(searched for: doi:10.1136/jech-2018-211654)
Frontiers in Artificial Intelligence, Volume 5; https://doi.org/10.3389/frai.2022.913093
Current technological and medical advances lend substantial momentum to efforts to attain new medical certainties. Artificial Intelligence can enable unprecedented precision and capabilities in forecasting the health conditions of individuals. But, as we lay out, this novel access to medical information threatens to exacerbate adverse selection in the health insurance market. We conduct an interdisciplinary conceptual analysis to study how this risk might be averted, considering legal, ethical, and economic angles. We ask whether it is viable and effective to ban or limit AI and its medical use as well as to limit medical certainties and find that neither of these limitation-based approaches provides an entirely sufficient resolution. Hence, we argue that this challenge must not be neglected in future discussions regarding medical applications of AI forecasting, that it should be addressed on a structural level and we encourage further research on the topic.
Journal of Epidemiology and Community Health, Volume 76, pp 623-625; https://doi.org/10.1136/jech-2021-216584
Too much data? Too much information? The COVID-19 pandemic has made the case. The WHO coined the term ‘infodemic’ to describe the issue of overabundance of information, including misinformation, disseminated in real time via multiple channels.1 2 A related concept is ‘datademic’ to describe the overabundance of data. I argue in this essay that infodemic intoxicates public health surveillance and decision-making, and that we need to revisit how we conduct surveillance in the age of big data by fostering a slow data culture.
BMJ, Volume 376; https://doi.org/10.1136/bmj.o161
DIGITAL HEALTH, Volume 8; https://doi.org/10.1177/20552076221102255
Background: “Digital public health” has emerged from an interest in integrating digital technologies into public health. However, significant challenges which limit the scale and extent of this digital integration in various public health domains have been described. We summarized the literature about these challenges and identified strategies to overcome them. Methods: We adopted Arksey and O’Malley's framework (2005) integrating adaptations by Levac et al. (2010). OVID Medline, Embase, Google Scholar, and 14 government and intergovernmental agency websites were searched using terms related to “digital” and “public health.” We included conceptual and explicit descriptions of digital technologies in public health published in English between 2000 and June 2020. We excluded primary research articles about digital health interventions. Data were extracted using a codebook created using the European Public Health Association's conceptual framework for digital public health. Results and analysis: Overall, 163 publications were included from 6953 retrieved articles with the majority (64%, n = 105) published between 2015 and June 2020. Nontechnical challenges to digital integration in public health concerned ethics, policy and governance, health equity, resource gaps, and quality of evidence. Technical challenges included fragmented and unsustainable systems, lack of clear standards, unreliability of available data, infrastructure gaps, and workforce capacity gaps. Identified strategies included securing political commitment, intersectoral collaboration, economic investments, standardized ethical, legal, and regulatory frameworks, adaptive research and evaluation, health workforce capacity building, and transparent communication and public engagement. Conclusion: Developing and implementing digital public health interventions requires efforts that leverage identified strategies to overcome diverse challenges encountered in integrating digital technologies in public health.
Published: 1 January 2022
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Frontiers in Public Health, Volume 9; https://doi.org/10.3389/fpubh.2021.756675
Recent advances in technology have led to the rise of new-age data sources (e.g., Internet of Things (IoT), wearables, social media, and mobile health). IoT is becoming ubiquitous, and data generation is accelerating globally. Other health research domains have used IoT as a data source, but its potential has not been thoroughly explored and utilized systematically in public health surveillance. This article summarizes the existing literature on the use of IoT as a data source for surveillance. It presents the shortcomings of current data sources and how NextGen data sources, including the large-scale applications of IoT, can meet the needs of surveillance. The opportunities and challenges of using these modern data sources in public health surveillance are also explored. These IoT data ecosystems are being generated with minimal effort by the device users and benefit from high granularity, objectivity, and validity. Advances in computing are now bringing IoT-based surveillance into the realm of possibility. The potential advantages of IoT data include high-frequency, high volume, zero effort data collection methods, with a potential to have syndromic surveillance. In contrast, the critical challenges to mainstream this data source within surveillance systems are the huge volume and variety of data, fusing data from multiple devices to produce a unified result, and the lack of multidisciplinary professionals to understand the domain and analyze the domain data accordingly.
