Case-Control Studies for Outbreak Investigations

1
Case-Control Studies for Outbreak
Investigations
2
Goals

• Describe the basic steps of conducting a
  case-control study
• Discuss how to select cases and controls
• Discuss how to conduct basic data analysis (odds,
  odds ratios, and matched analysis)
• Provide examples of recent outbreak
  investigations that have used the case-control
  study design

3
Quick Review of Case-Control Studies

• Analytic studies answer what is the relationship
  between exposure and disease?
• Case-control design often conducted with
  relatively few diseased individuals (so is
  efficient)
• Case-control design useful when studying a rare
  disease or investigating an outbreak 

4
Case Selection

• Depends on how the study investigator defines a
  case
• Case definition a set of standard criteria for
  deciding whether an individual should be
  classified as having the health condition of
  interest (1)
• Clinical criteria
• Restricted to time, place, person characteristics
• Simple, objective, and consistently applied

5
Case Selection

• Sources for identifying case-patients
• Medical records
• Laboratory results
• Surveillance systems
• Registries
• Mass screening programs
• Case-patients identify other persons who have
  similar illness

6
Case Selection Example

• August 2001 Illinois Department of Health
  notified of a cluster of cases of diarrheal
  illness associated with exposure to a
  recreational water park in central Illinois (2)
• Local media and community networks used to
  encourage ill persons to contact the local health
  department
• Case-patients asked if there were any other ill
  persons in their household or if anyone attending
  the water park with them was ill

7
Control Selection

• Most difficult part of a case-control study!
• We would like to be able to conclude that there
  is an association between exposure and disease in
  question
• Way the controls are selected is major
  determinant of whether this conclusion is valid
  (3)

8
Control Selection (1)

• Controls are persons who do not have the disease
  in question
• Should be representative of population from which
  cases arose (source population)
• If a control had developed the disease, would
  have been included as a case in the study
• Should provide good estimate of the level of
  exposure one would expect in that population

9
Control Selection

• Sources for controls
• Same health-care institutions or providers as
  cases
• Same institution or organization as cases (e.g.,
  schools, workplaces)
• Relatives, friends, or neighbors of cases
• Randomly from the source population (1)
• May choose multiple methods of control selection
• Source will depend on the scope of the outbreak
• May choose multiple controls per case to increase
  likelihood of identifying significant
  associations (usually no more than 3 controls per
  case)

10
Control Selection Example

• Persons served by the same health-care
  institution or providers as the cases
• August 2001 cluster of Ralstonia pickettii
  bacteremia among neonatal intensive care unit
  (NICU) infants at a California hospital (4)
• Controls were NICU infants who
• Had blood cultures taken during either cluster
  period (July 30-August 3 and August 19-30)
• Had blood cultures that did not yield R.
  pickettii and
• Had been in the hospital for at least 72 hours.
• Attempted to recruit 2 controls per
• case-patient

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Control Selection Example

• Members of the same institution or organization
• 2004 outbreak of varicella in a primary school
  in a suburb of Beijing, China (5)
• Case-control study to identify factors
  contributing to high rate of transmission and
  assess effectiveness of control measures
• Controls included randomly-selected students in
  grades K-2 of the primary school with no history
  of current or previous varicella
• One control recruited for each
• case-patient

12
Control Selection Example

• Relatives, friends, or neighbors
• August 2000 increase noted in Salmonella
  serotype Thompson isolates from Southern
  California patients with onset of illness in July
  (6)
• Preliminary interviews found many case-patients
  had eaten at Chain A restaurant in 5 days before
  illness onset
• Case-control study conducted to evaluate specific
  food and drink exposures at Chain A restaurants
• Controls were well friends or family members who
  shared meals with cases at Chain A during
  exposure period

13
Control Selection Example

• Random sample of the source population
• January-June 2004 aflatoxicosis outbreak in
  eastern Kenya resulted in 317 cases and 125
  deaths (7)
• Case-control study conducted to identify risk
  factors for contamination of implicated maize
• Randomly selected 2 controls from each case
  patients village
• Spun a bottle in front of village elders home
  and walked to fifth house in direction indicated
  by the bottle (or third house in sparsely
  populated areas)
• Random number list was used to select one
  household member

