 Hello everyone, welcome back to another session in Dentistry and more today we have a topic from Epidemology or public health industry that is screening for disease. So screening is nothing but actively searching a Disease in an apparently healthy people or a population So hope you remember the concept of causation and iceberg phenomena Where the iceberg represents the cases? But the tip of the iceberg is a symptomatic cases that is only what we see So these tip of the iceberg or the disease people with symptoms who goes to Doctors for the treatment, but the majority there is a 90 percentage of the disease people This 90 percentage are asymptomatic, but they still have disease They are below the water level They don't know they have the disease so they don't go to doctors for the treatment. So screening works in that section of the disease Population that is the asymptomatic cases. So screening is nothing but actively searching Cases in an asymptomatic population or apparently healthy people So uses of screening the first one is a prescriptive screening that is for our own benefit So the doctors or healthcare person do screening in order to find out a disease in a healthy people Okay, so screening procedures which is prescriptive in nature for the case detection Whereas a prospective screening here the Benefit is for the others not for the person because we are protecting these people In order not to spread the disease. It is very common nowadays because the COVID-19 disease where we quarantine People with disease because they don't spread to others It is actually benefiting the other people Because they don't get the disease from the person that is prospective screening screening for immigrants from infectious disease Now for the research purpose that is to estimate the prevalence and incidence which can help the research people and also to educate or to create awareness for lifestyle diseases such as diabetes or to Detect the disease so early so that the intervention can be done so early So the complications or mortality will be very less So what are the types of screening they are mass screening high risk screening Then the multi-phase screening mass screening is nothing but it is done on a mass of people irrespective of any high risk group or a particular risk Just like the tuberculosis which is very prevalent in third-world countries like India by 60 to 70 percentage are Having disease but majority are asymptomatic or carriers So in order to find the disease from such a huge population with 70 percentage of prevalence We need to do mass screening. We just cannot exclude any group Because the disease is so prevalent. So in such cases mass screening is the only strategy The second one that is high risk or selective screening where only a particular group can be Involved such as if for diabetes We can just use people with 40 plus or people's parents with diabetes history And if for breast cancer the women group of 40 plus can be particularly used Because majority of the cases will be in that sentence so we can just reduce our cost workflows or time whereas this Multi-phase screening it is done on different phases for any disease The first it starts with questionnaire then clinical examination and then there will be investigation which can be a blood investigation or The radiographic investigation because it is having many layers of investigation to find out the disease but it is Not having much benefit. It is showing increased cost So what are the criteria of screening tests? The three criteria is Acceptability repeatability and validity. Acceptability is nothing but it should be very acceptable for the people Repeatability is consistency and validity is accuracy Acceptability it should not be painful, discomforting or embarrassed for the people Repeatability is nothing but the reproducibility or the consistency. It should give the test should give consistent results when repeated more than once So there are three factors which involves in the repeatability The first one is observer variation then the subject variation and technical Here is observer variation We have two things that is intra-observer and Inter-observer intra means within the same person the same observer if I'm doing the examination I should be doing it two or three times so that I can minimize the error by taking average Whereas inter-observation is a little more complicated because there are two people involved so The proper training or the intensive training or the standardization of procedures only can minimize the error because Both person has to record the result accurately or consistently so We need to train the examiners very well or we need to standardize the procedure Whereas these errors are common in cases of x-ray, ECGs, blood pressure or histopathological specimen But it cannot be eliminated completely that is there Now the biological variation it can be due to the changes in parameters Or radiation in perceiving the symptoms And technical errors could be the defective instruments the faulty reagents But this is the most important thing and the Screening test that is a validity how valid how accurate the test in Finding out the disease. So what extent test accurately measures which it intends to measure The test which is to identify the HIV patients or test to identify the corona patient. So how accurately it detects the Person worth disease. Let's take the RT-PCR its accuracy is around 95 percentage means if population with 100 people With COVID-19 disease the RT-PCR would be able to detect 95 out of 100 still It is not able to detect five people with disease So that is the accuracy or validity what extent the test can measure what Intense to measure so it distinguish those who have disease from those who do not We can say glycosurus is a screening test for diabetes but GTT glucose tolerance test is a more valid just like the antigen test is a screening test Whereas the RT-PCR is a confirmatory test or more accurate test So validity has two components that is a sensitivity and specificity So this is a picture you need to visualize when thinking about sensitivity and specificity before that The validity means nothing but accuracy so how accurate The result of the test to its true value how accurate it is giving the The test is to its true value if 100 people are having disease how accurately it is reproducing the test result Whereas the reliability is precision how close are the results of a test on reputation? That is reliability Now we have some mathematical test for the evaluation of screening test One is a sensitivity specificity then positive predictive value negative predictive value percentage of false positive and percentage of false negative So before moving on you need to keep in mind that positive means case negative means healthy people false positive means false case that is healthy person false negative means false healthy that is actually a case So sensitivity it is the ability of