Medicine. Big data meets public health

Bayesian Pathway Analysis for Complex Interactions

Modern epidemiologic studies permit investigation of the complex pathways that mediate effects of social, behavioral, and molecular factors on health outcomes. Conventional analytical approaches struggle with high-dimensional data, leading to high likelihoods of both false-positive and false-negative inferences. Herein, we describe a novel Bayesian pathway analysis approach, the algorithm for learning pathway structure (ALPS), which addresses key limitations in existing approaches to complex data analysis. ALPS uses prior information about pathways in concert with empirical data to identify and quantify complex interactions within networks of factors that mediate an association between an exposure and an outcome. We illustrate ALPS through application to a complex gene-drug interaction analysis in the Predictors of Breast Cancer Recurrence (ProBe CaRe) Study, a Danish cohort study of premenopausal breast cancer patients (2002-2011), for which conventional analyses severely limit the quality of inference.

On multistep tumor growth models of fractional variable-order

Fractional mathematical oncology is a research topic that applies non-integer order calculus to tackle cancer problems such as tumor growth analysis or its optimal treatment. This work proposes a multistep exponential model with a fractional variable-order representing the evolution history of a tumor. Model parameters are tuned according to variable fractional order profiles while assessing their capability of fitting a clinical time series. The results point to the superiority of the proposed model in describing the experimental data, thus providing new perspectives for modeling tumor growth.

Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis

Precision medicine aims to empower clinicians to predict the most appropriate course of action for patients with complex diseases like cancer, diabetes, cardiomyopathy, and COVID-19. With a progressive interpretation of the clinical, molecular, and genomic factors at play in diseases, more effective and personalized medical treatments are anticipated for many disorders. Understanding patient’s metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic, and predictive biomarkers and paths ultimately providing optimal and personalized care for diverse, and targeted chronic and acute diseases. In clinical settings, we need to timely model clinical and multi-omics data to find statistical patterns across millions of features to identify underlying biologic pathways, modifiable risk factors, and actionable information that support early detection and prevention of complex disorders, and development of new therapies for better patient care. It is important to calculate quantitative phenotype measurements, evaluate variants in unique genes and interpret using ACMG guidelines, find frequency of pathogenic and likely pathogenic variants without disease indicators, and observe autosomal recessive carriers with a phenotype manifestation in metabolome. Next, ensuring security to reconcile noise, we need to build and train machine-learning prognostic models to meaningfully process multisource heterogeneous data to identify high-risk rare variants and make medically relevant predictions. The goal, today, is to facilitate implementation of mainstream precision medicine to improve the traditional symptom-driven practice of medicine, and allow earlier interventions using predictive diagnostics and tailoring better-personalized treatments. We strongly recommend automated implementation of cutting-edge technologies, utilizing machine learning (ML) and artificial intelligence (AI) approaches for the multimodal data aggregation, multifactor examination, development of knowledgebase of clinical predictors for decision support, and best strategies for dealing with relevant ethical issues.

The intersection of genomics and big data with public health: Opportunities for precision public health

Muin Khoury and co-authors discuss anticipated contributions of genomics and other forms of large-scale data in public health.

Exploring research trends in big data across disciplines: A text mining analysis

Using big data has been a prevailing research trend in various academic fields. However, no studies have explored the scope and structure of big data across disciplines. In this article, we applied topic modeling and word co-occurrence analysis methods to identify key topics from more than 36,000 big data publications across all academic disciplines between 2012 and 2017. The results revealed several topics associated with the storage, collection and analysis of large datasets; the publications were predominantly published in computational fields. Other identified research topics show the influence of big data methods and techniques in areas beyond computer science, such as education, urban informatics, business, health and medical sciences. In fact, the prevalence of these topics has increased over time. In contrast, some themes like parallel computing, network modeling and big data analytic techniques have lost their popularity in recent years. These results probably reflect the maturity of big data core topics and highlight flourishing new research trends pertinent to big data in new domains, especially in social sciences, health and medicine. Findings of this article can be beneficial for researchers and science policymakers to understand the scope and structure of big data in different academic disciplines.

The Ethics of Digital Well-Being: A Thematic Review

This article presents the first thematic review of the literature on the ethical issues concerning digital well-being. The term 'digital well-being' is used to refer to the impact of digital technologies on what it means to live a life that is good for a human being. The review explores the existing literature on the ethics of digital well-being, with the goal of mapping the current debate and identifying open questions for future research. The review identifies major issues related to several key social domains: healthcare, education, governance and social development, and media and entertainment. It also highlights three broader themes: positive computing, personalised human-computer interaction, and autonomy and self-determination. The review argues that three themes will be central to ongoing discussions and research by showing how they can be used to identify open questions related to the ethics of digital well-being.

Motives of contributing personal data for health research: (non-)participation in a Dutch biobank

Background

Large-scale, centralized data repositories are playing a critical and unprecedented role in fostering innovative health research, leading to new opportunities as well as dilemmas for the medical sciences. Uncovering the reasons as to why citizens do or do not contribute to such repositories, for example, to population-based biobanks, is therefore crucial. We investigated and compared the views of existing participants and non-participants on contributing to large-scale, centralized health research data repositories with those of ex-participants regarding the decision to end their participation. This comparison could yield new insights into motives of participation and non-participation, in particular the behavioural change of withdrawal.

Methods

We conducted 36 in-depth interviews with ex-participants, participants, and non-participants of a three-generation, population-based biobank in the Netherlands. The interviews focused on the respondents’ decision-making processes relating to their participation in a large-scale, centralized repository for health research data.

Results

The decision of participants and non-participants to contribute to the biobank was motivated by a desire to help others. Whereas participants perceived only benefits relating to their participation and were unconcerned about potential risks, non-participants and ex-participants raised concerns about the threat of large-scale, centralized public data repositories and public institutes, such as social exclusion or commercialization. Our analysis of ex-participants’ perceptions suggests that intrapersonal characteristics, such as levels of trust in society, participation conceived as a social norm, and basic societal values account for differences between participants and non-participants.

Conclusions

Our findings indicate the fluidity of motives centring on helping others in decisions to participate in large-scale, centralized health research data repositories. Efforts to improve participation should focus on enhancing the trustworthiness of such data repositories and developing layered strategies for communication with participants and with the public. Accordingly, personalized approaches for recruiting participants and transmitting information along with appropriate regulatory frameworks are required, which have important implications for current data management and informed consent procedures.