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Control Selection Example

• Multiple methods of control selection
• In waterpark outbreak in Illinois previously
  mentioned, recruited 1 control per case using 3
  methods (2)
• Case-patients asked to identify another healthy
  person
• Used local reverse-telephone directory based on
  residential address of case-patients
• Canvassed local schools and
• community groups

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Selection Bias

• Bias distortion of relationship between exposure
  and disease
• Systematic difference in way you select your
  controls compared to way you select your cases
  that could be related to the exposure could
  introduce bias
• Bias related to the way cases or controls are
  chosen for a study is selection bias

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Selection Bias Example

• Case-patients more likely to work on lower floors
  of an office building and employees on the lower
  floors are more likely to leave the building to
  go out for lunch
• If control population is mostly employees from
  upper floors, conclude there is a real difference
  between cases and controls associated with eating
  at a local deli
• But the difference is due to where they worked in
  the building, which resulted in how often they
  ate out

17
Selection Bias Example

• Outbreak at a gym and a majority of the
  case-patients are females
• Majority of the controls are male
• Found an association between illness and an
  aerobics class
• Outbreak was caused by the steam in the sauna in
  the womens locker room
• Relationship between illness and the aerobics
  class due to the fact that women are more likely
  to take an aerobics class than men

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Matching

• Validity is dependent on the similarity of cases
  and controls in all respects except for exposure
• Match cases and controls on characteristics
  like age and gender
• Matching factors should be important in disease
  development, but not the exposure under
  investigation
• Since matching variable will not be associated
  with either case or control status, it cannot
  confound, or distort, the exposure-disease
  association.
• Analysis of data must take matching
• into account

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Matching

• Individual matching (aka matched pairs)
• Matches each case with a control that has
  specific characteristics in common with the case
• Used when each case has unique and important
  characteristics
• Group matching (aka frequency matching, category
  matching)
• Proportion of controls with certain
  characteristics to be identical to the proportion
  of cases with these same characteristics
• Requires that all cases be selected first so
  investigator knows the proportions to which the
  controls should be matched
• If 30 of cases were male, would select so that
• 30 of controls were male

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Matching

• Can be time efficient, cost effective, and
  improve statistical power
• The more variables that are chosen as matching
  characteristics, the more difficult it is to find
  a suitable control to match to the case
• Once a variable is used for matching, no
  relationship can be discerned between this
  variable and the disease
• Dont match on anything you think might be a risk
  factor!

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Individual Matching Example

• Outbreak of tularemia in Sweden in 2000 (8)
• Selected two controls for each case
• Matched for age, sex, and place of residence
• Identified through computerized Swedish National
  Population Register (stores name, date of birth,
  personal identifying number, address of all
  citizens and residents)

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Group Matching Example

• Outbreak of Escherichia coli associated with
  petting zoo at 2004 North Carolina State Fair (9)
• Recruited 3 controls for each case
• Group-matched by age groups (1-5 years, 6-17
  years, and 18 years and older)
• Identified from list provided by fair officials
  of 23,972 persons who purchased tickets to the
  fair online, at kiosks, or in
• malls

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Conducting the Investigation

• Gather demographic information and exposure
  histories from cases and controls
• After you have collected the data you need, you
  can begin the analysis and calculate measures of
  association

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Analyzing the Data

• Odds ratio is calculated to measure the
  association between an exposure and a disease
  outcome

25
Calculating Odds

• Odds measure occurrence of an event compared to
  non-occurrence of same event
• Variables with two levels (binary variables) used
  to calculate an odds ratio
• Examples of binary variables yes/no responses
  (disease/no disease, exposed/not exposed)

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Calculating Odds

• Odds of exposure among cases calculated by
  dividing number of exposed cases by number of
  unexposed cases
• Odds of exposure among controls calculated by
  dividing number of exposed controls by number of
  unexposed controls

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An Odd Measure How are odds different from
probability or risk?

