Glossary for public health surveillance in the age of data science

Chioléro, Arnaud; Buckeridge, David L.

doi:10.1136/jech-2018-211654

Cited by 34 publications

(44 citation statements)

References 58 publications

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“…Visualization of data, a shared area of research in data science and informatics disciplines, has long been an important aspect of PH surveillance. 36 Unlike past visualizations in the form of static reports published on a PH website, the dashboard represents a modern example of PH surveillance applications that offer interaction with dynamic visualized data, such as drilling down into county-level data or selecting a subpopulation. Interaction allows epidemiologists to explore trends and patterns of data more easily than traditional, more time-consuming approaches in which data are manipulated in spreadsheets.…”

Section: Discussionmentioning

confidence: 99%

Leveraging data visualization and a statewide health information exchange to support COVID-19 surveillance and response: Application of public health informatics

Dixon

Grannis

McAndrews

et al. 2021

Journal of the American Medical Informatics Association

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Objective To support public health surveillance and response to COVID-19 through rapid development and implementation of novel visualization applications for data amalgamated across sectors. Materials and methods We developed and implemented population-level dashboards that collate information on individuals tested for and infected with COVID-19, in partnership with state and local public health agencies as well as health systems. The dashboards are deployed on top of a statewide health information exchange. One dashboard enables authorized users working in public health agencies to surveil populations in detail, and a public version provides higher-level situational awareness to inform ongoing pandemic response efforts in communities. Results Both dashboards have proved useful informatics resources. For example, the private dashboard enabled detection of a local community outbreak associated with a meat packing plant. The public dashboard provides recent trend analysis to track disease spread and community-level hospitalizations. Combined, the tools were utilized 133,637 times by 74,317 distinct users between June 21 and August 22, 2020. The tools are frequently cited by journalists and featured on social media. Discussion Capitalizing on a statewide health information exchange, in partnership with health system and public health leaders, Regenstrief biomedical informatics experts rapidly developed and deployed informatics tools to support surveillance and response to COVID-19. Conclusion The application of public health informatics methods and tools in Indiana holds promise for other states and nations. Yet development of infrastructure and partnerships will require effort and investment after the current pandemic in preparation for the next public health emergency.

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Section: Discussionmentioning

confidence: 99%

Leveraging data visualization and a statewide health information exchange to support COVID-19 surveillance and response: Application of public health informatics

Dixon

Grannis

McAndrews

et al. 2021

Journal of the American Medical Informatics Association

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“…Die kontinuierliche Weiterentwicklung des SUMO-Produktivsystems während des Betriebs wird über Beiträge aus der 3 https://art-decor.org/art-decor/decorproject--nokeda.…”

Section: Dynamischer Systembetriebunclassified

“…komplementiert durch Kenntnisse und Einschätzungen von Pfleger*innen und Ärzt*innen. Im Sinne einer syndromischen Surveillance können so Daten zu Häufigkeit, Schweregrad und Ausprägung relevanter Entitäten zusammengeführt werden und als Grundlage zur Public-Health-Handlungssteuerung genutzt werden [ 3 ]. Hierbei bezieht sich die Bezeichnung „syndromisch“ auf die vornehmliche Nutzung (i. d. R. wenig qualitätsgesicherter) klinischer Angaben, kann aber nach dem hier verwendeten umfassenderen Verständnis sämtliche Daten mit einbeziehen, die im jeweiligen Setting zur eindeutigen Definition abgrenzbarer (ggf.…”

Section: Introductionunclassified

Gewinnung von Echtzeitdaten aus der medizinischen Versorgung zur Handlungssteuerung in Public Health

