Table 6

Predictors of dispensing antibiotics without a prescription

PredictorsInteractionsUnivariate analysisMultivariate analysis
Number of interactions in which antibiotic was dispensed
N (%)
n=342
Total number of interactions
n=495
Or (95% CI)P valueOr (95% CI)P value
District
 Tabalong (rural district)99 (67.4%)1471
 Bekasi (urban district)243 (69.8%)3481.0 (1.0 to 1.0)0.786
Type of drug outlets
 Drugstore67 (50.4%)13311
 Pharmacy attached to GP/specialist clinics83 (65.9%)1262.4 (1.4 to 4.3) 0.002 2.2 (1.2 to 3.9) 0.007
 Standalone pharmacy192 (81.4%)2367.4 (4.1 to 13.4) <0.001 5.9 (3.2 to 10.8) <0.001
If a pharmacist or pharmacy technician was available during the visit
 Yes98 (64.9%)1511
 No or don’t know244 (70.9%)3441.3 (0.8 to 2.0)0.242
Gender of drug outlet staff
 Female264 (70.4%)3751.3 (0.8 to 2.1)0.348
 Male78 (65%)1201
Day or night visit
 Day visit225 (67.2%)3351
 Night visit117 (73.1%)1601.3 (0.8 to 2.1)0.261
Case
 Child diarrhoea78 (47.3%)16511
 Presumptive TB133 (80.6%)1654.6 (2.8 to 7.6) <0.001 5.7 (3.0 to 10.8) <0.001
 URTI131 (79.4%)1654.3 (2.6 to 7.0) <0.001 5.2 (2.7 to 9.8) <0.001
  • Model calibration was adequate as assessed by the Hosmer-Lemeshow test (p=0.48), with a sensitivity of 0.60, a specificity of 0.76, a positive predictive value of 0.85 and a negative predictive value of 0.46.