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Table 5 Exposure predictions for different strata.

From: Conduct of a personal radiofrequency electromagnetic field measurement study: proposed study protocol

Variable Category n Coefficient 95%-CI p-value
Age young adults (20-34 y) 56 reference - -
  adults (35-64) 69 0.77 0.59;1.01 0.06
  retired people (>64) 6 0.75 0.39;1.42 0.37
Gender Female 74 reference - -
  Male 57 0.93 0.72;1.20 0.58
Place of residence Urban 76 reference - -
  Suburban 55 1.27 0.97;1.66 0.08
Ownership of mobile phone Yes 119 reference - -
  No 12 0.70 0.44;1.11 0.13
Ownership of cordless phone Yes 79 reference - -
  No 52 0.91 0.68;1.21 0.51
Ownership of W-LAN Yes 50 reference - -
  No 81 0.95 0.72;1.25 0.72
Socio economic status Low 21 reference - -
  Middle 17 0.87 0.54;1.39 0.55
  High 93 1.10 0.77;1.58 0.59
  1. Coefficients of a multiple loglinear regression model using data from a Swiss RF-EMF population survey [15]. This model allows predicting average RF-EMF exposure in different population strata
  2. Intercept of the model: 0.11 mW/m2 (95%-CI: 0.08-0.17) (exposure during the day of a female person aged 20-34 living in an urban environment, owning a mobile phone, a cordless phone and wireless LAN at home, with the lowest socioeconomic status).
  3. To calculate total exposure of a woman with the same characteristics but who does not own a mobile phone, the value has to be multiplied by 0.70 resulting in an exposure of 0.08 mW/m2. Note that this is only an example to demonstrate the principle of an exposure prediction model. Lack of significance of coefficients for potentially relevant parameters may indicate that a larger sample size is needed for this type of exposure prediction model.