Table 4.
Mixed model regression analysis on antibacterial prescriptions in 2018.
| Predictor (Reference, Where Applicable) |
Univariable Analysis | Multivariable Analysis n = 26,589, GPs = 240 |
|||
|---|---|---|---|---|---|
| Prescriptions and GPs, n | Estimates (95% CI) | p | Estimates (95% CI) | p | |
| GP Characteristics | ICC = 0.09 | ||||
| Age | n = 26,595, GP = 240 | 0.005 (−0.001, 0.011) | 0.080 | ||
| N. consultations ≥ 6000 (<6000) | n = 26,595, GP = 240 | 0.355 (0.252, 0.457) | <0.001 | 0.281 (0.163, 0.398) | <0.001 |
| Female gender (male) | n = 26,595, GP = 240 | −0.205 (−0.316, −0.094) | <0.001 | −0.079 (−0.199, 0.042) | 0.204 |
| Years in practice | n = 22,808, GP = 204 | 0.006 (−0.0004, 0.012 ) | 0.068 | ||
| Self-Employed (employee) | n = 24,604, GP = 216 | 0.089 (−0.035, 0.213) | 0.161 | ||
| Employment level (100%) | n = 26,595, GP = 240 | ||||
| <50% | −0.483 (−0.694, −0.266) | <0.001 | −0.279 (−0.507, −0.051) | 0.017 | |
| 50–79% | −0.271 (−0.400, −0.143) | <0.001 | −0.100 (−0.247, 0.047) | 0.183 | |
| 80–99% | −0.096 (−0.235, 0.044) | 0.179 | −0.058 (−0.193, 0.077) | 0.402 | |
| Practice Characteristics | |||||
| Type of practice (single practice) | n = 26,595, GP = 240 | ||||
| Double practice | −0.136 (−0.451, 0.179) | 0.399 | |||
| Group practice | −0.086 (−0.247, 0.075) | 0.297 | |||
| Patient Characteristics | |||||
| Age | n = 26,595, GP = 240 | 0.002 (0.001, 0.003) | <0.001 | 0.002 (0.001, 0.003) | <0.001 |
| Male gender (female) * | n = 26,589, GP = 240 | −0.231 (−0.270, −0.193) | <0.001 | −0.234 (−0.272, −0.196) | <0.001 |
Note. Linear model of number of prescriptions with patient and general practitioners (GPs) characteristics as fixed effects and GPs as random effects (mixed model). n, number; CI, Confidence interval; ICC, Intraclass correlation. *: Sensitivity analyses: after excluding patients with ICPC (International Classification of Primary Care) codes available for urinary tract infections, gender was no longer associated with antibiotic prescribing (p = 0.505).