The PhenX Toolkit: Recommended Measurement Protocols for Social Determinants of Health Research.

Health disparities are driven by unequal conditions in the environments in which people are born, live, learn, work, play, worship, and age, commonly termed the Social Determinants of Health (SDoH). The availability of recommended measurement protocols for SDoH will enable investigators to consistently collect data for SDoH constructs. The PhenX (consensus measures for Phenotypes and eXposures) Toolkit is a web-based catalog of recommended measurement protocols for use in research studies with human participants. Using standard protocols from the PhenX Toolkit makes it easier to compare and combine studies, potentially increasing the impact of individual studies, and aids in comparability across literature. In 2018, the National Institute on Minority Health and Health Disparities provided support for an initial expert Working Group to identify and recommend established SDoH protocols for inclusion in the PhenX Toolkit. In 2022, a second expert Working Group was convened to build on the work of the first SDoH Working Group and address gaps in the SDoH Toolkit Collections. The SDoH Collections consist of a Core Collection and Individual and Structural Specialty Collections. This article describes a Basic Protocol for using the PhenX Toolkit to select and implement SDoH measurement protocols for use in research studies. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA. Basic Protocol: Using the PhenX Toolkit to select and implement SDoH protocols.

T-CLEARE: A Pilot Community-Driven Tissue-Clearing Protocol Repository.

Selecting and implementing a tissue-clearing protocol is challenging. Established more than 100 years ago, tissue clearing is still a rapidly evolving field of research. There are currently many published protocols to choose from, and each performs better or worse across a range of key evaluation factors (e.g., speed, cost, tissue stability, fluorescence quenching). Additionally, tissue-clearing protocols are often optimized for specific experimental contexts, and applying an existing protocol to a new problem can require a lengthy period of adaptation by trial and error. Although the primary literature and review articles provide a useful starting point for optimization, there is growing recognition that many articles do not provide sufficient detail to replicate or reproduce experimental results. To help address this issue, we have developed a novel, freely available repository of tissue-clearing protocols named T-CLEARE (Tissue CLEAring protocol REpository; https://doryworkspace.org/doryviz). T-CLEARE incorporates community responses to an open survey designed to capture details not commonly found in the scientific literature, including modifications to published protocols required for specific use cases and instances when tissue-clearing protocols did not perform well (negative results). The goal of T-CLEARE is to provide a forum for the community to share evaluations and modifications of tissue-clearing protocols for various tissue types and potentially identify best-in-class methods for a given application.

The PhenX Toolkit: Measurement Protocols for Assessment of Social Determinants of Health.

INTRODUCTION: Social determinants are structures and conditions in the biological, physical, built, and social environments that affect health, social and physical functioning, health risk, quality of life, and health outcomes. The adoption of recommended, standard measurement protocols for social determinants of health will advance the science of minority health and health disparities research and provide standard social determinants of health protocols for inclusion in all studies with human participants. METHODS: A PhenX (consensus measures for Phenotypes and eXposures) Working Group of social determinants of health experts was convened from October 2018 to May 2020 and followed a well-established consensus process to identify and recommend social determinants of health measurement protocols. The PhenX Toolkit contains data collection protocols suitable for inclusion in a wide range of research studies. The recommended social determinants of health protocols were shared with the broader scientific community to invite review and feedback before being added to the Toolkit. RESULTS: Nineteen social determinants of health protocols were released in the PhenX Toolkit (https://www.phenxtoolkit.org) in May 2020 to provide measures at the individual and structural levels for built and natural environments, structural racism, economic resources, employment status, occupational health and safety, education, environmental exposures, food environment, health and health care, and sociocultural community context. CONCLUSIONS: Promoting the adoption of well-established social determinants of health protocols can enable consistent data collection and facilitate comparing and combining studies, with the potential to increase their scientific impact.

The PhenX Toolkit: standard measurement resources for cancer outcomes and survivorship research.

