Medicine. Big data meets public health

Artificial intelligence and the impact of multiomics on the reporting of case reports

The integration of artificial intelligence (AI) and multiomics has transformed clinical and life sciences, enabling precision medicine and redefining disease understanding. Scientific publications grew significantly from 2.1 million in 2012 to 3.3 million in 2022, with AI research tripling during this period. Multiomics fields, including genomics and proteomics, also advanced, exemplified by the Human Proteome Project achieving a 90% complete blueprint by 2021. This growth highlights opportunities and challenges in integrating AI and multiomics into clinical reporting. A review of studies and case reports was conducted to evaluate AI and multiomics integration. Key areas analyzed included diagnostic accuracy, predictive modeling, and personalized treatment approaches driven by AI tools. Case examples were studied to assess impacts on clinical decision-making. AI and multiomics enhanced data integration, predictive insights, and treatment personalization. Fields like radiomics, genomics, and proteomics improved diagnostics and guided therapy. For instance, the "AI radiomics, genomics, oncopathomics, and surgomics project" combined radiomics and genomics for surgical decision-making, enabling preoperative, intraoperative, and postoperative interventions. AI applications in case reports predicted conditions like postoperative delirium and monitored cancer progression using genomic and imaging data. AI and multiomics enable standardized data analysis, dynamic updates, and predictive modeling in case reports. Traditional reports often lack objectivity, but AI enhances reproducibility and decision-making by processing large datasets. Challenges include data standardization, biases, and ethical concerns. Overcoming these barriers is vital for optimizing AI applications and advancing personalized medicine. AI and multiomics integration is revolutionizing clinical research and practice. Standardizing data reporting and addressing challenges in ethics and data quality will unlock their full potential. Emphasizing collaboration and transparency is essential for leveraging these tools to improve patient care and scientific communication.

Human Plasma Proteomics Links Modifiable Lifestyle Exposome to Disease Risk

Environmental exposures influence disease risk, yet their underlying biological mechanisms remain poorly understood. We present the Human Exposomic Architecture of the Proteome (HEAP), a framework and resource integrating genetic, exposomic, and proteomic data to uncover how lifestyle influences disease through plasma proteins. Applying HEAP to 2,686 proteins in 53,014 UK Biobank participants, we identified over 11,000 exposure-protein associations across 135 lifestyle exposures. Exposures explained a substantial portion of proteomic variation, with 9% of proteins more influenced by lifestyle than genetics. Mediation analyses across 270 diseases revealed proteins linking exposures to disease risk; for instance, IGFBP1 and IGFBP2 mediated the effects of exercise and diet on type 2 diabetes. These findings were supported by concordant proteomic shifts in interventional studies of exercise and GLP1 agonists, underscoring therapeutic relevance. HEAP provides a resource for advancing disease prevention and precision medicine by revealing mechanisms through which lifestyle shapes human health.

Statistical Methods for Baseline Adjustment and Cohort Analysis in Korean National Health Insurance Claims Data: A Review of PSM, IPTW, and Survival Analysis With Future Directions

The utilization of health insurance claims data has expanded significantly, enabling researchers to conduct epidemiological studies on a large scale. This review examines key statistical methods for addressing baseline differences and conducting cohort analyses using Korean National Health Insurance claims data. Propensity score matching and inverse probability of treatment weighting are widely used to mitigate selection bias and enhance causal inference in observational studies. These methods help improve study validity by balancing covariates between treatment and control groups. Additionally, survival analysis techniques, such as the Cox proportional hazards model, are essential for assessing time-to-event outcomes and estimating hazard ratios while accounting for censoring. However, the application of these statistical methods is accompanied by challenges, including unmeasured confounding, instability in weight estimation, and violations of model assumptions. To address these limitations, emerging approaches, such as Doubly robust estimation, machine learning-based causal inference, and the marginal structural model, have gained prominence. These techniques offer greater flexibility and robustness in real-world data analysis. Future research should focus on refining methodologies for integrating high-dimensional health datasets and leveraging artificial intelligence to enhance predictive modeling and causal inference. Furthermore, the expansion of international collaborations and the adoption of standardized data models will facilitate large-scale multi-center studies. Ethical considerations, including data privacy and algorithmic transparency, should also be prioritized to ensure responsible data use. Maximizing the utility of health insurance claims data requires interdisciplinary collaboration, methodological advancements, and the implementation of rigorous statistical techniques to support evidence-based healthcare policy and improve public health outcomes.

Perspectives in cattle reproduction for the next 20 years - A European context

Following a significant increase in herd and farm sizes after the removal of milk quotas in Europe, the past 10 years have seen a slight yet steady decline in the population of cattle. This includes a reduction of approximately 5 % in dairy and beef cattle. This trend is driven by various factors, such as changing market demands, economic shifts, and sustainability challenges in the livestock sector. Despite this, technological advancements in reproductive management have continued to enhance efficiency and sustainability, particularly in dairy production. The main areas of rapid development, which will continue to grow for improving fertility and management, include: i) genetic selection (including improved phenotypes for use in breeding programs), ii) nutritional management (including transition cow management), iii) control of infectious disease, iv) rapid diagnostics of reproductive health, v) development of more efficient ovulation/estrous synchronization protocols, vi) assisted reproductive management (and automated systems to improve reproductive management), vii) increased implementation of sexed semen and embryo transfer, viii) more efficient handling of substantial volumes of data, ix) routine implementation of artificial intelligence technology for rapid decision-making at the farm level, x) climate change and sustainable cattle production awareness, xi) new (reproductive) strategies to improve cattle welfare, and xii) improved management and technology implementation for male fertility. This review addresses the current status and future outlook of key factors that influence cattle herd health and reproductive performance, with a special focus on dairy cattle. These insights are expected to contribute to improved performance, health, and fertility of ruminants in the next 20 years.

Promoting appropriate medication use by leveraging medical big data

According to World Health Organization statistics, inappropriate medication has become an important factor affecting the safety of rational medication. In the gray area of medical insurance supervision, such as designated drugstores and medical institutions, there are lots of inappropriate medication phenomena regarding "big prescription for minor ailments." A traditional clinical decision support system is mostly based on established rules to regulate inappropriate prescriptions, which are not suitable for clinical environments and require intelligent review. In this study, we model the complex relationships between patients, diseases, and drugs based on medical big data to promote appropriate medication use. More specifically, we first construct the medication knowledge graph based on the historical prescription big data of tertiary hospitals and medical text data. Second, based on the medication knowledge graph, we employ a Gaussian mixture model to group patient population representation as physiological features. For diagnostic features, we employ pre-training word vector Bidirectional Encoder Representations from Transformers to enhance the semantic representation between diagnoses. In addition, to reduce adverse drug interactions caused by drug combinations, we employ a graph convolution network to transform drug interaction information into drug interaction features. Finally, we employ the sequence generation model to learn the complex relationships between patients, diseases, and drugs and provide an appropriate medication evaluation for doctor prescriptions in small hospitals from two aspects: drug list and medication course of treatment. In this study, we utilize the MIMIC III dataset alongside data from a tertiary hospital in Fujian Province to validate our model. The results show that our method is more effective than other baseline methods in the accuracy of the medication regimen prediction of rational medication. In addition, it achieved high accuracy in the appropriate medication detection of prescription in small hospitals.

