Related Experiment Video
Updated: Aug 12, 2026
09:34
Implementation of Portable Emissions Measurement Systems (PEMS) for the Real-driving Emissions (RDE) Regulation in Europe
Published on: December 4, 2016
Association between economic fluctuations and road mortality in OECD countries
1Flinders Health Economics Group, School of Medicine, Flinders University, Adelaide, Australia Gang.Chen@flinders.edu.au chengang1029@gmail.com.
European Journal of Public Health
|February 26, 2014
Summary
Road mortality rises with economic growth and falls during economic downturns in developed countries. This pro-cyclical relationship indicates that economic fluctuations significantly impact road safety trends.
Area of Science:
- Socioeconomics
- Transportation Safety
- Public Health
Background:
- Economic fluctuations are known to influence various societal aspects.
- Road safety is a critical public health concern with significant socioeconomic implications.
Purpose of the Study:
- To investigate the association between annual variations in economic activity and road mortality.
- To determine if road mortality exhibits cyclical patterns linked to economic cycles.
Main Methods:
- Utilized longitudinal data from 32 Organisation for Economic Co-operation and Development (OECD) countries spanning 1970-2010.
- Employed fixed-effects and random-coefficients regression models for data analysis.
Main Results:
- Road mortality demonstrates a pro-cyclical relationship with economic fluctuations.
- An average 1% increase in economic growth correlates with a 1.1% rise in road mortality, and vice versa.
- The cyclicality of road mortality was found to be symmetric.
Conclusions:
- Economic growth is associated with increased road mortality, while economic downturns correlate with decreased road mortality.
- Findings highlight the need for targeted road safety interventions during periods of economic expansion and contraction.
Related Concept Videos
01:21Introduction to Test of Independence
2.8K
In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
2.8K
01:16Hypothesis Test for Test of Independence
6.6K
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
H0: The two variables (factors)...
6.6K
01:08Determination of Expected Frequency
2.5K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.5K
01:12Relative Risk
1.5K
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
1.5K
01:21Causality in Epidemiology
1.3K
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...
1.3K
01:22Applications of Life Tables
232
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...
232