• In a bag containing 20 poker chips 4 red and 16
  blue
• Probability is the number of times something
  occurs divided by the total number of occurrences
• Probability of getting red is 4/20 (or 1/5 or
  20)
• Probability of getting blue is 16/20 (or 4/5 or
  80).
• Odds are the number of times something occurs
  divided by the number of times something does not
  occur
• Odds of getting red are 4/16 (or 1/4)
• Odds of picking blue are 16/4 (or 4/1)
• May refer to the odds of getting blue as 4 to 1
  against getting red
• Odds probability/(1-probability)
• If probability for picking red is 20, odds are
  0.20/(1-0.20) or 1/4
• Probability odds/(1odds)
• If odds of picking red is 1/4, probability is
  0.25/(10.25)0.20

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Calculating Odds

• A 2x2 table shows distribution of cases and
  controls

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Calculating Odds Ratios

• Odds ratio is odds of exposure among cases
  divided by odds of exposure among controls
• Exposure among cases is compared to exposure
  among controls to assess if and how exposure
  levels differ between cases and controls

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Calculating Odds Ratios

• Odds ratio calculated by dividing odds of
  exposure among cases (a/c) by odds of exposure
  among controls (b/d)
• Numerically the same as dividing the products
  obtained when multiplying diagonally across the
  2x2 table (ad/bc)
• Also known as cross-products ratio

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Calculating Odds Ratios

• To interpret odds ratio, compare value to 1
• If odds ratio 1 odds of exposure is the same
  for cases and controls (no association between
  disease and exposure)
• If odds ratio gt 1 odds of exposure among cases
  is greater than among controls (a positive
  association between disease and exposure)
• If odds ratio lt 1 odds of exposure among cases
  is less than among controls (a negative, or
  protective, association between disease and
  exposure)

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Calculating Odds Example

• Outbreak of Hepatitis A among patrons of a single
  Pennsylvania restaurant (10)
• 240 case-patients and 134 controls identified
• OR (218/22) (218x89) 19.6
• (45/89) (45x22)

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Matched Analysis

• If individual matching, 2x2 table set up
  differently
• Examine pairs in table, so have cases along one
  side and controls along the other, and each cell
  in the table contains pairs

34
Matched Analysis

• Cell e contains number of matched case-control
  pairs where both case and control were exposed
• Concordant cell (and cell h) because case and
  control have same exposure status
• Cell f contains number of matched case-control
  pairs where cases were exposed but controls were
  not exposed
• Discordant cell (as cell g) because case and
  control have different exposure status
• Only discordant cells give useful data the
  matched odds ratio calculated as cell f divided
  by cell g 
• Matched Odds Ratio f/g

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Odds vs. Risk

• Odds are qualitatively different from risk
  (calculated in a cohort study)
• Case-control studies select participants based on
  disease status and then measure exposure among
  the participants
• Can only approximate risk of disease given
  exposure
• Values needed to calculate risk are not available
  because entire population at risk is not included
  in the study
• Finding and accessing all who did not get sick
  would be difficult or impossible
• Case-control study allows us to use only a subset
  of controls and calculate the odds ratio as an
• estimate of the risk

36
Example Case-Control Study E. coli at fast-food
restaurant

• November 1999 childrens hospital notified
  Fresno County Health Department (California) of 5
  cases of E. coli O157 infections during a 2-week
  period (11)
• All case patients had eaten at popular fast-food
  restaurant chain A in 7-day period before onset
  of illness
• Local health officials and clinicians throughout
  California asked to enhance surveillance for E.
  coli O157 infections
• States bordering California asked to review
  medical histories of persons with recent E. coli
  O157 infections and arrange for subtyping of
  isolates
• 2 sequential case-control studies conducted
• in early December 1999

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Example Case-Control Study E. coli at fast-food
restaurant

• First study conducted to determine the restaurant
  associated with the outbreak
• Case defined as patient with
• An infection with the PFGE-defined outbreak
  strain of E. coli O157H7, diarrheal illness with
  more than 3 loose stools during a 24-hour period,
  and/or hemolytic uremic syndrome (HUS) during the
  first 2 weeks of November 1999 or
• Illness clinically compatible with E. coli
  O157H7 infection, without laboratory
  confirmation but with epidemiologic connection to
  the outbreak
• Control defined as person without a diarrheal
  illness or HUS during the first 2 weeks of
  November 1999

38
Example Case-Control Study E. coli at fast-food
restaurant

• Controls age-matched and systematically
  identified using computer-assisted telephone
  interviewing or residents in the same telephone
  exchange area as case patients.
• Attempted 2 controls per case
• Enrolled 10 cases and 19 matched controls
• Only chain A showed statistically significant
  association with illness among cases and controls

