Motility studies of cells often rely on computer software that analyzes time-lapse recorded movies and establishes cell trajectories fully automatically. This raises the question of reproducibility of results, since different programs could yield significantly different results of such automated analysis. The fact that the segmentation routines of such programs are often challenged by nanostructured surfaces makes the question more pertinent. Here we illustrate how it is possible to track cells on bright field microscopy images with image analysis routines implemented in an open-source cell tracking program, PACT (Program for Automated Cell Tracking). We compare the automated motility analysis of three cell tracking programs, PACT, Autozell, and TLA, using the same movies as input for all three programs. We find that different programs track overlapping, but different subsets of cells due to different segmentation methods. Unfortunately, population averages based on such different cell populations, differ significantly in some cases. Thus, results obtained with one software package are not necessarily reproducible by other software. Cell migration plays an essential role in many biological processes: e.g. wound healing, embryogenesis, inflammation, and metastasis where uncontrolled cell migration can lead to tumor spreading and hence can cause cancer progression. Studying these processes frequently require cell tracking, and most motility studies of monolayer cultures involve fluorescent labeling of cells, which allows for fluorescence microscopy studies 2]. This. This2].Representative programs for sparse cell culture analysis are listed in There are two main approaches to cell tracking in the current state-of-the-art 21]16][[16][21][[[16][21]In order to address the issue of background removal, and to compare the performance of different programs on real data against a baseline which we fully understand and control, we here introduce a new cell tracking program, which we call PACT (rogram for Āutomated ell racking). PACT is suitable for tracking motile cells on flat and nanostructured surfaces, and is simple enough for users to freely modify it according to their experimental needs. Since nanostructured surfaces are currently of great interest as cell culture substrates and can appear as a highly non-uniform background in the image, we emphasize the use of a reliable spatial image filter here – see details in the materials and methods section, and SuppInfo.zip file for the Matlab code.TLA 26].[26].12][.[26].12]We have imaged HeLa and NIH 3T3 cells on a microscope glass slide , and NIH 3T3 cells on flat silicon and silicon black [4b]. Silicon black samples were fabricated by reactive ion etching of silicon substrates 31]..31].4 cells per each well of 24-well plate and cultured on the tested materials (1cmx1cm) for 1 or 3 days. The time-lapse microscopy experiments were performed in a home-made cell culture chamber equipped with a bubble trap and adjustable medium flow, mounted on a temperature-controlled microscope stage.The cell lines were obtained from Risø National Laboratory, Denmark. The cells were grown in Dulbecco's Modified Eagle Medium: Nutrient Mixture F-12 (DMEM/F12) + GlutaMAX (Invitrogen) supplemented with either 10% fetal bovine serum – FBS (Sigma) in the case of HeLa cells, or 10% of newborn calf serum for NIH3T3 cells, 2 mM L-glutamine (Sigma), 100 U/mL penicillin (Sigma), 100 µg/mL streptomycin (Sigma) and grown until confluency. Cells were harvested by a standard trypsinization method, then seeded at a concentration of 5 x 10For the fluorescence microscopy experiments performed to compare brightfield images with fluorescence microscopy images of the actin cytoskeleton of the cells, the cells were treated with 2% glutaraldehyde in 0.05M cacodylate buffer for 15-20 minutes at the room temperature, washed in 1xPBS containing 0.05% Tween-20, and permeabilized with 0.1% Triton X-100 in 1xPBS for 1-5 minutes at room temperature. After being washed three times with 1xPBS containing 0.05% Tween-20, the cells were incubated in TRITC-conjugated phalloidin (Sigma-Aldrich) for 30 minutes, and rinsed three times with 1xPBS containing 0.05% Tween-20.2 and a Labview-controlled microscope stage. The acquisition technique was bright field microscopy, and reflected light microscopy was used as many of the substrates were not transparent to light. The fluorescence and bright field microscopy experiments with fixed cells were performed on an Olympus BX51 upright microscope. Analysis of bright field images was performed using the TLA setup file provided by its authors. It may be found in the SuppData.zip file. By default, TLA tracks cells imaged in bright field microscopy by first applying a low-pass Gaussian filter of size 25x25 pixels and standard deviation 11 pixels. A Wiener low-pass filter in a 15x15 pixel mask around each pixel is applied to remove the pixel noise in the image, and this is followed by the actual segmentation process.The videos contain 8-bit grayscale images recorded with a temporal resolution of 2-10 minutes. Each image pixel has a size of 0.977 x 0.977 µm and the resolution of the images was 1024 x 768. The recording device was a Zeiss Axiotech microscope equipped with a 10x Zeiss objective with a 19 mm working distance and a field of view of 1000 x 750 µmPACT was implemented using MATLAB (ver. 2008a) and is a text-based, interactive, open source application for the analysis of cell motility data with methods described by Selmeczi et al. . This anProcessing a time lapse movie in PACT consists of the following workflow: Tracking the individual moving objects through a sequence of images, where the objects have been localized by an image segmentation routine on images after a suitable image filtering process for optimal segmentation results. PACT employs a combination of two of the simplest and fastest segmentation methods: thresholding and edge detection. The result of a PACT processing is a time-ordered list of centroid coordinates for each object that is tracked. Root mean square deviation filtering  below) is an optional last step of processing and removes tracks of objects whose displacement is below a user-defined threshold. This option automatically removes tracks of non-motile cells, such as cells immobilized on the surface and lysed cells.Post-processing is the manual screening of the processed data using criteria of biological relevance, which results in final identification of biologically relevant cells amongst the objects tracked. Human judgment is needed to decide which tracks are of sufficient quality for inclusion in the data analysis. This involves discarding segments of tracks where cells either: (a) undergo division; (b) come into contact with one another; (c) carry foreign objects; (d) and optionally cells that do not display normal motility patterns (i.e. those that do not extend lamellipodia or filopodia).working parameters” in the main program may be used to change the name of the input files and to invert the images if needed. The frame may be color or gray-scale; PACT will remove the color component if present. 8-bit depth is sufficient if the contrast is good, else 16-, 24-, etc. may be needed.The source code of PACT is provided in Supporting Information 2. The program takes the recorded movie frames as input and outputs the cell centroid coordinates. The frames must be located in the same folder as the source code of the program and a new folder ("processed") is created where overlay images of original frames and the contours and cell centroids are stored. The section “I in two steps with a Gaussian function G and a boxcar function B and subtracting the result:For image filtering, a band-pass filter that is part of ‘IDL (Interactive Data Language) Particle Tracking’ package  is firstworking parameters section: the high-frequency limit by the standard deviation of G, and the low-frequency limit by the width of B. We found that a high-frequency limit of 2-3 pixels works well for most flat surfaces, and that as much as eight pixels may be required for the highly noisy backgrounds that we have encountered when imaging cells on silicon black. We typically set the low frequency limit to 30-50 pixels as this corresponds approximately to the size of an average cell in our microscopy setup and efficiently filters inhomogeneous background illumination.In Fourier space this amounts to applying a radially symmetric mask function which features a Gaussian decline at high frequencies and an abrupt fall at low frequencies. The limits of this band-pass filter are also set up in the peak exclusion threshold which defines the minimal brightness of a spot in the band-pass filtered image required for inclusion on the object list. The peak (brightest pixel) of the object is marked as a red cross on the image. The cell contour shown as a blue line in the image is defined as the isohypse located at a user-defined percentage (the contour cutoff) of the peak level . The segmentation is done manually for the first and last frames in the movie, and all intervening frames are processed automatically with an average of the contour cutoffs and linear interpolation of the peak exclusion thresholds set for these two frames. The linear interpolation is especially useful for long movies where the background and/or object brightness may change slowly in time due to factors external to the experiment (see section (ii) below).For the segmentation, PACT has a text-based, interactive user interface, which iteratively requests two parameters until the user is satisfied with the selection. The first parameter is the working parameters section.Further selection includes restrictions on minimal and maximal cell area  and minimal distance between cells to ensure that only independent cells are detected. These parameters may also be set in the The tracking process entails sorting this independent list of coordinates (determined at discrete times as the centroid of the cell footprint) into particle tracks using a routine part of ‘IDL Particle Tracking’ package . If desiTo assess the reliability of automated tracking and the relative performance of PACT to some other programs available, we have first evaluated the cell segmentation performance at the single-frame level. To that end we have done the following:Tested the segmentation efficiency of our code, namely how well it locates individual cells in comparison to the otherprograms available.Assessed how changes in focus can affect the accuracy of cell centroid positioningCompared PACT’s accuracy of centroid positioning in brightfield imaging and fluorescence imagingEvaluated the background removal efficiency on nanostructured surfaces by comparing segmentation and centroid position results from bright-field and fluorescence microscopy of the same sample.We have also tested the performance of PACT and other programs at tracking motile cells by performing a statistical analysis of tracks:We study the effect of the RMSD-filtering procedure used to remove non-motile cells and its effect on the cell motilityparameters.We compare pair-wise the statistics obtained by tracking with different programs.We compare the positional noise as function of software.We assess the reproducibility of our method by comparing cell track statistics determined from different movies of the same cell type/substrate-pair with PACT.We compare the velocity autocovariance functions calculated from tracks obtained with different programsBelow, these points are elaborated, and the ensuing discussion section deals with how these observations relate to each other and lead to the conclusions.We tested segmentation on a movie frame of HeLa cells on a glass substrate, NIH 3T3 on a flat silicon substrate, and NIH 3T3 on a silicon black substrate with PACT, TimeLapseAnalyzer (TLA) AutozellPACT has a cell segmentation efficiency comparable to that of TLA, and both are better than Autozell and CellTrack for HeLa/glass, while CellTrack performs better for 3T3/Si. The cell selection efficiency is very dependent on the software used and on the settings used in the segmentation. With training in how to use software, users can achieve 80-90% count rates compared to manual counting.The tracking efficiency was assessed by counting the objects picked in the representative images used in PACT was able to