The data on seroprevalence of anti-SARS-CoV-2 antibodies in Kazakhstani population are non-existent, but are needed for planning of public health interventions targeted to COVID-19 containment. The aim of the study was to estimate the seropositivity of SARS-CoV-2 infection in the Kazakhstani population from 2020 to 2021. We relied on the data obtained from the results from “IN VITRO” laboratories of enzyme-linked immunosorbent assays for class G immunoglobulins (IgG) and class M (IgM) to SARS-CoV-2. The association of COVID-19 seropositivity was assessed in relation to age, gender, and region of residence. Additionally, we related the monitoring of longitudinal seropositivity with COVID-19 statistics obtained from Our World in Data. The total numbers of tests were 68,732 for SARS-CoV-2 IgM and 85,346 for IgG, of which 22% and 63% were positive, respectively. The highest rates of positive anti-SARS-CoV-2 IgM results were seen during July/August 2020. The rate of IgM seropositivity was the lowest on 25 October 2020 (2%). The lowest daily rate of anti-SARS-CoV-2 IgG was 17% (13 December 2020), while the peak of IgG seropositivity was seen on 6 June 2021 (84%). A longitudinal serological study should be envisaged to facilitate understanding of the dynamics of the epidemiological situation and to forecast future scenarios.

This article discusses the role of innovative digital technologies in medicine to improve its competitiveness and efficiency of organization and management in health care, as well as improving the quality of treatment. Health care shows the diversity of achievements and the flow of innovations based on technology. Most of the innovations that the author will describe here are still at the initial stage of development. Everyone is impressive in their own way. But the good news is that these innovations have been opened up from the traditional healthcare system to “unified medicine”. The goal is to identify and prevent the disease, along with treatment. In an outdated model of medicine, the necessary data is scattered between paper files, or classified into an electronic medical record system. Today, its improved model is being implemented. This is an individual technology for continuous monitoring of vital functions and comprehensive presentation of information about the patient’s health status. The development of information technologies and home self-monitoring devices leads to the creation of convenient services for remote consultation of citizens who care about their health.

The article presents basic algorithms of R software using for correlation analysis and simple linear regression. Basic approaches to the interpretation of the results of analysis and evaluation of statistical regression models are presented.

Introduction. It was conducted a study of the structure and dynamics of death outcomes among Almaty residents over the COVID-19 pandemic. The leading causes of death in the population during this period may change, both through infectious morbidity and losses due to concomitant chronic non-communicable diseases associated with coronavirus infection. Material and methods. The material for the analysis was the registered data on case of deaths among Almaty residents in Kazakhstan between 2018 and 2020. We studied the structure and trend in cases of death by gender and age of patients, depending on the date and place of death and in view of the class of diseases. Results. During 2020, the number of deaths of Almaty residents increased in 1.37 and 1.40 times compared to 2018 and 2019. Differences in deaths on gender and age of patients were revealed. In trend, the number of deaths recorded in the hospital has significantly increased whereas the number of deaths at home has decreased. In 2020, five major classes of diseases dominated in the structure of causes of death: 1) I00-I99, 2) G00-G99, 3) C00-D48, 4) J00-J99, and 5) U07.1 and U07.2. Research limitations. All cases of deaths registered in Almaty were used to analyse the subject of the study, which increases the reliability of statistical conclusions. Conclusion. During the COVID-19 pandemic the structure of causes of death among residents of Almaty changed when compared with previous years. There was gain in deaths, which is primarily due to coronavirus infection and diseases associated with COVID-19. It is possible underreporting of deaths from COVID-19 among residents of Almaty which related with false registration of comorbid diseases.