[Impact of COVID-19 on mortality in the autonomous community of Castilla y León (Spain)]

Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection

The COVID-19 pandemic demonstrated the need to develop strategies to control a new viral infection. However, the different characteristics of the health system and population of each country and hospital would require the implementation of self-systems adapted to their characteristics. The objective of this work was to determine predictors that should identify the most severe patients with COVID-19 infection. Given the poor situation of the hospitals in the first wave, the analysis of the data from that period with an accurate and fast technique can be an important contribution. In this regard, machine learning is able to objectively analyze data in hourly sets and is used in many fields. This study included 291 patients admitted to a hospital in Spain during the first three months of the pandemic. After screening seventy-one features with machine learning methods, the variables with the greatest influence on predicting mortality in this population were lymphocyte count, urea, FiO2, potassium, and serum pH. The XGB method achieved the highest accuracy, with a precision of >95%. Our study shows that the machine learning-based system can identify patterns and, thus, create a tool to help hospitals classify patients according to their severity of illness in order to optimize admission.

Impact of the first wave of the COVID-19 pandemic on non-COVID inpatient care in southern Spain

We assessed the impact of the first wave of COVID-19 pandemic on non-COVID hospital admissions, non-COVID mortality, factors associated with non-COVID mortality, and changes in the profile of non-COVID patients admitted to hospital. We used the Spanish Minimum Basic Data Set with diagnosis grouped according to the Diagnostic Related Groups. A total of 10,594 patients (3% COVID-19; 97% non-COVID) hospitalised during the first wave in 2020 (27-February/07-June) were compared with those hospitalised within the same dates of 2017-2019 (average annual admissions: 14,037). We found a decrease in non-COVID medical (22%) and surgical (33%) hospitalisations and a 25.7% increase in hospital mortality among non-COVID patients during the first pandemic wave compared to pre-pandemic years. During the officially declared sub-period of excess mortality in the area (17-March/20-April, in-hospital non-COVID mortality was even higher (58.7% higher than the pre-pandemic years). Non-COVID patients hospitalised during the first pandemic wave (compared to pre-pandemic years) were older, more frequently men, with longer hospital stay and increased disease severity. Hospitalisation during the first pandemic wave in 2020, compared to hospitalisation during the pre-pandemic years, was an independent risk factor for non-COVID mortality (HR 1.30, 95% CI 1.07-1.57, p = 0.008), reflecting the negative impact of the pandemic on hospitalised patients.

Epidemiological Characteristics of Deaths from COVID-19 in Peru during the Initial Pandemic Response

BACKGROUND AND AIM

Peru is the country with the highest mortality rate from COVID-19 globally, so the analysis of the characteristics of deaths is of national and international interest. The aim was to determine the epidemiological characteristics of deaths from COVID-19 in Peru from 28 March to 21 May 2020.

METHODS

Deaths from various sources were investigated, including the COVID-19 Epidemiological Surveillance and the National System of Deaths (SINADEF). In all, 3851 deaths that met the definition of a confirmed case and had a positive result of RT-PCR or rapid test IgM/IgG, were considered for the analysis. We obtained the epidemiological variables and carried out an analysis of time defined as the pre-hospital time from the onset of symptoms to hospitalization, and hospital time from the date of hospitalization to death.

RESULTS

Deaths were more frequent in males (72.0%), seniors (68.8%) and residents of the region of Lima (42.7%). In 17.8% of cases, the death occurred out-of-hospital, and 31.4% had some comorbidity. The median of pre-hospital time was 7 days (IQR: 4.0-9.0) and for the hospital time was 5 days (IQR: 3.0-9.0). The multivariable analysis with Poisson regression with robust variance found that the age group, comorbidity diagnosis and the region of origin significantly influenced pre-hospital time; while sex, comorbidity diagnosis, healthcare provider and the region of origin significantly influenced hospital time.

CONCLUSION

Deaths occurred mainly in males, seniors and on the coast, with considerable out-of-hospital deaths. Pre-hospital time was affected by age group, the diagnosis of comorbidities and the region of origin; while, hospital time was influenced by gender, the diagnosis of comorbidities, healthcare provider and the region of origin.

