Public Health Informatics

In 2002, the United States (US) Centers for Disease Control and Prevention (CDC) launched the National Environmental Public Health Tracking Program (Tracking Program) to address the challenges in environmental health surveillance described by the Pew Environmental Commission (1). The report cited gaps in our understanding of how the environment affects our health and attributed these gaps to a dearth of surveillance data for environmental hazards, human exposures, and health effects. The Tracking Program's mission is to provide information from a nationwide network of integrated health and environmental data that drives actions to improve the health of communities. Accomplishing this mission requires a range of expertise from environmental health scientists to programmers to communicators employing the best practices and latest technical advances of their disciplines. Critical to this mission, the Tracking Program must identify and prioritize what data are needed, address any gaps found, and integrate the data into the network for ongoing surveillance.

Objective: To increase the availability and accessibility of standardized environmental health data for public health surveillance and decision-making.

The number of unintentional overdose deaths in New York City (NYC) has increased for seven consecutive years. In 2017, there were 1,487 unintentional drug overdose deaths in NYC. Over 80% of these deaths involved an opioid, including heroin, fentanyl, and prescription pain relievers.1 As part of a comprehensive strategy to reduce overdose mortality in NYC, the NYC Department of Health and Mental Hygiene’s (DOHMH) Overdose Education and Naloxone Distribution (OEND) Program makes naloxone kits available to laypeople free-of-charge through registered Opioid Overdose Prevention Programs (OOPPs). Naloxone kits contain two doses of naloxone and educational materials. The OEND Program distributes kits to registered OOPPs, which then dispense kits to individuals via community-based trainings. In this context, distribution refers to kits shipped to programs, whereas dispensing refers to kits given to individuals. Increased NYC funding has enabled recruitment of more OOPPs including syringe exchange programs, public safety agencies, shelters, drug treatment programs, health care facilities, and other community-based programs and greater dispensing of naloxone kits to laypeople. Naloxone distribution has undergone a dramatic expansion, from 2,500 kits in 2009 to 61,706 kits in 2017.2 In 2018, DOHMH aims to distribute more than 100,000 kits to OOPPs. In order to target naloxone dispensing to neighborhoods in NYC with the highest overdose burden, we developed a tracking system able to capture individual-level geographic data about naloxone kit recipients. Prior to the development of the tracking system, DOHMH collected quarterly, aggregate-level naloxone dispensing data from OOPPs. These data included only the OOPPs™ ZIP Codes but not recipient residence. OOPP ZIP Code was used as a proxy for kits dispensed to individuals. Without individual-level geographic information, however, we could not determine whether naloxone kit dispensing reached people in neighborhoods with high overdose mortality rates. To overcome these barriers, DOHMH developed a comprehensive but flexible individual-level data collection method.

Objective: Describe the development of an individual-level tracking system for community-based naloxone dispensing as part of New York City's (NYC) comprehensive plan to reduce overdose deaths. We present data from the first year of the initiative to illustrate results of the tracking system and describe the potential impact on naloxone dispensing program.

In 2002, the United States (US) Centers for Disease Control and Prevention (CDC) launched the National Environmental Public Health Tracking Program (Tracking Program) to address the challenges and gaps in the nation'™s environmental health surveillance infrastructure. The Tracking Program's mission is to provide information from a nationwide network of integrated health and environmental data that drives actions to improve the health of communities. As a primary objective of the Tracking Program, the Environmental Public Health Tracking Network (Tracking Network) was developed as an online surveillance system with data available for 23 topics and over 450 different health, environmental, and population measures. The integration and display of such disparate data can be challenging. For data consumers without scientific training, or even scientists and public health professionals with limited time, it can be difficult to examine and explore the data in an online surveillance system. Additionally, casual data consumers may not require complex data details; a big picture perspective may be appropriate to their needs. The Tracking Network which applies standardized data, a modern user interface, techniques catering to a variety of data consumers, and best practices in data visualization provides a dynamic data query system that allows users to visualize different types of environmental health data in numerous ways including a variety of charting, mapping, and graphing options. Objective: The presenter will demonstrate complex health and environment surveillance data visualization techniques within the CDC's Environmental Public Health Tracking Network.

