What are associations?

For purposes of understanding how serious health problems are, it is valuable to know how much disease there is in a population.  Knowing how many children have asthma, how many heart attacks people have every year, or how many people have learning disabilities is key to understanding these problems, developing plans to address them, and allocating resources. 

A step beyond understanding how much disease there is in a population is the comparison of how much disease exists within different groups of people.  For example, we can compare how much disease occurs in one city versus another, among different race and ethnic groups, or among different social classes.  Similarly, one can compare groups of people exposed to different environments, such as those who live either near or far away from sources of pollution, people who smoke versus those who don't, or people who eat different kinds of foods. 

Epidemiologists call these comparisons associations, and the study of associations is largely what the practice of Epidemiology is all about.  Finding a positive association is not the same as finding proof about what caused the disease.  There are many factors to consider when looking for evidence of causality.  However, knowing about associations can be useful in many ways.  For example, knowing that cigarettes are associated with lung cancer has taught us new things about what cancer is, who might have it, and how prevention efforts might be organized.  The patterns of disease described using associations may also tell us about disease disparities or the differences in the health among more privileged and less privileged people in society. 

Associations analysis

For this pilot project, we looked at the associations between asthma and traffic pollution and between birth outcomes and traffic pollution.  To do this, we used a method called land use regression to estimate NO₂ (nitrogen dioxide, a compound found in traffic pollution) levels at various geographic locations across the entire county.  We could then link to addresses in our health outcome records dataset to these estimates. 

We categorized all the individuals in our dataset by levels of NO₂ exposure based on how much NO₂ was near the individual's place of residence.  We then used a statistical method called logistic regression to see if rates of the health outcomes varied based on levels of NO₂ near people's homes.  For a details on how we estimated NO₂ levels, see the Traffic Findings section.  For more details about associations analysis, see the Data and Methods Overview section. 

Click below to see associations findings:

Associations between asthma and traffic pollution

Associations between birth outcomes and traffic pollution

Questions about associations or these results?

Click below:

FAQs on associations analysis and results

Overview of pilot project data and methods

Additional resources

For questions or more information, please contact:

Michelle Wong, MPH

mwong@dhs.ca.gov

  Return to the pilot project findings page

 Pilot Project Findings | Overview of Pilot Project Data and Methods | Glossary of Scientific and Statistical Terms | Additional Resources