Real-time mortality statistics during the COVID-19 pandemic: A proposal based on Spanish data, January-March, 2021

Objectives

During the COVID-19 pandemic, surveillance systems worldwide underestimated mortality in real time due to longer death reporting lags. In Spain, the mortality monitor "MoMo" published downward biased excess mortality estimates daily. I study the correction of such bias using polynomial regressions in data from January to March 2021 for Spain and the Comunitat Valenciana, the region with the highest excess mortality.

Methods

This adjustment for real-time statistics consisted of (1) estimating forthcoming revisions with polynomial regressions of past revisions, and (2) multiplying the daily-published excess mortality by these estimated revisions. The accuracy of the corrected estimates compared to the original was measured by contrasting their mean absolute errors (MAE) and root mean square errors (RMSE).

Results

Applying quadratic and cubic regressions improved the first communication of cumulative mortality in Spain by 2-3%, on average, and the flow in registered deaths by 20%. However, for the Comunitat Valenciana, those corrections improved the first publications of the cumulative mortality by 36-45%, on average; their second publication, by 23-30%; and the third, by 15-21%. The flow of deaths registered each day improved by 62-63% on their first publication, by 19-36% on the second, and by 12-17% on the third.

Conclusion

It is recommended that MoMo's estimates for excess mortality be corrected from the effect of death reporting lags by using polynomial regressions. This holds for the flows in each date and their cumulative sum, as well as national and regional data. These adjustments can be applied by surveillance systems in other countries.

Excess Mortality during the COVID-19 Pandemic in Cities of Chile: Magnitude, Inequalities, and Urban Determinants

We estimated excess mortality in Chilean cities during the COVID-19 pandemic and its association with city-level factors. We used mortality, and social and built environment data from the SALURBAL study for 21 Chilean cities, composed of 81 municipalities or "comunas", grouped in 4 macroregions. We estimated excess mortality by comparing deaths from January 2020 up to June 2021 vs 2016-2019, using a generalized additive model. We estimated a total of 21,699 (95%CI 21,693 to 21,704) excess deaths across the 21 cities. Overall relative excess mortality was highest in the Metropolitan (Santiago) and the North regions (28.9% and 22.2%, respectively), followed by the South and Center regions (17.6% and 14.1%). At the city-level, the highest relative excess mortality was found in the Northern cities of Calama and Iquique (around 40%). Cities with higher residential overcrowding had higher excess mortality. In Santiago, capital of Chile, municipalities with higher educational attainment had lower relative excess mortality. These results provide insight into the heterogeneous impact of COVID-19 in Chile, which has served as a magnifier of preexisting urban health inequalities, exhibiting different impacts between and within cities. Delving into these findings could help prioritize strategies addressed to prevent deaths in more vulnerable communities.

[Impact of the pandemic on primary care. SESPAS Report 2022]

La pandemia de COVID-19, que dura ya más de 2 años, ha tensionado la atención primaria (AP) de salud hasta una preocupante situación actual de saturación y agotamiento. La COVID-19 es una enfermedad infecciosa comunitaria que, debido al gran número de casos (alrededor de 10 millones declarados en enero de 2022), ha requerido un esfuerzo extraordinario de atención de los casos leves y de detección precoz de los casos potencialmente graves por parte de los centros de salud. Desafortunadamente, no se ha realizado hasta ahora una evaluación global de las actuaciones que permita aprender de esta nueva experiencia. Se describe en este artículo el impacto en la AP de las distintas fases por las que ha discurrido la pandemia. Se proponen soluciones para reforzar los criterios centrales que permiten mantener la AP como fundamento del estado de derecho, la longitudinalidad, la resolutividad, la accesibilidad y la coordinación y continuidad asistencial mediante el aporte de recursos y competencias a la AP. Se concluye que la AP debe seguir siendo la base del sistema sanitario y recuperar o reivindicar aquellas competencias y recursos que siempre debieron estar ubicadas en este nivel de atención.