Incorporating connectivity among Internet search data for enhanced influenza-like illness tracking

Big data collected from the Internet possess great potential to reveal the ever-changing trends in society. In particular, accurate infectious disease tracking with Internet data has grown in popularity, providing invaluable information for public health decision makers and the general public. However, much of the complex connectivity among the Internet search data is not effectively addressed among existing disease tracking frameworks. To this end, we propose ARGO-C (Augmented Regression with Clustered GOogle data), an integrative, statistically principled approach that incorporates the clustering structure of Internet search data to enhance the accuracy and interpretability of disease tracking. Focusing on multi-resolution %ILI (influenza-like illness) tracking, we demonstrate the improved performance and robustness of ARGO-C over benchmark methods at various geographical resolutions. We also highlight the adaptability of ARGO-C to track various diseases in addition to influenza, and to track other social or economic trends.

Artificial intelligence empowering public health education: prospects and challenges

Artificial Intelligence (AI) is revolutionizing public health education through its capacity for intricate analysis of large-scale health datasets and the tailored dissemination of health-related information and interventions. This article conducts a profound exploration into the integration of AI within public health, accentuating its scientific foundations, prospective progress, and practical application scenarios. It underscores the transformative potential of AI in crafting individualized educational programs, developing sophisticated behavioral models, and informing the creation of health policies. The manuscript strives to thoroughly evaluate the extant landscape of AI applications in public health, scrutinizing critical challenges such as the propensity for data bias and the imperative of safeguarding privacy. By dissecting these issues, the article contributes to the conversation on how AI can be harnessed responsibly and effectively, ensuring that its application in public health education is both ethically grounded and equitable. The paper's significance is multifold: it aims to provide a blueprint for policy formulation, offer actionable insights for public health authorities, and catalyze the progression of health interventions toward increasingly sophisticated and precise approaches. Ultimately, this research anticipates fostering an environment where AI not only augments public health education but also does so with a steadfast commitment to the principles of justice and inclusivity, thereby elevating the standard and reach of health education initiatives globally.

Supervised machine learning for the prediction of post-operative clinical outcomes of hip and knee replacements: a review

Prediction models are being increasingly used in the medical field to identify risk factors and possible outcomes. Some of these are presently being used to develop guidelines for improving clinical practice. The application of machine learning (ML), comprising a powerful set of computational tools for analysing data, has been clearly expanding in the role of predictive modelling. This paper reviews the latest developments of supervised ML techniques that have been used to analyse data related to post-operative total hip and knee replacements. The aim was to review the most recent findings of relevant published studies by outlining the methodologies employed (most-widely used supervised ML techniques), data sources, domains, limitations of predictive analytics and the quality of predictions.

Prognostic subgroups of chronic pain patients using latent variable mixture modeling within a supervised machine learning framework

The present study combined a supervised machine learning framework with an unsupervised method, finite mixture modeling, to identify prognostically meaningful subgroups of diverse chronic pain patients undergoing interdisciplinary treatment. Questionnaire data collected at pre-treatment and 1-year follow up from 11,995 patients from the Swedish Quality Registry for Pain Rehabilitation were used. Indicators measuring pain characteristics, psychological aspects, and social functioning and general health status were used to form subgroups, and pain interference at follow-up was used for the selection and the performance evaluation of models. A nested cross-validation procedure was used for determining the number of classes (inner cross-validation) and the prediction accuracy of the selected model among unseen cases (outer cross-validation). A four-class solution was identified as the optimal model. Identified subgroups were separable on indicators, predictive of long-term outcomes, and related to background characteristics. Results are discussed in relation to previous clustering attempts of patients with diverse chronic pain conditions. Our analytical approach, as the first to combine mixture modeling with supervised, targeted learning, provides a promising framework that can be further extended and optimized for improving accurate prognosis in pain treatment and identifying clinically meaningful subgroups among chronic pain patients.

Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities

The global prevalence of obesity presents a pressing challenge to public health and healthcare systems, necessitating accurate prediction and understanding for effective prevention and management strategies. This article addresses the need for improved obesity prediction models by conducting a comprehensive analysis of existing machine learning (ML) and deep learning (DL) approaches. This study introduces a novel hybrid model, Attention-based Bi-LSTM (ABi-LSTM), which integrates attention mechanisms with bidirectional Long Short-Term Memory (Bi-LSTM) networks to enhance interpretability and performance in obesity prediction. Our study fills a crucial gap by bridging healthcare and urban planning domains, offering insights into data-driven approaches to promote healthier living within urban environments. The proposed ABi-LSTM model demonstrates exceptional performance, achieving a remarkable accuracy of 96.5% in predicting obesity levels. Comparative analysis showcases its superiority over conventional approaches, with superior precision, recall, and overall classification balance. This study highlights significant advancements in predictive accuracy and positions the ABi-LSTM model as a pioneering solution for accurate obesity prognosis. The implications extend beyond healthcare, offering a precise tool to address the global obesity epidemic and foster sustainable development in smart cities.

Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review

The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.

Survey of Public Attitudes toward the Secondary Use of Public Healthcare Data in Korea

OBJECTIVES

Public healthcare data have become crucial to the advancement of medicine, and recent changes in legal structure on privacy protection have expanded access to these data with pseudonymization. Recent debates on public healthcare data use by private insurance companies have shown large discrepancies in perceptions among the general public, healthcare professionals, private companies, and lawmakers. This study examined public attitudes toward the secondary use of public data, focusing on differences between public and private entities.

METHODS

An online survey was conducted from January 11 to 24, 2022, involving a random sample of adults between 19 and 65 of age in 17 provinces, guided by the August 2021 census.

RESULTS

The final survey analysis included 1,370 participants. Most participants were aware of health data collection (72.5%) and recent changes in legal structures (61.4%) but were reluctant to share their pseudonymized raw data (51.8%). Overall, they were favorable toward data use by public agencies but disfavored use by private entities, notably marketing and private insurance companies. Concerns were frequently noted regarding commercial use of data and data breaches. Among the respondents, 50.9% were negative about the use of public healthcare data by private insurance companies, 22.9% favored this use, and 1.9% were "very positive."

CONCLUSIONS

This survey revealed a low understanding among key stakeholders regarding digital health data use, which is hindering the realization of the full potential of public healthcare data. This survey provides a basis for future policy developments and advocacy for the secondary use of health data.

Epistemic Rights and Responsibilities of Digital Simulacra for Biomedicine

Big data and AI have enabled digital simulation for prediction of future health states or behaviors of specific individuals, populations or humans in general. "Digital simulacra" use multimodal datasets to develop computational models that are virtual representations of people or groups, generating predictions of how systems evolve and react to interventions over time. These include digital twins and virtual patients for in silico clinical trials, both of which seek to transform research and health care by speeding innovation and bridging the epistemic gap between population-based research findings and their application to the individual. Nevertheless, digital simulacra mark a major milestone on a trajectory to embrace the epistemic culture of data science and a potential abandonment of medical epistemological concepts of causality and representation. In doing so, "data first" approaches potentially shift moral attention from actual patients and principles, such as equity, to simulated patients and patient data.

Conceptual modelling for life sciences based on systemist foundations

BACKGROUND

All aspects of our society, including the life sciences, need a mechanism for people working within them to represent the concepts they employ to carry out their research. For the information systems being designed and developed to support researchers and scientists in conducting their work, conceptual models of the relevant domains are usually designed as both blueprints for a system being developed and as a means of communication between the designer and developer. Most conceptual modelling concepts are generic in the sense that they are applied with the same understanding across many applications. Problems in the life sciences, however, are especially complex and important, because they deal with humans, their well-being, and their interactions with the environment as well as other organisms.

