| Literature DB >> 27054812 |
Roar Dyrkorn1, Svein Gjelstad2, Ketil Arne Espnes1, Morten Lindbæk2.
Abstract
OBJECTIVE: To analyse if peer academic detailing by experienced general practitioners (GPs) could be a useful way to change Medical Doctors, (MDs) prescription of antibiotics for acute respiratory tract infections (ARTIs) in out-of-hours service.Entities:
Keywords: Antibiotic prescriptions; Norway; general practice; out-of-hours service; peer academic detailing; respiratory tract infections
Mesh:
Substances:
Year: 2016 PMID: 27054812 PMCID: PMC4977941 DOI: 10.3109/02813432.2016.1163035
Source DB: PubMed Journal: Scand J Prim Health Care ISSN: 0281-3432 Impact factor: 2.581
Characteristics of 53 doctors working in out-of-hours health service divided by whether they received peer academic detailing on use of antibiotics in acute respiratory tract infections in out-of-hours service (Trondheim 2006–2008).
| Description | Intervention arm (22 GPs) | Control arm (31 GPs) |
|---|---|---|
| GP specialists | 8 | 12 |
| Male GPs (%) | 68 | 83 |
| Mean (95% CI) age in years | 39.7 (35.8–43.7) | 40.7 (31.1–44.4) |
| Mean (95% CI) consultation rate at baseline | 757 (384–1129) | 514 (320–708) |
| Mean (95% CI) consultation rate intervention year | 698 (364–1031) | 577 (358–796) |
| Mean (95% CI) prescription rate at baseline | 39.4 (34.5–44.3) | 43.4 (38.6–48.2) |
| Mean (95% CI) prescription rate intervention year | 41.3 (35.1–47.5) | 41.5 (35.5–47.4) |
Proportions and absolute changes in proportions (%) in antibiotic prescriptions from 53 doctors.
| Outcome | Intervention group (22 GPs) | Control group (31 GPs) |
|---|---|---|
| Proportion of ARTI episodes with antibiotic prescription | Mean (95% CI) | Mean (95% CI) |
| Before intervention | 39.4 (34.5 to 44.3) | 43.4 (38.6 to 48.2) |
| After intervention | 41.3 (35.1 to 47.5) | 41.5 (35.5 to 47.4) |
| Change | 1.9 | − 1.9 |
| Proportion of penicillin V | ||
| Before intervention | 65.5 (58.2 to 72.8) | 68.8 (62.0 to 75.5) |
| After intervention | 75.3 (69.4 to 81.2) | 69.2 (62.9 to 75.5) |
| Change | 9.8 | 0.4 (−5.2 to 6.1) |
| Proportion of penicillin with extended spectrum | ||
| Before intervention | 7.2 (2.7 to 11.7) | 4.3 (1.7 to 6.8) |
| After intervention | 6.7 (2.5 to 10.8) | 6.6 (2.7 to 10.5) |
| Change | −0.5 (−4.3 to 3.1) | 2.3 (−1.3 to 6.0) |
| Proportion of macrolides and lincosamides | ||
| Before intervention | 21.3 (15.6 to 26.9) | 22.7 (17.3 to 28.0) |
| After intervention | 12.5 (8.1 to 16.9) | 18.8 (12.6 to 24.9) |
| Change | −8.8 | −3.9 (−9.6 to 1.9) |
| Proportion of tetracyclines | ||
| Before intervention | 4.4 (2.7 to 6.1) | 2.9 (1.3 to 4.5) |
| After intervention | 2.2 (0.7 to 3.8) | 3.7 (1.4 to 6.0) |
| Change | −2.2 (−4.3 to 0.01) | 0.8 (−1.4 to 3.1) |
| Proportion of all other antibiotics in ATC J01 group | ||
| Before intervention | 0.6 (−0.07 to 1.3) | 0.4 (−0.1 to 0.9) |
| After intervention | 1.0 (−0.6 to 2.6) | 0.6 (−0.2 to 1.4) |
| Change | 0.4 (−1.4 to 2.2) | 0.2 (−0.7 to 1.1) |
*Statistical significance at p = 0.05 level.
Note: Values are based on means for each group, analysed by paired sample t-test.
The intervention group received peer academic detailing on prescription of antibiotics in acute respiratory tract infections in out-of-hours service (Trondheim 2006–2008).
Multilevel logistic regression analyses where the effect of the intervention on prescribing penicillin V versus other antibiotics for specific conditions.
| Diagnose | Observations | OR (95% CI) | |
|---|---|---|---|
| All diagnoses | 4550 | 1.60 (1.22–2.10) | <0.01 |
| URTI | 1443 | 1.66 (1.00–2.74) | 0.05 |
| Tonsillitis | 751 | 1.06 (0.41–2.73) | 0.91 |
| Sinusitis | 441 | 5.52 (1.84–16.6) | <0.01 |
| Acute bronchitis | 398 | 2.53 (0.93–6.87) | 0.07 |
| Pneumonia | 335 | 3.22 (1.13–9.22) | 0.03 |
| Acute otitis media | 1050 | 2.41 (1.16–5.03) | 0.02 |
| Other RTIs | 132 | 1.65 (0.29–9.39) | 0.57 |
Note: Estimates are based on an interaction variable of time and intervention. The highest OR represents the strongest effect of the intervention. The data are adjusted for patients’ age and gender, with doctors as clusters.