SINTEF Digital, Department of Health, Health services research group, Strindvegen 4, Trondheim, 7034, Norway.
Department of Public Health and Nursing, Norwegian University of Science and Technology, Håkon Jarlsgate 11, Trondheim, 7030, Norway.
BMC Public Health. 2024 May 13;24(1):1296. doi: 10.1186/s12889-024-18757-7.
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.
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.
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).
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.
先前的研究表明，社会经济地位（SES）是慢性病的强有力预测因素。然而，据我们所知，尚无研究表明 SES 如何影响肌痛性脑脊髓炎/慢性疲劳综合征（ME/CFS）的风险，这些研究不是基于自我报告或回顾性症状筛查。据我们所知，这因此是第一项利用个体水平登记数据分离和描述 ME/CFS 的社会经济决定因素并计算这些因素与 ME/CFS 诊断风险之间关系的研究。这使得 ME/CFS 人群能够客观操作，并有可能相对于对照组建立 SES 对 ME/CFS 诊断风险的影响模型。
我们对挪威 2016 年至 2018 年间所有被诊断患有 ME/CFS 的成年患者的登记数据进行了汇总横断面分析，并结合了挪威统计局 2011 年至 2018 年的社会经济数据。我们将 SES 操作化为研究开始时的家庭收入和教育程度。我们将 SES 对 ME/CFS 诊断风险的影响与具有 ME/CFS 临床特征的慢性疾病患者群体和挪威人口的健康随机样本进行比较。我们的模型通过逻辑回归分析进行估计。
当将 ME/CFS 诊断的风险与由四种特定慢性疾病组成的人群进行比较时，我们发现高教育程度与 ME/CFS 风险增加 19%（OR：1.19）相关，而高家庭收入与 ME/CFS 风险降低 17%（OR：0.83）相关。在我们的第二个模型中，我们与健康人群样本进行比较，发现低教育程度与 ME/CFS 风险降低 69%（OR：0.31）相关，而低家庭收入与 ME/CFS 风险增加 53%（OR：1.53）相关。
我们发现 SES 与 ME/CFS 风险之间存在统计学上显著的关联。然而，我们更详细的分析表明，我们的发现因我们比较 ME/CFS 患者的人群而异，并且 SES 的影响在与健康人群样本进行比较时更大，而不是与具有特定医院诊断的对照组进行比较时更大。
