Konty Kevin

Influenza surveillance has been a major focus of Data Science efforts to use novel data sources in population and public health. This interest reflects the public health utility of timely identification of flu outbreaks and characterization of their severity and dynamics. Such information can inform mitigation efforts including the targeting of interventions and public health messaging. The key requirement for influenza surveillance systems based on novel data streams is establishing their relationship with underlying influenza patterns. We assess the potential utility of wearable mHealth devices by establishing the aggregate responses to ILI along three dimensions: steps, sleep, and heart rate. Surveillance based on mHealth devices may have several desirable characteristics including 1) high resolution individual-level responses that can be prospectively analyzed in near real-time, 2) indications of physiological responses to flu that should be resistant to feedback loops, changes in health seeking behavior, and changes in technology use, 3) a growing user-base often organized into networks by providers or payers with increasing data quality and completeness, 4) the ability to query individual users underlying aggregate signals, and 5) demographic and geographic information enabling detailed characterization. These features suggest the potential of mHealth data to deliver faster, more locally relevant surveillance systems.

Objective: To describe population-level response to influenza-like illness (ILI) as measured by wearable mobile health (mHealth) devices across multiple dimensions including steps, heart rate, and sleep duration and to assess the potential for using large networks of mHealth devices for influenza surveillance.

According to the Substance Abuse and Mental Health Services Administration’s (SAMHSA) Drug Abuse Warning Network (DAWN) surveillance of drug-related ED visits, underage (B21 years) alcohol-alone visit rates have been increasing since 2004 to 2009 (1). Similarly, the ‘‘alcohol’’ syndrome for underage (12-20 years) ED visits also shows an overall increase from 2003 to 2009 in the percentage of alcohol-related visits (2). College-aged drinkers tend to binge drink at a higher frequency than the general population, putting them at greater risk for unintentional injuries and unsafe sex practices (3). Identifying collegespecific patterns for alcohol-associated morbidity have important policy implications to reduce excessive drinking and associated harms on and around college campuses.

Objective

To develop and implement a method for using emergency department records from a syndromic surveillance system to identify alcohol-related visits in New York City, estimate trends, and describe age-specific patterns. In particular, we are interested in college-aged morbidity patterns and how they differ from other age groups.

The New York City Department of Health and Mental Hygiene (NYC DOHMH) collects data daily from 50 of 61 (82%) emergency departments (EDs) in NYC representing 94% of all ED visits (avg daily visits ~10,000). The information collected includes the date and time of visit, age, sex, home zip code and chief complaint of each patient. Observations are assigned to syndromes based on the chief complaint field and are analyzed using SaTScan to identify statistically significant clusters of syndromes at the zip code and hospital level. SaTScan employs a circular spatial scan statistic and clusters that are not circular in nature may be more difficult to detect. FlexScan employs a flexible scan statistic using an adjacency matrix design.

Objective

To use the NYC DOHMH's ED syndromic surveillance data to evaluate FleXScan’s flexible scan statistic and compare it to results from the SaTScan circular scan. A second objective is to improve cluster detection in by improving geographic characteristics of the input files.

Syndromic Surveillance has been in use in New York City since 2001, with 2.5 million visits reported from 39 participating emergency departments, covering an estimated 75% of annual visits. As syndromic surveillance becomes increasingly spatial and tied to geography, the resulting spatial analysis is also evolving to provide new methodology and tools. In late 2004, the New York City Department of Health and Mental Hygiene (DOHMH) created the geographic information systems (GIS) Center of Excellence to identify ways in which GIS could enhance programs like syndromic surveillance. The DOHMH uses the SaTScan program for much of its spatial analysis (i.e. cluster analysis).

Objective

This paper describes a series of visualization enhancements and automation processes to efficiently depict syndromic surveillance data in GIS. Modelling the portrayal of events when merging existing syndromic surveillance with GIS can standardize and expedite results.

There has been much recent interest in using disease signatures to better recognize disease outbreaks. Conversely, the metrics used to describe these signatures can also be used to better characterize the outbreaks. Recent work at the New York City Department of Health has shown the ability to identify characteristic age-specific patterns during influenza outbreaks. One issue that remains is how to implement a search for such patterns using prospective outbreak detection tools such as SatScan.

A potential approach to this problem arises from another currently active research area: the simultaneous use of multiple datastreams. One form of this is to disaggregate a data stream with respect to a third variable such as age. Two drawbacks to this approach are that the categories used to make the streams have to be defined a priori and that relationships between the streams cannot be exploited. Furthermore, the resulting description is less rich as it describes outbreaks in a few non-overlapping age-specific streams. It would be desirable to look for age specific patterns with the age groupings implicitly defined.

Objective

This paper presents an implementation of a citywide SatScan analysis that uses age as a one-dimensional spatial variable. The resulting clusters identify age-specific clusters of respiratory and fever/flu syndromes in the New York City Emergency Department Data.

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.

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.

This paper describes HHC outpatient data and evaluates the performance for early influenza-like illness (ILI) outbreak detection. We compare its detection ability to that of the emergency department (ED) data and of the two data sources simultaneously.