Data Analytics

How are interactive story map journals situated within the genre of interactive, health science reporting? How can reporting information to public audiences be theorized through traditional and contemporary understandings of new media genres in technical, health and science communication (1-7). Although the polio vaccine has eradicated the disease in the United States, and 99% worldwide (8), PPS has emerged as a present-day condition that continues to affect many polio survivors years after the initial onset and recovery. Since the symptoms of PPS are oftentimes mis-identified as other illnesses, the diagnosis and management of disease is especially challenging for PPS survivors due to the limited knowledge of and access to PPS resources and support networks (9-11). In 2011, Esri created the ArcGIS story map initiative to meet a need for public audiences who sought how to critically think, better understand, communicate, and interact with world news events. ArcGIS is a geospatially-driven, new media platform that enables audiences to engage with interactive storytelling of news events. Public health and news reporting agencies are now turning to Esri and similar interactive, geospatially driven new media platforms for health and disease surveillance (12-14). Esri's ArcGIS mobile and web technology platform visually reports, maps and tracks population health data information. With the emergence of such new media applications, it is therefore important to recognize multimodal, visualization strategies that investigate how interaction design choices within the story map journal influence and engage public health audiences. In the field of technical and professional communication (15), applied concept of visual-material rhetorics is a useful mode of inquiry in the study of interactive story map journals. Propen's concept presents a new understanding of how researchers in disease and public health surveillance can analyze the effectiveness of text and new media technology in relationship to space, place, and geospatial mapping. More specifically, Propen's concept situates the visual-material as the applied use of text with visual, interactive multimodal components inclusive of images, video, and GPS/GIS mapping technologies.

Objective: A case study on the visual-material components of story map journals as visual, new media interactive health reporting used in population health surveillance. The story map journal is demonstrated an effective tool that visually reports, maps and tracks global support networks and health resources for post-polio (PPS) survivors.

Routine childhood administration of varicella-containing vaccine has resulted in the number of varicella (chickenpox) cases in Pennsylvania falling from nearly 3,000 cases in 2007 to less than 400 cases in 2017. Prior to 2018, the completeness of varicella case investigation data documented in Pennsylvania's electronic disease surveillance system (PA-NEDSS) was not routinely monitored by Department of Health (DOH) staff. A pilot project was initiated in April 2018 to monitor and improve completeness of select varicella case investigation variables.

Objective: The objective of this study was to evaluate the impact of efforts made to improve the completeness of select varicella (chickenpox) case investigation variables.

Traditionally, surveillance systems for dengue and other infectious diseases locate each individual case by home address, aggregate these locations to small areas, and monitor the number of cases in each area over time. However, human mobility plays a key role in dengue transmission, especially due to the mosquito day-biting habit, and relying solely on individuals' residential address as a proxy for dengue infection ignores a multitude of exposures that individuals are subjected to during their daily routines. Residence locations may be a poor indicator of the actual regions where humans and infected vectors tend to interact more, and hence, provide little information for dengue prevention. The increasing availability of geolocated data in online platforms such as Twitter offers a unique opportunity: in addition to identifying diseased individuals based on the textual content, we can also follow them in time and space as they move on the map and model their movement patterns. Comparing the observed mobility patterns for case and control individuals can provide relevant information to detect localized regions with higher risk of dengue infection. Incorporating the mobility of individuals into risk modeling requires the development of new spatial models that can cope with this type of data in a principled way and efficient algorithms to deal with the ever-growing amount of data. We propose new spatial scan models and exploit geo-located data from Twitter to detect geographic clusters of dengue infection risk.

Objective: We develop new spatial scan models that use individuals' movement data, rather than a single location per individual, in order to identify areas with a high relative risk of infection by dengue disease.

On November 20, 2017, several sites participating in the NSSP reported anomalies in their syndromic data. Upon review, it was found that between November 17-18, an EHR vendor’s syndromic product experienced an outage and errors in processing data. The ISDS DQC, NSSP, a large EHR vendor, and many of the affected sites worked together to identify the core issues, evaluate ramifications, and formulate solutions to provide to the entire NSSP CoP.

Objective: The National Syndromic Surveillance Program (NSSP) Community of Practice (CoP) works to support syndromic surveillance by providing guidance and assistance to help resolve data issues and foster relationships between jurisdictions, stakeholders, and vendors. During this presentation, we will highlight the value of collaboration through the International Society for Disease Surveillance (ISDS) Data Quality Committee (DQC) between jurisdictional sites conducting syndromic surveillance, the Centers for Disease Control and Prevention’s (CDC) NSSP, and electronic health record (EHR) vendors when vendor-specific errors are identified, using a recent incident to illustrate and discuss how this collaboration can work to address suspected data anomalies.

Drug overdose mortality is a growing problem in the United States. In 2017 alone over 72,000 deaths were attributed to drug overdose, most of which were caused by fentanyl and fentanyl analogs (synthetic opioids). While nearly every community has seen an increase in drug overdose, there is considerable variation in the degree of increase in specific communities. The Harris County community, which includes the City of Houston, has not seen the massive spikes observed in some communities, such as West Virginia, Kentucky, and Ohio. However, the situation in Harris County is complicated in mortality and drug use. From 2010 - 2016 Harris County has seen a fairly stable overdose-related mortality count, ranging from 450 - 618 deaths per year. Of concern, the last two years, 2015-2016, suggest a sharp increase has occurred. Another complexity is that Harris County drug related deaths seem to be largely from polysubstance abuse. Deaths attributed to cocaine, methamphetamine, and benzodiazipine all have risen in the past few years. Deaths associated with methamphetamine have risen from approximately 20 per year in 2010 - 2012 to 119 in 2016. This 6-fold increase is alarming and suggests a large-scale public health response is needed.

