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Welcome.

In this segment, we'll be talking about another

type of study design called a cross-sectional study.

This is yet another type of observational study design.

These are learning objectives for this segment on cross-sectional studies.

There are to list the basic characteristics and be able to

explain the cross-sectional study design and also to learn to identify the

advantages and disadvantages of the cross-sectional study design.

Cross sectional studies fall into the observational study design type.

They are one of the four types of

observational study designs we are covering in our course.

Like cohort studies, cross sectional studies conceptually

begin with a population base within which

the occurrence of disease or health outcome

and sometimes the simultaneous occurrence of the exposure

will be studied.

For example, the population could be

all individuals currently living in Harari, Ethiopia.

Or it could be all children ages five

to six currently attending kindergarten in Seattle, Washington.

Or it could be all taxi drivers currently working in Beijing, China.

A key aspect of a cross sectional study is that

the exposure and the outcome are assessed at the same point

in time within the specified study population.

If you conducted a cross sectional study in

1990, you would first define your study population.

For example, all adolescents in high schools

in the city of Rio de Janeiro, Brazil.

You would then survey all the high school students at

school and ask them about their exposures to traffic pollution.

For example, how close they live to heavily used roads.

At the same time, you would also ask

them if they currently had asthma, or asthma-like symptoms.

In this example, you obtained the

information about both their exposure, traffic-related

air pollution and their health outcome, asthma, at one point in time.

This is in contrast to both the cohort study in which you start with

defining the population, measuring the exposure, and

then ascertain the disease at a later date.

In this diagram the investigators start in 1970, measure

exposure, and then assess the health outcome in 2007.

The cross-sectional study design is also different from the case-control design.

As you might remember, the case-control design starts with selected cases

and controls, and then looks back at exposures in the past.

In this diagram, the investigators start in 2005,

selected cases and controls, and look back in the past at exposures in 1970.

Here's another diagram of the cross-sectional study design.

You start with defining your source population or

a population base, then you define your study population.

And next you sample study participants from that study population.

Among the study participants you asses both exposure and

disease or health outcome statue in the same point in time.

An easy way to think of a cross sectional study design is as

a snapshot of an exposure and or health outcome at one point in time.

Here's an example: among all individuals living in the

United States, what is the prevalence of type one diabetes?

Let's now apply the cross sectional study designed to the topic of distracted

driving by looking at a study done by Vera Lopez et al in 2013.

Using a mobile phone for either talking or texting

while driving a car can lead to traffic accidents.

The Vera Lopez study took place in 2011 and 2012 in Mexico.

Mexico like many other countries has a public health problem

with regards to high rates of deaths from traffic accidents.

Several municipalities have passed laws restricting mobile phone use

by drivers.

Researchers selected three cities in Mexico.

Their goal was to measure the prevalence of talking and

texting on mobile phones among drivers in the three cities.

A sample of 3% of all intersections with functioning

traffic lights in all three cities was randomly selected.

With this systematic sampling methodology, 7,940 drivers

and their vehicles were observed during 2011

to 2012.

The overall prevalence of mobile phone use while driving was 10.8%.

Now we will discuss the numerator and denominator

of the prevalence measure used in cross sectional studies.

Cross-sectional studies are often used to describe the occurrence

of a health outcome or exposure in the population.

The measure used to describe this occurrence is prevalence.

For the numerator you include all existing cases

of the health outcome or disease in a population group, ie, prevalent cases.

While for the denominator you include all existing persons in the study population

or among study participants, including both prevalent cases and non cases.

For example, among adults, 50 and older living in

Dallas, Texas, what is the prevalence of high blood pressure?

The numerator would be all

people 50 years or older, with existing high blood pressure.

The denominator would be all people 50 and older living in Dallas, Texas.

There are several ways in which cross sectional studies may be used.

Some cross sectional studies characterize the prevalence of a health outcome or

disease in a specified population in a defined period of time, ie, prevalence.

