Data Analytics

Syndromic surveillance involves monitoring big health datasets to provide early warning of threats to public health. Public health authorities use statistical detection algorithms to interrogate these datasets for aberrations that are indicative of emerging threats. The algorithm currently in use at Public Health England (PHE) for syndromic surveillance is the ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method (Morbey et al, 2015), which fits a mixed model to counts of syndromes on a daily basis. This research checks whether the RAMMIE method works across a range of public health scenarios and how it compares to alternative methods.

Objective:

To investigate whether alternative statistical approaches can improve daily aberration detection using syndromic surveillance in England.

Neonatal Abstinence Syndrome (NAS) rates have tripled for Missouri residents in the past three years. NAS is a condition infants suffer soon after birth due to withdrawal after becoming opioid-dependent in the womb. NAS has significant immediate health concerns and can have long term effects on child development and quality of life. The Missouri Department of Health and Senior Services (MODHSS) maintains the Patient Abstract System (PAS), a database of inpatient, emergency room, and outpatient records collected from non-federal hospitals and ambulatory surgical centers throughout the state. PAS records contain extensive information about the visit, patient, and diagnosis. When examining 2015 annual PAS data for NAS-associated discharges, Missouri analysts noticed a greater than 50% increase in discharges, even larger than anticipated in light of the opioid epidemic. Provisional 2016 data produced similar high rates, dispelling the notion that the trend was a transitional problem. In fact, provisional 2016 rates are 115% higher than NAS rates in 2015. In contrast, percentage change of opioid misuse emergency department visits (as defined by MODHSS) for Missouri women age 18-44 was +13% in 2015 and -12% in 2016.

Objective:

In this analysis we examine Missouri NAS discharge rates with special focus on the ICD-9-CM/ICD-10-CM transition and changes in code descriptions.

In 2013, the Utah Department of Health (UDOH) began working with hospital and reference laboratories to implement electronic laboratory reporting (ELR) of reportable communicable disease data. Laboratories utilize HL7 message structure and standard terminologies such as LOINC and SNOMED to send data to UDOH. These messages must be evaluated for validity, translated, and entered into Utah’s communicable disease surveillance system (UT-NEDSS), where they can be accessed by local and state investigators and epidemiologists. Despite the development and use of standardized terminologies, reporters may use different, outdated versions of these terminologies, may not use the appropriate codes, or may send local, home-grown terminologies. These variations cause problems when trying to interpret test results and automate data processing. UDOH has developed a two-step translation process that allows us to first standardize and clean incoming messages, and then translate them for consumption by UT-NEDSS. These processes allow us to efficiently manage several different terminologies and helps to standardize incoming data, maintain data quality, and streamline the data entry process.

Objective:

The objective of this abstract is to illustrate how the Utah Department of Health processes a high volume of electronic data. We do this by translating what reporters send within an HL7 message into "epidemiologist" language for consumption into our disease surveillance system.

Under the CDC STD Surveillance Network (SSuN) Part B grant, WA DOH is testing eICR of sexually transmitted infections (STI) with a clinical partner. Existing standard vocabulary codes were identified to represent previously-identified information gaps, or the need for new codes or concepts was identified.

Objective:

Previous research identified data gaps between traditional paper-based STI notifiable condition reporting and pilot electronic initial case reporting (eICR) relying on Continuity of Care Documents (CCDs) exported from our clinical partner’s electronic health record (EHR) software. Structured data capture is needed for automatic processing of eICR data imported into public health repositories and surveillance systems, similar to electronic laboratory reporting (ELR). Coding data gaps (between paper and electronic case reports) using standardized vocabularies will allow integration of additional questions into EHR or other data collection systems and may allow creation of standard Clinical Data Architecture (CDA) templates, Logical Observation Identifiers Names and Codes (LOINC) panels, or Fast Healthcare Interoperability Resources (FHIR) resources. Furthermore, identifying data gaps can inform improvements to other standards including nationwide standardization efforts for notifiable conditions.

Plague was first identified in Armenia in 1958 when Y. pestis was isolated and cultured from the flea species Ct. teres collected from the burrows of common voles in the northwestern part of the country. In the process of digitalizing archived data, a statistical and spatial analysis of the species composition of mammals and parasites involved in the epizootic process of plague between 1958 and 2016 was performed.

Objective:

The objective of this study was to determine the species composition of mammals and parasites involved in the epizootic process of plague in Armenia and their geographic distribution.

The frequency of disease outbreaks varies as a result of complex biological processes. Analysis of these frequencies can reveal patterns that can serve as a basis for predictions.