Objective: In this session, we will explore the results of a descriptive analysis of all drug overdose mortality data collected by the Harris County Medical Examiner's Office and how that data can be used to inform public health action.

Syndromic surveillance achieves timeliness by collecting prediagnostic data, such as emergency department chief complaints, from the start of healthcare interactions. The tradeoff is less precision than from diagnosis data, which takes longer to generate. As the use and sophistication of electronic health information systems increases, additional data that provide an intermediate balance of timeliness and precision are becoming available. Information about the procedures and treatments ordered for a patient can indicate what diagnoses are being considered. Procedure records can also be used to track the use of preventive measures such as vaccines that are also relevant to public health surveillance but not readily captured by typical syndromic data elements. Some procedures such as laboratory tests also provide results which can provide additional specificity about which diagnoses will be considered. If procedure and treatment orders and test results are included in existing syndromic surveillance feeds, additional specificity can be achieved with timeliness comparable to prediagnostic assessments.

Objective: To identify additional data elements in existing syndromic surveillance message feeds that can provide additional insight into public health concerns such as the influenza season.

Opioid overdoses have emerged within the last five to ten years to be a major public health concern. The high potential for fatal events, disease transmission, and addiction all contribute to negative outcomes. However, what is currently known about opioid use and overdose is generally gathered from emergency room data, public surveys, and mortality data. In addition, opioid overdoses are a non-reportable condition. As a result, state/national standardized procedures for surveillance or reporting have not been developed, and local government monitoring is frequently not specific enough to capture and track all opioid overdoses. Lastly, traditional means of data collection for conditions such as heart disease through hospital networks or insurance companies are not necessarily applicable to opioid overdoses, due to the often short disease course of addiction and lack of consistent health care visits. Overdose patients are also reluctant to follow-up or provide contact information due to law enforcement or personal reasons. Furthermore, collected data related to overdoses several months or years after the fact are useless in terms of short-term outreach. Therefore, given the potentially brief timeline of addiction or use to negative outcome, the current project set to create a near real-time surveillance and treatment/outreach system for opioid overdoses using an already existing EMS data collection framework.

Objective: To develop and implement a classifcation algorithm to identify likely acute opioid overdoses from text fields in emergency medical services (EMS) records.

Emergency department (ED) syndromic surveillance relies on a chief complaint, which is often a free-text field, and may contain misspelled words, syntactic errors, and healthcare-specific and/or facility-specific abbreviations. Cleaning of the chief complaint field may improve syndrome capture sensitivity and reduce misclassification of syndromes. We are building a spell-checker, customized with language found in ED corpora, as our first step in cleaning our chief complaint field. This exercise would elucidate the value of pre-processing text and would lend itself to future work using natural language processing (NLP) techniques, such as topic modeling. Such a tool could be extensible to other datasets that contain free-text fields, including electronic reportable disease lab and case reporting.

Objective: To share progress on a custom spell-checker for emergency department chief complaint free-text data and demonstrate a spell-checker validation Shiny application.

Data-driven decision-making is a cornerstone of public health emergency response; therefore, a highly-configurable and rapidly deployable data capture system with built-in quality assurance (QA; e.g., completeness, standardization) is critical. Additionally, to keep key stakeholders informed of developments during an emergency, data need to be shared in a timely and effective manner. Dynamic data visualization is a particularly useful means of sharing data with healthcare providers and the public.2 During Spring 2018, detection of canine influenza H3N2 among dogs in NYC caused concern in the veterinary community. Canine influenza is a highly contagious respiratory infection caused by an influenza A virus.3 However, no central database existed in NYC to monitor the outbreak and no single agency was responsible for data capture. Our team at the NYC Department of Health and Mental Hygiene (DOHMH) partnered with the NYC Veterinary Medical Association (VMA) to monitor the canine influenza H3N2 outbreak by building a web-based reporting platform and interactive dashboard.

Objective: The objectives of this project were to rapidly build and deploy a web-based reporting platform in response to a canine influenza H3N2 outbreak in New York City (NYC) and provide aggregate data back to the veterinary community as an interactive dashboard.

Despite considerable effort since the turn of the century to develop Natural Language Processing (NLP) methods and tools for detecting negated terms in chief complaints, few standardised methods have emerged. Those methods that have emerged (e.g. the NegEx algorithm) are confined to local implementations with customised solutions. Important reasons for this lack of progress include (a) limited shareable datasets for developing and testing methods (b) jurisdictional data silos, and (c) the gap between resource-constrained public health practitioners and technical solution developers, typically university researchers and industry developers. To address these three problems ISDS, funded by a grant from the Defense Threat Reduction Agency, organized a consultancy meeting at the University of Utah designed to bring together (a) representatives from public health departments, (b) university researchers focused on the development of computational methods for public health surveillance, (c) members of public health oriented non-governmental organisations, and (d) industry representatives, with the goal of developing a roadmap for the development of validated, standardised and portable resources (methods and data sets) for negation detection in clinical text used for public health surveillance.

Objective: This abstract describes an ISDS initiative to bring together public health practitioners and analytics solution developers from both academia and industry to define a roadmap for the development of algorithms, tools, and datasets to improve the capabilities of current text processing algorithms to identify negated terms (i.e. negation detection).