RESULTS

This work proposes a "systemist" perspective for creating a conceptual model of a life scientist's problem. We introduce the notion of a system and then show how it can be applied to the development of an information system for handling genomic-related information. We extend our discussion to show how the proposed systemist perspective can support the modelling of precision medicine.

CONCLUSION

This research recognizes challenges in life sciences research of how to model problems to better represent the connections between physical and digital worlds. We propose a new notation that explicitly incorporates systemist thinking, as well as the components of systems based on recent ontological foundations. The new notation captures important semantics in the domain of life sciences. It may be used to facilitate understanding, communication and problem-solving more broadly. We also provide a precise, sound, ontologically supported characterization of the term "system," as a basic construct for conceptual modelling in life sciences.

Ethics Principles for Artificial Intelligence-Based Telemedicine for Public Health

The use of artificial intelligence (AI) in the field of telemedicine has grown exponentially over the past decade, along with the adoption of AI-based telemedicine to support public health systems. Although AI-based telemedicine can open up novel opportunities for the delivery of clinical health and care and become a strong aid to public health systems worldwide, it also comes with ethical risks that should be detected, prevented, or mitigated for the responsible use of AI-based telemedicine in and for public health. However, despite the current proliferation of AI ethics frameworks, thus far, none have been developed for the design of AI-based telemedicine, especially for the adoption of AI-based telemedicine in and for public health. We aimed to fill this gap by mapping the most relevant AI ethics principles for AI-based telemedicine for public health and by showing the need to revise them via major ethical themes emerging from bioethics, medical ethics, and public health ethics toward the definition of a unified set of 6 AI ethics principles for the implementation of AI-based telemedicine. (Am J Public Health. 2023;113(5):577-584. https://doi.org/10.2105/AJPH.2023.307225).

Current ethical and social issues in epidemiology

PURPOSE

The American College of Epidemiology held its 2021 Annual Meeting virtually, September 8-10, with a conference theme of 'From Womb to Tomb: Insights from Health Emergencies'. The American College of Epidemiology Ethics Committee hosted a symposium session in recognition of the ethical and social challenges brought to light by the coronavirus disease 2019 pandemic and on the occasion of the publication of the third edition of the classic text, Ethics and Epidemiology. The American College of Epidemiology Ethics Committee invited the book editor and contributing authors to present at the symposium session titled 'Current Ethical and Social Issues in Epidemiology.' The purpose of this paper is to further highlight the ethical challenges and presentations.

METHODS

Three speakers with expertise in ethics, health law, health policy, global health, health information technology, and translational research in epidemiology and public health were selected to present on the social and ethical issues in the current landscape. Dr. S Coughlin presented on the 'Ethical and Social Issues in Epidemiology', Dr. L Beskow presented on 'Ethical Challenges in Genetic Epidemiology', and Dr. K Goodman presented on the 'Ethics of Health Informatics'.

RESULTS

New digital sources of data and technologies are driving the ethical challenges and opportunities in epidemiology and public health as it relates to the three emerging topic areas identified: (1) digital epidemiology, (2) genetic epidemiology, and (3) health informatics. New complexities such as the reliance on social media to control infectious disease outbreaks and the introduction of computing advancements are requiring re-evaluation of traditional bioethical frameworks for epidemiology research and public health practice. We identified several cross-cutting ethical and social issues related to informed consent, benefits, risks and harms, and privacy and confidentiality and summarized these alongside more nuanced ethical considerations such as algorithmic bias, group harms related to data (mis)representation, risks of misinformation, return of genomic research results, maintaining data security, and data sharing. We offered an integrated synthesis of the stages of epidemiology research planning and conduct with the ethical issues that are most relevant in these emerging topic areas.

CONCLUSIONS

New realities exist for epidemiology and public health as professional groups who are faced with addressing population health, and especially given the recent pandemic and the widespread use of digital tools and technologies. Many ethical issues can be understood in the context of existing ethical frameworks; however, they have yet to be clearly identified or connected with the new technical and methodological applications of digital tools and technologies currently in use for epidemiology research and public health practice. To address current ethical challenges, we offered a synthesis of traditional ethical principles in public health science alongside more nuanced ethical considerations for emerging technologies and aligned these with lifecycle stages of epidemiology research. By critically reflecting on the impact of new digital sources of data and technologies on epidemiology research and public health practice, specifically in the control of infectious outbreaks, we offered insights on cultivating these new areas of professional growth while striving to improve population health.

Seeing Beyond the Here and Now: How Corporate Purpose Combats Corporate Myopia

Corporations are currently confronting major, interlocking crises, including climate change, biodiversity loss, inequalities, and social isolation. When under threat, executives tend to focus inward and on the short term. This is particularly unfortunate because it is in such crises that executives need to see beyond the here and now in order to ride the storms. In this paper, we argue that corporate purpose helps organizations fight such myopia and offer four mechanisms through which this works: exposing new insights, seeing issues holistically, helping to sustain focus, and bringing unity and direction.

Funding: The authors acknowledge the generous funding from the Social Sciences and Humanities Council of Canada [Grant 895-2015-0026] that contributed to the broader project in which these ideas were generated.

Applications of artificial intelligence multiomics in precision oncology

Cancer is the second leading worldwide disease that depends on oncogenic mutations and non-mutated genes for survival. Recent advancements in next-generation sequencing (NGS) have transformed the health care sector with big data and machine learning (ML) approaches. NGS data are able to detect the abnormalities and mutations in the oncogenes. These multi-omics analyses are used for risk prediction, early diagnosis, accurate prognosis, and identification of biomarkers in cancer patients. The availability of these cancer data and their analysis may provide insights into the biology of the disease, which can be used for the personalized treatment of cancer patients. Bioinformatics tools are delivering this promise by managing, integrating, and analyzing these complex datasets. The clinical outcomes of cancer patients are improved by the use of various innovative methods implicated particularly for diagnosis and therapeutics. ML-based artificial intelligence (AI) applications are solving these issues to a great extent. AI techniques are used to update the patients on a personalized basis about their treatment procedures, progress, recovery, therapies used, dietary changes in lifestyles patterns along with the survival summary of previously recovered cancer patients. In this way, the patients are becoming more aware of their diseases and the entire clinical treatment procedures. Though the technology has its own advantages and disadvantages, we hope that the day is not so far when AI techniques will provide personalized treatment to cancer patients tailored to their needs in much quicker ways.

Machine learning based regional epidemic transmission risks precaution in digital society

The contact and interaction of human is considered to be one of the important factors affecting the epidemic transmission, and it is critical to model the heterogeneity of individual activities in epidemiological risk assessment. In digital society, massive data makes it possible to implement this idea on large scale. Here, we use the mobile phone signaling to track the users' trajectories and construct contact network to describe the topology of daily contact between individuals dynamically. We show the spatiotemporal contact features of about 7.5 million mobile phone users during the outbreak of COVID-19 in Shanghai, China. Furthermore, the individual feature matrix extracted from contact network enables us to carry out the extreme event learning and predict the regional transmission risk, which can be further decomposed into the risk due to the inflow of people from epidemic hot zones and the risk due to people close contacts within the observing area. This method is much more flexible and adaptive, and can be taken as one of the epidemic precautions before the large-scale outbreak with high efficiency and low cost.

