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

Global and pixel-wise analysis of simulated TCSPC polarisation resolved image data.

<p>Simulated polarisation resolved TCSPC data was generated with fluorescence lifetimes 3.0 and 1.2 ns and rotational correlation times of 30 ns and 1.0 ns. The simulated data was generated with the total initial anisotropy set to 0.4 across the image with the initial anisotropy contribution of the short component equal to 0.1, 0.2 and 0.3 in three bands from top to bottom across the image. (A) False colour images of the recovered initial anisotropy contribution for the long (left) and short (right) correlation time components analysed pixel-wise (top) and with global fitting (bottom); Histograms of estimates of the initial anisotropy contribution of the (B) long and (C) short correlation time components analysed pixel-wise (dashed lines) and with global fitting (solid lines).</p

Parameters from global fitting of a dose-response dataset using an NMT inhibitor with HeLa cells expressing ECFP-Gag and EYFP-Gag using a bi-exponential donor FRET model.

<p> represents the lifetimes of the donor-only decay and represents the fractional contribution of each component. represents the FRET efficiency for each component as defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070687#pone.0070687.e040" target="_blank">Equations 9</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070687#pone.0070687.e053" target="_blank">13</a>. All three parameters are determined globally. represents the fluorescence lifetime of the FRET population calculated from the fitted parameters and is therefore shown in italics.</p

Profiling of the CPU and memory requirements of the algorithm.

<p>(A) Fractional core activity of the four cores while performing the fit in Experiment 1, colour coded by algorithm stage as shown in the flowchart in (B) which shows the main stages of the algorithm. (C) CPU time spent on the different algorithm stages in Experiment 1. (E) Memory requirements for a global fit against image number for (blue) a five frame time gated FLIM dataset, (red) a TCSPC dataset and (green) a two channel polarisation resolved dataset. Numbers exclude the memory required for the MATLAB runtime engine (300 MB).</p

Global analysis of an IPA-3 dose-response dataset modulating the interaction between Rac1 and Pak1.

<p>Global analysis was applied to a multiwell plate dose-response dataset showing the effect of the inhibitor IPA-3 on interaction between Rac1 and Pak1 using an mTurquoise variant of the FLAIR biosensor in COS-7 cells stimulated with EGF. A) representative images from each inhibitor concentration showing distribution of fraction undergoing FRET. B) examples of automatic image segmentation with <i>(left)</i> donor intensity and <i>(right)</i> acceptor images shown in grey-scale with coloured segmented cell regions overlaid. <i>C</i>) plot of fraction of donor molecules undergoing FRET against IPA-3 concentration, averaged across segmented cells with fitted dose-response curve. Error bars indicate 95% confidence intervals on average FRET fraction over segmented cells at each dose. White scale bar represents 100 µm.</p

Parameters used for simulation of polarisation resolved TCSPC data of a fluorescent protein undergoing homo-FRET and fitted parameters using global and pixel-wise fitting.

<p>The mean and standard deviations are calculated over 10 independent datasets.</p

Global analysis of a polarisation resolved homo-FRET TCSPC dataset reading out PtdIns(3,4,5)P₃ accumulation at the membrane.

<p>A MEF transfected with EGFP-AKT-PH was imaged at two minute intervals and stimulated with 50 ng/ml PDGF after 6 minutes (indicated by black triangles). (A, top row) False colour map of the initial anisotropy contribution <i>r</i>₂ associated with homo-FRET over the time course. (A, bottom row) Integrated fluorescence intensity images over the time course. (B, C) Initial anisotropy contributions spatially averaged over the cell: <i>r₁</i> associated with the rotational correlation (B) and <i>r₂</i> associated with homo-FRET (C). Error bars represent the standard deviation across the image. (D,E) Exemplar fluorescence decays from the region indicated by a white triangle in the first (D) and last (E) frame with fit (top) and normalised residuals (bottom). The thin, fainter lines represent the experimental data while the thick, bolder lines represent the fitted model. Black lines represent the parallel component while grey lines represent the perpendicular component. Data are representative of three experiments. White scale bar represents 20 µm.</p

Global analysis of a multiwell plate with varying concentrations of fluorescent dyes.

<p>Global analysis was applied to a multiwell plate with varying concentrations of the fluorescent dyes Rhodamine B and Rhodamine 6G using a bi-exponential model. The relative concentration of Rhodamine 6G reduces across pairs of columns as described in the text. The dataset contains four fields FOV per well. A) plate map showing the measured fractional contribution of Rhodamine 6G for a representative FOV in each well. B) plot of the actual Rhodamine 6G contribution against measured contribution (crosses). C) plot of measured lifetime using a single exponential fit against actual Rhodamine 6G concentration. This dataset was collected as part of a previous study <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070687#pone.0070687-Kumar1" target="_blank">[22]</a>.</p

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