Prevalence of self-reported sensitivities to various environmental factors in Germany, Sweden, and Finland based on multiple classification criteria

Abstract

Overview

This study addresses the lack of standardized criteria and assessments in measuring the prevalence of self-reported sensitivities to various environmental factors. It specifically examines population-based data from Germany, Sweden, and Finland, and evaluates how the strictness of classification criteria influences the prevalence rates reported for such sensitivities, including electromagnetic fields (EMFs).

Methods

• Large-scale surveys were conducted in Germany (n = 2,515), Sweden (n = 3,253), and Finland (n = 1,467).
• Sensitivities to environmental factors—sounds, chemicals, buildings, electromagnetic fields (EMFs), and wind turbines—were rated on ordinal scales by participants in all samples.

Findings

• In Germany, approximately 25% reported mild reactions to sounds, chemicals, and buildings; about 10% reported mild reactions to EMFs and wind turbines.
• Strong reactions were reported by 1-2% for all factors.
• Nordic data indicated that more than 34% reported mild reactions to chemicals and sounds, whereas about 5% reported mild reactions to EMFs.
• Strong reactions in Nordic samples ranged from 1-8%.
• Germany had a higher reported prevalence of sensitivity to EMFs but a lower prevalence of sensitivity to chemicals and sounds compared to the Nordic countries.
• Significantly, for EMFs, 10.9% of the population in Germany is affected to some extent, including 0.8% with strong impact, closely matching previous national estimates.

Conclusion

These findings demonstrate that a considerable proportion of general populations in Germany, Sweden, and Finland report sensitivities to environmental factors, including electromagnetic fields. Exposure to EMFs is linked to self-reported health sensitivities, emphasizing the need for attention and robust assessment methods within population studies. The study underscores the importance of using ordinal scales rather than binary assessments to improve comparability and capture the range of sensitivities present in populations.