Medical Assistance in Dying, Data and Casting Assertions Aside

Various assertions have been made regarding why eligibility for medical assistance in dying (MAiD) should be expanded. Examining these and the studies used to support them should clear the way for thoughtful data monitoring and research into why some patients make death hastening requests. This will not only improve MAiD practices in Canada, but will lead to better more effective palliative care for patients whose suffering leads them to covet death.

Advancing Digital Solutions to Overcome Longstanding Barriers in Asthma and COPD Management

Maintenance therapy delivered via inhaler is central to asthma and chronic obstructive pulmonary disease (COPD) management. Poor adherence to inhaled medication and errors in inhalation technique have long represented major barriers to the optimal management of these chronic conditions. Technological innovations may provide a means of overcoming these barriers. This narrative review examines ongoing advances in digital technologies relevant to asthma and COPD with the potential to inform clinical decision-making and improve patient care. Digital inhaler devices linked to mobile apps can help bring about changes in patients' behaviors and attitudes towards disease management, particularly when they build in elements of interactivity and gamification. They can also support ongoing technique education, empowering patients and helping providers maximize the value of consultations and develop effective action plans informed by insights into the patient's inhaler use patterns and their respiratory health. When combined with innovative techniques such as machine learning, digital devices have the potential to predict exacerbations and prompt pre-emptive intervention. Finally, digital devices may support an advanced precision medicine approach to respiratory disease management and help support shared decision-making. Further work is needed to increase uptake of digital devices and integrate their use into care pathways before their full potential in personalized asthma and COPD management can be realized.

Documenting chaplain involvement: a pilot project exploring Pastoral Care and the integration of data science in a Central Florida Hospital

This paper intends to outline a data integration response to the demands placed on the pastoral care department through the COVID-19 pandemic. Uniquely, these demands accelerated the need to implement documentation of care directed towards staff to complement the data derived from patient visitation. The motivation for this initiative is in part, to provide a complete picture of the care provided by hospital chaplains using an evidence-based approach through the implementation of data science.

GenoREC: A Recommendation System for Interactive Genomics Data Visualization

Interpretation of genomics data is critically reliant on the application of a wide range of visualization tools. A large number of visualization techniques for genomics data and different analysis tasks pose a significant challenge for analysts: which visualization technique is most likely to help them generate insights into their data? Since genomics analysts typically have limited training in data visualization, their choices are often based on trial and error or guided by technical details, such as data formats that a specific tool can load. This approach prevents them from making effective visualization choices for the many combinations of data types and analysis questions they encounter in their work. Visualization recommendation systems assist non-experts in creating data visualization by recommending appropriate visualizations based on the data and task characteristics. However, existing visualization recommendation systems are not designed to handle domain-specific problems. To address these challenges, we designed GenoREC, a novel visualization recommendation system for genomics. GenoREC enables genomics analysts to select effective visualizations based on a description of their data and analysis tasks. Here, we present the recommendation model that uses a knowledge-based method for choosing appropriate visualizations and a web application that enables analysts to input their requirements, explore recommended visualizations, and export them for their usage. Furthermore, we present the results of two user studies demonstrating that GenoREC recommends visualizations that are both accepted by domain experts and suited to address the given genomics analysis problem. All supplemental materials are available at https://osf.io/y73pt/.

Natural Language Processing for Improved Characterization of COVID-19 Symptoms: Observational Study of 350,000 Patients in a Large Integrated Health Care System

BACKGROUND

Natural language processing (NLP) of unstructured text from electronic medical records (EMR) can improve the characterization of COVID-19 signs and symptoms, but large-scale studies demonstrating the real-world application and validation of NLP for this purpose are limited.

OBJECTIVE

The aim of this paper is to assess the contribution of NLP when identifying COVID-19 signs and symptoms from EMR.

METHODS

This study was conducted in Kaiser Permanente Southern California, a large integrated health care system using data from all patients with positive SARS-CoV-2 laboratory tests from March 2020 to May 2021. An NLP algorithm was developed to extract free text from EMR on 12 established signs and symptoms of COVID-19, including fever, cough, headache, fatigue, dyspnea, chills, sore throat, myalgia, anosmia, diarrhea, vomiting or nausea, and abdominal pain. The proportion of patients reporting each symptom and the corresponding onset dates were described before and after supplementing structured EMR data with NLP-extracted signs and symptoms. A random sample of 100 chart-reviewed and adjudicated SARS-CoV-2-positive cases were used to validate the algorithm performance.

RESULTS

A total of 359,938 patients (mean age 40.4 [SD 19.2] years; 191,630/359,938, 53% female) with confirmed SARS-CoV-2 infection were identified over the study period. The most common signs and symptoms identified through NLP-supplemented analyses were cough (220,631/359,938, 61%), fever (185,618/359,938, 52%), myalgia (153,042/359,938, 43%), and headache (144,705/359,938, 40%). The NLP algorithm identified an additional 55,568 (15%) symptomatic cases that were previously defined as asymptomatic using structured data alone. The proportion of additional cases with each selected symptom identified in NLP-supplemented analysis varied across the selected symptoms, from 29% (63,742/220,631) of all records for cough to 64% (38,884/60,865) of all records with nausea or vomiting. Of the 295,305 symptomatic patients, the median time from symptom onset to testing was 3 days using structured data alone, whereas the NLP algorithm identified signs or symptoms approximately 1 day earlier. When validated against chart-reviewed cases, the NLP algorithm successfully identified signs and symptoms with consistently high sensitivity (ranging from 87% to 100%) and specificity (94% to 100%).

CONCLUSIONS

These findings demonstrate that NLP can identify and characterize a broad set of COVID-19 signs and symptoms from unstructured EMR data with enhanced detail and timeliness compared with structured data alone.

General Practice Patients' Experiences and Perceptions of the WiserAD Structured Web-Based Support Tool for Antidepressant Deprescribing: Protocol for a Mixed Methods Case Study With Realist Evaluation

BACKGROUND

Research suggests that the rapid increase in worldwide antidepressant use is mainly due to a rise in long-term and potentially inappropriate use. It has been suggested that 1 in 3 antidepressant users among general practice patients are no longer experiencing clinical benefits from their medication and should commence deprescribing. However there are many barriers to antidepressant deprescribing for both patients and clinicians, which adds to the complex nature of reducing or ceasing the medication. As such, antidepressant deprescribing does not routinely occur in clinical practice. Evidence-based supports and interventions for safe and successful antidepressant deprescribing are needed to assist patients and their doctors. Interventions should also include an understanding of how an intervention works, why it works, and whom it is for.

OBJECTIVE

This study aims to evaluate how the WiserAD approach to antidepressant deprescribing works, whom it is for, and the underlying circumstances by (1) examining the experiences and perceptions of WiserAD among antidepressant users, (2) identifying the underlying mechanisms of the WiserAD approach to antidepressant deprescribing, and (3) describing in what contexts and to what extent the underlying mechanisms of WiserAD are suited for antidepressant users.

