A data management plan for the NESHIE observational study

With regard to the use and transfer of research participants' personal information, samples and other data nationally and internationally, it is necessary to construct a data management plan. One of the key objectives of a data management plan is to explain the governance of clinical, biochemical, laboratory, molecular and other sources of data according to the regulations and policies of all relevant stakeholders. It also seeks to describe the processes involved in protecting the personal information of research participants, especially those from vulnerable populations. In most data management plans, the framework therefore consists of describing the collection, organization, use, storage, contextualization, preservation, sharing and access of/to research data and/or samples. It may also include a description of data management resources, including those associated with analyzed samples, and identifies responsible parties for the establishment, implementation and overall management of the data management strategy. Importantly, the data management plan serves to highlight potential problems with the collection, sharing, and preservation of research data. However, there are different forms of data management plans and requirements may vary due to funder guidelines and the nature of the study under consideration. This paper leverages the detailed data management plans constructed for the 'NESHIE study' and is a first attempt at providing a comprehensive template applicable to research focused on vulnerable populations, particularly those within LMICs, that includes a multi-omics approach to achieve the study aims. More particularly, this template, available for download as a supplementary document, provides a modifiable outline for future projects that involve similar sensitivities, whether in clinical research or clinical trials. It includes a description of the management not only of the data generated through standard clinical practice, but also that which is generated through the analysis of a variety of samples being collected from research participants and analyzed using multi-omics approaches.

Improving data use in trachomatous trichiasis programmes: operationalisation of the TT Tracker

Trachoma is a disease of the eye and the leading infectious cause of blindness worldwide. Years of repeated infections can cause in-turning of the lashes so that they rub against the eyeball, causing pain, discomfort and, if left untreated, blindness. This is known as trachomatous trichiasis (TT) and can be remedied by surgery. To improve oversight and reporting of TT outreach, Sightsavers developed a mobile phone application called the TT Tracker so that TT surgeons, assistants and supervisors can collect and analyse information about surgical outcomes and performance and determine when and where follow-up appointments are required. The TT Tracker is being used by seven national programmes. Examples of use and programme improvements from Nigeria, Benin and Senegal are discussed here.

The face as folded object: Race and the problems with 'progress' in forensic DNA phenotyping

Forensic DNA phenotyping (FDP) encompasses a set of technologies aimed at predicting phenotypic characteristics from genotypes. Advocates of FDP present it as the future of forensics, with an ultimate goal of producing complete, individualised facial composites based on DNA. With a focus on individuals and promised advances in technology comes the assumption that modern methods are steadily moving away from racial science. Yet in the quantification of physical differences, FDP builds upon some nineteenth- and twentieth-century scientific practices that measured and categorised human variation in terms of race. In this article I complicate the linear temporal approach to scientific progress by building on the notion of the folded object. Drawing on ethnographic fieldwork conducted in various genetic laboratories, I show how nineteenth- and early twentieth-century anthropological measuring and data-collection practices and statistical averaging techniques are folded into the ordering of measurements of skin color data taken with a spectrophotometer, the analysis of facial shape based on computational landmarks and the collection of iris photographs. Attending to the historicity of FDP facial renderings, I bring into focus how race comes about as a consequence of temporal folds.

Cloud security in a bioanalytical world: considerations for use of third-party cloud services for bioanalysis

While using the cloud environment for various functions has become commonplace, relatively little attention has been given to considerations for the use of third-party cloud services for regulated bioanalytical workflow and data management. Little guidance has been provided as to how to utilize the cloud to support bioanalytical activities. It can be intimidating when considering how to go about using cloud services for data acquisition, but there are some general ideas to keep in mind when evaluating ways to accommodate regulated bioanalysis online. Determining how to incorporate the use of cloud storage with data that are generated from regulated bioanalytical analysis is an important step in maintaining the security of the data.

(Re-)use and (re-)analysis of publicly available metabolomics data

Metabolomics, the systematic measurement of small molecules (<1000 Da) in a given biological sample, is a fast-growing field with many different applications. In contrast to transcriptomics and proteomics, sharing of data is not as widespread in metabolomics, though more scientists are sharing their data nowadays. However, to improve data analysis tools and develop new data analytical approaches and to improve metabolite annotation and identification, sharing of reference data is crucial. Here, different possibilities to share (metabolomics) data are reviewed and some recent approaches and applications regarding the (re-)use and (re-)analysis are highlighted.

Trackman 4: Within and between-session reliability and inter-relationships of launch monitor metrics during indoor testing in high-level golfers

The aims of the present study were to: 1) investigate the within and between-session reliability of the Trackman 4 launch monitor system, and 2) determine the inter-relationships of some of these commonly used metrics. Golfers attended two test sessions at an indoor golf academy and performed 10 shots using their own driver. Results showed excellent within and between-session reliability for CHS (ICC = 0.99; SEM = 1.64-1.67 mph), ball speed (ICC = 0.97-0.99; SEM = 2.46-4.42 mph) and carry distance (ICC = 0.91-0.97; SEM = 7.80-14.21 mph). In contrast, spin rate showed the worst reliability (ICC = 0.02-0.60; SEM = 240.93-454.62 º/s) and also exhibited significant differences between test sessions (g = -0.41; p < 0.05), as did smash factor (g = 0.47; p < 0.05) and dynamic loft (g = -0.21; p < 0.05). Near perfect associations were evident in both test sessions between CHS and ball speed (r = 0.98-0.99), CHS and carry distance (r = 0.94-0.95), ball speed and carry distance (r = 0.97-0.98), and launch angle and dynamic loft (r = 0.98-0.99). Collectively, CHS, ball speed and carry distance serve as the most consistently reliable metrics making them excellent choices for practitioners working with golfers.

Predicting Big Five personality traits from smartphone data: A meta-analysis on the potential of digital phenotyping

OBJECTIVE

Since the first study linking recorded smartphone variables to self-reported personality in 2011, many additional studies have been published investigating this association. In the present meta-analyses, we aimed to understand how strongly personality can be predicted via smartphone data.

METHOD

Meta-analytical calculations were used to assess the association between smartphone data and Big Five traits. Because of the lack of independence of many included studies, analyses were performed using a multilevel approach.

RESULTS

Based on data collected from 21 distinct studies, extraversion showed the largest association with the digital footprints derived from smartphone data (r = .35), while remaining traits showed smaller associations (ranging from 0.23 to 0.25). For all traits except neuroticism, moderator analyses showed that prediction performance was improved when multiple features were combined together in a single predictive model. Additionally, the strength of the prediction of extraversion was improved when call and text log data were used to perform the prediction, as opposed to other types of smartphone data CONCLUSIONS: Our synthesis reveals small-to-moderate associations between smartphone activity data and Big Five traits. The opportunities, but also dangers of the digital phenotyping of personality traits based on traces of users' activity on a smartphone data are discussed.

Scoping the Priorities and Concerns of Parents: Infodemiology Study of Posts on Mumsnet and Reddit

BACKGROUND

Health technology innovation is increasingly supported by a bottom-up approach to priority setting, aiming to better reflect the concerns of its intended beneficiaries. Web-based forums provide parents with an outlet to share concerns, advice, and information related to parenting and the health and well-being of their children. They provide a rich source of data on parenting concerns and priorities that could inform future child health research and innovation.

OBJECTIVE

The aim of the study is to identify common concerns expressed on 2 major web-based forums and cluster these to identify potential family health concern topics as indicative priority areas for future research and innovation.

METHODS

We text-mined the r/Parenting subreddit (69,846 posts) and the parenting section of Mumsnet (99,848 posts) to create a large corpus of posts. A generative statistical model (latent Dirichlet allocation) was used to identify the most discussed topics in the corpus, and content analysis was applied to identify the parenting concerns found in a subset of posts.

