A novel methodology for epidemic risk assessment of COVID-19 outbreak.

We propose a novel data-driven framework for assessing the a-priori epidemic risk of a geographical area and for identifying high-risk areas within a country. Our risk index is evaluated as a function of three different components: the hazard of the disease, the exposure of the area and the vulnerability of its inhabitants. As an application, we discuss the case of COVID-19 outbreak in Italy. We characterize each of the twenty Italian regions by using available historical data on air pollution, human mobility, winter temperature, housing concentration, health care density, population size and age. We find that the epidemic risk is higher in some of the Northern regions with respect to Central and Southern Italy. The corresponding risk index shows correlations with the available official data on the number of infected individuals, patients in intensive care and deceased patients, and can help explaining why regions such as Lombardia, Emilia-Romagna, Piemonte and Veneto have suffered much more than the rest of the country. Although the COVID-19 outbreak started in both North (Lombardia) and Central Italy (Lazio) almost at the same time, when the first cases were officially certified at the beginning of 2020, the disease has spread faster and with heavier consequences in regions with higher epidemic risk. Our framework can be extended and tested on other epidemic data, such as those on seasonal flu, and applied to other countries. We also present a policy model connected with our methodology, which might help policy-makers to take informed decisions.

Artificial Intelligence in mental health and the biases of language based models.

BACKGROUND: The rapid integration of Artificial Intelligence (AI) into the healthcare field has occurred with little communication between computer scientists and doctors. The impact of AI on health outcomes and inequalities calls for health professionals and data scientists to make a collaborative effort to ensure historic health disparities are not encoded into the future. We present a study that evaluates bias in existing Natural Language Processing (NLP) models used in psychiatry and discuss how these biases may widen health inequalities. Our approach systematically evaluates each stage of model development to explore how biases arise from a clinical, data science and linguistic perspective. DESIGN/METHODS: A literature review of the uses of NLP in mental health was carried out across multiple disciplinary databases with defined Mesh terms and keywords. Our primary analysis evaluated biases within 'GloVe' and 'Word2Vec' word embeddings. Euclidean distances were measured to assess relationships between psychiatric terms and demographic labels, and vector similarity functions were used to solve analogy questions relating to mental health. RESULTS: Our primary analysis of mental health terminology in GloVe and Word2Vec embeddings demonstrated significant biases with respect to religion, race, gender, nationality, sexuality and age. Our literature review returned 52 papers, of which none addressed all the areas of possible bias that we identify in model development. In addition, only one article existed on more than one research database, demonstrating the isolation of research within disciplinary silos and inhibiting cross-disciplinary collaboration or communication. CONCLUSION: Our findings are relevant to professionals who wish to minimize the health inequalities that may arise as a result of AI and data-driven algorithms. We offer primary research identifying biases within these technologies and provide recommendations for avoiding these harms in the future.

Social determinants of health, data science, and decision-making: Forging a transdisciplinary synthesis.

Sandro Galea and co-authors discuss a forthcoming Collection on data science and social determinants of health.

Biomedical Research Goes Viral: Dangers and Opportunities.

Researchers around the globe have been mounting, accelerating, and redeploying efforts across disciplines and organizations to tackle the SARS-CoV-2 outbreak. However, humankind continues to be afflicted by numerous other devastating diseases in increasing numbers. Here, we outline considerations and opportunities toward striking a good balance between maintaining and redefining research priorities.

Socioecological indicators of senior financial exploitation: an application of data science to 8,800 substantiated mistreatment cases.

Senior financial exploitation (FE) is prevalent and harmful. Its often insidious nature and co-occurrence with other forms of mistreatment make detection and substantiation challenging. A secondary data analysis of N = 8,800 Adult Protective Services substantiated senior mistreatment cases, using machine learning algorithms, was conducted to determine when pure FE versus hybrid FE was occurring. FE represented N = 2514 (29%) of the cases with 78% being pure FE. Victim suicidal ideation and threatening behaviors, injuries, drug paraphernalia, contentious relationships, caregiver stress, and burnout and victims needing assistance were most important for differentiating FE vs non-FE-related mistreatment. The inability to afford housing, medications, food, and medical care as well as victims suffering from intellectual disability disorder(s) predicted hybrid FE. This study distinguishes socioecological factors strongly associated with the presence of FE during protective service investigations. These findings support existing and new indicators of FE and could inform protective service investigation practices.

