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Epidemiology

Positive and negative predictive value

Sensitivity and specificity

Test precision and accuracy

Odds ratio

0 / 9 complete

0 / 2 complete

0 / 2 complete

of complete

of complete

of complete

A researcher is investigating the association between the use of social media in teenagers and bipolar disorder. He gathers continuous data, which is divided into 2 groups: those who spend more than 2 hours per day on social media and those who spend less than 2 hours per day. The data is demonstrated below:

Which of the following is the odds ratio for developing bipolar disorder among tennagers who spend more than 2 hours per day on social media, when compared to those who spend less than 2 hours per day.

More than 2 hours of social media daily | Less than 2 hours of social media daily | |

Bipolar disorder | 300 | 200 |

No bipolar disorder | 100 | 400 |

Which of the following is the odds ratio for developing bipolar disorder among tennagers who spend more than 2 hours per day on social media, when compared to those who spend less than 2 hours per day.

2022

2021

2020

2019

2018

2017

2016

In statistics, the words probability and odds are often confused with each other, because they help measure the same thing - the chance that an outcome will occur - and in both cases, we need to know the same two things - the number of times an outcome actually happened, or didn’t happen, and the total number of times an outcome could have happened.

The probability is the number of times an outcome happened divided by the number of times the outcome could have happened, and is often represented by a capital P.

For example, let’s say we want to figure out the probability of having a heart attack for people with hypertension - or high blood pressure.

To do this, we could carry out a cohort study which is where we start with two exposure groups and follow them over time to see if they develop a certain outcome.

We could recruit 100 people with hypertension, The exposed group - and 100 people without hypertension - the non-exposed group - and organize our results in a 2 by 2 table, and keep track of how many of them have heart attacks in the next year.

The two outcomes - heart attack or no heart attack - labeled on the top, and the two exposure groups - hypertension or no hypertension - on the side, and each of the cells inside the box labeled as a, b, c, or d.

Now, let’s say there are 9 people that have heart attacks in the group with hypertension - cell a - and 3 people that have heart attacks in the group without hypertension - cell c.

That means that there are 100 minus 9, or 91, people that didn’t have heart attacks in the group with hypertension - cell b - and 100 minus 3, or 97, people that didn’t have heart attacks in the group without hypertension - cell d.

To calculate the relative risk, we need the probability of having a heart attack in the group with hypertension - so cell a, 9, divided by cell a plus cell b or 100, which gives us 0.09.

The odds ratio (OR) is a statistical measure used to compare the odds of an event occurring in one group to the odds of the same event occurring in another group. It is often used in retrospective studies, such as case-control studies, to compare the likelihood of an outcome (such as a disease or condition) occurring in one group of people compared to another group.

The odds ratio is calculated by dividing the odds of the event occurring in one group by the odds of the event occurring in the other group. For example, if the odds of a disease occurring in group A are 1 in 10, and the odds of the disease occurring in group B are 1 in 20, the odds ratio would be (1/10) / (1/20) = 2. This indicates that the odds of the disease occurring in group A are twice as high as the odds of the disease occurring in group B.

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