Developing pathways for community-led research with big data: a content analysis of stakeholder interviews

BACKGROUND

Big data (BD) informs nearly every aspect of our lives and, in health research, is the foundation for basic discovery and its tailored translation into healthcare. Yet, as new data resources and citizen/patient-led science movements offer sites of innovation, segments of the population with the lowest health status are least likely to engage in BD research either as intentional data contributors or as 'citizen/community scientists'. Progress is being made to include a more diverse spectrum of research participants in datasets and to encourage inclusive and collaborative engagement in research through community-based participatory research approaches, citizen/patient-led research pilots and incremental research policy changes. However, additional evidence-based policies are needed at the organisational, community and national levels to strengthen capacity-building and widespread adoption of these approaches to ensure that the translation of research is effectively used to improve health and health equity. The aims of this study are to capture uses of BD ('use cases') from the perspectives of community leaders and to identify needs and barriers for enabling community-led BD science.

METHODS

We conducted a qualitative content analysis of semi-structured key informant interviews with 16 community leaders.

RESULTS

Based on our analysis findings, we developed a BD Engagement Model illustrating the pathways and various forces for and against community engagement in BD research.

CONCLUSIONS

The goal of our Model is to promote concrete, transparent dialogue between communities and researchers about barriers and facilitators of authentic community-engaged BD research. Findings from this study will inform the subsequent phases of a multi-phased project with the ultimate aims of organising fundable frameworks and identifying policy options to support BD projects within community settings.

Epistemic virtues and data-driven dreams: On sameness and difference in the epistemic cultures of data science and psychiatry

Data science and psychiatry have diverse epistemic cultures that come together in data-driven initiatives (e.g., big data, machine learning). The literature on these initiatives seems to either downplay or overemphasize epistemic differences between the fields. In this paper, we study the convergence and divergence of the epistemic cultures of data science and psychiatry. This approach is more likely to capture where and how the cultures differ and gives insights into how practitioners from both fields find ways to work together despite their differences. We introduce the notions of "epistemic virtues" to focus on epistemic differences ethnographically, and "trading zones" to concentrate on how differences are negotiated. This leads us to the following research question: how are epistemic differences negotiated by data science and psychiatry practitioners in a hospital-based data-driven initiative? Our results are based on an ethnographic study in which we observed a Dutch psychiatric hospital department developing prediction models of patient outcomes based on machine learning techniques (September 2017 - February 2018). Many epistemic virtues needed to be negotiated, such as completeness or selectivity in data inclusion. These differences were traded locally and temporarily, stimulated by shared epistemic virtues (such as a systematic approach), boundary objects and socialization processes. Trading became difficult when virtues were too diverse, differences were enlarged by storytelling and parties did not have the time or capacity to learn about the other. In the discussion, we argue that our combined theoretical framework offers a fresh way to study how cooperation between diverse practitioners goes and where it can be improved. We make a call for bringing epistemic differences into the open as this makes a grounded discussion possible about the added value of data-driven initiatives and the role they can play in healthcare.

The Epidemiologic Toolbox: Identifying, Honing, and Using the Right Tools for the Job

There has been much debate about the relative emphasis of the field of epidemiology on causal inference. We believe this debate does short shrift to the breadth of the field. Epidemiologists answer myriad questions that are not causal and hypothesize about and investigate causal relationships without estimating causal effects. Descriptive studies face significant and often overlooked inferential and interpretational challenges; we briefly articulate some of them and argue that a more detailed treatment of biases that affect single-sample estimation problems would benefit all types of epidemiologic studies. Lumping all questions about causality creates ambiguity about the utility of different conceptual models and causal frameworks; 2 distinct types of causal questions include 1) hypothesis generation and theorization about causal structures and 2) hypothesis-driven causal effect estimation. The potential outcomes framework and causal graph theory help efficiently and reliably guide epidemiologic studies designed to estimate a causal effect to best leverage prior data, avoid cognitive fallacies, minimize biases, and understand heterogeneity in treatment effects. Appropriate matching of theoretical frameworks to research questions can increase the rigor of epidemiologic research and increase the utility of such research to improve public health.

The Population Health OutcomEs aNd Information EXchange (PHOENIX) Program - A Transformative Approach to Reduce the Burden of Chronic Disease

This concept article introduces a transformative vision to reduce the population burden of chronic disease by focusing on data integration, analytics, implementation and community engagement. Known as PHOENIX (The Population Health OutcomEs aNd Information EXchange), the approach leverages a state level health information exchange and multiple other resources to facilitate the integration of clinical and social determinants of health data with a goal of achieving true population health monitoring and management. After reviewing historical context, we describe how multilevel and multimodal data can be used to facilitate core public health services, before discussing the controversies and challenges that lie ahead.

Universal patterns in passenger flight departure delays

Departure delays are a major cause of economic loss and inefficiency in the growing industry of passenger flights. A departure delay of a current flight is inevitably affected by the late arrival of the flight immediately preceding it with the same aircraft. We seek to understand the mechanisms of such propagated delays, and to obtain universal metrics by which to evaluate an airline’s operational effectiveness in delay alleviation. Here we use big data collected by the American Bureau of Transportation Statistics to design models of flight delays. Offering two dynamic models of delay propagation, we divided all carriers into two groups exhibiting a shifted power law or an exponentially truncated shifted power law delay distribution, revealing two universal delay propagation classes. Three model parameters, extracted directly from dual data mining, help characterize each airline’s operational efficiency in delay mitigation. Therefore, our modeling framework provides the crucially lacking evaluation indicators for airlines, potentially contributing to the mitigation of future departure delays.

