Hilland GH, Anthun KS. Socioeconomic determinants of myalgic encephalomyelitis/chronic fatigue syndrome in Norway: a registry study.
BMC Public Health 2024;
24:1296. [PMID:
38741074 PMCID:
PMC11089728 DOI:
10.1186/s12889-024-18757-7]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 05/02/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND
Previous research has shown that socioeconomic status (SES) is a strong predictor of chronic disease. However, to the best of our knowledge, there has been no studies of how SES affects the risk of Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) that has not been based upon self-reporting or retrospectively screening of symptoms. As far as we know, this is therefore the first study that isolate and describe socioeconomic determinants of ME/CFS and calculate how these factors relate to the risk of ME/CFS diagnosis by utilizing individual level registry data. This allows for objective operationalization of the ME/CFS population, and makes it possible to model SES affect the risk of ME/CFS diagnosis, relative to control groups.
DATA AND METHODS
We conduct a pooled cross-sectional analysis of registry data from all adult patients diagnosed with ME/CFS from 2016 to 2018 in Norway, coupled with socioeconomic data from statistics Norway from 2011 to 2018. We operationalize SES as household income and educational attainment fixed at the beginning of the study period. We compare the effects of SES on the risk of ME/CFS diagnosis to a population of chronically ill patients with hospital diagnoses that share clinical characteristics of ME/CFS and a healthy random sample of the Norwegian population. Our models are estimated by logistic regression analyses.
RESULTS
When comparing the risk of ME/CFS diagnosis with a population consisting of people with four specific chronic diseases, we find that high educational attainment is associated with a 19% increase (OR: 1.19) in the risk of ME/CFS and that high household income is associated with a 17% decrease (OR:0.83) in risk of ME/CFS. In our second model we compare with a healthy population sample, and found that low educational attainment is associated with 69% decrease (OR:0.31) in the risk of ME/CFS and that low household income is associated with a 53% increase (OR: 1.53).
CONCLUSION
We find statistically significant associations between SES and the risk of ME/CFS. However, our more detailed analyses shows that our findings vary according to which population we compare the ME/CFS patients with, and that the effect of SES is larger when comparing with a healthy population sample, as opposed to controls with selected hospital diagnoses.
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