Related Experiment Video
Updated: Oct 16, 2025

07:57
A New Method for Inducing a Depression-Like Behavior in Rats
Published on: February 22, 2018
21.3K
Is there a link between all-cause mortality and economic fluctuations?
1Swedish Institute for Social Research, Stockholm University, Stockholm, Sweden.
Scandinavian Journal of Public Health
|October 20, 2021
Summary
Economic changes impact population health. Rising unemployment correlates with lower mortality rates across most age groups, while economic growth shows long-term benefits for mortality reduction.
Area of Science:
- Public Health
- Health Economics
- Demography
Background:
- All-cause mortality serves as a key global health indicator.
- Understanding the relationship between macroeconomic changes and mortality is crucial.
- Unemployment rate variations are utilized as a proxy for economic fluctuations.
Purpose of the Study:
- To quantify the short-term and long-term effects of macroeconomic shifts on all-cause mortality.
- To analyze the impact of economic changes on different age demographics.
Main Methods:
- Utilized time-series data from 21 OECD countries (1960-2018).
- Examined four mortality outcomes: total, infant, working-age (20-64), and old-age (65+).
- Employed error correction modeling to assess macroeconomic impacts on mortality.
Main Results:
- Increased unemployment showed a statistically significant association with decreased mortality in all groups except the elderly.
- Economic growth (GDP per capita) demonstrated significant long-term mortality-reducing effects.
- Old-age mortality was not significantly impacted by unemployment rate changes.
Conclusions:
- Macroeconomic fluctuations have differential impacts on mortality across age groups.
- Economic growth contributes to long-term improvements in population health.
- Policy interventions may need to consider age-specific mortality responses to economic changes.
Related Concept Videos
Causality in Epidemiology
1.1K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.1K
Cause and Effect
11.6K
While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
11.6K
Applications of Life Tables
139
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...
139
Correlation and Causation
40.0K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
40.0K
Regression Toward the Mean
6.6K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.6K
Factors Affecting Illness
4.6K
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,...
For instance, risk factors are connected to illness,...
4.6K