identify cell boundaries even when the cells were imaged slightly out of focus. Imaging out of focus in bright field microscopy has the advantage of enhancing the contrast between cells and background. This is due to light interference effects and blurring of details of cell organelles, which are visible in focus and can interfere with cell identification. We did not employ a blurring method (such as a Gaussian blur) to remove the fine-structure of cells, as that procedure would also negatively affect the sharpness of contrast between cell and background.Long-term time-lapse recordings may lead to drift and variable focus. Another source of noise can be the light conditions and also variations in threshold and filter settings for the image processing which in PACT also can influence the cell centroid measurement. The following experiment estimates the combined effect of these influences by varying focus, which changes the cell contour and light levels.We imaged NIH 3T3 fibroblasts fixed on a glass slide (see GradinaruSuppInfo.zip file) in overfocus at 2, 4, 6, 8, 10, 15, and 20 µm, respectively, as well as in focus (z=0 µm), which we consider a fairly wide range of focus fluctuations for a time lapse experiment. After segmentation and post-processing, 92 cells were found in all frames. We calculated the individual cell centroids throughout the stack. From these positions we subtracted average centroid value of each frame to remove stage drift, and then evaluated the average root- mean-square displacements of all 92 cells segmented, as a function of z . TypicalIn order to test the precision of the method, we compared the performance of our program with bright field images to images of dyed cells visualized by fluorescence microscopy. We have recorded images of fluorescently dyed NIH 3T3 fibroblasts fixed on glass and flat Si in both the bright field and fluorescence mode, To process images with grainy backgrounds, such as images recorded on a substrate of silicon black, the band-pass filter used by PACT  to process images is of critical importance. By setting the high-frequency limit to 5-10 pixels, i.e., higher than the recommended value of 1-3 pixels that is appropriate for removal of pixel noise, grainy images can be analyzed. On this substrate, cells appear as shadows on a very noisy background and we were unable to detect cells with other cell tracking programs , with thTo assess the deviation in cell count and position between fluorescence and brightfield imaging, we compared the fluorescence image with the bright-field images of individual NIH 3T3 fibroblasts fixed on silicon black; see In order to remove all fixed objects automatically, an additional, RMSD-based filter is applied to the tracks prior to their analysis. For each track, the program calculates the root-mean-squared deviation of the tracked point from its mean  value:0 is nearly equal to the mean squared velocity of the cells,one should clearly state the use of the RMSD filter and estimate its effect, since its elimination of the slow-moving cells has the potential of artificially inflating φ0.This is a small quantity if a cell does not move. In our experience, an RMSD threshold of approximately half a typical cell diameter (15 pixels in our images) is optimal for the exclusion of non-moving cells. This ensures that only cells that move at least one cell diameter over the course of a time-lapse experiment are used for further analyses. Most of the cells discarded this way  appear to be mechanically immobilized, attached permanently to the surface, or dead/lysed, and they should not be included in the motility analysis. The few cells that are not lysed or immobile, but simply happen to move very little for the duration of the observation, are also removed by this filter. This causes negligible error because their contribution to the velocity autocovariance function would be nearly zero if they were allowed to contribute to its statistics, while inclusion of a large number of non-moving cells and other objects would skew the statistics. Consequently, the RMSD-filter's elimination of these cells has minimal effect on our estimates for persistence times. Specifically, in our movies the RMSD filter only discarded two to three biologically relevant cells out of a full dataset of 70. We find that the convenience of having all fixed objects removed automatically greatly outweighs the negligible effect of their exclusion on statistics. In general, however, since the parameter φFollowing the post-processing step, for the purpose of assessing the relative performance of the various programs with regards to centroid positioning error, we have pair-wise compared the tracks of cells tracked by each of the three programs (forming 3 datasets), TLA, Autozell, and PACT, so as to avoid cell selection bias due to the differing tracking methods implemented. The difference between the tracks of the same cell obtained with two different tracking programs is due to a conglomerate of errors arising from of differences in the spatial filtering, thresholding and tracking algorithm employed by each program. To remove systematic error, the tracks common to all three datasets (the subsets) were first translationally aligned to minimize the pair-wise RMSD  of the tThe magnitude of pair-wise mismatches between the three programs may be found in pos. These values are listed in We have also evaluated the mean square displacements of cell centroids as a function of time for each of the three subsets to assess the centroid positional error for each of the three cell tracking programs. By fitting to the extended Fürth's formula (Supporting information Sect. 7), we determined the centroid positional measurement error, σi with variance 2σpos,i2) with the measured centroid coordinate value ri. The i subscript refers to the algorithm/program used:The results in 2pos,i), as well as any systematic, algorithm-specific, centroid positioning error. Thus the pair-wise RMSD is:In the limit of large N, the measured value of the centroid is biased by the choice of algorithm due to positional noise  between the mean squared pair-wise displacement  recording ∼12 hours of NIH 3T3 motility on glass, with ▵t=2 min between successive frames. Results are shown in 0) parameters were determined from a weighted least-squares fit to velocity autocovariance function data points. We note that the error bars of the autocovariance data points, shown in Supp. info. Figure S2, become increasingly underestimated as recording time goes by, due to data redundancy in the method used to calculate them (Supp. info. Eq. S1). This leads to artificially low weighing factors in the least-squares algorithm, which lowers the error bars of the fit parameters P and especially φ0. Additionally, the autocovariance data points themselves are correlated  as they are calculated from the same set of cell centroid coordinates. This artificially deflates the error bars of the fit parameters P and φ0 : P=36 ± 4 min and φ0=0.18 ± 0.02 µm2/min2. We conclude that the observed discrepancy between the motility parameters determined from the five movies is statistically insignificant: 4/5 parameter pairs match within 1σ (68.3% expected to match) after correction. This shows that this method of analysis is reproducible.The 5 pairs of  such that the position measurement is accepted as the true object position (fifteen tracks). The results are shown in We have compared the accuracy of various cell tracking programs by testing them on NIH 3T3 cells recorded on a glass substrate (movie in Gradinaru.zip). The cells in this movie were tracked by Autozell, MATLAB, and TLA, and following post-processing, the tracks found by each of the three programs  were analyzed. We have named the resulting tracks the 0 nearly halved, P nearly doubled) from the results obtained using PACT. The Autozell and PACT results are statistically equivalent as there is a clear overlap between the blue  and light red ellipsoid , and a possible overlap within 1σ between the two programs if the Autozell results were data overdispersion-corrected. However, both TLA ellipsoids (1σ) remain well outside the range of overlap with the other two programs. We speculated that the Kalman filter or the moving average filter was the culprit; however, the results do not change much upon removal of these effects. This is perhaps the best illustration of the increase in fit imprecision due to the correlation of the velocity autocovariance data points. Indeed, the velocity autocovariance functions determined using the TLA datasets show significant structure , which would indeed lead to significant underestimation of the error bars, manifested as the substantially smaller green ellipsoids in The motility parameters thus determined with Autozell and/or TLA differ substantially . The results are shown in 0 and P. We conclude that the observed discrepancy between the motility parameters determined with the three programs is statistically insignificant: 2/3 persistence times match within 1σ and all (3/3) φ0 parameters do as well (at most 68% expected to match), despite the shown error bars being underestimates of the real error bar levels.Only three of the tracks correspond to cells that were identified and tracked by all three programs. A comparison of cell motility parameters determined from raw data (i.e. without any filtering/smoothing procedure applied to the data) in 0 set of results. This is at least in part due to the substantial underestimation of the respective error bars, owing to the highly structured velocity autocovariance functions . Finally, to test the the possible effect of Autozell's tracking method, which involved truncating the centroid coordinate to the nearest pixel, we have similarly rounded the coordinates generated by PACT to the nearest pixel to generate the PACT/cut common dataset (Supp. Info. Table S2). We found that the motility parameters determined from this subset had not changed, and conclude that the systematic centroid truncation performed by Autozell is unlikely to affect the motility analysis results.We note, however, that the Kalman filtered- and moving average smoothed-subset yields a very different P-φThese observations raise the point of the importance of the definition of the cell centroid and the effect that this definition has on motility data. We elaborate on this below.The segmentation efficiency tested in (i) was performed by four programs listed in Different programs select somewhat different cells and hence do their respective measurements and statistics for somewhat different overlapping subpopulations. This amounts to filtering the original cell population with somewhat different filters, with the preferences of the segmentation algorithm of each program providing a different filter. The variations in segmentation combined with track formation algorithms such as the RMSD filter (v) can result in very different subpopulations of tracks from the same data set. This is borne out by the fact that only three trajectories were selected by all tracking programs. A single program may be prone to biased selection of subpopulations of cells with specific features, such as proximity to other cells and specific morphology features, all depending on the algorithms being used, sample details and users choice of settings. For these reasons, we recommend that before performing automated time-lapse analysis on a specific movie, the user should assess how well the program performs compared to manual counting and consider carefully if the undetected cells might result in a bias in the cell subpopulation tracked by the software.We have developed PACT for tracking cells on non-uniform backgrounds. To that end, we tested PACT on silicon black substrates. Cells on such substrates are often difficult to distinguish from the background even by eye. TLA and PACT are the only programs that successfully tracked cells in these images. The Wiener filter that TLA employs in its bright field tracking method, is comparable to the convolution filter implemented in PACT with regard to cell recognition. However, we have specifically designed PACT to reduce operator effort at the post-processing step, which is the most time-consuming step of the analysis. We accomplished this by implementing additional automated post-processing features: restrictions on the area of the