![Data Dissemination
• What should be said? To whom? Through
what communication medium? How should
the message be stated? What effect did the
message create?
• Determine answers based on the purpose of
the system.
• single overriding communication objective.
[What is new? Who is affected? What works
best?]
5/9/17 10](https://image.slidesharecdn.com/surveillanceandscreening-cp-240112071736-89f6f191/85/Surveillance-and-screening-cp-pptx-10-320.jpg)
Surveillance and screening-cp.pptx
- 2. What Surveillance Is • Systematic, ongoing… – Collection – Analysis – Interpretation – Dissemination • …of health data for planning Health action • investigation • control • prevention 2
- 3. • Estimates of a health problem • Natural history of disease • Detection of epidemics • Distribution and spread of a health event • Hypothesis testing • Evaluating control and prevention measures • Monitoring change • Detecting changes in health practice • Forecasting Trends • Facilitate planning How can surveillance data be used? Purpose 5/9/17 3
- 4. Surveillance Routine Active Agency solicited, Go & Get data, Complete report, Competent Personnel, Costly Passive Provider Initiated, Inexpensive Wait & Watch at OPD Problem with underreporting Sentinel Monitor Sites, events, providers & vectors; Reporting by selected Units/ Professionals, Could be active or Passive, Few selected units report for a specific time period, Easier to maintain Quality & Regularity, Denominator absent, Data collected are Not representative & NO generalization Focused/ Enhanced Situation / Process / Area; Case/outbreak investigations; Special surveys-Nutritional surveillance; Syndromic surveillance 5/9/17 4
- 5. Outcomes • Surveillance is outcome oriented. • Can measure frequency of an illness or injury (e.g., number of cases, incidence, prevalence) • Can measure severity of the condition (e.g., hospitalization rate, disability, case fatality) • Can measure impact of the condition (e.g., cost) • Orient data by person, place, and time 5/9/17 5
- 6. Planning a Surveillance System • Establish objectives • Develop case definitions • Determine data source or data collection mechanism • Field test methods • Develop and test analytic approach • Develop dissemination mechanism • Assure use of analysis and interpretation 5/9/17 6
- 7. What Should be Under Surveillance? • Establish priorities based on: – Frequency (incidence, prevalence, mortality) – Severity (case-fatality, hospitalization rate, disability rate, years of potential life lost) – Cost (direct and indirect) – Preventability – Communicability – Public interest – Will the data be useful for public health action? 5/9/17 7
- 8. Cycle of Surveillance • Data Collection –Pertinent, regular, timely • Consolidation and Interpretation –descriptive, evaluative, timely • Dissemination –Prompt, to all who need to know (data providers and action takers) • Action to Control and Prevent • Evaluation 5/9/17 8
- 9. Data Sources • Reporting from the surveillance system • OPD data • Indoor hospital data—public & private • Laboratory data • Any other? • Information should be collected from – Public as well as private sector – Urban and rural area – Govt/Pvt./NGOs/Charitable hospitals/health centres 5/9/17 9
- 10. Data Dissemination • What should be said? To whom? Through what communication medium? How should the message be stated? What effect did the message create? • Determine answers based on the purpose of the system. • single overriding communication objective. [What is new? Who is affected? What works best?] 5/9/17 10
- 11. Surveillance : Evaluation • Did the system generate needed answers to problems? • Was the information timely? • Was it useful for planners, researchers, etc? • How was the information used? • Was it worth the effort? • What can be done to make it better? 5/9/17 11
- 12. Worries of PHC MO/DHO • What information to gather? • How often to compile and analyze the data? • How often and whom to report to? • What proforma or format to use? • What action to take? • Knowledge of the standard case definition • Have a list of all reporting units. • Monitor receipt of reports in time. • Monitor completeness of report . 5/9/17 12
- 13. A good surveillance system does not necessarily ensure making of right decisions; but it reduces the chances of wrong ones Alexander D. Langmuir; NEJM 1963;268:182-191 5/9/17 13
- 15. Learning Objectives • Definition of screening; • Principles of Screening.
- 16. What is Screening • Screening is the testing of apparently healthy populations to identify previously undiagnosed diseases or people at high risk of developing a disease. • Screening aims to detect early disease before it becomes symptomatic. • Screening is an important aspect of prevention, but not all diseases are suitable for screening.
- 17. The Principles of Screening • The choice of disease for which to screen; • The nature of the screening test or tests to be used; • The availability of a treatment for those found to have the disease; • The relative costs of the screening.
- 18. • The disease must be an important health problem. • There should be a recognizable latent or early symptomatic stage. • The natural history of the disease, including latent to declared disease, should be adequately understood.
- 19. • There should be a suitable test or examination. • The test should be acceptable to the population.
- 20. True Disease Status Screening Test Positive Negative Total Positive True Positives (TP) False Positives (FP) TP+FP Negative False Negatives (FN) True Negatives (TN) FN+TN Total TP+FN FP+TN TP+FP+FN+TN Outcomes of a Screening Test
- 21. • There should be an acceptable treatment for the patients with recognized disease. • There should be facilities for diagnosis and treatment should be available. • There should be an agreed policy on whom to treat as patients.
- 22. • The cost of case finding (including diagnosis and treatment of patients diagnosed) should be economically balanced in relation to possible expenditure on medical care as a whole. • Case finding should be a continuing process and not a "once for all" project.
- 23. Summary • Screening is the testing of apparently healthy populations to identify previously undiagnosed diseases or people at high risk of developing a disease. • Principles of Screening: disease, test, treatment and cost. What is the next step? Define the validity of the screening test and put screening to use in the population.
