Assessing the heterogeneity of the impact of COVID-19 incidence on all-cause excess mortality among healthcare districts in Lombardy, Italy, to evaluate the local response to the pandemic: an ecological study

OBJECTIVES

The fragmentation of the response to the COVID-19 pandemic at national, regional and local levels is a possible source of variability in the impact of the pandemic on society. This study aims to assess how much of this variability affected the burden of COVID-19, measured in terms of all-cause 2020 excess mortality.

DESIGN

Ecological retrospective study.

SETTING

Lombardy region of Italy, 2015-2020.

OUTCOME MEASURES

We evaluated the relationship between the intensity of the epidemics and excess mortality, assessing the heterogeneity of this relationship across the 91 districts after adjusting for relevant confounders.

RESULTS

The epidemic intensity was quantified as the COVID-19 hospitalisations per 1000 inhabitants. Five confounders were identified through a directed acyclic graph: age distribution, population density, pro-capita gross domestic product, restriction policy and population mobility.Analyses were based on a negative binomial regression model with district-specific random effects. We found a strong, positive association between COVID-19 hospitalisations and 2020 excess mortality (p<0.001), estimating that an increase of one hospitalised COVID-19 patient per 1000 inhabitants resulted in a 15.5% increase in excess mortality. After adjusting for confounders, no district differed in terms of COVID-19-unrelated excess mortality from the average district. Minimal heterogeneity emerged in the district-specific relationships between COVID-19 hospitalisations and excess mortality (6 confidence intervals out of 91 did not cover the null value).

CONCLUSIONS

The homogeneous effect of the COVID-19 spread on the excess mortality in the Lombardy districts suggests that, despite the unprecedented conditions, the pandemic reactions did not result in health disparities in the region.

Strategies to convert hospital beds for COVID-19 patients to minimize emergency department overcrowding

Background: The shortage of hospital beds for COVID-19 patients has been one critical cause of Emergency Department (ED) overcrowding. Purpose: We aimed at elaborating a strategy of conversion of hospital beds, from non-COVID-19 to COVID-19 care, minimizing both ED overcrowding and the number of beds eventually converted. Research Design: Observational retrospective study. Study Sample: We considered the centralized database of all ED admissions in the Lombardy region of Italy during the second "COVID-19 wave" (October to December 2020). Data collection and Analysis: We analyzed all admissions to 82 EDs. We devised a family of Monte Carlo simulations to evaluate the performance of hospital beds' conversion strategies triggered by ED crowding of COVID-19 patients, determining a critical number of beds to be converted when passing an ED-specific crowding threshold. Results: Our results suggest that the maximum number of patients waiting for hospitalization could have been decreased by 70% with the proposed strategy. Such a reduction would have been achieved by converting 30% more hospital beds than the total number converted in the region. Conclusions: The disproportion between reduction in ED crowding and additionally converted beds suggests that a wide margin to improve the efficiency of the conversions exists. The proposed simulation apparatus can be easily generalized to study management policies synchronizing ED output and in-hospital bed availability.

Prognostic value of respiratory parameters for COVID-19 patients in the emergency department: results from the EC-COVID study

While several studies have evaluated the prognostic weight of respiratory parameters in patients with COVID-19, few have focused on patients' clinical conditions at the first emergency department (ED) assessment. We analyzed a large cohort of ED patients recruited within the EC-COVID study over the year 2020, and assessed the association between key bedside respiratory parameters measured in room air (pO2, pCO2, pH, and respiratory rate [RR]) and hospital mortality, after adjusting for key confounding factors. Analyses were based on a multivariable logistic Generalized Additive Model (GAM). After excluding patients who did not perform a blood gas analysis (BGA) test in room air or with incomplete BGA results, a total of 2458 patients were considered in the analyses. Most patients were hospitalized on ED discharge (72.0%); hospital mortality was 14.3%. Strong, negative associations with hospital mortality emerged for pO2, pCO2 and pH (p-values: < 0.001, < 0.001 and 0.014), while a significant, positive association was observed for RR (p-value < 0.001). Associations were quantified with nonlinear functions, learned from the data. No cross-parameter interaction was significant (all p-values were larger than 0.10), suggesting a progressive, independent effect on the outcome as the value of each parameter departed from normality. Our results collide with the hypothesized existence of patterns of breathing parameters with specific prognostic weight in the early stages of the disease.