This brief report introduces the Cancer Outcomes and Survivorship domain in the PhenX Toolkit (consensus measures for Phenotypes and eXposures), which includes 15 validated measurement protocols for cancer survivorship research that were recommended for inclusion in this publicly available resource. Developed with input from the scientific community, the domain provides researchers with well-established measurement protocols for evaluating physical and psychological effects, financial toxicity, and patient experiences with cancer care. The PhenX Toolkit, funded by the National Human Genome Research Institute since 2007, is an online resource that provides high-quality standard measurement protocols for a wide range of research areas (eg, smoking cessation, harm reduction and biomarkers, and social determinants of health). Use of the PhenX Cancer Survivorship Outcomes and Survivorship domain can simplify the selection of measurement protocols, data sharing, and comparisons across studies investigating the cancer survivorship experience.

Linking complex disease and exposure data-insights from an environmental and occupational health study.

The disparate measurement protocols used to collect study data are an intrinsic barrier to combining information from environmental health studies. Using standardized measurement protocols and data standards for environmental exposures addresses this gap by improving data collection quality and consistency. To assess the prevalence of environmental exposures in National Institutes of Health (NIH) public data repositories and resources and to assess the commonality of the data elements, we analyzed clinical measures and exposure assays by comparing the Caribbean Consortium for Research in Environmental and Occupational Health study with selected NIH environmental health resources and studies. Our assessment revealed that (1) environmental assessments are widely collected in these resources, (2) biological assessments are less prevalent, and (3) NIH resources can help identify common data for meta-analysis. We highlight resources to help link environmental exposure data across studies to support data sharing. Including NIH data standards in environmental health research facilitates comparing and combining study data, and the use of NIH resources and adoption of standard measures will allow integration of multiple studies and increase the scientific impact of individual studies.

Smoking cessation, harm reduction, and biomarkers protocols in the PhenX Toolkit: Tools for standardized data collection.

The use of standard protocols in studies supports consistent data collection, improves data quality, and facilitates cross-study analyses. Funded by the National Institutes of Health, the PhenX (consensus measures for Phenotypes and eXposures) Toolkit is a catalog of recommended measurement protocols that address a wide range of research topics and are suitable for inclusion in a variety of study designs. In 2020, a PhenX Working Group of smoking cessation experts followed a well-established consensus process to identify and recommend measurement protocols suitable for inclusion in smoking cessation and smoking harm reduction studies. The broader scientific community was invited to review and provide feedback on the preliminary recommendation of the Working Group. Fourteen selected protocols for measuring smoking cessation, harm reduction, and biomarkers research associated with smoking cessation were released in the PhenX Toolkit ( https://www.phenxtoolkit.org) in February 2021. These protocols complement existing PhenX Toolkit content related to tobacco regulatory research, substance use and addiction research, and other measures of smoking-related health outcomes. Adopting well-established protocols enables consistent data collection and facilitates comparing and combining data across studies, potentially increasing the scientific impact of individual studies.

Identifying Datasets for Cross-Study Analysis in dbGaP using PhenX.

Identifying relevant studies and harmonizing datasets are major hurdles for data reuse. Common Data Elements (CDEs) can help identify comparable study datasets and reduce the burden of retrospective data harmonization, but they have not been required, historically. The collaborative team at PhenX and dbGaP developed an approach to use PhenX variables as a set of CDEs to link phenotypic data and identify comparable studies in dbGaP. Variables were identified as either comparable or related, based on the data collection mode used to harmonize data across mapped datasets. We further added a CDE data field in the dbGaP data submission packet to indicate use of PhenX and annotate linkages in the future. Some 13,653 dbGaP variables from 521 studies were linked through PhenX variable mapping. These variable linkages have been made accessible for browsing and searching in the repository through dbGaP CDE-faceted search filter and the PhenX variable search tool. New features in dbGaP and PhenX enable investigators to identify variable linkages among dbGaP studies and reveal opportunities for cross-study analysis.

Correlation Analysis of Variables From the Atherosclerosis Risk in Communities Study.

