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Cross sectional study

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Cross sectional study

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A study is designed to assess the impact of air pollution in urban areas on the prevalence of asthma in children and adolescents <18-years-old. Data on the prevalence of asthma in children living in industrialized urban areas is collected and compared to equivalent data from the same time period of children living in rural areas. The urban and rural data sets were collected two years ago and are now being analyzed. Which of the following study types best describes the design of this study?

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Content Reviewers:

Rishi Desai, MD, MPH

A cross- sectional study is a study design where an exposure and an outcome are measured at the same time.

For example, let’s say you want to figure out if people who are obese – which means having a body mass index or BMI of 30 or higher - have higher serum cholesterol levels compared to people who are not obese – having a body mass index below 30.

To do this, you might look at the medical records of 100 people to see who has high cholesterol levels and who has low cholesterol levels and compare that to how many people in each group are obese or not obese.

You can think about a cross-sectional study like a snapshot of the population at a certain point in time.

Since you can only collect the information you see in that one moment, you don’t know what happens before or after the snapshot was taken.

So, we can only collect information on prevalence – the proportion of exposures or outcomes that already exist at a certain time – and not incidence – the proportion of new exposures or outcomes that occur in a certain time period.

In terms of prevalence, there’s an outcome prevalence and an exposure prevalence.

The outcome prevalence is the proportion of people who have an outcome in the exposed group and the non-exposed group.

In a cross-sectional study this can be organized in a 2 by 2 table, with the exposure – obesity or no obesity – on the side and the outcome – high or low cholesterol levels – on the top, and each box is labeled a, b, c, or d.

Cell a includes individuals who have high cholesterol and who are obese; cell b includes individuals who have low cholesterol and who are obese; cell c includes individuals who have high cholesterol and who are not obese; and cell d includes individuals who have low cholesterol and who are not obese.

We can calculate an outcome prevalence to figure out if high cholesterol – the outcome – is more prevalent for people who are obese or not obese – the exposure.

Let’s say that there are 50 people in cell a, 15 in cell b, 5 in cell c, and 30 in cell d.

To find the outcome prevalence, we first calculate the proportion of people who have high cholesterol in the group that is obese – so “a” divided by “a plus b”, or 50 divided by 65, which rounds to 0.77.

Then we compare it with the proportion of people who have high cholesterol in the group who are not obese – so “c” divided by “c plus d”, or 5 divided by 35, which rounds to 0.14.

And, 0.77 divided by 0.14 equals 5.5, which means that the people who are obese have about 5.5 times the prevalence of high cholesterol compared to people who are not obese.

We can similarly calculate the exposure prevalence, which is a comparison of the proportion of people who have an exposure in the outcome group and the no outcome groups.

For example, we might calculate the exposure prevalence to figure out if obesity – the exposure – is more prevalent for people who have high cholesterol or low cholesterol – the outcome.

To find the exposure prevalence, we first calculate the proportion of people who are obese in the group that has high cholesterol– so “a” divided by “a plus c”, or 50 divided by 55, which is 0.91.

We then compare it to the proportion of people who are obese in the group that has low cholesterol – so “b” divided by “b plus d”, or 15 divided by 45, which is 0.33.