Table 2

Results of the hierarchical binary logistic regression analysis examining the role of the FFM personality traits in reporting the development of ADRs between T1 and T2 while controlling for sociodemographic and health indicators at T1 and the change in health status from T1 to T2

Block 1Block 2Block 3
BSEWaldp valuesExp(B)BSEWaldp valuesExp(B)BSEWaldp valuesExp(B)
Sociodemographic variables (T1)
Nagelkerke R2 =0.058
Χ2 (5)=50.37, p<0.0001
Health indicators
 Number of EHIF diagnoses (T1)
 Number of medicines taken (T1)
 Change in the number of medicines from T1 to T20.100.0310.070.0021.
 Change in BMI from T1 to T20.040.031.620.2041.
Nagelkerke R2 =0.108
Χ2 (2)=31.01, p<0.0001
NEO PI-3 domain scales (T2)
Nagelkerke R2 =0.116
Χ2 (2)=6.37, p=0.041
  • Number of EHIF diagnoses is the number of diagnoses recorded in the Estonian Health Insurance Fund for the year of data collection.

  • Number of medicines taken is the number of medicines taken during the previous 2 months, either regularly or for treating specific diseases.

  • Gender: 0=male, 1=female; Education: higher education was defined as the reference or baseline category (1).

  • ADR, adverse drug reaction; BMI, body mass index; EHIF, Estonian Health Insurance Fund; FFM, Five-Factor Model; NEO PI-3 , NEO Personality Inventory-3; T1, first measurement; T2, second measurement (on average 5.3 years after T1).