for heat-related illness and maximum temperature in Toronto, Ontario during the summer of 2005.

The 2005 Youth Risk Behavior Survey of 9th to 12th graders in Miami-Dade County public schools found that 69.7% of students tried alcohol, 28.3% tried marijuana, and 6.3% tried cocaine in their lifetime. Results also showed that Hispanics had a higher percentage of usage when compared to Blacks or Whites. The 2007 White House Office of National Drug Control Policy special report entitled “Hispanic Teens and Drugs” also concluded that Hispanics were at the highest risk for substance abuse. With the county’s 60% Hispanic population, this issue is of concern for the community. This is the first study to compare multiple sources of data to describe substance abuse among youth from areas such as healthcare utilization to criminal charges.

Prehospital  EMS  data  is  rarely  mentioned  in  discus-sions  surrounding  syndromic  surveillance  for  covert  bio-terrorism  attacks  or  for  the  monitoring  of  syn-dromic  illness  such  as  bird  flu.    However,  EMS  dis-patch data may serve as the very first marker in such an event.  EMS dispatch data has many useful advan-tages  in  syndromic  surveillance.    These  include  the  ability to monitor across wide areas of geography and a  single  data  collection  source.    Additionally,  EMS  dispatchers  are  a  medically  trained  core  group  of  in-dividuals that use a single standardized set of interro-gation  questions  and  methods  with  specific  dispatch  codes  regarding  patient  conditions.    This  data  would  arguably be a more reliable source of data than mul-tiple  different  inputs  from  multiple  individuals  at  various clinics and hospitals emergency departments.  EMS  data  is  also  able  to  look  at  a  much  broader  group  of  individuals  both  by  volume  of  calls  and  by  geography,  since  they  are  instantaneously  able  to  capture  the  location  of  the  callers  when  dialing  911. EMS  dispatch  is  also  able  to  monitor  patient  move-ment to different accepting facilities.

Objective

This paper describes how the surveillance of actual EMS real time events occurring during normal operations were analyzed using a syndromic surveillance system and how these events can be used as surrogate markers for how a bio-surveillance system would act if an actual covert or overt terrorist event or pandemic illness were to occur

Drug-related deaths have increased over the past decade throughout the United States. In New York City (NYC), every year there are approximately 900 psychoactive drug-related fatalities with the majority involving opioids. Unintentional drug overdose is the fourth leading cause of early adult death in NYC, and high rates of drug-related morbidity among drug users are evidenced by over 30,000 drug mentions in NYC emergency departments each year. Moreover, nonfatal overdose may be common among chronic drug users. Despite the relationship between fatal and non-fatal overdose clusters and continued increases in drug-related morbidity and mortality, no regular surveillance system currently exists. The implementation of a drug-related early warning system can inform and target a comprehensive public health response addressing the significant health problem of overdose morbidity and mortality.

Objective

This presentation describes how multiple syndromic data sources from emergency medical services ambulance dispatches and emergency department visits can be combined to routinely monitor citywide spatial patterns of adverse drug events and drug morbidity. This information can be used to target information, treatment and prevention services to drug “hotspots,” to provide early warning for drug-related morbidity, and to detect potential increased risk for overdose death.

Estimation of representative spatial probabilities and expected counts from baseline data can cause problems in applying spatial scan statistics when observed events are sparse in a large percentage of the spatial zones (e.g., zip codes or census tracts) found in the data records. In applications of scan statistics to datasets with fine spatial resolution, such as census tracts or block groups, such highly skewed data distributions are likely to occur. If the spatial distribution estimation process does not handle the zones with low counts correctly, bias in the determination of statistically significant clusters will occur.

In any 8-week baseline period, some of the sparse-data zones have no counts at all. If ignored, the zero-count spatial zones will result in division by zero in the loglikelihood ratio evaluation. The traditional method of setting a floor on the expected counts in each spatial zone leads to a loss of sensitivity when the number of zero count zones is a significant fraction of all the zones. One alternative method for estimating spatial probabilities is to add one count to the sum of baseline counts in each spatial zone. This method has been used in a study of spatial cluster detection using medical 911 call data from San Diego County with good results. However, when this method was applied to data with a more highly skewed spatial distribution, issues were uncovered which led to this investigation of alternatives.

Objective

Modifications to spatial scan statistics are investigated for prospective cluster detection at fine-resolution with highly skewed spatial distributions having many spatial zones with very few cases. Several alternative methods for the estimation of spatial probabilities and expected counts from counts in a baseline data window are evaluated with the Poisson spatial scan statistic and the space-time permutation scan statistic using goodness-of-fit statistics and cluster rates to compare performance.

This paper describes the spatial pattern of New York City (NYC) heat-related emergency medical services (EMS) ambulance dispatches and emergency department visits (ED) and explores how this information can be used in planning for and response to heat-related health events.

To examine data from 911/Emergency Medical Services (EMS) and determine whether these data provide a useful addition to syndromic surveillance (SS) when used with emergency department (ED) chief complaint (CC) data.