The need to test chemicals in a timely and cost-effective manner has driven the development of new alternative methods (NAMs) that utilize in silico and in vitro approaches for toxicity prediction. There is a wealth of existing data from human studies that can aid in understanding the ability of NAMs to support chemical safety assessment. This study aims to streamline the integration of data from existing human cohorts by programmatically identifying related variables within each study. Study variables from the Atherosclerosis Risk in Communities (ARIC) study were clustered based on their correlation within the study. The quality of the clusters was evaluated via a combination of manual review and natural language processing (NLP). We identified 391 clusters including 3,285 variables. Manual review of the clusters containing more than one variable determined that human reviewers considered 95% of the clusters related to some degree. To evaluate potential bias in the human reviewers, clusters were also scored via NLP, which showed a high concordance with the human classification. Clusters were further consolidated into cluster groups using the Louvain community finding algorithm. Manual review of the cluster groups confirmed that clusters within a group were more related than clusters from different groups. Our data-driven approach can facilitate data harmonization and curation efforts by providing human annotators with groups of related variables reflecting the themes present in the data. Reviewing groups of related variables should increase efficiency of the human review, and the number of variables reviewed can be reduced by focusing curator attention on variable groups whose theme is relevant for the topic being studied.

Standard metadata for 3D microscopy.

Recent advances in fluorescence microscopy techniques and tissue clearing, labeling, and staining provide unprecedented opportunities to investigate brain structure and function. These experiments' images make it possible to catalog brain cell types and define their location, morphology, and connectivity in a native context, leading to a better understanding of normal development and disease etiology. Consistent annotation of metadata is needed to provide the context necessary to understand, reuse, and integrate these data. This report describes an effort to establish metadata standards for three-dimensional (3D) microscopy datasets for use by the Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative and the neuroscience research community. These standards were built on existing efforts and developed with input from the brain microscopy community to promote adoption. The resulting 3D Microscopy Metadata Standards (3D-MMS) includes 91 fields organized into seven categories: Contributors, Funders, Publication, Instrument, Dataset, Specimen, and Image. Adoption of these metadata standards will ensure that investigators receive credit for their work, promote data reuse, facilitate downstream analysis of shared data, and encourage collaboration.

Tools for standardized data collection: Speech, Language, and Hearing measurement protocols in the PhenX Toolkit.

The PhenX Toolkit (https://www.phenxtoolkit.org/) is an online catalog of recommended measurement protocols to facilitate cross-study analyses for biomedical research. An expert review panel (ERP) reviewed and updated the PhenX Toolkit Speech and Hearing domain to improve the precision and consistency of speech, language, and hearing disorder phenotypes. A three-member ERP convened in August 2018 to review the measurement protocols in the PhenX Speech and Hearing domain. Aided by three additional experts in voice assessment, vertigo, and stuttering, the ERP updated the 28 protocols to reflect the latest science and technology. ERP recommendations include six new protocols, five updated protocols (from the same source), and one retired protocol. New additions include two voice-related, three hearing-related, and two speech-related protocols. Additions reflect new phone/tablet applications for hearing and language, and clinical evaluations of voice. "Language" was added to the domain name, which is now "Speech, Language, and Hearing," to represent language-related protocols. These protocols can facilitate the assessment of speech, language, and hearing in clinical and population research. Common data elements (i.e., use of the same variables across studies) used by geneticists, otolaryngologists, audiologists, speech-language pathologists, and in other disciplines can lead to cross-study data integration and increased statistical power when studies are combined.

Using the PhenX Toolkit to Select Standard Measurement Protocols for Your Research Study.

The goals of PhenX (consensus measures for Phenotypes and eXposures) are to promote the use of standard measurement protocols and to help investigators identify opportunities for collaborative research and cross-study analysis, thus increasing the impact of individual studies. The PhenX Toolkit (https://www.phenxtoolkit.org/) offers high-quality, well-established measurement protocols to assess phenotypes and exposures in studies with human participants. The Toolkit contains protocols representing 29 research domains and 6 specialty collections of protocols that add depth to the Toolkit in specific research areas (e.g., COVID-19, Social Determinants of Health [SDoH], Blood Sciences Research [BSR], Mental Health Research [MHR], Tobacco Regulatory Research [TRR], and Substance Abuse and Addiction [SAA]). Protocols are recommended for inclusion in the PhenX Toolkit by Working Groups of domain experts using a consensus process that includes input from the scientific community. For each PhenX protocol, the Toolkit provides a detailed description, the rationale for inclusion, and supporting documentation. Users can browse protocols in the Toolkit, search the Toolkit using keywords, or use Browse Protocols Tree to identify protocols of interest. The PhenX Toolkit provides data dictionaries compatible with the database of Genotypes and Phenotypes (dbGaP), Research Electronic Data Capture (REDCap) data submission compatibility, and data collection worksheets to help investigators incorporate PhenX protocols into their study design. The PhenX Toolkit provides resources to help users identify published studies that used PhenX protocols. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Using the PhenX Toolkit to support or extend study design.

