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Background. The COVID-19 pandemic has affected all socio-economic and political spheres, affected economic development, the lifestyle of the population and the healthcare system in all countries. It had a significant impact on the mortality rates of the population in Russia. Assess the impact of the pandemic of a new coronavirus infection on the structure of mortality in Penza and Penza region. Materials and methods. The analysis of the structure of mortality of the population in the territory of Penza and the Penza region (according to ICD-10) for 2019-2021 was carried out based on the database of the Regional Bureau of Forensic Medicine. Results. The overall mortality rate in the city of Penza and the Penza region since the beginning of the COVID-19 pandemic has increased from 13.1% to 17.1% and 20.3% in 2019, 2020 and 2021, respectively. Since the start of the pandemic of a new coronovirus infection in 2020, there has been a significant increase in the death rate from infectious and parasitic diseases (class I). Conclusions. The effect of the COVID- 19 pandemic on the level and structure of all-cause mortality among the total population in the analyzed territory is traced.

The problem of premature infants is well recognized in Russia and all over the world. The article discusses the mortality level and structure of extremely premature infant during 2015–2019. The aim of the study was to identify significant cause-effect relations for high mortality of premature infants and infants with extremely low body weight (ELBW) according to reports from obstetric departments, pediatric outpatient clinics, children's hospitals, statistical agencies, queries results from Territorial Compulsory Medical Insurance Fund (TCMIF) in Saint Petersburg and Russian Association of Human Reproduction (RAHR). The main demographic indices are presented by the Petrostat association. Russian Federal State Statistics Service (Rosstat) forms provided by the Center for Analysis and Prediction of Maternal and Child Health of the Saint Petersburg Health care Committee (No. 32 “Information on medical care for pregnant women, women in labour and new mothers” (approved by order of Rosstat No. 591 of 27.11.2015); No. 19 “Information on children with disabilities” registered in children's clinics (approved by order of Rosstat No. 866 of 27.12.2016); No. 14 “Information on the activities of medical facilities units providing inpatient medical care” (approved by order of Rosstat No. 723 of 05.12.2014); No. 30 “Information on the medical facility” (approved by order of Rosstat No. 483 of 03.08.2018 “On the approval of statistical tools for the organization of federal statistical observation in the field of healthcare by the Ministry of Health of the Russian Federation”) presented by Saint Petersburg Medical Informational and Analytical Center (MIAC)), and answers from TCMIF in Saint Petersburg and RARCH have been investigated. The authors have analyzed the fertility and mortality rates of premature infants, the group of children with ELBW has been established. High mortality levels have been revealed in the first weeks of life (22–23), they were mainly associated with infectious processes. The authors associate the premature delivery increase with the widespread implementation of assisted reproductive technology (ART). ART can be performed either via compulsory medical insurance funds, or other non-government sources. The assumption was proposed that there are possible violations of ART indications, contraindications, and the number of procedures. The ART efficacy is difficult to estimate as well due to insufficient information provided by Rosstat report form No. 32. The need of implementation of the new statistical form that will cover the data on the of ART administration, the possibility of efficacy estimation of modern invasive methods for infertility treatment and mandatory submission of reports about the use of these methods by medical facilities is discussed.

This paper studies if the accuracies of mortality models (LC model vs. 4-parametric model) are aggravated if a mortality structure changes due to the impact of COVID-19. LC model (LCM) uses dimension reduction for fitting to the log mortality matrix so that the performance of the dimension reduction method may not be good when the matrix structure changes. On the other hand, 4-parametric factor model (4-PFM) is designed to use factors for fitting to log mortality data by age groups so that it would be less affected by the change of the mortality structure. In fact, the forecast accuracies of LCM are better than those of 4-PFM when life-tables are used whereas those of 4-PFM are better when the mortality structure changes. Thus this result shows that 4-PFM is more reliable in performance to the structural changes of the mortality. To support the accuracy changes of LCM the functional aspect is explained by computing eigenvalues produced by singular vector decomposition. [ABSTRACT FROM AUTHOR]

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