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Table 3 Odds ratios and 95% confidence intervals from multilevel logistic regressions of poor health on individual and regional characteristics N = 107,087

From: Determinants of health disparities between Italian regions

Dependent variable:
Poor health
Model 1 Model 2 Model 3
  OR 95% CI OR 95% CI OR 95% CI
Individual level factors       
Age       
Less than 35 0.12 (0.10, 0.14) 0.12 (0.10, 0.14) 0.12 (0.10, 0.14)
35-44 0.37 (0.33, 0.42) 0.37 (0.33, 0.42) 0.37 (0.33, 0.42)
45-64 ref   ref   ref  
65-74 1.82 (1.69, 1.96) 1.82 (1.69, 1.96) 1.82 (1.69, 1.96)
75 and over 3.74 (3.47, 4.03) 3.74 (3.47, 4.03) 3.74 (3.47, 4.03)
Gender       
Female ref   ref   ref  
Male 1.00 (0.94, 1.07) 1.00 (0.94, 1.07) 1.00 (0.94, 1.07)
Marital status       
Married ref   ref   ref  
Separated/divorced 1.43 (1.26, 1.61) 1.43 (1.26, 1.62) 1.43 (1.26, 1.62)
Widowed 1.28 (1.20, 1.37) 1.28 (1.20, 1.37) 1.28 (1.20, 1.37)
Single 1.41 (1.29, 1.53) 1.41 (1.29, 1.53) 1.41 (1.29, 1.53)
Education       
College degree ref   ref   ref  
High school 1.16 (0.99, 1.36) 1.16 (0.99, 1.36) 1.16 (0.99, 1.36)
Less than high school 2.07 (1.79, 2.39) 2.07 (1.79, 2.39) 2.07 (1.79, 2.39)
Employment status       
Employee ref   ref   ref  
Self-employed 0.73 (0.62, 0.86) 0.73 (0.62, 0.86) 0.73 (0.62, 0.86)
Retired 2.17 (1.95, 2.41) 2.17 (1.95, 2.41) 2.17 (1.95, 2.42)
Not working/other 3.01 (2.72, 3.34) 3.01 (2.71, 3.33) 3.00 (2.71, 3.33)
Regional level factors       
Economic disadvantage1    1.21 (1.09,1.34) 0.99 (0.84, 1.12)
Poor living conditions1      1.41 (1.04, 1.92)
Satisfaction with healthcare1,3      0.96 (0.85, 1.08)
Capital intensity in healthcare2,3      1.00 (0.97, 1.02)
Share of private healthcare expenditure1      0.94 (0.88, 0.99)
Social isolation (no friends)      0.99 (0.91, 1.09)
Obesity rate1      1.24 (0.61, 2.54)
Region level variance 0.07 (0.03, 0.13) 0.04 (0.02, 0.08) 0.03 (0.01, 0.05)
ICC 0.020 (0.010, 0.038) 0.012 (0.006, 0.024) 0.008 (0.004, 0.016)
  1. 1: Rescaled so that OR represent change in poor health associated with a 10% change in regional factor.
  2. 2: Number of medical equipment machines per 10,000 residents
  3. 3: These variables are measured so that larger values represent a beneficial effect on health (and a negative effect on poor health).