An association between air pollution and daily most frequently visits of eighteen outpatient diseases in an industrial city

Toxic effects of air pollutants were individually identified in various organs of the body. However, the concurrent occurrences and the connection of diseases in multiple organs arise from air pollution has not been concurrently studied before. Here we hypothesize that there exist connected health effects arise from air pollution when diseases in various organs were considered together. We used medical data from hospital outpatient visits for various organs in the body with a disease-air pollution model that represents each of the diseases as a function of the environmental factors. Our results show that elevated air pollution risks (above 40%) concurrently occurred in diseases of spondylosis, cerebrovascular, pneumonia, accidents, chronic obstructive pulmonary disease (COPD), influenza, osteoarthritis (OA), asthma, peptic ulcer disease (PUD), cancer, heart, hypertensive, diabetes, kidney, and rheumatism. Air pollutants that were associated with elevated health risks are particular matters with diameters equal or less than 2.5 μm (PM_2.5), nitrogen dioxide (NO₂), ozone (O₃), particular matters with diameters equal or less than 10 μm (PM₁₀), carbon monoxide (CO), and nitrogen oxide (NO). Concurrent occurrences of diseases in various organs indicate that the immune system tries to connectively defend the body from persistent and rising air pollution.

Drug Abuse Research Trend Investigation with Text Mining

Drug abuse poses great physical and psychological harm to humans, thereby attracting scholarly attention. It often requires experience and time for a researcher, just entering this field, to find an appropriate method to study drug abuse issue. It is crucial for researchers to rapidly understand the existing research on a particular topic and be able to propose an effective new research method. Text mining analysis has been widely applied in recent years, and this study integrated the text mining method into a review of drug abuse research. Through searches for keywords related to the drug abuse, all related publications were identified and downloaded from PubMed. After removing the duplicate and incomplete literature, the retained data were imported for analysis through text mining. A total of 19,843 papers were analyzed, and the text mining technique was used to search for keyword and questionnaire types. The results showed the associations between these questionnaires, with the top five being the Addiction Severity Index (16.44%), the Quality of Life survey (5.01%), the Beck Depression Inventory (3.24%), the Addiction Research Center Inventory (2.81%), and the Profile of Mood States (1.10%). Specifically, the Addiction Severity Index was most commonly used in combination with Quality of Life scales. In conclusion, association analysis is useful to extract core knowledge. Researchers can learn and visualize the latest research trend.

Big Data− Análisis informétrico de documentos indexados en Scopus y Web of Science

El fenómeno Big Data es reciente, como lo demuestran las escasas publicaciones sobre el tema, lo cual incentiva esta investigación cuyos objetivos son compilar y referenciar documentos académicos incluidos en las bases de datos Scopus y Web of Science y analizar los contenidos. El método empleado es la investigación descriptiva, de primera aproximación, que consistió en la búsqueda y recuperación de información en Scopus y Web of Science en el periodo 2008-2018. Se analizaron 39 documentos, los cuales corresponden a 70 autores distribuidos en 14 títulos de revistas científicas, cuyo tipo de contribución se distribuye en 19 artículos, 10 comentarios, seis cartas al editor y cuatro reseñas. Otro de los resultados relevantes es que hay una alta concentración de publicaciones en Science y Nature. Los fenómenos Big Data y la CI son de reciente cuño y se encuentran en redefiniciones y conformaciones de dominios de estudios constantes. Encontramos un interés por las investigaciones Big Data; por otra parte, después de un análisis conceptual, proponemos una definición de Big Data.

Individuals with spinal cord injury have greater odds of substance use disorders than non-sci comparisons

BACKGROUND

Individuals with spinal cord injury (SCI) are disproportionately affected by numerous physical and behavioral health disparities, but the literature lacks a clear understanding of the association between SCI and substance use disorders. Identifying such behavioral health disparities in persons with disabilities is an increasingly central focus for public health researchers and represents a critical first step for prevention.

METHOD

The present study utilized a large database of deidentified electronic health records to examine the association between SCI and substance use disorders related to alcohol, cannabis, opioid, and nicotine. Examining data from patients 16 years or older who had patient encounters at the Penn State Hershey Medical Center from January 1, 1997 to April 30, 2018, the current study included data from 1,466,985 unique patients - 6192 of which held SCI diagnoses. Age-adjusted odds ratios were calculated using logistic regression.

RESULTS

Compared to non-SCI patients, individuals with SCI were at increased odds of having alcohol use disorder (OR: 4.19, 95% CI [3.67, 4.80]), cannabis use disorder (OR: 7.83, 95% CI [6.32, 9.69]), opioid use disorder (OR: 7.97, 95% CI [6.59, 9.66]), and nicotine use disorder (OR: 4.66, 95% CI [4.40, 4.94]). Patient sex did not moderate any of the four associations.

CONCLUSION

This study provides early indication that individuals with SCI may be disproportionately at-risk for substance use disorders and provides a foundation for future mechanistic and translational research. This evidence is a valuable step towards improving the health and quality of life for individuals with SCI.

A narrative review of the use of agent-based modeling in health behavior and behavior intervention

Studies of health behaviors and behavior intervention have begun to explore the potential of agent-based modeling (ABM). A review of how ABMs have been used in health behavior, behavior intervention, and corresponding insights is warranted. The goal of this study was to provide a narrative review of the applications of ABMs in health behavior change and intervention. I will focus on two perspectives: (a) the mechanism of behavior and behavior change and (b) ABMs' use for behavior intervention. I identified and reviewed 17 ABMs applied to behaviors including physical activity, diet, alcoholic drinking, smoking, and drug use. Among these ABMs, I grouped their mechanisms of behavior change into four categories and evaluated the advantages and disadvantages of each mechanism. For behavior intervention, I evaluated the use of ABMs on levels of individual, interpersonal, and neighborhood environment. Various behavior change mechanisms and simplifications existed because of our limited knowledge of behaviors at the individual level. Utility maximization was the most frequently used mechanism. ABMs offered insights for behavior intervention including the benefits of upstream interventions and multilevel intervention, as well as balances among various factors, outcomes, and populations. ABMs have been used to model a diversity of behaviors, populations, and interventions. The use of ABMs in health behavior is at an early stage, and a major challenge is our limited knowledge of behaviors at the individual level.

Medical research using governments' health claims databases: with or without patients' consent?

Taking advantage of its single-payer, universal insurance system, Taiwan has leveraged its exhaustive database of health claims data for research purposes. Researchers can apply to receive access to pseudonymized (coded) medical data about insured patients, notably their diagnoses, health status and treatments. In view of the strict safeguards implemented, the Taiwanese government considers that this research use does not require patients' consent (either in the form of an opt-in or in the form of an opt-out). A group of non-governmental organizations has challenged this view in the Taiwanese Courts, but to no avail. The present article reviews the arguments both against and in favor of patients' consent for re-use of their data in research. It concludes that offering patients an opt-out would be appropriate as it would best balance the important interests at issue.