RESULTS

A model with 25 topics produced the highest coherence and a wide range of meaningful parenting concern topics. The most frequently expressed parenting concerns are related to their child's sleep, self-care, eating (and food), behavior, childcare context, and the parental context including parental conflict. Topics directly associated with infants, such as potty training and bottle feeding, were more common on Mumsnet, while parental context and screen time were more common on r/Parenting.

CONCLUSIONS

Latent Dirichlet allocation topic modeling can be applied to gain a rapid, yet meaningful overview of parent concerns expressed on a large and diverse set of social media posts and used to complement traditional insight gathering methods. Parents framed their concerns in terms of children's everyday health concerns, generating topics that overlap significantly with established family health concern topics. We provide evidence of the range of family health concerns found at these sources and hope this can be used to generate material for use alongside traditional insight gathering methods.

Domesticating data: Traveling and value-making in the data economy

Data are versatile objects that can travel across contexts. While data's travels have been widely discussed, little attention has been paid to the sites from where and to which data flow. Drawing upon ethnographic fieldwork in two connected data-intensive laboratories and the concept of domestication, we explore what it takes to bring data 'home' into the laboratory. As data come and dwell in the home, they are made to follow rituals, and as a result, data are reshaped and form ties with the laboratory and its practitioners. We identify four main ways of domesticating data. First, through storytelling about the data's origins, data practitioners draw the boundaries of their laboratory. Second, through standardization, staff transform samples into digital data that can travel well while ruling what data can be let into the home. Third, through formatting, data practitioners become familiar with their data and at the same time imprint the data, thus making them belong to their home. Finally, through cultivation, staff turn data into a resource for knowledge production. Through the lens of domestication, we see the data economy as a collection of homes connected by flows, and it is because data are tamed and attached to homes that they become valuable knowledge tools. Such domestication practices also have broad implications for staff, who in the process of 'homing' data, come to belong to the laboratory. To conclude, we reflect on what these domestication processes-which silence unusual behaviours in the data-mean for the knowledge produced in data-intensive research.

Comparing a Data Entry Tool to Provider Insights Alone for Assessment of COVID-19 Hospitalization Risk: Pilot Matched Cohort Comparison Study

BACKGROUND

In March 2020, the World Health Organization declared COVID-19 a global pandemic, necessitating an understanding of factors influencing severe disease outcomes. High COVID-19 hospitalization rates underscore the need for robust risk prediction tools to determine estimated risk for future hospitalization for outpatients with COVID-19. We introduced the "COVID-19 Risk Tier Assessment Tool" (CRTAT), designed to enhance clinical decision-making for outpatients.

OBJECTIVE

We investigated whether CRTAT offers more accurate risk tier assignments (RTAs) than medical provider insights alone.

METHODS

We assessed COVID-19-positive patients enrolled at Emory Healthcare's Virtual Outpatient Management Clinic (VOMC)-a telemedicine monitoring program, from May 27 through August 24, 2020-who were not hospitalized at the time of enrollment. The primary analysis included patients from this program, who were later hospitalized due to COVID-19. We retroactively formed an age-, gender-, and risk factor-matched group of nonhospitalized patients for comparison. Data extracted from clinical notes were entered into CRTAT. We used descriptive statistics to compare RTAs reported by algorithm-trained health care providers and those produced by CRTAT.

RESULTS

Our patients were primarily younger than 60 years (67% hospitalized and 71% nonhospitalized). Moderate risk factors were prevalent (hospitalized group: 1 among 11, 52% patients; 2 among 2, 10% patients; and ≥3 among 4, 19% patients; nonhospitalized group: 1 among 11, 52% patients, 2 among 5, 24% patients, and ≥3 among 4, 19% patients). High risk factors were prevalent in approximately 45% (n=19) of the sample (hospitalized group: 11, 52% patients; nonhospitalized: 8, 38% patients). Approximately 83% (n=35) of the sample reported nonspecific symptoms, and the symptoms were generally mild (hospitalized: 12, 57% patients; nonhospitalized: 14, 67% patients). Most patient visits were seen within the first 1-6 days of their illness (n=19, 45%) with symptoms reported as stable over this period (hospitalized: 7, 70% patients; nonhospitalized: 3, 33% patients). Of 42 matched patients (hospitalized: n=21; nonhospitalized: n=21), 26 had identical RTAs and 16 had discrepancies between VOMC providers and CRTAT. Elements that led to different RTAs were as follows: (1) the provider "missed" comorbidity (n=6), (2) the provider noted comorbidity but undercoded risk (n=10), and (3) the provider miscoded symptom severity and course (n=7).

CONCLUSIONS

CRTAT, a point-of-care data entry tool, more accurately categorized patients into risk tiers (particularly those hospitalized), underscored by its ability to identify critical factors in patient history and clinical status. Clinical decision-making regarding patient management, resource allocation, and treatment plans could be enhanced by using similar risk assessment data entry tools for other disease states, such as influenza and community-acquired pneumonia. The COVID-19 pandemic has accelerated the adoption of telemedicine, enabling remote patient tools such as CRTAT. Future research should explore the long-term impact of outpatient clinical risk assessment tools and their contribution to better patient care.

Effects and Limitations of a Unique, Nationwide, Self-Exclusion Service for Gambling Disorder and Its Self-Perceived Effects and Harms in Gamblers: Protocol for a Qualitative Interview Study

BACKGROUND

Voluntary self-exclusion from gambling is a common but underdeveloped harm reduction tool in the management of gambling problems or gambling disorders. Large-scale, multi-operator, and operator-independent self-exclusion services are needed. A recent nationwide multi-operator self-exclusion service in Sweden (Spelpaus), involving both land- and web-based gambling sites, is promising, but recent data have revealed limitations to this system and possibilities to breach one's self-exclusion through overseas web-based gambling. More knowledge is needed about the benefits and challenges of such an extensive self-exclusion service, and its effects as perceived by gamblers.

OBJECTIVE

This study protocol describes the rationale and design of a qualitative interview study addressing the effects and limitations perceived by individuals with gambling problems and their concerned significant others. The study aims to provide an in-depth experience of this novel self-exclusion service and to inform stakeholders and policymakers in order to further improve harm reduction tools against gambling problems.

METHODS

Individuals with gambling problems will be recruited primarily through social media and also from a treatment unit, if needed, for a qualitative interview study. Recorded interview material will be analyzed through content analysis, and recruitment will continue until saturation in the material is reached. This study will provide in-depth information about a harm reduction tool that is promising and commonly used, but which has proven to be breached by a significant number of users, potentially limiting its efficiency. The aim is to interview a sufficient number of gamblers until saturation has been obtained in the interview material. Saturation will be considered through a continuous analysis, comparing recently collected data to previously collected data.

RESULTS

Results will be reported as the themes and subthemes identified after the thorough analysis and coding of the transcribed text material and will be accompanied by citations representing relevant themes and subthemes. Results are planned to be provided before the end of 2023.

CONCLUSIONS

This study will likely provide new insights into user perspectives on a multi-operator self-exclusion service that involves both web- and land-based gambling operators, and which according to previous literature attracts many gamblers but also appears to have limitations and challenges in the target group of individuals with gambling problems. Policy and legislation implications, as well as clinical implications for treatment providers, will be discussed. Results and conclusions will be disseminated to policy makers in Sweden and internationally, as well as to peer organizations, treatment providers, and the research community.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05693155; https://clinicaltrials.gov/study/NCT05693155.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/47528.