Using Precision Public Health to Manage Climate Change: Opportunities, Challenges, and Health Justice.

Amid public health concerns over climate change, "precision public health" (PPH) is emerging in next generation approaches to practice. These novel methods promise to augment public health operations by using ever larger and more robust health datasets combined with new tools for collecting and analyzing data. Precision strategies to protecting the public health could more effectively or efficiently address the systemic threats of climate change, but may also propagate or exacerbate health disparities for the populations most vulnerable in a changing climate. How PPH interventions collect and aggregate data, decide what to measure, and analyze data pose potential issues around privacy, neglecting social determinants of health, and introducing algorithmic bias into climate responses. Adopting a health justice framework, guided by broader social and climate justice tenets, can reveal principles and policy actions which may guide more responsible implementation of PPH in climate responses.

Distributing Local Resources for Public Health Preparedness Grants: A Data-Driven Approach.

OBJECTIVES: To simulate allocations of Public Health Emergency Preparedness funds to counties using alternative metrics of need, minimum allocation amounts, and the proportion earmarked for discretionary considerations. DESIGN: We developed a county-level community resilience index of 57 New York State counties using publicly available indicators, which we incorporated into an interactive spreadsheet of 8 hypothetical allocation formulas with different combinations of population size, the index and its 5 domains, and population density. Simulations were compared with the 2013-2014 fiscal year grant allocation. RESULTS: New York allocated $6.27 million to counties outside New York City, with a median allocation of $78 038, ranging from $50 825 to $556 789. These allocations would vary under different strategies, with the largest changes among sparsely populated counties that currently receive a minimum allocation of $50 825. Allocations were sensitive to changes in minimum allocation, amount earmarked for discretionary allocation, and need indicator. CONCLUSIONS: Population-based approaches are commonly used but ignore important dimensions of need. It is feasible to include robust local community resilience measures in formulas, and interactive spreadsheet models can help stakeholders evaluate the consequences of alternative funding strategies.

Beyond the Randomized Clinical Trial: Innovative Data Science to Close the Pediatric Evidence Gap.

Despite the application of advanced statistical and pharmacometric approaches to pediatric trial data, a large pediatric evidence gap still remains. Here, we discuss how to collect more data from children by using real-world data from electronic health records, mobile applications, wearables, and social media. The large datasets collected with these approaches enable and may demand the use of artificial intelligence and machine learning to allow the data to be analyzed for decision making. Applications of this approach are presented, which include the prediction of future clinical complications, medical image analysis, identification of new pediatric end points and biomarkers, the prediction of treatment nonresponders, and the prediction of placebo-responders for trial enrichment. Finally, we discuss how to bring machine learning from science to pediatric clinical practice. We conclude that advantage should be taken of the current opportunities offered by innovations in data science and machine learning to close the pediatric evidence gap.

Using big data analytics to improve HIV medical care utilisation in South Carolina: A study protocol.

