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Introduction to Epidemiology and Surveillance


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Dr. Alex Keenan from the Health Protection Agency gives a short introduction into epidemiology and surveillance.

Published in: Health & Medicine, Technology

Introduction to Epidemiology and Surveillance

  1. 1. An Introduction to Epidemiology pt 01 Dr Alex Keenan, Epidemiology and Surveillance Analyst, Cheshire & Merseyside HPU 27 th April 2010
  2. 2. Learning Outcomes <ul><li>Basic Understanding of Epidemiology including analysis during outbreak situations </li></ul><ul><ul><ul><ul><li>Exercise 3 </li></ul></ul></ul></ul><ul><li>Importance of Surveillance How? Why? What? </li></ul><ul><ul><ul><ul><li>Exercise 1 </li></ul></ul></ul></ul><ul><li>Importance of Data including integrity, consistency, accuracy and limitations of data sources </li></ul><ul><ul><ul><ul><li>Exercise 2 </li></ul></ul></ul></ul>
  3. 3. Aims of Session 1 <ul><li>Understanding of Epidemiology </li></ul><ul><li>Importance of Surveillance </li></ul><ul><li>Surveillance Systems </li></ul><ul><li>Importance of Data Quality </li></ul><ul><li>Interpretation of Data </li></ul><ul><li>Data Sources </li></ul>
  4. 4. Definitions of Epidemiology <ul><li>Epidemiology is the study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to control of health problems. A Dictionary of Epidemiology, Last J. (Ed.) </li></ul><ul><li>Epidemiology is the study of factors affecting the health and illness of populations, and serves as the foundation of logic of interventions made in the interest of public health and preventive medicine. Wikipedia </li></ul>
  5. 5. Surveillance <ul><li>Why Bother? </li></ul>
  6. 6. Surveillance – functions WHO recommended surveillance standards, Second edition <ul><li>The core functions in surveillance of any health event are: </li></ul><ul><li>Case Detection </li></ul><ul><li>Reporting </li></ul><ul><li>Investigation & Confirmation </li></ul><ul><li>Analysis & Interpretation </li></ul><ul><li>Action </li></ul><ul><ul><li>Control / Response </li></ul></ul><ul><ul><li>Policy </li></ul></ul><ul><ul><li>Feedback </li></ul></ul>Case definition Agreed system/process A means to follow up cases Effective (secure) storage and skills to analyse Political will to act – perceived importance Requires…
  7. 7. Surveillance Systems Database Policy makers PCTs / LAs / SHAs Health Practitioners Dissemination Laboratory / clinic Data Analysis Specialist Laboratory Supplementary data
  8. 8. Data Quality <ul><li>Input </li></ul><ul><ul><ul><li>Consistency </li></ul></ul></ul><ul><ul><ul><li>Data accuracy </li></ul></ul></ul><ul><ul><ul><li>Data reliability (integrity) </li></ul></ul></ul><ul><li>Output </li></ul><ul><ul><ul><li>Standardised Outputs </li></ul></ul></ul><ul><ul><ul><li>Consistency of Outputs </li></ul></ul></ul><ul><ul><ul><li>Routine e.g. monthly, annual </li></ul></ul></ul><ul><ul><ul><li>Ad Hoc for outbreak situations e.g. swine flu </li></ul></ul></ul>
  9. 9. Weekly Surveillance Data
  10. 10. Campylobacter – Weekly Surveillance (1)
  11. 11. Campylobacter – Weekly Surveillance (2)
  12. 12. Data Interpretation <ul><li>Interrogating Datasets </li></ul><ul><ul><ul><ul><ul><li>Define whether increase in reports or increase in cases </li></ul></ul></ul></ul></ul><ul><li>Compare with Previous Years (Temporal) </li></ul><ul><ul><ul><ul><ul><li>Decide if increase is higher than expected </li></ul></ul></ul></ul></ul><ul><li>Compare Other Geographical Areas (Spatial) </li></ul><ul><ul><ul><ul><ul><li>Is increase confined to one area </li></ul></ul></ul></ul></ul><ul><li>Compare Age Groups </li></ul><ul><ul><ul><ul><ul><li>Is increase confined to one particular age group </li></ul></ul></ul></ul></ul>
  13. 13. Campylobacter – Weekly Surveillance (3)
  14. 14. Temporal Distribution
  15. 15. Measles – Outbreak (1)
  16. 16. Measles – Outbreak (2)
