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. 2019 Jan 18;53:14. doi: 10.11606/S1518-8787.2019053000406

Table 2. Time interval between the first appointment and the start of breast cancer therapy (≤ or > 160 days) for 600 women treated in nine public hospitals of the Federal District, Brazil, between September 2012 and September 2014, with simple logistic regression using socioeconomic and pathway characteristics.

Variable Time interval between the first appointment and therapy OR 95%CI
≤ 160 days > 160 days
Place of residence
Federal District 209 (34.8%) 186 (31.0%) 1.00
Outside of the Federal District 94 (15.7%) 111 (18.5%) 1.32 0.94–1.85
Self-reported skin colorb
White 128 (21.3%) 98 (16.3%) 1.00
Brown or black 176 (29.4%) 198 (32.9%) 1.47 1.05–2.05
Family income (US$ per month)b
> 502.20 164 (28.1%) 108 (18.5%) 1.00
≤ 502.20 127 (21.8%) 184 (31.6%) 2.20 1.58–3.07
Educational level (years)b
> 8 160 (26.5%) 123 (20.5%) 1.00
≤ 8 143 (23.9%) 174 (29.0%) 1.57 1.14–2.17
First appointment locationb
Private services 127 (21.2%) 72 (11.9%) 1.00
Public services 175 (29.1%) 226 (37.8%) 2.31 1.62–3.28
Number of health services coveredb
≤ 2 181 (29.9%) 156 (25.9%) 1.00
> 2 121 (20.3%) 142 (23.9%) 1.36 0.98–1.88
Use of informal relationships to expedite some serviceb
Yes 134 (22.2%) 100 (16.8%) 1.00
No 166 (27.4%) 200 (33.7%) 1.63 1.17–2.28
Cancer staging
< IIB 128 (21.3%) 127 (21.2%)
≥ IIB 175 (29.2%) 170 (28.3%) 0.98 0.71–1.35
Anatomopathological biopsy analysis in private servicesb
Yes 161 (26.8%) 99 (16.5%) 1.00
No 142 (23.7%) 198 (33.0%) 2.27 1.63–3.16
Regulation of appointments with specialistsc
Before the regulation 68 (30.9%) 108 (49.1%) 1.00
After the regulation 29 (13.2%) 15 (6.8%) 0.33 0.16–0.65
a

Differences that correspond to information loss.

b

Variables that produced p ≤ 0.25 values.

c

n = 220, referring to women from the Federal District whose pathway began in primary care services with subsequent care in specialized or therapy services. Due to presenting different amounts when considering other independent variables, this variable was excluded from the adjustment of the multiple model.