INTRODUCTION: Linkage and retention in HIV medical care remains problematic in the USA. Extensive health utilisation data collection through electronic health records (EHR) and claims data represent new opportunities for scientific discovery. Big data science (BDS) is a powerful tool for investigating HIV care utilisation patterns. The South Carolina (SC) office of Revenue and Fiscal Affairs (RFA) data warehouse captures individual-level longitudinal health utilisation data for persons living with HIV (PLWH). The data warehouse includes EHR, claims and data from private institutions, housing, prisons, mental health, Medicare, Medicaid, State Health Plan and the department of health and human services. The purpose of this study is to describe the process for creating a comprehensive database of all SC PLWH, and plans for using BDS to explore, identify, characterise and explain new predictors of missed opportunities for HIV medical care utilisation. METHODS AND ANALYSIS: This project will create person-level profiles guided by the Gelberg-Andersen Behavioral Model and describe new patterns of HIV care utilisation. The population for the comprehensive database comes from statewide HIV surveillance data (2005-2016) for all SC PLWH (N≈18000). Surveillance data are available from the state health department's enhanced HIV/AIDS Reporting System (e-HARS). Additional data pulls for the e-HARS population will include Ryan White HIV/AIDS Program Service Reports, Health Sciences SC data and Area Health Resource Files. These data will be linked to the RFA data and serve as sources for traditional and vulnerable domain Gelberg-Anderson Behavioral Model variables. The project will use BDS techniques such as machine learning to identify new predictors of HIV care utilisation behaviour among PLWH, and 'missed opportunities' for re-engaging them back into care. ETHICS AND DISSEMINATION: The study team applied for data from different sources and submitted individual Institutional Review Board (IRB) applications to the University of South Carolina (USC) IRB and other local authorities/agencies/state departments. This study was approved by the USC IRB (#Pro00068124) in 2017. To protect the identity of the persons living with HIV (PLWH), researchers will only receive linked deidentified data from the RFA. Study findings will be disseminated at local community forums, community advisory group meetings, meetings with our state agencies, local partners and other key stakeholders (including PLWH, policy-makers and healthcare providers), presentations at academic conferences and through publication in peer-reviewed articles. Data security and patient confidentiality are the bedrock of this study. Extensive data agreements ensuring data security and patient confidentiality for the deidentified linked data have been established and are stringently adhered to. The RFA is authorised to collect and merge data from these different sources and to ensure the privacy of all PLWH. The legislatively mandated SC data oversight council reviewed the proposed process stringently before approving it. Researchers will get only the encrypted deidentified dataset to prevent any breach of privacy in the data transfer, management and analysis processes. In addition, established secure data governance rules, data encryption and encrypted predictive techniques will be deployed. In addition to the data anonymisation as a part of privacy-preserving analytics, encryption schemes that protect running prediction algorithms on encrypted data will also be deployed. Best practices and lessons learnt about the complex processes involved in negotiating and navigating multiple data sharing agreements between different entities are being documented for dissemination.

Dynamic, data-driven typologies of long-term smoking, cessation, and their correlates: Findings from the International Tobacco Control (ITC) Netherlands Survey.

RATIONALE: Efforts towards tobacco control are numerous, but relapse rates for smoking cessations remain high. Behavioral changes necessary for continuous cessation appear complex, variable and subject to social, biological, psychological and environmental determinants. Currently, most cessation studies concentrate on short-to midterm behavioral changes. Besides, they use fixed typologies, thereby failing to capture the temporal changes in smoking/cessation behaviors, and its determinants. OBJECTIVE: To obtain long-term, data-driven longitudinal patterns or profiles of smoking, cessation, and related determinants in a cohort of adult smokers, and to investigate their dynamic links. METHODS: The dataset originated from the International Tobacco Control (ITC) Netherlands Project, waves 2008 to 2016. Temporal dynamics of smoking/cessation, psychosocial constructs, and time-varying determinants of smoking were extracted with Group-Based Trajectory Modeling technique. Their associations were investigated via multiple regression models. RESULTS: Substantial heterogeneity of smoking and cessation behaviors was unveiled. Most respondents were classified as persistent smokers, albeit with distinct levels of consumption. For a minority, cessation could be sustained between 1 and 8 years, while others showed relapsing or fluctuating smoking behavior. Links between smoking/cessation trajectories with those of psychosocial and sociodemographic variables were diverse. Notably, changes in two variables were aligned to behavioral changes towards cessation: decreasing number of smoking peers and attaining a higher self-perceived control. CONCLUSION: The unveiled heterogeneity of smoking behavior over time and the varied cross-dependencies between smoking data-driven typologies and those of underlying risk factors underscore the need of individually tailored approaches for motivational quitting.

Applying Data Science methods and tools to unveil healthcare use of lung cancer patients in a teaching hospital in Spain.

