For patients with breast cancer, geographic and social disparities are independent determinants of access to specialized surgeons. A eleven-year population-based multilevel analysis

Table 3 Remoteness and patients’ socio-economic characteristics: univariate and multilevel logistic regression analysis as predictive factors of being operated on by a high-volume breast cancer surgeon

	Univariate logistic regression analysis		Multilevel logistic regression analysis
	Odds ratio (95 % CI*)	p-value	Odds ratio (95 % CI*)	p-value
Individual data
Age
< 50 years old	0.90 (0.76; 1.07)	<0.001		0.179
50 to 74 years old	1
> 74 years old	0.68 (0.55; 0.83)
T Stage - size
T1	1	<0.001		0.189
T2	0.79 (0.66-0.95)
T3 and T4	0.58 (0.41-0.81)
Circumstances of diagnosis
Screening	1	<0.001	1	0.005
Not screening	0.72 (0.62; 0.83)	<0.001	0.78 (0.66-0.93)	0.005
Time to go to the nearest reference cancer care centre
< 10 minutes	1	<0.001	1	<0.001
10 to 20 minutes	0.98 (0.80; 1.21)		0.86 (0.68-1.07)
20 to 35 minutes	0.70 (0.56; 0.88)		0.56 (0.43-0.73)
> 35 minutes	0.50 (0.42; 0.61)		0.38 (0.29-0.50)
Aggregate data
Place of residence
Rural	0.72 (0.62; 0.84)	<0.001	0.68 (0.53-0.87)	0.002
Urban	1	<0.001	1	0.002
Townsend index
Quintile 1 (most affluent)	1	<0.001	1	0.013
Quintile 2	0.84 (0.60; 1.18)		0.84 (0.58-1.21)
Quintile 3	0.73 (0.52; 1.03)		0.74 (0.52-1.08)
Quintile 4	0.67 (0.49; 0.93)		0.69 (0.48-0.97)
Quintile 5 (most deprived)	0.65 (0.49; 0.88)		0.61 (0.44-0.85)