Screening Presentation PDF

Screening Maryam Zamanian Phd of epidemiology 1 Lesson Objectives  Explain screening.  Explain the difference between screening and disease detection.  Define sensitivity and specificity.  Define positive predictive value and negative predictive value.  Calculate and interpret se...

Screening Maryam Zamanian Phd of epidemiology 1 Lesson Objectives  Explain screening.  Explain the difference between screening and disease detection.  Define sensitivity and specificity.  Define positive predictive value and negative predictive value.  Calculate and interpret sensitivity and specificity.  Calculate and interpret positive and negative predictive value.  Describe the characteristics and application of the ROC curve.  State the applications and differences between parallel and Serial tests.  State possible biases in screening.  State the characteristics of an appropriate screening test.  State the characteristics of a disease suitable for screening. 2 Iceberg phenomenon 3 Screening Normal blood pressure High blood pressure Diagnosed high blood pressure Society 4 Population screening “application of a test to asymptomatic people to detect occult disease or a precursor state” (Alan Morrison, Screening in Chronic Disease, 1985) 5 Population screening “Screening includes methods; technique; procedure and examinations for early and rapid detection of unrecognized disease or defect in apparently healthy persons(before they have symptoms of the disease. ).” 6 For what it is done? Screening is carried out in the hope that earlier diagnosis and subsequent treatment favorably alters the natural history of the disease in significant proportion of those who are identified as “positive” The goal of this program is to discover conditions in their earliest and most treatable stages. The program only works for certain conditions where there is effective and sensitive screening tests and effective treatment. In these cases, minimally invasive treatments can be used, and improved life expectancy should result. Ultimate objective is to reduce mortality and morbidity 7 CRITERIA for screening for diseases Acceptable, cost-effective, accurate, repeatable and safe screening tests are essential The disease screened must be common and should have a detectable preclinical stage, for which effective treatment should be available(Early detection & treatment, reduces morbidity & mortality- improve clinical outcomes) A large proportion of people at risk should participate in screening, investigations and treatment The local health services infrastructure should be sufficiently developed to provide the diagnostic, treatment and follow-up services for positives 8 Screening tests / Diagnostic tests Done on apparently healthy. Done on those with indication or sick. Applied on groups. Applied on single patient Essentially indicates suspicion of disease Result provides a definite diagnosis Based on one criteria or cut-off point. Based on evaluation of a number of symptoms, sign and lab findings. Simple, acceptable to patients and staff maybe invasive, but justifiable as necessary to establish diagnosis Less accurate. More accurate. Less expensive. More expensive. Not basis for treatment. Used as a basis for treatment. 9  Preclinical phase of disease Clinical phase Symptoms Disease detectable Critical point for develop detecting disease Disease detectable Death Biologic onset via screening tools of disease Screening time Lead time Flow diagram for a screening program Population + Screening test - Diagnostic procedures Unconfirmed diagnosis Intervention Confirmed diagnosis 11 Gold standard Gold standard To determine the sensitivity and specificity of a screening test, we compare the results of the test in question, with a gold standard test that accurately determines whether individuals are healthy or sick. 12 What is used as a “gold standard” 1. Most definitive diagnostic procedure e.g. microscopic examination of a tissue specimen 2. Best available laboratory test e.g. polymerase chain reaction (PCR) for HIV virus 3. Comprehensive clinical evaluation e.g. clinical assessment of arthritis 13 Why don't we always use the Gold Standard? It's expensive It's time-consuming It's difficult (it's invasive!) Sometimes it's not possible 14  Validity How well the test measures what it is supposed to measure. (The ability of a test to distinguish between sick and healthy people) It has two components: 1-Sensitivity 2- Specificity 15 Validity: 1) Sensitivity Probability (proportion) of correct classification of cases Cases found / all cases 16 Validity: 2) Specificity Probability (proportion) of correct classification of noncases Noncases identified / all noncases 17  Test Cut off Health Disease True Negative True Positive False Negative False Positive 18 Screening test False True Positive positive + Screening _ True Negative False negative 19 Screening test Disease Yes No True Positive False Positive Pos. Test False Negative True Negative Neg. 20 Question? In a screening program for congenital hypothyroidism in newborns, serum TSH levels are measured. The question for health officials is: If some newborns do indeed have congenital hypothyroidism, how well does the test detect these newborns? How well does the test detect healthy newborns? 