SCREENING Dr Farhat R Malik Assistant Professor Department

SCREENING Dr. Farhat R Malik Assistant Professor Department of Community Health Sciences Peshawar Medical College

Session Objectives 2 At the end of session the students will be able to: • • • Define screening Explain why we do screening. Classify types of screening. Differentiate between screening & diagnostic tests. Enlist criteria for screening. Calculate & interpret the following from the 2*2 table: Sensitivity, Specificity, Positive Predictive and Negative Predictive value 6/16/2021

Do you recognize this? ? ?

Definition Screening, in medicine, is a strategy used in a population to identify the possible presence of an as-yet-undiagnosed disease in individuals without signs or symptoms. This can include individuals with pre-symptomatic or unrecognized symptomatic disease.

Why we do Screening Early detection of disease Control the complications Delay the onset of clinical disease Reduce morbidity Prolong survival

Uses of Screening Identify the cases Control the disease Prevent the disease Research Educational

Screening vs. Diagnosis 7 Screening Diagnosis Done on Apparently healthy Cases (signs and symptoms) Applied on Groups , populations Individuals Based on One criterion (cut off) Signs symptoms, lab findings Cost Relatively cheaper Expensive Time taken Relatively rapid Time consuming Accuracy Relatively inaccurate Accurate Basis for treatment Cannot be used as basis Useful basis for treatment Initiative from Investigator Cases with complaints 6/16/2021

Can you name some diseases recommended for screening in Pakistan HIV/AIDS ? ? ? ? ?

Types of Screening Mass Selective/ High Risk/ Targeted Individual Opportunistic Accidental

When is screening successful? 11 Suitable disease Suitable test Suitable screening program 6/16/2021

Suitable disease? 12 Serious consequences Progressive Treatment must be effective at early stage Example of suitable diseases are breast cancer and hypertension 6/16/2021

Natural history of disease 13 Years 30 A (Biological onset) 35 40 B (Disease detectable by screening) 45 50 53 55 60 C (Symptoms develop) 65 70 D (Death) 6/16/2021

Natural history of disease 14 Pre-clinical phase Detectable pre-clinical phase Lead Time Duration of time by which the diagnosis is advanced as a result of screening. 6/16/2021

Suitable Test? 15 A test should classify people with pre-clinical disease as positive, and people without preclinical diseases as negative. ◦ Whether the test does what it is supposed to do (Is it valid? ) Validity is ability of a test to distinguish between who has a disease and who does not ◦ Whether the test gives the same result on repetition under similar conditions? (Is it reliable? ) Reliability is the repeatability of a test 6/16/2021

Validity vs. Reliability 16 6/16/2021

2× 2 Table for Screening True Disease Status 17 Results of screening test Yes No Total Positive a b a+b Negative c d c+d Total a+c b+d a+b+c+d 6/16/2021

2× 2 Table for. True. Screening Disease Status 18 Positive Results of screening test Negative Total Yes No Total a b a+b (True Positive) (False Positive) c d (False Negative) (True Negative) a+c b+d c+d a+b+c+d 6/16/2021

Sensitivity 19 True Disease Status Yes No Total Sensitivity: Positive Results of screening test Negative Total a b (True Positive) (False Positive) c d (False Negative) (True Negative) a+c b+d a+b The probability of testing positive if the disease is truly present c+d Sensitivity= a/(a+c) a+b+c+d 6/16/2021

Specificity 20 Yes. Disease Status No True Positive Negative Results of screening test Total a b (True Positive) (False Positive) c d (False Negative) (True Negative) a+c b+d Total a+b Specificity: The probability of screening negative if the disease is truly absent c+d a+b+c+d Specificity= d/(b+d) 6/16/2021

21 Recap - Validity of Screening Test • Sensitivity – the ability of the test to correctly identify those who HAVE the disease • Specificity – the ability of the test to correctly identify those who DO NOT HAVE the disease 6/16/2021

True Disease Status Yes 22 Positive Results of screening test Negative Total No a b (True Positive) (False Positive) c d (False Negative) (True Negative) a+c b+d Total a+b c+d a+b+c+d Positive predictive value = probability of the person having the disease when the test is positive = a/ (a+b) Negative predictive value = probability of person not having the disease when the test is negative =d/ (c+d) Positive & Negative Predictive Values 6/16/2021

Predictive value of a test 23 • • Reflects diagnostic power of a test(yield) The probability that a patient with a positive test has in fact the disease in question – Positive • predictive value The probability that a patient with a negative test has in fact not got the disease in question • Negative predictive value 6/16/2021

Example 24 Confirmed Not Confirmed Total Positive y ph 132 983 a+b Negative 45 63, 650 c+d Total a+c b+d a+b+c+d Breast Cancer a am m r og M 6/16/2021

Reliability 25 Can the results of a test be replicated? If a test is valid but not reliable, results are meaningless 6/16/2021

Factors that affect reliability 26 § § Subject variation (biological variation): blood pressure, blood sugar, serum cholesterol Measurement (instrument) bias 6/16/2021

Factors that affect reliability 27 § § Intra observer variation: variation within an individual subject by the same observer e. g. blood pressure variation in an individual Inter observer variation: variation between different observers’ recorded test results 6/16/2021

Lead Time Bias 28 Woman A Woman B diagnosed at diagnosed after screening symptoms’ development Years 30 A (Biological onset) 35 40 B (Disease detectable by screening) 45 50 53 55 Both woman A and B died from breast cancer at age 70 60 C (Symptoms develop) 65 70 D (Death) Survival for Woman B = ? years Apparent Survival for Woman A = 25 years Both women died at the same age but lead-time bias makes it seem as though Woman A has a 8 year longer survival. 6/16/2021

Lead time bias 29 Incorrect perception that a disease has a longer survival, when the longer survival is actually due to earlier detection 6/16/2021

MCQs Example 30 False negative means; a. b. c. d. Persons have disease but show negative test results Persons have no disease but show negative test results Persons have disease but show positive test results Persons have no disease but show positive test results 6/16/2021

SEQ Examples 31 1. 2. Difference between screening and diagnosis. Calculate the following from the data given in 2*2 table; Gold Standard Test New Screenin g Test Positive Negative Positive 22 7 Negative 4 142 Sensitivity Negative Predictive Value 6/16/2021

Jazak. Allah Khairun Kaseera 32 6/16/2021