Faculty Opinions recommendation of Effect of antibiotic prescribing in primary care on antimicrobial resistance in individual patients: systematic review and meta-analysis.

Author(s):  
Allen Cheng
BMJ ◽  
2010 ◽  
Vol 340 (may18 2) ◽  
pp. c2096-c2096 ◽  
Author(s):  
C. Costelloe ◽  
C. Metcalfe ◽  
A. Lovering ◽  
D. Mant ◽  
A. D. Hay

BJGP Open ◽  
2021 ◽  
pp. BJGPO.2021.0106
Author(s):  
Mina Bakhit ◽  
Emma Baillie ◽  
Natalia Krzyzaniak ◽  
Mieke van Driel ◽  
Justin Clark ◽  
...  

BackgroundAntibiotic prescribing is a major concern that contributes to the problem of antibiotic resistance.AimTo assess the effect on antibiotic prescribing in primary care of telehealth (TH) consultations compared to face-to-face (F2F).Design & settingSystematic review and meta-analysis of adult or paediatric patients with a history of a community acquired acute infection (respiratory, urinary, or skin and soft tissue). We included studies that compared synchronous TH consultations (phone or video based) to F2F consultations in primary care.MethodWe searched PubMed, Embase, Cochrane CENTRAL (inception-2021), clinical trial registries and citing-cited references of included studies. Two review authors independently screened the studies and extracted the data.ResultsWe identified 13 studies. The one small randomised controlled trial found a non-significant 25% relative increase in antibiotic prescribing in the TH group. The remaining 10 were observational studies but did not control well for confounding, and therefore at high risk of bias. When pooled by specific infections, there was no consistent pattern. The six studies of sinusitis – including one before-after study - showed significantly less prescribing for acute rhinosinusitis in TH consultations, whereas the two studies of acute otitis media showed a significant increase. Pharyngitis, conjunctivitis, and urinary tract infections showed not-significant higher prescribing in the TH group. Bronchitis showed no change.ConclusionsThe impact of telehealth on prescribing appears to vary between conditions with more increases than reductions. However, there is insufficient evidence to draw strong conclusions, and higher quality research is urgently needed.


One Health ◽  
2021 ◽  
pp. 100286
Author(s):  
Biruk Alemu Gemeda ◽  
Ayalew Assefa ◽  
Megarsa Bedasa Jaleta ◽  
Kebede Amenu ◽  
Barbara Wieland

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e037405
Author(s):  
Daniel Dedman ◽  
Melissa Cabecinha ◽  
Rachael Williams ◽  
Stephen J W Evans ◽  
Krishnan Bhaskaran ◽  
...  

ObjectiveTo identify observational studies which used data from more than one primary care electronic health record (EHR) database, and summarise key characteristics including: objective and rationale for using multiple data sources; methods used to manage, analyse and (where applicable) combine data; and approaches used to assess and report heterogeneity between data sources.DesignA systematic review of published studies.Data sourcesPubmed and Embase databases were searched using list of named primary care EHR databases; supplementary hand searches of reference list of studies were retained after initial screening.Study selectionObservational studies published between January 2000 and May 2018 were selected, which included at least two different primary care EHR databases.Results6054 studies were identified from database and hand searches, and 109 were included in the final review, the majority published between 2014 and 2018. Included studies used 38 different primary care EHR data sources. Forty-seven studies (44%) were descriptive or methodological. Of 62 analytical studies, 22 (36%) presented separate results from each database, with no attempt to combine them; 29 (48%) combined individual patient data in a one-stage meta-analysis and 21 (34%) combined estimates from each database using two-stage meta-analysis. Discussion and exploration of heterogeneity was inconsistent across studies.ConclusionsComparing patterns and trends in different populations, or in different primary care EHR databases from the same populations, is important and a common objective for multi-database studies. When combining results from several databases using meta-analysis, provision of separate results from each database is helpful for interpretation. We found that these were often missing, particularly for studies using one-stage approaches, which also often lacked details of any statistical adjustment for heterogeneity and/or clustering. For two-stage meta-analysis, a clear rationale should be provided for choice of fixed effect and/or random effects or other models.


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