Allostatic (over)Load GitHub Repository
Researchers have long studied allostatic (over)load as an estimated measure of individual cumulative stress over a lifetime. Often called the overall ‘wear and tear’ from social and environmental stressors, allostatic (over)load shows promise as a practical indicator of general health trends in community settings. This data processing workflow aims to document our overall approach and reasoning when calculating allostatic (over)load for data analysis and knowledge sharing. The included repository features an R script for generating datasets using this workflow from the following data sources:
- All of Us Research Program data repository
- Health and Retirement Study (HRS)
- National Health and Nutrition Examination Survey (NHANES)
Our allostatic (over)load measurement process, along with the linked repository, provides a reproducible workflow to process secondary data and offers insights into protocol-driven measurement practices in community environments.
Longitudinal Stress Measures GitHub Repository
This repository will include an R script to develop datasets for studying the longitudinal view of multiple temporal measures of stress among participants at mid-age and beyond, using the Health and Retirement Study (HRS) as the data source.
How do I cite these resources?
Beese, S., Cross, J., Bergquist, M., Dietrich, M., Ripplinger, W., Williams, O., Rice, D., DeJong, T. (2025). Allostatic (over)Load [Repository]. GitHub. Retrieved from https://github.com/CHORDSLab/Allostatic-over-Load-Repository
Cross, J. and Beese, S. (2025). Longitudinal Stress Measures [Repository]. GitHub. Retrieved from https://github.com/CHORDSLab/HRS-Stress-Measures-and-Supplementary-Datasets
Beese, S., Cross, J., Rice, D., DeJong, T. (2025). Allostatic (over)Load Measurement: Workflow and repository. medRxiv 2025.07.31.25332519; DOI: doi.org/10.1101/2025.07.31.25332519
Coming Soon…
Washington State (WA) PLACES Story Map -estimated spring 2026
This interactive GIS story map will provide users with the ability to explore data insights that drive chronic disease prevalence across all areas of WA, with an emphasis on the socio-environmental influences of different geographic contexts.
Beese and DeJong to author Exposure Mixture Analysis methods chapter
Castner, J. (1 Ed.). (2027). Environmental Health Nursing: Research, Evidence, and Impact. (Beese, S. & DeJong, T; Exposure Mixture Analysis chapter 16). [In development] Wiley Press.