Public Health Surveillance

Introduction: response to this, the Centers for Disease Control and Prevention (CDC), CSTE, and the American Association of Poison Control Centers (AAPCC) members created the Poison Center Public Health Community of Practice (CoP). The CoP acts as a platform, to facilitate sharing experiences, identify best practices, and develop relationships among federal agencies, state and local health departments (HD), and PCs. Since its inception, the CoP garnered over 250 members, hosted more than 25 webinars regarding PC-HD collaborations, and produced five newsletters highlighting subjects pertinent to PC and HD personnel. To date, the CoP's primary focus has been to strengthen PC-HD partnerships; however, recent events highlight opportunities to expand the public health impact of the CoP. In this roundtable, we will discuss how the CoP was leveraged by federal and state health agencies to build new multidisciplinary and inter-agency relationships and how these experiences have led to the proposed guidance.

Objective: - To discuss the development of a set of tools for interagency collaborations on health surveillance - To determine the core contents of the tools based on known gaps in health surveillance - To determine collaborators in development and timelines for completion

Opioid abuse has increased exponentially in recent years throughout the United States, leading to an increase in the incidence of emergency response activities, hospitalization, and mortality related to opioid overdose. As a result, states that have been hit particularly hard during this period such as Wisconsin have allocated considerable resources to addressing this crisis via enhanced public health surveillance and outreach, procurement and administration of medical countermeasures, prescription drug monitoring programs, targeted preventive and acute treatment, first responder and hospital staff training, cross-agency collaboration, and Incident Management System activities. Central to these efforts is the identification of the primary drivers of opioid overdose and death to improve the precision and efficacy of targeted public health interventions to address the opioid crisis. The present study sought to accomplish this end by syncing rich data sources at the point of emergency response (EMS ambulance runs) to ultimate mortality outcomes (vital death records).

Objective: To identify the correlates of opioids as an underlying cause of death by linking coroner/medical examiner vital death records with emergency medical service (EMS) ambulance run data. By combining death data to EMS ambulance runs, the goal was to determine characteristics of the emergency response particularly for opioid overdose events that may connect to increased mortality.

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.

Adverse Childhood Experiences (ACEs) have been linked to a variety of detrimental health and social outcomes. In the last 20 years, the association between ACEs with several adult health risk behaviors, conditions, and diseases including suicides, and substance abuse, mental health disturbances and impaired memory, nervous, endocrine and immune systems impairments, and criminal activities have been studied. One of the challenges in studying and timely diagnosis of ACEs is that the links between specific childhood experiences and their health outcomes are not totally clear. Similarly, an integrated dataset builtfrom multiple sources is often required for effective ACEs surveillance. The SPACES project aims at providing a semantic infrastructure to facilitate data sharing and integration and answer causal queries to improve ACEs surveillance.

Objective: We introduce the Semantic Platform for Adverse Childhood Experiences (ACEs) Surveillance (SPACES). It facilitates the access to the relevant integrated information, enables discovering the causality pathways and assists researchers, clinicians, public health practitioners, social workers, and health organization in studying the ACEs, identifying the trends, as well as planning and implementing preventive and therapeutic strategies.

In North Carolina there has been an escalation of poisoning deaths. In 2011, the number of fatal poisonings was 1,368 deaths, with 91% classified as drug overdoses with the majority of those due to opioid analgesics.[1] Far greater numbers of drug overdoses result in hospitalization, emergency department (ED) or outpatient clinic visits, or resolve without the individual seeking medical attention. Although public health authorities have long employed death data for drug overdose surveillance in NC, little attention has been paid to the use of ED data for this purpose. Through the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT), NC collects information on 99.5% of all acute-care ED visits across the state, primarily for syndromic surveillance purposes. Despite the timeliness and completeness of this data system, drug overdose surveillance is a challenge due to lack of a standardized definition for the positive identification of opioid overdoses. In this study we used NC DETECT ED data to describe visits due to drug, and more specifically, opioid overdoses. Objective: To describe the epidemiologic characteristics for emergency department visits due to drug overdoses in North Carolina.

For public health surveillance to achieve its desired purpose of reducing morbidity and mortality, surveillance data must be linked to public health response. While there is evidence of the growing popularity of syndromic surveillance (1,2), the impact or value added with its application to public health responses is not well described (3).

Objective

To describe if and how syndromic surveillance data influenced public health decisions made during the 2009 H1N1 pandemic within the context of other existing public health surveillance systems.

The ability to rapidly detect any substantial change in disease incidence is of critical importance to facilitate timely public health response and, consequently, to reduce undue morbidity and mortality. Unlike testing methods (1, 2), modeling for spatio-temporal disease surveillance is relatively recent, and this is a very active area of statistical research (3). Models describing the behavior of diseases in space and time allow covariate effects to be estimated and provide better insight into etiology, spread, prediction and control. Most spatio-temporal models have been developed for retrospective analyses of complete data sets (4). However, data in public health registries accumulate over time and sequential analyses of all the data collected so far is a key concept to early detection of disease outbreaks. When the analysis of spatially aggregated data on multiple diseases is of interest, the use of multivariate models accounting for correlations across both diseases and locations may provide a better description of the data and enhance the comprehension of disease dynamics.

Objective

This study deals with the development of statistical methodology for on-line surveillance of small area disease data in the form of counts. As surveillance systems are often focused on more than one disease within a predefined area, we extend the surveillance procedure to the analysis of multiple diseases. The multivariate approach allows for inclusion of correlation across diseases and, consequently, increases the outbreak detection capability of the methodology

Tracking emergency department (ED) asthma visits is an important part of asthma surveillance, as ED visits can be preventable and may represent a failure of asthma control efforts. When using limited clinical ED datasets for secondary purposes such as public health surveillance, it is important to employ a standard approach to operationally defining ED visits attributable to asthma. The prevailing approach uses only the primary ICD-9-CM diagnosis assigned to the ED visit; however, doing so may underestimate the public health impact of asthma. We conducted this pilot study to determine the value of including ED visits with asthma-related diagnoses in secondary or tertiary positions. For example, for an ED visit with a primary diagnosis of upper respiratory infection and secondary diagnosis of asthma, it is possible that the infection triggered the asthma exacerbation and the visit could be attributed to both infection and asthma.

Objective

Determine operational definition of ED visits attributable to asthma for public health surveillance purposes.

Although U.S. Mother to Child transmission (MCT) rates of HIV have been reduced from approximately 25% to less than 2%, transmissions continue to occur.1 This reduction comes in a large part from treating pregnant mothers with antiretroviral medications.2 Despite these efforts, Louisiana has one of the highest rates of MCT of HIV in the U.S.3 Real-time identification of pregnancy status would allow high risk HIV-infected pregnant women to be targeted for follow-up. In Louisiana, laboratories are required to report positive HIV tests to SHP, most of which are received in electronic lab reporting (ELR) format. Although pregnancy status is not a variable provided on lab reports, some reports do contain information that is useful in identifying pregnancy status.

Objective

To identify, in real-time, pregnancy status of HIV-infected women through information found in laboratory reports received by the STD/HIV Program (SHP) at the Louisiana Office of Public Health. This identification will be used for targeted follow-up.