Disruptive Technologies for Environment and Health Research: An Overview of Artificial Intelligence, Blockchain, and Internet of Things

The purpose of this descriptive research paper is to initiate discussions on the use of innovative technologies and their potential to support the research and development of pan-Canadian monitoring and surveillance activities associated with environmental impacts on health and within the health system. Its primary aim is to provide a review of disruptive technologies and their current uses in the environment and in healthcare. Drawing on extensive experience in population-level surveillance through the use of technology, knowledge from prior projects in the field, and conducting a review of the technologies, this paper is meant to serve as the initial steps toward a better understanding of the research area. In doing so, we hope to be able to better assess which technologies might best be leveraged to advance this unique intersection of health and environment. This paper first outlines the current use of technologies at the intersection of public health and the environment, in particular, Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT). The paper provides a description for each of these technologies, along with a summary of their current applications, and a description of the challenges one might face with adopting them. Thereafter, a high-level reference architecture, that addresses the challenges of the described technologies and could potentially be incorporated into the pan-Canadian surveillance system, is conceived and presented.

Precluding rare outcomes by predicting their absence

Forecasting extremely rare events is a pressing problem, but efforts to model such outcomes are often limited by the presence of multiple causes within classes of events, insufficient observations of the outcome to assess fit, and biased estimates due to insufficient observations of the outcome. We introduce a novel approach for analyzing rare event data that addresses these challenges by turning attention to the conditions under which rare outcomes do not occur. We detail how configurational methods can be used to identify conditions or sets of conditions that would preclude the occurrence of a rare outcome. Results from Monte Carlo experiments show that our approach can be used to systematically preclude up to 78.6% of observations, and application to ground-truth data coupled with a bootstrap inferential test illustrates how our approach can also yield novel substantive insights that are obscured by standard statistical analyses.

Unweaving tangled mortality and antibiotic consumption data to detect disease outbreaks - Peaks, growths, and foresight in swine production

As our capacity to collect and store health data is increasing, a new challenge of transforming data into meaningful information for disease monitoring and surveillance has arisen. The aim of this study was to explore the potential of using livestock mortality and antibiotic consumption data as a proxy for detecting disease outbreaks at herd level. Changes in the monthly records of mortality and antibiotic consumption were monitored in Danish swine herds that became positive for porcine reproductive and respiratory syndrome (PRRS) and porcine pleuropneumonia. Laboratory serological results were used to identify herds that changed from a negative to a positive status for the diseases. A dynamic linear model with a linear growth component was used to model the data. Alarms about state changes were raised based on forecast errors, changes in the growth component, and the values of the retrospectively smoothed values of the growth component. In all cases, the alarms were defined based on credible intervals and assessed prior and after herds got a positive disease status. The number of herds with alarms based on mortality increased by 3% in the 3 months prior to laboratory confirmation of PRRS-positive herds (Se = 0.47). A 22% rise in the number of weaner herds with alarms based on the consumption of antibiotics for respiratory diseases was found 1 month prior to these herds becoming PRRS-positive (Se = 0.22). For porcine pleuropneumonia-positive herds, a 10% increase in antibiotic consumption for respiratory diseases in sow herds was seen 1 month prior to a positive result (Se = 0.5). Monitoring changes in mortality data and antibiotic consumption showed changes at herd level prior to and in the same month as confirmation from diagnostic tests. These results also show a potential value for using these data streams as part of surveillance strategies.

Data Linkage: Canadian and Australian Perspectives on a Valuable Methodology for Intellectual and Developmental Disability Research

Data linkage holds great promise for generating new information about people with intellectual and developmental disabilities (IDD) as a population, yet few centers have developed the infrastructure to utilize this methodology. Two examples, from Canada and Australia, describe their efforts in building data linkage capabilities, and how linked databases can be used to identify persons with IDD and used for population-based research. The value of data linkage is illustrated through new estimates of prevalence of IDD; health service utilization patterns; associations with sociodemographic characteristics, and with physical and mental health conditions (e.g., chronic diseases, injury, fertility, and depression); and findings on equity in medical treatments. Examples are provided of findings used for governmental policy and program planning.

Air pollution as cause of mental disease: Appraisal of the evidence

A causal association of air pollution with mental diseases is an intriguing possibility raised in a Short Report just published in PLOS Biology. Despite analyses involving large data sets, the available evidence has substantial shortcomings, and a long series of potential biases may invalidate the observed associations. Only bipolar disorder shows consistent results, with similar effects across United States and Denmark data sets, but the effect has modest magnitude, appropriate temporality is not fully secured, and biological gradient, plausibility, coherence, and analogy offer weak support. The signal seems to persist in some robustness analyses, but more analyses by multiple investigators, including contrarians, are necessary. Broader public sharing of data sets would also enhance transparency.

Explainable statistical learning in public health for policy development: the case of real-world suicide data

BACKGROUND

In recent years, the availability of publicly available data related to public health has significantly increased. These data have substantial potential to develop public health policy; however, this requires meaningful and insightful analysis. Our aim is to demonstrate how data analysis techniques can be used to address the issues of data reduction, prediction and explanation using online available public health data, in order to provide a sound basis for informing public health policy.

METHODS

Observational suicide prevention data were analysed from an existing online United Kingdom national public health database. Multi-collinearity analysis and principal-component analysis were used to reduce correlated data, followed by regression analyses for prediction and explanation of suicide.

RESULTS

Multi-collinearity analysis was effective in reducing the indicator set of predictors by 30% and principal component analysis further reduced the set by 86%. Regression for prediction identified four significant indicator predictors of suicide behaviour (emergency hospital admissions for intentional self-harm, children leaving care, statutory homelessness and self-reported well-being/low happiness) and two main component predictors (relatedness dysfunction, and behavioural problems and mental illness). Regression for explanation identified significant moderation of a well-being predictor (low happiness) of suicide behaviour by a social factor (living alone), thereby supporting existing theory and providing insight beyond the results of regression for prediction. Two independent predictors capturing relatedness needs in social care service delivery were also identified.