METHODS

A mixed methods case study with realist evaluation will be conducted among participants in the WiserAD randomized controlled trial for antidepressant deprescribing. Quantitative data will be obtained from up to 12 participants from the intervention and control arms at baseline and 3-month follow-up. Baseline data will be used to characterize the sample using descriptive statistics. Paired samples t tests will also be performed to compare responses between baseline and 3-month follow-up for participant self-management, skills, confidence and knowledge, beliefs about medicines, current emotional health, and well-being symptoms. Qualitative data from the same participants will be collected via narrative interview at 3-month follow-up. Quantitative and qualitative data will be converged to form a "case," and analysis will be conducted within each case with comparisons made across multiple cases.

RESULTS

Recruitment of participants commenced in October 2022 and will be completed by March 2023. Analysis will be completed by June 2023.

CONCLUSIONS

To our knowledge, this will be the first realist evaluation of an antidepressant deprescribing intervention in general practice. Findings from this evaluation may assist in the implementation of the WiserAD approach to antidepressant deprescribing in routine clinical practice.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/42526.

Big Data in a Nano World: A Review on Computational, Data-Driven Design of Nanomaterials Structures, Properties, and Synthesis

The recent rise of computational, data-driven research has significant potential to accelerate materials discovery. Automated workflows and materials databases are being rapidly developed, contributing to high-throughput data of bulk materials that are growing in quantity and complexity, allowing for correlation between structural-chemical features and functional properties. In contrast, computational data-driven approaches are still relatively rare for nanomaterials discovery due to the rapid scaling of computational cost for finite systems. However, the distinct behaviors at the nanoscale as compared to the parent bulk materials and the vast tunability space with respect to dimensionality and morphology motivate the development of data sets for nanometric materials. In this review, we discuss the recent progress in data-driven research in two aspects: functional materials design and guided synthesis, including commonly used metrics and approaches for designing materials properties and predicting synthesis routes. More importantly, we discuss the distinct behaviors of materials as a result of nanosizing and the implications for data-driven research. Finally, we share our perspectives on future directions for extending the current data-driven research into the nano realm.

Barriers and Facilitators of Obtaining Social Determinants of Health of Patients With Cancer Through the Electronic Health Record Using Natural Language Processing Technology: Qualitative Feasibility Study With Stakeholder Interviews

BACKGROUND

Social determinants of health (SDoH), such as geographic neighborhoods, access to health care, education, and social structure, are important factors affecting people's health and health outcomes. The SDoH of patients are scarcely documented in a discrete format in electronic health records (EHRs) but are often available in free-text clinical narratives such as physician notes. Innovative methods like natural language processing (NLP) are being developed to identify and extract SDoH from EHRs, but it is imperative that the input of key stakeholders is included as NLP systems are designed.

OBJECTIVE

This study aims to understand the feasibility, challenges, and benefits of developing an NLP system to uncover SDoH from clinical narratives by conducting interviews with key stakeholders: (1) oncologists, (2) data analysts, (3) citizen scientists, and (4) patient navigators.

METHODS

Individuals who frequently work with SDoH data were invited to participate in semistructured interviews. All interviews were recorded and subsequently transcribed. After coding transcripts and developing a codebook, the constant comparative method was used to generate themes.

RESULTS

A total of 16 participants were interviewed (5 data analysts, 4 patient navigators, 4 physicians, and 3 citizen scientists). Three main themes emerged, accompanied by subthemes. The first theme, importance and approaches to obtaining SDoH, describes how every participant (n=16, 100%) regarded SDoH as important. In particular, proximity to the hospital and income levels were frequently relied upon. Communication about SDoH typically occurs during the initial conversation with the oncologist, but more personal information is often acquired by patient navigators. The second theme, SDoH exists in numerous forms, exemplified how SDoH arises during informal communication and can be difficult to enter into the EHR. The final theme, incorporating SDoH into health services research, addresses how more informed SDoH can be collected. One strategy is to empower patients so they are aware about the importance of SDoH, as well as employing NLP techniques to make narrative data available in a discrete format, which can provide oncologists with actionable data summaries.

CONCLUSIONS

Extracting SDoH from EHRs was considered valuable and necessary, but obstacles such as narrative data format can make the process difficult. NLP can be a potential solution, but as the technology is developed, it is important to consider how key stakeholders document SDoH, apply the NLP systems, and use the extracted SDoH in health outcome studies.

State-Level COVID-19 Symptom Searches and Case Data: Quantitative Analysis of Political Affiliation as a Predictor for Lag Time Using Google Trends and Centers for Disease Control and Prevention Data

BACKGROUND

Across each state, the emergence of the COVID-19 pandemic in the United States was marked by policies and rhetoric that often corresponded to the political party in power. These diverging responses have sparked broad ongoing discussion about how the political leadership of a state may affect not only the COVID-19 case numbers in a given state but also the subjective individual experience of the pandemic.

OBJECTIVE

This study leverages state-level data from Google Search Trends and Centers for Disease Control and Prevention (CDC) daily case data to investigate the temporal relationship between increases in relative search volume for COVID-19 symptoms and corresponding increases in case data. I aimed to identify whether there are state-level differences in patterns of lag time across each of the 4 spikes in the data (RQ1) and whether the political climate in a given state is associated with these differences (RQ2).

METHODS

Using publicly available data from Google Trends and the CDC, linear mixed modeling was utilized to account for random state-level intercepts. Lag time was operationalized as number of days between a peak (a sustained increase before a sustained decline) in symptom search data and a corresponding spike in case data and was calculated manually for each of the 4 spikes in individual states. Google offers a data set that tracks the relative search incidence of more than 400 potential COVID-19 symptoms, which is normalized on a 0-100 scale. I used the CDC's definition of the 11 most common COVID-19 symptoms and created a single construct variable that operationalizes symptom searches. To measure political climate, I considered the proportion of 2020 Trump popular votes in a state as well as a dummy variable for the political party that controls the governorship and a continuous variable measuring proportional party control of federal Congressional representatives.

RESULTS

The strongest overall fit was for a linear mixed model that included proportion of 2020 Trump votes as the predictive variable of interest and included controls for mean daily cases and deaths as well as population. Additional political climate variables were discarded for lack of model fit. Findings indicated evidence that there are statistically significant differences in lag time by state but that no individual variable measuring political climate was a statistically significant predictor of these differences.

CONCLUSIONS

Given that there will likely be future pandemics within this political climate, it is important to understand how political leadership affects perceptions of and corresponding responses to public health crises. Although this study did not fully model this relationship, I believe that future research can build on the state-level differences that I identified by approaching the analysis with a different theoretical model, method for calculating lag time, or level of geographic modeling.

Developing a care home data platform in Scotland: a mixed methods study of data routinely collected in care homes

BACKGROUND

care homes collect extensive data about their residents, and their care, in multiple ways, for multiple purposes. We aimed to (i) identify what data are routinely collected and (ii) collate care home managers' views and experiences of collecting, using and sharing data.

METHODS

we examined data collected in six care homes across Lothian, Scotland. We extracted the meta-data, cross-referenced definitions and assessed the degree of harmonisation between care homes and with data sets currently in use in Scotland and internationally. We interviewed care home managers about their views and experiences of collecting, using and sharing data.

RESULTS

we identified 15 core data items used routinely, with significant heterogeneity in tools and assessments used, and very limited harmonisation. Two overarching themes were identified of importance to the development of a care home data platform: (i) the rationale for collecting data, including to (a) support person-centred care, (b) share information, (c) manage workforce and budget and (d) provide evidence to statutory bodies and (ii) the reality of collecting data, including data accuracy, and understanding data in context.