Dogs, cats and other companion animals have played an integral role in many aspects of human life. Human and companion animal (CAs) interactions have a wide range of benefits to human health [1-3]. The threat of zoonotic transmission between CAs and humans is exacerbated by proximity (56% of dog owners and 62% of cat owners sleep with their animal next to them [4]) and the number of diseases CAs share with humans. Many of these highlighted zoonoses are spread by direct contact, and others are vector-transmitted (e.g., fleas, ticks, flies, and mosquitos). Within the realm of the One-Health concept, CAs can serve multiple roles in zoonotic transmission chains between humans and animals. They can serve as intermediate hosts between wildlife reservoirs and humans, or as possible sentinel or proxy species for emerging diseases [5]. Given the large number of CAs within the United States (estimated 72 million pet dogs, 81 million pet cats), understanding and preventing the diseases prevalent in CA populations is of utmost importance. Biosurveillance is a critical component of One Health initiatives including zoonotic disease mitigation and control. As Lead Service for Veterinary Animal and Public Health Services, the Army has a responsibility to champion biosurveillance efforts to support One Health initiatives, improving Servicemember, family, and retiree health across the Joint Force. Additionally, with military personnel experiencing apparent increased rates of job-reducing ailments such as diarrheal, bacterial and viral disease [6- 8], it is essential that the Army focus on maximizing their operational potential by minimizing the amount of time personnel are sick from these transmissible diseases and observing potential sources of infection. By observing the zoonotic disease burden in privately owned (POAs) and government-owned (GOAs) animals, public health investigators can increase focus on what transmittable diseases are at greatest risk of being spread from companion animals to military personnel. To address this potential source of infection, the Department of Defense (DoD) sought and continues to seek to establish a centralized and integrated veterinary zoonotic surveillance system to provide Commanders with a clear picture of disease burden [9]. With this assigned responsibility, the Army Veterinary Service (VS) seeks to centralize and enhance surveillance efforts through the Remote Online Veterinary Record (ROVR) Electronic Health Record (EHR), an enterprise web-based application to support the Army VS, accurately establishing a zoonotic epidemiological baseline and sustaining consistent future reporting.

Objective: We assesed the feasibility of a zoonotic disease surveillance system through the current EHR (ROVR) for all POAs and GOAs. Additionally, we conducted a retrospective observational study querying and collecting reported zoonoses of interest, for 2017.

The West Africa Ebola outbreak of 2014-2016 demonstrated the importance of strong disease surveillance systems and the severe consequences of weak capacity to detect and respond to cases quickly. Challenges in the transmission and management of surveillance data were one factor that contributed to the delay in detecting and confirming the Ebola outbreak. To help address this challenge, we have collaborated with the U.S. Centers for Disease Control and Prevention (CDC), the Ministry of Health (MOH) in Guinea, the World Health Organization and various partners to strengthen the disease surveillance system through the implementation of an electronic reporting system using an open source software tool, the District Health Information Software Version 2 (DHIS 2). These efforts are part of the Global Health Security Agenda objective to strengthen real-time surveillance. This online system enables prefecture health offices to enter aggregate weekly disease reports from health facilities and for that information to be immediately accessible to designated staff at prefecture, regional and national levels. Incorporating DHIS 2 includes several advantages for the surveillance system. For one, the data is available in real time and can be analyzed quickly using built-in data analysis tools within DHIS 2 or exported to other analysis tools. In contrast, the existing system of reporting using Excel spreadsheets requires the MOH to manually compile spreadsheets from all the 38 prefectures to have case counts for the national level. For the individual case notification system, DHIS 2 enables a similar accessibility of information that does not exist with the current paper-based reporting system. Once a case notification form is completed in DHIS 2, the case-patient information is immediately accessible to the laboratories receiving specimens and conducting testing for case confirmation. The system is designed so that laboratories enter the date and time that a specimen is received, and any test results. The results are then immediately accessible to the reporting district and to the stakeholders involved including the National Health Security Agency and the Expanded Program on Vaccination. In addition, DHIS 2 can generate email and short message service (SMS) messages to notify concerned parties at critical junctures in the process, for example, when a laboratory result is available for a given case.