21 Sensitivity & Specificity Disease Yes No Pos. a b Test Neg. c d a+c b+d 22  Disease Present Disease Absent Test Positive True Positive False Positive Test Negative False Negative True Negative Sensitivity = 23  Disease Present Disease Absent Test Positive True Positive False Positive Test Negative False Negative True Negative Specificity = 24  True Disease Status Cases Non-cases True False Positive positive positive a+b Screening a b Test c d Results False True Negative c+d negative negative a+c b+d True positives a Sensitivity = = All cases a+c True negatives d Specificity = = All non-cases b+d 25  True Disease Status Cases Non-cases Positive 140 1,000 1,140 Screening a b Test c d Results 19,000 19,060 Negative 60 200 20,000 True positives 140 Sensitivity = = = 70% All cases 200 Specificity = True negatives = 19,000 = 95% All non-cases 20,000 26 Question? A 35-year-old man has visited a doctor to check his health. The doctor has also performed clinical and laboratory examinations on him for this purpose. The test results revealed that this person is HBS positive. In reviewing this person's history, no reason was found for him to be positive for HBS. Now the question arises: based on this test result, how sure can we be that this person is really HBS positive? 27 PPV & NPV Disease Yes No Pos. a b a+b Test Neg. c d c+d 28 Interpreting test results: predictive value Probability (proportion) of those tested who are correctly classified / Cases identified all positive tests / Noncases identified all negative tests 29  Disease Present Disease Absent Test Positive True Positive False Positive Test Negative False Negative True Negative PPV= 30  Disease Present Disease Absent Test Positive True Positive False Positive Test Negative False Negative True Negative NPV= 31  True Disease Status Cases Non-cases True False Positive positive positive a+b Screening a b Test c d Results False True Negative c+d negative negative a+c b+d True positives a PPV = = All positives a+b True negatives d NPV = = All negatives c+d 32  True Disease Status Cases Non-cases Positive 140 1,000 1,140 Screening a b Test c d Results 19,000 19,060 Negative 60 200 20,000 True positives 140 PPV = = = 12.3% All positives 1,140 True negatives 19,000 NPV = = = 99.7% All negatives 19,060 33 Sensitivity Ability of test to detect people who have disease Specificity Ability of test to detect people who do not have disease Positive Predictive Value Likelihood that a person with a positive (PPV) test result, truly has disease Negative Predictive Value Likelihood that a person with a negative (NPV) test result, truly does not have disease Measures used in screening  Sensitivity is the likelihood that those with disease will be picked up by the screening test  Specificity is the likelihood that those with a negative screening test will not have the disease  Positive predictive value is the likelihood that those with a positive test will have the disease  Negative predictive value is the likelihood that those with a negative test will not have the disease 35  Gold Standard Positive Negative Total True positive False Positive positive A+B False True Screening Test Negative negative negative C+D Total A+C B+D N Sensitivity = TP/TP+FN= A/A+C Specificity= TN/TN+FP= D/B+D Positive predictive value (PPV) = TP/TP+FP= A/A+B Negative predictive value (NPV) = TN/TN+FN= D/C+D 36  Gold Standard Positive Negative Total Positive 80 100 180 Test A Negative 20 800 820 Total 100 900 1000 Sensitivity = TP/TP+FN= 80/80+20= 80% Specificity= TN/TN+FP= 800/800+100=88.8% PPV = TP/TP+FP=80/80+100= 44.4 % NPV= TN/TN+FN=800/800+20=97.5% 37 Measures for screening Sensitivity and Specificity Positive predictive value and Negative predictive value Disease Total Yes No Screening Positive 300 30 130 test Negative 20 3000 3020 Total 320 3030 3350 38 Measures for screening Sensitivity and Specificity Positive predictive value and Negative predictive value Disease Total Sensitivity = 300/320 = 94% Yes No Specificity= 3000/3030 = 99% Screening Positive 300 30 330 test Negative 20 3000 3020 PPV= 300/330 = 91% Total 320 3030 3350 NPV= 3000/3020 = 99% 39 Relationship of disease prevalence with PPV As we have seen, the higher the prevalence, the higher the positive predictive value. Therefore, a screening program is most productive and efficient if it is directed to a high-risk target population. 