ABSTRACT
The objects of the study were the daily data on the population morbidity and mortality due to coronavirus disease 2019 (COVID-19) in Russian regions, as well as regional medical, demographic and environmental data recorded in recent years. COVID-19 is a contagious disease caused by the novel coronavirus (SARS-CoV-2). The mathematical methods consist of correlation and regression analysis, methods of testing statistical hypotheses. First, a multiple Variable Structure Regression should be specified. The intercept in the model differs from region to region, depending on the combination of values for dummy variables. The role of the dependent variable Y-t was chosen as the cumulative mortality published by the operational headquarters for the regions that has been linked to day t, so that COVID-19 was considered the main cause of death. The complex of explanatory variables included two factorial variables that changed daily, and had a lag relative to t value. Also, this complex included a number of variables that did not change with the growth of t: the explanatory variable with the region's availability with doctors of certain specialties;and four dummy variables. One of them coded the region's belonging to the two southern Russian Federal Districts. Three other variables characterized the increased air pollution in settlements recorded in recent years, as well as the level of radiation pollution of the region's territory and the population health estimated for 10 classes of diseases (for the circulatory system, endocrine system, etc.). The values of such dummy variables were obtained from open data from the Federal State Statistics Service (Rosstat) etc. The model parameters were estimated by the least squares method using the training table, which included 40 Russia's regions, the t parameter for variable Y-t was assessed starting from November, 1, 2021. As a result, a statistical model was built with an approximation error equal to 3%. For 3/4 regions of the regions examined this error was 1.94 (+/- 1.5)% for the value Y t that has been fixed on the 1st Nov. The plots show daily prediction for mortality rate due to COVID-19 in the first half of November for seven Russian regions compared with actual data. The model can be useful in development of medical and demographic policy in geographic regions, as well as generating adjusted compartment models that based on systems of differential equations (SEIRF, SIRD, etc.).
ABSTRACT
This article is based on a report presented at the Scientific Session of the RAS General Meeting (Moscow, December 15, 2021). The reaction of society to the pandemic in Russia and other countries of the world is analyzed from an anthropological point of view. The features of the behavior and psychological reaction of residents of different regions, professional groups, and ethnocultural communities are considered with account for gender, age, and cultural characteristics (collectivismâindividualism, loosenessâtightness, power distance). Particular attention is paid to phobias and social activity during the pandemic; the growing role of nation-states in overcoming the consequences of the pandemic is discussed. The results presented can be used as an additional source of information for taking effective measures finally to overcome the pandemic and, most importantly, its negative social and political consequences.
ABSTRACT
BACKGROUND AND AIMS: We aimed to analyze the outcomes of HD patients with COVID-19 hospitalized in the Moscow region, Russia, and to compare it with those in the general population. METHOD: Data were obtained retrospectively from the Moscow region COVID-19 register database, which comprises all hospitalizations with suspected or confirmed COVID-19 between February 2020 and November 2021. A total of 384 327 patients were included;1 435 of them were ESRD patients. RESULTS: Among ESRD patients there were 1386 HD patients and 49 kidney graft recipients. Thus, during the specified period, 48.5% of all prevalent HD patients of the Moscow region and only 7.8% of the graft recipients required hospitalization. Due to a few number of hospital admissions among kidney recipients they were excluded from the further analyses. We observed typical 4 waves of hospital admissions in the general population, but not in HD patients. In these patients, we noted a peak in December 2020 with a subsequent decrease in February, 2021;then the number of hospitalizations remained stable. The proportion of HD patients was approximately 0.5% of all patients with COVID-19 admitted to hospital. Almost all HD patients with COVID-19 were hospitalized regardless of disease severity. The mean age of hospitalized HD patients was significantly more than that in the general population: 68.95 ± 13.69 years versus 59.18 ± 17.11 years, P < 0.001. Of note, the mean age of HD patients in Russia is 56.3 ± 11.7 years. The proportion of men among hospitalized HD patients with COVID-19 reached 50.4% versus 43.5% in the general population. HD was associated with a significant increase in the risk of critical but stable and extremely critical (+ worsened: terminal and clinical death) condition at admission (Figure 1A): RR = 3.36 [95% confidence interval (95% CI) 3.12-3.59], P < 0.001 and RR = 4.83 (95% CI 3.93-5.92), P < 0.001, respectively. HD patients were significantly more likely to need for any kind of respiratory support (oxygen mask and mechanical ventilation (MV)) or MV alone (Figure 1B): RR = 1.72 (95% CI 1.63-1.81), P < 0.001 and RR = 4.67 (95% CI 4.18-5.21), P < 0.001, respectively. HD was associated with a significant increase in the risk of death (Figure 1C): RR = 3.48 (95% CI 3.24-3.72), P < 0.001. HD significantly increased the risk of death in patients without oxygen support and in patients with need for an oxygen mask (Figure 2A): RR = 3.56 (95% CI 2.97-4.25), P < 0.001 and RR = 2.47 (95% CI 2.18-2.78), P < 0.001, respectively. For patients requiring MV, mortality was >95% in both cohorts: RR = 0.999 (95% CI 0.955-1.01), P = 0.309. Deceased patients were older than survivors both in HD patients [73 (IQR 65-82) versus 69 (IQR 59-78) years;P < 0.001] and in the general population [72 (IQR 63- 82) versus 60 (IQR 48-69) years;P < 0.001], however, the difference between medians was significantly greater in the general population: 13 (95% CI 12-14) versus 5 (95% CI 3-6) years. Heart and lung diseases increased the risk of death. In the general population concomitant heart diseases worsened the prognosis to a greater extent compared with lung diseases: RR = 2.69 (95% CI 2.64-2.74), P < 0.001 and RR = 1.3 (95% CI 1.26-1.35), P < 0.001, respectively. In HD patients pre-existing lung diseases had a greater impact on the risk of death than heart diseases: RR = 2.02 (95% CI 1.71-2.41), P < 0.001 and RR = 3.05 (95% CI 2.73-3.41), P < 0.001, respectively. In the multivariate model, significant predictors of death in HD patients were need for MV (OR = 9.81, 95% CI 8.48-17.8;P < 0.001) and lung diseases (OR = 2.92, 95% CI 1.92-5.42;P < 0.001], but not heart diseases, age and gender. CONCLUSION: HD patients with COVID-19 have a significantly worse prognosis compared with the general population. The main risk factors for death are need for respiratory support and pre-existing lung diseases.