BMJ, Volume 374; https://doi.org/10.1136/bmj.n2126
Published: 21 August 2021
Introduction To better assess the epidemiological situation of acute respiratory illness in Germany over time, we used emergency department data for syndromic surveillance before and during the COVID-19 pandemic. Methods We included routine attendance data from emergency departments who continuously transferred data between week 10-2017 and 10-2021, with ICD-10 codes available for >75% of the attendances. Case definitions for acute respiratory illness (ARI), severe ARI (SARI), influenza-like illness (ILI), respiratory syncytial virus disease (RSV) and Coronavirus disease 2019 (COVID-19) were based on a combination of ICD-10 codes, and/or chief complaints, sometimes combined with information on hospitalisation and age. Results We included 1,372,958 attendances from eight emergency departments. The number of attendances dropped in March 2020, increased during summer, and declined again during the resurge of COVID-19 cases in autumn and winter of 2020/2021. A pattern of seasonality of acute respiratory infections could be observed. By using different case definitions (i.e. for ARI, SARI, ILI, RSV) both the annual influenza seasons in the years 2017-2020 and the dynamics of the COVID-19 pandemic in 2020-2021 were apparent. The absence of a flu season during the fall and winter of 2020/2021 was visible, in parallel to the resurge of COVID-19 cases. The proportion of SARI among ARI cases peaked in April-May 2020 and November 2020-January 2021. Conclusion Syndromic surveillance using routine emergency department data has the potential to monitor the trends, timing, duration, magnitude and severity of illness caused by respiratory viruses, including both influenza and SARS-CoV-2.
Scientific Reports, Volume 11, pp 1-10; https://doi.org/10.1038/s41598-021-95658-4
Comparison of COVID-19 trends in space and over time is essential to monitor the pandemic and to indirectly evaluate non-pharmacological policies aimed at reducing the burden of disease. Given the specific age- and sex- distribution of COVID-19 mortality, the underlying sex- and age-distribution of populations need to be accounted for. The aim of this paper is to present a method for monitoring trends of COVID-19 using adjusted mortality trend ratios (AMTRs). Age- and sex-mortality distribution of a reference European population (N = 14,086) was used to calculate age- and sex-specific mortality rates. These were applied to each country to calculate the expected deaths. Adjusted Mortality Trend Ratios (AMTRs) with 95% confidence intervals (C.I.) were calculated for selected European countries on a daily basis from 17th March 2020 to 29th April 2021 by dividing observed cumulative mortality, by expected mortality, times the crude mortality of the reference population. These estimated the sex- and age-adjusted mortality for COVID-19 per million population in each country. United Kingdom experienced the highest number of COVID-19 related death in Europe. Crude mortality rates were highest Hungary, Czech Republic, and Luxembourg. Accounting for the age-and sex-distribution of the underlying populations with AMTRs for each European country, four different patterns were identified: countries which experienced a two-wave pandemic, countries with almost undetectable first wave, but with either a fast or a slow increase of mortality during the second wave; countries with consistently low rates throughout the period. AMTRs were highest in Eastern European countries (Hungary, Czech Republic, Slovakia, and Poland). Our methods allow a fair comparison of mortality in space and over time. These might be of use to indirectly estimating the efficacy of non-pharmacological health policies. The authors urge the World Health Organisation, given the absence of age and sex-specific mortality data for direct standardisation, to adopt this method to estimate the comparative mortality from COVID-19 pandemic worldwide.
