After you have reviewed all of

the week four lectures you should be able

to do the items listed in the learning objectives.

These include defining the different types of measures of association

including risk ratio, rate ratio, odds ratio and prevalence ratio.

And you should also be able to define risk difference.

Rate differences, odds differences and prevalence differences.

In addition you should be able to recognize which measures of

disease occurrence and association are often used with various study designs.

And interpret both statistically significant and

not statistically significant measures of association.

And their related confidence intervals.

Let's start with a few examples of measures of association.

These examples may be similar to ones you might have heard of in the news.

Epidemiologic research on smoking and

lung cancer has found that people who smoke are 15 to 30 times as likely

to get lung cancer, or die from lung cancer, than people who do not smoke.

This is an example of a measure of association.

We will learn about how understand and interpret

statistics such as this one during this weeks lectures.

Another example of a measure of association is

from Malaria researchers who conducted a study of mosquito

nets in Mozambique.

One of the measures of association, a

rate ratio, the researchers calculated was 0.16.

How is this interpreted?

Houses treated with insecticide treated mosquito nets had

a rate of mosquito entry that was 0.16 times.

The rate of mosquito entry in house with treated nets.

This ratio can also be

interpreted as houses treated with mosquito netting reduced

entry rates of an Offalese Gambia mosquitos by 84%.

Thus, the research provides evidence that the use of insecticide

treated mosquito nets, reduces exposure to mosquitoes in the home.

Another example, is injury prevention and motor vehicle safety.

An important topic.

This slide includes a measure of association.

In this case, an odds ratio from a study done on common driving distractions.

Drivers who are composing or sending a text message had 23 times the odds of

a safety critical event compared with drivers who

were not composing or sending a text message.

Let's talk now about the definitions and formulas for measures of association.

So, up to this point in the MOOC we have covered measures of disease occurrence.

You may

recall these include prevalence, risks, rates, and odds.

Here are the actual formulas for these measures of disease occurrence.

I want to review them with you now because

they are the building blocks of the measures of association.

Before we go any further, I'd like to

highlight the differences and similarities among these various measures.

Most importantly, risks and rates both use incident or new cases.

Whereas prevalence

uses prevalent, i.e existing cases.

And odds can use either incident or prevalent cases.