Champions for School Health-An NASN Initiative to Increase Vaccine Confidence, Equity, and Uptake in COVID-19 and School-Required Vaccinations: Part 2

NASN, with generous funding from Kaiser Permanente (KP) and partnered with the Institute for Educational Leadership, developed and implemented the Champions for School Health (CSH) grant initiative. The CSH initiative awarded 54 Implementation Grants in two funding cycles in 2022, funding school districts and community-based organizations (CBOs) to increase access to the pediatric COVID-19 vaccine as well as school-required immunizations and to increase vaccine confidence among underserved populations in KP's footprint: California, Colorado, Georgia, Hawaii, Maryland, Oregon, Virginia, Washington, and the District of Columbia. These grantees administered a total of 17,630 COVID-19 vaccines to individuals ages 5 or older and 34,025 routine immunizations, of which 8,233 school-required vaccinations went to children of ages 5-11 years. Over 851,000 people were reached by vaccine education events in all nine KP markets. A notable takeaway from the project's results was the new partnerships created and the continuation of existing partnerships by the grantees. NASN's implementation of the CSH initiative and results provides a model and a source of critical data on how school health services and community-based organizations can partner to provide hyper-local responses to community/public health crises. This Part 2 article provides an overview of the key results of the project.

Next-Generation Models for Predicting Winning Times in Elite Swimming Events: Updated Predictions for the Paris 2024 Olympic Games

PURPOSE

To evaluate statistical models developed for predicting medal-winning performances for international swimming events and generate updated performance predictions for the Paris 2024 Olympic Games.

METHODS

The performance of 2 statistical models developed for predicting international swimming performances was evaluated. The first model employed linear regression and forecasting to examine performance trends among medal winners, finalists, and semifinalists over an 8-year period. A machine-learning algorithm was used to generate time predictions for each individual event for the Paris 2024 Olympic Games. The second model was a Bayesian framework and comprised an autoregressive term (the previous winning time), moving average (past 3 events), and covariates for stroke, gender, distance, and type of event (World Championships vs Olympic Games). To examine the accuracy of the predictions from both models, the mean absolute error was determined between the predicted times for the Budapest 2022 World Championships and the actual results from said championships.

RESULTS

The mean absolute error for prediction of swimming performances was 0.80% for the linear-regression machine-learning model and 0.85% for the Bayesian model. The predicted times and actual times from the Budapest 2022 World Championships were highly correlated (r = .99 for both approaches).

CONCLUSIONS

These models, and associated predictions for swimming events at the Paris 2024 Olympic Games, provide an evidence-based performance framework for coaches, sport-science support staff, and athletes to develop and evaluate training plans, strategies, and tactics, as well as informing resource allocation to athletes, based on their potential for the Paris 2024 Olympic Games.

Data Collection: Time to revisit the WHY, WHAT, and HOW

This is the first in a series of three articles looking at school health data collection from identification of data points to utilizing data to share your story and submitting your data to contribute to the National School Health Data Set: Every Student Counts! Many school nurses cringe at the mention of data collection. However, everything we do as school nurses is data driven. Every documented assessment, observation, and conversation provides the school nurse with data. The barriers often noted to participating in formal data collection efforts are time, workload, access to an electronic health record, and not understanding the WHY, WHAT, and HOW. The key to data collection is identifying the data already being collected and starting where you are. Data collection is not something new that you need to find a way to fit into your already busy schedule. WHAT do you currently collect? WHY are you collecting the data you have? HOW do you collect it? WHAT do you do with the data? These are all very important questions, but let's take a closer look at the WHY, WHAT, and HOW behind data collection.

Development of interactive dashboards for monitoring endemic animal pathogens in Ontario, Canada: Ontario interactive animal pathogen dashboards

The advancement of web-based technologies makes it possible to build user interfaces or web pages that present and summarize complex data in easy-to-read graphical formats that emphasize key information. Taking advantage of this technologic progress, we addressed the need for real-time visualizations of trends for major pathogens in the largest livestock industries in Ontario: poultry, swine, and cattle. These visualizations were built using test data from the laboratory information management system of the Animal Health Laboratory at the University of Guelph, a large veterinary diagnostic laboratory in Ontario. The data were processed using R software and used to construct interactive and dynamic visualizations using Tableau Desktop v.2021.4 (Tableau Software). We designed 12 dashboards: in chickens-influenza A virus, fowl adenovirus, infectious bronchitis virus, and infectious laryngotracheitis virus; in turkeys-influenza A virus; in swine, influenza A virus, rotavirus, and porcine reproductive and respiratory syndrome virus; in cattle-bovine viral diarrhea virus, Mycoplasma bovis, Salmonella Dublin in individual samples, and Salmonella Dublin in bulk tank milk samples. Data for each pathogen are presented in 2 dashboards. One shows the data of the last 10 y (general view) and the other the data of the last 3 y, but in more detail (comprehensive view). Information on gaining access to all dashboards is available at https://iapd.lsd.uoguelph.ca/. The visualizations provide near-real-time access to aggregated assay results for selected pathogens for veterinarians, animal health regulatory agencies, researchers, and other users who are interested in livestock pathogen surveillance.

Calculator for predicting the probability of faculty promotion in an academic medical centre

OBJECTIVE

The academic medical centre (AMC), with over 2200 faculty members, annually manages approximately 300 appointments and promotions. Considering these large numbers, we explored whether machine learning could predict the probability of obtaining promotional approvals.

METHODS

We examined variables related to academic promotion using predictive analytical methods. The data included candidates' publications, the H-index, educational contributions and leadership or service within and outside the AMC.

RESULTS

Of the five methods employed, the random forest algorithm was identified as the 'best' model through our leave-one-out cross-validation model evaluation process.

CONCLUSIONS

To the best of our knowledge, this is the first study on the AMC. The developed model can be deployed as a 'calculator' to evaluate faculty performance and assist applicants in understanding their chances of promotion based on historical data. Furthermore, it can act as a guide for tenure and promotion committees in candidate review processes. This increases the transparency of the promotion process and aligns faculty aspirations with the AMC's mission and vision. It is possible for other researchers to adopt the algorithms from our analysis and apply them to their data.

Data-Driven Optimization of DIA Mass Spectrometry by DO-MS

Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever-increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data-independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of MS methods (DO-MS). The DO-MS app v2.0 (do-ms.slavovlab.net) allows one to optimize and evaluate results from both label-free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant to single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication of quality figures that can be easily shared. The source code is available at github.com/SlavovLab/DO-MS.

Early trends in ECMO mortality during the first quarters of 2019 and 2020: Could we have predicted the onset of the pandemic?

OBJECTIVE

To compare mortality trends in patients requiring Extracorporeal Membrane Oxygenation (ECMO) support between the first quarters of 2019 and 2020 and determine whether these trends might have predicted the severe acute respiratory syndrome coronavirus-2 (SARS)-Cov-2 pandemic in the United States.

METHODS

We analyzed 5% Medicare claims data at aggregate, state, hospital, and encounter levels using MS-DRG (Medicare Severity-Diagnosis Related Group) codes for ECMO, combining state-level data with national census data. Necessity and sufficiency relations associated with change in mortality between the 2 years were modeled using qualitative comparative analysis (QCA). Multilevel, generalized linear modeling was used to evaluate mortality trends.

RESULTS

Based on state-level data, there was a 3.36% increase in mortality between 2019 and 2020. Necessity and sufficiency evaluation of aggregate data at state and institutional levels did not identify any association or combinations of risk factors associated with this increase in mortality. However, multilevel and generalized linear models using disaggregated patient-level data to evaluate institution mortality and patient death, identified statistically significant differences between the first (p = .019) and second (p = .02) months of the 2 years, the first and second quarters (p < .001 and p = .042, respectively), and the first 6 months (p < .001) of 2019 and 2020.

CONCLUSION

Mortality in ECMO patients increased significantly during the first quarter of 2020 and may have served as an early warning of the SARS-Cov-2 pandemic. Granular data shared in real-time may be used to better predict public health threats.

Automated identification of unstandardized medication data: a scalable and flexible data standardization pipeline using RxNorm on GEMINI multicenter hospital data

Objective

Patient data repositories often assemble medication data from multiple sources, necessitating standardization prior to analysis. We implemented and evaluated a medication standardization procedure for use with a wide range of pharmacy data inputs across all drug categories, which supports research queries at multiple levels of granularity.

