1. Basic Investigation of Outbreaks Karin Galil, MD MPH Centers for Disease Control and Prevention Atlanta, Georgia

2. Outline  Identify the outbreak  Investigate the outbreak  Interpret results  Institute control measures  Report results

3. Identify Potential Outbreaks  What is an outbreak ?  How can one detect outbreaks ?  Why should one look for outbreaks ?

4. Outbreak: Definition  An increase in the occurrence of a complication or disease above the background rate  One rare event  e.g. GAS surgical site infection  Many episodes of common occurrence  e.g. MRSA surgical site infections

5. Background Rate of Disease  Ongoing surveillance  Determine rates—compare within and between institutions  Trends  Requires common, accepted case definitions  Retrospective review of data

6. Pitfalls in Rate Estimates  Case definitions  Numerator  Different definition  increased or decreased number  Population at risk  Denominator  Different definition  increased or decreased rate

7. Who Identifies Potential Outbreaks ?  Routine surveillance  Infection control  Registries  Clinical staff  Laboratory staff

8. Reasons to Investigate  Outbreak control  Increased knowledge  Pathogen  Risk factors for acquisition  Transmission  Epidemiology

9. Clusters that Suggest Nosocomial Transmission  Similar cases on one unit or among similar patients  Cases associated with invasive device  HCW and patients with same infection  Typical nosocomial pathogen  multiply-resistant  opportunistic

10. Determining Risk Factors for Disease  Known risk factors in hospital-acquired infections:  Invasive devices  Severe illness or underlying disease  Environmental factors  Especially immunocompromised patients (e.g. aspergillosis)

11. Institute Control Measures  Immediate control measures needed even before investigation begun or completed Simple: e.g. improved handwashing Complex: cohorting patients, closing unit, halting use of device or product

12. Before the Investigation  Cooperation  All involved personnel and administration  Laboratory capacity  Antimicrobial susceptibility testing, typing (molecular and nonmolecular methods)  Resources  Personnel, supplies, lead investigator, statistician

13. The Investigation  Define “case”  Find cases  Confirm outbreak  Review charts  Describe epidemiology  Generate hypothesis  Test hypothesis  Analyze data  Communicate results

14. Case Definitions  “Working” case definition  Person, place, time  Clinical, laboratory or diagnostic findings  Confirmed vs. possible cases  Case definitions usually change during the investigation

15. Example: Case Definition “A case of multi-drug resistant tuberculosis was defined as any patient in Hospital X diagnosed with active tuberculosis from January 1, 1999 to December 31, 1999 whose isolate was resistant to at least isoniazid and rifampin.”

16. Case Finding Use case definition to find other cases in the source population  Large potential source population: discharge diagnoses, microbiology log books, emergency room visits, use of diagnostic technique  Small population (unit of hospital): review charts of entire cohort

17. Line Listing NameAgeSexWardOnsetOutcome

18. Confirm the Outbreak  Calculate background rate of disease  Compare rate during outbreak with background rate  Define periods from incubation time to last case (or present)

19. Rate Ratio = attack rate (outbreak period) attack rate (background period)

20. Pseudo-Outbreaks  Clusters of positive cultures in patients without evidence of disease  Perceived increase in infections  New or enhanced surveillance  Different laboratory methods

21. Descriptive Epidemiology  Line listing of case-patients (person, place, time)  Demographic information  Clinical information  Epidemic curve  Point source  Person-to-person

22. Point Source Outbreak  Shorter duration  Sharp peak in epidemic curve  Rapid resolution  May resolve without intervention

23. Epidemic Curve: Point Source Outbreak

24. Epidemic Curve: Contaminated Product Number of persons with abscess 19951996 N=87

25. Bloodstream Infections and Pyrogenic Reactions Extrinsic Contamination Bloodstream infectionPyrogenic reaction

26. Person-to-Person or Contaminated Equipment  Poor infection control technique or contaminated patient equipment  Long duration  May not resolve without intervention  If HCW and patients affected, plot separately and together to determine mode of transmission

27. Clues  Location  Tb skin test conversion associated with outpatient HIV clinic  air flow  Patient characteristics  Immunocompromised patients  Persons of a certain age  Persons with same disease/procedure

28. Hypotheses  What caused the outbreak ?  Available data from the outbreak  Published literature  Expert opinion  Hypothesis testing

29. Epidemiologic Studies  Case-control studies  Cases : disease  Controls : equal likelihood of exposure as cases  Cohort studies  Cohort selected on the basis of exposure status

