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Related Concept Videos

Factors Affecting Illness01:18

Factors Affecting Illness

When a person's physical, emotional, intellectual, social development or spiritual functioning is compromised, this deviation from a healthy normal state is called illness. Illness creates stress that in turn harms individuals. Irritation, anger, denial, hopelessness, and fear are behavioral and emotional changes an individual experiences in the phases of illness. A variety of factors influence a person's health and well-being.
For instance, risk factors are connected to illness, disability,...
Actuarial Approach01:20

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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.
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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...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
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,...

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

Published on: January 8, 2020

Mortality and morbidity risks and economic behavior.

Avraham Stoler1, David Meltzer

  • 1Department of Economics, DePaul University, Chicago, IL, USA. astoler@depaul.edu

Health Economics
|February 7, 2012
PubMed
Summary
This summary is machine-generated.

High mortality risk in young adulthood deters human capital investment. This study used Huntington's disease data to show a clear negative link between early death risk and investing in oneself.

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Last Updated: May 25, 2026

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

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Published on: January 8, 2020

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

Area of Science:

  • Health Economics
  • Genetics
  • Sociology

Background:

  • Theoretical models suggest high mortality/morbidity risk reduces human capital investment.
  • Empirical investigation is challenging due to omitted variables and reverse causation.
  • Human capital investment can influence mortality and morbidity outcomes.

Purpose of the Study:

  • To investigate the causal relationship between mortality/morbidity risk and human capital investment.
  • To overcome empirical challenges using an exogenous risk factor.
  • To analyze the impact of genetic predisposition to disease on life choices.

Main Methods:

  • Collected data on human capital investments, fertility, and economic choices.
  • Utilized individuals at risk for Huntington's disease as a study group.
  • Leveraged Huntington's disease as a source of exogenous mortality and morbidity risk.

Main Results:

  • A significant negative correlation was observed between mortality/morbidity risk and human capital investment.
  • Individuals facing higher risks of early death invested less in human capital.
  • The findings support the theoretical link between risk perception and investment behavior.

Conclusions:

  • High risk of mortality and morbidity significantly deters human capital investment.
  • Exogenous genetic disorders provide a unique opportunity to study this relationship.
  • Policy implications may involve mitigating risk perception to encourage human capital development.