DISCUSSION

considerable information is collected by care home staff, in varied formats, with heterogeneity of scope and definition, for range of reasons. We discuss the issues that should be considered to ensure that individual resident-level form the strong foundations for any data platform for care homes, which must also include, robust infrastructure and clear interoperability, with appropriate governance. It must be co-produced by academics, policy makers and sector representatives, with residents, their families and care staff.

Using data linkage to monitor COVID-19 vaccination: development of a vaccination linked data repository

The COVID-19 Vaccination Linked Data Repository (CVLDR) was established in 2021 to assist with the implementation and management of the COVID-19 vaccination program in the State of Western Australia (WA). The CVLDR contains a number of datasets including the Australian Immunisation Register, hospital admissions, emergency department attendances, notifiable infectious disease, and laboratory data. Datasets in the CVLDR are linked using a probabilistic method at the WA Department of Health. Quality assurance mechanisms have been established to identify and mitigate potential errors in the linkage. Each of the datasets has varying degrees of data quality and completeness, however most are of high standard, underpinned by legislation. The linking of the datasets within the CVLDR has allowed for increased public health utility in the immunisation program including the areas of vaccine safety, effectiveness, and coverage.

Case-Finding Strategies for Drug-Resistant Tuberculosis: Protocol for a Scoping Review

BACKGROUND

Transmission of drug-resistant tuberculosis (DR-TB) is ongoing. Finding individuals with DR-TB and initiating treatment as early as possible is important to improve patient clinical outcomes and to break the chain of transmission to control the pandemic. To our knowledge systematic reviews assessing effectiveness, cost-effectiveness, acceptability, and feasibility of different case-finding strategies for DR-TB to inform research, policy, and practice have not been conducted, and it is unknown whether enough research exists to conduct such reviews. It is unknown whether case-finding strategies are similar for DR-TB and drug-susceptible TB and whether we can draw on findings from drug-susceptible reviews to inform decisions on case-finding strategies for DR-TB.

OBJECTIVE

This protocol aims to describe the available literature on case-finding for DR-TB and to describe case-finding strategies.

METHODS

We will screen systematic reviews, trials, qualitative studies, diagnostic test accuracy studies, and other primary research that specifically sought to improve DR-TB case detection. We will exclude studies that invited individuals seeking care for TB symptoms, those including individuals already diagnosed with TB, or laboratory-based studies. We will search the academic databases including MEDLINE, Embase, The Cochrane Library, Africa-Wide Information, CINAHL, Epistemonikos, and PROSPERO with no language or date restrictions. We will screen titles, abstracts, and full-text articles in duplicate. Data extraction and analyses will be performed using Excel (Microsoft Corp).

RESULTS

We will provide a narrative report with supporting figures or tables to summarize the data. A systems-based logic model, developed from a synthesis of case-finding strategies for drug-susceptible TB, will be used as a framework to describe different strategies, resulting pathways, and enhancements of pathways. The search will be conducted at the end of 2021. Title and abstract screening, full text screening, and data extraction will be undertaken from January to June 2022. Thereafter, analysis will be conducted, and results compiled.

CONCLUSIONS

This scoping review will chart existing literature on case-finding for DR-TB-this will help determine whether primary studies on effectiveness, cost-effectiveness, acceptability, and feasibility of different case-finding strategies for DR-TB exist and will help formulate potential questions for a systematic review. We will also describe case-finding strategies for DR-TB and how they fit into a model of case-finding pathways for drug-susceptible TB. This review has some limitations. One limitation is the diverse, inconsistent use of intervention terminology within the literature, which may result in missing relevant studies. Poor reporting of intervention strategies may also cause misunderstanding and misclassification of interventions. Lastly, case-finding strategies for DR-TB may not fit into a model developed from strategies for drug-susceptible TB. Nevertheless, such a situation will provide an opportunity to refine the model for future research. The review will guide further research to inform decisions on case-finding policies and practices for DR-TB.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/40009.

The Use of Passive Smartphone Data to Monitor Anxiety and Depression Among College Students in Real-World Settings: Protocol for a Systematic Review

BACKGROUND

College students are particularly at risk of depression and anxiety. These disorders have a serious impact on public health and affect patients' daily lives. The potential for using smartphones to monitor these mental conditions, providing passively collected physiological and behavioral data, has been reported among the general population. However, research on the use of passive smartphone data to monitor anxiety and depression among specific populations of college students has never been reviewed.

OBJECTIVE

This review's objectives are (1) to provide an overview of the use of passive smartphone data to monitor depression and anxiety among college students, given their specific type of smartphone use and living setting, and (2) to evaluate the different methods used to assess those smartphone data, including their strengths and limitations.

METHODS

This review will follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two independent investigators will review English-language, full-text, peer-reviewed papers extracted from PubMed and Web of Science that measure passive smartphone data and levels of depression or anxiety among college students. A preliminary search was conducted in February 2022 as a proof of concept.

RESULTS

Our preliminary search identified 115 original articles, 8 of which met our eligibility criteria. Our planned full study will include an article selection flowchart, tables, and figures representing the main information extracted on the use of passive smartphone data to monitor anxiety and depression among college students.

CONCLUSIONS

The planned review will summarize the published research on using passive smartphone data to monitor anxiety and depression among college students. The review aims to better understand whether and how passive smartphone data are associated with indicators of depression and anxiety among college students. This could be valuable in order to provide a digital solution for monitoring mental health issues in this specific population by enabling easier identification and follow-up of the patients.

TRIAL REGISTRATION

PROSPERO CRD42022316263; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=316263.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/38785.

To Address Women's Health Inequity, It Must First Be Measured

Research and data collection related to what is historically known as "women's health" is consistently underfunded and marginalizes the health risks and experiences of women of color and transgender people. In the wake of the pandemic, the United States has an opportunity to redesign and reimagine a modern public health data infrastructure that centers equity and elevates the health and well-being of under-represented communities, including the full spectrum of gender identities. This piece offers a blueprint for transformational change in how the United States collects, interprets, and shares critical data to deliver greater health justice for all.

An economy of details: standards and data reusability

Reusability has been a key issue since the origins of the parts-based approach to synthetic biology. Starting with the BioBrick™ standard part, multiple efforts have aimed to make biology more exchangeable. The reusability of parts and other deoxyribonucleic acid-based data has proven over time to be challenging, however. Drawing on a series of qualitative interviews and an international workshop, this article explores the challenges of reusability in real laboratory practice. It shows particular ways that standards are experienced as presenting shortcomings for capturing the kinds of contextual information crucial for scientists to be able to reuse biological parts and data. I argue that researchers in specific laboratories develop a sense of how much circumstantial detail they need to share for others to be able to make sense of their data and possibly reuse it. When choosing particular reporting formats, recharacterizing data to gain closer knowledge or requesting additional information, researchers enact an 'economy of details'. The farther apart two laboratories are in disciplinary, epistemological, technical and geographical terms, the more detailed information needs to be captured for data to be reusable across contexts. In synthetic biology, disciplinary distance between computing science and engineering researchers and experimentalist biologists is reflected in diverging views on standards: what kind of information should be included to enable reusability, what kind of information can be captured by standards at all and how they may serve to produce and circulate knowledge. I argue that such interdisciplinary tensions lie at the core of difficulties in setting standards in synthetic biology.

