 Interest incidence and prevalence. You can go back and look at some of our other videos and we discuss how to calculate prevalence Comparing these two incidence is the number of new cases over the number of people at risk This is actually looking at new specific cases coming in not a total overall So if you look at this picture on the right, this is a great explanation of how this works So incidence is just like the faucet. It's the new water that's coming into the faucet and filling up the bathtub There's nothing to do with anything that's already in the bathtub. There's nothing to do with things that are in the pipe yet It's what the new water that's coming in and starting to increase in the volume of the bathtub Prevalence however is the number of existing cases. So this using this bathtub example That's the number of cases that are in the bathtub over the total number of people in the population So how many people want to use the bathtub versus how many are actually in it? Okay? So in terms of prevalence prevalence is about equal to incidence and diseases that are short duration So we think of common colds you only last for five days maybe a couple weeks The incidence and prevalence are gonna be about the same the new people coming into the bathtub We're gonna be about equal to the people going out and recovering and it's gonna be about the same as the number of people that are in it Prevalence will increase or will be greater than incidence for chronic diseases. So things like diabetes so we have a large number of patients that are living with diabetes and That will be a larger number than the new people coming in So as we increase prevalence Therefore we're going to increase our positive predictive value and we're also going to decrease our negative predictive value Let's think through a couple of the things that could affect the incidence and prevalence If we increase the survival time, so we're able to treat better. That means we're not going to change incidence They're still going to be same number of people that are coming up with the disease, but we're going to increase the prevalence So there's not going to be as many people dying Through mortality if we increase the mortality so more people die We're not going to change the number of people that continue to get this medic to get this disease But we will decrease the prevalence If we increase the recovery so the number of people that come out of that tub We're not going to change the incidence. It's still going to be the faucet still going to be pouring in But we're going to decrease the prevalence so increasing mortality or increasing recovery Will not change incidence will only decrease prevalence If we increase our vaccination efforts So we give someone a vaccine to help decrease the the actual onset of a disease then that's going to decrease the incidence Decreasing incidence is going to decrease the prevalence and if we lower our risk factors So we lower the smoking that could increase our risk of having lung cancer We're going to lower the disease incidence and in turn lower the disease prevalence Finally, let's discuss precision and accuracy Precision is also known as reliability We need consistent and reproducible results. This doesn't have any absence or it doesn't have any random variations This is just going to be how reliable or how precise a measurement is Accuracy however is more of a validity It's discussing validity and how close our test results are to the actual true values We don't have systemic error or bias in this test. So obviously Error or bias will actually decrease our accuracy So let's look at these bullseyes here So if you throw a dart and you throw it in the pattern of a None of these darts are close to the bullseye None of them are close to each other. There's not neither accuracy nor precision in a However, if you throw the darts and you hit B You're not really hitting close to the bullseye, but you are hitting close together So there's precision being that you're precisely hitting the same spots But it's not accurate by hitting the middle of the bullseye in C They're all equal distance around the bullseye So there is some accuracy in that you're hitting the same distance from the bullseye, but it's not precise And then in D as you see all the the darts are Landing close to the bullseye. They're close together. So in this case D shows us the best typical D shows us the best results because the results are precise and they're accurate So we want to have studies that are both precise and accurate, but some studies may need to be more reliable So that we can reproduce it some of them may need to be more valid Or they're closest to the true values, but they may be scattered a little bit