Genomic medicine implementation protocols in the PhenX Toolkit: tools for standardized data collection.

PURPOSE: The PhenX Toolkit ( www.phenxtoolkit.org ), an online catalog of recommended measurement protocols, facilitates cross-study analyses for research with human participants. The PhenX Steering Committee recommended genomic medicine implementation as a new research domain, with the following scope: genomic knowledge and education (both patients and providers); implementation science; changes in management and treatment; return of results; patient outcomes; and ethical, legal, and social issues (ELSI) associated with genomic research. METHODS: A seven-member expert Working Group convened in October 2019 to identify well-established measurement protocols for a new genomic medicine implementation domain and used the established PhenX consensus process to select measurement protocols for inclusion in the PhenX Toolkit. RESULTS: The Working Group recommended 15 measurement protocols for inclusion in the PhenX Toolkit, with priority given to those with empirical evidence supporting validity. Consortia funded by the National Institutes of Health, and particularly the National Human Genome Research Institute, proved critical in identifying protocols with established utility in this research domain, and identified protocols that were developed through a rigorous process for scope elements that lacked formally validated protocols. CONCLUSION: Use of these protocols, which were released in September 2020, can facilitate standard data collection for genomic medicine implementation research.

The PhenX Toolkit: Establishing Standard Measures for COVID-19 Research.

The PhenX (consensus measures for Phenotypes and eXposures) Toolkit (https://www.phenxtoolkit.org/) is a publicly available, web-based catalog of recommended, well-established measurement protocols of phenotypes and exposures. The goal of PhenX is to facilitate the use of standard measures, enhance data interoperability, and promote collaborative and translational research. PhenX is driven by the scientific community and historically has depended on working groups of experts to recommend measures for release in the PhenX Toolkit. The urgent need for recommended, standard measures for COVID-19 research triggered the development of a "rapid release" process for releasing new content in the PhenX Toolkit. Initially, PhenX collaborated with the National Institutes of Health (NIH) Office of Behavioral and Social Sciences Research, the National Human Genome Research Institute, and the NIH Disaster Research Response (DR2) program to create a library of COVID-19 measurement protocols. With additional support from NIH, PhenX adapted crowdsourcing techniques to accelerate prioritization and recommendation of protocols for release in the PhenX Toolkit. Prioritized COVID-19-specific protocols were used to anchor and define specialty collections of protocols that were subject to review and approval by the PhenX Steering Committee. In addition to the COVID-19-specific protocols, the specialty collections include existing, well-established PhenX protocols, use of which will further enhance data interoperability and cross-study analysis. The COVID-19 specialty collections are Behaviors and Risks; Ethnicity, Race and Demographics; History, Treatment and Outcomes; Information Resources; Psychosocial and Mental Health; and Socioeconomic. The development and usage of PhenX COVID-19 specialty collections are described in this article. © 2021 The Authors. Basic Protocol: Selecting COVID-19 protocols.

The China tuberculosis clinical trials consortium network: a model for international TB clinical trials capacity building.