Aggressive measures, rising inequalities, and mass formation during the COVID-19 crisis: An overview and proposed way forward

A series of aggressive restrictive measures were adopted around the world in 2020-2022 to attempt to prevent SARS-CoV-2 from spreading. However, it has become increasingly clear the most aggressive (lockdown) response strategies may involve negative side-effects such as a steep increase in poverty, hunger, and inequalities. Several economic, educational, and health repercussions have fallen disproportionately on children, students, young workers, and especially on groups with pre-existing inequalities such as low-income families, ethnic minorities, and women. This has led to a vicious cycle of rising inequalities and health issues. For example, educational and financial security decreased along with rising unemployment and loss of life purpose. Domestic violence surged due to dysfunctional families being forced to spend more time with each other. In the current narrative and scoping review, we describe macro-dynamics that are taking place because of aggressive public health policies and psychological tactics to influence public behavior, such as mass formation and crowd behavior. Coupled with the effect of inequalities, we describe how these factors can interact toward aggravating ripple effects. In light of evidence regarding the health, economic and social costs, that likely far outweigh potential benefits, the authors suggest that, first, where applicable, aggressive lockdown policies should be reversed and their re-adoption in the future should be avoided. If measures are needed, these should be non-disruptive. Second, it is important to assess dispassionately the damage done by aggressive measures and offer ways to alleviate the burden and long-term effects. Third, the structures in place that have led to counterproductive policies should be assessed and ways should be sought to optimize decision-making, such as counteracting groupthink and increasing the level of reflexivity. Finally, a package of scalable positive psychology interventions is suggested to counteract the damage done and improve humanity's prospects.

Big data from customers and non-customers through crowdsourcing, citizen science and crowdfunding

Purpose

The unprecedented growth in the volume, variety and velocity with which data is generated and collected over the last decade has led to the spread of big data phenomenon. Organizations have become increasingly involved in the collection and analysis of big data to improve their performance. Whereas the focus thus far has mainly been on big data collected from customers, the topic of how to collect data also from those who are not yet customers has been overlooked. A growing means of interacting with non-customers is through crowd-based phenomena, which are therefore examined in this study as a way to further collect big data. Therefore, this study aims to demonstrate the importance of jointly considering these phenomena under the proposed framework.

Design/methodology/approach

This study seeks to demonstrate that organizations can collect big data from a crowd of customers and non-customers through crowd-based phenomena such as crowdsourcing, citizen science and crowdfunding. The conceptual analysis conducted in this study produced an integrated framework through which companies can improve their performance.

Findings

Grounded in the resource-based view, this paper argues that non-customers can constitute a valuable resource insofar as they can be an additional source of big data when participating in crowd-based phenomena. Companies can, in this way, further improve their performance.

Originality/value

This study advances scientific knowledge of big data and crowd-based phenomena by providing an overview of how they can be jointly applied to further benefit organizations. Moreover, the framework posited in this study is an endeavour to stimulate further analyses of these topics and provide initial suggestions on how organizations can jointly leverage crowd-based phenomena and big data.

A Study on Epidemic Information Screening, Prevention and Control of Public Opinion Based on Health and Medical Big Data: A Case Study of COVID-19

The outbreak of the coronavirus disease 2019 (COVID-19) represents an alert for epidemic prevention and control in public health. Offline anti-epidemic work is the main battlefield of epidemic prevention and control. However, online epidemic information prevention and control cannot be ignored. The aim of this study was to identify reliable information sources and false epidemic information, as well as early warnings of public opinion about epidemic information that may affect social stability and endanger the people's lives and property. Based on the analysis of health and medical big data, epidemic information screening and public opinion prevention and control research were decomposed into two modules. Eight characteristics were extracted from the four levels of coarse granularity, fine granularity, emotional tendency, and publisher behavior, and another regulatory feature was added, to build a false epidemic information identification model. Five early warning indicators of public opinion were selected from the macro level and the micro level to construct the early warning model of public opinion about epidemic information. Finally, an empirical analysis on COVID-19 information was conducted using big data analysis technology.

Traceable machine learning real-time quality control based on patient data

Objectives

Patient-based real-time quality control (PBRTQC) has gained attention as an alternative/integrative tool for internal quality control (iQC). However, it is still doubted for its performance and its application in real clinical settings. We aim to generate a newly and easy-to-access patient-based real-time QC by machine learning (ML) traceable to standard reference data with assigned values by National Institute of Metrology of China (NIM), and to compare it with PBRTQC for clinical validity evaluation.

Methods

For five representative biochemistry analytes, 1,195 000 patient testing results each were collected. After data processing, independent training and test sets were divided. Machine learning internal quality control (MLiQC) was set up by Random Forest in ML and was validated by way of both metrology algorithm traceability and 4 PBRTQC methods recommended by IFCC analytical working group.

Results

MLiQC were established. As an example of albumin (ALB) at the critical bias, the uncertainty of MLiQC was 0.14%, which was evaluated by standard reference data produced by NIM. Compared with four optimal PBRTQC methods at critical bias, the average of the number of patient samples from a bias introduced until detected (ANPed) of MLiQC averagely decreased from 600 to 20. The median and 95 quantiles of NPeds (MNPed and 95NPed) of MLiQC were superior to all optimal PBRTQCs above 90% for all test items.

Conclusions

MLiQC is highly superior to PBRTQC and well-suited in real settings. The validation of the model from two aspects of algorithm traceability and clinical effectiveness confirms its satisfactory performance.

Research on adaption to air pollution in Chinese cities: Evidence from social media-based health sensing

Air pollution seriously threats to human health. Understanding the health effects of air pollution is of great importance for developing countermeasures. However, little is known about the real-time impacts of air pollution on the human heath in a comprehensive way in developing nations, like China. To fill this research gap, the Chinese urbanites' health were sensed from more than 210.82 million Weibo (Chinese Twitter) data in 2017. The association between air pollution and the health sensing were quantified through generalized additive models, based on which the sensitivities and adaptions to air pollution in 70 China's cities were assessed. The results documented that the Weibo data can well sense urbanites' health in real time. With the different geographical characteristics and socio-economic conditions, the Chinese residents have adaption to air pollution, indicated by the spatial heterogeneity of the sensitivities to air pollution. Cities with good air quality in South China and East China were more sensitive to air pollution, while cities with worse air quality in Northwest China and North China were less sensitive. This research provides a new perspective and methodologies for health sensing and the health effect of air pollution.

Digital Health Solutions and State of Interoperability: Landscape Analysis of Sierra Leone

Background

The government and partners have invested heavily in the health information system (HIS) for service delivery, surveillance, reporting, and monitoring. Sierra Leone’s government launched its first digital health strategy in 2018. In 2019, a broader national innovation and digital strategy was launched. The health pillar direction will use big data and artificial intelligence (AI) to improve health care in general and maternal and child health in particular. Understanding the number, distribution, and interoperability of digital health solutions is crucial for successful implementation strategies.

Objective

This paper presents the state of digital health solutions in Sierra Leone and how these solutions currently interoperate. This study further presents opportunities for big data and AI applications.

Methods

All the district health management teams, all digital health implementing organizations, and a stratified sample of 72 (out of 1284) health facilities were purposefully selected from all health districts and surveyed.

Results

The National Health Management Information System’s (NHMIS’s) aggregate reporting solution populated by health facility forms HF1 to HF9 was, by far, the most used tool. A health facility–based weekly aggregate electronic integrated disease surveillance and response solution was also widely used. Half of the health facilities had more than 2 digital health solutions in use. The different digital health software solutions do not share data among one another, though aggregate reporting data were sent as necessary. None of the respondents use any of the health care registries for patient, provider, health facility, or terminology identification.

