Your SlideShare is downloading. ×
Screening for diseases by Dr. San
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Screening for diseases by Dr. San

772
views

Published on

Published in: Health & Medicine

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
772
On Slideshare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
84
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Screening for Diseases Dr San San Oo
  • 2. Learning outcomes 1. To describe the concept of screening 2. To differentiate between screening test and diagnostic test 3. To explain the concept of “lead time” 4. To understand aims and objectives of screening 5. To list the uses of screening8/12/2012 Dr.san san oo_commed 2
  • 3. 6. To enumerate the types of screening 7. To describe the basic requirements of a screening test 8. To calculate the validity (sensitivity and specificity) of a screening test and interpret them 9. To calculate the predicative accuracy of a screening test and interpret them 10.To set the cutoff levels of a screening test for different diseases8/12/2012 Dr.san san oo_commed 3
  • 4. Introduction• Necessary to distinguish – Who have the disease – Who do not• Important challenge – Clinical arena (for patient care) – Public health arena (for early disease detection and intervention)• Quality of screening and diagnostic tests – a critical issue8/12/2012 Dr.san san oo_commed 4
  • 5. Concept of Screening• The search for unrecognized disease or defect by means of rapidly applied tests, examinations or other procedures in apparently healthy individuals• A fundamental aspect of prevention• ACTIVE SEARCH FOR DISEASE8/12/2012 Dr.san san oo_commed 5
  • 6. Screening test and diagnostic testScreening test Diagnostic test• Apparently healthy • With indications or sick• Groups • Single patients• Test results are arbitrary and • Diagnosis not final, the sum of final all evidence• One criterion or cut-off • Numbers of symptoms, signs and lab investigations• Less accurate • More accurate• Less expensive • More expensive• Not a basis for treatment • Basis for treatment• Initiatives from investigators or • Initiatives from a patient with agency a complaint8/12/2012 Dr.san san oo_commed 6
  • 7. Concept of “lead time”8/12/2012 Dr.san san oo_commed 7
  • 8. • “Lead time” – the advantage gained by screening i.e. the period between diagnosis by early detection and diagnosis by other means• A = usual outcome of the disease• B= outcome to be expected when disease is detected at the earliest possible moment• B-A = benefits of the programmes8/12/2012 Dr.san san oo_commed 8
  • 9. Aims and objectives Apparently healthy (Screening tests) Apparently normal Apparently abnormal (Periodic re screening) Normal Intermediate Abnormal (Periodic re- (Surveillance) (Treatment) screening)8/12/2012 Dr.san san oo_commed 9
  • 10. Uses of screening1. Case detection – Prescriptive screening – Presumptive identification of unrecognized disease – E.g. Breast cancer, cervical cancer, diabetes2. Control of disease – Prospective screening – For benefits of others – E.g. screening of immigrants from infectious diseases8/12/2012 Dr.san san oo_commed 10
  • 11. 3. Research purposes – More basic knowledge about natural history of diseases – E.g. chronic diseases (cancer, hypertension)4. Educational opportunities – Creating public awareness and educating heath professionals – E.g. screening for diabetes8/12/2012 Dr.san san oo_commed 11
  • 12. Types of screening1. Mass screening – Whole population – Sub groups2. High risk or selective screening – High risk groups – Screening of diabetes, hypertension, breast cancer in other members of family3. Multiphasic screening – Two or more screening tests at one time8/12/2012 Dr.san san oo_commed 12
  • 13. Criteria for screening• Two considerations1. The disease2. The test8/12/2012 Dr.san san oo_commed 13
  • 14. IATROGENIC1. Condition should be important (I)2. An acceptable treatment should be available for disease (A)3. Diagnostic and treatment facilities should be available (T)4. A recognizable early symptomatic stage is required (R)5. Opinions on who treat must be agreed (O)8/12/2012 Dr.san san oo_commed 14
  • 15. 6. The safety of the test is guaranteed (G)7. The test examination must be acceptable to the patient (E)8. The untreated natural history of the disease must be known (N)9. The test should be inexpensive (I)10. Screening must be continuous (C)8/12/2012 Dr.san san oo_commed 15