Methods

The GEMINI-RxNorm system automates the use of multiple RxNorm tools in tandem with other datasets to identify drug concepts from pharmacy orders. GEMINI-RxNorm was used to process 2 090 155 pharmacy orders from 245 258 hospitalizations between 2010 and 2017 at 7 hospitals in Ontario, Canada. The GEMINI-RxNorm system matches drug-identifying information from pharmacy data (including free-text fields) to RxNorm concept identifiers. A user interface allows researchers to search for drug terms and returns the relevant original pharmacy data through the matched RxNorm concepts. Users can then manually validate the predicted matches and discard false positives. We designed the system to maximize recall (sensitivity) and enable excellent precision (positive predictive value) with efficient manual validation. We compared the performance of this system to manual coding (by a physician and pharmacist) of 13 medication classes.

Results

Manual coding was performed for 1 948 817 pharmacy orders and GEMINI-RxNorm successfully returned 1 941 389 (99.6%) orders. Recall was greater than 0.985 in all 13 drug classes, and the F1-score and precision remained above 0.90 in all drug classes, facilitating efficient manual review to achieve 100% precision. GEMINI-RxNorm saved time substantially compared with manual standardization, reducing the time taken to review a pharmacy order row from an estimated 30 to 5 s and reducing the number of rows needed to be reviewed by up to 99.99%.

Discussion and Conclusion

GEMINI-RxNorm presents a novel combination of RxNorm tools and other datasets to enable accurate, efficient, flexible, and scalable standardization of pharmacy data. By facilitating efficient manual validation, the GEMINI-RxNorm system can allow researchers to achieve near-perfect accuracy in medication data standardization.

Imaginaries of omniscience: Automating intelligence in the US Department of Defense

The current reanimation of artificial intelligence includes a resurgence of investment in automating military intelligence on the part of the US Department of Defense. A series of programs set forth a technopolitical imaginary of fully integrated, comprehensive and real-time 'situational awareness' across US theaters of operation. Locating this imaginary within the history of 'closed world' discourse, I offer a critical reading of dominant scholarship within military circles that sets out the military's cybernetic model of situational awareness in the form of the widely referenced Observe, Orient, Decide, Act or OODA Loop. I argue that the loop's promise of dynamic homeostasis is held in place by the enduring premise of objectivist knowledge, enabled through a war apparatus that treats the contingencies and ambiguities of relations on the ground as noise from which a stable and unambiguous signal can be extracted. In contrast, recent challenges to the closed-world imaginary, based on critical scholarship and investigative journalism, suggest that the aspiration to closure is an engine for the continued destructiveness of US interventions and the associated regeneration of enmity. To challenge these technopolitics of violence we need a radically different kind of situational awareness, one that recognizes the place of ignorance in perpetuating the project of militarism. Only that kind of awareness can inform the public debate required to re-envision a future place for the US in the world, founded in alternative investments in demilitarization and commitments to our collective security.

Textual data transformations using natural language processing for risk assessment

Underlying information about failure, including observations made in free text, can be a good source for understanding, analyzing, and extracting meaningful information for determining causation. The unstructured nature of natural language expression demands advanced methodology to identify its underlying features. There is no available solution to utilize unstructured data for risk assessment purposes. Due to the scarcity of relevant data, textual data can be a vital learning source for developing a risk assessment methodology. This work addresses the knowledge gap in extracting relevant features from textual data to develop cause-effect scenarios with minimal manual interpretation. This study applies natural language processing and text-mining techniques to extract features from past accident reports. The extracted features are transformed into parametric form with the help of fuzzy set theory and utilized in Bayesian networks as prior probabilities for risk assessment. An application of the proposed methodology is shown in microbiologically influenced corrosion-related incident reports available from the Pipeline and Hazardous Material Safety Administration database. In addition, the trained named entity recognition (NER) model is verified on eight incidents, showing a promising preliminary result for identifying all relevant features from textual data and demonstrating the robustness and applicability of the NER method. The proposed methodology can be used in domain-specific risk assessment to analyze, predict, and prevent future mishaps, ameliorating overall process safety.

Data-driven approach to predicting the risk of pressure injury: A retrospective analysis based on changes in patient conditions

AIMS

To determine the risk of pressure injury development in the intensive care unit based on changes in patient conditions.

DESIGN

This retrospective study was based on secondary data analysis.

METHODS

Patient data from electronic health records were retrospectively obtained and we included 438 and 1752 patients with and without pressure injury, respectively, among those admitted to the medical and surgical intensive care units (ICUs) from January 2017-February 2020. Changes in patient conditions were analysed based on the first and last objective data values from the day of ICU admission to the day before the onset of pressure injury and categorised as follows: improved, maintained normal, exacerbated and unchanged. Logistic regression was performed to identify the significant predictors of pressure injury development based on 11 variables.

RESULTS

The 11 selected variables were age, body mass index, activity, acute physiology and chronic health evaluation II score, nursing severity level, pulse and albumin, haematocrit, C-reactive protein, total bilirubin and blood urea nitrogen levels. The risk for a pressure injury was high with exacerbation of or persistently abnormal levels of nursing severity, albumin, haematocrit, C-reactive protein, blood urea nitrogen and pulse >100 beat/min.

CONCLUSION

Periodic monitoring of haematological variables is important for preventing pressure injury in the intensive care unit.

REPORTING METHOD

The study followed STROBE guidelines.

PATIENT OR PUBLIC CONTRIBUTION

This study contributes to the utilisation of patient data from electronic health records.

RELEVANCE TO CLINICAL PRACTICE

In addition to other pressure injury risk assessment tools, ICU nurses can help prevent pressure injuries by assessing patients' blood test results, thereby promoting patient safety and enhancing the efficacy of nursing practice.

What evidence for a cholera vaccine? Jaime Ferrán's submissions to the Prix Bréant

This article analyses how the French Academy of Sciences assessed Jaime Ferrán's cholera vaccine submitted for the Prix Bréant in the 1880s. Ferrán, a Spanish independent physician, discovered the treatment in 1884 and tried it on thousands of patients during the cholera outbreak in Valencia the following year. His evaluation sparked a controversy in Spain and abroad on the vaccine's efficacy. The Bréant jury did not see any evidence for it in Ferrán's submission, a decision usually interpreted in terms of French scientific nationalism (or simple chauvinism): an outsider from the scientific periphery could not be awarded the Bréant. Drawing on the archival records of the award, we suggest that Ferrán failed instead to provide data that the Academy could consider unbiased, according to the contemporary standards for data presentation. We will illustrate these standards at work in the assessment of another submission from Spain, by Philip Hauser, who received the Bréant for the thoroughness of his statistical endeavour.

Personal Location as Health-Related Data: Public Knowledge, Public Concern, and Personal Action

OBJECTIVES

Personal health information (PHI), including health status and behaviors, are often associated with personal locations. Smart devices and other technologies routinely collect personal location. Therefore, technologies collecting personal location do not just create generic questions of privacy, but specific concerns related to PHI.

METHODS

To assess public opinion on the relationship between health, personal location, and privacy, a national survey of US residents was administered online in March 2020. Respondents answered questions about their use of smart devices and knowledge of location tracking. They also identified which of the locations they could visit were most private and how to balance possibilities that locations may be private but can also be useful to share.

RESULTS

Of respondents that used smart devices (n = 688), a majority (71.1%) indicated they knew they had applications tracking their location, with respondents who were younger (P < .001) and male (P = .002) and with more education (P = .045) more likely to indicate "yes." When all respondents (N = 828) identified the locations on a hypothetical map they felt were most private, health-related locations (substance use treatment center, hospital, urgent care) were the most selected.

CONCLUSIONS

The historical notion of PHI is no longer adequate and the public need greater education on how data from smart devices may be used to predict health status and behaviors. The COVID-19 pandemic brought increased attention to personal location as a tool for public health. Given healthcare's dependence upon trust, the field needs to lead the conversation and be viewed as protecting privacy while usefully leveraging location data.

