1. SCREENING Asst. Prof. Sumattna Glangkarn RN, MSc. (Epidemiology), PhD (Nursing studies) sumattana.g@msu.ac.th sglangkarn@hotmail.co.uk

2. Levels of Prevention Primordial prevention Primary prevention Secondary prevention Tertiary prevention

3. Primordial prevention Phase of disease: Underlying economic, social, and environmental conditions leading to causation Aim: Establish and maintain conditions that minimised hazards to health Actions: Measures that inhibit the emergence of environmental, economic, social and behavioural conditions. Target: Total population or selected groups; achieved through public health policy and health promotion.

4. Primary prevention Phase of disease: Specific causal factors Aim: Reduce the incidence of disease Actions: Protection of health by personal and communal efforts, such as enhancing nutritional status, providing immunizations, and eliminating environmental risks Target: Total population, selected groups and healthy individuals; achieved through public health policy.

5. Secondary prevention Phase of disease: Early stage of disease Aim: Reduce the prevalence of disease by shortening its duration Actions: Measures available to individuals and communities for early detection and prompt intervention to control disease and minimise disability (e.g. through screening programmes). Target: Individuals at high risk and patients; achieved through preventive medicine.

6. Tertiary prevention Phase of disease: Late stage of disease (treatment, rehabilitation) Aim: Reduce the number and/or impact of complications Actions: Measures aimed at softening the impact of long-term disease and disability; minimising suffering, maximising potential years of useful life. Target: Patients; achieved through rehabilitation.

7. Screening test Screening is performed in order to identify whether have a disease for which they currently have no symptoms Screening is not performed to diagnose illness. It aims to improve the outcomes of those who are affected, by detecting a disease before its symptoms have developed. If the disease can be diagnosed and treated at an early stage, illness and mortality can be reduced.

8. In practice, screening test are never completely accurate. ‘False-positive’ results, in which the test indicates that a subject has the disease when it reality they do not. ‘False-negative’ results, in which the test indicates that there is no disease present, when it reality the subject does have the disease. A good screening test should keep false-positive and false-negative results to an absolute minimum.

9. The UK National Screening Committee’s criteria for appraising the viability, effectiveness and appropriateness of a screening programme Condition Test Treatment Screening programme

10. Condition The condition should be an important health problem. The epidemiology and natural history of the condition, including development from latent to declared disease, should be adequately understood and there should be a detectable risk factor, disease marker, latent period or early symptomatic stage. All of the cost-effective primary prevention interventions should have been implemented as far as is practicable. If the carriers of a mutation are identified as a result of screening the natural history of people with this status should be understood, including the psychological implications.

11. Test There should be a simple, safe, precise and validated screening test. The distribution of test values in the target population should be known, and a suitable-cut-off level should be defined and agreed. The test should be acceptable to the population. There should be an agreed policy on the further diagnostic investigation of individuals with a positive test result, and on the choices available to those individuals.

12. Treatment There should be an effective treatment or intervention for patients identified through early detection, with evidence of early treatment leading to better outcomes than late treatment. There should be agreed evidence-based policies covering which individuals should be offered treatment, and the appropriate treatment to be offered. Clinical management of the condition and patient outcomes should be optimised by all healthcare providers prior to participation in a screening programme.

13. Screening programme There should be evidence from high- quality randomised controlled trials that the screening programme is effective in reducing mortality or morbidity. There should be evidence that the complete screening programme (including testing, diagnostic procedures, treatment/intervention) is clinically, socially and ethically acceptable both to health professional and to the public. The benefit from the screening programme should outweigh the physical and psychological harm (caused by the test, diagnostic procedures and treatment).

14. The opportunity cost of the screening programme (including testing, diagnosis and treatment, administration, training and quality assurance) should be economically balanced in relation to expenditure on medical care as a whole (i.e. value for money). There should be a plan for managing and monitoring the screening programme and an agreed set of quality assurance standards. Adequate staffing and facilities for testing, diagnosis, treatment and programme management should be made available prior to the commencement of the screening programme.

15. All other options for managing the condition should have been considered (e.g. improving treatment, providing other services), to ensure that no more cost-effective interventions could be introduced or current interventions increased within the resources available. Evidence-based information explaining the consequences of testing, investigation and treatment should be made available to potential participants to assist them in making an informed choice.

16. Public pressure both to widen the eligibility criteria for reducing the screening interval and to increase the sensitivity of the testing process should be anticipated. Decisions about these parameters should be scientifically justifiable to the public. If screening is for a mutation the programme should be acceptable to people identified as carriers and to other family members

17. Evaluation of proficiency of test  Precision (or Repeatability or Reliability) is the ability of a measurement to give consistent results on repeated trials  Validity (or Accuracy) is the ability of a measuring instrument to give a true value. Validity can be evaluated only if there exists an accepted and independent (gold standard) method for confirming the condition.

18. SENSITIVITY and SPECIFICITY Sensitivity is the ability of a test to give positive results in a group of persons with the disease (True positive) Specificity is the ability of a test to give negative results in a group of persons without the disease (True negative)

19. Screening test

20. SENSITIVITY and SPECIFICITY Sensitivity = TPx 100 = a x 100 TP + FN a + c Specificity =TNx 100 = d x 100 FP + TN b + d

21. ACCURACY Accuracy = TP + TN x 100 Grand total =a + d x 100 a + b + c + d

22. Conditions that requires high sensitivity test The disease is fatal if missed. If it is detected at an early stage, the patients would have high probability of surviving, or getting cured. The disease has the high potential of spread to other people if not detected. The confirmatory test is available for those who have screened as positive.

