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Abstract Background A previous study reported significant excess mortality among non-COVID-19 patients due to disrupted surgical care caused by resource prioritization for COVID-19 cases in France. The primary objective was to investigate if a similar impact occurred for medical conditions and determine the effect of hospital saturation on non-COVID-19 hospital mortality during the first year of the pandemic in France. Methods We conducted a nationwide population-based cohort study including all adult patients hospitalized for non-COVID-19 acute medical conditions in France between March 1, 2020 and 31 May, 2020 (1st wave) and September 1, 2020 and December 31, 2020 (2nd wave). Hospital saturation was categorized into four levels based on weekly bed occupancy for COVID-19: no saturation ( 5% and ≤ 15%), moderate saturation (> 15% and ≤ 30%), and high saturation (> 30%). Multivariate generalized linear model analyzed the association between hospital saturation and mortality with adjustment for age, sex, COVID-19 wave, Charlson Comorbidity Index, case-mix, source of hospital admission, ICU admission, category of hospital and region of residence. Results A total of 2,264,871 adult patients were hospitalized for acute medical conditions. In the multivariate analysis, the hospital mortality was significantly higher in low saturated hospitals (adjusted Odds Ratio/aOR = 1.05, 95% CI [1.34–1.07], P

ABSTRACTIntroduction: Prolonged Hospital Length of Stay (PLOS) is an indicator of deteriorated efficiency in Quality of Care. One goal of public health management is to reduce PLOS by identifying its most relevant predictors. The objective of this study is to explore Machine Learning (ML) models that best predict PLOS.Methods: Our dataset was collected from the French Medico-Administrative database (PMSI) as a retrospective cohort study of all discharges in the year 2015 from a large university hospital in France (APHM). The study outcomes were LOS transformed into a binary variable (long vs. short LOS) according to the 90th percentile (14 days). Logistic regression (LR), classification and regression trees (CART), random forest (RF), gradient boosting (GB) and neural networks (NN) were applied to the collected data. The predictive performance of the models was evaluated using the area under the ROC curve (AUC).Results: Our analysis included 73,182 hospitalizations, of which 7,341 (10.0%) led to PLOS. The GB classifier was the most performant model with the highest AUC (0.810), superior to all the other models (all p-values

Summary: Background: Patients with schizophrenia represent a vulnerable, underserved, and undertreated population who have been neglected in health disparities work. Understanding of end-of-life care in patients with schizophrenia and cancer is poor. We aimed to establish whether end-of-life care delivered to patients with schizophrenia and cancer differed from that delivered to patients with cancer who do not have diagnosed mental illness. Methods: We did a population-based cohort study of all patients older than 15 years who had a diagnosis of advanced cancer and who died in hospital in France between Jan 1, 2013, and Dec 31, 2016. We divided this population into cases (ie, patients with schizophrenia) and controls (ie, patients without a diagnosis of mental illness) and compared access to palliative care and indicators of high-intensity end-of-life care between groups. In addition to unmatched analyses, we also did matched analyses (matched in terms of age at death, sex, and site of primary cancer) between patients with schizophrenia and matched controls (1:4). Multivariable generalised linear models were done with adjustment for social deprivation, year of death, time from cancer diagnosis to death, metastases, comorbidity, and hospital type (ie, specialist cancer centre vs non-specialist centre). Findings: The main analysis included 2481 patients with schizophrenia and 222 477 controls. The matched analyses included 2477 patients with schizophrenia and 9896 controls. Patients with schizophrenia were more likely to receive palliative care in the last 31 days of life (adjusted odds ratio 1·61 [95% CI 1·45–1·80]; p