Chapter 8

Correlational (Ecological) Studies

A correlational (ecological, aggregate) study uses population-level data to examine the relationship

between exposure rates and disease rates.

Examples

• Does the percentage of adults with multiple sclerosis tend to be higher in countries farther from the equator?

• Does the prevalence of diabetes tend to be higher in provinces with a higher prevalence of obesity?

• Does the rate of asthma tend to be higher in cities with higher levels of air pollution?

Overview

• Population-level data are used to look for associations between two or more group characteristics

Data Sources

One (or more) data source(s) that contains comparable information about the population characteristics of interest must be identified.

Information about all the variables of interest must be available for a suitable number of populations, which can be grouped by place or time.

Examples of Populations

• All member nations of the United Nations• All 50 states from the United States• The largest 20 metropolitan areas in the United

Kingdom• All the counties in the state of Michigan• A random sample of census tracts in New York City• Historic data for the past several decades from one or

more place-based populations

Exposures and Outcomes

• At least one characteristic of the populations being examined is designated as an exposure– Exposures are often environmental measures likely

to be fairly consistent across an entire population• At least one characteristic is designated as an

outcome

Aggregate Data

• Population characteristics are in the form of aggregate (grouped) data, such as:– the proportion of each population with a particular

characteristic– the average value of the variable in the population

Examples of Exposures

• The percentage of adults age 30 and older who have not completed at least 12 years of education

• The mean income in the population• The median age• The number of rainy days over a given year in the

population• The average ultraviolet radiation index during midday

in the hottest month of the year

Examples of Diseases

• The prevalence of obesity among adults• The mean BMI (body mass index) among adults • The annual mortality rate from asthma

Cautions

• Correlational studies are valid only if the data points are comparable.

• If multiple sources of data are used or if the data were collected over a lengthy period of time, then the definition of exposure or disease may differ from one population to another and may not be comparable.

• In some populations, exposures and diseases may be routinely undercounted or routinely over-diagnosed compared to other populations.

Data Management

• Data should be entered into a spreadsheet• Each population (A, B, C, etc.) is in its own row• Each exposure and each outcome is in its own column

Analysis: Correlation• On a scatterplot used to illustrate correlation, each point

represents one population in the study. • The exposure is plotted on the x-axis, and the outcome or

disease is plotted on the y-axis.

Analysis: Correlation

• When all the points fall neatly in a line, then the correlation is strong.

• When the points are not exactly linear but a line for trend can be drawn, then the correlation is mild or moderate.

• When the points appear to be randomly placed and no obvious line can be drawn through them, then the correlation is weak or nonexistent.

Analysis: Correlation• If higher levels of exposure are linked to higher rates of

disease, then the slope is positive. • If higher levels of exposure are linked to lower rates of

disease, then the slope is negative.

Analysis: Correlation

• For continuous variables and other variables with responses that can be plotted on a number line, a Pearson correlation coefficient (r) should be used to calculate the correlation.

• For variables that assign a rank to responses or that have ordered categories, use the Spearman rank-order correlation (designated by the letter r or the Greek letter r (rho) in most statistical programs).

Analysis: Correlation

• r = –1: all points lie perfectly on a line with a negative slope

• r = 1: all points lie perfectly on a line with a positive slope

• r = 0: no association between the exposure and outcome

• r2 shows how strong a correlation is without indicating the direction of the association

Analysis

• Use linear regression models when the goal is to:– compare more than two variables– understand the relationship between two variables

while controlling or adjusting for the effects of other variables

Age Adjustment

When the populations being compared have very different age structures, age adjustment may be necessary to make a fair comparison among populations.

Avoiding the Ecological Fallacy

Correlational studies compare groups rather than individuals.

No individual-level data are included in the analysis, only population-level data.

The incorrect attribution of population-level associations to individuals is called the ecological fallacy.

Avoiding the Ecological Fallacy

Even though a population with a higher rate of exposure to something has a higher rate of disease than populations with lower exposure rates, individuals in that population who have a high level of exposure do not necessarily have the disease.

Avoiding the Ecological Fallacy

The experience of an individual in a population may vary significantly from the population average.

It would be incorrect to assume that any one individual from a country with a high average body mass index (BMI) will be obese or that an individual from a country with a low average BMI will not be obese.

Avoiding the Ecological Fallacy

However, it is appropriate to identify trends in populations and to use those observations to generate hypotheses for individual-level studies that will test for relationships between the characteristics of interest in individuals.

FIGURE 8- 1 Key Characteristics of Correlational (Ecological) Studies