The role of continuous glucose monitoring in physical activity and nutrition management: perspectives on present and possible uses

PURPOSE

Continuous glucose monitoring (CGM) is on the rise as the prevalence of obesity and diabetes increases. This review aimed to explore the use of CGM and its potential novel applications in physical activity and nutrition management.

METHODS

We searched PubMed, Web of Science, and Wiley Online Library databases using the keywords 'continuous glucose monitor,' 'nutrition,' 'physical activity,' and 'numerical modeling.'

RESULTS

Continuous blood glucose measurement is useful for individuals with obesity and diabetes. Long-term blood glucose data allow for personalized planning of nutritional composition, meal timing, and physical activity type and intensity, as well as help prevent hypoglycemia and hyperglycemia. Thus, understanding the limitations of CGM is important for its effective use.

CONCLUSION

CGM systems are being increasingly used to monitor and identify appropriate blood glucose controlling interventions. Blood glucose level is influenced by various factors such as nutrient composition, meal timing, physical activity, circadian rhythm, and cortisol levels. Numerical modeling can be used to analyze the complex relationship between stress, sleep, nutrition, and physical activity, which affect blood glucose levels. In future, blood glucose, sleep, and stress data will be integrated to predict appropriate lifestyle levels for blood glucose management. This integrated approach improves glucose control and overall wellbeing, potentially reducing societal costs.

Demonstrating Trustworthiness to Patients in Data-Driven Health Care

Patient data is used to drive an ecosystem of advanced digital tools in health care, like predictive models or artificial intelligence-based decision support. Patients themselves, however, receive little information about these technologies or how they affect their care. This raises important questions about patient trust and continued engagement in a health care system that extracts their data but does not treat them as key stakeholders. This essay explores these tensions and provides steps forward for health systems as they design advanced health information-technology (IT) policies and practices. It centers patients, their concerns, and the ways they perceive trustworthiness to reframe advanced health IT in service of patient interests.

Prescribing Patterns of Dupilumab for Atopic Dermatitis in Adults: Retrospective, Observational Cohort Study

BACKGROUND

Atopic dermatitis (AD) is a common inflammatory disease caused by a type 2 T helper cell-mediated immune response to environmental antigens. Approximately 1 in 5 patients with AD presents with moderate to severe disease, and treatments approved by the Food and Drug Administration include emollients, topical glucocorticoids, and calcineurin inhibitors. Dupilumab, a fully human monoclonal antibody, improves AD via inhibition of interleukin-4 and interleukin-13.

OBJECTIVE

Our aim was to characterize the prescribing patterns of dupilumab for AD in adults at a large university-affiliated health system.

METHODS

A retrospective, observational cohort study was conducted using electronic data from the Observational Health Data Sciences and Informatics database, assessing data from the University of Colorado Medical Campus and its affiliates. The outcome measured was the prevalence of dupilumab prescribed for adults with AD (n=6421), between March 28, 2013, and March 28, 2021. We assessed whether the characteristics of patients who received dupilumab were different from those who did not. Each patient characteristic was assessed using a univariate logistic regression with the binary outcome of receiving or not receiving dupilumab.

RESULTS

We found a population prevalence of 5.6% (6421/114,476) for AD. In our cohort, Black patients with AD were more than twice as likely to have received dupilumab compared to White patients (odds ratio 2.352, 95% CI 1.58-3.39). Patients with a diagnosis of atopic neurodermatitis were approximately twice as likely to have received dupilumab compared to those with other diagnostic variants of AD (odds ratio 1.87, 95% CI 1.01-3.22).

CONCLUSIONS

Our results demonstrate that both patient racial characteristics and specific AD diagnoses were associated with variations in dupilumab prescription patterns.

Predicting Undesired Treatment Outcomes With Machine Learning in Mental Health Care: Multisite Study

Background

Predicting which treatment will work for which patient in mental health care remains a challenge.

Objective

The aim of this multisite study was 2-fold: (1) to predict patients' response to treatment in Dutch basic mental health care using commonly available data from routine care and (2) to compare the performance of these machine learning models across three different mental health care organizations in the Netherlands by using clinically interpretable models.

Methods

Using anonymized data sets from three different mental health care organizations in the Netherlands (n=6452), we applied a least absolute shrinkage and selection operator regression 3 times to predict the treatment outcome. The algorithms were internally validated with cross-validation within each site and externally validated on the data from the other sites.

Results

The performance of the algorithms, measured by the area under the curve of the internal validations as well as the corresponding external validations, ranged from 0.77 to 0.80.

Conclusions

Machine learning models provide a robust and generalizable approach in automated risk signaling technology to identify cases at risk of poor treatment outcomes. The results of this study hold substantial implications for clinical practice by demonstrating that the performance of a model derived from one site is similar when applied to another site (ie, good external validation).

Perceived Use Cases, Barriers, and Requirements for a Smart Health-Tracking Toilet Seat: Qualitative Focus Group Study

BACKGROUND

Smart bathroom technology offers unrivaled opportunities for the automated measurement of a range of biomarkers and other data. Unfortunately, efforts in this area are mostly driven by a technology push rather than market pull approach, which decreases the chances of successful adoption. As yet, little is known about the use cases, barriers, and desires that potential users of smart bathrooms perceive.

OBJECTIVE

This study aimed to investigate how participants from the general population experience using a smart sensor-equipped toilet seat installed in their home. The study contributes to answering the following questions: What use cases do citizens see for this innovation? and What are the limitations and barriers to its everyday use that they see, including concerns regarding privacy, the lack of fit with everyday practices, and unmet expectations for user experience?

METHODS

Overall, 31 participants from 30 households participated in a study consisting of 3 (partially overlapping) stages: sensitizing, in which participants filled out questionnaires to trigger their thoughts about smart bathroom use and personal health; provotyping, in which participants received a gentle provocation in the form of a smart toilet seat, which they used for 2 weeks; and discussion, in which participants took part in a web-based focus group session to discuss their experiences.

RESULTS

Participants mostly found the everyday use of the toilet, including installation and dismantling when necessary, to be relatively easy and free of complications. Where complications occurred, participants mentioned issues related to the design of the prototype, technology, or mismatches with normal practices in using toilets and hygiene. A broad range of use cases were mentioned, ranging from signaling potentially detrimental health conditions or exacerbations of existing conditions to documenting physical data to measuring biomarkers to inform a diagnosis and behavioral change. Participants differed greatly in whether they let others use, or even know about, the seat. Ownership and control over their own data were essential for most participants.

CONCLUSIONS

This study showed that participants felt that a smart toilet seat could be acceptable and effective, as long as it fits everyday practices concerning toilet use and hygiene. The range of potential uses for a smart toilet seat is broad, as long as privacy and control over disclosure and data are warranted.

New horizons in the role of digital data in the healthcare of older people

There are national and global moves to improve effective digital data design and application in healthcare. This New Horizons commentary describes the role of digital data in healthcare of the ageing population. We outline how health and social care professionals can engage in the proactive design of digital systems that appropriately serve people as they age, carers and the workforce that supports them.

KEY POINTS

Healthcare improvements have resulted in increased population longevity and hence multimorbidity. Shared care records to improve communication and information continuity across care settings hold potential for older people. Data structure and coding are key considerations. A workforce with expertise in caring for older people with relevant knowledge and skills in digital healthcare is important.

Exploring Hearing Care Technology from Clinic to Capability

Healthcare systems are traditionally a clinician-led and reactive structure that does not promote clients managing their health issues or concerns from an early stage. However, when clients are proactive in starting their healthcare earlier than later, they can achieve better outcomes and quality of life. Hearing healthcare and the rehabilitation journey currently fit into this reactive and traditional model of care. With the development of service delivery models evolving to offer services to the consumer online and where they are predominately getting their healthcare information from the internet and the advancement of digital applications and hearing devices beyond traditional hearing aid structures, we are seeing a change in how consumers engage in hearing care. Similarly, as the range of hearing devices evolves with increasingly blended and standard levels of technology across consumer earbuds/headphones and medical grade hearing aids, we are seeing a convergence of consumers engaging earlier and becoming increasingly aware of hearing health needs. This article will discuss how the channels, service, and technology are coming together to reform traditionally clinician-led healthcare models to an earlier consumer-led model and the benefits and limitations associated with it. Additionally, we look to explore advances in hearing technologies and services, and if these will or can contribute to a behavioral change in the hearing healthcare journey of consumers.

