Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Life Tables01:22

Life Tables

243
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,...
243
Applications of Life Tables01:22

Applications of Life Tables

145
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...
145
Actuarial Approach01:20

Actuarial Approach

159
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,...
159
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

317
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
317
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

352
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...
352
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

634
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:
634

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Cause-of-death data series to monitor cause-specific mortality across time and space.

Scientific data·2026
Same author

Loneliness as a mediator between family separation and life satisfaction among Ukrainian refugee women in Germany.

Scientific reports·2026
Same author

Socioeconomic inequalities in life expectancy within and between native-born and foreign-born populations: a comparative study of 10 European countries.

International journal of epidemiology·2026
Same author

Inequality in life expectancy losses by education in Finland during COVID-19 pandemic.

International journal for equity in health·2026
Same author

Geographical divides in male premature mortality in the CEE-FSU European region: an ecological study of 2320 spatial units in 12 countries, 2003-2019.

BMJ public health·2026
Same author

Educational inequalities in cervical cancer mortality in the Baltic countries and Finland in the context of organized screening: A register-based study 2000-2015.

International journal of cancer·2026

Related Experiment Video

Updated: Oct 22, 2025

Measurement of Lifespan in Drosophila melanogaster
10:00

Measurement of Lifespan in Drosophila melanogaster

Published on: January 7, 2013

34.8K

Method for reconstructing mortality by educational groups.

László Németh1, Domantas Jasilionis2, Henrik Brønnum-Hansen3

  • 1Max Planck Institute for Demographic Research, Konrad-Zuse-Straße 1., 18057, Rostock, Germany. nemeth@demogr.mpg.de.

Population Health Metrics
|August 27, 2021
PubMed
Summary
This summary is machine-generated.

This study developed a new method to estimate mortality differences by education in Denmark and Sweden. Results show a widening longevity gap between the most and least educated individuals in both countries.

Keywords:
Educational differencesLife expectancyOld-age mortality

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.4K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.8K

Related Experiment Videos

Last Updated: Oct 22, 2025

Measurement of Lifespan in Drosophila melanogaster
10:00

Measurement of Lifespan in Drosophila melanogaster

Published on: January 7, 2013

34.8K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.4K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.8K

Area of Science:

  • Demography
  • Public Health
  • Sociology

Background:

  • Lack of educational attainment data in older populations hinders mortality disparity studies in Denmark and Sweden.
  • Missing educational distribution data limits analysis of longevity improvements due to population changes.

Purpose of the Study:

  • To propose a transparent method for robust education-specific mortality estimates and population weights.
  • To address data limitations in studying mortality disparities by education.

Main Methods:

  • Utilized a nonparametric approach to redistribute unknown cases.
  • Extrapolated mortality curves using aggregate population data from the Human Mortality Database.

Main Results:

  • Reconstructed harmonized education-specific life tables for Sweden and Denmark (1991-2015).
  • Life tables align with national data, showing plausible age- and education-specific patterns.
  • Observed widening longevity gap between highest and lowest educated individuals for both sexes.

Conclusions:

  • The developed method is simple, transparent, and applicable to countries with missing socio-economic data at older ages.
  • Provides robust estimates for education-specific mortality and population weights.