Objective: The objective is to share the progress and challenges in the implementation of the District Health Information Software Version 2 (DHIS 2) as an electronic disease surveillance system platform in Guinea, West Africa, to inform Global Health Security Agenda efforts to strengthen real-time surveillance in low-resource settings.

BioSense 2.0 protects the health of the American people by providing timely insight into the health of communities, regions, and the nation by offering a variety of features to improve data collection, standardization, storage, analysis, and collaboration. BioSense 2.0 is the result of a partnership between the Centers for Disease Control and Prevention (CDC) and the public health community to track the health and well-being of communities across the country. As part of the redesign effort, new fat pipe system architecture has recently been implemented to improve the features and capabilities of the system.

Objective

The objective of this presentation is to provide an overview of the technical architecture of BioSense 2.0.

The importance transmitting clinical information to public health for disease surveillance is well-documented. Conventional reporting processes require health care providers to complete paper-based notifiable condition reports which are transmitted by fax and mail to public health agencies. These processes result in incomplete reports, inconsistencies in reporting frequencies among different diseases and reporting delays as well as time-consuming follow-up by public health to get needed information. One strategy to address these issues is to electronically pre-populate report forms with available clinical, lab and patient data to streamline reporting workflows, increase data completeness and, ultimately, provide access to more timely and accurate surveillance data for public health organizations. Prior to implementing an intervention that includes using pre-populated forms, we conducted interviews in clinical and public health settings to identify the barriers and facilitators to adopting and utilizing the forms and their potential impact on workflow and perceived burden. These interviews are a component of a larger mixed methods evaluation that will triangulate pre- and post-intervention quantitative data quality measures with qualitative results.

Objective

Introduction of new health information technologies can produce unanticipated consequences on existing user behaviors, workflow, etc. Prior to implementing a public health reporting intervention, we conducted a series of interviews regarding workflow and perceptions of task burden with respect to notifiable condition reporting.

The Florida Department of Health (FDOH) electronically receives both urgent care center (UCC) data and hospital emergency department (ED) data from health care facilities in 43 of its 67 counties through its Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE-FL). Each submitted record is assigned to one of eleven ESSENCE Syndrome categories based on parsing of chief complaint data. The UCC data come from 22 urgent care centers located in Central Florida, and the ED data come from 161 hospitals located across the state. Traditionally, the data from these two sources are grouped and viewed together. To date, limited investigation has been carried out on the validity of grouping data from UCCS and EDs in ESSENCE-FL. This project will investigate and describe the differences between the data received from these two sources and provide best practices for grouping and analyzing these data sources.

Objective

To identify best practices for grouping emergency department and urgent care data in a syndromic surveillance system.

Early warning surveillance (EWS) is a key factor in the fight against tropical infectious diseases(1). However, the process of carrying out EWS is complex as it involves several actors and requires the use of diverse human, material and technological resources for data collection, analysis, and diffusion(2). Modern EWS systems make use of state of the art technologies and technics which require much financial input and adequate technological expertise for the users. More so, the culture and habits of users in DCs make it very difficult to run such EWS systems in this milieu. In this paper, we propose a generic early warning surveillance architecture that tackles the stages from just after data collection, through data analysis to feedback and that is adapted to the context of limited resource countries.

Objective

Build a computer aided Early warning disease surveillance system adapted for Developing Countries (DCs) facing limited financial, human, intellectual, organizational, technological, and infrastructural resources.