30. Case-Control Study  Advantages: small number of cases, better for rare diseases, diseases with long latency periods, multiple exposures  Disadvantages: selection and recall bias, not good if exposure is rare, cannot measure disease incidence rate (OR vs. RR)

31. Cohort Study  Advantages: can study rare exposures, can calculate disease incidence rates, selection bias less likely  Disadvantages: feasibility, not suited to rare diseases

32. Collect Data  Complete: same data for cases and controls  Unbiased: same way to avoid bias

33. Potential Types of Bias  Selection bias  Self-selection  Diagnostic bias  Information bias  Differential vs. misclassification  Recall bias

34. Questionnaire  Design questionnaire  Demographic information  Potential risk factors  Outcomes  Field test  Complete for on all patients

35. Enter and Clean Data  Line listing  Statistical program  EpiInfo, SAS, STATA  Clean data  Correct errors

36. Data Analysis  Descriptive statistics  Univariate analysis  Stratified analysis  Complex analysis

37. Descriptive Statistics  Vital first step  Describe person, place, time  Describe frequency of all variables collected  Look for errors  Decide on further analyses based on these results

38. . aba+b cdc+d a+cb+dN Exposure Yes No Disease YesNo

39. Risk Estimate  OR/RR >> 1  Strong positive association  OR/RR = 1  No association  OR/RR << 1  Strong negative association

40. Statistical Significance  Confidence Intervals  Include 1  Exclude 1  P value  p > 0.05  p << 0.05

41. Univariate Analysis: Categorical Variables  Categorical variables (yes/no; young/old)  Odds Ratio (OR)  case-control study  Relative Risk (RR)  cohort study

42. Odds Ratio  Case-control study  OR = odds that person with disease was exposed compared to odds that a person without disease was not exposed to risk factor  OR estimates the relative risk

43. Odds Ratio OR = ad / bc

44. Odds Ratio DiseaseNo disease Exposure14721 No exposure 5813 191534

45. Calculating the Odds Ratio OR = ad / bc OR = (14)(8) / (7)(5) OR = 3.2

46. Relative Risk  Cohort study  RR = risk ratio = incidence rate ratio = relative rate  RR = risk of disease among exposed compared to risk among the unexposed

47. Relative Risk RR = a(c+d) / c(a+b)

48. Confidence Intervals  Sampling  estimates the OR or RR  95% confidence Intervals—if we resampled numerous times, our estimate would fall within these bounds 95% of the time  Finite population correction

49. Statistical Tests for 2x2 Tables  Chi-square test  Fisher’s exact test—if value of any cell <5  P value indicates level of certainty that association was not due to chance alone

50. Risk Estimate vs. P Value  OR or RR –direction & strength of association  >>1: strong association  = 1 : no association  <<1: strong inverse association  P Value—level of certainty about the estimate of the association  <<.05: unlikely to be due to chance

51. Univariate Analysis: Continuous Variables  Continuous variables (e.g. age, bp)  Distribution  Normal (bell-shaped) Mean and standard deviation  Not normal Median and range

52. Stratified Analysis  Simple stratified analysis  Control for one variable  Logistic/linear regression models  Control for multiple variables at once  Control for confounding and effect modification  Non-linear relationships

53. Microbiologic Investigation  Alert lab: save all specimens + positive cultures  Typing of organisms  Species identification  Biotyping  Antimicrobial susceptibility testing  Advanced typing (serotyping, plasmid analysis, phage typing, isoenzyme electrophoresis, genetic fingerprinting)

54. Environmental Investigation  Are inanimate objects linked with the outbreak ?  Were infections clustered in one area ?  Consider infected devices, medications/products, airflow patterns

55. Interpret Results  Is there an association ?  It is statistically significant ?  Was study biased ?  Are the results plausible ?  Did the exposure precede the outcome ?  Are results consistent with other studies ?  Is there a dose-response effect ?

56. Control the Outbreak  Routine infection control procedures  Guidelines for universal precautions  Specific guidelines for patient-care equipment  Specific interventions for the ongoing outbreak  Clues—person, place, time

57. Evaluate Control Measures Did the control measures stop the outbreak?  Were there multiple modes of transmission ?  Were control measures implemented properly ?  Were control measures sufficient ?

58. Implement Successful Control Measures

59. Report Results  Inform all concerned parties of results  Hospital staff, consultants, health department  Contaminated products/devices— government authorities, manufacturers  Media — spokesperson

60. Investigations are:  Challenging  Time - consuming  Imperfect