object and minimal inter-cell distance. These features make PACT less likely to find false positives and this is reflected in the percentage of objects that pass the post-processing step of PACT in comparison with TLA: 81% versus 72% for flat Si and 52% versus 31% for silicon black. We conclude that PACT is more useful for the sparse cell culture motility analyses that we perform in this paper. PACT can, however, be less efficient for other applications such as dense cell cultures.The positional error was shown in Section (vii) to be of the order 1-2µm. Compared to this noise level, the cell centroids appear to be fairly reliably determined: They are not strongly influenced by variations in focus and threshold settings, but scatter with an RMSD of 1µm for a 20 µm focus variation in the test performed in Section (ii). The brightfield image centroid position correlates with the fluorescence microscopy results with an RMSD on flat surfaces of 2.8µm as shown in (iii), while nanostructured substrates increase the RMSD to 4.3µm. This indicates that bright field microscopy can be used for motility analysis with precision comparable to that of fluorescence microscopy and that the overall process is not strongly influenced by variations in the experimental procedure and image segmentation.After removal of positional noise effects, the pair-wise RMSD of the tracks compared in Section (vi) are similar to the positional noise level itself. That is also supported by our experiments introduced in sections (iii and iv), which show that the PACT centroids measured from bright-field images are comparable to a “golden standard” of fluorescence images of the same samples. These results indicate that the different programs can be used to assess cell centroids reliably (accurately and precisely) from bright-field images.We tested the internal consistency of the velocity auto-covariance function measurements on cell motility in Section (viii) and found that the results are consistent, especially when accounting the artificial underestimation of the motility parameters’ error bars due to data overdispersion. Comparing the motility parameters obtained with different programs in (ix), The motility parameters obtained from different programs in Finally, we have implemented the features present in PACT  in TLA.0 in a statistically significant manner when all sources of errors are accounted for, but comparison between data obtained with different programs should be done with caution, since different programs well may select different cell subpopulations based on their segmentation algorithm. For nanostructured substrates this is can be important, as they appear to make cells display a wide phenotypic variability [35]. Comparison of data obtained with different programs doing automated analysis is hence difficult, unless detailed tests have been done for how each program performs in segmentation and how its results compare to those of other programs.In conclusion, our study shows that cell tracking can be done with different programs, often with reasonable segmentation efficiency and precision: the pair-wise RMSD of tracks output by two different programs is 2-3µm, consistent with a positional noise per dataset of ∼1µm. Our analysis of results for the velocity auto-covariance function demonstrates that different programs provide comparable precision in the motility parameters P and φClick here for additional data file.Click here for additional data file.


To describe trends in the incidence of non-traumatic amputations among people with and without diabetes and estimate the relative risk of an individual with diabetes undergoing a lower extremity amputation compared to an individual without diabetes in the Republic of Ireland.All adults who underwent a nontraumatic amputation during 2005 to 2009 were identified using HIPE  data. Participants were classified as having diabetes or not having diabetes. Incidence rates were calculated using the number of discharges for diabetes and non-diabetes related lower extremity amputations as the numerator and estimates of the resident population with and without diabetes as the denominator. Age-adjusted incidence rates were used for trend analysis.Total diabetes-related amputation rates increased non-significantly during the study period; 144.2 in 2005 to 175.7 in 2009 per 100,000 people with diabetes (p = 0.11). Total non-diabetes related amputation rates dropped non-significantly from 12.0 in 2005 to 9.2 in 2009 per 100,000 people without diabetes (p = 0.16). An individual with diabetes was 22.3 (95% CI 19.1–26.1) times more likely to undergo a nontraumatic amputation than an individual without diabetes in 2005 and this did not change significantly by 2009.This study provides the first national estimate of lower extremity amputation rates in the Republic of Ireland. Diabetes-related amputation rates have remained steady despite an increase in people with diabetes. These estimates provide a base-line and will allow follow-up over time. Diabetic foot disease is a major health problem and unfortunately, Lower Extremity Amputation (LEA) remains a common outcome. LEA is a significant complication that is costly to individuals economically, socially and psychologically 5 people with diabetes Lower Extremity Amputations are an important indicator of the quality of care of diabetes in a population There is a lack of published data on LEA rates in the ROI. Base-line information on LEA rates in the ROI will facilitate comparison with other countries, benchmarking against best practice and tracking of potential improvements in the future Aggregate data from a routine dataset was obtained via Health Atlas. Ethical approval was not required. As this data cannot be linked to individual patients, individual patient consent was not obtained. All data included is publicly available and no personal information was handled.This study is a retrospective review of data on hospital discharges from the Hospital In-Patient Enquiry (HIPE) dataset over a 5 year period in the ROI. HIPE collects information on day and in-patient discharges from acute public hospitals including private patients treated in Health Service Executive hospitals. If a patient dies in hospital, a discharge summary is completed and the HIPE system collects this patient’s details. Data extracted for each hospital discharge includes patient age, length of hospital stay, discharge status, principal diagnosis and procedure and up to 20 secondary diagnoses and procedures. HIPE is available on Health Atlas; a mapping software system st January 2005 and 31st December 2009 were initially identified. Next, all discharges for LEA procedures performed on patients between 1st January 2005 and 31st December 2009 were identified through an ICD-10 procedure code  in any procedure field. LEAs were categorised as major or minor. A major LEA was defined as through or proximal to the ankle joint ; a minor LEA as one distal to the ankle joint  The numbers of discharges for patients with diabetes (ICD-10 codes E10–E11) for any cause between 1The denominators used were the total estimated resident population with and without diabetes in the ROI in each year of the study. The Institute of Public Health, Ireland provided estimates of the prevalence rate of diabetes in the population in 2007 in a study ‘Making Chronic Conditions Count: A systematic approach to estimating and forecasting estimate of the diabetic population in Ireland as population prevalence on the island of Ireland’. Statistical analyses were performed using the Stats Direct statistical package. Age-adjusted incidence rates were standardised to the EU standard population and the 95% confidence intervals were based on the Poisson distribution. Incidence rates in 3 different age-categories  were calculated. Cuzick’s trend test was used to test for significant changes over time. A two-sided p<0.05 was considered statistically significant. The relative risk of an individual with diabetes undergoing a LEA  compared with that of an individual without diabetes was estimated with 95% confidence intervals.total population .During 2005–2009, 2,776 patients underwent non-traumatic LEA in the ROI. Of all amputations, 1654 (53.5%) occurred among people with diabetes.The median length of stay for a LEA in people with diabetes was 24 days (range 1–539 days); with a total of 60,332 bed days occupied over the 5 year study period. The mortality rate during a hospital admission for LEA in patients with diabetes was 6.4%. The majority of patients with diabetes admitted for LEA were discharged home post-operatively (64.6%). A further 16.1% of patients were discharged to nursing homes and the remaining 12.9% were discharged elsewhere .The numbers of discharges for diabetes-related LEAs used as the numerator are described in The estimated population without diabetes used as the denominator to calculate nondiabetes-related LEA rates is described in The risk of an individual with diabetes undergoing a LEA was 22.3 times that of an individual without diabetes in 2005 and did not change significantly by 2009 (p = 0.4) see .Diabetes is on the rise and is impacting on health services in the ROI. This is reflected in a significant increase in the age-standardised rate of hospital discharges for all patients with diabetes during 2005–2009 per 100,000 total population.This study determined incidence rates of diabetes and non-diabetes related LEAs in people with and without diabetes. A non-significant rise in total (major & minor) diabetes-related LEA rates from 144.2 to 175.7 per 100,000 people with diabetes was found. We are cognisant that our results should be compared to data from studies of similar methodology Other studies have used slightly different methods to determine the numerator but are worthy of mention. In the UK, Canavan et al described a decrease in total LEA rates, 564.3 to 176 per 100,000 population with diabetes in the South Tees from 1995–2000; while in Ipswich from 1995 to 2005, total amputations fell from 532 to 160 per 100,000 people with diabetes In the Netherlands, incidence was reported as 363 per 100,000 people with diabetes in 2000 Outside Europe, among U.S. Medicare beneficiaries with diabetes, the annual incidence of total LEA was 500 in 2006, 460 in 2007, and 450 in 2008 per 100,000 people with diabetes An early minor amputation can prevent a later major amputation An increase in diabetes-related LEA rates in the 30–59 year old age-category was observed. Although this increase did not reach statistical significance, it is, nonetheless, worrisome . As the It is suggested that ideally, the relative risk of a person with diabetes undergoing a LEA should be similar to that of a person without diabetes This study has a number of strengths; it is population based and uses methodology comparable to other countries As all LEA procedures are carried out in hospital, the numbers of discharges for LEAs serve as a proxy measure of the number of LEAs in this study and were used as the numerator. The use of this numerator to calculate LEA incidence, instead of the actual number of LEA procedures performed has been criticised This study does not include auto-amputations . Whether autoamputation is an optimal management strategy in patients with diabetes is the subject of debate Concerns have been raised on the accuracy of routinely recorded datasets such as HIPE In the denominator, the population with diabetes was estimated from prevalence rates calculated by the Institute of Public Health for 1 year, 2007, and these were applied to all years in the study . Thus, t. This study provides the first national estimate of LEA rates which will serve as a base-line to facilitate tracking of changes and potential improvements with the new national programme and healthcare system reconfiguration.With optimal care, many LEAs in people with diabetes are preventable It is re-assuring that these first Irish LEA rates are comparable to the UK, despite a disparity between countries in resources contributed to the care of the diabetic foot. It will be challenging to reduce existing LEA rates as levels of diabetes and obesity continue to rise. In addition, there are limited resources available for diabetes care in the current economic climate. Future trends in LEA rates need to be monitored as well as trends in the prevalence of diabetes.