Conclusions

Many digital health solutions are currently used at health facilities in Sierra Leone. The government can leverage current investment in HIS from surveillance and reporting for using big data and AI for care. The vision of using big data for health care is achievable if stakeholders prioritize individualized and longitudinal patient data exchange using agreed use cases from national strategies. This study has shown evidence of distribution, types, and scale of digital health solutions in health facilities and opportunities for leveraging big data to fill critical gaps necessary to achieve the national digital health vision.

A Literature Review of Big Data-Based Urban Park Research in Visitor Dimension

Urban parks provide multiple benefits to human well-being and human health. Big data provide new and powerful ways to study visitors’ feelings, activities in urban parks, and the effect they themselves have on urban parks. However, the term “big data” has been defined variably, and its applications on urban parks have so far been sporadic in research. Therefore, a comprehensive review of big data-based urban park research is much needed. The review aimed to summarize the big data-based urban park research in visitor dimension by a systematic review approach in combination with bibliometric and thematic analyses. The results showed that the number of publications of related articles has been increasing exponentially in recent years. Users’ days data is used most frequently in the big data-based urban park research, and the major analytical methods are of four types: sentiment analysis, statistical analysis, and spatial analysis. The major research topics of big data-based urban park research in visitor dimension include visitors’ behavior, visitors’ perception and visitors’ effect. Big data benefits urban park research by providing low-cost, timely information, a people-oriented perspective, and fine-grained site information. However, its accuracy is insufficient because of coordinate, keyword classification and different kinds of users. To move forward, future research should integrate multiple big data sources, expand the application, such as public health and human–nature interactions, and pay more attention to the big data use for overcoming pandemic. This review can help to understand the current situation of big data-based urban park research, and provide a reference for the studies of this topic in the future.

C.33https://orcid.org/0000-0001-7283-2541VeloEnkelejda24https://orcid.org/0000-0002-8963-6421ZittraCarina261Agencia de Salud Pública de Barcelona (ASPB), Plaça Lesseps 8 entresol, 08023, Barcelona, Spain2Andrija Stampar Teaching Institute of Public Health (ASTIPH), Mirogojska c. 16, 10 000, Zagreb, Croatia3Anticimex Spain (Anticimex), C/ Jesús Serra Santamans, 5, Planta 3, 08174, Sant Cugat del Vallès, Barcelona, Spain4Associació Mediambiental Xatrac (Xatrac), C/ Pius Font i Quer, S/N, 17310, Lloret de Mar, Girona, Spain5Austrian Agency for Health and Food Safety, Division for Public Health (AGES), Währinger Strasse 25a, 1090, Vienna, Austria6British Forces Cyprus, Joint Services Health Unit (JSHU), Cyprus7Center for Rickettsiosis and Arthropod-Borne Diseases, Hospital Universitario San Pedro-CIBIR (CRETAV-CIBIR), C/Piqueras 98, 3° planta, 26006, La Rioja, Spain8Centre for Monitoring of Vectors, National Reference Centre, Netherlands Food and Consumer Product Safety Authority (CMV-NVWA), Geertjesweg 15, 6706 EA, Wageningen, Netherlands9Ecodevelopment S.A. (ECODEV), Thesi Mezaria, PO Box 2420, 57010 Filyro, Greece10University of Zaragoza, Faculty of Veterinary Medicine of Zaragoza, Animal Health Department (UNIZAR), C/ Miguel Servet 177, 50013, Zaragoza, Spain11Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe), Viale dell’Università 10, 35020, Legnaro (Padua), Italy12National Institute of Health, Centre for Vectors and Infectious Diseases Research (INSA-CEVDI), Avenida Padre Cruz, 1649-016, Lisboa, Portugal13PHI Center for Public Health-Skopje (CPH), blv.3rd Macedonian brigade, no.18, Skopje, North Macedonia14Sapienza University, Department Public Health and Infectious Diseases (UNIROMA1), Piazzale Aldo Moro 5, 00198, Rome, Italy15Universidad Iberoamericana (UNIBE), Avenida Francia 129, 10203, Santo Domingo, Dominican Republic16University Balearic Islands, Applied Zoology and Animal Conservation Research Group (UIB), Ctra. Valldemossa km 7.5, 07122, Palma, Spain17University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca (USAMV-CN), Calea Mănăştur 3-5, Cluj-Napoca, 400372, Romania18University of Amsterdam, Department of Theoretical and Computational Ecology, Institute for Biodiversity and Ecosystem Dynamics (UvA), Science Park 904, 1098XH, Amsterdam, Netherlands19University of Applied Scieces and Arts of Southern Switzerland, Institute of Microbiology (SUPSI), Via Flora Ruchat-Roncati 15, 6850, Mendrisio Switzerland, Switzerland20University of Extremadura, Veterinary Faculty, Department of Animal Health (Uex), Av/ Universidad S/N 10003 Cáceres, Spain21University of Novi Sad, Faculty of Agriculture, Laboratory for Medical and Veterinary Entomology (UNSFA), Trg Dositeja Obradovića 8, 21000, Novi Sad, Serbia22University of Pécs (UP), Ifúság útja 6, 7624, Pécs, Hungary23University of Veterinary Medicine Vienna, Institute of Parasitology (Vetmeduni), Veterinärplatz 1, 1210, Vienna, Austria24Institute of Public Health, Department of Epidemiology and Control of Infectious Diseases, Vectors’ Control Unit (IPH), Str: “Aleksander Moisiu”, No. 80, Tirana, Albania25Universidad de Murcia, Departamento de Zoología y Antropología Física (UM), Campus de Espinardo, 30100 Murcia, Spain26University of Vienna, Department of Functional and Evolutionary Ecology (UNIVIE), Djerassiplatz 1, 1030, Vienna, Austria27Benaki Phytopathological Institute, Laboratory of Insects and Parasites of Medical Importance (BPI), 8, Stefanou Delta str., 14561 Kifissia, Athens, Greece28National Centre of Infectious and Parasitic Diseases (NCIPD), 26, Yanko Sakazov blvd., 1504, Sofia, Bulgaria29Ss. Cyril and Methodius University in Skopje, Faculty of Veterinary Medicine-Skopje (FVMS), Lazar Pop-Trajkov 5-7, 1000, Skopje, North Macedonia30Institute of Tropical Medicine, Department of Biomedical Sciences, Unit of Entomology (ITM), Nationalestraat 155, 2000, Antwerp, Belgium31Centre d’Estudis Avançats de Blanes (CEAB-CSIC), C/ d’accés a la Cala St. Francesc 14, 17300 Blanes, Girona, Spain32Universidad Cardenal Herrera CEU-CEU Universities, Facultad de Veterinaria, Veterinary Public Health and Food Science and Technology, Department of Animal Production and Health (PASAPTA), C/ Tirant lo Blanc, 7, 46115 Alfara del Patriarca, Valencia, Spain33Medical Entomology, UK Health Security Agency (UKHSA), Porton Down, Salisbury, SP4 0JG, United Kingdom34University of Primorska, Faculty of Mathematics, Natural Sciences and Information Technologies (UP FAMNIT), Glagoljaška ulica 8, 6000, Koper, Slovenia35Hacettepe University, Department of Biology, Ecology Section, Vector Ecology Research Group (HU-VERG), Hacettepe University, Beytepe Campus, 06800, Ankara, Turkey36Estación Biológica de Doñana, Departamento de Ecología de los Humedales (EBD-CSIC), Avda. Américo Vespucio 26, 41092, Sevilla, Spain37Centro de Educación Superior Hygiea (HYGIEA), Av. de Pablo VI, 9, 28223, Pozuelo de Alarcón, Madrid, Spain38Istituto Superiore di Sanità, Department of Infectious Diseases (ISS), Viale Regina Elena, 299, 00161, Roma, Italy39Museo di Scienze di Trento (MUSE), Corso del Lavoro e della Scienza, 3, 38122, Trento, Italy, Mosquito Alert Community. Mosquito alert: leveraging citizen science to create a GBIF mosquito occurrence dataset