The Holy grail of healthcare analytics: what it takes to get there?

BACKGROUND

Indian healthcare is rapidly growing and needs efficiency more than ever, which can be achieved by leveraging healthcare analytics. National Digital Health Mission has set the stage for digital health and getting the right direction from the very beginning is important. The current study was, therefore, undertaken to find what it takes for an apex tertiary care teaching hospital to leverage healthcare analytics.

AIM

To study the existing Hospital Information System (HIS) at AIIMS, New Delhi and assess the preparedness to leverage healthcare analytics.

METHODOLOGY

A three-pronged approach was used. First, concurrent review and detailed mapping of all running applications was done based on nine parameters by a multidisciplinary team of experts. Second, capability of the current HIS to measure specific management related KPIs was evaluated. Third, user perspective was obtained from 750 participants from all cadres of healthcare workers, using a validated questionnaire based on Delone and McLean model.

RESULTS

Interoperability issues between applications running within the same institute, impaired informational continuity with limited device interface and automation were found on concurrent review. HIS was capturing data to measure only 9 out of 33 management KPIs. User perspective on information quality was very poor which was found to be due to poor system quality of HIS, though some functions were reportedly well supported by the HIS.

CONCLUSION

It is important for hospitals to first evaluate and strengthen their data generation systems/HIS. The three-pronged approach used in this study provides a template for other hospitals.

Investigating the Effects of Hyperbaric Oxygen Treatment in Necrotizing Soft Tissue Infection With Transcriptomics and Machine Learning (the HBOmic Study): Protocol for a Prospective Cohort Study With Data Validation

BACKGROUND

Necrotizing soft tissue infections (NSTIs) are complex multifactorial diseases characterized by rapid bacterial proliferation and progressive tissue death. Treatment is multidisciplinary, including surgery, broad-spectrum antibiotics, and intensive care; adjunctive treatment with hyperbaric oxygen (HBO₂) may also be applied. Recent advances in molecular technology and biological computation have given rise to new approaches to infectious diseases based on identifying target groups defined by activated pathophysiological mechanisms.

OBJECTIVE

We aim to capture NSTI disease signatures and mechanisms and responses to treatment in patients that receive the highest standard of care; therefore, we set out to investigate genome-wide transcriptional responses to HBO₂ treatment during NSTI in the host and bacteria.

METHODS

The Effects of Hyperbaric Oxygen Treatment Studied with Omics (HBOmic) study is a prospective cohort study including 95 patients admitted for NSTI at the intensive care unit of Copenhagen University Hospital (Rigshospitalet), Denmark, between January 2013 and June 2017. All participants were treated according to a local protocol for management of NSTI, and biological samples were obtained and stored according to a standard operational procedure. In the proposed study, we will generate genome-wide expression profiles of whole-blood samples and samples of infected tissue taken before and after HBO₂ treatment administered during the initial acute phase of infection, and we will analyze the profiles with unsupervised hierarchical clustering and machine learning. Differential gene expression will be compared in samples taken before and after HBO₂ treatment (N=85), and integration of profiles from blood and tissue samples will be performed. Furthermore, findings will be compared to NSTI patients who did not receive HBO₂ treatment (N=10). Transcriptomic data will be integrated with clinical data to investigate associations and predictors.

RESULTS

The first participant was enrolled on July 27, 2021, and data analysis is expected to begin during autumn 2022, with publication of results immediately thereafter.

CONCLUSIONS

The HBOmic study will provide new insights into personalized patient management in NSTIs.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01790698; https://clinicaltrials.gov/ct2/show/NCT01790698.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/39252.

Chatbot-Delivered Cognitive Behavioral Therapy in Adolescents With Depression and Anxiety During the COVID-19 Pandemic: Feasibility and Acceptability Study

BACKGROUND

Symptoms of depression and anxiety, suicidal ideation, and self-harm have escalated among adolescents to crisis levels during the COVID-19 pandemic. As a result, primary care providers (PCPs) are often called on to provide first-line care for these youth. Digital health interventions can extend mental health specialty care, but few are evidence based. We evaluated the feasibility of delivering an evidence-based mobile health (mHealth) app with an embedded conversational agent to deliver cognitive behavioral therapy (CBT) to symptomatic adolescents presenting in primary care settings during the pandemic.

OBJECTIVE

In this 12-week pilot study, we evaluated the feasibility of delivering the app-based intervention to adolescents aged 13 to 17 years with moderate depressive symptoms who were treated in a practice-based research network (PBRN) of academically affiliated primary care clinics. We also obtained preliminary estimates of app acceptability, effectiveness, and usability.

METHODS

This small, pilot randomized controlled trial (RCT) evaluated depressive symptom severity in adolescents randomized to the app or to a wait list control condition. The primary end point was depression severity at 4-weeks, measured by the 9-item Patient Health Questionnaire (PHQ-9). Data on acceptability, feasibility, and usability were collected from adolescents and their parent or legal guardian. Qualitative interviews were conducted with 13 PCPs from 11 PBRN clinics to identify facilitators and barriers to incorporating mental health apps in treatment planning for adolescents with depression and anxiety.

RESULTS

The pilot randomized 18 participants to the app (n=10, 56%) or to a wait list control condition (n=8, 44%); 17 participants were included in the analysis, and 1 became ineligible upon chart review due to lack of eligibility based on documented diagnosis. The overall sample was predominantly female (15/17, 88%), White (15/17, 88%), and privately insured (15/17, 88%). Mean PHQ-9 scores at 4 weeks decreased by 3.3 points in the active treatment group (representing a shift in mean depression score from moderate to mild symptom severity categories) and 2 points in the wait list control group (no shift in symptom severity category). Teen- and parent-reported usability, feasibility, and acceptability of the app was high. PCPs reported preference for introducing mHealth interventions like the one in this study early in the course of care for individuals presenting with mild or moderate symptoms.

CONCLUSIONS

In this small study, we demonstrated the feasibility, acceptability, usability, and safety of using a CBT-based chatbot for adolescents presenting with moderate depressive symptoms in a network of PBRN-based primary care clinics. This pilot study could not establish effectiveness, but our results suggest that further study in a larger pediatric population is warranted. Future study inclusive of rural, socioeconomically disadvantaged, and underrepresented communities is needed to establish generalizability of effectiveness and identify implementation-related adaptations needed to promote broader uptake in pediatric primary care.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04603053; https://clinicaltrials.gov/ct2/show/NCT04603053.

Invited Commentary: The Importance of Descriptive Epidemiology

Lesko et al. (Am J Epidemiol. 2022;191(12):2063-2070) propose a framework for descriptive epidemiology. This framework helps lay out some of the key issues in producing a useful descriptive work. Lesko et al. help emphasize the importance and value of descriptive work in epidemiology and public health. In this commentary, related issues are discussed and open questions are raised.

