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One or many environmental intolerance(s)? A cluster analysis over two representative samples

International Journal of Hygiene and Environmental Health · 2026 · Unclear · Low evidence

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Across two representative datasets, cluster analysis distinguished people with versus without SAEFs, but did not find evidence for empirically distinct SAEF subtype clusters. The SAEF cluster showed higher chemical, noise, and electromagnetic field sensitivit…

Study snapshot

PublicationInternational Journal of Hygiene and Environmental Health · 2026
Study typecross_sectional
Reported directionUnclear
Evidence ratingLow

Abstract

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, noise sensitivity, electromagnetic field sensitivity, and sleep. People in the SAEF cluster scored higher on markers of psychopathology (e.g., anxiety, depression), and more women were in the SAEF cluster. 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.

Machine Enhanced Logic

AI evidence extraction

Structured fields below were generated from the paper’s abstract and metadata to make the evidence easier to inspect. Automated extraction may be incomplete or incorrect; verify important details against the original paper.

Study typecross_sectional
Effect directionunclear
PopulationRepresentative population-based samples from Sweden (Västerbotten Environmental Health Study) and Finland (Österbotten Environmental Health Study)
Sample size2809
ExposureNot supplied
Evidence strengthLowConfidence: 74% · Peer reviewed: yes

Main findings

Across two representative datasets, cluster analysis distinguished people with versus without SAEFs, but did not find evidence for empirically distinct SAEF subtype clusters. The SAEF cluster showed higher chemical, noise, and electromagnetic field sensitivity and worse sleep, along with higher markers of psychopathology (e.g., anxiety, depression), and included more women.

Outcomes measured

  • Symptoms associated with environmental factors (SAEFs) and attribution to environmental sources (chemicals, electromagnetic fields, noise, other)
  • Cluster structure of SAEFs (presence/absence; subtype differentiation)
  • Chemical sensitivity
  • Noise sensitivity
  • Electromagnetic field sensitivity
  • Sleep
  • Markers of psychopathology (e.g., anxiety, depression)
  • Sex distribution

Limitations

  • Cross-sectional design limits causal inference
  • Cluster analysis results depend on selected variables and partitioning methods
  • No exposure quantification for electromagnetic fields (e.g., frequency, SAR, duration) described in abstract
View raw extracted JSON
{
  "study_type": "cross_sectional",
  "exposure": {
    "band": null,
    "source": "other",
    "frequency_mhz": null,
    "sar_wkg": null,
    "duration": null
  },
  "population": "Representative population-based samples from Sweden (Västerbotten Environmental Health Study) and Finland (Österbotten Environmental Health Study)",
  "sample_size": 2809,
  "outcomes": [
    "Symptoms associated with environmental factors (SAEFs) and attribution to environmental sources (chemicals, electromagnetic fields, noise, other)",
    "Cluster structure of SAEFs (presence/absence; subtype differentiation)",
    "Chemical sensitivity",
    "Noise sensitivity",
    "Electromagnetic field sensitivity",
    "Sleep",
    "Markers of psychopathology (e.g., anxiety, depression)",
    "Sex distribution"
  ],
  "main_findings": "Across two representative datasets, cluster analysis distinguished people with versus without SAEFs, but did not find evidence for empirically distinct SAEF subtype clusters. The SAEF cluster showed higher chemical, noise, and electromagnetic field sensitivity and worse sleep, along with higher markers of psychopathology (e.g., anxiety, depression), and included more women.",
  "effect_direction": "unclear",
  "limitations": [
    "Cross-sectional design limits causal inference",
    "Cluster analysis results depend on selected variables and partitioning methods",
    "No exposure quantification for electromagnetic fields (e.g., frequency, SAR, duration) described in abstract"
  ],
  "evidence_strength": "low",
  "confidence": 0.74,
  "peer_reviewed_likely": "yes",
  "keywords": [
    "environmental intolerance",
    "symptoms associated with environmental factors",
    "SAEF",
    "chemical sensitivity",
    "noise sensitivity",
    "electromagnetic field sensitivity",
    "cluster analysis",
    "population-based",
    "psychopathology",
    "sleep"
  ],
  "suggested_hubs": [
    {
      "slug": "electromagnetic-hypersensitivity",
      "weight": 0.9,
      "reason": "Study includes electromagnetic field sensitivity as a key SAEF dimension and examines whether SAEF subtypes form distinct clusters."
    }
  ]
}
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AI-extracted fields are generated from the abstract and metadata and may be incomplete or incorrect. This material is for informational purposes and is not medical advice.

Identifiers and source

DOI: 10.1016/j.ijheh.2026.114764
PubMed: Not supplied

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