Data Analytics

Since 1 January 2016, the Auvergne and Rhone-Alpes regions have merged as part of the territorial reform. The new region is composed of 12 departments and accounts for more than 8 million inhabitants. Its territory is heterogeneous in population density with very urban areas (Clermont-Ferrand, Grenoble, Lyon and Saint-Etienne) and important mountainous areas (Arc Alpin, Massif Central). In France since 2004, the syndromic surveillance system SurSaUD® [1] coordinated by the French Public Health Agency (Sant© publique France) collects morbidity data on a daily basis from two data sources: the emergency departments (ED) network Oscour® and the emergency general practitioners SOS Medecins associations. In Auvergne-Rhone-Alpes, the number of structures participating in the scheme has gradually increased from 2006 to today; as of 1 September 2018, all emergency services (N = 84) and all SOS ©decins associations (N = 7) transmit their data on a daily basis. Both data sources collect medical diagnoses, using ICD10 codes in the ED network and specific medical thesaurus in SOS Medecins Associations. These data are routinely analyzed to detect and follow-up various expected or unusual public health events all over the territory [2]. A reflection on the analysis of monitoring data at the sub-regional level was conducted in the region in order to refine the analyses carried out and better meet the expectations of local partners.

Objective: Define analytic areas at a sub-regional level to better meet the needs of local decision-makers.

In 2017, the dialysis centre of East Reunion Hospital Group (ERHG) based in Saint-Benoit highlighted an increase in bacteraemia's rates. It was a significant rising compared to previous years. Indeed, ERHG is participating since 2013 to the France haemodialysis infections network surveillance (DIALIN) [1], created in 2005 and that is allowing assessing bacteraemia. DIALIN is a multicentre prospective permanent survey that has followed six voluntary centres in 2005 and forty-two in 2016. Objectives of this network are firstly to produce data about acquired infections in haemodialysis sector such as infection incidence rate and standardized ratios allowing centres to compare themselves and, secondly, to improve the quality of care. The current study describe how a root cause analysis has been conducted through the ALARM risk assessment methodology to set up action plans and to reduce the phenomenon [2] [3].

Objective: To investigate the bacteraemia increase in haemodialysis sector based on data from specific dialysis nosocomial infections national network surveillance (DIALIN) and through an Association of Litigation and Risk Management protocol (ALARM).

In late 2015, two economists studying health-related data inadvertently discovered an alarming trend: death rates for middle-aged, white Americans were dramatically increasing from drug overdoses (Kolata, 2015), particularly opioids (CDC, 2015). The opioid epidemic has since been widely publicized in the media. However, as critics have argued, the government's response to the crack epidemic differs dramatically from an arguably equally devastating œdrug epidemic that hit many inner US cities thirty years ago ”the influx of crack cocaine. More specifically, opioid addicts, who tend to be white, have been positioned as patients, whereas in the 1970s and 80s during the war on drugs, heroin and crack addicts, respectively, who tended to be people of color, were criminalized (Hart, 2017; Hutchinson, 2017).

Objective: I analyze a collection of data visualizations created during the crack and opioid epidemics, respectively, published by mainstream news media using three criteria: genre, subject matter, and language used to describe the graphic. I use precarity as a theoretical framework--that is, a politically induced condition in which certain populations suffer from failing social and economic networks of support and become differentially exposed to injury, violence, and death (Butler, 2009, p. 35)--to argue that visualizations created during the crack epidemic positioned addicts as criminals whereas opioid addicts have been positioned as patients in need of treatment.

Mortality is an indicator of the severity of the impact of an event on the population. In France mortality surveillance is part of the syndromic surveillance system SurSaUD and is carried out by Santé publique France, the French public health agency. The set-up of an Electronic Death Registration System (EDRS) in 2007 enabled to receive in real-time medical causes of death in free-text format. This data source was considered as reactive and valuable to implement a reactive mortality surveillance system using medical causes of death (1). The reactive mortality surveillance system is based on the monitoring of Mortality Syndromic Groups (MSGs). An MSG is defined as a cluster of medical causes of death (pathologies, syndromes, symptoms) that meet the objectives of early detection and impact assessment of events (2). Since causes of death are entered in free-text format, their automatic classifications into MSGs require the use of natural language processing methods. We observe a constant increase in the use of these methods to classify medical information and for health surveillance over the last two decades (3).

Objective: This study aims to implement and evaluate two automatic classification methods of free-text medical causes of death into Mortality Syndromic Groups (MSGs) in order to be used for reactive mortality surveillance.

Disease mapping is a method used to descript the geographical variation in risk (heterogeneity of risk) and to provide the potential reason (factors or confounders) to explain the distribution. Possibly the most famous uses of disease mapping in epidemiology were the studies by John Snow of the cholera epidemics in London. Accurate estimation relative risk of small areas such as mortality and morbidity, by different age, ethnic group, interval and regions, is important for government agencies to identify hazards and mitigate disease burden. Recently, as the innovative algorithms and the available software, more and more disease risk index has been pouring out. This abstract will provide several estimation risk index, from raw incidence to model-based relative risks, and use visual approach to display them.