PURPOSE: Our primary goal was to study the use of outpatient attendances by lung cancer patients in Hospital Universitario Puerta de Hierro Majadahonda (HUPHM), Spain, by leveraging our Electronic Patient Record (EPR) and structured clinical registry of lung cancer cases as well as assessing current Data Science methods and tools. METHODS/PATIENTS: We applied the Cross-Industry Standard Process for Data Mining (CRISP-DM) to integrate and analyze activity data extracted from the EPR (9.3 million records) and clinical data of lung cancer patients from a previous registry that was curated into a new, structured database based on REDCap. We have described and quantified factors with an influence in outpatient care use from univariate and multivariate points of view (through Poisson and negative binomial regression). RESULTS: Three cycles of CRISP-DM were performed resulting in a curated database of 522 lung cancer patients with 133 variables which generated 43,197 outpatient visits and tests, 1538 ER visits and 753 inpatient admissions. Stage and ECOG-PS at diagnosis and Charlson Comorbidity Index were major contributors to healthcare use. We also found that the patients' pattern of healthcare use (even before diagnosis), the existence of a history of cancer in first-grade relatives, smoking habits, or even age at diagnosis, could play a relevant role. CONCLUSIONS: Integrating activity data from EPR and clinical structured data from lung cancer patients and applying CRISP-DM has allowed us to describe healthcare use in connection with clinical variables that could be used to plan resources and improve quality of care.

Comparison of data science workflows for root cause analysis of bioprocesses.

Root cause analysis (RCA) is one of the most prominent tools used to comprehensively evaluate a biopharmaceutical production process. Despite of its widespread use in industry, the Food and Drug Administration has observed a lot of unsuitable approaches for RCAs within the last years. The reasons for those unsuitable approaches are the use of incorrect variables during the analysis and the lack in process understanding, which impede correct model interpretation. Two major approaches to perform RCAs are currently dominating the chemical and pharmaceutical industry: raw data analysis and feature-based approach. Both techniques are shown to be able to identify the significant variables causing the variance of the response. Although they are different in data unfolding, the same tools as principal component analysis and partial least square regression are used in both concepts. Within this article we demonstrate the strength and weaknesses of both approaches. We proved that a fusion of both results in a comprehensive and effective workflow, which not only increases better process understanding. We demonstrate this workflow along with an example. Hence, the presented workflow allows to save analysis time and to reduce the effort of data mining by easy detection of the most important variables within the given dataset. Subsequently, the final obtained process knowledge can be translated into new hypotheses, which can be tested experimentally and thereby lead to effectively improving process robustness.

Data Analytics and Modeling for Appointment No-show in Community Health Centers.

OBJECTIVES: Using predictive modeling techniques, we developed and compared appointment no-show prediction models to better understand appointment adherence in underserved populations. METHODS AND MATERIALS: We collected electronic health record (EHR) data and appointment data including patient, provider and clinical visit characteristics over a 3-year period. All patient data came from an urban system of community health centers (CHCs) with 10 facilities. We sought to identify critical variables through logistic regression, artificial neural network, and naïve Bayes classifier models to predict missed appointments. We used 10-fold cross-validation to assess the models' ability to identify patients missing their appointments. RESULTS: Following data preprocessing and cleaning, the final dataset included 73811 unique appointments with 12,392 missed appointments. Predictors of missed appointments versus attended appointments included lead time (time between scheduling and the appointment), patient prior missed appointments, cell phone ownership, tobacco use and the number of days since last appointment. Models had a relatively high area under the curve for all 3 models (e.g., 0.86 for naïve Bayes classifier). DISCUSSION: Patient appointment adherence varies across clinics within a healthcare system. Data analytics results demonstrate the value of existing clinical and operational data to address important operational and management issues. CONCLUSION: EHR data including patient and scheduling information predicted the missed appointments of underserved populations in urban CHCs. Our application of predictive modeling techniques helped prioritize the design and implementation of interventions that may improve efficiency in community health centers for more timely access to care. CHCs would benefit from investing in the technical resources needed to make these data readily available as a means to inform important operational and policy questions.