The Mosquito Alert dataset includes occurrence records of adult mosquitoes collected worldwide in 2014-2020 through Mosquito Alert, a citizen science system for investigating and managing disease-carrying mosquitoes. Records are linked to citizen science-submitted photographs and validated by entomologists to determine the presence of five targeted European mosquito vectors: Aedes albopictus, Ae. aegypti, Ae. japonicus, Ae. koreicus, and Culex pipiens. Most records are from Spain, reflecting Spanish national and regional funding, but since autumn 2020 substantial records from other European countries are included, thanks to volunteer entomologists coordinated by the AIM-COST Action, and to technological developments to increase scalability. Among other applications, the Mosquito Alert dataset will help develop citizen science-based early warning systems for mosquito-borne disease risk. It can also be reused for modelling vector exposure risk, or to train machine-learning detection and classification routines on the linked images, to assist with data validation and establishing automated alert systems.

Clinical Network for Big Data and Personalized Health: Study Protocol and Preliminary Results

The use of secondary hospital-based clinical data and electronical health records (EHR) represent a cost-efficient alternative to investigate chronic conditions. We present the Clinical Network Big Data and Personalised Health project, which collects EHRs for patients accessing hospitals in Central-Southern Italy, through an integrated digital platform to create a digital hub for the collection, management and analysis of personal, clinical and environmental information for patients, associated with a biobank to perform multi-omic analyses. A total of 12,864 participants (61.7% women, mean age 52.6 ± 17.6 years) signed a written informed consent to allow access to their EHRs. The majority of hospital access was in obstetrics and gynaecology (36.3%), while the main reason for hospitalization was represented by diseases of the circulatory system (21.2%). Participants had a secondary education (63.5%), were mostly retired (25.45%), reported low levels of physical activity (59.6%), had low adherence to the Mediterranean diet and were smokers (30.2%). A large percentage (35.8%) were overweight and the prevalence of hypertension, diabetes and hyperlipidemia was 36.4%, 11.1% and 19.6%, respectively. Blood samples were retrieved for 8686 patients (67.5%). This project is aimed at creating a digital hub for the collection, management and analysis of personal, clinical, diagnostic and environmental information for patients, and is associated with a biobank to perform multi-omic analyses.

Food Sales and Adult Weight Status: Results of a Cross-Sectional Study in England

Ecological studies often use supermarket location as a proxy measure of the food environment. In this study, we used data on sales at a leading mainstream supermarket chain to explore how area-level supermarket use is associated with overweight and obesity in English adults. Sales data were aggregated to local authority level and joined to a national dataset describing self-reported height and weight and fruit and vegetable consumption. Regression models showed a modest association between higher levels of unhealthy food sales relative to health food sales and increased odds of being overweight and obese. Although effect sizes were small, they persisted after adjustment for area-level deprivation. Supermarket sales data provide additional understanding in the study of food environments and their impact on increasing weight status. Future health policies should consider using ‘big data’ combined with other research methods to address the increasing consumption of unhealthy and highly processed foods.

Infectious diseases prevention and control using an integrated health big data system in China

BACKGROUND

The Yinzhou Center for Disease Prevention and Control (CDC) in China implemented an integrated health big data platform (IHBDP) that pooled health data from healthcare providers to combat the spread of infectious diseases, such as dengue fever and pulmonary tuberculosis (TB), and to identify gaps in vaccination uptake among migrant children.

METHODS

IHBDP is composed of medical data from clinics, electronic health records, residents' annual medical checkup and immunization records, as well as administrative data, such as student registries. We programmed IHBDP to automatically scan for and detect dengue and TB carriers, as well as identify migrant children with incomplete immunization according to a comprehensive set of screening criteria developed by public health and medical experts. We compared the effectiveness of the big data screening with existing traditional screening methods.

RESULTS

IHBDP successfully identified six cases of dengue out of a pool of 3972 suspected cases, whereas the traditional method only identified four cases (which were also detected by IHBDP). For TB, IHBDP identified 288 suspected cases from a total of 43,521 university students, in which three cases were eventually confirmed to be TB carriers through subsequent follow up CT or T-SPOT.TB tests. As for immunization screenings, IHBDP identified 240 migrant children with incomplete immunization, but the traditional door-to-door screening method only identified 20 ones.

CONCLUSIONS

Our study has demonstrated the effectiveness of using IHBDP to detect both acute and chronic infectious disease patients and identify children with incomplete immunization as compared to traditional screening methods.

Data science approaches to confronting the COVID-19 pandemic: a narrative review

During the COVID-19 pandemic, more than ever, data science has become a powerful weapon in combating an infectious disease epidemic and arguably any future infectious disease epidemic. Computer scientists, data scientists, physicists and mathematicians have joined public health professionals and virologists to confront the largest pandemic in the century by capitalizing on the large-scale 'big data' generated and harnessed for combating the COVID-19 pandemic. In this paper, we review the newly born data science approaches to confronting COVID-19, including the estimation of epidemiological parameters, digital contact tracing, diagnosis, policy-making, resource allocation, risk assessment, mental health surveillance, social media analytics, drug repurposing and drug development. We compare the new approaches with conventional epidemiological studies, discuss lessons we learned from the COVID-19 pandemic, and highlight opportunities and challenges of data science approaches to confronting future infectious disease epidemics. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.

Profile and outcomes of emergency department presentations based on mode of arrival: A state-wide retrospective cohort study

OBJECTIVE

Understanding how people arrive to the ED assists in planning health services' response to fluctuating ED demand. The present study aimed to describe and compare demographics, clinical characteristics and health service outcomes of adult ED patient presentations based on mode of arrival: brought in by police (BIBP)/brought in by ambulance (BIBA)/privately arranged transport (PAT).

METHODS

A retrospective cohort study of ED patient presentations made between 1 January 2018 and 31 December 2020 from all public hospital EDs across Queensland, Australia. Descriptive and inferential analyses were performed to ascertain presentation characteristics and predictors of health service outcomes.

RESULTS

From 4 707 959 ED presentations, 0.9% were BIBP, 34.8% were BIBA and 64.0% were PAT. Presentations BIBP were younger and comprised a higher proportion of mental health problems and Emergency Examination Authority orders compared to presentations BIBA or PAT. Compared to presentations BIBP or PAT, presentations BIBA were more likely to be assigned more urgent triage scores, be admitted to hospital, and have a longer ED length of stay (LOS). Compared to other modes of arrival, presentations arriving by PAT were more likely to be discharged and have a shorter ED LOS.

CONCLUSION

Presentations BIBA and BIBP encountered a longer ED LOS and higher admission rates than PAT, suggesting more complex care needs than those from PAT. Clinical care pathways for specific modes of arrival that support pre-hospital providers and patients and are considerate of the throughput and output stages of ED care may be needed.