Objective:

The goal of this study was to identify the periodicity of seven zooanthroponoses in humans, and set epidemic thresholds for future occurrences.

In recent years, individuals have been using social network sites like Facebook, Twitter, and Reddit to discuss health-related topics. These social media platforms consequently became new avenues for research and applications for researchers, for instance disease surveillance. Reddit, in particular, can potentially provide more in-depth contextual insights compared to Twitter, and Reddit members discuss potentially more diverse topics than Facebook members. However, identifying relevant discussions remains a challenge in large datasets like Reddit. Thus, much previous research using Reddit data focused on selected few topically-oriented sub-communities. Although such approach allows for topically focused datasets, a large portion of related data can be missed. In this research, we examine all sub-communities in which members are discussing e-cigarettes in order to determine if investigating these other sub-communities could result in a better smoking surveillance system.

Objective:

We aim to explore how to effectively leverage social media for vaping electronic cigarette (e-cigarette) surveillance. This study examines how members of a social media platform called Reddit utilize topically-oriented sub-communities for e-cigarette discussions.

Nearly 100 people per day die from opioid overdose in the United States. Further, prescription opioid abuse is assumed to be responsible for a 15-year increase in opioid overdose deaths. However, with increasing use of social media comes increasing opportunity to seek and share information. For instance, 80% of Internet users obtain health information online, including popular social interaction sites like Reddit (http://www.reddit.com), which had more than 82.5 billion page views in 20153. In Reddit, members often share information, and include URLs to supplement the information. Understanding the frequency of URL sharing and types of shared URLs can improve our knowledge of information seeking/sharing behaviors as well as domains of shared information on social media. Such knowledge has the potential to provide opportunities to improve public health surveillance practice. We use Reddit to track opioid related discussions and then investigate types of shared URLs among Reddit members in those discussions.

Objective:

We aim to understand (1) the frequency of URL sharing and (2) types of shared URLs among opioid related discussions that take place in the social media platform called Reddit.

Standard syndrome definitions for ED visits in ESSENCE rely on chief complaints. Visits with more words in the chief complaint field are more likely to match syndrome definitions. While using ESSENCE, we observed geographic differences in chief complaint length, apparently related to differences in electronic health record (EHR) systems, which resulted in disparate syndrome matching across Idaho regions. We hypothesized that chief complaint and diagnosis code co-occurrence among ED visits to facilities with long chief complaints could help identify terms that would improve syndrome match among facilities with short chief complaints.

Objective:

We sought to use free text mining tools to improve emergency department (ED) chief complaint and discharge diagnosis data syndrome definition matching across facilities with differing robustness of data in the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) application in Idaho’s syndromic surveillance system.

Pneumonia, an infection of the lung due to bacterial, viral or fungal pathogens, is a significant cause of morbidity and mortality worldwide. In the past few decades, the threat of emerging pathogens presenting as pneumonia, such as Severe Acute Respiratory Syndrome, avian influenza A(H5N1) and A(H7N9), and Middle East Respiratory Syndrome coronavirus has emphasised the importance of the surveillance of pneumonia and other severe respiratory infections. An unexpected increase in the number of hospital admissions for pneumonia or severe respiratory infections could be a signal of a change in the virulence of the influenza viruses or other respiratory pathogens circulating in the community, or an alert of an emerging pathogen which warrants further public health investigation. The purpose of this study was to develop a forecasting model to prospectively forecast the number of emergency department (ED) admissions due to pneumonia in Singapore, a tropical country. We hypothesise that there is complementary information between hospital-based and community-based surveillance systems. The clinical spectrum of many respiratory pathogens causing pneumonia ranges from asymptomatic or subclinical infection to severe or fatal pneumonia, and it is usually difficult to distinguish between the different pathogens in the absence of a laboratory test. Infected persons could present with varying degrees of severity of the infection, and seek treatment at different healthcare facilities. Hospital-based surveillance captures the more severe manifestation of the infection while community-based surveillance captures the less severe manifestation of the infection and enables earlier detection of the infection. Thus, the integration of information from the two surveillance systems should improve the prospective forecasting of ED admissions due to pneumonia. We also investigate if the inclusion of influenza data from the laboratory surveillance system would improve the forecasting model, since influenza circulates all-year round in Singapore and is a common aetiology for pneumonia.

Objective:

To develop a forecasting model for weekly emergency department admissions due to pneumonia using information from hospital-based, community-based and laboratory-based surveillance systems.