BACKGROUND: With the second largest tuberculosis (TB) burden globally, China is committed to actively engage in international TB clinical trials to contribute to global TB research. However, lack of research capacity among local sites has been identified as a barrier. MAIN TEXT: The China Tuberculosis Clinical Trials Consortium (CTCTC) was initiated by Beijing Chest Hospital with investment from the US National Institutes of Health and technical support from Family Health International 360 in 2013, as a nationwide collaborative clinical trial network to strengthen selected clinical site research capacity and attract TB clinical trials. The program aims to: 1) recruit leading hospitals that care for TB patients; 2) conduct on-site assessment to identify capacity gaps and needs for improvement; 3) design and deliver capacity building activities; 4) attract and deliver high quality results for TB clinical trials. A total of 24 sites have joined CTCTC, covering 20 provinces in China. Twenty-two sites have been accredited by the National Medical Products Administration (NMPA) to be qualified to conduct TB clinical trials. The onsite assessment, extensive trainings among the CTCTC sites and young investigators have resulted in better understanding and improvement of the site capacity in conducting TB clinical trials. The establishment and growth of the CTCTC network has benefited from the good leadership, effective international cooperation and local commitment. Issues in human resources, regulatory environment and sustainability have been challenging the network from continuing growth. Clinical researchers have full-time clinical responsibilities in China and it is thus important to build a cadre of other human resources to assist. The regulatory environment is becoming friendlier in China to introduce international clinical trials to the CTCTC network. CONCLUSIONS: The CTCTC, with mature management structure and sustainable development model, which are distilled five key lessons for other developing countries or investigators of interest. They are the respectively using assessment-based approach to design tailored training package, understanding the availability of clinical researchers, providing solutions to maintain sustainability, understanding local regulatory environments and working with an international organization with local on-site team, respectively. Although, the experiences and capacity of China's TB hospitals in conducting clinical research vary. Considerable efforts to continue building the capacity are still needed, although the gap is smaller for a few top-tier hospitals.

Common Data Elements for National Institute of Mental Health-Funded Translational Early Psychosis Research.

The National Institutes of Health has established the PhenX Toolkit as a web-based resource containing consensus measures freely available to the research community. The National Institute of Mental Health (NIMH) has introduced the Mental Health Research Core Collection as part of the PhenX Toolkit and recently convened the PhenX Early Psychosis Working Group to generate the PhenX Early Psychosis Specialty Collection. The Working Group consisted of two complementary panels for clinical and translational research. We review the process, deliberations, and products of the translational research panel. The Early Psychosis Specialty Collection rationale for measure selection as well as additional information and protocols for obtaining each measure are available on the PhenX website (https://www.phenxtoolkit.org). The NIMH strongly encourages investigators to use instruments from the PhenX Mental Health Research Collections in NIMH-funded studies and discourages use of alternative measures to collect similar data without justification. We also discuss some of the potential advances that can be achieved by collecting common data elements across large-scale longitudinal studies of early psychosis.

Nonparticipation reasons in a randomized international trial of a new latent tuberculosis infection regimen.

BACKGROUND/AIMS: Efficient recruitment of eligible participants, optimizing time and sample size, is a crucial component in conducting a successful clinical trial. Inefficient participant recruitment can impede study progress, consume staff time and resources, and limit quality and generalizability or the power to assess outcomes. Recruitment for disease prevention trials poses additional challenges because patients are asymptomatic. We evaluated candidates for a disease prevention trial to determine reasons for nonparticipation and to identify factors that can be addressed to improve recruitment efficiency. METHODS: During 2001-2009, the Tuberculosis Trials Consortium conducted Study 26 (PREVENT TB), a randomized clinical trial at 26 sites in four countries, among persons with latent tuberculosis infection at high risk for tuberculosis disease progression, comparing 3 months of directly observed once-weekly rifapentine plus isoniazid with 9 months of self-administered daily isoniazid. During March 2005-February 2008, non-identifying demographic information, risk factors for experiencing active tuberculosis disease, and reasons for not enrolling were collected from screened patients to facilitate interpretation of trial data, to meet Consolidated Standards of Reporting Trials standards, and to evaluate reasons for nonparticipation. RESULTS: Of the 7452 candidates screened in Brazil, Canada, Spain, and the United States, 3584 (48%) were not enrolled, because of ineligibility (41%), site decision (10%), or patient choice (49%). Among those who did not enroll by own choice, and for whom responses were recorded on whether they would accept treatment outside of the study (n = 1430), 68% reported that they planned to accept non-study latent tuberculosis infection treatment. Among 1305 patients with one or more reported reasons for nonparticipation, study staff recorded a total of 1886 individual reasons (reason count: median = 1/patient; range = 1-9) for why patients chose not to enroll, including grouped concerns about research (24% of 1886), work or school conflicts (20%), medication or health beliefs (16%), latent tuberculosis infection beliefs (11%), and patient lifestyle and family concerns (10%). CONCLUSION: Educational efforts addressing clinical research concerns and beliefs about medication and health, as well as study protocols that accommodate patient-related concerns (e.g. work, school, and lifestyle) might increase willingness to enter clinical trials. Findings from this evaluation can support development of communication and education materials for clinical trial sites at the beginning of a trial to allow study staff to address potential participant concerns during study screening.