Plasmodium falciparum infected erythrocytes to bind to a range of host receptors including those displayed on endothelial cells has been associated with the pathology of this infection. Investigations into this adhesive phenomenon have used protein and cell-based adhesion assays to quantify the ability of infected red blood cells to bind. These adhesion assays tend to have relatively high inherent variability and so require multiple experiments in order to provide good quantitation. This means that investigators doing these experiments must count many fields of adherent parasites, a task that is time-consuming and laborious. To address this issue and to facilitate cytoadherence research, developed automated protocols were developed for counting parasite adhesion.The ability of mature forms of Parasite adhesion assays were mainly carried out under static conditions using purified receptors, which is the simplest form of these assays and is translatable to the field. Two different software platforms were used, one commercial (Image Pro-Plus (Media Cybernetics)) and one available in the public domain (ImageSXM) based on the freely available NIH Image software. The adhesion assays were performed and parasite binding quantified using standard manual techniques. Images were also captured using video microscopy and analysed using the two automated systems. The results generated by each system were compared using the Bland and Altman method for assessing the agreement between two methods.Both automated counting programs showed concordance compared to the 'gold standard' manual counting within the normal range of adhesion seen with these assays, although the ImageSXM technique had some systematic bias. There was some fall-off in accuracy at very high parasite densities, but this can be resolved through good design of the experiments. Cell based assays were also used as inputs to one of the automated systems (ImageSXM) and produced variable, but encouraging, results.The automated counting programs are an accurate and practical way of quantifying static parasite binding assays to purified proteins. They are less accurate when applied to cell based systems, but can still provide a reasonable level of accuracy to give a semi-quantitative readout. Plasmodium falciparum malaria remains a major burden in terms of its contribution to morbidity and mortality in resource poor countries [ountries . Despiteountries , with seountries -6. The bD-glucose, 6 mM NaOH, 25 μg ml-1 gentamicin sulphate, 2 mM L-glutamine and 10% human serum) at a pH of 7.2, in a gas mixture of 96% nitrogen, 3% carbon dioxide and 1% oxygen. Parasites were synchronized using 5% sorbitol. Prior to use, parasites were washed twice in binding buffer  and re-suspended at 1% haematocrit (Coulter counter) and 3% parasitaemia (Giemsa staining). All parasite lines are subject to antigenic switching in vitro and so stabilates of ICAM-1 selected ItG-ICAM were used up to three weeks post-selection to minimize the effect of mixed populations.The parasite line ItG-ICAM  was usedth passage.Characterized human umbilical vein endothelial cells (HUVEC) and human dermal microvascular endothelial cells (HDMEC) were obtained from Promocell. Cells were grown in manufacturer's recommended medium and then frozen down in 7.5% DMSO/92.5% FCS. HUVEC and HDMEC were used at 6-1) [-1) (R&D Systems) were spotted onto 60 mm diameter bacteriological Petri dishes and incubated in a humidified chamber for 2 h at 37°C. Proteins were aspirated off and dishes blocked overnight at 4°C in 1% BSA/PBS. Blocking solution was removed, the dish washed in binding buffer and 2 ml of parasite suspension at 3% parasitaemia and 1% haematocrit added to each dish. Dishes were incubated at 37°C for 1 h, with re-suspension for every 10 min. Unbound RBC and pRBC were removed by repeated washing, bound cells fixed with 1% glutaraldehyde for 1 h and then stained with 5% Giemsa for 20 min. Levels of ICAM-1 adhesion were quantitated microscopically or using automated image analysis (see below). CD36 adhesion was included solely as a positive control for the binding assay and was not included in the automated counting analysis in this study.Static protein binding assays were carried out as previously described . Briefly-1)  or CD36 -1rTNF . Cells were washed with binding buffer and incubated with 0.5 ml of parasite suspension  for 1 h at 37°C, with gentle resuspension every 10 min. A 1 h gravity wash removed unbound cells, adherent cells were fixed using 2% glutaraldehyde for 1 h and then stained with 5% Giemsa for 20 min. Coverslips were dried and mounted on slides using DPX mounting buffer (BDH Lab Supplies). Levels of adhesion were quantitated microscopically or using automated image analysis (see below).Static cell binding assays were carried out using a modified version of a previously described method . HUVEC oMicroscopy and image capture was carried out using a Nikon Eclipse TE2000 microscope with cooled CCD camera (QICAM) and remotely operated stage (Prior Optiscan) using the Image-Pro Plus 5.1 suite of image capture tools  at 300× magnification. Eleven separate replicate experiments were carried out with twenty fields captured per assay.2 fields were selected by the user and counted for each of 12 ICAM-1 protein spots  per plate (CD36 was used only as a control for binding), and the mean of these counts multiplied by 4 to give a count of pRBC/mm2 (4 × 0.25 mm2 fields).pRBC were manually counted at 200× magnification. 6 × 0.25 mmsimultaneously within IPP 5.1. The program was then able to export the cell count information and user defined attributes to an Excel (Microsoft Corporation) database, which had been pre-prepared to analyse the data and output an pRBC/mm2 result.As an adjunct to image capture using IPP 5.1, a program was written using the Visual Basic for Applications (Microsoft Corporation) programming language that allowed any specified number of images to be analysed Following image capture, adherent pRBC appear as dark spots on a light background. Therefore IPP 5.1 was calibrated to recognize dark areas on the image. After initial testing, 3 potential sources of error were identified: incorrectly including uninfected red blood cells (uRBC) and non-cellular objects such as dust and dark marks on the camera lens in the final count, and incorrectly counting several clustered or overlapping cells as one single dark object. These problems were addressed as follows:IPP 5.1 includes an algorithm which resolves clustering by determining the mean area of objects it identifies as single  and then dividing the area of any objects identified as clusters (anything beyond 2 standard deviations from the mean 'single object' area) by the mean single object area and adjusting the count accordingly.The area of objects such as dust, fibres and marks on the camera lens were measured using the IPP 5.1 image analysis suite. These were then compared to the area of pRBC.Un-infected cells: Uninfected RBC are generally un- or lightly stained when compared to pRBC and appear as brighter objects in images. Also, as a result of them maintaining their concave shape and in contrast to pRBC, they have a tendency to be brighter in the centre. With this in mind, attributes based on optical density, the degree of 'clumpiness'  and the 'margination'  were measured and compared with those of pRBC.Image SXM [NIH Image [Image SXM as a starting platform, specialist image analysis solutions have been developed that address the needs of users in the life sciences for specific applications. MIASMA  [mage SXM  is an imIH Image  in the 1cations)  is the rImage SXM using the CodeWarrior compiler system (Metrowerks). The PCA takes minimal input from the user  and then carries out a batch analysis of all images in a set of folders. The output is (i) a text file giving the details of the pRBC count in each image and (ii) a summary of the batch analysis with the average pRBC count for images in each folder. In addition, a colour-coded image is generated for each image that shows the pRBC that were identified and counted. After the PCA routines have completed, a blink comparator allows the user to compare these output images with the original images to determine whether or not the analysis ran with satisfactory accuracy.The PCA routines were written in the Pascal source code of Each image is processed to compensate for differences in brightness and/or contrast and then a background subtraction routine is applied to minimize the influence of cell structure on pRBC recognition. The geometric characteristics of each candidate for a pRBC, which appear as dark objects on a light background, are measured -- area, perimeter, and lengths of the major and minor axes of the best-fit ellipse. These measurements are used to (i) veto those objects that do not meet geometric criteria and (ii) determine if the object is two or more touching pRBC.The criteria for valid pRBC were determined from a batch of test images:2/area < 100 (a test of the convolution of the object's outline)(i) perimeter(ii) major axis/minor axis < 3 (a test of the aspect ratio of the object)n touching pRBC use a combination of area, perimeter and aspect ratio, with the value of n being calculated from the object's area relative to the mean area of all objects found in the image.The criteria for an object being recognized as The blink comparator can be used to check an image with the corresponding output image that shows which pixels were identified as cytoplasm and pRBC. This can be done on a selection of images before or after a full PCA run is executed. If there is any consistent under/overestimate of the pRBC count then the user can adjust global parameters that fine tune the PCA algorithms and run the analysis again. If there is no consistent under/over-estimate of the pRBC count but some objects in some images have been identified incorrectly, then the user can opt to apply a manual override to adjust the analysis on an image-by-image basis.The results obtained using the Image ProPlus and SXM automatic methods .For the comparison of the SXM and manual methods, the mean difference between the two measurements was statistically significant for the 0 μg/ml and 50 μg/ml concentration levels and was close to statistical significance (0.05 < p < 0.10) for the 12.5 μg/ml and 25 μg/ml concentration levels. The automatic SXM method tended to systematically over-estimate counts at the 0 μg/ml and 12.5 μg/ml concentration levels but to under-estimate counts at the 25 μg/ml and 50 μg/ml concentration levels.Table Additional file One of the major challenges for malaria cytoadherence assays has been the counting. These assays are also notoriously variable, requiring several replicates that add to this burden. In this paper we have investigated two automated methods for parasite counting based on the simplest format, namely static assays performed with purified protein. The counting software used represent commercial (Image ProPlus) and publically-available (ImageSXM) platforms and we have attempted to optimize both so that they are able to cover a broad range of values generated by the assays. Of the two methods the Image ProPlus platform more closely represented the counts obtain manually with no significant differences at all concentrations tested. ImageSXM did show some bias, and this varied based on the concentration of receptor used, and therefore the number of adherent parasites per field.The superiority of the commercial software may reflect, in part, the better user interface available with the Image ProPlus package and better familiarity with the Image SXM software might have improved the outcome. However, despite having access to an expert on ImageSXM (SBD), the software backage was tested without extensive intervention to see how it would perform under 'standard' conditions. As well as the small bias seen with ImageSXM we also encountered some