Table 1

Characteristics of all respondents (n=4020), and prevalence estimates on the interest in GP advice on health protection (=yes) relative to the respondents’ characteristics; including results of regression models on associations between these characteristics and an interest in receiving GP advice (yes vs no)

Total sample, n=4020 unweighted data
% (n)
Interested in GP advice on health protection against heat=yes (vs no)
Weighted data, n=892
% (n, 95% CI)
Unweighted data
OR* (95% CI)
Sex†
Male (reference)47.2 (1899)19.9 (368, 18.1 to 21.8)1
Female52.8 (2121)26.6 (524, 24.7 to 28.6)1.39 (1.21 to 1.60)
Age in years†
14–248.4 (337)14.1 (64, 11.1 to 17.7)Continuous, per year
1.01 (1.01 to 1.01)
25–3921.9 (881)22.0 (183, 19.20 to 24.9)
40–5932.3 (1300)20.1 (257, 17.9 to 22.4)
60–7425.7 (1033)28.0 (243, 25.0 to 31.1)
75+11.7 (469)38.2 (146, 33.3 to 43.2)
Educational attainment‡
High (reference)30.8 (1237)19.4 (231, 17.2 to 21.8)1
Medium37.5 (1506)22.8 (326, 20.7 to 25.1)1.07 (0.89 to 1.28)
Low29.5 (1185)30.0 (314, 27.2 to 32.9)1.26 (1.04 to 1.51)
Household income/€§
High26.3 (1056)21.3 (213, 18.8 to 23.9)Continuous, see
0.91 (0.83 to 0.99)
Medium60.8 (2446)23.6 (556, 21.9 to 25.4)
Low12.8 (513)27.1 (124, 23.1 to 31.4)
Migration background†
No (reference)82.0 (3298)22.1 (663, 20.7 to 23.7)1
Yes14.2 (570)25.9 (179, 22.7 to 29.3)1.62 (1.34 to 2.00)
Region of residence**
Rural area (reference)38.2 (1535)17.7 (279, 15.9 to 19.7)1
Urban area41.9 (1686)28.4 (464, 26.2 to 30.7)2.17 (1.82 to 2.59)
Metropolitan area19.9 (799)24.7 (150, 21.3 to 28.3)2.07 (1.67 to 2.57)
Cohabitation††
Other household (reference)62.0 (2494)21.6 (631, 20.1 to 23.1)1
Single-person household38.0 (1526)29.4 (262, 26.4 to 23.5)1.20 (1.02 to 1.40)
  • Data are presented as column percentages (number), row percentages (number, 95% CI), and as OR together with 95% CI around OR, statically significant results are highlighted in bold.

  • *Adjustment sets for regression analyses were derived by application of directed acyclic graphs (more details—including the graphs—have been published together with the analysis protocol https://osf.io/ycz7n).

  • †Univariate logistic regression model: no adjustment is necessary or possible—as it would produce a collider bias—to estimate the total effect of the independent variable on the outcome.

  • ‡Multivariable logistic regression model adjusted for the variable: migration background.

  • §Multivariable logistic regression model adjusted for the variables: sex, age, educational attainment, migration background.

  • ¶Entered as a continuous variable in regression analyses (range from 0 (€0 income) to 7 (€7000 or more).

  • **Multivariable logistic regression model for the variables: age, educational attainment, income per person, migration background, cohabitation.

  • ††Multivariable logistic regression model for the variables: age, educational attainment, income per person, migration background, region of residence.

  • GP, general practitioner.