  • 16. Some screening testsPregnancy Infancy• Anaemia • Hearing defects• Hypertension toxaemia • Visual defects• Rh status • Haemoglobinopathies• Syphilis (VDRL) • Spina bifida• Diabetes• HIV• Neural tube defects• Down’s syndrome8/12/2012 Dr.san san oo_commed 16
  • 17. Middle aged men and women Elderly• Hypertension • Cancer• Cancer • Glaucoma• Diabetes mellitus • Cataract• Serum cholesterol • Chronic bronchitis• obesity • Nutritional disorders8/12/2012 Dr.san san oo_commed 17
  • 18. Validity• The extent the test accurately measures what it purports to measure• The ability of a test to separate or distinguish those who have the disease from those who do not• Two components (expressed as %) 1. Sensitivity 2. Specificity8/12/2012 Dr.san san oo_commed 18
  • 19. Test with dichotomous results (positive or negative)8/12/2012 Dr.san san oo_commed 19
  • 20. Two by two tableScreening test Diagnosis (Gold standard test) Total Diseased Not diseasedPositive a (True positives) b (False negatives) a+bNegative c (False negatives) d (True negatives) c+dTotal a+c b+d a+b+c+d8/12/2012 Dr.san san oo_commed 20
  • 21. Evaluation of a screening test1. Sensitivity2. Specificity3. Predictive value of a positive test4. Predictive value of a negative test5. Percentage of false negatives6. Percentage of false positives8/12/2012 Dr.san san oo_commed 21
  • 22. Sensitivity• The ability of a test to Screening Diagnosis Total test identify correctly those who have the disease Diseased Not diseased• Proportion of individuals with the Positive a b a+b (True (False disease who are positives) positives) correctly identified by the test Negative c d c+d (False (True• True positives negatives) negatives)• a/a+c Total a+c b+d a+ b+c +d8/12/2012 Dr.san san oo_commed 22
  • 23. • A measure of the probability of correctly diagnosing a case• The probability that any given case will be identified by the test• A 80% sensitivity means • 80% of the diseased people screened by the test will give a “true positive” result • The proportion of diseased people who are correctly identified as “positive” by the test is 80%8/12/2012 Dr.san san oo_commed 23
  • 24. Specificity• The ability of a test to Screening Diagnosis Total test identify correctly those who do not have the Diseased Not diseased disease Positive a b a+b• Proportion of individuals (True (False without the disease who positives) positives) are correctly identified by Negative c d c+d the test (False (True negatives) negatives)• True negatives• d/b+d Total a+c b+d a+b+c+d8/12/2012 Dr.san san oo_commed 24
  • 25. • A measure of the probability of correctly identifying a non-diseased person with a screening test• A 90% specificity means • 90% of the non-diseased people screened by the test will give “ true negative” result • The proportion of non-diseased people who are correctly identified as negative by the test is 90%8/12/2012 Dr.san san oo_commed 25
  • 26. Example (1)Screening test Diagnosis (cervical biopsy) TotalPap smear Diseased Not diseasedPositive 160 240 400Negative 40 560 600Total 200 800 1,000Sensitivity = 160/200 * 100 = 80%•80% of women having Ca cervix screened by Pap smear will give “ true positive” result.•The proportion of women having Ca cervix who are correctly identified as positive byPap smear is 80%.Specificity = 560/800 * 100 = 70%•70% of women not having Ca cervix screened by Pap smear will give “true negative”result.•The proportion of women not having Ca cervix who are correctly identified as negativeby Pap smear is 70%.8/12/2012 Dr.san san oo_commed 26
  • 27. False negatives• Patients who actually Screening Diagnosis Total have the disease are told test that they do not have the disease Diseased Not diseased• c/a + c• False reassurance Positive a b a+b (True (False• Ignore the development positives) positives) of symptoms and signs• Critical Negative c d c+d (False (True – if effective intervention is negatives) negatives) available (e.g. cancer)• Very sensitive test has Total a+c b+d a+b+c+d fewer FN8/12/2012 Dr.san san oo_commed 27
  • 28. False positives• Patients who do not Screening Diagnosis Total have the disease are test told that they have Diseased Not• b/b+d diseased• Further tests Positive a b a+b• Expenses (True (False positives) positives)• Anxiety and worry• Limitation in Negative c d c+d employment (False negatives) (True negatives)• A high specificity screening test has fewer Total a+c b+d a+b+c+d FP8/12/2012 Dr.san san oo_commed 28