Objective: The purpose is to propose a serial of approach for estimation for disease risk for ILI in "small area" and present the risk values by spatio-temporal disease mapping or an interactive visualization with HTML format.

The use of new technologies such as Online Maps and the QR Code facilitates the knowledge dissemination in the health science, aiding in diagnostic elucidation and intelligent decisions making, thus offering an improvement in the quality of care provided to patients. Cases with suspected spotted fever should be approached as potentially serious, which may develop with shock within a few hours and, if not addressed can progress to death. In the case of spotted fever, early onset determines the cure of these cases.

Objective: To perform the spatial distribution of Spotted Fever in the Metropolitan Area of Sao Paulo Municipality (MRSP), coverage area of Epidemiological Surveillance Group VII of Santo Andre (GVE7), to determine clusters of disease incidence, and through QR Code to be able to access data from any smartphone as an aid to the early treatment of new suspected cases.

An interdisciplinary team convened by ISDS to translate public health use-case needs into well-defined technical problems recently identified the need for new pre-syndromic surveillance methods that do not rely on existing syndromes or pre-defined illness categories1. Our group has recently developed Multidimensional Semantic Scan (MUSES), a pre-syndromic surveillance approach that (1) uses topic modeling to identify newly emerging syndromes that correspond to rare or novel diseases; and (2) uses multidimensional scan statistics to identify emerging outbreaks that correspond to these syndromes and are localized to a particular geography and/or subpopulation2,3. Through a blinded evaluation on retrospective free-text ED chief complaint data from NYC DOHMH, we demonstrate that MUSES has great potential to serve as a safety net for public health surveillance, facilitating a rapid, targeted, and effective response to emerging novel disease outbreaks and other events of relevance to public health that do not fit existing syndromes and might otherwise go undetected.

Objective: We present a new approach for pre-syndromic disease surveillance from free-text emergency department (ED) chief complaints, and evaluate the method using historical ED data from New York City's Department of Health and Mental Hygiene (NYC DOHMH).

The public health problem identified by Alabama Department of Public Health Syndromic Surveillance (AlaSyS) was that the data reflected in the user application of ESSENCE (Electronic Surveillance System for the Early Notification of Community-based Epidemics) was underestimating occurrences of syndromic alerts preventing Alabama Department of Public Health (ADPH) from timely recognition of potential public health threats. Syndromic surveillance (SyS) data in ESSENCE were not reliable for up to a week after the visit date due to slow processing, server downtime, and untimely data submission from the facilities. For AlaSyS, 95 percent of data should be submitted within 24 hours from time of visit, for near real time results. The slow data processing caused latency in the data deeming it less useful for surveillance purposes, consequently the data was not meaningful for daily alerts. For example, if a user ran a report to assess the number of Emergency Department (ED) visits that mentioned heroin in the chief complaint (CC), depending on the status of the data coming from the facility (processing, sending, or offline), the number of visits visible to the user could vary from one to several days. With the opioid epidemic Alabama is currently facing, this delay poses a major public health problem.

Objective: To monitor and improve the data quality captured in syndromic surveillance for Alabama Department of Public Health Syndromic Surveillance (AlaSyS).

Hepatitis C virus (HCV) infection is a leading cause of liver disease-related morbidity and mortality in the United States and HCV incidence has been increasing. Mental illness may impact the likelihood of initial HCV infection, progress and adherence to treatment along the hepatitis C care cascade, and risk of subsequent reinfection for those cured of hepatitis C. The relationship between HCV infection and mental illness is not well understood and many studies have lacked sufficient sample size to adjust for important confounders. We sought to explore the association between chronic HCV infection and mental illness after adjusting for important confounders.

Objective: Using data from the 2011-“2015 IBM MarketScan® Commercial Claims and Encounters, we sought to assess the relationship between mental health outcomes and chronic hepatitis C infection after adjusting for important confounders. Persons with HCV antibody and RNA test results between 2011 and 2015 and continuous enrollment in fee-for-service plans were included in the analysis.

Hepatitis C virus (HCV) infection is a leading cause of liver disease-related morbidity and mortality in the United States. Approximately 75% of people infected with chronic HCV were born between 1945 and 1965. Since 2012, the CDC has recommended one-time screening for chronic HCV infection for all persons in this birth cohort (baby boomers). The United States Preventive Services Task Force (USPSTF) subsequently made the same recommendation in June 2013. We estimated the rate of HCV testing between 2011 and 2017 among persons with commercial health insurance coverage and compared rates by birth cohort.

Objective: Using the two largest commercial laboratory data sources nationally, we estimated the annual rates of hepatitis C testing among individuals who were recommended to be tested (i.e., baby boomer cohort born between 1945 and 1965) by the CDC and United States Preventive Services Task Force. This panel will discuss strengths and weaknesses for monitoring hepatitis C testing using alternative data sources including self-reported data, insurance claims data, and laboratory testing data.