Related Experiment Video
Updated: Oct 14, 2025

10:00
Measurement of Lifespan in Drosophila melanogaster
Published on: January 7, 2013
34.8K
Mortality in Russia Since the Fall of the Soviet Union
1Brandeis University, Waltham, MA USA.
Summary
Government policies significantly impacted Russian adult mortality rates following the Soviet Union
Area of Science:
- Public Health
- Economics
- Demography
Background:
- Adult mortality surged in Russia and former Soviet republics after the 1991 collapse of the Soviet system.
- Significant fluctuations in mortality rates have occurred since the Soviet Union's dissolution.
- Understanding the drivers of these mortality trends is crucial for public health policy.
Purpose of the Study:
- To document changes in Russian adult mortality from 1989 to the present.
- To review economic and public health literature on the causes of mortality shifts.
- To analyze the impact of post-2000 alcohol and tobacco control policies on declining mortality rates.
Main Methods:
- Documentation of mortality changes in Russia since 1989.
- Review of existing economic and public health research.
- Focus on the post-2000 period, examining policy impacts.
Main Results:
- Government policies are identified as critical factors influencing both increases and decreases in male mortality.
- The causes behind the mortality crisis and its subsequent reversal are complex and not fully understood.
- Alcohol and tobacco control policies may have played a role in recent mortality declines.
Conclusions:
- Government interventions are pivotal in shaping adult mortality trends in Russia.
- Further empirical research is needed to fully elucidate the causes of Russia's mortality crisis and its reversal.
- The interplay between policy, socioeconomic factors, and health outcomes requires continued investigation.
Related Concept Videos
Applications of Life Tables
138
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...
138
Life Tables
236
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,...
236
Regression Toward the Mean
6.6K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.6K
Actuarial Approach
151
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,...
151
Survival Curves
374
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
374
Assumptions of Survival Analysis
218
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
218