CONCLUSIONS

We demonstrate the effectiveness of regression techniques in the analysis of online public health data. Regression analysis for prediction and explanation can both be appropriate for public health data analysis for a better understanding of public health outcomes. It is therefore essential to clarify the aim of the analysis (prediction accuracy or theory development) as a basis for choosing the most appropriate model. We apply these techniques to the analysis of suicide data; however, we argue that the analysis presented in this study should be applied to datasets across public health in order to improve the quality of health policy recommendations.

Personal clinical history predicts antibiotic resistance of urinary tract infections

Antibiotic resistance is prevalent among the bacterial pathogens causing urinary tract infections. However, antimicrobial treatment is often prescribed “empirically”, in the absence of antibiotic susceptibility testing, risking mismatched and therefore ineffective treatment. Here, linking a 10-year longitudinal dataset of over 700,000 community-acquired UTIs with over 5,000,000 individually-resolved records of antibiotic purchases, we identify strong associations of antibiotic resistance with the demographics, records of past urine cultures and history of drug purchases of the patients. When combined together, these associations allow for machine learning-based personalized drug-specific predictions of antibiotic resistance, thereby enabling drug-prescribing algorithms that match antibiotic treatment recommendation to the expected resistance of each sample. Applying these algorithms retrospectively, over a one-year test period, we find that they much reduce the risk of mismatched treatment compared to the current standard-of-care. The clinical application of such algorithms may help improve the effectiveness of antimicrobial treatments.

Exploring the system capacity to meet occupational health and safety needs: the case of the ready-made garment industry in Bangladesh

Background

Since the 2013 Rana Plaza incident in Bangladesh, the government of Bangladesh has been under pressure to improve health and safety conditions for workers in the ready-made garment industry. Its efforts have focused heavily on structural safety of the buildings but have largely ignored broader occupational health system issues. This qualitative study explores contextual factors and system challenges that create barriers for ensuring a healthy and safe workplace in the ready-made garment industry in Bangladesh.

Methods

Data were collected through key informant interviews (n = 14) with government officials from the Department of Inspection for Factories and Establishments (DIFE), factory employers, factory doctors and representatives from the Bangladesh Garment Manufacturers and Exporters Association (BGMEA). A thematic analysis was conducted using Atlas-ti v 5.2.

Results

A thematic analysis suggests that the capacity of the DIFE to provide adequate occupational health services remains a problem. There is a shortage of both appropriately trained staff and equipment to monitor occupational health and safety in factories and to gather useful data for evidence-based decision-making. Another barrier to effective occupational health and safety of workers is the lack of cooperation by employers in recording data on workers’ health and safety problems. Finally, government officials have limited resources and power to enforce compliance with regulations. Such deficiencies threaten the health and safety of this important, largely female, working population.

Conclusion

This case example focused on the valuable ready-made garment industry sector of Bangladesh’s economy. It identifies the critical need for occupational health system strengthening. Specifically system capacity needs to be improved by both increasing human resources for in-factory hazards and health monitoring, regulatory inspection, enforcement, and improved training of government officials around monitoring and reporting.

Electronic supplementary material

The online version of this article (10.1186/s12913-019-4291-y) contains supplementary material, which is available to authorized users.

Artificial intelligence (AI) and cancer prevention: the potential application of AI in cancer control programming needs to be explored in population laboratories such as COMPASS

Understanding the risk factors that initiate cancer is essential for reducing the future cancer burden. Much of our current cancer control insight is from cohort studies and newer large-scale population laboratories designed to advance the science around precision oncology. Despite their promise for improving diagnosis and treatment outcomes, their current reductionist focus will likely have little impact shifting the cancer burden. However, it is possible that these big data assets can be adapted to have more impact on the future cancer burden through more focus on primary prevention efforts that incorporate artificial intelligence (AI) and machine learning (ML). ML automatically learns patterns and can devise complex models and algorithms that lend themselves to prediction in big data, revealing new unexpected relationships and pathways in a reliable and replicable fashion that otherwise would remain hidden given the complexities of big data. While AI has made big strides in several domains, the potential application in cancer prevention is lacking. As such, this commentary suggests that it may be time to consider the potential of AI within our existing cancer control population laboratories, and provides justification for why some small targeted investments to explore their impact on modelling existing real-time cancer prevention data may be a strategic cancer control opportunity.

Signals Among Signals: Prioritizing Nongenetic Associations in Massive Data Sets

Massive data sets are often regarded as a panacea to the underpowered studies of the past. At the same time, it is becoming clear that in many of these data sets in which thousands of variables are measured across hundreds of thousands or millions of individuals, almost any desired relationship can be inferred with a suitable combination of covariates or analytic choices. Inspired by the genome-wide association study analysis paradigm that has transformed human genetics, X-wide association studies or "XWAS" have emerged as a popular approach to systematically analyzing nongenetic data sets and guarding against false positives. However, these studies often yield hundreds or thousands of associations characterized by modest effect sizes and miniscule P values. Many of these associations will be spurious and emerge due to confounding and other biases. One way of characterizing confounding in the genomics paradigm is the genomic inflation factor. An analogous "X-wide inflation factor," denoted λX, can be defined and applied to published XWAS. Effects that arise in XWAS may be prioritized using replication, triangulation, quantification of measurement error, contextualization of each effect in the distribution of all effect sizes within a field, and pre-registration. Criteria like those of Bradford Hill need to be reconsidered in light of exposure-wide epidemiology to prioritize signals among signals.

Taking patient involvement seriously: a critical ethical analysis of participatory approaches in data-intensive medical research

BACKGROUND

Data-intensive research in medicine and healthcare such as health-related big data research (HBDR) implies that data from clinical routine, research and patient-reported data, but also non-medical social or demographic data, are aggregated and linked in order to optimize biomedical research. In this context, notions of patient participation and involvement are frequently invoked to legitimize this kind of research and improve its governance. The aim of this debate paper is to critically examine the specific use and ethical role of participatory concepts in the context of HBDR and data-intensive research in medicine and healthcare.

DISCUSSION

We introduce basic conceptual distinctions for the understanding of participation by looking at relevant fields of application in politics, bioethics and medical research. Against this backdrop, we identify three paradigmatic participatory roles that patients/subjects are assigned within the field of HBDR: participants as providers of biomaterials and data, participants as administrators of their own research participation and participants as (co-)principal investigators. We further illustrate these roles by exemplary data-intensive research-initiatives. Our analysis of these initiatives and their respective participatory promises reveals specific ethical and practical shortcomings and challenges. Central problems affecting, amongst others, ethical and methodological research standards, as well as public trust in research, result from the negligence of essential political-ethical dimensions of genuine participation.

