Primary care utilisation patterns among an urban immigrant population in the Spanish National Health System

<p>Abstract</p> <p>Background</p> <p>There is evidence suggesting that the use of health services is lower among immigrants after adjusting for age and sex. This study takes a step forward to compare primary care (PC) utilisation patterns between immigrants and the native population with regard to their morbidity burden.</p> <p>Methods</p> <p>This retrospective, observational study looked at 69,067 individuals representing the entire population assigned to three urban PC centres in the city of Zaragoza (Aragon, Spain). Poisson models were applied to determine the number of annual PC consultations per individual based on immigration status. All models were first adjusted for age and sex and then for age, sex and case mix (ACG System^®).</p> <p>Results</p> <p>The age and sex adjusted mean number of total annual consultations was lower among the immigrant population (children: IRR = 0.79, p < 0.05; adults: IRR = 0.73, p < 0.05). After adjusting for morbidity burden, this difference decreased among children (IRR = 0.94, p < 0.05) and disappeared among adults (IRR = 1.00). Further analysis considering the PC health service and type of visit revealed higher usage of routine diagnostic tests among immigrant children (IRR = 1.77, p < 0.05) and a higher usage of emergency services among the immigrant adult population (IRR = 1.2, p < 0.05) after adjusting for age, sex and case mix.</p> <p>Conclusions</p> <p>Although immigrants make lower use of PC services than the native population after adjusting the consultation rate for age and sex, these differences decrease significantly when considering their morbidity burden. These results reinforce the 'healthy migration effect' and discount the existence of differences in PC utilisation patterns between the immigrant and native populations in Spain.</p

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Reliability of dissimilarity measures for multi-voxel pattern analysis

Representational similarity analysis of activation patterns has become an increasingly important tool for studying brain representations. The dissimilarity between two patterns is commonly quantified by the correlation distance or the accuracy of a linear classifier. However, there are many different ways to measure pattern dissimilarity and little is known about their relative reliability. Here, we compare the reliability of three classes of dissimilarity measure: classification accuracy, Euclidean/Mahalanobis distance, and Pearson correlation distance. Using simulations and four real functional magnetic resonance imaging (fMRI) datasets, we demonstrate that continuous dissimilarity measures are substantially more reliable than the classification accuracy. The difference in reliability can be explained by two characteristics of classifiers: discretization and susceptibility of the discriminant function to shifts of the pattern ensemble between imaging runs. Reliability can be further improved through multivariate noise normalization for all measures. Finally, unlike conventional distance measures, crossvalidated distances provide unbiased estimates of pattern dissimilarity on a ratio scale, thus providing an interpretable zero point. Overall, our results indicate that the crossvalidated Mahalanobis distance is preferable to both the classification accuracy and the correlation distance for characterizing representational geometries