41  Positive predictive value, Sensitivity, specificity, and prevalence Prevalence (%) PV+ (%) Se (%) Sp (%) 0.1 1.4 70 95 1.0 12.3 70 95 5.0 42.4 70 95 50.0 93.3 70 95 42 Uses of sensitive test  When we do not want to miss a single case of the disease.  When we have reasons to suspect a dangerous but treatable disease, such as tuberculosis, syphilis, Hodgkin's disease.  In order to finally rule out the disease ("rule out").  In general, a sensitive test is more valuable to the doctor when the result is negative. 43 Uses of specific test  When a false positive test result causes physical, psychological, or economic harm to the patient.  In order to definitively confirm the disease ("rule in")  In general, a specific test is more valuable to the physician when the result is positive. 44 Relationship between sensitivity & :specificity As sensitivity increases, the specificity of the test decreases, and as specificity increases, the sensitivity of the.test decreases 45  ROC Curves 1  Area under ROC curve: 0.8 1 = Perfect diagnostic test 0.6 0.5 = Useless diagnostic test Sensitivity 0.4 If the area is 1.0, you have an ideal 0.2 test, because it achieves both 100% 0 sensitivity and 100% specificity. 0 0.2 0.4 0.6 0.8 1 If the area is 0.5, then you have a test which has effectively 50% 1- Specificity sensitivity and 50% specificity. This is a test that is no better than flipping a coin. 46 ROC Curves  The ROC curve helps us determine the best cutoff point for sensitivity and specificity.  Generally, the best cutoff point for the ROC curve is near the shoulder of the curve, unless there is a clinical reason to reduce false negatives or false positives.  The ROC curve is also a valuable way to compare different tests for a particular diagnosis. 47 The CAGE is both more sensitive and more specific than the MAST and includes a much larger area under its curve. Multiple Tests PARALLEL TESTS A number of tests are administered to a person at the same time. If the result of one or more tests is positive, the person is considered sick. When we need a quick check, such as in emergency cases. 49 Multiple Tests SERIAL TESTS If the result of one test is positive, the next test is administered. Only those who have negative results in all tests are considered healthy. In non-emergency situations Also, when some tests for a disease are expensive or dangerous, and such tests will only be prescribed after the disease is confirmed by cheaper and simpler tests. 50 51 Lead Time  The time interval between the detection of a disease through screening and the detection of the same disease normally with signs and symptoms. 52 Lead time bias: Make diagnosis of disease but no mortality benefit Screening may advance the time of diagnosis through earlier detection, but due to the weakness of human knowledge, the patient's death cannot be delayed. As a result, the gap between diagnosis and death increases, and ultimately it is mistakenly believed that screening has increased the patient's life expectancy. Given that screening picks up disease at an earlier stage – the time between diagnosis and death increases without any actual increase in survival 53 Lead time bias: 54 Lead Time Bias     Survival Time Time Screened Diagnosis Patient Group Confirmed Expires Lead Time Time  Control Group  Survival Time Symptoms Dx. Confirmed Patient Expires Length Bias: “Overdiagnosis” and “Pseudo-disease”  Patients who are detected in a timely manner through a regular screening program have had longer presymptomatic stages (and possibly less severe disease or a better prognosis) than those who are detected spontaneously over time (with the onset of symptoms or tests, etc.).  Therefore, it is possible that patients detected in screening have a better prognosis (e.g., lower mortality- less aggressive ) than patients detected with symptoms (spontaneously and without screening), and it is mistakenly believed that screening has been beneficial and, for example, has increased the survival of patients. 56 Length bias: 57 Length Bias  More Aggressive        More Indolent Time 58 Volunteer Bias  People who volunteer for screening may differ from others (who did not volunteer) in some characteristics that are related to prognosis.  This can lead to the misconception that screening has led to a better prognosis (e.g., fewer deaths), while the nature of the disease of the volunteers participating in screening was different from that of others, and their better prognosis was not related to screening. 59  Thanks! ?Any Question 60