Trends and patterns in antibiotic prescribing among out-of-hours primary care providers in England, 2010–14

Objectives: Antimicrobial resistance is a global threat, increasing morbidity and mortality. In England, publicly funded clinical commissioning groups (CCGs) commission out-of-hours (OOH) primary care services outside daytime hours. OOH consultations represent 1% of in-hours general practice (GP) consultations. Antibiotic prescriptions increased 32% in non-GP community services between 2010 and 2013. We describe OOH antibiotic prescribing patterns and trends between 2010 and 2014. Methods: We: estimated the proportion of CCGs with OOH data available; described and compared antibiotic prescribing by volume of prescribed items, seasonality and trends in GP and OOH, using linear regression; and compared the proportion of broad-spectrum to total antibiotic prescriptions in OOHs with their respective CCGs in terms of seasonality and trends, using binomial regression. Results: Data were available for 143 of 211 (68%) CCGs. OOH antibiotic prescription volume represented 4.5%-5.4% of GP prescription volume and was stable over time ( P  =   0.37). The proportion of broad-spectrum antibiotic prescriptions increased in OOH when it increased in the CCG they operated in (regression coefficient 0.98; 95% CI 0.96-0.99). Compared with GP, the proportion of broad-spectrum antibiotic prescriptions in OOH was higher but decreased both in GP and OOH (-0.57%, 95% CI - 0.54% to - 0.6% and -0.76%, 95% CI - 0.59% to - 0.93% per year, respectively). Conclusions: OOH proportionally prescribed more antibiotics than GPs although we could not comment on prescribing appropriateness. OOH prescribing volume was stable over time, and followed GP seasonal patterns. OOH antibiotic prescribing reflected the CCGs they operated in but with relatively more broad-spectrum antibiotics than in-hours GP. Understanding factors influencing prescribing in OOH will enable the development of tailored interventions promoting optimal prescribing in this setting

FISH_PROJ20_FLUO

<p>Fluorescence data set with 20 projections</p

FISH_PROJ800_TRANS_part3

<p>Transmission data set with 800 projections, part 3 of 16.</p

FISH_PROJ800_FLUO_part10

<p>Fluorescence data set with 800 projections, part 10 of 16.</p

FISH_PROJ20_TRANS

<p>Transmission data set with 20 projections</p

FISH_PROJ800_FLUO_part16

<p>Fluorescence data set with 800 projections, part 16 of 16.</p

DSI obtained for the segmented vasculature images.

<p>The plot shows the DSI for the segmentation obtained from the images reconstructed from the down-sampled data sets using different numbers of projection images. Error bars represent the maximum absolute error estimated for the images obtained from each down-sampled data set.</p

TraNac Tg(KDR:mCherry) zebrafish embryo 2D transaxial slices reconstructed from the densely sampled (800 projections) and down-sampled data sets of 50 and 30 projections using the conventional FBP method and iterative image reconstruction strategy.

<p>Zebrafish head slices reconstructed (A) from the transmission projection images using (left) the FBP algorithm and (right) the iterative image reconstruction strategy. (B) Slices reconstructed from the fluorescence projection images using (left) the FBP algorithm and (right) the iterative image reconstruction strategy. The number of projections used to reconstruct the images is indicated in each subfigure (left). Reconstructed slices are presented in a 0–255 contrast scale as indicated by the color bars. Scale bar pertains to all images.</p

FISH_PROJ64_FLUO

<p>Fluorescence data set with 64 projections</p

SSIM obtained for the images reconstructed from the down-sampled data sets using the FBP method.

<p>The plots show the SSIM for the images reconstructed from transmission and fluorescence measurements using different numbers of projection images. Error bars represent the maximum absolute error estimated for the images obtained from each down-sampled data set.</p