CONCLUSIONS

Based on the conceptual distinctions introduced, we formulate basic criteria for justified appeals to participatory approaches in HBDR and data-intensive research in medicine and healthcare in order to overcome these shortcomings. As we suggest, this is not only a matter of conceptual clarity, but a crucial requirement for maintaining ethical standards and trust in HBDR and related medical research.

Toward precision public health

OBJECTIVES

The information in this article is based on a presentation given at the American Institute of Dental Public Health's 2018 Colloquium on Precision Public Health (PPH). A brief introduction to precision medicine provides context for PPH. Precision medicine tailors treatment and prevention strategies for individual patients based on variability of genetic, lifestyle, and environmental factors. An overview of PPH with examples of pilot studies with different research approaches bridges a discussion on an initiative undertaken by the University of Florida to build infrastructure and expertise for PPH research.

METHODS

PPH use better and more precise data to target disease prevention and control in the right population at the right time. To facilitate the identification of relevant open access data sources for PPH research a "one-stop" shop was created and tested. Case studies were conducted to validate the data portal's usefulness in identifying at-risk populations.

RESULTS

Details of portal data types, research challenges, and university-wide integrated programs are included. Case studies indicated that providing a "one-stop shop" of relevant data sources is an effective tool to aid researchers in identifying at-risk populations.

CONCLUSIONS

Research studies undertaken by University of Florida graduate students illustrate how PPH aligns with essential public health services including community engagement to reduce disparities in health care. Assurance of a competent workforce in PPH research approaches may be improved by training new researchers graduating from health science programs with knowledge of data and tools. The move toward PPH is in early stages and much work lies ahead.

A Pilot Randomized Controlled Trial of a Digital Intervention Aimed at Improving Food Purchasing Behavior: The Front-of-Pack Food Labels Impact on Consumer Choice Study

BACKGROUND

Most food in the United Kingdom is purchased in supermarkets, and many of these purchases are routinely tracked through supermarket loyalty card data. Using such data may be an effective way to develop remote public health interventions and to measure objectively their effectiveness at changing food purchasing behavior.

OBJECTIVE

The Front-of-pack food Labels: Impact on Consumer Choice (FLICC) study is a pilot randomized controlled trial of a digital behavior change intervention. This pilot trial aimed to collect data on recruitment and retention rates and to provide estimates of effect sizes for the primary outcome (healthiness of ready meals and pizzas purchased) to inform a larger trial.

METHODS

The intervention consisted of a website where participants could access tailored feedback on previous purchases of ready meals and pizzas, set goals for behavior change, and model and practice the recommended healthy shopping behavior using traffic light labels. The control consisted of Web-based information on traffic light labeling. Participants were recruited via email from a list of loyalty card holders held by the participating supermarket. All food and drink purchases for the participants for the 6 months before recruitment, during the 6-week intervention period, and during a 12-week washout period were transferred to the research team by the participating supermarket. Healthiness of ready meals and pizzas was measured using a predeveloped scale based solely on the traffic light colors on the foods. Questionnaires were completed at recruitment, end of the intervention, and end of washout to estimate the effect of the intervention on variables that mediate behavior change (eg, belief and intention formation).

RESULTS

We recruited 496 participants from an initial email to 50,000 people. Only 3 people withdrew from the study, and purchase data were received for all other participants. A total of 208 participants completed all 3 questionnaires. There was no difference in the healthiness of purchased ready meals and pizzas between the intervention and control arms either during the intervention period (P=.32) or at washout (P=.59).

CONCLUSIONS

Although the FLICC study did not find evidence of an impact of the intervention on food purchasing behavior, the unique methods used in this pilot trial are informative for future studies that plan to use supermarket loyalty card data in collaboration with supermarket partners. The experience of the trial showcases the possibilities and challenges associated with the use of loyalty card data in public health research.

TRIAL REGISTRATION

ISRCTN Registry ISRCTN19316955; http://www.isrctn.com/ISRCTN19316955 (Archived by WebCite at http://www.webcitation.org/76IVZ9WjK).

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1186/s40814-015-0015-1.

Experience and reflection from China's Xiangya medical big data project

The construction of medical big data includes several problems that need to be solved, such as integration and data sharing of many heterogeneous information systems, efficient processing and analysis of large-scale medical data with complex structure or low degree of structure, and narrow application range of medical data. Therefore, medical big data construction is not only a simple collection and application of medical data but also a complex systematic project. This paper introduces China's experience in the construction of a regional medical big data ecosystem, including the overall goal of the project; establishment of policies to encourage data sharing; handling the relationship between personal privacy, information security, and information availability; establishing a cooperation mechanism between agencies; designing a polycentric medical data acquisition system; and establishing a large data centre. From the experience gained from one of China's earliest established medical big data projects, we outline the challenges encountered during its development and recommend approaches to overcome these challenges to design medical big data projects in China more rationally. Clear and complete top-level design of a project requires to be planned in advance and considered carefully. It is essential to provide a culture of information sharing and to facilitate the opening of data, and changes in ideas and policies need the guidance of the government. The contradiction between data sharing and data security must be handled carefully, that is not to say data openness could be abandoned. The construction of medical big data involves many institutions, and high-level management and cooperation can significantly improve efficiency and promote innovation. Compared with infrastructure construction, it is more challenging and time-consuming to develop appropriate data standards, data integration tools and data mining tools.

S. Dawkins H, Nowak KJ, Weeramanthri TS. Genomic Testing for Human Health and Disease Across the Life Cycle: Applications and Ethical, Legal, and Social Challenges

The expanding use of genomic technologies encompasses all phases of life, from the embryo to the elderly, and even the posthumous phase. In this paper, we present the spectrum of genomic healthcare applications, and describe their scope and challenges at different stages of the life cycle. The integration of genomic technology into healthcare presents unique ethical issues that challenge traditional aspects of healthcare delivery. These challenges include the different definitions of utility as applied to genomic information; the particular characteristics of genetic data that influence how it might be protected, used and shared; and the difficulties applying existing models of informed consent, and how new consent models might be needed.