- 24. 24 Outline 1. Performance characteristics of a test – Sensitivity – Specificity – Choice of a threshold 2. Performance of a test in a population – Positive predictive value of a test (PPV) – Negative predictive value of a test (NPV) – Impact of disease prevalence, sensitivity and specificity on predictive values
- 25. 25 1. Performance characteristics of a test in a laboratory setting
- 26. 26 Population with affected and non-affected individuals Affected Non-affected
- 27. 27 A perfect diagnostic test identifies the affected individuals only Affected Non-affected
- 28. 28 In reality, tests are not perfect Affected Non-affected
- 29. Sensitivity of a test The sensitivity of a test is the ability of the test to identify correctly the affected individuals Proportion of persons testing positive among affected individuals Affected persons Test result + - True positive (TP) False negative (FN) Sensitivity (Se) = TP / (TP + FN) 2 9
- 30. Estimating the sensitivity of a test • Identify affected individuals with a gold standard • Obtain a wide panel of samples that are representative of the population of affected individuals – Recent and old cases – Severe and mild cases – Various ages and sexes • Test the affected individuals • Estimate the proportion of affected individuals that are positive with the test 30
- 31. Example: Sensitivity a new ELISA IgM test for acute Q-fever Patients with acute Q-fever ELISA IgM test result + True positive (TP) 148 - False negative (FN) 2 150 Sensitivity = TP / (TP + FN) 148 / 150 = 98.7% 31
- 32. Specificity of a test Specificity (Sp) = TN / (TN + FP) The specificity of a test is the ability of the test to identify correctly non- affected individuals Proportion of persons testing negative among non-affected individuals 32 Non-affected persons Test result + - False positive (FP) True negative (TN)
- 33. Estimating the specificity of a test • Identify non-affected individuals – Negative with a gold standard – Unlikely to be infected • Obtain a wide panel of samples that are representative of the population of non-affected individuals • Test the non-affected individuals • Estimate the proportion of non-affected individuals that are negative with the test 33
- 34. Specificity of a new ELISA IgM test for acute Q-fever Persons without acute Q-fever ELISA IgM test result + False positive (FP) 10 - True negative (TN) 190 200 Specificity = TN / (TN + FP) 190 / 200 = 95% 34
- 35. 35 Disease TN Sp = TN + FP Performance of a test FP TN No TP Se = TP + FN Test TP FN Yes + -
- 36. To whom sensitivity and specificity matters most? INTRINSIC characteristics of the test ► To laboratory specialists! 36
- 37. 37 Using several tests • One way out of the dilemma is to use several tests that complement each other • First use test with a high sensitivity (e.g. screening for HIV by ELISA) • Second use test with a high specificity (e.g. confirmation of HIV by western blot)
- 38. 38 2. Performance of a test in a population
- 39. How well does the test perform in a real population? Status of persons Affected Non-affected Test Positive True + False + A+B Negative False - True - C+D A+C B+D A+C+B+D • The test is now used in a real population • This population is made of – Affected individuals – Non-affected individuals • The proportion of affected individuals is the prevalence 39
- 40. Predictive value of a positive test The predictive value of a positive test is the probability that an individual testing positive is truly affected Proportion of affected persons among those testing positive 40
- 41. Positive predictive value (PPV) of a test Status of persons Affected Non- affected Test Positive A B A+B Negative C D C+D A + C B+D A+C+B+D PPV = A / (A+B) 41
- 42. Predictive value of a negative test The predictive value of a negative test is the probability that an individual testing negative is truly non-affected Proportion of non-affected persons among those testing negative 42
- 43. Negative predictive value (NPV) of a test Status of persons Affected Non- affected Test Positive A B A+B Negative C D C+D A+C B+D A+C+B+D NPV = D / (C+D) 43
- 44. 44 • ELISA IgM test – Sensitivity = 98% – Specificity = 95% • Population in low endemic area – Prevalence = 0.5% • Patients with atypical pneumonia – Prevalence = 20% • 10,000 tests performed in each group Example: Screening for acute Q-fever in two settings
- 45. 45 Example: Screening for acute Q-fever in a population in a low endemic area Prevalence = 0.5% PPV = 8.97% NPV = 99.98% IgM ELISA test sensitivity = 98% IgM ELISA test specificity = 95% Q-fever Yes No Total IgM ELISA + 49 497 546 - 1 9,453 9,454 50 9,950 10,000
- 46. 46 Example: Screening for acute Q-fever in patients with atypical pneumonia Prevalence = 20% PPV = 83.05% NPV = 99.48% IgM ELISA test sensitivity = 98% IgM ELISA test specificity = 95% Q-fever Yes No Total IgM ELISA + 1,960 400 2,360 - 40 7,600 7,640 2,000 8,000 10,000
- 47. To whom predictive values matters most? • Look at denominators! – Persons testing positive – Persons testing negative ► To clinicians – probability that a individual with a positive test is really sick? – probability that a individual with a negative test is really healthy? ► To epidemiologists! – proportion of positive tests corresponding to true patients? – proportion of negative tests corresponding to healthy subjects? 47
- 48. 48 • Sensitivity and specificity matter to laboratory specialists – Studied on panels of positives and negatives – Intrinsic characteristics of a test • Capacity to identify the affected • Capacity to identify the non-affected • Predictive values matter to clinicians and epidemiologists – Studied on homogeneous populations – Dependent on the disease prevalence – Performance of a test in real life • How to interpret a positive test • How to interpret a negative test Summary
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