  17. 17. Exercise (1) - ~15 mins <ul><li>Each group has a graph of data to interpret. For each one, assess: </li></ul><ul><ul><ul><ul><li>What is the data showing? </li></ul></ul></ul></ul><ul><ul><ul><ul><li>What else would you like to know? </li></ul></ul></ul></ul><ul><ul><ul><ul><li>What action might it inform? </li></ul></ul></ul></ul><ul><ul><ul><ul><li>What are the strengths and weaknesses of the data? </li></ul></ul></ul></ul><ul><li>Nominate someone to feed back from each group </li></ul>
  18. 18. Exercise (2) ~ 15 mins <ul><li>What routine data sources do you think that you might use when studying an epidemiological issue? </li></ul><ul><li>What sources are available? </li></ul><ul><li>How is the data accessed? </li></ul><ul><li>How easy is it to access the data? </li></ul><ul><li>What is the timeliness of accessibility? </li></ul>
  19. 19. Hospital Utilisation Data <ul><li>Hospital Episode Statistics </li></ul><ul><li>Presentation at Accident & Emergency </li></ul><ul><li>Attendance at outpatients clinics </li></ul><ul><li>Patients registered for Specialist Clinics </li></ul><ul><li>Korner data </li></ul><ul><li>Laboratory data </li></ul>
  20. 20. General Practice Data <ul><li>General Practice Research Database </li></ul><ul><li> </li></ul><ul><li>Established in 1987 </li></ul><ul><li>Largest computerised database of medical records in world </li></ul><ul><li>Currently 450 PCT practices </li></ul><ul><li>Records for 3.4 million patients (13 million total) </li></ul><ul><li>46 million patient years of validated data </li></ul><ul><li>Includes data on demographics, symptoms, therapy, referrals and lifestyle factors </li></ul>
  21. 21. NHS Direct <ul><li>Organisation started with 3 pilot sites 1998 </li></ul><ul><li>National 2000 </li></ul><ul><li>Online 24 million visitors per year </li></ul><ul><li>Phone service 7 million calls per year </li></ul><ul><li>Interactive TV to 16 million households </li></ul><ul><li>2.5 million users per month </li></ul><ul><li>Record sex, age, postcode, primary symptom, time and date of call </li></ul>
  22. 22. Other sources of Routine Data <ul><li>Cancer registries </li></ul><ul><li> </li></ul><ul><li>Surveys </li></ul><ul><li> </li></ul><ul><li>Mortality Figures </li></ul><ul><li> </li></ul><ul><li>National Poisons Information Service </li></ul><ul><li> </li></ul>
  23. 23. Routine Environmental Data <ul><li>Air Quality </li></ul><ul><li> </li></ul><ul><li>Pollution Inventory </li></ul><ul><li> </li></ul><ul><li>Radiation </li></ul><ul><li> </li></ul><ul><li>Contaminated Land </li></ul><ul><li>Local Authority public registers </li></ul>
  24. 24. Congenital Abnormalities <ul><li>European Surveillance of Congenital Abnormalities </li></ul><ul><li>Started in 1979 </li></ul><ul><li>More than 1.5 million births surveyed per year in Europe ~ 29% of European Birth Population </li></ul><ul><li>43 registries in 20 European countries </li></ul><ul><ul><ul><li>Structural defects </li></ul></ul></ul><ul><ul><ul><li>Chromosomal abnormalities </li></ul></ul></ul><ul><ul><ul><li>Inborn metabolism errors </li></ul></ul></ul><ul><ul><ul><li>Hereditary diseases </li></ul></ul></ul><ul><li> </li></ul>
  25. 25. Merseyside and Cheshire Congenital Anomaly Survey <ul><li>Started as foetal anomaly survey in 1992 </li></ul><ul><li>1995 - Member of EUROCAT </li></ul><ul><li>Approximately 1200 notifications of congenital anomalies per year </li></ul><ul><li>Reporting voluntary </li></ul><ul><li>Delivery within geographic area irrespective of place of residence </li></ul><ul><li>~ 27000 births each year </li></ul><ul><li>[email_address] </li></ul>
  26. 26. <ul><li>The key to successful analysis of routine data for epidemiological studies </li></ul><ul><li>Good case definition </li></ul><ul><li>Knowledge of the limitations of routine data </li></ul><ul><li>Careful selection of non exposed population </li></ul><ul><li>Care with use of small numbers </li></ul>
  27. 27. Session 02 <ul><li>Outbreaks, Clusters and some Maths </li></ul>