Disease evolution and risk-based disease trajectories in congestive heart failure patients

Congestive Heart Failure (CHF) is among the most prevalent chronic diseases worldwide, and is commonly associated with comorbidities and complex health conditions. Consequently, CHF patients are typically hospitalized frequently, and are at a high risk of premature death. Early detection of an envisaged patient disease trajectory is crucial for precision medicine. However, despite the abundance of patient-level data, cardiologists currently struggle to identify disease trajectories and track the evolution patterns of the disease over time, especially in small groups of patients with specific disease subtypes. The present study proposed a five-step method that allows clustering CHF patients, detecting cluster similarity, and identifying disease trajectories, and promises to overcome the existing difficulties. This work is based on a rich dataset of patients' records spanning ten years of hospital visits. The dataset contains all the health information documented in the hospital during each visit, including diagnoses, lab results, clinical data, and demographics. It utilizes an innovative Cluster Evolution Analysis (CEA) method to analyze the complex CHF population where each subject is potentially associated with numerous variables. We have defined sub-groups for mortality risk levels, which we used to characterize patients' disease evolution by refined data clustering in three points in time over ten years, and generating patients' migration patterns across periods. The results elicited 18, 23, and 25 clusters respective to the first, second, and third visits, uncovering clinically interesting small sub-groups of patients. In the following post-processing stage, we identified meaningful patterns. The analysis yielded fine-grained patient clusters divided into several finite risk levels, including several small-sized groups of high-risk patients. Significantly, the analysis also yielded longitudinal patterns where patients' risk levels changed over time. Four types of disease trajectories were identified: decline, preserved state, improvement, and mixed-progress. This stage is a unique contribution of the work. The resulting fine partitioning and longitudinal insights promise to significantly assist cardiologists in tailoring personalized interventions to improve care quality. Cardiologists could utilize these results to glean previously undetected relationships between symptoms and disease evolution that would allow a more informed clinical decision-making and effective interventions.

Analysis of ocular emergencies in a reference eye center in Brazil

PURPOSE

To determine the incidence of ocular emergencies and patient profiles in a public health eye center in Brazil.

METHODS

The medical record database of the Fundação Altino Ventura, Recife, Brazil was retrospectively analyzed and included all patients assisted at the ophthalmic emergency room between January 2017 and January 2018. Medical records with incomplete data and outpatient complaints were excluded. For records with multiple visits, only the initial visit was considered.

RESULTS

In 1 year, 134,788 patients (mean age at admission: 38.7 ± 22 years; range: 0-99 years) were admitted at the emergency room of the Fundação Altino Ventura. The most frequent diagnoses were conjunctivitis (52,732 cases; 37.3%), blepharitis (7,213 cases; 5.1%), and corneal/conjunctival foreign body (6,925 cases; 4.9%). Corneal/conjunctival foreign body and ocular trauma had an eight- and two-fold higher incidence in male patients, respectively (both p<0.001). Female patients presented a two-fold higher incidence of trichiasis and blepharitis than males (p<0.001). Corneal/conjunctival foreign body and ocular trauma affected more patients in a productive age (>15 years), while corneal ulcers, blepharitis, and trichiasis affected more elderly patients. All diagnostic clusters (e.g., infectious diseases, ocular trauma, foreign bodies, retinopathies, eyelid disorders, corneal diseases, glaucomatous crisis, and neuroophthalmological diseases) were more common during the spring season (p<0.001).

CONCLUSION

The most common ocular emergencies in the present study were infectious diseases and foreign body. However, the incidence of ophthalmological emergencies was influenced by the age and sex of patients, as well as the time of the year.

Sharing administrative health data with private industry: A report on two citizens' juries

BACKGROUND

There is good evidence of both community support for sharing public sector administrative health data in the public interest and concern about data security, misuse and loss of control over health information, particularly if private sector organizations are the data recipients. To date, there is little research describing the perspectives of informed community members on private sector use of public health data and, particularly, on the conditions under which that use might be justified.

METHODS

Two citizens' juries were held in February 2020 in two locations close to Sydney, Australia. Jurors considered the charge: 'Under what circumstances is it permissible for governments to share health data with private industry for research and development?'

RESULTS

All jurors, bar one, in principle supported sharing government administrative health data with private industry for research and development. The support was conditional and the juries' recommendations specifying these conditions related closely to the concerns they identified in deliberation.

CONCLUSION

The outcomes of the deliberative processes suggest that informed Australian citizens are willing to accept sharing their administrative health data, including with private industry, providing the intended purpose is clearly of public benefit, sharing occurs responsibly in a framework of accountability, and the data are securely held.

PATIENT AND PUBLIC CONTRIBUTION

The design of the jury was guided by an Advisory Group including representatives from a health consumer organization. The jurors themselves were selected to be descriptively representative of their communities and with independent facilitation wrote the recommendations.

The US COVID Atlas: A dynamic cyberinfrastructure surveillance system for interactive exploration of the pandemic

Distributed spatial infrastructures leveraging cloud computing technologies can tackle issues of disparate data sources and address the need for data-driven knowledge discovery and more sophisticated spatial analysis central to the COVID-19 pandemic. We implement a new, open source spatial middleware component (libgeoda) and system design to scale development quickly to effectively meet the need for surveilling county-level metrics in a rapidly changing pandemic landscape. We incorporate, wrangle, and analyze multiple data streams from volunteered and crowdsourced environments to leverage multiple data perspectives. We integrate explorative spatial data analysis (ESDA) and statistical hotspot standards to detect infectious disease clusters in real time, building on decades of research in GIScience and spatial statistics. We scale the computational infrastructure to provide equitable access to data and insights across the entire USA, demanding a basic but high-quality standard of ESDA techniques. Finally, we engage a research coalition and incorporate principles of user-centered design to ground the direction and design of Atlas application development.

Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance

The launch of the United Nations (UN) 17 Sustainable Development Goals (SDGs) in 2015 was a historic event, uniting countries around the world around the shared agenda of sustainable development with a more balanced relationship between human beings and the planet. The SDGs affect or impact almost all aspects of life, as indeed does the technological revolution, empowered by Big Data and their related technologies. It is inevitable that these two significant domains and their integration will play central roles in achieving the 2030 Agenda. This research aims to provide a comprehensive overview of how these domains are currently interacting, by illustrating the impact of Big Data on sustainable development in the context of each of the 17 UN SDGs.

Epidemiology of Heart Failure: A Contemporary Perspective

Designated as an emerging epidemic in 1997, heart failure (HF) remains a major clinical and public health problem. This review focuses on the most recent studies identified by searching the Medline database for publications with the subject headings HF, epidemiology, prevalence, incidence, trends between 2010 and present. Publications relevant to epidemiology and population sciences were retained for discussion in this review after reviewing abstracts for relevance to these topics. Studies of the epidemiology of HF over the past decade have improved our understanding of the HF syndrome and of its complexity. Data suggest that the incidence of HF is mostly flat or declining but that the burden of mortality and hospitalization remains mostly unabated despite significant ongoing efforts to treat and manage HF. The evolution of the case mix of HF continues to be characterized by an increasing proportion of cases with preserved ejection fraction, for which established effective treatments are mostly lacking. Major disparities in the occurrence, presentation, and outcome of HF persist particularly among younger Black men and women. These disturbing trends reflect the complexity of the HF syndrome, the insufficient mechanistic understanding of its various manifestations and presentations and the challenges of its management as a chronic disease, often integrated within a context of aging and multimorbidity. Emerging risk factors including omics science offer the promise of discovering new mechanistic pathways that lead to HF. Holistic management approaches must recognize HF as a syndemic and foster the implementation of multidisciplinary approaches to address major contributors to the persisting burden of HF including multimorbidity, aging, and social determinants of health.