problems with the analysis of images from different batches, where SXM had less flexibility to cope with changes in exposure and contrast. Despite this SXM still performed well producing results that were consistent with the other methods and without the need to purchase commercial software. ImageSXM software was also applied to cell-based assays, which bring an added level of complexity to automated counting due to the presence of many 'particles' contributed by the endothelium. The results  produced some systematic errors when compared to the 'gold-standard' manual counting but still provided data that were consistent with the other techniques, and with the flexibility to be applied to the next generation of cell-based adhesion assays.The authors declare that they have no competing interests.DP carried out the binding assays, designed the IPP counting routine, performed the counting , participated in the design of the study and drafted the section on IPP counting. KM and TS performed binding assays and performed the counting. SB wrote the routines for MIASMA based on ImageSXM and drafted the section on MIASMA-based counting. BF performed the statistical analysis and drafted the section on this analysis. AC conceived of and designed the study and drafted the manuscript. All authors read and approved the final manuscript.Table of pRBC binding data. The binding results using manual and automated counting for replicate experiments to measure pRBC adhesion to ICAM-1.Click here for fileAutomated counting of cell-based assays. An example of cell-based adhesion assay automated counting using Image SXM from a single assay. The results from manual and SXM methods have been plotted against each other to demonstrate the level of concordance, and three images have been included to provide one example of successful automated counting and two images where the automated system does not match the manual counts.Click here for file


Emergency departments are medical treatment facilities, designed to provide episodic care to patients suffering from acute injuries and illnesses as well as patients who are experiencing sporadic flare-ups of underlying chronic medical conditions which require immediate attention. Supply and demand for emergency department services varies across geographic regions and time. Some persons do not rely on the service at all whereas; others use the service on repeated occasions. Issues regarding increased wait times for services and crowding illustrate the need to investigate which factors are associated with increased frequency of emergency department utilization. The evidence from this study can help inform policy makers on the appropriate mix of supply and demand targeted health care policies necessary to ensure that patients receive appropriate health care delivery in an efficient and cost-effective manner. The purpose of this report is to assess those factors resulting in increased demand for emergency department services in Ontario. We assess how utilization rates vary according to the severity of patient presentation in the emergency department. We are specifically interested in the impact that access to primary care physicians has on the demand for emergency department services. Additionally, we wish to investigate these trends using a series of novel regression models for count outcomes which have yet to be employed in the domain of emergency medical research.Data regarding the frequency of emergency department visits for the respondents of Canadian Community Health Survey (CCHS) during our study interval (2003-2005) are obtained from the National Ambulatory Care Reporting System (NACRS). Patients' emergency department utilizations were linked with information from the Canadian Community Health Survey (CCHS) which provides individual level medical, socio-demographic, psychological and behavioral information for investigating predictors of increased emergency department utilization. Six different multiple regression models for count data were fitted to assess the influence of predictors on demand for emergency department services, including: Poisson, Negative Binomial, Zero-Inflated Poisson, Zero-Inflated Negative Binomial, Hurdle Poisson, and Hurdle Negative Binomial. Comparison of competing models was assessed by the Vuong test statistic.The CCHS cycle 2.1 respondents were a roughly equal mix of males (50.4%) and females (49.6%). The majority (86.2%) were young-middle aged adults between the ages of 20-64, living in predominantly urban environments (85.9%), with mid-high household incomes (92.2%) and well-educated, receiving at least a high-school diploma (84.1%). Many participants reported no chronic disease (51.9%), fell into a small number (0-5) of ambulatory diagnostic groups (62.3%), and perceived their health status as good/excellent (88.1%); however, were projected to have high Resource Utilization Band levels of health resource utilization (68.2%). These factors were largely stable for CCHS cycle 3.1 respondents. Factors influencing demand for emergency department services varied according to the severity of triage scores at initial presentation. For example, although a non-significant predictor of the odds of emergency department utilization in high severity cases, access to a primary care physician was a statistically significant predictor of the likelihood of emergency department utilization  and the rate of emergency department utilization  in low severity cases.Using a theoretically appropriate hurdle negative binomial regression model this unique study illustrates that access to a primary care physician is an important predictor of both the odds and rate of emergency department utilization in Ontario. Restructuring primary care services, with aims of increasing access to undersupplied populations may result in decreased emergency department utilization rates by approximately 43% for low severity triage level cases. Emergency departments are medical treatment facilities, designed to provide episodic care to patients suffering from acute injuries and illnesses as well as patients who are experiencing sporadic flare-ups of underlying chronic medical conditions which require urgent medical attention . The sciIncreased patient volumes at emergency departments, resulting from changes in patient preference/demand characteristics, decreasing supply of emergency department resources , or long term structural changes to patient case mix as a result of demographic trends have resulted in documented challenges in the delivery of emergency department services. These challenges include: increasingly long wait times, ambulance diversions, and crowding. Despite considerable research in this area, a lack of consensus exists as to the most appropriate strategies for addressing these problems. A review of available literature can sometimes illustrate contradictory findings regarding the characteristics of those individuals whom exhibit increased (sometimes coined "inappropriate") demand for emergency department services. One area of controversy is whether lack of access to a primary care physician in the community is attributable to increased utilization of emergency department services. In an Ontario based study, Chan  found thAnother distinguishing feature of this study is the methodology employed to assess the factors' contributing to demand for emergency department services. Previous studies have either dichotomized the count of emergency department visits at some threshold (indicating non-frequent users versus frequent users) and modeled the transformed outcome using logistic regression ,11 whereThe Canadian Community Health Survey (CCHS) cycles 1.1 to 5.1 are national surveys which have been conducted by Statistics Canada from 2000 to 2010 [In the province of Ontario CCHS respondents were asked to provide their Ontario health card numbers and to consent to linkage of their CCHS responses with personal health care utilization data. Those consenting in cycles 1.1-3.1 were linked to the Ontario Registered Persons Database (RPDB), the province's health care registry. Once linked with the RPDB, health card numbers were used to link respondents with fee-for-service billing claims that physicians report to the Ontario Health Insurance Plan (OHIP). These data were collected for 2000-2001 and 2002-2003 observation periods. Approximately 94% of all physician encounters in the province are included in this database. A small number of physicians are salaried employees and hence do not directly bill OHIP for patient encounters. Records of all emergency department visits were also submitted to the Canadian Institute for Health Information (CIHI) as part of the National Ambulatory Care Reporting System (NACRS), for which close to 100% of emergency department claims in the province are included. The data were accessed at the Institute for Clinical Evaluative Sciences (ICES) as part of a comprehensive research agreement with the Ontario Ministry of Health and Long-Term Care (MOHLTC).The study setting of Ontario is Canada's most populous province and the second largest province in terms of geographic area. The study population was restricted to individuals between the ages of 20 and 79 years to avoid proxy responses that could be assigned to children and older seniors. The cycles 2.1 collection period was January 2003 through December 2003 and cycle 3.1 was January 2005 through December 2005.The number of emergency department visits during the 365 day interval following the interview date were tallied for fiscal years 2003 through 2006 for each individual respondent of CCHS cycle 2.1 and 3.1, and counted using the NACRS database. The scrambled Ontario health card number was used as a unique key to link individual level medical, socio-demographic, psychological and behavioral data from the CCHS 2.1 and 3.1 to emergency department visit data. We defined a potentially avoidable emergency department visit as one with a Canadian Triage and Acuity Scale (CTAS) score of 4 or 5 (less urgent), where the patient was not admitted to the hospital following observation by the physician. We defined an unavoidable emergency department visit, as one with a CTAS score of 1, 2 or 3 (urgent) and no diagnostic code indicating an injury. We assume these emergency department visits are unlikely treatable in a primary care environment. We excluded emergency department visits where the patient left without being seen and excluded transfers  and pregnant women. Outcome variables for each participant are the number of less urgent and the number of urgent emergency department visits. In regression models, participants with no emergency department visits were included with zero visits for both less urgent and urgent emergency department visits.We used the John Hopkins University Ambulatory Care Groups Case Mix Adjustment System (version 7) to summarize the degree of comorbidity experienced by Ontarians during the two year period prior to the interview date. This software reads in international classification of disease (ICD) codes from physician and hospital-based claims and categorizes patients as having up to 32 different Ambulatory Diagnostic Group (ADG) labels. An individual can be assigned to multiple ADG's depending on their respective diagnoses. We collapsed the 32-category variable into three categories, specifically: individuals falling into 0-5, 6-9 and greater than 9 ADG's. This three level categorical variable was used as an indicator of comorbidity in all subsequent analyses.The Ambulatory Care Groups Case Mix Adjustment software also generates Resource Utilization Bands (RUB), which estimate expected resource utilization. Patient level RUB categorization is determined through consideration of age, sex, and disease diagnoses. Different categories of RUB are associated with different levels of expected resource use and overall cost to the health care system over a given period of time. RUB