Inter-regional transfers for pandemic surges were associated with reduced mortality rates

Background The objective of this study was to better understand the factors associated with the heterogeneity of in-hospital COVID-19 morbidity and mortality across France, one of the countries most affected by COVID-19 in the early months of the pandemic. Methods This geo-epidemiological analysis was based on data publicly available on government and administration websites for the 96 administrative departments of metropolitan France between March 19 and May 11, 2020, including Public Health France, the Regional Health Agencies, the French national statistics institute, and the Ministry of Health. Using hierarchical ascendant classification on principal component analysis of multidimensional variables, and multivariate analyses with generalised additive models, we assessed the associations between several factors (spatiotemporal spread of the epidemic between Feb 7 and March 17, 2020, the national lockdown, demographic population structure, baseline intensive care capacities, baseline population health and health-care services, new chloroquine and hydroxychloroquine dispensations, economic indicators, degree of urbanisation, and climate profile) and in-hospital COVID-19 incidence, mortality, and case fatality rates. Incidence rate was defined as the cumulative number of in-hospital COVID-19 cases per 100 000 inhabitants, mortality rate as the cumulative number of in-hospital COVID-19 deaths per 100 000, and case fatality rate as the cumulative number of in-hospital COVID-19 deaths per cumulative number of in-hospital COVID-19 cases. Findings From March 19 to May 11, 2020, hospitals in metropolitan France notified a total of 100 988 COVID-19 cases, including 16 597 people who were admitted to intensive care and 17 062 deaths. There was an overall cumulative in-hospital incidence rate of 155·6 cases per 100 000 inhabitants (range 19·4–489·5), in-hospital mortality rate of 26·3 deaths per 100 000 (1·1–119·2), and in-hospital case fatality rate of 16·9% (4·8–26·2). We found clear spatial heterogeneity of in-hospital COVID-19 incidence and mortality rates, following the spread of the epidemic. After multivariate adjustment, the delay between the first COVID-19-associated death and the onset of the national lockdown was positively associated with in-hospital incidence (adjusted standardised incidence ratio 1·02, 95% CI 1·01–1·04), mortality (adjusted standardised mortality ratio 1·04, 1·02–1·06), and case fatality rates (adjusted standardised fatality ratio 1·01, 1·01–1·02). Mortality and case fatality rates were higher in departments with older populations (adjusted standardised ratio for populations with a high proportion older than aged >85 years 2·17 [95% CI 1·20–3·90] for mortality and 1·43 [1·08–1·88] for case fatality rate). Mortality rate was also associated with incidence rate (1·0004, 1·0002–1·001), but mortality and case fatality rates did not appear to be associated with baseline intensive care capacities. We found no association between climate and in-hospital COVID-19 incidence, or between economic indicators and in-hospital COVID-19 incidence or mortality rates. Interpretation This ecological study highlights the impact of the epidemic spread, national lockdown, and reactive adaptation of intensive care capacities on the spatial distribution of COVID-19 morbidity and mortality. It provides information for future geo-epidemiological analyses and has implications for preparedness and response policies to current and future epidemic waves in France and elsewhere. Funding None.

