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. 2015 Nov 11;2015(11):CD010907. doi: 10.1002/14651858.CD010907.pub2

2. Antibiotic prescriptions per index consultation or population rate over time.

Author Outcome Measurement time point Intervention (n) Control Effect estimate P value Notes
          Adjusted odds ratio (95% CI)    
Francis (2009) Antibiotics prescribed at the index consultation 14 days (30 practices) Patients = 50/256 (19.5%) (31 practices)
 Patients = 111/272 (40.8%) 0.29 (0.14 to 0.60)a NR ICC = 0.24
Altiner (2007) Rate of antibiotic prescriptions (per acute cough and per GP) 6 weeks GPs = 42
 Patients = 1021 GPs = 44
 Patients = 1143 0.38 (0.26 to 0.56)b < 0.001 ICC=0.20
12 months GPs = 28
 Patients = 787 GPs = 33
 Patients = 920 0.55 (0.38 to 0.80)b 0.002
Briel (2006) Uptake of antibiotic prescriptions as reported by pharmacists < 2 weeks after the consultation 14 days GPs = 15
 Patients = 259 GPs = 15
 Patients = 293 0.86 (0.40 to 1.93)c NR ICC = 0.04
Design effect = 1.6
          Adjusted risk ratio (95% CI)    
Little (2013) Antibiotic prescription index consultation Practices = 61
 Patients = 2332 Practices = 61
 Patients = 1932 0.69 (0.54 to 0.87)d < 0.0001 —
Légaré (2012) % patients who decided to use antibiotics immediately after the consultation Index consultation Practice units = 6
 GPs = 77
 Patients = 181 Practice units = 6
 GPs = 72
 Patients = 178 0.50 (0.30 to 0.70)e — —
          Adjusted risk difference (95% CI)    
Légaré (2011) % patients who decided to use antibiotics immediately after the consultation Index consultation Medicine groups = 2
 GPs = 18
 Patients = 81 Medicine groups
 GPs = 14
 Patients = 70 ‐16 (‐31 to 1)f 0.08 —
Butler (2012) Total no. dispensed oral antibiotic items per 1000 registered patients for the year after the intervention 12‐month period Practices = 34 Patients = 7053 Practices = 34 Patients = 7050 ‐4.2 (‐0.6 to ‐7.7) 0.02 —
Cals (2009) Antibiotic prescribing at the index consultation Index consultation n/N = 55/201
% crude (95% CI)G
27.4 (25.6 to 36.6)		 n/N = 123/230
% crude (95% CI)g
53.5 (43.8 to 63.2)		 ‐26.1 (% crude) < 0.01h ICC = 0.12
Cals (2013) Proportion of episodes of respiratory tract infections during follow‐up for which a GP was seen and that antibiotics were prescribed for Mean 3.67 years follow‐up n = 178
% (95% CI)
26.3 (20.6 to 32.0)		 n = 201
% (95% CI)
39.1 (33.1 to 45.1)		 ‐10.4i 0.02i —
Welschen (2006) % practice encounters for acute symptoms of the respiratory tract for which antibiotics were prescribed Index consultation Review groups = 6 Review groups = 6 –10.7 (–20.3 to –1.0)j — Practice =
0.17
Review group =
0.09

aTwo level (practice and patient) random intercept logistic regression models.
 bAfter backward elimination, four explanatory variables remained in the model: patients' disease severity, measured on a four‐point scale (odds ratio 4.8, 95% CI 3.9 to 5.9 per step on scale, P value < 0.001), and average practice severity (severity of the disease rated by the GP) (odds ratio 0.14, 95% CI 0.06 to 0.33, P value < 0.001 per category step on the scale), patients having fever (odds ratio 1.80, 95% CI 1.35 to 2.39, P value < 0.001 compared with no fever) and frequency of fever in practice, as determined by the log odds (odds ratio 1.31, 95% CI 1.08 to 1.59, P value = 0.007 per category step on the scale).
 cLogistic regression with random effects for each cluster and patient covariates (age, sex, education, days with restrictions at baseline).
 dThe adjusted model adjusted for baseline prescribing and clustering by physician and practice, and additionally controlled for age, smoking, sex, major cardiovascular or respiratory comorbidity, baseline symptoms, crepitations, wheeze, pulse higher than 100 beats per minute, temperature higher than 37.8°C, respiratory rate, blood pressure, physician's rating of severity and duration of cough.
 eAdjusted for cluster design, baseline values and patient age group (for analyses at teaching unit and physician levels).
 fP value adjusted for baseline values and the study's cluster design.
 gCalculated and inflated for clustering by using standard deviation inflated by variance inflation factor.
 hCalculated from second order penalised quasi‐likelihood multilevel logistic regression model adjusted for variance at general practitioner and practice level (random intercept at practice and general practitioner level). Models included both interventions and interaction term of interventions. 
 iP values from multilevel linear regression model to account and correct for variation at the level of family physician, and to adjust for both interventions, RTI‐episodes treated with antibiotics during baseline period, chronic obstructive pulmonary disease comorbidity.
 jIntervention effect in multi‐level analysis

CI: confidence interval
 GP: general practitioner
 NR: not reported