Related Experiment Video
Updated: Jun 21, 2025
04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
10.1K
[Comprehensive analysis of population mortality by cause: regional features]
A E Krasnov1, E A Bakirova2, E N Mingazova3,4,5
1Biomedical University of Innovation and Continuing Education of the State Scientific Center of the Russian Federation - A. I. Burnazyan Federal Medical Biophysical Center of the Federal Medical Biological Agency of Russia, 123098, Moscow, Russia, aekmu@mail.ru.
Summary
Chronic non-communicable diseases (CNCDs) are leading causes of death in Tatarstan. Cardiovascular diseases (CVD) are the primary cause, with COVID-19 significantly impacting mortality and reducing average lifespans.
Area of Science:
- Public Health
- Epidemiology
- Cardiology
Background:
- Chronic non-communicable diseases (CNCDs) represent the leading causes of mortality in the Russian Federation and the Republic of Tatarstan.
- In 2022, the mortality structure in Tatarstan was dominated by circulatory system diseases (46%), neoplasms (15%), and external causes (7.3%).
- The COVID-19 pandemic exacerbated population mortality, particularly from cardiovascular diseases (CVD) and the infection itself, which caused nearly 20% of deaths in 2021.
Purpose of the Study:
- To analyze the impact of COVID-19 on mortality patterns in the Republic of Tatarstan.
- To investigate the specific trends in cardiovascular disease (CVD) mortality in relation to the pandemic.
- To compare mortality risks from various disease categories between men and women in the region.
Main Methods:
- Analysis of mortality data from the Republic of Tatarstan for 2021 and 2022.
- Comparison of mortality rates across different disease categories (circulatory, neoplasms, external causes, respiratory, endocrine).
- Assessment of the impact of COVID-19 on average age at death for cardiovascular causes.
Main Results:
- COVID-19 significantly increased overall mortality, with a substantial rise in deaths attributed to cardiovascular diseases (CVD).
- The pandemic lowered the average age of death from CVD by 4.5 years in men and 4 years in women.
- Mortality risks differ by sex: women have higher risks from circulatory and endocrine diseases, while men face higher risks from respiratory diseases, cancer, and external causes.
Conclusions:
- Circulatory system diseases remain the leading cause of death in Tatarstan, with COVID-19 exacerbating this trend.
- The pandemic has had a profound impact on mortality, particularly affecting cardiovascular health and reducing life expectancy.
- Understanding sex-specific mortality patterns is crucial for targeted public health interventions in the region.
Keywords:
Republic of Tatarstanaverage age of deathexpected probability of deathmedical demographymortalitypopulationstatisticsMore Related Videos
06:46Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
248
04:37Comprehensive Analysis of Procoagulant Platelets Exhibiting Features of Necrosis, Apoptosis and Platelet Activation
Published on: May 23, 2025
330
Related Concept Videos
01:22Applications of Life Tables
59
Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
59
01:21Cancer Survival Analysis
340
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
340
01:20Actuarial Approach
73
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
73
01:20Comparing the Survival Analysis of Two or More Groups
175
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
175
01:22Life Tables
90
A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
90
01:25Statistical Methods for Analyzing Epidemiological Data
347
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
347