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Related Concept Videos

Applications of Life Tables01:22

Applications of Life Tables

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...
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,...
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...
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Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
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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.

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Related Experiment Video

Updated: May 7, 2026

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

Patterns of widowhood mortality.

Allison R Sullivan1, Andrew Fenelon

  • 1Correspondence should be addressed to Andrew Fenelon, Population Studies & Training Center, Brown University, 68 Waterman Street, Providence, RI 02912.

The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences
|October 1, 2013
PubMed
Summary
This summary is machine-generated.

Widowhood increases mortality risk by 48%. Socioeconomic status protects widows, contrary to prior research, highlighting the critical role of social support in health and longevity.

Keywords:
Hazard modelKey Words:Marital statusMortalitySocial supportWidowhood.

Related Experiment Videos

Last Updated: May 7, 2026

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

Area of Science:

  • Gerontology
  • Sociology
  • Public Health

Background:

  • Widowhood is a significant risk factor for mortality.
  • Previous research indicates socioeconomic status (SES) is not protective and men experience higher mortality increases than women post-bereavement.

Purpose of the Study:

  • To examine the magnitude, explanations, and variations in the association between widowhood and mortality.
  • To investigate the role of SES and gender in widowhood mortality.
  • To explore the impact of the expectedness of a spouse's death on mortality risk.

Main Methods:

  • Utilized data from the Health and Retirement Study.
  • Employed Cox proportional hazard models to estimate the association between widowhood and mortality.

Main Results:

  • Widowhood is associated with a 48% increase in mortality risk.
  • Approximately one-third of this increase is attributable to socioeconomic disadvantage (selection).
  • Contrary to previous findings, SES was found to be protective for widows. Men's mortality risk increased more with unexpected spousal death, while this was less pronounced for women.

Conclusions:

  • Widowhood's detrimental effect on mortality underscores the strong relationship between social support and individual health outcomes.
  • Findings reinforce the established importance of social support for health and longevity.