Data-Driven Investment Strategies for Peer-to-Peer Lending: A Case Study for Teaching Data Science.

We develop a number of data-driven investment strategies that demonstrate how machine learning and data analytics can be used to guide investments in peer-to-peer loans. We detail the process starting with the acquisition of (real) data from a peer-to-peer lending platform all the way to the development and evaluation of investment strategies based on a variety of approaches. We focus heavily on how to apply and evaluate the data science methods, and resulting strategies, in a real-world business setting. The material presented in this article can be used by instructors who teach data science courses, at the undergraduate or graduate levels. Importantly, we go beyond just evaluating predictive performance of models, to assess how well the strategies would actually perform, using real, publicly available data. Our treatment is comprehensive and ranges from qualitative to technical, but is also modular-which gives instructors the flexibility to focus on specific parts of the case, depending on the topics they want to cover. The learning concepts include the following: data cleaning and ingestion, classification/probability estimation modeling, regression modeling, analytical engineering, calibration curves, data leakage, evaluation of model performance, basic portfolio optimization, evaluation of investment strategies, and using Python for data science.

Over and under-regulation in the Colorado Cannabis industry - A data-analytic perspective.

With the State of California legalizing recreational cannabis sales on January 1, 2018, the regulatory process is once more in the forefront of cannabis research. Colorado, often held up as a model of legalization policy, was the first state to implement retail sale of recreational cannabis on January 1st, 2014. However, a combination of subsequent under-regulation and over-regulation, inconsistently applied across issues such as retail licencing, chemical testing, cannabis derivatives, municipality approval for growers, and financing, have not only held back the industry in Colorado but also negatively impacted public health, oversight, and have potentially increased the availability of illegal cannabis. We argue that a data-analytic approach to the industry is potentially the most effective way to resolve these concerns, since in the absence of consistent and reliable data, policymakers are apt to satisfy individual policy concerns without considering the industry as a whole. In this paper we present a data-analytic framework for the cannabis industry, offering a theoretically-driven justification for our approach, and describe implications for research on drug and information policy. The framework may serve as a model for other states or countries contemplating cannabis legalisation. As four new states legalised recreational cannabis in 2016, the implications of this research for policymakers has dramatically increased.

Big Data and Data Science in Critical Care.

The digitalization of the health-care system has resulted in a deluge of clinical big data and has prompted the rapid growth of data science in medicine. Data science, which is the field of study dedicated to the principled extraction of knowledge from complex data, is particularly relevant in the critical care setting. The availability of large amounts of data in the ICU, the need for better evidence-based care, and the complexity of critical illness makes the use of data science techniques and data-driven research particularly appealing to intensivists. Despite the increasing number of studies and publications in the field, thus far there have been few examples of data science projects that have resulted in successful implementations of data-driven systems in the ICU. However, given the expected growth in the field, intensivists should be familiar with the opportunities and challenges of big data and data science. The present article reviews the definitions, types of algorithms, applications, challenges, and future of big data and data science in critical care.

Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century.

Addressing minority health and health disparities has been a missing piece of the puzzle in Big Data science. This article focuses on three priority opportunities that Big Data science may offer to the reduction of health and health care disparities. One opportunity is to incorporate standardized information on demographic and social determinants in electronic health records in order to target ways to improve quality of care for the most disadvantaged populations over time. A second opportunity is to enhance public health surveillance by linking geographical variables and social determinants of health for geographically defined populations to clinical data and health outcomes. Third and most importantly, Big Data science may lead to a better understanding of the etiology of health disparities and understanding of minority health in order to guide intervention development. However, the promise of Big Data needs to be considered in light of significant challenges that threaten to widen health disparities. Care must be taken to incorporate diverse populations to realize the potential benefits. Specific recommendations include investing in data collection on small sample populations, building a diverse workforce pipeline for data science, actively seeking to reduce digital divides, developing novel ways to assure digital data privacy for small populations, and promoting widespread data sharing to benefit under-resourced minority-serving institutions and minority researchers. With deliberate efforts, Big Data presents a dramatic opportunity for reducing health disparities but without active engagement, it risks further widening them.