New horizons in the analysis of hospital readmissions of older adults.

This short communication highlights analytical methods that can be usefully applied to the problem of hospital readmissions of older adults. The limitations of the models currently used in studies of hospital readmissions are described. In summary, analyses of hospital readmissions face two important methodological and statistical problems not accounted for by these currently used statistical models: the potential recurrence of readmissions, and death, a terminal event which absorbs the readmission process. Not addressing the issue raised by recurrent events and terminal event generates biased estimates. We discuss an approach for the analysis of hospital readmission risk and death in the same framework. Understanding the features of this kind of approaches is essential at a time when high-quality data on hospital readmission in older patients are becoming available to a large number of researchers. Models adapted for the analysis of recurrent and terminal events are presented, and their application to studies of hospital readmission are explained, with reference to two cohorts of several thousand older individuals.

The Impacts of COVID-19 Pandemic on the Food Sector and on Supermarket Employees in France during the First Lockdown Period.

During the first lockdown period due to the COVID-19 pandemic, from the 17 March 2020 to the 11 May 2020 in France, essential professionals (nursing staff, police officers, supermarket staff, etc.) continued to be physically present at their workplaces. The present study focuses on exploring impacts of the pandemic on supermarket staff and on the food sector in France: COVID transmission among supermarket workers, working conditions, food supply, etc. For that, two anonymous surveys were addressed to supermarket employees and to supermarket supervisors. In total, 1746 responses from employees and 171 responses from supervisors were recorded all over France. Over 70% of employees and almost 50% of supervisors were women and over 50% of employees were between 25 and 40 years old. The following main trends in terms of physical and psychological impacts are revealed: 7% of employees working during the lockdown reported having COVID, although a still poorly developed screening and lack of diagnostic tests during the first lockdown should be kept in mind. The working conditions changed; higher work load, a more stressful environment, inappropriate client attitude, a lack of recognition, fatigue, and shortages were reported. A lack of government recognition, namely no prime allocations to supermarket staff during the lockdown period, is also often mentioned. Finally, no priority was given for store employees in terms of childcare.

Risk Factors for Hospital Readmission and Death After Discharge of Older Adults from Acute Geriatric Units: Taking the Rank of Admission into Account.

OBJECTIVE: To analyze the impact of the number of hospital readmissions on the risks of further hospital readmission and death after adjustment for a range of risk factors. METHODS: We performed a multicentre prospective study of the DAMAGE cohort in the Hauts-de-France region of France. Patients aged 75 and over hospitalized initially in an acute geriatric unit (AGU) were included and followed up for 12 months. The risk of hospital readmission was analyzed using a Cox model, and its extension for recurrent events and the risk of death were analyzed using a Cox model for time-dependent variables. RESULTS: A total of 3081 patients were included (mean (SD) age: 86.4 (5.5)). In the multivariate analysis, the relative risk (95% confidence interval [CI]) of hospital readmission rose progressively to 2.66 (1.44; 5.14), and the risk of death rose to 2.01 (1.23; 3.32) after five hospital admissions, relative to a patient with no hospital readmissions. The number of hospital readmissions during the follow-up period was the primary risk factor and the best predictor of the risk of hospital readmission and the risk of death. CONCLUSION: Hospital readmission is the primary risk factor for further hospital readmissions and for death in older subjects discharged from an AGU.

Joint latent class model: Simulation study of model properties and application to amyotrophic lateral sclerosis disease.

BACKGROUND: In many clinical applications, evolution of a longitudinal marker is censored by an event occurrence, and, symmetrically, event occurrence can be influenced by the longitudinal marker evolution. In such frameworks joint modeling is of high interest. The Joint Latent Class Model (JLCM) allows to stratify the population into groups (classes) of patients that are homogeneous both with respect to the evolution of a longitudinal marker and to the occurrence of an event; this model is widely employed in real-life applications. However, the finite sample-size properties of this model remain poorly explored. METHODS: In the present paper, a simulation study is carried out to assess the impact of the number of individuals, of the censoring rate and of the degree of class separation on the finite sample size properties of the JLCM. A real-life application from the neurology domain is also presented. This study assesses the precision of class membership prediction and the impact of covariates omission on the model parameter estimates. RESULTS: Simulation study reveals some departures from normality of the model for survival sub-model parameters. The censoring rate and the number of individuals impact the relative bias of parameters, especially when the classes are weakly distinguished. In real-data application the observed heterogeneity on individual profiles in terms of a longitudinal marker evolution and of the event occurrence remains after adjusting to clinically relevant and available covariates; CONCLUSION: The JLCM properties have been evaluated. We have illustrated the discovery in practice and highlights the usefulness of the joint models with latent classes in this kind of data even with pre-specified factors. We made some recommendations for the use of this model and for future research.

Importance of previous hospital stays on the risk of hospital re-admission in older adults: a real-life analysis of the PAERPA study population.

BACKGROUND: consideration of the first hospital re-admission only and failure to take account of previous hospital stays, which are the two significant limitations when studying risk factors for hospital re-admission. The objective of the study was to use appropriate statistical models to analyse the impact of previous hospital stays on the risk of hospital re-admission among older patients. METHODS: an exhaustive analysis of hospital discharge and health insurance data for a cohort of patients participating in the PAERPA ('Care Pathways for Elderly People at Risk of Loss of Personal Independence') project in the Hauts de France region of France. All patients aged 75 or over were included. All data on hospital re-admissions via the emergency department were extracted. The risk of unplanned hospital re-admission was estimated by applying a semiparametric frailty model, the risk of death by applying a time-dependent semiparametric Cox regression model. RESULTS: a total of 24,500 patients (median [interquartile range] age: 81 [77-85]) were included between 1 January 2015 and 31 December 2017. In a multivariate analysis, the relative risk (95% confidence interval [CI]) of hospital re-admission rose progressively from 1.8 (1.7-1.9) after one previous hospital stay to 3.0 (2.6-3.5) after five previous hospital stays. The relative risk [95%CI] of death rose slowly from 1.1 (1.07-1.11) after one previous hospital stay to 1.3 (1.1-1.5) after five previous hospital stays. CONCLUSION: analyses of the risk of hospital re-admission in older adults must take account of the number of previous hospital stays. The risk of death should also be analysed.