The impacts of COVID-19 measures on drug markets and drug use among a cohort of people who use methamphetamine in Victoria, Australia

BACKGROUND AND AIMS

Few studies of the impacts of the coronavirus disease 2019 (COVID-19) public health measures on drug markets and drug use patterns have used longitudinal data. We aimed to examine whether COVID-19 measures were associated with increases in methamphetamine price, decreases in methamphetamine use frequency and subsequent changes in secondary outcomes of other drug use frequency in metropolitan Melbourne and regional Victoria.

DESIGN

Longitudinal analysis framework was used from a longitudinal cohort of people who use methamphetamine.

SETTING

Victoria state, Australia.

PARTICIPANTS

One hundred eighty-five VMAX study participants who reported a methamphetamine purchase after the onset of the pandemic were used for the price paid analysis. Methamphetamine or other drug use frequency analysis was performed using 277 participants who used methamphetamine during the pandemic or in the year before the pandemic.

MEASUREMENTS

Price paid per gram of methamphetamine derived from the most recent purchase price and most recent purchase quantity. Frequency of methamphetamine and other drug use measured as the average number of days per week used in the last month.

FINDINGS

Compared with pre-COVID-19 period, methamphetamine prices increased by AUD351.63 (P value <0.001) and by AUD456.51 (P value <0.001) in Melbourne and regional Victoria, respectively, during the period in which the most intense public health measures were implemented in Victoria. Although prices decreased after harder restrictions were lifted (by AUD232.84, P value <0.001 and AUD263.68, P value <0.001, in Melbourne and regional Victoria, respectively), they remained higher than pre-COVID-19 levels. A complementary 76% decrease was observed in relation to methamphetamine use frequency in regional Victoria (P value = 0.006) that was not offset by any changes in the frequency of use of other drugs such as alcohol, tobacco or other illicit drugs.

CONCLUSION

COVID-19 public health measures in Victoria state, Australia, appear to have been associated with major price changes in the methamphetamine market and decreased frequency of use of the drug.

Using Hypothesis-Led Machine Learning and Hierarchical Cluster Analysis to Identify Disease Pathways Prior to Dementia: Longitudinal Cohort Study

BACKGROUND

Dementia development is a complex process in which the occurrence and sequential relationships of different diseases or conditions may construct specific patterns leading to incident dementia.

OBJECTIVE

This study aimed to identify patterns of disease or symptom clusters and their sequences prior to incident dementia using a novel approach incorporating machine learning methods.

METHODS

Using Taiwan's National Health Insurance Research Database, data from 15,700 older people with dementia and 15,700 nondementia controls matched on age, sex, and index year (n=10,466, 67% for the training data set and n=5234, 33% for the testing data set) were retrieved for analysis. Using machine learning methods to capture specific hierarchical disease triplet clusters prior to dementia, we designed a study algorithm with four steps: (1) data preprocessing, (2) disease or symptom pathway selection, (3) model construction and optimization, and (4) data visualization.

RESULTS

Among 15,700 identified older people with dementia, 10,466 and 5234 subjects were randomly assigned to the training and testing data sets, and 6215 hierarchical disease triplet clusters with positive correlations with dementia onset were identified. We subsequently generated 19,438 features to construct prediction models, and the model with the best performance was support vector machine (SVM) with the by-group LASSO (least absolute shrinkage and selection operator) regression method (total corresponding features=2513; accuracy=0.615; sensitivity=0.607; specificity=0.622; positive predictive value=0.612; negative predictive value=0.619; area under the curve=0.639). In total, this study captured 49 hierarchical disease triplet clusters related to dementia development, and the most characteristic patterns leading to incident dementia started with cardiovascular conditions (mainly hypertension), cerebrovascular disease, mobility disorders, or infections, followed by neuropsychiatric conditions.

CONCLUSIONS

Dementia development in the real world is an intricate process involving various diseases or conditions, their co-occurrence, and sequential relationships. Using a machine learning approach, we identified 49 hierarchical disease triplet clusters with leading roles (cardio- or cerebrovascular disease) and supporting roles (mental conditions, locomotion difficulties, infections, and nonspecific neurological conditions) in dementia development. Further studies using data from other countries are needed to validate the prediction algorithms for dementia development, allowing the development of comprehensive strategies to prevent or care for dementia in the real world.

Electronic Phenotype for Advanced Chronic Kidney Disease in a Veteran Health Care System Clinical Database: Systems-Based Strategy for Model Development and Evaluation

BACKGROUND

Identifying advanced (stages 4 and 5) chronic kidney disease (CKD) cohorts in clinical databases is complicated and often unreliable. Accurately identifying these patients can allow targeting this population for their specialized clinical and research needs.

OBJECTIVE

This study was conducted as a system-based strategy to identify all prevalent Veterans with advanced CKD for subsequent enrollment in a clinical trial. We aimed to examine the prevalence and accuracy of conventionally used diagnosis codes and estimated glomerular filtration rate (eGFR)-based phenotypes for advanced CKD in an electronic health record (EHR) database. We sought to develop a pragmatic EHR phenotype capable of improving the real-time identification of advanced CKD cohorts in a regional Veterans health care system.

METHODS

Using the Veterans Affairs Informatics and Computing Infrastructure services, we extracted the source cohort of Veterans with advanced CKD based on a combination of the latest eGFR value ≤30 ml·min-1·1.73 m-2 or existing International Classification of Diseases (ICD)-10 diagnosis codes for advanced CKD (N18.4 and N18.5) in the last 12 months. We estimated the prevalence of advanced CKD using various prior published EHR phenotypes (ie, advanced CKD diagnosis codes, using the latest single eGFR <30 ml·min-1·1.73 m-2, utilizing two eGFR values) and our operational EHR phenotypes of a high-, intermediate-, and low-risk advanced CKD cohort. We evaluated the accuracy of these phenotypes by examining the likelihood of a sustained reduction of eGFR <30 ml·min-1·1.73 m-2 over a 6-month follow-up period.

RESULTS

Of the 133,756 active Veteran enrollees at North Florida/South Georgia Veterans Health System (NF/SG VHS), we identified a source cohort of 1759 Veterans with advanced nondialysis CKD. Among these, 1102 (62.9%) Veterans had diagnosis codes for advanced CKD; 1391(79.1%) had the index eGFR <30 ml·min-1·1.73 m-2; and 928 (52.7%), 480 (27.2%), and 315 (17.9%) Veterans had high-, intermediate-, and low-risk advanced CKD, respectively. The prevalence of advanced CKD among Veterans at NF/SG VHS varied between 1% and 1.5% depending on the EHR phenotype. At the 6-month follow-up, the probability of Veterans remaining in the advanced CKD stage was 65.3% in the group defined by the ICD-10 codes and 90% in the groups defined by eGFR values. Based on our phenotype, 94.2% of high-risk, 71% of intermediate-risk, and 16.1% of low-risk groups remained in the advanced CKD category.

CONCLUSIONS

While the prevalence of advanced CKD has limited variation between different EHR phenotypes, the accuracy can be improved by utilizing two eGFR values in a stratified manner. We report the development of a pragmatic EHR-based model to identify advanced CKD within a regional Veterans health care system in real time with a tiered approach that allows targeting the needs of the groups at risk of progression to end-stage kidney disease.