  28. 28. Aims of Session 2 <ul><li>Outbreaks </li></ul><ul><li>Clusters </li></ul><ul><li>Studies </li></ul>
  29. 29. What is an outbreak? <ul><li>Observed number of cases greater than expected for a defined place and time period </li></ul><ul><li>Two or more cases with a common exposure </li></ul><ul><li>One case of serious/rare disease e.g. Ebola/plague/smallpox </li></ul>
  30. 30. How do clusters arise? <ul><li>Human pattern recognition </li></ul><ul><li>Desire to explain things </li></ul><ul><li>Genuine clusters in time, space and person </li></ul>
  31. 31. Clusters can be… In Time e.g. cases of legionella In Place e.g. meningococcal cases in same school class In Person e.g. cases of breast cancer in a family The term cluster denotes the suspicion of an increased frequency of some event occurring, not that any increase has been demonstrated
  32. 32. How do outbreaks come to light? <ul><li>Acute/unusual event: </li></ul><ul><ul><li>call to HPA, NHS or LA from a health professional, school, public, hotel staff, media etc. </li></ul></ul><ul><li>Routine surveillance: </li></ul><ul><ul><li>data show an increase over the normal background level for the particular place and time of year </li></ul></ul>
  33. 33. Types of outbreak <ul><li>Common source: </li></ul><ul><ul><ul><li>Point – peak one IP after exposure </li></ul></ul></ul><ul><ul><ul><li>Intermittent – irregular pattern </li></ul></ul></ul><ul><ul><ul><li>Continuous – irregular pattern </li></ul></ul></ul><ul><li>Propagated (person to person): </li></ul><ul><ul><ul><li>Successive series of increasing peaks about one IP apart </li></ul></ul></ul><ul><li>Mixture of the above </li></ul>
  34. 34. Figure. Measles cases by date of onset of rash. Region of Madrid, March 2006. (Cases reported until 16th March, 2006)                                                                                                                                                                 Example of propagated outbreak (see
  35. 35. Example of Point Source Outbreak
  36. 36. Why investigate outbreaks? <ul><li>Control of disease </li></ul><ul><li>Get new evidence about: </li></ul><ul><ul><ul><li>optimal outbreak management </li></ul></ul></ul><ul><ul><ul><li>prevention of outbreaks </li></ul></ul></ul><ul><ul><ul><li>behaviour of novel organisms </li></ul></ul></ul><ul><li>Political, legal or public concerns </li></ul>
  37. 37. How to use epidemiology in outbreaks <ul><li>Descriptive </li></ul><ul><ul><ul><ul><ul><li>Date of Onset – Epidemiological Curve </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Age Groups </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Sex </li></ul></ul></ul></ul></ul><ul><li>Analytical </li></ul><ul><ul><ul><ul><ul><li>Single Variable Analysis e.g. Odds Ratios </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Multi Variable Analysis </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><li>Awareness of Bias e.g. Recall – collect evidence ASAP </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Agree Questionnaire before asking questions to avoid multiple calls </li></ul></ul></ul></ul>
  38. 38. Examples of Types of Outbreak Investigation <ul><li>Case Control </li></ul><ul><ul><ul><ul><ul><li>Matched </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Unmatched </li></ul></ul></ul></ul></ul><ul><li>Cohort </li></ul><ul><ul><ul><ul><ul><li>All attendees </li></ul></ul></ul></ul></ul>
  39. 39. Risk Factors – some examples <ul><li>Age </li></ul><ul><li>Sex </li></ul><ul><li>All foods consumed </li></ul><ul><li>Toilet Visited </li></ul><ul><li>Foreign Travel </li></ul><ul><li>Other restaurants / takeaways / parties attended </li></ul><ul><li>Swimming Pools </li></ul><ul><li>Farms Visited </li></ul>
  40. 40. Odds Ratios (I) <ul><li>The odds ratio is a calculation that is used to measure the strength of a relationship between 2 variables. </li></ul><ul><li>Odds Ratio (Cross Product) = </li></ul>Ate / Exposed Didn’t Eat / Not Exposed Ill / Disease a b Not ill / No Disease c d