23. Conditions that requires high specificity test The false positive will give fatal impression for the persons screened. The disease is not yet detected by other method, or the diagnosis has to be done through more painful or more complicated methods such as liver biopsy.

24. Example: A pregnancy test is administered to 100 pregnant women and 100 non-pregnant women, the result are shown:-

25. Sensitivity, Specificity and Accuracy Sensitivity = 95x 100 = 95 % 100 Specificity =98x 100 = 98 % 100 Accuracy=95 + 98 x 100 = 96.5% 100+100

26. Example: Three screening test, A, B and C were applied to 1,000 patients with diabetes mellitus (diagnosed on the basis of glucose tolerance tests) and to 3,000 persons free of diabetes. Test A yielded positive results in 900 diabetics and 1,200 non diabetics. Test B gave positive results in 600 diabetics and 300 non diabetics. Test C was positive in 850 diabetics and 450 non diabetics.

27. FIND:- Test A, B, C Sensitivity = ? Specificity = ? Accuracy = ?

28. Results of Test A

29. Results of Test B

30. Results of Test C

31. Competency of the Tests

32. Test A : Best when we want a highly sensitive test Test B : Best when we want a highly specificity test Test C : Most valid of the three because it has high both in sensitivity and specificity. Competency of the Tests

33. Predictive value Ability to detect unrecognised disease and estimation of number of cases in the population Positive predictive value : probability of the person having the disease when the test is positive Negative predictive value : probability of the person not having the disease when the test is negative

34. PPV & NPV  Positive predictive value (PPV) =ax 100 a + b  Negative predictive value (NPV) =dx 100 c + d

35. Example: A test with sensitivity 95% and specificity 95% is applied to a population of 10,000 with estimated prevalence of a specified disease 10% Find :1. Positive predictive value 2. Negative predictive value 3. Efficiency of the test 4. % False positive of the positive test (to be used for mass screening purpose)

36. Calculation : Total population= 10,000 Prevalence of disease=10% Estimate sick persons=0.1 x 10,000 =1,000 (a + c) Estimate non-sick= 10,000 – 1,000 =9,000 (b + d)

37. Calculation (Cont.): From sick persons: Sensitivity of the test=95% True positive persons=0.95 x 1,000 =950 (a) False negative persons=1,000 - 950 =50 (c)

38. Calculation (Cont.): From non sick persons: Specificity of the test=95% True negative persons=0.95 x 9,000 =8,550 (d) False positive persons=9,000 – 8,550 =450 (b)

39. Result of Analysis of a screening test

40.  Positive predictive value =950x 100 1,400 =67.9%  Negative predictive value =8,550x 100 8,600 =99.4% Calculation (Cont.):

41.  Accuracy of the test = 950 + 8,550 x 100 10,000 =95.0%  False Positive of the positive test =450x 100 1,400 =32.1% (100- 67.9) Calculation (Cont.):

42. Example: From the previous example, suppose that the prevalence of the specified disease in the study population is 50% Find :1. Positive predictive value 2. Negative predictive value 3. Efficiency of the test 4. False positive of the test

43. Calculation : Total population=10,000 Prevalence of disease= 50% Estimate sick persons= 0.5 x 10,000 =5,000 (a + c) Then non-sick persons= 10,000 – 5,000 =5,000 (b + d)

44. Calculation (Cont.): From the group of sick persons: Sensitivity of the test=95% True positive persons= 0.95 x 5,000 =4,750 (a) False negative persons= 5,000 – 4,750 =250 (c)

45. Calculation (Cont.): From the group of non-sick persons: Specificity of the test=95% True negative persons= 0.95 x 5,000 =4,750 (d) False positive persons= 5,000 – 4,750 =250 (b)

46. Result of Analysis of a screening test

47.  Positive predictive value = 4,750x 100 5,000 =95.0%  Negative predictive value =4,750 x 100 5,000 = 95.0% Calculation (Cont.):

48.  Accuracy of the test= 4,750 + 4,750 x 100 10,000 =95.0%  False Positive of the positive test = 250x 100 5,000 =5% (100-95) Calculation (Cont.):

49. Screening and Prevalence When the same test is used for screening in populations with higher prevalence of disease, the lower false positives would be obtained.

50. จงหาค่า Positive predictive value เมื่อนำ การทดสอบที่มีคุณสมบัติต่อไปนี้มาใช้ใน ชุมชนที่มีอัตราความชุกของโรคต่างกัน (Pop = 10,000) Test 1 มี Sensitivity 95% และ Specificity 95% Test 2 มี Sensitivity 98% และ Specificity 98% Prevalence (%) Positive predictive value Test 1Test 2 0.1?? 1.0?? 2.0?? 5.0?? 50.0??

51. Test 1 มี Sensitivity 95% และ Specificity 95% Test 2 มี Sensitivity 98% และ Specificity 98% Prevalence (%) Positive predictive value Test 1Test 2 0.11.94.6 1.016.133.1 2.027.950.0 5.050.072.0 50.095.098.0

52. Type of error that can happen after making a decision True positive (Sensitivity) Correct decision False positive, Type II error (  ) Error of commission Proportion of well persons diagnosed as sick False negative, Type I error (  ) Error of omission Proportion of sick persons diagnosed as well True negative (Specificity) Correct decision