Over- and under-treatment of TB patients in Eastern China: an analysis based on health insurance claims data.

OBJECTIVE: Poor compliance with existing guidelines for tuberculosis (TB) care and treatment is an issue of concern in China. We assessed health service use by TB patients over the entire treatment process and compared it to the recommended guidelines. METHODS: We collected insurance claims data in three counties of one province of Eastern China. Patient records with a diagnosis of 'pulmonary TB' in 2015 and 2016 were extracted. Treatment duration, number of outpatient (OP) visits and hospital admissions, as well as total cost, out-of-pocket (OOP) payments and effective reimbursement rates were analysed. RESULTS: A total of 1394 patients were included in the analysis. More than 48% received over the 8 months of treatment that TB guidelines recommend, and over 28% received less. 49% of Urban and Rural Resident Basic Medical Insurance (URRBMI) TB patients were hospitalised while 30% of those with Urban Employee Basic Medical Insurance (UEBMI) had at least one admission. Median total cost for patients with hospital admission was almost 10 times that of patients without. By comparison, the average OOP was 5 times higher. UEBMI patients had a shorter treatment period, more outpatient visits but considerably fewer hospital admissions than URRBMI patients. CONCLUSIONS: We found an alarming extent of TB over- and under-treatment in our study population. There is an urgent need to improve compliance with treatment guidelines in China and to better understand the drivers of divergence. Extending the coverage of health insurance schemes and increasing reimbursement rates for TB outpatient services would seem to be key factors in reducing both the overall cost and financial burden on patients.

OBJECTIF: Le mauvais respect des directives existantes en matière de soins et de traitement de la tuberculose (TB) est un sujet préoccupant en Chine. Nous avons évalué l'utilisation des services de santé par les patients TB tout au long du processus de traitement et l'avons comparée aux directives recommandées. MÉTHODES: Nous avons collecté des données sur les réclamations d'assurance dans trois comtés d'une province de l'est de la Chine. Les dossiers de patients avec un diagnostic de «TB pulmonaire¼ en 2015 et 2016 ont été extraits. La durée du traitement, le nombre de visites ambulatoires et d'hospitalisations, ainsi que le coût total, les paiements directs et les taux de remboursement effectifs ont été analysés. RÉSULTATS: 1.394 patients ont été inclus dans l'analyse. Plus de 48% ont reçu plus de 8 mois du traitement recommandé par les directives TB et plus de 28% en ont reçu moins. 49% des patients TB résidents urbains et ruraux de l'assurance médicale de base (URRBMI) ont été hospitalisés, tandis que 30% de ceux avec une assurance médicale de base des employés urbains (UEBMI) ont eu au moins une admission. Le coût total moyen pour les patients hospitalisés était près de 10 fois plus élevé que celui des patients non hospitalisés. En comparaison, le payement direct moyen était 5 fois plus élevé. Les patients UEBMI ont eu une période de traitement plus courte, plus de visites ambulatoires mais beaucoup moins d'hospitalisations que les patients URRBMI. CONCLUSIONS: Nous avons trouvé une étendue alarmante de sur- et sous-traitement de la TB dans notre population d'étude. Il est urgent d'améliorer le respect des directives de traitement en Chine et de mieux comprendre les facteurs de divergence. L'extension de la couverture des schémas d'assurance santé et l'augmentation des taux de remboursement des services ambulatoires pour la TB sembleraient être des facteurs essentiels pour réduire à la fois le coût global et la charge financière pour les patients.