values vary from 0-5, with higher values associated with higher utilization levels. For this study, RUBs were categorized as ≥ 4 (very high), 3 (high), 2 (medium), and 0-1 (low). The ACG measures were determined using two years of diagnostic data  from physician and hospital-based claims.Individual-level variables that were included in the regression models were gender , age , total household income , education , number of chronic medical conditions  from the following list , perceived health status , number of ADG's , RUB status , access to a primary care physician in the community , and location of primary residence .Population studies which seek to estimate demand for emergency department services or hospitalization typically exhibit a large proportion of zeroes, representing the persons that do not use any of the services being investigated during the observational period of interest. Factors influencing the demand for these services are routinely modeled using Poisson or negative binomial regression. While the negative binomial regression model does not impose as stringent a set of restrictions on the conditional mean-variance relationship as the Poisson model, neither is ideal for handling data with a large proportion of zeroes. Failure to account for the mass of zeros that are occurring at a greater proportion than would be predicted by either the Poisson or negative binomial models may result in biased parameter estimates and misleading inferences. Several methods are proposed for analyzing data with excess zeros, including: the ZIP, ZINB, HP and HNB regression models. Given the lack of use of these models in emergency medical research we will describe each method below.Before proceeding to any multiple regression modeling, descriptive statistics were generated to characterize the sample under investigation. For continuously distributed variables we presented means and standard deviations; whereas, for categorical variables we presented counts and percentages.i) experienced by patient i follows a Poisson distribution:Perhaps the most parsimonious and widely implemented method for modeling count data in the public health sciences is Poisson regression. The Poisson regression model assumes that the number of events  and an estimated vector of coefficients (β). Mathematically, this relationship takes the following form:where μTaking the natural logarithm of the conditional mean allows for the response under consideration to vary linearly as a function of observed predictor variables multiplied by the effect of their corresponding regression coefficients. Various numerical maximization methods exist for iteratively estimating the values of the coefficient vector, β, and the associated -covariance matrix. variance Estimates are typically found by finding the parameter estimates that maximize the following log-likelihood function:Since the natural logarithm of the likelihood function for the Poisson regression model is globally concave, a unique maximum can be found if it exists . A restrA less parsimonious, but more flexible extension to the Poisson regression model is the negative binomial regression model. The negative binomial regression model does not assume that the conditional variance of the response is equal to the conditional mean. A simple extension to the specification of the Poisson conditional mean leads to a negative binomial regression model, which is illustrated below:i, that is assumed to be uncorrelated with the observation vector, xi. Typically one assumes that i) has a mean that of 1 and variance of υ then the conditional mean of yi is still μi; however, the conditional variance becomes μi(1 +uμi) = μi + uμi2. As u approaches zero binomial regression model converges toward the Poisson model, with a conditional mean that is equal to the conditional variance, μi [Above, the conditional mean for the Poisson model has been adjusted by adding an individual specific random term, εance, μi ,21. For i represents the mean number of events that is expected for an individual with observation vector xi, u represents the negative binomial dispersion parameter and Γ(·) represents the gamma function. Determination of regression coefficients in negative binomial regression proceeds by maximizing the following log-likelihood function with respect to the unknown parameters:Above, μThe negative binomial regression model is a useful model for accounting for data in which unobserved heterogeneity or temporal/spatial correlation is present; however, it is not necessarily an optimal model for dealing with data that contain an excess mass of zeroes at the corner of its empirical distribution.i, and a patient falls into group 2 with probability (1 - ψi), where ψi is an estimable parameter from available data. Group 1, although homogeneously comprised of persons with zero demand for emergency services over a given interval of time are derived from a combination of processes - resulting from the binomial probability, ψi, and the zeroes which accumulate naturally in the Poisson or negative binomial densities. Distinguishing between the two sources of zeroes is not possible, as it is a form of discrete unobserved heterogeneity [Zero Inflated Poisson (ZIP) regression models were introduced by Lambert  as a metogeneity . The proSimilarly, for zero-inflated negative binomial model, the probability density function is given by:i, the is modeled using logistic regression . As a result, the probability of an excess zero is given by:For both the ZIP and ZINB models the probability of an excess zero, ψi, which itself is formed from a set of predictor variables, zi, multiplied by their associated logistic regression coefficients, ε. For the ZIP model the conditional mean and variance are:In other words, the probability of an excess zero is a function of some observed linear predictor, ηFor the ZINB model, the conditional mean is the same as for the ZIP model; however, the conditional variance differs. The equations for both the conditional mean and variance of the ZINB model are given below:i as the probability of excess zeroes, it can be observed that as ψi tends toward zero then the probability densities, as well as the conditional mean and variances of the ZIP and ZINB models converge toward the corresponding formulas for the Poisson and negative binomial models, respectively [Considering ψectively ,19,21. DI(·) is an indicator function.Here One issue with the application of zero-inflated modeling strategies for emergency department demand is that interpretively some of the zeroes in ZIP/ZINB models are considered to be structural; whereas, others are assumed to arise as a result of a sampling process. Conceptually, it is hard to imagine even the healthiest individuals in the Ontario population not being "at risk" for an emergency department visit and hence representing a structural zero. As a result, even though the ZIP and ZINB models may fit our data well, a more parsimonious explanation of the phenomena under investigation can be derived using a hurdle modeling approach.i. Further, the probability of a non-zero occurrence is given by (1 - ψi). Therefore, with probability (1 - ψi) we will observe a positive integer count from either a truncated Poisson or negative binomial density . In a mathematically more precise manner, Mullahy [Hurdle models account for excess zeroes but are specified and interpreted slightly differently than the ZIP and ZINB models discussed above. The hurdle regression approach was introduced by Mullahy  and inco Mullahy  illustrai and γ represent the respective variables and coefficients associated with the zero/non-zero (hurdle) process. The xi and β are the respective variables and coefficients associated with the count process. Lastly, the fzero(·) function represents either a Bernoulli or right censored count density; whereas, the fcount(·) function typically represents a left truncated Poisson or negative binomial density.Above, zi) of a zero versus a non-zero (1 - ψi) count, coupled with a left truncated Poisson density (with mean μi) for the count process, then our overall hurdle Poisson (HP) density looks as follows:If we purport to use a Bernoulli density to model the probability (ψi) of a zero versus a non-zero (1 - ψi) count, coupled with a left truncated negative binomial density  for the count process, then our overall hurdle negative binomial (HNB) density looks as follows:Similarly, if we purport to use a Bernoulli density to model the probability  < Cα) = 1- α be a critical threshold V is less than -Cα evidence exists which favors the second model relative to the first. Conversely, if V is greater than Cα then evidence exists which favors the first model relative to the second. Finally, if V if less than or equal to Cα and greater than or equal to -Cα then weak evidence exists, and we cannot decisively determine which model is favored over the other.The Vuong statistic is asymptotically distributed as a N variable. Calculating a normal based random confidence interval can be used to assess whether model 2 is favored over model 1, whether model 1 is favored over model 2, or whether insufficient evidence exists to claim either model is favored over the other . MathemaAll statistical computation was carried out using SAS version 9.2 . For all regression modeling we used Proc NLMIXED, specifying the likelihood equations, as shown above, and maximizing them directly using numerical methods. Maximization began from various starting points and the final gradient vectors and hessian matrices were investigated to ensure proper convergence of estimated model parameters.Descriptive statistics for our sample are presented in Table Emergency department utilization was determined for each respondent, one year following their respective CCHS 2.1 and 3.1 interview dates. A summary of the respondents utilization patterns was stratified by triage scale, with triage scale rankings 1-3 collapsed into a single category (high severity) and triage scale rankings 4-5 collapsed into a separate category (low severity). Emergency department utilization rates were recorded for both CCHS cycles 2.1 and 3.1 and presented in Table Rate ratios, odds ratios (where applicable), and 95% confidence intervals for the six count regression models applied to triage scale 1-3 and triage scale 4-5 emergency department visits, from combined CCHS cycles 2.1 and 3.1 data, are presented in Table Similarly, when the Vuong test is applied to the combined CCHS cycle 2.1 and 3.1 dataset, stratified by low severity  emergency department visits, the results suggest that the HNB model is a good fit for these data Table . ResultsPoisson regression is a commonly employed method for analyzing count data. Our results illustrate that the Poisson regression model is a candidate model for analyzing the number of emergency department visits observed in the CCHS 2.1 and 3.1 datasets; however, alternative methodologies exist which may yield better fits to the observed data. Extra variation in the count data can be handled by extensions to the familiar Poisson model or by using a NB regression approach. Health utilization data, such as the number of emergency department visits made by an individual during a fixed window of follow-up time, are typically characterized by a large proportion of zeroes, representing those individuals who exhibit zero demand for the service during the study interval. Further, some individuals exhibit large demand for emergency department services, resulting in an empirical distribution of counts with a long right tail and extra-Poisson variation. Modified Poisson and NB regression models are able to deal with both extra variation (overdispersion) and the excess of zeros which are typically observed in medical utilization data. The HNB model is an extension of the NB model  and is a natural choice for modeling data that exhibit both extra variation and excess of zeros, especially