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Related Concept Videos

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,...
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...
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,...
Hazard Rate01:11

Hazard Rate

The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
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...
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: Jul 18, 2026

Measurement of Lifespan in Drosophila melanogaster
10:00

Measurement of Lifespan in Drosophila melanogaster

Published on: January 7, 2013

Network type and mortality risk in later life.

Howard Litwin1, Sharon Shiovitz-Ezra

  • 1Paul Baerwald School of Social Work and Social Welfare, Hebrew University, Mount Scopus, Jerusalem, 91905-IL, Israel. mshowie@huji.ac.il

The Gerontologist
|December 16, 2006
PubMed
Summary

Social network type significantly impacts mortality risk in older adults. Diverse and friend-focused networks are linked to lower mortality, highlighting the importance of social connections in later life.

Related Experiment Videos

Last Updated: Jul 18, 2026

Measurement of Lifespan in Drosophila melanogaster
10:00

Measurement of Lifespan in Drosophila melanogaster

Published on: January 7, 2013

Area of Science:

  • Gerontology
  • Social Epidemiology
  • Public Health

Background:

  • Social networks are crucial for well-being in older adults.
  • Understanding the relationship between social structures and health outcomes is vital for public health initiatives.
  • Previous research has not fully explored the impact of specific social network types on mortality risk in later life.

Purpose of the Study:

  • To investigate the association between different types of social networks and the risk of all-cause mortality over a 7-year period in older adults.
  • To identify which social network structures are protective against mortality in later life.

Main Methods:

  • Secondary analysis of a 1997 national survey of 5,055 Israeli adults aged 60+ linked to the National Death Registry (up to 2004).
  • Categorization of participants into six network types: diverse, friend focused, neighbor focused, family focused, community-clan, and restricted.
  • Cox proportional hazards regressions were used, controlling for demographic, socioeconomic, and health factors, with analyses stratified by age group (60-69, 70-79, 80+).

Main Results:

  • Social network type was significantly associated with mortality risk in individuals aged 70-79 and 80 and older.
  • Participants in diverse and friend-focused networks exhibited a lower risk of mortality.
  • Community-clan networks also showed a reduced mortality risk compared to restricted networks.

Conclusions:

  • Gerontological practitioners should assess older adults' social networks to inform client care.
  • The study's network type parameters can aid in developing practical assessment tools for social networks.
  • Interventions should be tailored to the specific social network types of elderly clients to optimize health outcomes.