  41. 41. Odds Ratios Example <ul><li>During a Wedding people became ill and we tried to ascertain if there was a link to any particular food. </li></ul><ul><li>Odds Ratio (Egg Salad) = = = 12 </li></ul>Egg Salad Ate Didn’t Eat Ill 8 1 Not ill 1 4
  42. 42. Exercise (3) ~ 30 mins <ul><li>Each group has a table of data to interpret. For each one, assess: </li></ul><ul><ul><ul><ul><li>What is the data showing? </li></ul></ul></ul></ul><ul><ul><ul><ul><li>What action might it inform? </li></ul></ul></ul></ul><ul><li>Then analyse the data as you see fit </li></ul><ul><li>Nominate someone to feed back from each group </li></ul>
  43. 43. Outbreak at an event in Liverpool July 2009 <ul><li>1000 - 1200 Attendees at event </li></ul><ul><li>Several Reports of illness associated with people who attended event and ate food from event </li></ul><ul><li>Questionnaires returned and completed by 200 people </li></ul><ul><li>148 of those were ill </li></ul><ul><li>Unmatched Case Control Study </li></ul>
  44. 44. Investigation <ul><li>Environmental </li></ul><ul><ul><ul><ul><ul><li>Environmental Health Officers visited premises where food was prepared to investigate possible issues </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Samples taken from premises </li></ul></ul></ul></ul></ul><ul><li>Microbiological </li></ul><ul><ul><ul><ul><ul><li>Samples taken from premises analysed </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Stool samples analysed from those who were ill </li></ul></ul></ul></ul></ul><ul><li>Epidemiological </li></ul><ul><ul><ul><ul><ul><li>Epidemiological Curve </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Identify Risk Factors </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Perform Odds Analysis </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Further more detailed analysis </li></ul></ul></ul></ul></ul>
  45. 45. Epidemiological Curve
  46. 46. Risk Factors Identified <ul><li>Age </li></ul><ul><li>Sex </li></ul><ul><li>Spring Rolls </li></ul><ul><li>Chicken Lollipop </li></ul><ul><li>Beef Cutlet </li></ul><ul><li>Vegetable Samosa </li></ul><ul><li>Chicken Curry </li></ul><ul><li>Rice </li></ul>
  47. 47. Single Variable Analysis <0.01 6.45 – 32.47 15.70 Rice <0.01 6.99 – 31.84 15.40 Chicken Curry <0.01 1.99 – 7.22 3.61 Vegetable Samosa <0.01 3.12 – 12.56 6.21 Beef Cutlet <0.01 2.80 – 10.52 6.05 Chicken Lollipop <0.01 2.60 – 9.77 4.87 Spring Roll 0.34 0.71 – 2.67 1.30 Sex 0.056 0.99 – 1.04 1.02 Age p – value Confidence Interval Odds Ratio
  48. 48. Further Analysis <ul><li>More detailed analysis involving (stepwise) multivariate logistic regression </li></ul><ul><li>People Ate more than 1 food so can take into account several foods eaten </li></ul><ul><li>Bias – people from same household likely to all reply or not reply </li></ul>
  49. 49. Multivariate Analysis <0.0001 4.34 – 36.88 12.63 Rice 0.003 1.54 – 8.51 3.63 Chicken Curry p- value Confidence Interval Odds Ratio
  50. 50. Summary of Epidemiological Findings <ul><li>Unmatched case control analysis performed </li></ul><ul><li>Point source outbreak </li></ul><ul><li>Age not a risk factor </li></ul><ul><li>Sex not a risk factor </li></ul><ul><li>All foods identified as risk factors during single variable analysis </li></ul><ul><li>Chicken Curry and Rice identified as most likely risk factors to be associated with illness </li></ul>
  51. 51. A Selection of Useful Reference Books <ul><li>A Dictionary of Epidemiology, Last J. (Ed.) </li></ul><ul><li>Research Methods in Health, Bowling A. </li></ul><ul><li>A – Z of Medical Statistics a companion for critical appraisal , Pereira-Maxwell F. </li></ul><ul><li>Essential Public Health, Donaldson & Donaldson </li></ul><ul><li>Oxford Handbook of Public Health Practice, Pencheon D. et. al. (Eds.) </li></ul>
  52. 52. <ul><li>[email_address] </li></ul><ul><li>[email_address] </li></ul>