  • 29. Sensitivity or Specificity ?• 100% as much as possible (Ideal)• Gain sensitivity at the expense of specificity and vice versa (Practice)• High sensitivity with fewer false negatives – Effective intervention especially at the early stage of the natural history of disease• High specificity with fewer false positives – Serious and untreatable• No screening test is perfect i.e. 100% sensitivity and 100% specificity8/12/2012 Dr.san san oo_commed 29
  • 30. Tests of continuous variables• Blood pressure No “positive” or• Blood glucose level “negative” result• The use of cut-off values8/12/2012 Dr.san san oo_commed 30
  • 31. 8/12/2012 Dr.san san oo_commed Downloaded from: StudentConsult (on 27 November 2010 02:13 AM) 31 © 2005 Elsevier
  • 32. Trade-off between sensitivity and specificity• Cut off level at 80 mg/dl – All diabetes are identified (100% sensitivity) – Many FP – Very low specificity• Cut off level at 200 mg/dl – All non diabetes are correctly identified (100% specificity) – Many FN – Very low sensitivity8/12/2012 Dr.san san oo_commed 32
  • 33. Dilemma• High cutoff or low cutoff ?• Only have 2 groups – Test positives – Test negatives• Depend on the relative importance of – False positives – False negatives8/12/2012 Dr.san san oo_commed 33
  • 34. Decision• When the disease is – Lethal High sensitivity – Early detection low cutoff values improves the prognosis (E.g. cervical cancer, breast cancer) – Tolerable FP• When the disease – Tx not change much High specificity – Need to limit FP high cutoff values (E.g. diabetes)8/12/2012 Dr.san san oo_commed 34
  • 35. How to choose the best cutoff points• The Receiver operator curve (ROC)8/12/2012 Dr.san san oo_commed 35
  • 36. Receiver Operator Characteristic (ROC) Curve ROC curve to determine best cutoff point for scc by means of meanrlu• Plot true positive rate 100 (sensitivity) against 90 50 10 false positive rate 100 80 (1-specificity) for several s 70 1000 (mean rlu) choice of positively e n 60 criterion 10000 s i 50• choose closest to top left ti 40 25000 50000 corner to maximized the vi 30 discriminative ability of y t 20 the test 10 0 0 20 40 60 80 100 8/12/2012 Dr.san san oo_commed 1- specificity 36
  • 37. Receiver Operator Characteristic (ROC) Curve ROC curve to determine best cutoff point for Wilsom Risk sum• The area under the curve scoring to detect difficulty of endotracheal intubation represent overall 100 0 1 90 accuracy of the test 80• useful to compare two 70 2 test sensitivity 60 3 50 40 30 20 5 10 0 8/12/2012 Dr.san san oo_commed 37 0 20 40 60 80 100 1- specificity
  • 38. If the test results are positive, what is the probability that this patient has the disease?8/12/2012 Dr.san san oo_commed Downloaded from: StudentConsult (on 26 November 2010 11:10 AM) 38 © 2005 Elsevier
  • 39. Predictive accuracy• Diagnostic power of the test• Depend upon 1. Sensitivity 2. Specificity 3. Prevalence of disease• Two measures 1. Predictive value of a positive test 2. Predictive value of a negative test8/12/2012 Dr.san san oo_commed 39
  • 40. Predictive value of a positive test• The probability that an Screening Diagnosis Total test individual with a Diseased Not positive test result has diseased the disease Positive a b a+b• a/a+b (True positives) (False positives)• A 44% PPV means Negative c d c+d • 44% of the people with (False (True positive test result have the negatives) negative) disease in question Total a+c b+d a+b+c+d8/12/2012 Dr.san san oo_commed 40
  • 41. Predictive value of a negative test• The probability that an Screening Diagnosis Total test individual with a Diseased Not negative test result diseased does not have the Positive a b a+b disease (True (False positives) positives)• d/c+d Negative c d c+d• A 98% NPV means (False (True negatives) negatives) • 98% of the people with negative test result do not Total a+c b+d a+b+c+d have the disease in question8/12/2012 Dr.san san oo_commed 41
  • 42. Example (2)Screening test Diagnosis (cervical biopsy) TotalPap smear Diseased Not diseasedPositive 160 240 400Negative 40 560 600Total 200 800 1,000PPV = 160/400 * 100 = 40%•40% of women with positive Pap smear result suffered from Ca cervix.NPV = 560/600 * 100 = 93%•93% of women with negative Pap smear result do not suffer from Ca cervix.8/12/2012 Dr.san san oo_commed 42