The multidimensional legal nature of personal genomic sequence data: A South African perspective

This article provides a comprehensive analysis of the various dimensions in South African law applicable to personal genomic sequence data. This analysis includes property rights, personality rights, and intellectual property rights. Importantly, the under-investigated question of whether personal genomic sequence data are capable of being owned is investigated and answered affirmatively. In addition to being susceptible of ownership, personal genomic sequence data are also the object of data subjects' personality rights, and can also be the object of intellectual property rights: whether on their own qua trade secret or as part of a patented invention or copyrighted dataset. It is shown that personality rights constrain ownership rights, while the exploitation of intellectual property rights is constrained by both personality rights and ownership rights. All of these rights applicable to personal genomic sequence data should be acknowledged and harmonized for such data to be used effectively.

Federated learning for preserving data privacy in collaborative healthcare research

Generalizability, external validity, and reproducibility are high priorities for artificial intelligence applications in healthcare. Traditional approaches to addressing these elements involve sharing patient data between institutions or practice settings, which can compromise data privacy (individuals' right to prevent the sharing and disclosure of information about themselves) and data security (simultaneously preserving confidentiality, accuracy, fidelity, and availability of data). This article describes insights from real-world implementation of federated learning techniques that offer opportunities to maintain both data privacy and availability via collaborative machine learning that shares knowledge, not data. Local models are trained separately on local data. As they train, they send local model updates (e.g. coefficients or gradients) for consolidation into a global model. In some use cases, global models outperform local models on new, previously unseen local datasets, suggesting that collaborative learning from a greater number of examples, including a greater number of rare cases, may improve predictive performance. Even when sharing model updates rather than data, privacy leakage can occur when adversaries perform property or membership inference attacks which can be used to ascertain information about the training set. Emerging techniques mitigate risk from adversarial attacks, allowing investigators to maintain both data privacy and availability in collaborative healthcare research. When data heterogeneity between participating centers is high, personalized algorithms may offer greater generalizability by improving performance on data from centers with proportionately smaller training sample sizes. Properly applied, federated learning has the potential to optimize the reproducibility and performance of collaborative learning while preserving data security and privacy.

Indigenous Scientist: Digital & Health Science Transformation

As an indigenous scientist, I have dedicated all my professional life to protecting people using informatics for public policy to the privacy of users, patients, clients, and citizens as a human right and obligation as part of the United Nations international development goals. I am reflecting on my earliest knowledge of the impact of data and information privacy on my journey as scientist. I was just a number out of many other numbers as a indigenous child. The aim of this paper is to share my own personal experience together with one of my students. Now working with data as a scientific task within the data modeling to measure poverty. As a datum with human value, I was a 1) Female child with young parents, 2) Low socioeconomic status & 3) Identified as an indigenous person within a minor language group. These three data descriptions described me as a person who needed protection of my human dignity and identity as a child, based on all the protocols of social services for providing help. In conclusion, as scientists, we need to remember when using client data in vulnerable contexts and protection of their privacy, due to the potential risk of active discrimination. Thanks to my extensive education in Australia, I became an outlying datum that deviated from the data modeling applied to me. Today, I work for Privacy digital standards to impact real life with respect to human dignity and obtain accurate scientific interpretations of human beings' realities.

Data transforming: A concept analysis

PURPOSE

The aims of this study are to clarify the concept of how data retrieved from electronic health records (EHR) are transformed into nurses' tacit knowledge for evidence-based practice from a cognitive perspective at a macro-organizational level, and to identify this concept's attributes, antecedents, and consequences in the nursing field.

SOURCE

A literature review was conducted by performing a search on scientific databases using the key terms "data," "transform," "EHR," "nursing," "tacit knowledge," "organization," "data," "interpretation," and "healthcare." Forty-nine articles and four books were selected for the analysis. The process was audited by two independent experts to ensure neutrality and credibility.

CONCLUSION

Data transforming is a complex cognitive process among different groups of data stakeholders at a macro-organizational level. The concept of data transforming has three attributes: analytical, respectful, and social. The antecedents of these attributes are skillful, immersive, and mission-driven. They have either positive or negative consequences for frontline nurses. These findings not only add to the body of knowledge but also serve as an important impetus for further theory development and research in nursing.

An Evaluation of Primary Studies Published in Predatory Journals Included in Systematic Reviews From High-Impact Dermatology Journals: Cross-sectional Study

BACKGROUND

Predatory publishing is a deceptive form of publishing that uses unethical business practices, minimal to no peer review processes, or limited editorial oversight to publish articles. It may be problematic to our highest standard of scientific evidence-systematic reviews-through the inclusion of poor-quality and unusable data, which could mislead results, challenge outcomes, and undermine confidence. Thus, there is a growing concern surrounding the effects predatory publishing may have on scientific research and clinical decision-making.

OBJECTIVE

The objective of this study was to evaluate whether systematic reviews published in top dermatology journals contain primary studies published in suspected predatory journals (SPJs).

METHODS

We searched PubMed for systematic reviews published in the top five dermatology journals (determined by 5-year h-indices) between January 1, 2019, and May 24, 2021. Primary studies were extracted from each systematic review, and the publishing journal of these primary studies was cross-referenced using Beall's List and the Directory of Open Access Journals. Screening and data extraction were performed in a masked, duplicate fashion. We performed chi-square tests to determine possible associations between a systematic review's inclusion of a primary study published in a SPJ and particular study characteristics.

RESULTS

Our randomized sample included 100 systematic reviews, of which 31 (31%) were found to contain a primary study published in a SPJ. Of the top five dermatology journals, the Journal of the American Academy of Dermatology had the most systematic reviews containing a primary study published in an SPJ. Systematic reviews containing a meta-analysis or registered protocol were significantly less likely to contain a primary study published in a SPJ. No statistically significant associations were found between other study characteristics.

CONCLUSIONS

Studies published in SPJs are commonly included as primary studies in systematic reviews published in high-impact dermatology journals. Future research is needed to investigate the effects of including suspected predatory publications in scientific research.

The Need for a Data Ecosystem for Youth Mental Health in The Netherlands

The Netherlands is missing nationally representative data on child and adolescent mental health, e.g., on prevalence, course, and consequences of psychological disorders and mental health care utilization. Researchers and policy makers also lack a basic data infrastructure that is necessary to provide timely and reliable data crucial for benchmarking and informed decision making. In this article, we describe the necessity for a clear and well-organized overview of data on youth mental health and mental health care. We look back on three key moments in time to illustrate the breadth of the desire for data. Barriers in collecting structured, national data on a frequent basis are discussed, and several recommendations are provided of what is needed to move towards a data ecosystem that can help us to track the development and mental well-being of all children and youth and the impact of the care they receive.

The Apple Watch for Monitoring Mental Health-Related Physiological Symptoms: Literature Review

BACKGROUND

An anticipated surge in mental health service demand related to COVID-19 has motivated the use of novel methods of care to meet demand, given workforce limitations. Digital health technologies in the form of self-tracking technology have been identified as a potential avenue, provided sufficient evidence exists to support their effectiveness in mental health contexts.

OBJECTIVE

This literature review aims to identify current and potential physiological or physiologically related monitoring capabilities of the Apple Watch relevant to mental health monitoring and examine the accuracy and validation status of these measures and their implications for mental health treatment.

METHODS

A literature review was conducted from June 2021 to July 2021 of both published and gray literature pertaining to the Apple Watch, mental health, and physiology. The literature review identified studies validating the sensor capabilities of the Apple Watch.