Other cross-sectional

studies obtain data on the prevalence of exposure and the health outcome

or disease for the purpose of examining the association of these two variables.

For example, is smoking prevalence among

adolescents related to smoking prevalence of parents.

Now, let's look at how to conduct a cross-sectional study.

First, a cross-sectional study begins with a defined study population.

From which data

on the presence or absence of the health outcome in individuals are gathered.

Second, the researcher ascertains the prevalence proportion

of the health outcome in the study population.

The prevalence odds ratio and prevalence ratio are commonly used

measures of association when data are obtained from cross sectional studies.

Some cross-sectional studies ascertain just the prevalence of a

health outcome, while other

cross-sectional studies ascertain the prevalence

proportion of a health outcome, among the exposed and unexposed persons.

For example, in this diagram, the prevalence proportion, for both exposed,

is a over N1 and non exposed is c over N0.

For example, let's think about the

prevalence of type I diabetes in undergraduates.

That would be a divided by N.

The prevalence of type 1 diabetes in female undergrads is

a divided by N1, and the prevalence in male undergrads is c over N0.

In comparing females and males, one sex is considered exposed if there is

some evidence from the literature of a

difference in type 1 diabetes between sexes.

Here's the example with smoking.

You may want to answer the question, what is the proportion of student smokers

who have a parent who smokes?

On this slide we will calculate a prevalence proportion.

The exposure in this example is, having at least one parent

who smokes, and the outcome is middle school student who smokes.

P1 equals a divided by N1. In this example it is 50 divided

by 220 or 22.7%. The interpretation is 22.7% of

student smokers have a parent who also smokes.

It is important to recall from a previous lecture that under steady-state

conditions, prevalence equals rate times average

duration of the disease or health outcome.

Note that there are some limitations of cross-sectional studies.

For instance, the prevalence is influenced by

the rate and duration of the health outcome.

For example, persons who survive longer with a health outcome or disease will

be more likely to be counted in the numerator of a prevalence proportion.

Short term survivors are not as likely to be counted,

as they are by definition around for a shorter time.

Sometimes there can be issues with interpreting cross-sectional studies.

Antecedent-consequent bias effects cross-sectional studies and

case control studies but not cohort studies.

In cohort studies, persons are selected for study because they're

exposed or not exposed while they're still at risk and

thus disease free.

For example, if you were investigated diet and arthritis.

In a cohort study, we obtain data on diet at base line.

Before any of the study subjects have evidence of arthritis.

In a cross-sectional study, we ascertain dietary patterns at the same

time as we obtain data on the presence or absence of arthritis.

Thus, you cannot be sure that the exposure preceded the disease as they

are both ascertained at the same time.

So, what are cross-sectional studies used for?

Cross-sectional studies can be used for different purposes.

They are widely used to estimate the occurrence

of risk factors or health outcomes in the population.

For example, a study to look at the prevalence of elevated

blood lead in toddlers or the prevalence of asthma in children.

National examples of cross-sectional studies of great

importance are the decennial census, the Nation Health and Nutrition Survey or

NHANES or the prevalence of HIV positive antibodies in military recruits.

Opinion polls and political polls are basically cross-sectional studies.

Surveillance of changes in smoking habits or of

other behavioral risk factors are sequential cross-sectional studies.

Similarly,

surveillance of long lasting diseases such as AIDS are cross-sectional.

Other cross-sectional studies obtain data on the

prevalence of exposure and the health outcome

for the purpose of comparing, or looking

at the relationship among these two variables.

One example we discussed in this segment was the

proportion of student smokers who had a parent who smokes.

In this example we calculated the prevalence of the exposure,

parents who smoke and that prevalence of

the health outcome, the proportion of student smokers.

This concludes the segment on the cross-sectional study design.

Remember, the most important thing to remember about a cross-sectional

study design is that it's a cut or a snapshot

at one point in time in which you measure both

the exposure and the health outcome at the same time.

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Cross-Sectional Studies

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