Errata: Rare Compound Heterozygous Missense Mutation of the SCN5A Gene with Childhood-Onset Sick Sinus Syndrome in Two Chinese Sisters: A Case Report.

An error appeared in the article entitled "Rare Compound Heterozygous Missense Mutation of the SCN5A Gene with Childhood-Onset Sick Sinus Syndrome in Two Chinese Sisters: A Case Report" by Yanyun Wang, Siyu Long, Chenxi Wei, and Xiaoqin Wang (Vol. 64 No.2, 299-305, 2023). The name of the first affiliation on page 299 was wrong. It should be "Laboratory of Molecular Translational Medicine, Center for Translational Medicine, West China Second University Hospital, Sichuan University, Chengdu, China" and not "Laboratory of Molecular Translational Medicine, Center for Translational Medicine, Sichuan University, Chengdu, China".

Promoting Data Harmonization of COVID-19 Research in Pregnant and Pediatric Populations.

This Viewpoint investigates the use of common data elements to promote data harmonization in COVID-19­related studies of pediatric and pregnant populations.

Evaluation of the National Center for Health Statistics Data Presentation Standards for Rates From Vital Statistics and Sample Surveys.

For the CIs used in the Standards for rates from vital statistics and complex health surveys, this report evaluates coverage probability, relative width, and the resulting percentage of rates flagged as statistically unreliable when compared with previously used standards. Additionally, the report assesses the impact of design effects and the denominator's sampling variability, when applicable.

Consistency of Physician Data Across Health Insurer Directories.

This study assesses the consistency of information across publicly available physician directories from 5 large national health insurers.

Accelerating Food Allergy Research: Need for a Data Commons.

Food allergy is a significant health problem affecting approximately 8% of children and 11% of adults in the United States. It exhibits all the characteristics of a "complex" genetic trait; therefore, it is necessary to look at very large numbers of patients, far more than exist at any single organization, to eliminate gaps in the current understanding of this complex chronic disorder. Advances may be achieved by bringing together food allergy data from large numbers of patients into a Data Commons, a secure and efficient platform for researchers, comprising standardized data, available in a common interface for download and/or analysis, in accordance with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. Prior data commons initiatives indicate that research community consensus and support, formal food allergy ontology, data standards, an accepted platform and data management tools, an agreed upon infrastructure, and trusted governance are the foundation of any successful data commons. In this article, we will present the justification for the creation of a food allergy data commons and describe the core principles that can make it successful and sustainable.

Brain Data Standards - A method for building data-driven cell-type ontologies.

Large-scale single-cell 'omics profiling is being used to define a complete catalogue of brain cell types, something that traditional methods struggle with due to the diversity and complexity of the brain. But this poses a problem: How do we organise such a catalogue - providing a standard way to refer to the cell types discovered, linking their classification and properties to supporting data? Cell ontologies provide a partial solution to these problems, but no existing ontology schemas support the definition of cell types by direct reference to supporting data, classification of cell types using classifications derived directly from data, or links from cell types to marker sets along with confidence scores. Here we describe a generally applicable schema that solves these problems and its application in a semi-automated pipeline to build a data-linked extension to the Cell Ontology representing cell types in the Primary Motor Cortex of humans, mice and marmosets. The methods and resulting ontology are designed to be scalable and applicable to similar whole-brain atlases currently in preparation.

Data Cleaning Process for mHealth Log Data to Inform Health Worker Performance.

Log data, captured during use of mobile health (mHealth) applications by health providers, can play an important role in informing nature of user engagement with the application. The log data can also be employed in understanding health provider work patterns and performance. However, given that these logs are raw data, they require robust cleaning and curation if accurate conclusions are to be derived from analyzing them. This paper describes a systematic data cleaning process for mHealth-derived logs based on Broeck's framework, which involves iterative screening, diagnosis, and treatment of the log data. For this study, log data from the demonstrative mUzima mHealth application are used. The employed data cleaning process uncovered data inconsistencies, duplicate logs, missing data within logs that required imputation, among other issues. After the data cleaning process, only 39,229 log records out of the initial 91,432 usage logs (42.9%) could be included in the final dataset suitable for analyses of health provider work patterns. This work highlights the significance of having a systematic data cleaning approach for log data to derive useful information on health provider work patterns and performance.

The tension between ethics and rigor when using Amazon MTurk for eating disorder research: Response to commentaries on Burnette et al. (2021).

