In recent decades, climate change has been responsible for an increase in the average temperature of the troposphere and of the oceans, with consequences on the frequency and intensity of many extreme weather phenomena. Climate change’s effects on natural disasters can be expected to induce a rise in humanitarian crises. In addition, it will surely impact the population’s long-term general health, especially among the most fragile. There are foreseeable health risks that both ambulatory care organizations and hospitals will face as global temperatures rise. These risks include the geographic redistribution of infectious (particularly zoonotic) diseases, an increase in cardiac and respiratory illnesses, as well as a host of other health hazards. Some of these risks have been detailed for most developed countries as well as for some developing countries. Using these existing risk assessments as a template, organizational innovations as well as implementation strategies should be proposed to mitigate the disruptive effects of these health risks on emergency departments and by extension, reduce the negative impact of climate change on the populations they serve.

Objective Emergency department (ED) crowding impacts negatively on quality of care. The aim was to determine the association between ED quality and input, throughput and output-associated variables. Methods This 1-year, prospective, observational, cohort study determined the daily percentage of patients leaving the ED in <4 h (ED quality and performance indicator; EDQPI). According to the median EDQPI two groups were defined: best-days and bad-days. Hospital and ED variables and time interval metrics were evaluated as predictors. Results Data were obtained for 67 307 patients over 364 days. Differences were observed between the two groups in unadjusted analysis: number of daily visits, number of patients as a function of final disposition, number boarding in the ED, and time interval metrics including wait time to triage nurse and ED provider, time from ED admission to decision, time from decision to departure and length of stay (LOS) as a function of final disposition. Five variables remained significant predictors for bad-days in multivariate analysis: wait time to triage nurse (OR 2.36; 95% CI 1.36 to 4.11; p=0.002), wait time to ED provider (OR 1.93; 95% CI 1.05 to 3.54; p=0.03), number of patients admitted to hospital (OR 1.86; 95% CI 1.09 to 3.19; p=0.02), LOS of non-admitted patients (OR 9.5; 95% CI 5.17 to 17.48; p<0.000001) and LOS of patients admitted to hospital (OR 2.46; 95% CI 1.44 to 4.2; p=0.0009). Conclusions Throughput is the major determinant of EDQPI, notably time interval reflecting the work dynamics of medical and nursing teams and the efficacy of fast-track routes for low-complexity patients. Output also significantly impacted on EDQPI, particularly the capacity to reduce the LOS of admitted patients.

Predictors of unscheduled return visits (URV), best time-frame to evaluate URV rate and clinical relationship between both visits have not yet been determined for the elderly following an ED visit. We conducted a prospective-observational study including 11,521 patients aged ≥75-years and discharged from ED (5,368 patients (53.5%)) or hospitalized after ED visit (6,153 patients). Logistic Regression and time-to-failure analyses including Cox proportional model were performed. Mean time to URV was 17 days; 72-hour, 30-day and 90-day URV rates were 1.8%, 6.1% and 10% respectively. Multivariate analysis indicates that care-pathway and final disposition decisions were significantly associated with a 30-day URV. Thus, we evaluated predictors of 30-day URV rates among non-admitted and hospitalized patient groups. By using the Cox model we found that, for non-admitted patients, triage acuity and diagnostic category and, for hospitalized patients, that visit time (day, night) and diagnostic categories were significant predictors (p<0.001). For URV, we found that 25% were due to closely related-clinical conditions. Time lapses between both visits constituted the strongest predictor of closely related-clinical conditions. Our study shows that a decision of non-admission in emergency departments is linked with an accrued risk of URV, and that some diagnostic categories are also related for non-admitted and hospitalized subjects alike. Our study also demonstrates that the best time frame to evaluate the URV rate after an ED visit is 30 days, because this is the time period during which most URVs and cases with close clinical relationships between two visits are concentrated. Our results suggest that URV can be used as an indicator or quality.

Influenza vaccination (IV) coverage remains low in France. Objectives were to assess patient knowledge and behaviors and missed opportunities for vaccination (MO) and their impact on vaccine uptake. This is a prospective-observational study, including emergency department patients at risk for severe influenza. Patients were interviewed about their knowledge and behaviors. We evaluated the health-care voucher scheme (HCVS) and MO. 868 patients were included. Vaccine uptake was 33.2%, 42% of patients knew about the possible severity of influenza, 23% thought that they were not at risk for severe influenza, 39% knew that they have an indication for the vaccine, and 4.3% to 11.5% expressed reservations concerning IV side effects and effectiveness. HCVS was used by 44.3% of patients, but only 14.8% had been vaccinated. MO were reported by 484 patients (69.4%) declaring 1104 consultations and 148 IV propositions (86.6%). Predictors of vaccine uptake (p<0.0001) were: knowledge of serious and fatal influenza forms [OR 0.36 (CI95% 0.25–0.5)]; confidence in influenza vaccine effectiveness [0.38 (0.2–0.7)]; opposition to vaccines [0.22 (0.1–0.48)]; visit to general practitioner [4.53 (2.9–7.1)]; general practitioner proposed IV [2.1 (1.2–3.4)]. Our results indicate that high rate of missed opportunities, some patient behaviors and general practitioner visits may explain low influenza vaccine uptake, and that HCVS use is a complex process. Of interest, we found that the patient’s knowledge of the potential severity of influenza is not sufficient to promote vaccine, suggesting that the information strategy must be adapted to each patient behavior.

ED-visits and through-ED admissions to medical/surgical wards (MSW) and intensive care unit (ICU) during influenza, COVID-19 and lockdown periods were evaluated in a four-hospital prospective observational study from November 2018 to March 2020. ED visit characteristics and main diagnostic categories were assessed. Analysis of 368,262 ED-visits highlighted a significantly increasing trend in ED-visits during influenza followed by a significantly decreasing trend after lockdown. For MSW-admissions, a pattern of growth during influenza was followed by a fall that began during COVID-19 pandemic and intensified during the lockdown. For ICU-admissions, a significant rise during the COVID-19 pandemic was followed by diminution during the lockdown period. During lockdown, significantly diminishing trends were shown for all diagnostic categories (between −40.8% and −73.6%), except influenza-like illness/COVID cases (+31.6%), Pulmonary embolism/deep vein thrombosis (+33.5%) and frequent users (+188.0%). The present study confirms an increase in demand during the influenza epidemic and during the initial phase of the COVID-19 epidemic, but a drop in activity during the lockdown, mainly related to non-COVID conditions. Syndromic surveillance of ILI cases in ED is a tool for monitoring influenza and COVID-19, and it can predict ED activity and the need for MSW and ICU beds.