Frontiers in Public Health, Volume 9; https://doi.org/10.3389/fpubh.2021.645260
Background: Digital data sources have become ubiquitous in modern culture in the era of digital technology but often tend to be under-researched because of restricted access to data sources due to fragmentation, privacy issues, or industry ownership, and the methodological complexity of demonstrating their measurable impact on human health. Even though new big data sources have shown unprecedented potential for disease diagnosis and outbreak detection, we need to investigate results in the existing literature to gain a comprehensive understanding of their impact on and benefits to human health.Objective: A systematic review of systematic reviews on identifying digital data sources and their impact area on people's health, including challenges, opportunities, and good practices.Methods: A multidatabase search was performed. Peer-reviewed papers published between January 2010 and November 2020 relevant to digital data sources on health were extracted, assessed, and reviewed.Results: The 64 reviews are covered by three domains, that is, universal health coverage (UHC), public health emergencies, and healthier populations, defined in WHO's General Programme of Work, 2019–2023, and the European Programme of Work, 2020–2025. In all three categories, social media platforms are the most popular digital data source, accounting for 47% (N = 8), 84% (N = 11), and 76% (N = 26) of studies, respectively. The second most utilized data source are electronic health records (EHRs) (N = 13), followed by websites (N = 7) and mass media (N = 5). In all three categories, the most studied impact of digital data sources is on prevention, management, and intervention of diseases (N = 40), and as a tool, there are also many studies (N = 10) on early warning systems for infectious diseases. However, they could also pose health hazards (N = 13), for instance, by exacerbating mental health issues and promoting smoking and drinking behavior among young people.Conclusions: The digital data sources presented are essential for collecting and mining information about human health. The key impact of social media, electronic health records, and websites is in the area of infectious diseases and early warning systems, and in the area of personal health, that is, on mental health and smoking and drinking prevention. However, further research is required to address privacy, trust, transparency, and interoperability to leverage the potential of data held in multiple datastores and systems. This study also identified the apparent gap in systematic reviews investigating the novel big data streams, Internet of Things (IoT) data streams, and sensor, mobile, and GPS data researched using artificial intelligence, complex network, and other computer science methods, as in this domain systematic reviews are not common.
Published: 24 March 2021
Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz, Volume 64, pp 412-417; https://doi.org/10.1007/s00103-021-03300-5
Zusammenfassung: Echtzeitdaten 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.
Frontiers in Veterinary Science, Volume 8; https://doi.org/10.3389/fvets.2021.633977
The biggest change brought about by the “era of big data” to health in general, and epidemiology in particular, relates arguably not to the volume of data encountered, but to its variety. An increasing number of new data sources, including many not originally collected for health purposes, are now being used for epidemiological inference and contextualization. Combining evidence from multiple data sources presents significant challenges, but discussions around this subject often confuse issues of data access and privacy, with the actual technical challenges of data integration and interoperability. We review some of the opportunities for connecting data, generating information, and supporting decision-making across the increasingly complex “variety” dimension of data in population health, to enable data-driven surveillance to go beyond simple signal detection and support an expanded set of surveillance goals.
Published: 24 February 2021
Journal of the American Medical Informatics Association, Volume 28, pp 1363-1373; https://doi.org/10.1093/jamia/ocab004
Objective We sought to support public health surveillance and response to coronavirus disease 2019 (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. Conclusions 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.
Published: 1 November 2020
Journal: American journal of public health
American journal of public health, Volume 110, pp 1614-1614; https://doi.org/10.2105/ajph.2020.305907
Journal of Medical Internet Research, Volume 22; https://doi.org/10.2196/20924
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.
Epidemiologia, Volume 1, pp 2-4; https://doi.org/10.3390/epidemiologia1010002
The creation of new journal about epidemiology is a good opportunity to think about the state of the field and to make proposals for its development
BMJ, Volume 370; https://doi.org/10.1136/bmj.m2731
This week, 13 June The debate on how to use mathematical simulations to predict the impact of covid-19 on population health and healthcare needs is ongoing.1 While some researchers have predicted a huge number of deaths and overwhelmed healthcare systems,2 others have forecast a much smaller burden.3 Now, predictions aim to estimate the probability and the scale of a second wave.1 Many prediction …