Dear Editor, The coronavirus disease 2019 (COVID-19) pandemic has challenged hospital organizations worldwide, not only because of the novelty of the disease, but also because of the high volume of patients in need of critical care over a short time period [1]. ICU mortality of COVID-19 patients depends on patient-related and caregiver-related factors in addition to organizational aspects of the unit, where those patients are hospitalized. We sought to identify various organizational factors associated with ICU mortality among COVID-19 patients. We performed a nationwide study based on the medical information system from all public and private hospitals in France. All adults admitted to a French ICU for severe COVID-19 acute respiratory failure, with SAPS II greater than 15 and who received invasive ventilation, between January 1, 2020, and April 26, 2020 were included. The primary outcome was all-cause mortality during the ICU stay. We computed a modified Poisson regression model to estimate the influence on patient mortality of organizational factors including a potential weekend effect (death probability among patients discharged from ICU on Saturday or Sunday compared to other weekdays), hospital location in French regions, and ICU team experience over time (cumulative number of COVID-19 patients already admitted to the ICU) [2]. A total of 9809 patients from 350 hospitals were analyzed, with a median of 17 severe COVID-19 patients (range 1–230) and 4 related deaths (0–97) per ICU. Patients mean age was 63.2 years (SD 11.6), SAPS II was 45.4 (16.9) and ICU length of stay 20.5 days (16.1). Overall, 3069 (31.3%) patients died in ICU. After adjusting for patient-related confounders, the risk of death increased among weekend ICU discharges (relative Risk 1.54, 95% CI 1.45–1.64). Patient mortality was also higher within ICUs located in the Paris (1.62, 1.35–1.94) and Northeast (1.24, 1.02–1.49) regions (Table 1). Table 1 Factors associated with ICU mortality among COVID-19 patients Unadjusted Adjusted Factors Relative risks (95% CI) p value Relative risks (95% CI) p value Day of ICU discharge  Weekend 1.65 (1.54–1.77)  < 0.001 1.54 (1.45–1.64)  < 0.001  Other weekdays 1 Reference 1 Reference ICU location in France  Paris region 1.59 (1.3–1.95)  < 0.001 1.62 (1.35–1.94)  < 0.001  Northeast 1.35 (1.1–1.68) 0.005 1.24 (1.02–1.49) 0.029  Northwest 1.07 (0.83–1.37) 0.604 1.14 (0.93–1.4) 0.194  Southeast 1.28 (1.03–1.58) 0.024 1.11 (0.93–1.33) 0.258  Southwest 1 Reference 1 Reference ICU team experience over time a  Very high [44–229 patients] 0.82 (0.74–0.9)  < 0.001 0.97 (0.86–1.1) 0.664  High [20–43 patients] 0.86 (0.79–0.94) 0.001 0.98 (0.9–1.07) 0.661  Low [8–19 patients] 0.88 (0.81–0.96) 0.004 0.94 (0.87–1.02) 0.147  Very low [0–7 patients] 1 Reference 1 Reference Patient ICU admission date  April 13 to April 26 0.88 (0.76–1.02) 0.092 1.14 (0.97–1.35) 0.113  March 30 to April 12 0.72 (0.65–0.8)  < 0.001 1.01 (0.89–1.15) 0.873  March 16 to March 29 0.81 (0.73–0.9)  < 0.001 1.08 (0.97–1.2) 0.164  January 01 to March 15 1 Reference 1 Reference Patient sex  Male 1.06 (0.99–1.13) 0.080 1.04 (0.98–1.09) 0.229  Female 1 Reference 1 Reference Patient age, year  80+  5.38 (3.62–8)  < 0.001 3.92 (2.96–5.2)  < 0.001  75–79 3.91 (2.64–5.78)  < 0.001 2.77 (2.11–3.64)  < 0.001  70–74 2.96 (2.01–4.35)  < 0.001 2.12 (1.61–2.78)  < 0.001  60–69 2.36 (1.6–3.48)  < 0.001 1.78 (1.37–2.3)  < 0.001  40–59 1.34 (0.92–1.95) 0.127 1.17 (0.91–1.51) 0.218  18–39 1 Reference 1 Reference Patient SAPS II a  Very high [56–120] 3.03 (2.66–3.44)  < 0.001 1.79 (1.6–2.01)  < 0.001  High [43–55] 2.12 (1.87–2.4)  < 0.001 1.39 (1.25–1.55)  < 0.001  Low [33–42] 1.65 (1.46–1.88)  < 0.001 1.27 (1.13–1.42)  < 0.001  Very low [15–32] 1 Reference 1 Reference Charlson comorbidity index  3+  1.36 (1.36–1.51)  < 0.001 1.03 (0.94–1.13) 0.553  2 1.15 (1.15–1.27) 0.010 0.96 (0.89–1.05) 0.403  1 1.3 (1.17–1.43)  < 0.001 1.07 (0.97–1.18) 0.179  0 1 Reference 1 Reference Hemodynamic support  Yes 2.1 (1.84–2.4)  < 0.001 1.60 (1.42–1.8)  < 0.001  No 1 Reference 1 Reference Renal replacement therapy  Yes 2.23 (2.07–2.4)  < 0.001 1.84 (1.72–1.97)  < 0.001  No 1 Reference 1 Reference Patient median household income a , €  Very low [11,726–18,115] 1.19 (1.1–1.28)  < 0.001 1.23 (1.14–1.33)  < 0.001  Low [18,125–20,083] 1.11 (1.01–1.22) 0.025 1.12 (1.03–1.22) 0.009  High [20,083–22,582] 1.08 (0.99–1.18) 0.094 1.11 (1.03–1.2) 0.009  Very high [22,583–43,350] 1 Reference 1 Reference 9809 critically ill COVID-19 patients from 350 hospitals were analyzed. Using modified Poisson regression model (with a robust error variance) accounting for patient clustering within hospitals and for patient related confounders (sex, age, SAPS II, Charlson comorbidity index, hemodynamic support, renal replacement therapy, patient median household income) and the date of patient ICU admission, we estimated adjusted relative risks with their 95% confidence intervals (95% CI) aCategorized into quartiles Three findings result from this large data analysis limited to available medical information that may not always consider all possible confounders accurately. First, weekends were associated with an increased likelihood of patient death at the end of ICU stay. Understaffing frequently occurs during weekends [3] and this result can be interpreted as a lack of available health professionals, given the patients’ needs [4]. Second, excess mortality may arise when healthcare organizations are overwhelmed. Paris and Northeast regions exhibited by far the highest number of severe COVID-19 patients to treat in France and corresponding ICUs appeared to be rapidly saturated [5]. Finally, no learning curve for ICU management of COVID-19 patients was evidenced. A potential explanation is that “practice makes perfect” effect may be counterbalanced by high-volume of admissions leading to excessive workload and surpassing bed capacity to provide optimal care. In the aftermath of the COVID-19 pandemic, ICU organizational aspects significantly influenced patient outcome. The capacity of healthcare systems to reshape quickly seems crucial to population survival in the context of health crises. Solutions to avoid overwhelming situations may include appropriate staffing, temporary units’ openings, and close collaborations between ICUs from the same territory for optimal patient repartition.

Despite the high risk for patient harm during unanticipated ICU evacuations, critical care providers receive little to no training on how to perform safe and effective ICU evacuations. We reviewed the pertinent published literature and offer suggestions for the critical care provider regarding ICU evacuation. The suggestions in this article are important for all who are involved in pandemics or disasters with multiple critically ill or injured patients, including front-line clinicians, hospital administrators, and public health or government officials.