Missed Opportunities: Evolution of Patients Leaving without Being Seen or against Medical Advice during a Six-Year Period in a Swiss Tertiary Hospital Emergency Department.

Aim. The study aimed at describing the evolution over a 6-year period of patients leaving the emergency department (ED) before being seen ("left without being seen" or LWBS) or against medical advice ("left against medical advice" or LAMA) and at describing their characteristics. Methods. A retrospective database analysis of all adult patients who are admitted to the ED, between 2005 and 2010, and who left before being evaluated or against medical advice, in a tertiary university hospital. Results. During the study period, among the 307,716 patients who were registered in the ED, 1,157 LWBS (0.4%) and 1,853 LAMA (0.9%) patients were identified. These proportions remained stable over the period. The patients had an average age of 38.5 ± 15.9 years for LWBS and 41.9 ± 17.4 years for LAMA. The median time spent in the ED before leaving was 102.4 minutes for the LWBS patients and 226 minutes for LAMA patients. The most frequent reason for LAMA was related to the excessive length of stay. Conclusion. The rates of LWBS and LAMA patients were low and remained stable. The patients shared similar characteristics and reasons for leaving were largely related to the length of stay or waiting time.

Multivariate QST-FST comparisons: a neutrality test for the evolution of the g matrix in structured populations.

Neutrality tests in quantitative genetics provide a statistical framework for the detection of selection on polygenic traits in wild populations. However, the existing method based on comparisons of divergence at neutral markers and quantitative traits (Q(st)-F(st)) suffers from several limitations that hinder a clear interpretation of the results with typical empirical designs. In this article, we propose a multivariate extension of this neutrality test based on empirical estimates of the among-populations (D) and within-populations (G) covariance matrices by MANOVA. A simple pattern is expected under neutrality: D = 2F(st)/(1 - F(st))G, so that neutrality implies both proportionality of the two matrices and a specific value of the proportionality coefficient. This pattern is tested using Flury's framework for matrix comparison [common principal-component (CPC) analysis], a well-known tool in G matrix evolution studies. We show the importance of using a Bartlett adjustment of the test for the small sample sizes typically found in empirical studies. We propose a dual test: (i) that the proportionality coefficient is not different from its neutral expectation [2F(st)/(1 - F(st))] and (ii) that the MANOVA estimates of mean square matrices between and among populations are proportional. These two tests combined provide a more stringent test for neutrality than the classic Q(st)-F(st) comparison and avoid several statistical problems. Extensive simulations of realistic empirical designs suggest that these tests correctly detect the expected pattern under neutrality and have enough power to efficiently detect mild to strong selection (homogeneous, heterogeneous, or mixed) when it is occurring on a set of traits. This method also provides a rigorous and quantitative framework for disentangling the effects of different selection regimes and of drift on the evolution of the G matrix. We discuss practical requirements for the proper application of our test in empirical studies and potential extensions.