RESULTS

A total of 5583 paper titles were identified, with 115 (2.06%) reviewed in full. Of these 115 papers, 19 (16.5%) were related to Apple Watch validation or comparison studies. Most studies showed that the Apple Watch could measure heart rate acceptably with increased errors in case of movement. Accurate energy expenditure measurements are difficult for most wearables, with the Apple Watch generally providing the best results compared with peers, despite overestimation. Heart rate variability measurements were found to have gaps in data but were able to detect mild mental stress. Activity monitoring with step counting showed good agreement, although wheelchair use was found to be prone to overestimation and poor performance on overground tasks. Atrial fibrillation detection showed mixed results, in part because of a high inconclusive result rate, but may be useful for ongoing monitoring. No studies recorded validation of the Sleep app feature; however, accelerometer-based sleep monitoring showed high accuracy and sensitivity in detecting sleep.

CONCLUSIONS

The results are encouraging regarding the application of the Apple Watch in mental health, particularly as heart rate variability is a key indicator of changes in both physical and emotional states. Particular benefits may be derived through avoidance of recall bias and collection of supporting ecological context data. However, a lack of methodologically robust and replicated evidence of user benefit, a supportive health economic analysis, and concerns about personal health information remain key factors that must be addressed to enable broader uptake.

Governing with health code: Standardising China's data network systems during COVID-19

Noting the infrastructural turn in platform studies, the article conceives China's health code system, Jian Kang Ma (JKM), deployed to manage the COVID-19 crisis as a new social infrastructure that manifests the symbolic and material power of the Party State. Using the platform walkthrough method and documentary inquiry, we unpack the structures of platform governance and identify actors of the power to appreciate the socio-political dynamics of platform algorithms. JKM's structural power is not monolithic in the name of the Party State but supports a process of structuration that operates across multiple actors, administrative bodies and, governing layers. JKM has centralised data systems through the building of a nationwide algorithmic standard of COVID-19 governance. JKM typified the political dynamics of deterritorialisation, a reference to the state's governing mindset of eradicating local variants of policy implementation and governing autonomy in China. The removal of local power in pandemic administration has led to the production of a unified national subject. Such a comprehensive approach begs for greater nuance and sophisticated knowledge about those indigenous logics that platforms and algorithms operate and are embedded in, thus contributing to de-westernising platform studies.

Equity as a Guiding Principle for the Public Health Data System

The growing centering of equity in health has elevated a conversation about how those interests should translate within the systems and sectors that influence health. In particular, the public health data system has been relatively limited in capturing the drivers and consequences of health inequity as well as the varying dimensions of equity. This article examines what it means to use equity as a guiding principle throughout the components and functions of a modern public health data system. As with other articles in this supplement, this article builds from a literature review, environmental scan, and deliberations from the National Commission to Transform Public Health Data Systems to summarize current gaps to integrate equity throughout the system. It outlines opportunities for the technology and data science sectors specifically to engage given the access that these sectors have to information that would illuminate and frame the nuances and impacts of health inequity.

Testing the Effectiveness of an Animated Decision Aid to Improve Recruitment of Control Participants in a Case-Control Study: Web-Based Experiment

BACKGROUND

Participation in case-control studies is crucial in epidemiological research. The self-sampling bias, low response rate, and poor recruitment of population representative controls are often reported as limitations of case-control studies with limited strategies to improve participation. With greater use of web-based methods in health research, there is a further need to understand the effectiveness of different tools to enhance informed decision-making and willingness to take part in research.

OBJECTIVE

This study tests whether the inclusion of an animated decision aid in the recruitment page of a study website can increase participants' intentions to volunteer as controls.

METHODS

A total of 1425 women were included in a web-based experiment and randomized to one of two experimental conditions: one in which they were exposed to a simulated website that included the animation (animation; n=693, 48.6%), and one in which they were exposed to the simulated website without the animation (control; n=732, 51.4%). The simulated website was adapted from a real website for a case-control study, which invites people to consider taking part in a study that investigates differences in purchasing behaviors between women with and without ovarian cancer and share their loyalty card data collected through 2 high street retailers with the researchers. After exposure to the experimental manipulation, participants were asked to state (1) their intention to take part in the case-control study, (2) whether they would be willing to share their loyalty card for research, and (3) their willingness to be redirected to the real website after completing the survey. Data were assessed using ordinal and binary logistic regression, reported in percentages (%), adjusted odds ratio (AOR), and 95% confidence intervals.

RESULTS

Including the animation in the simulated website did not increase intentions to participate in the study (AOR 1.09; 95% CI 0.88-1.35) or willingness to visit the real study website after the survey (control 50.5% vs animation 52.6%, AOR 1.08; 95% CI 0.85-1.37). The animation, however, increased the participants' intentions to share the data from their loyalty cards for research in general (control 17.9% vs animation 26%; AOR 1.64; 95% CI 1.23-2.18).

CONCLUSIONS

While the results of this study indicate that the animated decision aid did not lead to greater intention to take part in our web-based case-control study, they show that they can be effective in increasing people's willingness to share sensitive data for health research.

A Novel Modulation and Demodulation Method Based on Binary Frequency Shift Keying for Wireless Power and Data-Parallel Transmission

It is usually necessary but difficult to achieve reliable communication between the primary side and pick-up side in the wireless power transfer (WPT) system due to magnetic interferences. In this paper, a novel parallel transmission method for wireless power and data is proposed, which is based on the frequency shift keying (FSK) modulation and demodulation. The data are transmitted by changing the working frequency of the inverter and then demodulated based on the phase-locked loop (PLL) technology. In this way, the signal before the rectifier circuit for the data demodulation can overcome the influence of power transmission on the data transmission. Finally, a 426 W prototype has been built to verify the effectiveness of the proposed transmission method. The experimental results showed that when the output power changed from tens of watts to hundreds of watts, the data transmission was capable of achieving a stable transmission with a 10 kbps baud rate.

Tabular strategies for metadata in ecology, evolution, and the environmental sciences

Data support knowledge development and theory advances in ecology and evolution. We are increasingly reusing data within our teams and projects and through the global, openly archived datasets of others. Metadata can be challenging to write and interpret, but it is always crucial for reuse. The value metadata cannot be overstated-even as a relatively independent research object because it describes the work that has been done in a structured format. We advance a new perspective and classify methods for metadata curation and development with tables. Tables with templates can be effectively used to capture all components of an experiment or project in a single, easy-to-read file familiar to most scientists. If coupled with the R programming language, metadata from tables can then be rapidly and reproducibly converted to publication formats including extensible markup language files suitable for data repositories. Tables can also be used to summarize existing metadata and store metadata across many datasets. A case study is provided and the added benefits of tables for metadata, a priori, are developed to ensure a more streamlined publishing process for many data repositories used in ecology, evolution, and the environmental sciences. In ecology and evolution, researchers are often highly tabular thinkers from experimental data collection in the lab and/or field, and representations of metadata as a table will provide novel research and reuse insights.

The experience of staff utilizing data to evaluate and improve person-centred practice: An action research study

AIM

Explore how nurses and midwives use patient experience data collected from a mobile health app to influence the development of person-centred practice.

