Kielland, Anne, Liu, Jing, Tyldum, Guri et al. · Journal of health psychology · 2026 · DOI
This study looked at how well official medical records capture ME/CFS cases and who gets diagnosed. Researchers found that medical register data misses many cases and disproportionately underrepresents people from lower-income backgrounds. They used a new online method to find ME/CFS patients more accurately and checked whether official diagnoses matched recognized diagnostic criteria.
Accurate data about who has ME/CFS and their characteristics is essential for understanding disease burden, directing research resources, and advocating for patient needs. This study reveals that official diagnoses systematically undercount cases in vulnerable populations, which has implications for clinical recognition, research recruitment, and health equity in ME/CFS care.
This study does not prove that socioeconomic status causes ME/CFS, only that diagnostic coding is biased by social deprivation. It also does not establish prevalence figures for ME/CFS overall—it identifies methodological problems in obtaining unbiased prevalence data. The findings reflect diagnostic and registration patterns, not necessarily true disease distribution.
About the PEM badge: “PEM required” means post-exertional malaise was an explicit required diagnostic criterion for participant inclusion in this study — not that PEM was studied, observed, or discussed. Studies using criteria that do not require PEM (e.g. Fukuda, Oxford) are tagged “PEM not required”. How the atlas works →
The first block is for the primary paper and is the citation you should use in research work. The atlas-snapshot line only applies if you are specifically referring to this atlas’s reading of the paper on the date shown.
Primary citation
Kielland, Anne, Liu, Jing, Tyldum, Guri, & Jason, Leonard (2026). Improving myalgic encephalomyelitis population sampling: Applying an online respondent-driven method to address biases in G93.3 register data.. Journal of health psychology. https://doi.org/10.1177/13591053251325690
BibTeX
@article{mecfsatlas-kielland-2026-improving-myalgic,
author = {Kielland, Anne and Liu, Jing and Tyldum, Guri and Jason, Leonard},
title = {Improving myalgic encephalomyelitis population sampling: Applying an online respondent-driven method to address biases in G93.3 register data.},
journal = {Journal of health psychology},
year = {2026},
doi = {10.1177/13591053251325690},
note = {PubMed: 40125942},
url = {https://www.mecfsatlas.com/evidence/kielland-2026-improving-myalgic},
}Atlas snapshot reference
ME/CFS Atlas. Generator v1 / Scanner v1.4 / policy v0.1. Accessed 2026-05-26. https://www.mecfsatlas.com/evidence/kielland-2026-improving-myalgic
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