when zeros are structural. Although the NB regression model fits these data well, and has fewer estimated parameters than the HNB model, we tend to favor the slightly more complex hurdle model. The theoretical framework of the HNB model is an ideal choice for modeling medical utilization data as it allows researchers to simultaneously interpret the factors which influence the odds of using the medical service and the rate/intensity at which utilization occurs in those who do exhibit positive demand for the service.Our results demonstrate the suitability of both the NB model and the HNB model for analyzing emergency department demand in the CCHS cycle 2.1 and 3.1 datasets. As an aside the ZINB model also fit these data well; however, the zeroes in this model are a mix from the Bernoulli component of the model and the count component of the model, and hence interpretation is not as simple. The Vuong test, which is designed for comparing non-nested regression models, suggests the HNB model is the most appropriate approach to modeling emergency department demand in this study.The impact of covariates on the odds of visiting the emergency department for a less severe visit  versus a more severe visits  are quite different. For example, gender is not associated with the likelihood of emergency department utilization in the analysis characterized by less severe visits. However, male gender is statistically significantly associated with increased odds of at least one emergency department visit in the analysis stratified by more severe cases. This result indicates the importance of stratifying our analyses according to the severity of the triage scale, as the factors influencing the emergency department utilization may vary as a function of the severity of a cases initial presentation.The impact of access to a primary care physician on emergency department utilization rates is an interesting finding in our analysis. Once again, the impact of this covariate differs according to the severity of presentation. For more severe cases , having access to a family doctor did not influence the odds of emergency department utilization, nor did it impact the rate of utilization in those who demonstrated positive demand for the service over the study interval. For less severe emergency department visits  we estimate that having access to a primary care provider significantly reduces the likelihood (OR = 0.69) of a visit. Further, given that a visit occurs, the rate of utilization is also significantly lower in those with access to a primary care provider (RR = 0.57). From a policy perspective, this finding suggests that having access to a primary care provider has the opportunity to reduce more than 40 percent of less urgent emergency department visits. Hence, strategies to increase the supply/access to primary health care professionals may result in reduced demand for emergency department services and fewer issues related to crowding, wait times and variable quality of care in Ontario's emergency departments.To our knowledge this study is a unique population based Canadian study, which links a large national survey to provincial health utilization databases to assess the impact of individual level characteristics on the emergency department demand. Our sample size is large and outcome measures are complete. Results of this study are based on regression models that are theoretically appropriate and statistically had the best fit compared to other potential models which were investigated. Some of the findings of this study have important policy implications and if adopted may result in reducing the number of less urgent emergency department visits that are occurring in Ontario.One limitation of our study is that we did not examine the impact of contextual factors, such as: accessibility to nearby walk in clinics, the number of primary care providers in a respondents' census tract or postal code region or the distance to nearest emergency department at the area level. Nor did we stratify our analyses according to other pertinent factors, such as: the day of the week (weekday versus weekend) or the time of the day. An advanced multi-level modeling framework can be extended to the HNB regression model fit to these data to assess the impact of contextual factors on the likelihood and intensity of emergency department visits when the impact of individual level characteristics are adjusted for. Similar methodological approaches can be adopted for stratified analyses.Demand for emergency department services can be appropriately modeled using simple extensions to count based regression models, such as the HNB model. This model simultaneously accounts for excess zeroes, a skewed empirical distribution (extra-variation) and unobserved heterogeneity that is common in medical demand data. Additionally, the two component interpretation of the hurdle models makes them ideal for understanding factors which affect those who experience no demand for emergency department services versus those persons that experience positive demand for emergency department services.This analysis also revealed that the factors which influence the likelihood and intensity of emergency department services vary according to the severity of initial presentation. Some important factors that differed between the two stratified analyses were access to a primary care physician and urban-versus-rural residence. While access to a primary care physician was an irrelevant factor on both the odds and intensity of emergency department utilization in high severity cases, this factor was a statistically significant predictor of the likelihood and rate of emergency department services in low severity cases. Our findings suggest that access to a primary care physician could reduce the odds of a low severity emergency department visit by approximately 31% and further reduce the rate of low severity emergency department visits by approximately 43%. This suggests that re-structuring health care services in Ontario, such that access to primary care physicians is enhanced, may result in a reduced number of low severity cases presenting in the emergency department.The authors declare that they have no competing interests.RM performed the analysis and interpreted the results. RM and CM drafted the paper. MA and BZ cut the data. RM and RHG conceptualized the research. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-227X/11/13/prepub


To study 1-year effectiveness of an intensive, culturally targeted lifestyle intervention in general practice for weight status and metabolic profile of South-Asians at risk of type 2 diabetes.536 South-Asians at risk of type 2 diabetes were randomized to an intervention (n = 283) or control (n = 253) group. The intervention, which was targeted culturally to the South-Asian population, consisted of individual lifestyle counselling, a family session, cooking classes, and supervised physical activity programme. All components of the intervention were carried out by professionals as part of their daily clinical practice. The control group received generic lifestyle advice. Change in weight status and metabolic profile were assessed after 1 year.After 1 year, 201 participants were lost to follow-up. Remaining participants in intervention (n = 177) and control (n = 158) group had similar baseline characteristics. Weight loss in the intervention group was 0.2±3.3 kg, weight gain in the control group was 0.4±3.1 kg (p = 0.08). Changes in other weight-related measurements did not differ significantly between groups. Furthermore, there were no differences between groups in changes of metabolic profile. All results remained similar after repeating analyses in a multiple imputed dataset.An intensive, culturally targeted, lifestyle intervention of 1 year did not improve weight status and metabolic profile of South-Asians at risk of type 2 diabetes. The laborious recruitment, high drop-out, and lack of effectiveness emphasise the difficulty of realising health benefits in practice and suggest that this strategy might not be the optimal approach for this population.NTR1499Nederlands Trial Register  The prevalence of obesity and its related diseases has grown to epidemic proportions in the last few decades; at this point, type 2 diabetes mellitus has become one of the most common chronic diseases in industrialised countries Several studies have shown that the prevalence and incidence of type 2 diabetes and its associated metabolic risk factors vary between ethnic groups; in particular, people of South Asian origin living in industrialised countries are disproportionally affected The opportunity to reduce obesity and to prevent type 2 diabetes among high-risk individuals through intensive lifestyle intervention has been established in several efficacy trials Therefore, the aim of this study was to determine the effectiveness after 1 year of an intensive lifestyle intervention, which was culturally targeted to the population, on the weight status and metabolic profile  of 18 to 60-year-old South Asians at risk of type 2 diabetes  registered in general practices in The Hague, Netherlands.The protocol for this trial and supporting CONSORT checklist are available as supporting information; see The Institutional Review Board of the Academic Medical Center , Academic Medical Center, Amsterdam) approved the study. All participants provided oral and written informed consent.All those studied were participants of the DHIAAN study, a randomized controlled trial  in general practice. The aim was to study the effectiveness of a culturally targeted, intensive, lifestyle intervention to prevent type 2 diabetes and cardiovascular risk factors among Hindustani Surinamese The term ‘Hindustani-Surinamese’ refers to people of South Asian ancestral origin and their offspring who migrated to the Netherlands via Suriname. The Hindustani Surinamese are the descendants of the labourers from North India – Uttar Pradesh, Uttaranchal, and West Bihar – who were indentured between 1873 and 1917. The two large migration waves of Hindustani Surinamese to the Netherlands were caused mainly by the political situation in Suriname. The first wave took place at the time of the independence of Suriname in 1975, and the second wave, at the time of Desi Bouterse's coup in February 1980 The detailed methods of this study and the process of adapting the lifestyle intervention for the socio-cultural and socio-cognitive determinants of Hindustani Surinamese have been described earlier We asked all screening participants to fill out a brief questionnaire, undergo a physical exam, and provide a fasting blood sample. The 968 participants who were invited and screened between 18 May 2009 and 18 April 2010 also took an oral glucose tolerance test (75 g).We invited the screening participants to participate in the trial if they had an impaired fasting glucose (5.7–6.9 mmol/l), an impaired glucose tolerance (2-h post-load glucose of 7.8–10.9 mmol/l), a glycated haemoglobin level of 42–46 mmol/mol, and/or a value of 2.39 or more for the homeostasis model assessment of estimated insulin resistance (HOMA-IR) We offered all participants in the intervention group a lifestyle intervention. The design of this intervention was in line with the design of the proven efficacious intervention used in the Study on lifestyle intervention and impaired glucose tolerance Maastricht (SLIM), which aimed to evaluate the effect on glucose tolerance in a European Dutch population We used motivational interviewing to base the culturally appropriate, intensive, lifestyle intervention on individual lifestyle counselling. The lifestyle counselling in our study consisted of six to eight sessions  in the first 6 months, followed by two booster sessions in the next 6 months, and this counselling aimed at both diet and physical activity. The counsellors were trained dieticians who were familiar with the Hindustani