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

Krzyzanowski, Michelle; Ives, Cataia; Jones, Nancy Lee; Entwisle, Barbara; Fernández, Alicia; Cullen, Theresa; Darity, William A.; Fossett, Mark; Remington, Patrick L.; Taualii, Maile; Wilkins, Consuelo H.; Pérez‐Stable, Eliseo J.; Rajapakse, Nishadi; Breen, Nancy; Zhang, Xinzhi; Maiese, Deborah; Mandal, Meisha; Hwang, Stephen Y.; Huggins, Wayne; Gridley, Lauren; Riley, Amanda Flynn; Ramos, Erin M.; Hamilton, Carol M.

doi:10.1016/j.amepre.2023.03.003

Cited by 10 publications

(3 citation statements)

References 21 publications

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“…Increased emphasis on adhering to Findable, Accessible, Interoperable, and Reusable (FAIR) principles and data sharing has increased the utility of standard measures or common data elements for research (Wilkinson et al, 2016). Standard protocols for the measurement of SDoH play a crucial role in health disparities research, as they provide a structured framework for assessing the impact of different elements of social support across health outcomes and across communities (Krzyzanowski et al, 2023). By following these SDoH protocols, researchers of health disparities can minimize bias, facilitate replication and validation of research studies, and improve the sharing and synthesis of research findings across studies, which is essential for improving health outcomes.…”

Section: Commentary Background Informationmentioning

confidence: 99%

“…SDoH have been shown to influence a range of health outcomes; for example, poverty is highly correlated with poorer health outcomes (CDC, 2022). Because SDoH have a significant impact on a variety of health outcomes, it is crucial for the research community to define and adopt SDoH measurement standards (Krzyzanowski et al, 2023). These may vary from environmental exposures (e.g., air quality) that may directly impact biologic and physiologic functioning to access to health care and more.…”

Section: Implications/relevancementioning

confidence: 99%

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

Ives,

Krzyzanowski,

Marshall

et al. 2024

Current Protocols

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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

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Section: Commentary Background Informationmentioning

confidence: 99%

Section: Implications/relevancementioning

confidence: 99%

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

Ives,

Krzyzanowski,

Marshall

et al. 2024

Current Protocols

Self Cite

View full text Add to dashboard Cite

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“…The core collection used in this study includes demographic data such as race, age, gender identity, annual family income, access to health services, among other SDOH factors. Protocols from the PhenX SDOH Toolkit are well-established and have demonstrated reliability, reproducibility, and validity [38,39]. The PASSS consists of 20 items across 5 domains (i.e., emotional support, validation support, informational support, companionship support, instrumental support) and a scale of 1-7 that examines the perception of various support mechanisms related to engaging in PA.…”

Section: Plos Onementioning

confidence: 99%

Analysis of multi-level barriers to physical activity among nursing students using regularized regression

Capan,

Bigelow,

Kathuria

et al. 2024

PLoS ONE

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Physical inactivity is a growing societal concern with significant impact on public health. Identifying barriers to engaging in physical activity (PA) is a critical step to recognize populations who disproportionately experience these barriers. Understanding barriers to PA holds significant importance within patient-facing healthcare professions like nursing. While determinants of PA have been widely studied, connecting individual and social factors to barriers to PA remains an understudied area among nurses. The objectives of this study are to categorize and model factors related to barriers to PA using the National Institute on Minority Health and Health Disparities (NIMHD) Research Framework. The study population includes nursing students at the study institution (N = 163). Methods include a scoring system to quantify the barriers to PA, and regularized regression models that predict this score. Key findings identify intrinsic motivation, social and emotional support, education, and the use of health technologies for tracking and decision-making purposes as significant predictors. Results can help identify future nursing workforce populations at risk of experiencing barriers to PA. Encouraging the development and employment of health-informatics solutions for monitoring, data sharing, and communication is critical to prevent barriers to PA before they become a powerful hindrance to engaging in PA.

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A guide to large data sets for population‐based cancer research: Strengths, limitations, and pitfalls

Martin,

Chan,

Cheung

et al. 2024

Cancer

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With the proliferation of cancer research based on large databases, misalignment of research questions and data set capabilities is inevitable. Nationally maintained databases are appealing to cancer researchers because of the ease of access to large amounts of patient data available for analysis and risk estimation. Data sets that are commonly used in cancer research include the National Cancer Database, the SEER (Surveillance, Epidemiology, and End Results) program of the National Cancer Institute, the SEER–Medicare database, the American College of Surgeons National Surgical Quality Improvement Program, and the Healthcare Cost and Utilization Project databases, among others. Each data set has pros and cons with respect to variable availability and the ability to analyze cancer‐specific outcomes. It is critical for researchers to understand the strengths and limitations of each database. Changing variable definitions, the length of postoperative data collection, and the availability of patient‐reported outcomes or social determinants of health data are examples of factors that researchers must consider when selecting a data set for research purposes. For the current review, the authors summarized the advantages and disadvantages of various national data sets for cohort studies in cancer populations.

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

Cited by 10 publications

References 21 publications

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

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

Analysis of multi-level barriers to physical activity among nursing students using regularized regression

A guide to large data sets for population‐based cancer research: Strengths, limitations, and pitfalls

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