Thinking about the idea of consent in data science genomics: How 'informed' is it?

In this paper we argue that 'informed' consent in Big Data genomic biobanking is frequently less than optimally informative. This is due to the particular features of genomic biobanking research which render it ethically problematic. We discuss these features together with details of consent models aimed to address them. Using insights from consent theory, we provide a detailed analysis of the essential components of informed consent which includes recommendations to improve consent performance. In addition, and using insights from philosophy of mind and language and psycholinguistics we support our analyses by identifying the nature and function of concepts (ideas) operational in human cognition and language together with an implicit coding/decoding model of human communication. We identify this model as the source of patients/participants poor understanding. We suggest an alternative, explicit model of human communication, namely, that of relevance-theoretic inference which obviates the limitations of the code model. We suggest practical strategies to assist health service professionals to ensure that the specific information they provide concerning the proposed treatment or research is used to inform participants' decision to consent. We do not prescribe a standard, formal approach to decision-making where boxes are ticked; rather, we aim to focus attention towards the sorts of considerations and questions that might usefully be borne in mind in any consent situation. We hope that our theorising will be of real practical benefit to nurses and midwives working on the clinical and research front-line of genomic science.

The multiplicity of analysis strategies jeopardizes replicability: lessons learned across disciplines

For a given research question, there are usually a large variety of possible analysis strategies acceptable according to the scientific standards of the field, and there are concerns that this multiplicity of analysis strategies plays an important role in the non-replicability of research findings. Here, we define a general framework on common sources of uncertainty arising in computational analyses that lead to this multiplicity, and apply this framework within an overview of approaches proposed across disciplines to address the issue. Armed with this framework, and a set of recommendations derived therefrom, researchers will be able to recognize strategies applicable to their field and use them to generate findings more likely to be replicated in future studies, ultimately improving the credibility of the scientific process.

Establishing a sorting protocol for healthcare databases

BACKGROUND

Health information records in many countries, especially developing countries, are still paper based. Compared to electronic systems, paper-based systems are disadvantageous in terms of data storage and data extraction. Given the importance of health records for epidemiological studies, guidelines for effective data cleaning and sorting are essential. They are, however, largely absent from the literature. The following paper discusses the process by which an algorithm was developed for the cleaning and sorting of a database generated from emergency department records in Lebanon.

DESIGN AND METHODS

Demographic and health related information were extracted from the emergency department records of three hospitals in Beirut. Appropriate categories were selected for data categorization. For health information, disease categories and codes were selected according to the International Classification of Disease 10th Edition.

RESULTS

A total of 16,537 entries were collected. Demographic information was categorized into groups for future epidemiological studies. Analysis of the health information led to the creation of a sorting algorithm which was then used to categorize and code the health data. Several counts were then performed to represent and visualize the data numerically and graphically.

CONCLUSIONS

The article describes the current state of health information records in Lebanon and the associated disadvantages of a paper-based system in terms of storage and data extraction. Furthermore, the article describes the algorithm by which health information was sorted and categorized to allow for future data analysis using paper records.

Data-Driven Surveillance: Effective Collection, Integration, and Interpretation of Data to Support Decision Making

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.

Exploring the disparities in park accessibility through mobile phone data: Evidence from Fuzhou of China

Parks are a major public service infrastructure for urban residents. Due to the unbalance geographic distribution of public parks within an urban, residents may have uneven access to this service. Despite there has been an efflorescent literature references, there is no consensus on how to properly measure the accessibility of park. The traditional place-based or infrastructure-based approach is often criticized for inappropriately defining spatial units or threshold distances. Taking a fast urbanization region-Fuzhou City, China as a case, this study overcomes this deficiency by employing the method of two-step floating catchment area (2SFCA) to evaluate the park accessibility using mobile phone data (during December 10, 2018 to December 16, 2018), which is people-based information with actual park users' origin-destination trajectory of park users. The results indicate that the threshold distance is 2 km from the visitors' home to park regardless of level, and the total number of visitors is relative fewer in weekend than that in workdays. The spatial distribution of park effective area presents a notably decreasing trend from the urban center to its periphery; however, the spatial distribution of park accessibility is more scattered and irregular. Finally, different key factors of park accessibility are identified for different locations using Geographically weighted regression (GWR) technique. Our study has a good implication for urban park planner and manager to implement differentiated planning policies for parks with full consideration of holistic factors.

Fractional Mathematical Oncology: On the potential of non-integer order calculus applied to interdisciplinary models

Mathematical Oncology investigates cancer-related phenomena through mathematical models as comprehensive as possible. Accordingly, an interdisciplinary approach involving concepts from biology to materials science can provide a deeper understanding of biological systems pertaining the disease. In this context, fractional calculus (also referred to as non-integer order) is a branch in mathematical analysis whose tools can describe complex phenomena comprising different time and space scales. Fractional-order models may allow a better description and understanding of oncological particularities, potentially contributing to decision-making in areas of interest such as tumor evolution, early diagnosis techniques and personalized treatment therapies. By following a phenomenological (i.e. mechanistic) approach, the present study surveys and explores different aspects of Fractional Mathematical Oncology, reviewing and discussing recent developments in view of their prospective applications.

Validating Biobehavioral Technologies for Use in Clinical Psychiatry

The last decade has witnessed the development of sophisticated biobehavioral and genetic, ambulatory, and other measures that promise unprecedented insight into psychiatric disorders. As yet, clinical sciences have struggled with implementing these objective measures and they have yet to move beyond "proof of concept." In part, this struggle reflects a traditional, and conceptually flawed, application of traditional psychometrics (i.e., reliability and validity) for evaluating them. This paper focuses on "resolution," concerning the degree to which changes in a signal can be detected and quantified, which is central to measurement evaluation in informatics, engineering, computational and biomedical sciences. We define and discuss resolution in terms of traditional reliability and validity evaluation for psychiatric measures, then highlight its importance in a study using acoustic features to predict self-injurious thoughts/behaviors (SITB). This study involved tracking natural language and self-reported symptoms in 124 psychiatric patients: (a) over 5-14 recording sessions, collected using a smart phone application, and (b) during a clinical interview. Importantly, the scope of these measures varied as a function of time (minutes, weeks) and spatial setting (i.e., smart phone vs. interview). Regarding reliability, acoustic features were temporally unstable until we specified the level of temporal/spatial resolution. Regarding validity, accuracy based on machine learning of acoustic features predicting SITB varied as a function of resolution. High accuracy was achieved (i.e., ~87%), but only when the acoustic and SITB measures were "temporally-matched" in resolution was the model generalizable to new data. Unlocking the potential of biobehavioral technologies for clinical psychiatry will require careful consideration of resolution.

Preparing for Emerging Zoonotic Viruses

The risk of emergence and spread of novel human pathogens originating from an animal reservoir has increased in the past decades. However, the unpredictable nature of disease emergence makes surveillance and preparedness challenging. Knowledge of general risk factors for emergence and spread, combined with local level data is needed to develop a risk-based methodology for early detection. This involves the implementation of the One Health approach, integrating human, animal and environmental health sectors, as well as social sciences, bioinformatics and more. Recent technical advances, such as metagenomic sequencing, will aid the rapid detection of novel pathogens on the human-animal interface.