Sobre uma política de ciência e tecnologia para a saúde

RESUMO Frente ao conjunto de políticas de ciência e tecnologia existentes no Brasil, o texto reivindica um olhar diferenciado sobre a política de pesquisa em saúde. Isso decorre de sua magnitude física, de sua tradição histórica e de sua articulação com uma política pública de saúde na qual a intersetorialidade é valorizada. O texto se divide em três partes, precedidas de uma advertência sobre o impacto da conjuntura atual do País sobre a política geral de ciência e tecnologia. Em primeiro lugar, propõe uma abordagem metodológica para a definição das fronteiras da pesquisa em saúde. Em seguida, reivindica para o campo da saúde coletiva um papel de protagonismo na construção dessa política. Finalmente, apresenta e discute alguns desafios atuais postos para a política.

Deep learning for predicting toxicity of chemicals: a mini review

Humans and wildlife inhabit a world with panoply of natural and synthetic chemicals. Alarmingly, only a limited number of chemicals have undergone comprehensive toxicological evaluation due to limitations of traditional toxicity testing. High-throughput screening assays provide a higher-speed alternative for conventional toxicity testing. Advancement of high-throughput bioassay technology has greatly increased chemical toxicity data volumes in the past decade, pushing toxicology research into a "big data" era. However, traditional data analysis methods fail to effectively process large data volumes, presenting both a challenge and an opportunity for toxicologists. Deep learning, a machine learning method leveraging deep neural networks (DNNs), is a proven useful tool for building quantitative structure-activity relationship (QSAR) models for toxicity prediction utilizing these new large datasets. In this mini review, a brief technical background on DNNs is provided, and the current state of chemical toxicity prediction models built with DNNs is reviewed. In addition, relevant toxicity data sources are summarized, possible limitations are discussed, and perspectives on DNN utilization in chemical toxicity prediction are given.

Sampling and Sampling Frames in Big Data Epidemiology

Purpose of Review

The 'big data' revolution affords the opportunity to reuse administrative datasets for public health research. While such datasets offer dramatically increased statistical power compared with conventional primary data collection, typically at much lower cost, their use also raises substantial inferential challenges. In particular, it can be difficult to make population inferences because the sampling frames for many administrative datasets are undefined. We reviewed options for accounting for sampling in big data epidemiology.

Recent Findings

We identified three common strategies for accounting for sampling when the data available were not collected from a deliberately constructed sample: 1) explicitly reconstruct the sampling frame, 2) test the potential impacts of sampling using sensitivity analyses, and 3) limit inference to sample.

Summary

Inference from big data can be challenging because the impacts of sampling are unclear. Attention to sampling frames can minimize risks of bias.

Data-intensive Undergraduate Research Project Informs to Advance Healthcare Analytics

The overarching framework for incorporating informatics into the Wesley College (Wesley) undergraduate curriculum was to teach emerging information technologies that prepared undergraduates for complex high-demand work environments. Federal and State support helped implement Wesley's undergraduate Informatics Certificate and Minor programs. Both programs require project-based coursework in Applied Statistics, SAS Programming, and Geo-spatial Analysis (ArcGIS). In 2015, the State of Obesity listed the obesity ranges for all 50 US States to be between 21-36%. Yet, the Center for Disease Control and Prevention (CDC) mortality records show significantly lower obesity-related death-rates for states with very high obesity-rates. This study highlights the disparities in the reported obesity-related death-rates (specified by an ICD-10 E66 diagnosis code) and the obesity-rate percentages recorded for all 50 US States. Using CDC mortality-rate data, the available obesity-rate information, and ArcGIS, we created choropleth maps for all US States. Visual and statistical analysis shows considerable disparities in the obesity-related death-rate record-keeping amongst the 50 US States. For example, in 2015, Vermont with the sixth lowest obesity-rate had the highest reported obesity-related death-rate. In contrast, Alabama had the fifth highest adult obesity-rate in the nation, yet, it had a very low age-adjusted mortality-rate. Such disparities make comparative analysis difficult.

Protect us from poor-quality medical research

Much of the published medical research is apparently flawed, cannot be replicated and/or has limited or no utility. This article presents an overview of the current landscape of biomedical research, identifies problems associated with common study designs and considers potential solutions. Randomized clinical trials, observational studies, systematic reviews and meta-analyses are discussed in terms of their inherent limitations and potential ways of improving their conduct, analysis and reporting. The current emphasis on statistical significance needs to be replaced by sound design, transparency and willingness to share data with a clear commitment towards improving the quality and utility of clinical research.

Five year trends (2008-2012) in cardiac implantable electrical device utilization in five European nations: a case study in cross-country comparisons using administrative databases

Aims

Common methodologies for analysis of analogous data sets are needed for international comparisons of treatment and outcomes. This study tests using administrative hospital discharge (HD) databases in five European countries to investigate variation/trends in pacemaker (PM) and implantable cardioverter defibrillator (ICD) implant rates in terms of patient characteristics/management, device subtype, and initial implantation vs. replacement, and compares findings with existing literature and European Heart Rhythm Association (EHRA) reports.

Methods and results

HD databases from 2008 to 2012 in Austria, England, Germany, Italy and Slovenia were interrogated to extract admissions (without patient identification) associated with PM and ICD implants and replacements, using direct cross-referencing of procedure codes and common methodology to compare aggregate data. 1 338 199 records revealed 212 952 PM and 62 567 ICD procedures/year on average for a 204.4 million combined population, a crude implant rate of about 104/100 000 inhabitants for PMs and 30.6 for ICDs. The first implant/replacement rate ratios were 81/24 (PMs) and 25/7 (ICDs). Rates have increased, with cardiac resynchronization therapy (CRT) subtypes for both devices rising dramatically. Significant between- and within-country variation persists in lengths of stay and rates (Germany highest, Slovenia lowest). Adjusting for age lessened differences for PM rates, scarcely affected ICDs. Male/female ratios remained stable at 56/44% (PMs) and 79/21% (ICDs). About 90% of patients were discharged to home; 85-100% were inpatient admissions.

Conclusion

To aid in policymaking and track outcomes, HD administrative data provides a reliable, relatively cheap, methodology for tracking implant rates for PMs and ICDs across countries, as comparisons to EHRA data and the literature indicated.

