Pattern of SARS-CoV-2 infection among dependant elderly residents living in long-term care facilities in Marseille, France, March-June 2020.

OBJECTIVES: This study aimed to report the results of SARS-CoV-2 PCR-based screening campaigns conducted on dependent elderly residents (compared with staff members) in long-term care facilities (LTCFs) in Marseille, France, and the follow-up of positive cases. METHODS: Data from 1691 elderly residents and 1000 members of staff were retrospectively collected through interviewing the medical teams in 24 LTCFs and using the hospitals' electronic health recording systems. RESULTS: Elderly residents were predominantly female (64.8%) with a mean age of 83.0 years. SARS-CoV-2 detection among residents (226, 13.4%) was significantly higher than among staff members (87, 8.7%) (P < 0.001). Of the 226 infected residents, 37 (16.4%) were detected on a case-by-case basis due to their COVID-19 symptoms and 189 (83.6%) were detected through mass screening. Most (77.0%) had possible COVID-19 symptoms, including respiratory symptoms and signs (44.5%) and fever (46.5%); 23.0% were asymptomatic. A total of 116 (51.4%) patients received a course of oral hydroxychloroquine and azithromycin (HCQ-AZM) for ≥ 3 days; 47 (20.8%) died. Through multivariate analysis, the death rate was positively associated with being male (30.7% vs. 14.0%, OR = 3.95, P = 0.002), aged > 85 years (26.1% vs. 15.6%, OR = 2.43, P = 0.041) and receiving oxygen therapy (39.0% vs. 12.9%, OR = 5.16, P < 0.001) and negatively associated with being diagnosed through mass screening (16.9% vs. 40.5%, OR = 0.20, P= 0.001) and receiving HCQ-AZM treatment ≥ 3 days (15.5% vs. 26.4%, OR = 0.37, P = 0.02). CONCLUSION: The high proportion of asymptomatic COVID-19 patients and independent factors for mortality suggest that early diagnosis and treatment of COVID-19 patients in LTCFs may be effective in saving lives.

Predictive risk score for unplanned 30-day rehospitalizations in the French universal health care system based on a medico-administrative database.

BACKGROUND: Reducing unplanned rehospitalizations is one of the priorities of health care policies in France and other Western countries. An easy-to-use algorithm for identifying patients at higher risk of rehospitalizations would help clinicians prioritize actions and care concerning discharge transitions. Our objective was to develop a predictive unplanned 30-day all-cause rehospitalization risk score based on the French hospital medico-administrative database. METHODS: This was a retrospective cohort study of all 2015 discharges from acute-care inpatient hospitalizations in a tertiary-care university center comprising four hospitals. The study endpoint was unplanned 30-day all-cause rehospitalization via emergency departments, and we collected sociodemographic, clinical, and hospital characteristics based on hospitalization database computed for reimbursement of fees. We derived a predictive rehospitalization risk score using a split-sample design and multivariate logistic regression, and we compared the discriminative properties with the LACE index risk-score. RESULT: Our analysis included 118,650 hospitalizations, of which 4,127 (3.5%) led to rehospitalizations via emergency departments. Variables independently associated with rehospitalization were age, gender, state-funded medical assistance, as well as disease category and severity, Charlson comorbidity index, hospitalization via emergency departments, length of stay (LOS), and previous hospitalizations 6 months before. The predictive rehospitalization risk score yielded satisfactory discriminant properties (C statistic: 0.74) exceeding the LACE index (0.66). CONCLUSION: Our findings indicate that the possibility of unplanned rehospitalization remains high for some patient characteristics, indicating that targeted interventions could be beneficial for patients at the greatest risk. We developed an easy-to-use predictive rehospitalization risk-score of unplanned 30-day all-cause rehospitalizations with satisfactory discriminant properties. Future works should, however, explore if other data from electronic medical records and other databases could improve the accuracy of our predictive rehospitalization risk score based on medico-administrative data.