DESIGN

Participatory action research, underpinned by the Person-Centred Nursing Framework and Practice Development principles.

METHODS

Six clinical units in a large health district engaged in three action cycles from 2018 to 2020 using a mobile health app. Nursing/midwifery staff on the units (N = 177) utilized data collected via the app to evaluate and improve person-centred practice. A pre-post survey using the PCPI-S was conducted to evaluate staff perceptions of person-centredness. Data from the surveys (n = 101 in 2018 and n = 102 in 2020) and 17 semi-structured interviews were used to understand the influence working with these data had on person-centred practice. The Guidelines for Best Practices in the Reporting of Participatory Action Research have been used to report this study.

RESULTS

Improvements in person-centred practice were noted across both data sets. There was a statistically significant increase in two domains of the PCPI-S in the independent t-test and across all three domains in the paired t-test results. Thematic analysis resulted in the identification of six themes: Getting everyone on board, once we understood, keeping on track, there's a person in the bed, knowing you're doing a good job and improving over time.

CONCLUSION

Engaging with the data collected from the app in a facilitated and collaborative way results in increases in person-centredness.

IMPACT

This study provides insight into how nurses and midwives used data from a mHealth app to evaluate and improve person-centred practice. Utilizing the data generated by the app resulted in increased person-centredness amongst staff and changes to practice and culture. Nursing and midwifery teams who are supported to engage with patient experience data in an action-oriented way will see person-centred practice improvements, affecting patients and staff.

Too Much of a Good Thing? A Governing Knowledge Commons Review of Abundance in Context

The economics of abundance, along with the sociology of abundance, the law of abundance, and so forth, should be re-framed, linked, and situated in a common context for empirical rather than conceptual research. Abundance may seem to be a new, big thing, between anxiety over information overload, Big Data, and related technological disruptions. But scholars know that abundance is an ancient phenomenon, which only seemed to disappear as twentieth century social science focused on scarcity instead. Restoring the study of abundance, and figuring out how to solve the problems that abundance might create, means shedding disciplinary blinders and going back to basics. How does abundance, in various forms, create or alleviate social problems? We explain and illustrate how the Governing Knowledge Commons (GKC) framework provides a useful research tool to generate and test hypotheses about abundance in various economic, social, cultural, and legal settings.

The Role of Information Boxes in Search Engine Results for Symptom Searches: Analysis of Archival Data

Background

Search engines provide health information boxes as part of search results to address information gaps and misinformation for commonly searched symptoms. Few prior studies have sought to understand how individuals who are seeking information about health symptoms navigate different types of page elements on search engine results pages, including health information boxes.

Objective

Using real-world search engine data, this study sought to investigate how users searching for common health-related symptoms with Bing interacted with health information boxes (info boxes) and other page elements.

Methods

A sample of searches (N=28,552 unique searches) was compiled for the 17 most common medical symptoms queried on Microsoft Bing by users in the United States between September and November 2019. The association between the page elements that users saw, their characteristics, and the time spent on elements or clicks was investigated using linear and logistic regression.

Results

The number of searches ranged by symptom type from 55 searches for cramps to 7459 searches for anxiety. Users searching for common health-related symptoms saw pages with standard web results (n=24,034, 84%), itemized web results (n=23,354, 82%), ads (n=13,171, 46%), and info boxes (n=18,215, 64%). Users spent on average 22 (SD 26) seconds on the search engine results page. Users who saw all page elements spent 25% (7.1 s) of their time on the info box, 23% (6.1 s) on standard web results, 20% (5.7 s) on ads, and 10% (10 s) on itemized web results, with significantly more time on the info box compared to other elements and the least amount of time on itemized web results. Info box characteristics such as reading ease and appearance of related conditions were associated with longer time on the info box. Although none of the info box characteristics were associated with clicks on standard web results, info box characteristics such as reading ease and related searches were negatively correlated with clicks on ads.

Conclusions

Info boxes were attended most by users compared with other page elements, and their characteristics may influence future web searching. Future studies are needed that further explore the utility of info boxes and their influence on real-world health-seeking behaviors.

The history of seed banking and the hazards of backup

Seeds and other plant materials in seed and gene bank collections are rarely considered adequately conserved today unless genetically identical duplicate samples have been created and safely stored elsewhere. This paper explores the history of seed banking to understand how, why and with what consequences copying collections came to occupy this central place. It highlights a shift in the guiding metaphor for long-term preservation of seed collections, from banking to backup. To understand the causes and consequences of this shift in metaphor, the paper traces the intertwined histories of the central long-term seed storage facility of the United States (opened in 1958) and the international seed conservation system into which that facility was integrated in the 1970s. This account reveals how changing conceptions of security, linked to changing economic, political and technological circumstances, transformed both the guiding metaphors and the practices of seed conservation in these institutions. Early instantiations of long-term cold storage facilities vested security in robust infrastructures and the capacities of professional staff; between the 1960s and 1990s, this configuration gave way to one in which security was situated in copies rather than capacities. This observation ultimately raises questions about the security promised and achieved through present-day infrastructures for crop genetic resources conservation.

Differential Ratings of Perceived Exertion: Relationships With External Intensity and Load in Elite Men's Football

PURPOSE

To examine the utility of differential ratings of perceived exertion (dRPE) for monitoring internal intensity and load in association football.

METHODS

Data were collected from 2 elite senior male football teams during 1 season (N = 55). External intensity and load data (duration × intensity) were collected during each training and match session using electronic performance and tracking systems. After each session, players rated their perceived breathlessness and leg-muscle exertion. Descriptive statistics were calculated to quantify how often players rated the 2 types of rating of perceived exertion differently (dRPEDIFF). In addition, the association between dRPEDIFF and external intensity and load was examined. First, the associations between single external variables and dRPEDIFF were analyzed using a mixed-effects logistic regression model. Second, the link between dRPEDIFF and session types with distinctive external profiles was examined using the Pearson chi-square test of independence.

RESULTS

On average, players rated their session perceived breathlessness and leg-muscle exertion differently in 22% of the sessions (range: 0%-64%). Confidence limits for the effect of single external variables on dRPEDIFF spanned across largely positive and negative values for all variables, indicating no conclusive findings. The analysis based on session type indicated that players differentiated more often in matches and intense training sessions, but there was no pattern in the direction of differentiation.

CONCLUSIONS

The findings of this study provide no evidence supporting the utility of dRPE for monitoring internal intensity and load in football.

Virtual surgical planning and data ownership: Navigating the provider-patient-vendor relationship

The practice of modern craniomaxillofacial surgery has been defined by emergent technologies allowing for the acquisition, storage, utilization, and transfer of massive amounts of sensitive and identifiable patient data. This alone has thrust providers into an unlikely and unprecedented role as the stewards of vast databases of digital information. This data powers the potent surgical tool of virtual surgical planning, a method by which craniomaxillofacial surgeons plan and simulate procedural outcomes in a digital environment. Further complicating this new terrain is the involvement of third-party contractors-a necessary presence in bringing raw data to bear in the office, virtual space, and operating room. The individual privileges and responsibilities of patients, providers, and vendors towards data are situated within the most recent U.S. court rulings and regulations. This paper offers guidance for overseeing the safe and responsible transfer to third-party contractors, and provides suggestions for negotiating the trinary relationship between physicians, their patients, and the vendors offering this transformative technology.

