Socioeconomic status and ME/CFS

Key points

• Socioeconomic status (SES) indicates economic and social status, and can be measured using characteristics such as household income, area of residence, occupational status, and level of education.
• Low SES with an increased risk of chronic disease(s) – such as cardiovascular disease, and type 2 diabetes, less is known about how SES impacts the risk of ME/CFS – also a chronic disease.
• A research team in Norway explored the association between SES and ME/CFS in more detail, specifically looking at household income and educational attainment.
• The paper considered how values of household income, and educational attainment differed between the three groups of participants – an “ME/CFS population” – who had a record of the diagnostic code for “Post viral and related fatigue syndromes”, a chronic disease control group, and a healthy control group.
• Results suggest that compared with healthy controls, a significantly lower number of those in the “ME/CFS  population” had low educational attainment.
  • This finding could potentially be explained if those with lower education levels are less likely to have a diagnosis of ME/CFS – rather than low education itself decreasing the risk of the disease.
  • More research is needed to understand the association between education and ME/CFS in detail.
• Additionally, those in “the ME/CFS population” were more likely than healthy controls to be in the low-income group 5-7 years before the diagnosis of “Post viral and related fatigue syndromes”.
  • As ME/CFS often takes years to diagnose following the onset of symptoms, more research is needed to understand whether it could be the symptoms of undiagnosed ME/CFS that have an impact on household income.

Background

Socioeconomic status (SES) – an indication of economic and social status, is often measured using characteristics such as household income, area of residence, occupational status, and level of education.

Although research has linked low SES with an increased risk of chronic disease(s) – such as cardiovascular disease, and type 2 diabetes, less is known about how SES impacts the risk of ME/CFS – also a chronic disease.

Authors of a paper – published in BMC Public Health, noted that existing research has produced conflicting results; some studies indicate that those with high SES have an increased risk of ME/CFS, while others suggest that it is those who have low-middle SES who have the highest risk of the disease.

In their article, the researchers discuss that these inconsistencies could be explained by differences in how SES is measured, for example; studies considering household income and risk of ME/CFS may observe very different findings to those investigating level of education and ME/CFS.

Therefore, the research team decided to explore the relationship between ME/CFS and SES further, specifically looking at household income and educational attainment.

What did the study do?

The researchers considered information on SES – measured in 2011, from three groups of participants in Norway:

• “The ME/CFS population”: 5,556 individuals who, between 2016 and 2018, had received a diagnosis from a hospital of the ICD code G93.3 – the code for “Post viral and related fatigue syndromes”.
• Healthy controls: A random sample of 5,562 individuals from the Norwegian population.
• Chronic disease controls: 60,425 individuals, who, between 2009 and 2018, had received a diagnosis from a hospital of one or more of the following ICD codes; C50 “Malignant neoplasm of breast”, M79.7 “Fibromyalgia”, G35 “Multiple sclerosis”, and A69.2 “Lyme disease”.
  • These chronic diseases were chosen as “they either share clinical characteristics with ME/CFS (e.g., fatigue and idiopathic pain) and/or disproportionately affect women, which previous research has shown is the case also for ME/CFS”.

Although in the article the authors state they look at the association between SES and “risk of ME/CFS”, the analysis presented in the paper appears to consider how values for SES – household income and educational attainment, differed between the three groups of participants – and whether those in “the ME/CFS population” were more or less likely than the control groups to have:

• Low or high household income compared with medium household income.
• Low or high educational attainment compared with medium household income.  

These categories were defined as:

	Household income	Educational attainment
Low	Those with household income below 40% of the median household income.	Those who had completed only pre-school and elementary school,
Medium	Those with household income more than 40% but less than 70% of the median household income.	Those who had completed lower secondary school and secondary school,
High	Those with household income above 70% of the median household income.	Those who had completed a university degree, at any level.

Importantly, the analysis took also into account participant age, gender, occupational status, and marital status – all measured in 2011.

What did the study find?

Results suggest that compared with the healthy control group, those with ME/CFS were:

• 68% less likely to be in the low educational attainment group compared with the medium education group.
• 53% more likely to be in the low household income group compared with the medium income group.