We respond to commentaries on our 2021 paper "Concerns and recommendations for using Amazon MTurk for eating disorder research." The commentators raised many thoughtful and nuanced points regarding data validity and ethical means of online data collection. We echo concerns about the ethics of recruiting via platforms such as MTurk, and highlight tensions between recommendations for ethical data collection and ensuring data integrity. Especially, we highlight the consistent finding that MTurk workers display elevated (often remarkably so) rates of psychopathology, and argue such findings merit further scrutiny to ensure both data are valid and workers not exploited.

Ethical concerns arising from recruiting workers from Amazon's Mechanical Turk as research participants: Commentary on Burnette et al. (2021).

In this commentary, we respond to Burnette et al.'s (2021) paper, which gives significant practical recommendations to improve data quality and validity while gathering data via Amazon's Mechanical Turk (MTurk). We argue that it is also important to acknowledge and review the specific ethical issues that might arise when recruiting MTurk workers as participants. We particularly raise three main ethical concerns that need to be addressed when recruiting research participants from participant recruitment platforms: participants' economic vulnerability, participants' sensitivity, and power dynamics between participants and researchers. We elaborate on these issues by discussing the ways in which they may appear and be responded to. We conclude that considering the ethical aspects of data collection and the potential impacts of data collection on those involved would complement Burnette et al.'s recommendations. Consequently, data collection processes should be transparent as well, in addition to data screening processes.

Practice makes perfect: Commentary on Burnette et al. (2021).

Underrepresented identities have been overlooked in the development of measures assessing eating disorders; therefore, limited normative data exist for these identities. To address this, Burnette et al. sought to provide Eating Disorder Examination-Questionnaire and Eating Attitudes Test-26 norms for transgender adults using Amazon's MTurk. However, they were unable to achieve this goal due to what they perceived as high rates of invalid responses. Instead, they provided recommendations for conducting MTurk research. However, little or no evidence supports the validity of several recommendations, partly because their study was not designed to derive or validate recommendations. By their own admission, their strategies failed to address what they identified as the central problem. We express concern about Burnette et al.'s recommendations because (a) the recommendations are built on assumptions about the problem that may not be true; and (b) the recommendations are not provided within the context of limitations of self-report/online data collection writ large. We detail these concerns and propose that strategies for mitigating inattentive/invalid responding be subjected to validation prior to being recommended to prevent the implementation of procedures that result in the exclusion of the target population, individuals who we historically, and perhaps still, unjustly exclude from research.

The National Health and Nutrition Examination Survey (NHANES), 2021-2022: Adapting Data Collection in a COVID-19 Environment.

The National Health and Nutrition Examination Survey (NHANES) is a unique source of national data on the health and nutritional status of the US population, collecting data through interviews, standard exams, and biospecimen collection. Because of the COVID-19 pandemic, NHANES data collection was suspended, with more than a year gap in data collection. NHANES resumed operations in 2021 with the NHANES 2021-2022 survey, which will monitor the health and nutritional status of the nation while adding to the knowledge of COVID-19 in the US population. This article describes the reshaping of the NHANES program and, specifically, the planning of NHANES 2021-2022 for data collection during the COVID-19 pandemic. Details are provided on how NHANES transformed its participant recruitment and data collection plans at home and at the mobile examination center to safely collect data in a COVID-19 environment. The potential implications for data users are also discussed. (Am J Public Health. 2021;111(12):2149-2156. https://doi.org/10.2105/AJPH.2021.306517).

Advancements in the National Vital Statistics System to Meet the Real-Time Data Needs of a Pandemic.

The National Center for Health Statistics' (NCHS's) National Vital Statistics System (NVSS) collects, processes, codes, and reviews death certificate data and disseminates the data in annual data files and reports. With the global rise of COVID-19 in early 2020, the NCHS mobilized to rapidly respond to the growing need for reliable, accurate, and complete real-time data on COVID-19 deaths. Within weeks of the first reported US cases, NCHS developed certification guidance, adjusted internal data processing systems, and stood up a surveillance system to release daily updates of COVID-19 deaths to track the impact of the COVID-19 pandemic on US mortality. This report describes the processes that NCHS took to produce timely mortality data in response to the COVID-19 pandemic. (Am J Public Health. 2021;111(12):2133-2140. https://doi.org/10.2105/AJPH.2021.306519).