International journal of hygiene and environmental health · 2026
One or many environmental intolerance(s)? A cluster analysis over two representative samples.
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Petzke Tara M, Rippe Ralph C A, Witthöft Michael, Szemerszky Renáta, Köteles Ferenc, Lemogne Cédric, Nordin Steven, Pitron Victor
Аннотация
OBJECTIVE: People with symptoms associated with environmental factors (SAEFs) attribute somatic symptoms to chemicals, electromagnetic fields, noise, or other environmental sources. Debates are ongoing whether these different types constitute different disorders ("splitting") or rather different presentations of the same underlying disorder ("lumping"), and which characteristics contribute to this disorder/these disorders.
METHODS: To shed further light on this question, we performed a k-prototypes cluster analysis of two representative population-based datasets. We selected 23 clinically relevant variables from the Västerbotten Environmental Health Study (N = 1576), a representative dataset from Sweden. Common measures of cluster partitioning were used, and cluster profiles inspected. We then replicated the analysis in the Österbotten Environmental Health Study dataset (N = 1233), a representative dataset from Finland.
RESULTS: The cluster analysis distinguished between people with versus without SAEF, but did not provide evidence for empirically different SAEF clusters. Inspecting the profiles of the two clusters revealed that the main differences were in chemical sensitivity (rSweden=0.53,rFinland=0.71), noise sensitivity (rSweden=0.56,rFinland=0.61), electromagnetic field sensitivity (rSweden=0.36,rFinland=0.58), and sleep (rSweden=0.66,rFinland=0.30).People in the SAEF cluster scored higher on markers of psychopathology (e.g., anxiety: rSweden=0.42,rFinland=0.22, depression: rSweden=0.49,rFinland=0.22), and more women were in the SAEF cluster (Cramer's VSweden=0.19,VFinland=0.29,allp<.001).
CONCLUSIONS: The data supports the idea that different SAEF subtypes share similar clinical features. In terms of underlying mechanisms, this suggests that similar biopsychosocial determinants might be involved in shaping symptom experience over distinct SAEF subtypes. People with different SAEFs might thus profit from similar interventions.
METHODS: To shed further light on this question, we performed a k-prototypes cluster analysis of two representative population-based datasets. We selected 23 clinically relevant variables from the Västerbotten Environmental Health Study (N = 1576), a representative dataset from Sweden. Common measures of cluster partitioning were used, and cluster profiles inspected. We then replicated the analysis in the Österbotten Environmental Health Study dataset (N = 1233), a representative dataset from Finland.
RESULTS: The cluster analysis distinguished between people with versus without SAEF, but did not provide evidence for empirically different SAEF clusters. Inspecting the profiles of the two clusters revealed that the main differences were in chemical sensitivity (rSweden=0.53,rFinland=0.71), noise sensitivity (rSweden=0.56,rFinland=0.61), electromagnetic field sensitivity (rSweden=0.36,rFinland=0.58), and sleep (rSweden=0.66,rFinland=0.30).People in the SAEF cluster scored higher on markers of psychopathology (e.g., anxiety: rSweden=0.42,rFinland=0.22, depression: rSweden=0.49,rFinland=0.22), and more women were in the SAEF cluster (Cramer's VSweden=0.19,VFinland=0.29,allp<.001).
CONCLUSIONS: The data supports the idea that different SAEF subtypes share similar clinical features. In terms of underlying mechanisms, this suggests that similar biopsychosocial determinants might be involved in shaping symptom experience over distinct SAEF subtypes. People with different SAEFs might thus profit from similar interventions.