There were no significant differences between “the ME/CFS population” and healthy controls in relation to high education or high household income.

There were no significant differences between those in “the ME/CFS population” and those in the chronic disease control group in relation to measures of SES – education, and household income. Although the researchers noted that those with ME/CFS were 19% more likely to have high – compared with medium, levels of education relative to the chronic disease control group, the confidence intervals presented alongside this result – a measure of statistical significance, indicate that this finding is not significant.

What do these findings mean?

While the results could indicate that low educational status decreases the risk of receiving a diagnosis of ME/CFS (in this study a diagnosis of the ICD code G93.3 – the code for “Post viral and related fatigue syndromes”), the authors suggest that this finding may be a reflection of the interaction between those with lower education and health professionals, and that those with low education levels may be more likely to have undiagnosed ME/CFS. In the paper, two examples are given which support this suggestion:

• A study showing that those with higher SES are more likely to ask for, and receive diagnoses compared to those with lower SES.
• A paper by Jason and colleagues who found that when 18,675 individuals were screened for ME/CFS, 90% of those with middle to lower SES meeting “CFS” diagnostic criteria had not received a diagnosis from a physician.

In the discussion, the researchers state:

“it is highly possible that our findings reflect the underlying differences between high and low levels of education in meeting with the healthcare services, and it is not an epidemiological effect in and of itself”

The findings also show that those with ME/CFS were more likely than healthy controls to be in the low household income group compared with the medium income group – this finding was not discussed in detail by the researchers.

Strengths

By leaving a 5-7 year period between SES measurement in 2011 and assessment for the diagnostic code relating to symptoms of ME/CFS between 2016 and 2018, the researchers said that they had been able to take into account – to some degree, the “long and complex diagnostic process” for the disease. However, ME Research UK notes that in some instances, the delay in diagnosis is much longer than 7 years.

Limitations

The authors recognise that the participants included in the study with “ME/CFS” were diagnosed in specialised healthcare services in Norway – this excludes all individuals that are either only diagnosed in primary care or that have undiagnosed ME/CFS.

It is also noted in the discussion that due to the lack of validated biomarker for ME/CFS, the diagnosis of the disease – or of an ICD code that most closely matches the symptoms of ME/CFS, was made by individual doctors based on a combination of diagnostic tools or guidelines and subjective assessments of the participant’s symptoms. This means that there is an increased possibility that individuals are misdiagnosed – either with ME/CFS when another medical condition explains their symptoms, or are given another diagnosis when ME/CFS is the cause of the symptoms.  

An additional limitation for this study was that they were unable to consider occupational status and ME/CFS risk in detail. Although the study did have information on whether or not participants were working in 2011, there was not enough detailed information on their profession to consider occupational status as a measure of SES. Therefore, future research should consider the association between occupational status and ME/CFS.

Another potential limitation of this study is the information used – some of which was extracted from hospital records. Studies that use data – such as hospital and GP records, collected for purposes other than the project itself, are potentially limited as the data was not necessarily intended for use in research, or designed to answer a specific research question.

Conclusion

This study suggests that compared with healthy controls, a significantly lower number of people with a record of the diagnostic code for “Post viral and related fatigue syndromes” in Norway had low educational attainment. The study team thought that this finding could potentially be explained if those with lower education levels are less likely to have a diagnosis of ME/CFS – rather than low education itself decreasing the risk of the disease. More research is needed to understand the association between education and ME/CFS in detail – and to explore whether low education itself is a risk factor for the disease, or whether, as the authors of this study suggest, those with low education levels experience higher levels of undiagnosed ME/CFS.

Additionally, results suggested that those in “the ME/CFS population” were more likely than healthy controls to be in the low-income group 5-7 years before the diagnosis of “Post viral and related fatigue syndromes”. As ME/CFS often takes years to diagnose following the onset of symptoms, more research is needed to understand whether it could be the symptoms of undiagnosed ME/CFS that have an impact on household income. It is also worth considering that, as “the ME/CFS population” in this study included predominantly women (78.3%), household income may have been lower in this group due to increased periods of maternity – or parental, leave.