a test to identify correctly all those with disease that is true positive 90% is sensitivity means 95 90% of the disease people are screened and they give the positive result and ten percentage will be false negative that means if a population of 100 people and we are having a test and the test giving 90 positive result that is 100 disease people and this test is saying that out of 190 are having disease So what about the ten percentage this test is saying actually they are false negative They are saying there is no disease, but they are actually having disease, right? Because all 100 people are having disease, but this test is able to detect only 90 out of those 100 The remaining turn is false negative that is sensitivity ability to detect the correctly detect the disease cases Whereas specificity Identifying correctly those who do not have the disease that is true negative Before it was true positive true positive means case true negative means healthy So 90% specificity means 90 by 90% of this non-disease people will be true negative Then percentage will be false first day that is if 100 people we take and nobody's having disease Okay, this test after this test this test is saying that 90% is true negative. That means 90% is not having disease, but it is saying that 10% is having disease But actually they do not have any disease So this is a error of this test This is able to give only 90% of the specificity that is the 10% of the population This test is giving wrong information So that is sensitivity and specificity any test any test is Mature in this two value sensitivity and specificity to take any test will be measured They'll be mentioning about its sensitivity and specificity So the perfect test is 100% is sensitive and 100% is specific. That is very rare So let's take these all things into a mathematical table that is A, B, C and D A is true positive that is it is actually a disease and and test also it is giving positive Actually it is disease, but in test it is giving negative that is false negative. Okay Actually, there is no decision, but it test it is giving positive. That is false positive Actually, it is no no disease and test also giving negative that is true negative. So this is A, B, C and D So total positive cases are A plus B. It also includes false positive. Total negative cases are C plus D It also includes false negative. Total cases are A plus C and Total Healthy or not disease are B plus D or without people without disease So sensitivity is this is persons with positive test divided by all persons in population with disease. So this is the Denominator all persons with disease A plus C That is total case. So it will be This is person with positive test A by A plus C whereas specificity is a non-disease person with negative test that is D divided by all persons in population without disease that is B plus D. So sensitivity is A by A plus C Specificity is D by B plus D. So never learn this A by B C by D A plus B C plus D because it is very confusing So you need to be Be very thorough with this concept. What is true positive? What is false positive? What is false negative and false true negatives? next we have Positive predictive value and negative predictive value. Positive predictive value is a proportion of patients who test positive who actually have the disease So this is a proportion of patients With positives who actually have the disease that is A by A plus B. A plus B is the total positives and A is the actual diseases from the total positives Whereas the negative test is a proportion of Patients who test negative who are actually free of disease So D is the actual people without disease by the test giving that is C plus D is the total negative by the test So this is just horizontal A by A plus B D by C plus D whereas the sensitivity is A by A plus C or D by B plus D That is specificity Now let's take an example where the total population is thousand Out of them 100 is having disease 900 is not having disease. Okay So we have done a test in that 80 has given positives So 20 is Negative. This is a test result. Okay. Actually 100 is having disease So true positive is 80 then The false negative is 20 True negative 800 This is what the test has given and 100 is false positive But actually 900 is a total negative or healthy 100 is a Total cases, but the test is able to give only 80 out of 100 and 800 out of 900 cases and healthy respectively So total positive is 180 and total negative is 820 and total. So let's see one by one What is sensitivity? Sensitivity is nothing but A by A plus C that is 80 by 80 plus 20 100 that is 80 percentage and specificity is a D by B plus D That is 800 by 800 plus 100 that is 800 by 900 89 percentage So this particular test is able to detect 80 cases out of 100 cases and this test is able to detect 89 healthy people out of 100 healthy people So its sensitivity is 80 percentage and specificity is 189 percentage So now let's see the positive predictive value. It is a proportion of people with positive among the total positive that is 80 by A by A plus B 80 by 80 plus 100 that is 80 by 180 44 percent. This is a proportion of the Positively tested among the total positive or the cases among the total positives. This is a healthy people among the total negatives it is 800 by 800 plus 20 that is 98 percentage is an 80 predictive value and 44 percentage is a positive predictive value Now we have the percentage of false negative and percentage of false positive. That is C by A plus C and B by B plus D. It gives a percentage of false negative and false positive First false positive is B and false negative is C. So just take the denominator of disease and non-desist So this is an example you can try it out I have the answer with me So we need to find out the sensitivity, specificity, positive predictive value and anti-predictive value And this percentage of false negative and false positive. So sensitivity will be 40 by 140 Specificity will be 9840 by 9860 Then the positive predictive value will be 40 by 60 then negative predictive value will be 840 by 9 940 So here you have the answer. Sensitivity 40 by 140 specificity 9 840 by 9 860 false negative is 100 by 140 false positive by 20 by 9 100860 positive predictive value is 40 by 60 and this and 100840 by 9000 So this is a mathematical evaluation of any test Ideally speaking sensitivity and specificity should be 100 or close to 100 Okay, so less than 90% age won't be much accepted So That is all about a screening The only thing is you need to be very thorough with these false positive and false negative True positive and true negative and nothing but the cases and healthy people and how we calculate the sensitivity specificity And never learn this A, B, C, D Always try to understand the concept and Then apply it. So you won't remember you won't forget it for a very long period So if you understood this topic of screening, I'll come up with a new topic in dentistry and more. Thank you