ABSTRACT
The editorial touches upon the problem of the possible impact of COVID-19 on CKD patients, mediated by the forced reorganization of the health care system in a whole, the redistribution of its resources in the context of the COVID-19 pandemic. Lack of regular outpatient monitoring, delayed diagnosis and therapy in patients with kidney dysfunction are factors of adverse clinical outcomes - accelerated disease progression, ESKD development and the need for KRT, life-threatening complications, reduced quality of life and survival. The data of a pooled analysis of the impact of the pandemic on specialized renal care and its availability in a number of regions of the Northwest Federal District of Russia and the Moscow Region are presented: a fall in hospital admissions, outpatient consultations and a decrease in the use of hospital beds (on average, by 37 %, 40 % and 32 %, respectively). Principles and conditions of the functioning of health systems associated in the COVID-19 pandemic have been discussed. The main approaches to maintaining the standard level of renal patients care have been formulated, aimed at preventing an unfavorable patient-oriented CKD outcomes. © 2021 Patristica et Mediaevalia. All rights reserved.
ABSTRACT
The search for sensitive and specific markers enabling timely identification of patients with a life-threat-ening novel coronavirus infection (COVID-19) is important for a successful treatment. The aim of the study was to examine the association of molecular biomarkers of air-blood barrier damage, surfactant proteins SP-A and SP-D and Club cell protein CC16, with the outcome of patients with COVID-19. Materials and methods. A cohort of 109 patients diagnosed with COVID-19 was retrospectively divided into two groups. Group 1 comprised survivor patients discharged from the ICU (n=90). Group 2 included the patients who did not survive (n=19). Association of disease outcome and SP-A, SP-D, and CC16 levels in blood serum, clinical, and laboratory data were examined taking into account the day of illness at the time of bio-material collection. Results. The non-survivors had higher SP-A (from days 1 to 10 of symptoms onset) and lower CC16 (from days 11 to 20 of symptoms onset) levels vs survivors discharged from ICU. No significant differences in SP-D levels between the groups were found. Conclusion. According to the study results, the surfactant protein SP-A and Club cell protein CC16 are associated with increased COVID-19 mortality.
ABSTRACT
The question of the secularity of society still remains open, since scientists have proposed only cautious speculative answers, while every scientist understands that in the social sciences it is a sad experience of predictions, that history is random and therefore unpredictable and the future always remains fundamentally open. The process of transformation of postmodern society, the development of which is actively influenced by the current pandemy of COVID-19, entailed the revival of religious values and the formation of a qualitatively new religious consciousness. In connection with the rise in the social status of religious consciousness and the widespread dissemination of religious ideas, primarily at the everyday level, the analysis of individual religious consciousness as one of the ways to comprehend the world is of particular importance. The social nature of religious consciousness is manifested not only in the fact that religious values are perceived as a kind of khanism of social regulation, but also in the fact that they serve as epistemological guidelines and often compete with scientific values. This determines the growing philosophical interest in the analysis of the epistemological functions of religion and secular reality, as well as the cognitive capabilities of religious consciousness, which is impossible without a consistent study of the methodological basis of religious knowledge.
ABSTRACT
The paper dwells on certain mathematical models showing how epidemics develop, namely, logistic ones, SIR-model, and some others. There is also a review of articles that focus on such models showing dynamics of incidence with COVID-19 infection. These models are often successfully applied for data collected in a whole country but on a regional level there are difficulties due to peculiarities of calculating mortality figures in Russia. In this case regression models can be useful with their obvious advantage at the initial stage in an epidemic process. They also include exogenous variables that influence mortality, for example, a number of doctors and nurses per a hospital, how well hospitals are equipped with ALV devices, and a number of available beds in them. Our research goal was to build up a linear regression model that could be used as a basis for estimating regional mortality caused by COVID-19 as well as for more efficient distribution of all the resources mentioned above. The model is built as per a set of resource parameters including data on «active cases». Preliminary three variables that showed data on resources available to communicable diseases departments in hospitals were transformed into a new single one via linear transformation. Then the model was tested on a training sample containing an endogenous variable on mortality and four factor ones including prevalence of active virus carriers. Regions were included into training data with different lags;they were included into such daily samples when death cases were registered rarely. Then the estimated model was applied with other values. It turned out to be quite efficient in estimating COVID-induced mortality for regions from trainings samples as well as for several others (for certain intervals). As a result, we built a regression model and estimated its precision;the model showed a relation between mortality in a region and prevalence of active SARS-CoV-2 carriers and availability of resources to hospitals in it. It can be useful when these resources are distributed. It can also be used to build SIRD, SEIR, and SEIRF models at a regional level when choosing parameters in them related to mortality. A methodology itself that can be similarly applied for other epidemic processes also deserves certain attention. © Stepanov V.S., 2020