Table 2.

Multilevel logistic models (random intercept) for drinking status and monthly RSOD for men and women separately

	Drinking status		Monthly RSOD
Fixed effects	Men (33 countries/46 254 individuals)	Women (33 countries/55 269 individuals)	Men (28 countries/36 259 individuals)	Women (27 countries/43 124 individuals)
Individual level	OR (95%CI)		OR (95%CI)
education
Low (≤10 years)	1	1	1	1
Middle (>10 and <13 years	1.52 (1.42–1.63)	1.90 (1.80–2.01)	0.97 (0.90–1.04)	0.98 (0.88–1.10)
High (bachelor, Master, PhD)	1.84 (1.70–2.00)	2.89 (2.68–3.11)	0.77 (0.71–0.83)	0.92 (0.80–1.05)
Age (in decades)	0.88 (0.85–0.90)	0.95 (0.93–0.96)	0.79 (0.77–0.81)	0.72 (0.69–0.75)
Age squared	0.96 (0.94–0.98)	0.92 (0.91–0.93)	0.97 (0.95–0.98)	–
Country level
GNI (in ten thousand)	1.38 (1.01–1.89)	1.86 (1.23–2.80)	0.79 (0.61–1.03)	0.89 (0.61–1.31)
Gini (in tens)a	0.65 (0.45–0.95)	0.74 (0.45–1.21)	0.65 (0.49–0.87)	0.64 (0.43–0.95)
Gender Gap Index (in 1/10)b	1.10 (0.60–2.01)	1.60 (0.73–3.51)	1.01 (0.60–1.70)	1.25 (0.59–2.67)
Cross level interaction
Education*GNI	1.21 (1.15–1.27)	1.09 (1.04–1.14)	1.06 (1.00–1.12)	0.88 (0.79–0.98)
Random effects	Beta (SE)		Beta (SE)
Variance between countries	0.51 (0.13)	0.88 (0.22)	0.30 (0.08)	0.51 (0.15)

a: That means if the Gini coefficient was for example 30.0 we used a value of 3.0 in the regression to get better interpretable ORs

b: That means if the Gender Gap Index was for example 0.60 we used a value of 6.0 in the regression to get better interpretable ORs