An overview of real-world data sources for oncology and considerations for research

Generating evidence on the use, effectiveness, and safety of new cancer therapies is a priority for researchers, health care providers, payers, and regulators given the rapid pace of change in cancer diagnosis and treatments. The use of real-world data (RWD) is integral to understanding the utilization patterns and outcomes of these new treatments among patients with cancer who are treated in clinical practice and community settings. An initial step in the use of RWD is careful study design to assess the suitability of an RWD source. This pivotal process can be guided by using a conceptual model that encourages predesign conceptualization. The primary types of RWD included are electronic health records, administrative claims data, cancer registries, and specialty data providers and networks. Careful consideration of each data type is necessary because they are collected for a specific purpose, capturing a set of data elements within a certain population for that purpose, and they vary by population coverage and longitudinality. In this review, the authors provide a high-level assessment of the strengths and limitations of each data category to inform data source selection appropriate to the study question. Overall, the development and accessibility of RWD sources for cancer research are rapidly increasing, and the use of these data requires careful consideration of composition and utility to assess important questions in understanding the use and effectiveness of new therapies.

Historical Context of Cardiac Rehabilitation: Learning From the Past to Move to the Future

Contemporary myocardial infarction (MI) care and management has evolved dramatically since the 1950's; yet outpatient rehabilitation remains underutilized. Deepening our understanding of the origins and history of cardiac rehabilitation highlights a contemporary shift required for policy and practice related to secondary prevention of coronary disease in light of societal changes as well as medical, digital and surgical advancements. Contemporary "cardiac rehabilitation" began when bed rest and physical inactivity was recommended and commonplace for MI survivors. Today, most patients who survive an MI, undergo reperfusion therapy, a short inpatient stay and are discharged with minimal physical morbidity. Despite this, the majority of modern day programs continue to be structured in the same way they have been for the past 50 years and this model has become incongruent with the contemporary context, especially in the COVID-19 era. This review aims to describe the historical foundations of cardiac rehabilitation to inform solutions and meet the demands of contemporary MI management. Delivering health systems reform to address modernization is current healthcare challenge where a united and interdisciplinary effort is needed.

COVID-19 Return to Sport: NFL Injury Prevalence Analysis

Background

Sport injuries have been common among athletes across the globe for decades and have the potential to disrupt athletic careers, performance, and psyche. Many health professionals and organizations have undertaken injury mitigation strategies to prevent sport injuries through protective equipment, training protocols, and a host of other evidence-based practices. Many of these specialized training methods were disrupted due to protocols to mitigate the spread of COVID-19. This research examines the effects of the COVID-19 pandemic in relation to the prevalence of athletic injuries in the National Football League (NFL).

Objective

During the COVID-19 pandemic, NFL teams and athletes across all levels of sport were reported to have reduced training in preparation for their seasons due to protocols to mitigate the spread of COVID-19. This study compares the prevalence of injury during the 2018, 2019, and 2020 NFL seasons, with the aim to determine the potential causes of the differences in injury prevalence.

Methods

Official injury reports from each team were counted during the 17-week regular season of each year (2018, 2019, and 2020). The data were analyzed using an unpaired t test to compare the injury prevalence between each of the three seasons.

Results

The 2018 season produced a total of 1561 injuries and a mean of 48.8 injuries per team. The 2019 season produced a total of 1897 injuries and a mean of 59.3 injuries per team, while the 2020 season produced a total of 2484 injuries and a mean of 77.6 injuries per team. An unpaired t test was performed using the data to compare the mean number of injuries per team during each of the seasons. Comparison of the 2020 season against the 2019 season showed a statistically significant difference (P<.001); comparison of the 2020 season to the 2018 season found a statistically significant difference (P<.001); and comparison between the 2019 and the 2018 seasons found a statistically significant difference (P=.03).

Conclusions

Although the 2019 and 2018 seasons showed a statistically significant difference (P=.03), this difference is not as large when we compare the 2020 seasons versus the 2019 (P<.001) and 2018 (P<.001) seasons. The astronomical increase in injury prevalence during the 2020 season over the previous years raises the possibility that there was a reduced physiological adaptation to stress, due to the limited amount of training as a result of the closure of practice facilities in order to slow the spread of COVID-19.

You cannot find what you are not looking for! detecting malaria outbreaks in Uganda: a case study

Malaria is the leading cause of morbidity and mortality in Uganda, with nearly half of the population becoming infected in any given year. Uganda relies on analyzing high-quality surveillance data to help detect outbreaks, determine which areas or population groups are most affected, and help target resources to where they are most needed. In March 2019, over 300 health facilities from different districts in Uganda reported substantially higher malaria cases than usual. In 13 districts, health facilities reported that the number of malaria cases was so high that they were experiencing stock outs of antimalarial drugs. Although seasonal increases in cases had been expected, districts reported that the number of cases being identified were overwhelming the capacity of the health facilities. Uganda´s National Malaria Control Division tasked a team of epidemiologists to investigate this unprecedented increase in malaria cases. National Malaria Control Division were interested in how malaria epidemiology had been changing in recent years, and whether they had missed something that would have predicted the situation they were facing in 2019. This case study describes the steps taken to conduct a descriptive analysis of routine malaria surveillance data and demonstrates how to detect malaria outbreaks using historical data. It is useful for training Field Epidemiologists and public health officers involved in analysis of surveillance data.

Groundhog Day: research without old data and old references

BACKGROUND

The use of older data and references is becoming increasingly disfavored for publication. A myopic focus on newer research risks losing sight of important research questions already addressed by now-invisible older studies. This creates a 'Groundhog Day' effect as illustrated by the 1993 movie of this name in which the protagonist has to relive the same day (Groundhog Day) over and over and over within a world with no memory of it. This article examines the consequences of the recent preference for newer data and references in current publication practices and is intended to stimulate new consideration of the utility of selected older data and references for the advancement of scientific knowledge.

METHODS

Examples from the literature are used to exemplify the value of older data and older references. To illustrate the recency of references published in original medical research articles in a selected sample of recent academic medical journals, original research articles were examined in recent issues in selected psychiatry, medicine, and surgery journals.

RESULTS

The literature examined reflected this article's initial assertion that journals are emphasizing the publication of research with newer data and more recent references.

CONCLUSIONS

The current valuation of newer data above older data fails to appreciate the fact that new data eventually become old, and that old data were once new. The bias demonstrated in arbitrary policies pertaining to older data and older references can be addressed by instituting comparable treatment of older and newer data and references.

Peer Teaching as Bioinformatics Training Strategy: Incentives, Challenges, and Benefits

Bioinformatics is essential for basic and clinical research. Peer-to-peer (P2P) teaching was used to respond to the bioinformatics training needs at a research-intensive institution. In addition to the data collected from the workshops, personal experiences of the teachers were used to understand incentives, challenges, and benefits of P2P teaching. Developing communication skills such as confidence in teaching, explaining complex concepts, and better understanding of topics benefited P2P teachers. Lack of time and classroom management were identified as major challenges. Hence, P2P teaching can be beneficial not only for bioinformatics trainees but also as a professional development opportunity for peer teachers.