Misinterpretations of Significance Testing Results Near the P-Value Threshold in the Urologic Literature

Background The outcome of a statistical test is to accept or reject a null hypothesis. Reporting a metric as "trending toward significance" is a misinterpretation of the p-value. Studies highlighting the prevalence of statistical errors in the urologic literature remain scarce. We evaluated abstracts from 15 urology journals published within the years 2000-2021 and provided a quantitative measure of a common statistical mistake-misconstruing the function of null hypothesis testing by reporting "a trend toward significance." Materials and methods We performed an audit of 15 urology journals, looking at articles published from January 1, 2000, to January 1, 2022. A word recognition function in Microsoft Excel was utilized to identify the use of the word "trend" in the abstracts. Each use of the word "trend" was manually investigated by two authors to determine whether it was improperly used in describing non-statistically significant data as trending toward significance. Statistics and data analysis were performed using Python libraries: pandas, scipy.stats, and seaborn. Results This study included 101,134 abstracts from 15 urology journals. Within those abstracts, the word "trend" was used 2,509 times, 572 uses of which were describing non-statistically significant data as trending toward significance. There was a statistically significant difference in the rate of errors between the 15 journals (p < 0.01). The highest rate of improper use of the word "trend" was found in Bladder Cancer with a rate of 1.6% (p < 0.01) of articles. The lowest rate of improper use was found in European Urology, with a rate of 0.3% (p < 0.01). Our analysis found a moderate correlation between the number of articles published and the number of misuses of the word "trend" within each journal and across all journals every year (r = 0.61 and 0.70, respectively). Conclusion The overall rate of p-value misinterpretation never exceeded 2% of articles in each journal. There is significance in the difference in misinterpretation rates between the different journals. Authors' utilization of the word "trend" describing non-significant p-values as being near significant should be used with caution.

Samrakshan Yodha Dashboard of Diagnostic and Performance Metrics for Fetal Radiologists

We used the data collection experience of the initial 3 years of Samrakshan to develop a live dashboard for individual practitioners to examine diagnostic and performance metrics in real-time and to assess trends. The dashboard was created in MS Excel (Microsoft 365 MSO version 2209) and the output provides useful information on actionable items like compliance with low-dose aspirin and estimates of preterm and term pre-eclampsia and fetal growth restriction, congenital anomalies, the proportion of preterm births, and perinatal mortality estimates. The output will help individual practitioners to generate practice-related actionable evidence and can further optimize service delivery for local populations. The dashboard can be used on any platform with MS Excel and does not require the installation of any additional software or license. The dashboard is provided as a free, open-access resource by the Samrakshan Program of Indian Radiological and Imaging Association.

Design of tables for the presentation and communication of data in ecological and evolutionary biology

Tables and charts have long been seen as effective ways to convey data. Much attention has been focused on improving charts, following ideas of human perception and brain function. Tables can also be viewed as two-dimensional representations of data; yet, it is only fairly recently that we have begun to apply principles of design that aid the communication of information between the author and reader. In this study, we collated guidelines for the design of data and statistical tables. These guidelines fall under three principles: aiding comparisons, reducing visual clutter, and increasing readability. We surveyed tables published in recent issues of 43 journals in the fields of ecology and evolutionary biology for their adherence to these three principles, as well as author guidelines on journal publisher websites. We found that most of the over 1000 tables we sampled had no heavy grid lines and little visual clutter. They were also easy to read, with clear headers and horizontal orientation. However, most tables did not aid the vertical comparison of numeric data. We suggest that authors could improve their tables by the right-flush alignment of numeric columns typeset with a tabular font, clearly identify statistical significance, and use clear titles and captions. Journal publishers could easily implement these formatting guidelines when typesetting manuscripts.

Signaling and meaning in organizational analytics: coping with Goodhart's Law in an era of digitization and datafication

The future of work will be measured. The increasing and widespread adoption of analytics, the use of digital inputs and outputs to inform organizational decision making, makes the communication of data central to organizing. This article applies and extends signaling theory to provide a framework for the study of analytics as communication. We report three cases that offer examples of dubious, selective, and ambiguous signaling in the activities of workers seeking to shape the meaning of data within the practice of analytics. The analysis casts the future of work as a game of strategic moves between organizations, seeking to measure behaviors and quantify the performance of work, and workers, altering their behavioral signaling to meet situated goals. The framework developed offers a guide for future examinations of the asymmetric relationship between management and workers as organizations adopt metrics to monitor and evaluate work.

Evaluating the Discriminatory Ability of the Sickle Cell Data Collection Program's Administrative Claims Case Definition in Identifying Adults With Sickle Cell Disease: Validation Study

BACKGROUND

Sickle cell disease (SCD) was first recognized in 1910 and identified as a genetic condition in 1949. However, there is not a universal clinical registry that can be used currently to estimate its prevalence. The Sickle Cell Data Collection (SCDC) program, funded by the Centers for Disease Control and Prevention, funds state-level grantees to compile data within their states from various sources including administrative claims to identify individuals with SCD. The performance of the SCDC administrative claims case definition has been validated in a pediatric population with SCD, but it has not been tested in adults.

OBJECTIVE

The objective of our study is to evaluate the discriminatory ability of the SCDC administrative claims case definition to accurately identify adults with SCD using Medicaid insurance claims data.

METHODS

Our study used Medicaid claims data in combination with hospital-based medical record data from the Alabama, Georgia, and Wisconsin SCDC programs to identify individuals aged 18 years or older meeting the SCDC administrative claims case definition. In order to validate this definition, our study included only those individuals who were identified in both Medicaid's and the partnering clinical institution's records. We used clinical laboratory tests and diagnostic algorithms to determine the true SCD status of this subset of patients. Positive predictive values (PPV) are reported overall and by state under several scenarios.

RESULTS

There were 1219 individuals (354 from Alabama and 865 from Georgia) who were identified through a 5-year time period. The 5-year time period yielded a PPV of 88.4% (91% for data from Alabama and 87% for data from Georgia), when only using data with laboratory-confirmed (gold standard) cases as true positives. With a narrower time period (3-year period) and data from 3 states (Alabama, Georgia, and Wisconsin), a total of 1432 individuals from these states were included in our study. The overall 3-year PPV was 89.4% (92%, 93%, and 81% for data from Alabama, Georgia, and Wisconsin, respectively) when only considering laboratory-confirmed cases as true cases.

CONCLUSIONS

Adults identified as having SCD from administrative claims data based on the SCDC case definition have a high probability of truly having the disease, especially if those hospitals have active SCD programs. Administrative claims are thus a valuable data source to identify adults with SCD in a state and understand their epidemiology and health care service usage.

Evaluating the Feasibility of a Multiplayer Role-Playing Game as a Behavioral Health Intervention in Adolescent Patients With Chronic Physical or Mental Conditions: Protocol for a Cohort Study

BACKGROUND

Numerous studies have revealed that adolescents with chronic physical or mental conditions (CPMCs) are at an increased risk for depression and anxiety, with serious direct and indirect negative effects on treatment adherence, family functioning, and health-related quality of life. As game-based approaches are effective interventions in treating anxiety and depression, we propose to explore the use of a multiplayer role-playing game (RPG) as a potential intervention for social isolation, anxiety, and depression.

OBJECTIVE

The objectives of this study were to (1) determine the feasibility of using Masks, a multiplayer RPG, as an intervention for social isolation, anxiety, and depression in adolescents with CPMCs; (2) evaluate the viability of the research process; and (3) gauge participation in and engagement with RPG-based interventions.

METHODS

This study is a remote synchronous game-based intervention for adolescents with CPMCs aged 14-19 years. Eligible participants completed a web-based baseline survey to assess anxiety, depression, and social isolation and to identify their gaming habits. After completing the baseline survey, they participated in 5 moderated Masks game sessions. In Masks, players assume the roles of young superheroes; select their character types, superpowers; and perform actions determined by the game's rule system and dice rolls. All game sessions were played using Discord, a communication platform commonly used by gaming communities. Games were led and moderated by game masters (GMs). After each game session, participants completed surveys to assess changes in anxiety, depression, and social isolation, and their attitude toward the game and the user experience. The participants also completed an exit survey after all 5 game sessions (modified version of the Patient Health Questionnaire and the Generalized Anxiety Disorder Questionnaire, and 17 open-ended questions). The GMs rated each game session and reported on gameplay, player behavior, comfort, and engagement levels of the players.