Surinamese culture and dietary habits. We offered the participants a family session with the dietician to decrease the social pressure to eat unhealthily and to increase the social support for a healthful lifestyle within the family. If desired, participants could also attend two cooking classes to improve their skills in how to adjust traditional dishes to make them more healthful.Furthermore, we offered to supervise a 20-week physical activity programme for all participants in the intervention group. This programme, “exercise on prescription”, has been described elsewhere We invited the participants in the control groups to join two group sessions led by student dieticians (at baseline and after 1 year). The sessions provided generic information about type 2 diabetes and discussed current guidelines for diet and physical activity. These participants received two flyers (at 3 and 9 months) with simple generic lifestyle advice.One trial visit for the intervention group and one for the control group was planned at baseline and after 1 year A trained interviewer interviewed each participant face to face during the visit. The interviewer noted the self-identified ethnicity and determined the educational level. The categories of educational level were: low – secondary education, primary education, or less; middle – low vocational training, lower secondary education, intermediate vocational training, and higher secondary education; and high – higher vocational training or university. Trained research staff, who were blinded to the allocation of participants to the intervention or control groups, used a standardised protocol for physical examinations. They measured weight on a mechanical scale  to the nearest 500 g. Height, waist circumference, and hip circumference were measured to the nearest 0.01 m. Body fat was measured to the nearest 0.1% by means of bioelectrical impedance analysis . All anthropometric measurements were obtained twice and the means were used for analysis. Blood pressure was measured in the seated position , up to five times. We calculated the mean from the first two measurements with less than 5 mm Hg difference in both systolic blood pressure and diastolic blood pressure Both at baseline and after 1 year, all participants provided a fasting blood sample and were offered an oral glucose tolerance test. Measurements of fasting plasma glucose , 2-h post-load glucose [75 gram] , insulin , HbA1c (high-performance liquid chromatography), total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides  were carried out according to a standardised protocol at the General Practice Laboratory Foundation, Etten-Leur, Netherlands. HOMA-IR was calculated as (fasting plasma glucose * fasting plasma insulin)/22.5 At the time of registration of the trial, the intended primary outcome was the 3-year incidence of type 2 diabetes. However, as the initial response rate was lower than expected, the recruitment period had to be extended to 2 years. Due to a fixed end date of the study (grant restrictions), the follow-up time was reduced to 2 years. As this period was too short to properly investigate differences in incidence of type 2 diabetes between the control and intervention group, we changed the primary outcome to the more proximal outcomes, namely changes in weight and other weight-related measurements  after 1 year. Secondary outcomes were changes in glucose metabolism, blood pressure, and lipid profile after 1 year. The changes of the primary outcomes of the trial were approved by the Ethics Committee of the Academic Medical Center In the current analysis, we included all those who participated both in the baseline measurement and the follow-up measurement after 1 year, but we excluded anyone without weight data . Of the t-tests and chi-square tests.We also determined whether those who remained in the intervention and control groups had similar baseline characteristics. We described the characteristics with means (with standard deviations) or percentages, and we tested for differences between groups using independent sample t-tests to compare these outcomes at baseline and after 1 year in both the intervention group and the control group. In order to determine the effectiveness of the intervention, we compared the changes in these measurements between those assigned to the intervention group and the control group at baseline. We used independent sample t-tests to study the differences in continuous measures and chi-square tests to compare the differences in categorical measures. We verified the consistency of the findings across age and sex groups. Additionally, we repeated the main analysis comparing the changes in the intervention versus the changes in the control group with non-parametric tests.Then we described the 1-year changes in weight status, other anthropometric measurements, blood pressure, lipid profile, and glucose metabolism. We used paired We carried out several additional analyses. First, to determine whether potential dependencies influenced our results between participants belonging to the same general practice, we used a two-level linear regression model in which participants (level 1) were nested within general practices (level 2). To allow for dependencies, we incorporated a random intercept (level 2) into the model. We then determined whether the estimates for the fixed effects in these analyses were similar to the results of the original analyses, and we graphically investigated the random components of the model. Given that there were only 14 people with family members in the study who had follow-up data available, we refrained from carrying out a similar multilevel analysis with the family data.Moreover, in order to verify the robustness of our results after the loss to follow-up, we repeated the main analyses after using multiple imputation to replace the missing values for the changes after 1 year in weight status, blood pressure, lipid profile, and glucose metabolism within the intervention group. First, we described the participation in the different components of the intervention. Second, we compared differences in the change in weight status and the metabolic profile between those attending lifestyle counselling (≥2 sessions attended on top of an intake visit) and those not attending, and between those maintaining participation (≥6 sessions attended on top of an intake visit) and those not maintaining participation. We also distinguished between those who had participated in at least 2 components or in all components of the intervention with those who had not. We based these categories on the goals specified per session in the protocol of the intervention. Finally, we conducted several explanatory analyses to further examine potential factors associated with the effects of the intervention after 1 year We used SPSS 19.0  for all analyses.The original power calculation has already been described The mean age of the total population was 45.1 (SD 9.9) years, 50.7% were men, and 11.5% had a low level of education . The meaThe intervention group had a mean weight loss of 0.2 kg after 1 year (p = 0.32), whereas those in the control group gained 0.4 kg .Finally, the explanatory analyses in the intervention group showed that 81% had participated in the individual lifestyle counselling sessions, with a median number of 5 sessions (IQR 1−9) per person . Moreover, 26% participated in a supplemental family session, 26% in the cooking classes and 22% in the supervised exercise sessions. We did not find differences in the change of any metabolic risk factors between those who attended or maintained lifestyle counselling and those who did not . In addiWe investigated the effectiveness of an intensive, culturally targeted lifestyle intervention in general practice among 18 to 60-year-old Hindustani Surinamese, a population of South Asian origin with a high risk of type 2 diabetes, living in the Netherlands. We found that the lifestyle intervention did not effectively change the weight status, glucose metabolism, blood pressure, or lipid profile in the target population. After 1 year of follow-up, the metabolic risk factors of the intervention and control groups were similar. The attendance and maintenance of the intervention did not explain the lack of change in weight and metabolic profile in the intervention group.This study has both strengths and limitations that we wish to mention before discussing our main findings. One strength of the study is that it is unique in terms of population , age (young population), and the culturally targeted design of the intervention In spite of the intensive invitation procedures, there was a high loss to follow-up (197 of the 536 participants). This drop-out emphasizes the difficulty of realising health gain by means of a lifestyle intervention in general practice. At the same time, we consider this a limitation of our study. Although those lost to follow-up and those with follow-up data available after 1 year had similar baseline characteristics and loss to follow-up was not associated with group allocation, we cannot rule out selection bias in our study. We attempted to account for the effects of the loss to follow-up by repeating our analyses in a multiply imputed dataset. These analyses did not change our interpretation of the results.Another limitation was that we measured weight to the nearest 500 g, which influenced the precision of the reported weight and weight change after 1 year. However, this applies equally to the measurements taken in the intervention group and the control group, so that it is unlikely that this limitation has majorly influenced the reported difference in change of weight between the two groups.We found that a culturally targeted intensive lifestyle intervention in general practice did not effectively change the metabolic risk factors among our Hindustani Surinamese study population after 1 year of follow-up. Our results are in line with results from studies that have investigated the effectiveness in clinical practice of lifestyle interventions for the prevention of type 2 diabetes among other ethnic populations The results of our study pinpoint that, despite the potential efficacy among South Asians We do wish to mention that, although the lifestyle intervention was not effective in our overall population, we did find that participants who achieved a relatively large weight loss after 1 year (i.e. those within the highest quartile of weight loss), regardless whether they had been assigned to the control group or the intervention group, showed more improvement in lipid profile and glucose metabolism than participants in the lowest quartile of weight loss Click here for additional data file.Protocol S1Description of full study protocol in English.(PDF)Click here for additional data file.Protocol S2Full trial protocol after changes in design of study .(DOC)Click here for additional data file.Protocol S3Full trial protocol before start study .(DOC)Click here for additional data file.Protocol S4Copy trial protocol approval by ethics committee before changes to the protocol part 1.(PDF)Click here for additional data file.Protocol S5Copy trial protocol approval by ethics committee before changes to the protocol part 2.(PDF)Click here for additional data file.Protocol S6Copy trial protocol approval by ethics committee after changes to the protocol part 1.(PDF)Click here for additional data file.Protocol S7Copy trial protocol approval by ethics committee after changes to the protocol part 2.(PDF)Click here for additional data file.Table S1Difference in baseline characteristics between participants with and without follow-up data available after 1year.(DOC)Click here for additional data file.Table S2Comparison of change in metabolic parameters between those with high and low weight loss, defined by the highest versus lowest quartiles of weight change.(DOC)Click here for additional data file.