Demographic and spatial trends in diabetes-related virtual nursing examinations

Diabetes currently affects nearly 30 million Americans, but the distribution of cases is not uniform across all demographics or every state. In the course of their education, nurses learn how to become important conduits for information on diabetes management during their eventual interactions with patients. Exploring the status and trends of diabetes-related knowledge in nursing students is one method to explore the idea that one's community affects how one sees disease. However, they are not yet experts, which places them in a period of transition. This study used data mined from the Shadow Health Digital Clinical Experience™ virtual patient exams conducted by nursing students between the years of 2012 and 2015 to find any potential demographic or spatial trends within simulation performance results from nursing students who examined a virtual patient with self-managed diabetes. Findings of the analysis indicated that age and experience affected the way in which an examination was conducted, where older and more experienced nursing students asked 8% fewer examination questions, yet showed 32% more empathy and offered 76% more educational statements than their younger counterparts. Spatial trends were less pronounced, although deeper analysis revealed that students in states closer to the national mean for population rate with diabetes perform better, show more empathy, and offer more educational statements during examinations compared to states well above or well below the national mean. This suggests that targeted information may be preferable to "one-size-fits-all" public health awareness and education programs for diabetes programs used uniformly across the country.

Digital Interventions for Mental Disorders: Key Features, Efficacy, and Potential for Artificial Intelligence Applications

Mental disorders are highly prevalent and often remain untreated. Many limitations of conventional face-to-face psychological interventions could potentially be overcome through Internet-based and mobile-based interventions (IMIs). This chapter introduces core features of IMIs, describes areas of application, presents evidence on the efficacy of IMIs as well as potential effect mechanisms, and delineates how Artificial Intelligence combined with IMIs may improve current practices in the prevention and treatment of mental disorders in adults. Meta-analyses of randomized controlled trials clearly show that therapist-guided IMIs can be highly effective for a broad range of mental health problems. Whether the effects of unguided IMIs are also clinically relevant, particularly under routine care conditions, is less clear. First studies on IMIs for the prevention of mental disorders have shown promising results. Despite limitations and challenges, IMIs are increasingly implemented into routine care worldwide. IMIs are also well suited for applications of Artificial Intelligence and Machine Learning, which provides ample opportunities to improve the identification and treatment of mental disorders. Together with methodological innovations, these approaches may also deepen our understanding of how psychological interventions work, and why. Ethical and professional restraints as well as potential contraindications of IMIs, however, should also be considered. In sum, IMIs have a high potential for improving the prevention and treatment of mental health disorders across various indications, settings, and populations. Therefore, implementing IMIs into routine care as both adjunct and alternative to face-to-face treatment is highly desirable. Technological advancements may further enhance the variability and flexibility of IMIs, and thus even further increase their impact in people's lives in the future.

The Sex-Specific Detrimental Effect of Diabetes and Gender-Related Factors on Pre-admission Medication Adherence Among Patients Hospitalized for Ischemic Heart Disease: Insights From EVA Study

Background: Sex and gender-related factors have been under-investigated as relevant determinants of health outcomes across non-communicable chronic diseases. Poor medication adherence results in adverse clinical outcomes and sex differences have been reported among patients at high cardiovascular risk, such as diabetics. The effect of diabetes and gender-related factors on medication adherence among women and men at high risk for ischemic heart disease (IHD) has not yet been fully investigated. Aim: To explore the role of sex, gender-related factors, and diabetes in pre-admission medication adherence among patients hospitalized for IHD. Materials and Methods: Data were obtained from the Endocrine Vascular disease Approach (EVA) (ClinicalTrials.gov Identifier: NCT02737982), a prospective cohort of patients admitted for IHD. We selected patients with baseline information regarding the presence of diabetes, cardiovascular risk factors, and gender-related variables (i.e., gender identity, gender role, gender relations, institutionalized gender). Our primary outcome was the proportion of pre-admission medication adherence defined through a self-reported questionnaire. We performed a sex-stratified analysis of clinical and gender-related factors associated with pre-admission medication adherence. Results: Two-hundred eighty patients admitted for IHD (35% women, mean age 70), were included. Around one-fourth of the patients were low-adherent to therapy before hospitalization, regardless of sex. Low-adherent patients were more likely diabetic (40%) and employed (40%). Sex-stratified analysis showed that low-adherent men were more likely to be employed (58 vs. 33%) and not primary earners (73 vs. 54%), with more masculine traits of personality, as compared with medium-high adherent men. Interestingly, women reporting medication low-adherence were similar for clinical and gender-related factors to those with medium-high adherence, except for diabetes (42 vs. 20%, p = 0.004). In a multivariate adjusted model only employed status was associated with poor medication adherence (OR 0.55, 95%CI 0.31-0.97). However, in the sex-stratified analysis, diabetes was independently associated with medication adherence only in women (OR 0.36; 95%CI 0.13-0.96), whereas a higher masculine BSRI was the only factor associated with medication adherence in men (OR 0.59, 95%CI 0.35-0.99). Conclusion: Pre-admission medication adherence is common in patients hospitalized for IHD, regardless of sex. However, patient-related factors such as diabetes, employment, and personality traits are associated with adherence in a sex-specific manner.

The Future of Maternal and Child Health

INTRODUCTION

The purpose of this commentary is to start a national conversation about the future of maternal and child health (MCH). In the coming decades, we will have unprecedented opportunities to improve MCH, but will also face unprecedented threats.

METHODS

This paper examines emerging opportunities and threats to MCH, and discusses strategies for leading the future of MCH.

RESULTS

Scientific advancements will continue to drive improvements in MCH, but to unleash its full potential for improving population health future MCH research must become more transdisciplinary, translational, and precise. Technological innovations could dramatically transform our work in MCH while big data could enhance predictive analytics and precision health; our challenge will be to assure equitable access. The greatest gains in MCH will continue to come from improving social conditions, which will require advancing MCH in all policies. Climate change, infectious outbreaks and antimicrobial resistance pose increasing threats to MCH, which can be averted by reducing global warming, implementing global early warning systems, and instituting responsible antimicrobial stewardship. The growing burden of chronic diseases in children and adults need to be addressed from an ecological and life course perspective. The water crisis in Flint shined a spotlight on the growing health threats from America's decaying infrastructure.

DISCUSSION

We can lead the future of MCH by starting a national conversation, improving MCH research, and preparing future MCH workforce, but the future of MCH will depend on our effectiveness in bringing about social and political change in the coming decades.