Engorgement des centres d'urgences: une raison légitime de refuser l'accès aux patients non urgents [Emergency department overcrowding: a legitimate reason to refuse access to urgent care for non-urgent patients?].

Non-urgent cases represent 30-40% of all ED consults; they contribute to overcrowding of emergency departments (ED), which could be reduced if they were denied emergency care. However, no triage instrument has demonstrated a high enough degree of accuracy to safely rule out serious medical conditions: patients suffering from life-threatening emergencies have been inappropriately denied care. Insurance companies have instituted financial penalties to discourage the use of ED as a source of non-urgent care, but this practice mainly restricts access for the underprivileged. More recent data suggest that in fact most patients consult for appropriate urgent reasons, or have no alternate access to urgent care. The safe reduction of overcrowding requires a reform of the healthcare system based on patients' needs rather than access barriers.

Mapping Bias Overestimates Reference Allele Frequencies at the HLA Genes in the 1000 Genomes Project Phase I Data.

Next-generation sequencing (NGS) technologies have become the standard for data generation in studies of population genomics, as the 1000 Genomes Project (1000G). However, these techniques are known to be problematic when applied to highly polymorphic genomic regions, such as the human leukocyte antigen (HLA) genes. Because accurate genotype calls and allele frequency estimations are crucial to population genomics analyses, it is important to assess the reliability of NGS data. Here, we evaluate the reliability of genotype calls and allele frequency estimates of the single-nucleotide polymorphisms (SNPs) reported by 1000G (phase I) at five HLA genes (HLA-A, -B, -C, -DRB1, and -DQB1). We take advantage of the availability of HLA Sanger sequencing of 930 of the 1092 1000G samples and use this as a gold standard to benchmark the 1000G data. We document that 18.6% of SNP genotype calls in HLA genes are incorrect and that allele frequencies are estimated with an error greater than ±0.1 at approximately 25% of the SNPs in HLA genes. We found a bias toward overestimation of reference allele frequency for the 1000G data, indicating mapping bias is an important cause of error in frequency estimation in this dataset. We provide a list of sites that have poor allele frequency estimates and discuss the outcomes of including those sites in different kinds of analyses. Because the HLA region is the most polymorphic in the human genome, our results provide insights into the challenges of using of NGS data at other genomic regions of high diversity.