Grabenhenrich

Schranz

Boender

et al. 2021

Bundesgesundheitsbl

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ZusammenfassungEchtzeitdaten aus der medizinischen Versorgung spielen für die Handlungsteuerung in Public Health eine entscheidende Rolle. Dies zeigt die COVID-19-Pandemie besonders deutlich: Viele Public-Health-Akteure sind auf die aktuellen Daten aus dem klinischen und Versorgungsgeschehen angewiesen, um wichtige Entscheidungen treffen und Empfehlungen geben zu können. Die Automatisierung der Verarbeitungs- und Kommunikationsprozesse ist essenziell, damit eine Kontinuität des Datenflusses gewährleistet wird und Ressourcen geschont werden. Bisher standen der Entwicklung digital automatisierter Echtzeitsysteme mit wissenschaftlichem Qualitätsanspruch verschiedene technische, fachliche und organisatorische Herausforderungen im Weg. Die COVID-19-Pandemie dient seit ihrem Beginn Anfang 2020 als Motor für zukunftsfähige Systementwicklungen.In diesem Beitrag wird zunächst beschrieben, wie ein Echtzeitdatensystem aufgebaut sein muss, damit eine automatisierte Datenverarbeitung möglich ist. Wichtige Aspekte bei der Zusammenführung der Daten, ihrer Aufbereitung, Bereitstellung und Kommunikation werden dargestellt. Als Beispiel dient ein System, das Routinedaten aus Notaufnahmen in Echtzeit verarbeitet und diese Public-Health-Akteuren bereitstellt. Es setzt sich zusammen aus dem Notaufnahmeregister des Aktionsbündnisses für Informations- und Kommunikationstechnologie in Intensiv- und Notfallmedizin (AKTIN) der Universität Magdeburg und der RWTH Aachen und dem Surveillance Monitor (SUMO) des Robert Koch-Instituts.Die Entwicklung zukunftsfähiger Echtzeitsysteme zur Verarbeitung von Forschungsdaten aus der medizinischen Versorgung kann nur durch die Zusammenarbeit verschiedenster Akteure gelingen. Eine wichtige Grundlage für den langfristigen Erfolg ist die Entwicklung eines gesetzlichen Rahmens.

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“…It applies specifically to a population of people who were previously free of infection and were not vaccinated [ 51 ]. Existing surveillance systems use data that are subject to significant measurement error and other contaminants [ 52 , 53 ]. Moreover, timely information is needed to improve statistical methods that extract information from data sets posted on websites [ 54 - 56 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Panel Estimate–Based Health Surveillance of SARS-CoV-2 Infection Rates to Inform Public Health Policy: Model Development and Validation

Oehmke¹,

Oehmke²,

Singh³

et al. 2020

J Med Internet Res

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Background SARS-CoV-2, the novel coronavirus that causes COVID-19, is a global pandemic with higher mortality and morbidity than any other virus in the last 100 years. Without public health surveillance, policy makers cannot know where and how the disease is accelerating, decelerating, and shifting. Unfortunately, existing models of COVID-19 contagion rely on parameters such as the basic reproduction number and use static statistical methods that do not capture all the relevant dynamics needed for surveillance. Existing surveillance methods use data that are subject to significant measurement error and other contaminants. Objective The aim of this study is to provide a proof of concept of the creation of surveillance metrics that correct for measurement error and data contamination to determine when it is safe to ease pandemic restrictions. We applied state-of-the-art statistical modeling to existing internet data to derive the best available estimates of the state-level dynamics of COVID-19 infection in the United States. Methods Dynamic panel data (DPD) models were estimated with the Arellano-Bond estimator using the generalized method of moments. This statistical technique enables control of various deficiencies in a data set. The validity of the model and statistical technique was tested. Results A Wald chi-square test of the explanatory power of the statistical approach indicated that it is valid (χ210=1489.84, P<.001), and a Sargan chi-square test indicated that the model identification is valid (χ2946=935.52, P=.59). The 7-day persistence rate for the week of June 27 to July 3 was 0.5188 (P<.001), meaning that every 10,000 new cases in the prior week were associated with 5188 cases 7 days later. For the week of July 4 to 10, the 7-day persistence rate increased by 0.2691 (P=.003), indicating that every 10,000 new cases in the prior week were associated with 7879 new cases 7 days later. Applied to the reported number of cases, these results indicate an increase of almost 100 additional new cases per day per state for the week of July 4-10. This signifies an increase in the reproduction parameter in the contagion models and corroborates the hypothesis that economic reopening without applying best public health practices is associated with a resurgence of the pandemic. Conclusions DPD models successfully correct for measurement error and data contamination and are useful to derive surveillance metrics. The opening of America involves two certainties: the country will be COVID-19–free only when there is an effective vaccine, and the “social” end of the pandemic will occur before the “medical” end. Therefore, improved surveillance metrics are needed to inform leaders of how to open sections of the United States more safely. DPD models can inform this reopening in combination with the extraction of COVID-19 data from existing websites.

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Glossary for public health surveillance in the age of data science

Cited by 34 publications

References 58 publications

Leveraging data visualization and a statewide health information exchange to support COVID-19 surveillance and response: Application of public health informatics

Leveraging data visualization and a statewide health information exchange to support COVID-19 surveillance and response: Application of public health informatics

Gewinnung von Echtzeitdaten aus der medizinischen Versorgung zur Handlungssteuerung in Public Health

Dynamic Panel Estimate–Based Health Surveillance of SARS-CoV-2 Infection Rates to Inform Public Health Policy: Model Development and Validation

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