RESULTS

As of March 2020, six participants were recruited for the pilot study to participate in moderated web-based game sessions of Masks; 3 completed all game sessions and all required assessments. Although the number of participants was too low to draw generalizable conclusions, self-reported clinical outcomes did seem to indicate a positive change in depression, anxiety, and social isolation symptoms. Qualitative analysis of postgame survey data from participants and GMs indicated high levels of engagement and enjoyment. Furthermore, the participants provided feedback about improved mood and engagement related to weekly participation in Masks. Lastly, responses to the exit survey showed interest in future RPG-related studies.

CONCLUSIONS

We established a workflow for gameplay and evaluated a research protocol for evaluating the impact of RPG participation on isolation, anxiety, and depression symptoms in adolescents with CPMCs. Preliminary data collected from the pilot study support the validity of the research protocol and the use of RPG-based interventions in larger clinical studies.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR1-10.2196/43987.

Digital Education for the Deployment of Artificial Intelligence in Health Care

Artificial Intelligence (AI) represents a significant milestone in health care's digital transformation. However, traditional health care education and training often lack digital competencies. To promote safe and effective AI implementation, health care professionals must acquire basic knowledge of machine learning and neural networks, critical evaluation of data sets, integration within clinical workflows, bias control, and human-machine interaction in clinical settings. Additionally, they should understand the legal and ethical aspects of digital health care and the impact of AI adoption. Misconceptions and fears about AI systems could jeopardize its real-life implementation. However, there are multiple barriers to promoting electronic health literacy, including time constraints, overburdened curricula, and the shortage of capacitated professionals. To overcome these challenges, partnerships among developers, professional societies, and academia are essential. Integrating specialists from different backgrounds, including data specialists, lawyers, and social scientists, can significantly contribute to combating digital illiteracy and promoting safe AI implementation in health care.

Convergence in Mobility Data Sets From Apple, Google, and Meta

BACKGROUND

The higher movement of people was one of the variables that contributed to the spread of the infectious agent SARS-CoV-2 during the COVID-19 pandemic. Governments worldwide responded to the virus by implementing measures that would restrict people's movements, and consequently, the spread of the disease. During the onset of the pandemic, the technology companies Apple, Google, and Meta used their infrastructure to anonymously gather mobility reports from their users.

OBJECTIVE

This study aims to compare mobility data reports collected by Apple, Google, and Meta (formerly Facebook) during the COVID-19 pandemic and a major winter storm in Texas in 2021. We aim to explore the hypothesis that different people exhibit similar mobility trends during dramatic events and to emphasize the importance of this type of data for public health measures. The study also aims to promote evidence for companies to continue releasing mobility trends data, given that all 3 companies have discontinued these services.

METHODS

In this study, we collected mobility data spanning from 2020 to 2022 from 3 major tech companies: Apple, Google, and Meta. Our analysis focused on 58 countries that are common to all 3 databases, enabling us to conduct a comprehensive global-scale analysis. By using the winter storm that occurred in Texas in 20201 as a benchmark, we were able to assess the robustness of the mobility data obtained from the 3 companies and ensure the integrity of our findings.

RESULTS

Our study revealed convergence in the mobility trends observed across different companies during the onset of significant disasters, such as the first year of the COVID-19 pandemic and the winter storm that impacted Texas in 2021. Specifically, we observed strong positive correlations (r=0.96) in the mobility data collected from different tech companies during the first year of the pandemic. Furthermore, our analysis of mobility data during the 2021 winter storm in Texas showed a similar convergence of trends. Additionally, we found that periods of stay-at-home orders were reflected in the data, with record-low mobility and record-high stay-at-home figures.

CONCLUSIONS

Our findings provide valuable insights into the ways in which major disruptive events can impact patterns of human mobility; moreover, the convergence of data across distinct methodologies highlights the potential value of leveraging mobility data from multiple sources for informing public health decision-making. Therefore, we conclude that the use of mobility data is an asset for health authorities to consider during natural disasters, as we determined that the data sets from 3 companies yielded convergent mobility patterns. Comparatively, data obtained from a single source would be limited, and therefore, more difficult to interpret, requiring careful analysis.

A Wireless Sensor Network for Residential Building Energy and Indoor Environmental Quality Monitoring: Design, Instrumentation, Data Analysis and Feedback

This article outlines the implementation and use of a large wireless instrumentation solution to collect data over a long time period of a few years for three collective residential buildings. The sensor network consists of a variety of 179 sensors deployed in building common areas and in apartments to monitor energy consumption, indoor environmental quality, and local meteorological conditions. The collected data are used and analyzed to assess the building performance in terms of energy consumption and indoor environmental quality following major renovation operations on the buildings. Observations from the collected data show energy consumption of the renovated buildings in agreement with expected energy savings calculated by an engineering office, many different occupancy patterns mainly related to the professional situation of the households, and seasonal variation in window opening rates. The monitoring was also able to detect some deficiencies in the energy management. Indeed, the data reveal the absence of time-of-day-dependent heating load control and higher than expected indoor temperatures because of a lack of occupant awareness on energy savings, thermal comfort, and the new technologies installed during the renovation such as thermostatic valves on the heaters. Lastly, we also provide feedback on the performed sensor network from the experiment design and choice of measured quantities to data communication, through the sensors' technological choices, implementation, calibration, and maintenance.

Developing Biodiversity Baselines to Develop and Implement Future Conservation Targets

With the recent launch of the Kunming-Montreal global biodiversity framework (GBF), and the associated monitoring framework, understanding the framework and data needed to support it is crucial. Unfortunately, whilst the monitoring framework was meant to provide key data to monitor progress towards goals and targets, most indicators are too unclear for detection or marking progress. The most common datasets for this task, such as the IUCN redlist of species, have major spatial inaccuracies, and lack the temporal resolution to track progress, whilst point-based datasets lack data from many regions, in addition to species coverage. Utilising existing data will require the careful use of existing data, such as the use of inventories and projecting richness patterns, or filling data gaps before developing species-level models and assessments. As high-resolution data fall outside the scope of explicit indicators within the monitoring framework, using essential biodiversity variables within GEOBON (which are noted in the prelude of the monitoring framework) as a vehicle for data aggregation provides a mechanism for collating the necessary high-resolution data. Ultimately developing effective targets for conservation will require better species data, for which National Biodiversity Strategic Action Plans (NBSAPs) and novel mechanisms for data mobilisation will be necessary. Furthermore, capitalising on climate targets and climate biodiversity synergies within the GBF provides an additional means for developing meaningful targets, trying to develop urgently needed data to monitor biodiversity trends, prioritising meaningful tasks, and tracking our progress towards biodiversity targets.

The public availability of hospital CHNA reports: limitations and potential to study hospital investments in the next phase of public health

Nonprofit hospitals have been required to complete and make publicly available their community benefit reports for more than a decade, a sign of changing expectations for private health care organizations to explicitly collaborate with public health departments to improve community health. Despite these important changes to practice and policy, no governmental agency provides statistics regarding compliance with this process. To better understand the nature and usefulness of the data provided through these processes, we led a research team that collected and coded Community Health Needs Assessment (CHNA) and Implementation Strategy (IS) Reports for a nationally representative sample of hospitals between 2018 and 2022. We utilized descriptive statistics to understand the frequency of noncompliance; t-tests and chi-square tests were employed to identify characteristics associated with incomplete documents. Approximately 95% of hospitals provided a public CHNA, and approximately 86% made their IS available. The extent of compliance with the CHNA/IS mandate indicates that these documents, paired with existing public health and policy data, offer considerable potential for understanding the investments nonprofit hospitals make to improve health outcomes and health equity in the communities they serve.