  • 43. Relationship between Predictive value and Disease Prevalence• There are two community with different breast cancer prevalence; – 50/1,000pop and 30/1,000pop.• Both community has total population of 1,600• If we are going to apply a screening test with 95% sensitivity and 85% specificity• what will be the predictive value of positive and negative in that communities?8/12/2012 Dr.san san oo_commed 43
  • 44. Calculation for community with50/1,000 pop Breast No breast Totals cancer D+ cancer D- Test T+ 76(step 4) 228(step 7) 304(step8) sensitivity Test T - 4(step 6) 1292(step 5) 1296(step 5) specificity Totals 80(step 2) 1520(step 3) 1,600(step 1) prevalence PVP=76/304= 0.25 PVN=1292/1296=.0.9978/12/2012 Dr.san san oo_commed 44
  • 45. Calculation for community with30/1,000 pop Breast No breast Totals cancer D+ cancer D- Test T+ 45.6(step 4) 232.8(step 7) 278.4(step8) sensitivity Test T - 2.4(step 6) 1319.2(step 5) 1321.6(step 5) specificity Totals 48(step 2) 1552(step 3) 1,600(step 1) prevalence PVP=45.6/278.4= 0.16 PVN=1319.2/1321.6=.0.9988/12/2012 Dr.san san oo_commed 45
  • 46. The higher the prevalence the greater the predictive value of positive8/12/2012 Dr.san san oo_commed Downloaded from: StudentConsult (on 26 November 2010 11:38 AM) 46 © 2005 Elsevier
  • 47. Why should we be concerned ?• Directed to – High risk target population• Most productive and efficient• More motivated to participate• More likely to take recommended action8/12/2012 Dr.san san oo_commed 47
  • 48. Efficiency of a test – The percentage of all true positive and true negative results – a+d / a+b+c+d – The higher the value, the more efficient the measure8/12/2012 Dr.san san oo_commed 48
  • 49. Is test useful?• Likelihood ratio (LR) – The likelihood that the test result would be expected in a patient with the condition compared to the likelihood that the same result would be expected in a patient without the condition – Unlike predictive values, likelihood ratios are not influenced by prevalence of the disease8/12/2012 Dr.san san oo_commed 49
  • 50. • Likelihood ratio (Positive) – Divide the sensitivity by 1- specificity• Likelihood ratio (Negative) – Divide the 1- sensitivity by specificity8/12/2012 Dr.san san oo_commed 50
  • 51. Likelihood Ratios Positive Likelihood ratio positive D+ D-(LR+) is the ratio of thesensitivity of a test to the false T+ a b a+bpositive error rate of the test(1- specificity) T- c d c+d The higher the ratio is thebetter the test. a+c b+d a+b+c+LR+ = [a/(a+c)] / [b/(b+d)] 8/12/2012 Dr.san san oo_commed 51
  • 52. Likelihood Ratios Negative Likelihood ratio negative(LR-) is the ratio of thefalse negative error rate of D+ D-a test (1- sensitivity )to thespecificity of the test T+ a b a+b The closer the ratio is to 0 thebetter the test. T- c d c+d a+c b+d a+b+c+d LR- = [c/(a+c)] / [d/(b+d)]8/12/2012 Dr.san san oo_commed 52
  • 53. Summary• Concept of a screening test• How good is a screening test? (Validity)• Question for physician (Predictive accuracy)• Cutoff values• Is test useful? (LR)8/12/2012 Dr.san san oo_commed 53
  • 54. References1. Park. K., 2009. Park’s Textbook of Preventive and Social Medicine. pp 123-130. 20th Edition.2. Gordis. L., 2009. Epidemiology. pp 85-108. 4th Edition3. Petrie. A., and Sabin. C.,2000. Medical Statistics at a Glance. pp 90-928/12/2012 Dr.san san oo_commed 54
  • 55. Assignment Pelvic scan Ovarian cancer Total (n) Present Absent abnormal 15 20 35 normal 5 60 65 Total 20 80 100 A hundred women at high risk of ovarian carcinoma have a pelvic ultrasound scan. The findings after scan and surgery are shown in the table. Calculate the following measures and interpret them. 1. Sensitivity 2. Specificity 3. False negatives 4. False positives 5. Positive Predictive value 6. Negative Predictive value8/12/2012 Dr.san san oo_commed 55
  • 56. • A new screening test with sensitivity of 80% and specificity of 90% was performed on 1,000 persons for detection of avian influenza H5N1 infection. The prevalence of disease was 20% in the general population. Compute the following and interpret them. – Construct 2x2 table. – Calculate positive predictive value of the test. – Calculate false positive of positive test.8/12/2012 Dr.san san oo_commed 56