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

Actuarial Approach01:20

Actuarial Approach

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,...
Bonanno's Theory of Grieving01:17

Bonanno's Theory of Grieving

Grieving is a complex psychological and emotional process that varies significantly among individuals. George Bonanno's research on bereavement identified four distinct patterns of grieving, offering a nuanced understanding of how people cope with significant loss, such as the death of a spouse, over extended periods. These patterns — resilience, recovery, chronic dysfunction, and delayed grief — highlight the diversity in emotional responses and adaptive mechanisms.
Resilience
The resilience...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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.
Life Tables01:22

Life Tables

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,...
Life Histories01:29

Life Histories

Constrained by limited energy and resources, organisms must compromise between offspring quantity and parental investment. This trade-off is represented by two primary reproductive strategies; K-strategists produce few offspring but provide substantial parental support, whereas r-strategists produce much progeny that receives little care. These strategies are related to an organism’s survival likelihood across its lifespan, which is represented by a survivorship curve. Three general types of...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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,...

You might also read

Related Articles

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

Sort by
Same author

Tex15 is required for vomeronasal sensory neuron diversity and male pheromone detection.

bioRxiv : the preprint server for biology·2025
Same author

Grandparent-Grandchild Coresidence Among Middle-Aged and Older Adults Around the Globe.

Populations·2025
Same author

Heat risks in agriculture: Microclimate variability and worker safety in sweet corn and tobacco.

Journal of occupational and environmental hygiene·2025
Same author

Neural Responses to Intranasal Oxytocin in Youths With Severe Irritability.

The American journal of psychiatry·2024
Same author

Mediating effect of amygdala activity on response to fear vs. happiness in youth with significant levels of irritability and disruptive mood and behavior disorders.

Frontiers in behavioral neuroscience·2023
Same author

Systemic immune derangements are shared across various CNS pathologies and reflect novel mechanisms of immune privilege.

Neuro-oncology advances·2023

Related Experiment Videos

Who is hurt by procyclical mortality?

Ryan Edwards1

  • 1Department of Economics, Queens College, City University of New York, Flushing, NY 11367, USA. redwards@qc.cuny.edu

Social Science & Medicine (1982)
|November 4, 2008
PubMed
Summary

Economic upturns may increase mortality rates, challenging traditional views. This study finds that mortality is procyclical, with educated working-age men bearing a heavier burden, not disadvantaged groups.

Area of Science:

  • Public Health
  • Health Economics
  • Sociology

Background:

  • Renewed interest exists in the relationship between economic fluctuations and health/mortality.
  • Traditional views posited recessions worsen health; recent findings suggest mortality is procyclical.
  • Procyclical mortality, linked to accidents, cardiovascular disease, and substance use, may disproportionately affect vulnerable populations during economic booms.

Purpose of the Study:

  • To investigate the relationship between economic conditions and mortality across individual characteristics.
  • To examine whether socioeconomically disadvantaged groups are disproportionately affected by procyclical mortality.
  • To analyze mortality patterns by education level and age group during economic fluctuations.

Main Methods:

Related Experiment Videos

  • Utilized the U.S. National Longitudinal Mortality Study (NLMS) data.
  • Analyzed mortality data from the 1980s and 1990s.
  • Examined mortality differentials based on individual characteristics, including education and age.

    • Main Results:

    • Found scant evidence that disadvantaged groups face significantly higher exposure to procyclical mortality.
    • Identified that working-age men with higher education levels appear to bear a greater mortality burden during economic expansions.
    • Observed that individuals with lower education levels experienced countercyclical mortality patterns.

    Conclusions:

    • The study challenges the assumption that economic booms uniformly increase mortality risk for all, particularly disadvantaged groups.
    • Higher mortality risk during economic expansions is concentrated among educated working-age men.
    • Mortality patterns related to economic cycles vary significantly by education level, suggesting complex socioeconomic interactions.