39
Example Case-Control Study E. coli at fast-food
restaurant

• Second case-control study involving patrons of
  chain A restaurants conducted to determine
  specific menu item or ingredient associated with
  illness (11)
• Case defined as above but restricted to those who
  had eaten at chain A and who could be matched
  with meal companion-controls
• 8 cases and 16 meal companion-controls enrolled
• Consumption of a beef taco was found to be
  statistically associated with illness
• Traceback investigation implicated an upstream
  supplier of beef, but farm investigation was not
  possible

40
Example Case-Control Study Listeriosis with
deli meat

• July and August 2002 22 cases of listeriosis
  were reported in Pennsylvania, a nearly 3-fold
  increase over baseline (12)
• Subtyping identified cluster of cases caused by
  single Liseteria monocytogenes strain
• CDC asked health departments in northeast United
  States to conduct active case finding, prompt
  reporting of listeriosis cases and retrieval of
  clinical isolates for rapid PFGE testing
• Conducted case-control study to identify cause of
  increase in cases

41
Example Case-Control Study Listeriosis with
deli meat

• Case-patient defined as person with
  culture-confirmed listeriosis between July 1 and
  November 30, 2002, whose infection was caused by
  the outbreak strain
• Control defined as person with culture-confirmed
  listeriosis between July 1 and November 30, 2002,
  whose infection was caused by any other
  non-outbreak strain of L. monocytogenes, and who
  lived in a state with at least 1 case patient
• Interviewed with standard questionnaire including
  more than 70 specific food items to gather
  medical and food histories during the 4 weeks
  preceding culture for L. monocytogenes.

42
Example Case-Control Study Listeriosis with
deli meat

• Study obtained data from 38 case-patients and 53
  controls
• Infection strongly associated with consumption of
  precooked turkey breast products sliced at the
  deli counter of groceries and restaurants
• Based on traceback investigation, 4 turkey
  processing plants investigated outbreak strain
  of L. monocytogenes found in plant A and in
  turkey breast products from plant B
• Both plants suspended production and recalled
  more than 30 million pounds of products,
  resulting in one of the largest meat recalls in
  US history 

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Conclusion

• Important to keep in mind the hypothesis you are
  testing
• Consideration of underlying population that gave
  rise to cases will help select appropriate
  controls
• Improper selection of controls can introduce bias
  and result in a spurious association between
  exposure and illness
• If controls are representative of the source
  population, case-control studies are an efficient
  way to conduct an analytic study to determine the
  relationship between exposures and a disease

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References

• 1. Gregg MB. Field Epidemiology. 2nd ed. New
  York, NY Oxford University Press 2002.
• 2. Causer LM, Handzel T, Welch P, et al. An
  outbreak of Cryptosporidium hominis infection at
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  Infect. 2006134(1)147-156.
• 3. Gordis L. Epidemiology. 2nd ed. Philadelphia,
  PA WB Saunders Company 2000.
• 4. Kimura AC, Calvet H, Higa JI, et al. Outbreak
  of Ralstonia pickettii bacteremia in a neonatal
  intensive care unit. Pediatr Infect Dis J.
  2005241099-1103.
• 5. Ma H, Fontaine R. Varicella outbreak among
  primary school students--Beijing, China, 2004.
  MMWR Morb Mortal Wkly Rep. 200655(suppl)39-43.

45
References

• 6. Kimura AC, Palumbo MS, Meyers H, Abbott S,
  Rodriguez R, Werner SB. A multi-state outbreak
  of Salmonella serotype Thompson infection from
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• 7. Azziz-Baumgartner E, Lindblade K, Gieseker K,
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  Case-control study of an acute aflatoxicosis
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• 8. Eliasson H, Lindbäck J, Nuorti JP, et al. The
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• 9. Goode B, OReilly C. Outbreak of Shiga toxin
  producing E. coli (STEC) infections associated
  with a petting zoo at the North Carolina State
  Fair Raleigh, North Carolina, November 2004. NC
  Dept of Health and Human Services June 29, 2005.
  Available at www.epi.state.nc.us/epi/gcdc/ecoli/E
  ColiReportFinal062905.pdf.

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References

• 10.Wheeler C, Vogt TM, Armstrong GL, et al. An
  outbreak of hepatitis A associated with green
  onions. N Engl J Med. 2005 353890-897.
• 11.Jay M, Garrett V, Mohle-Boetani JC, et al. A
  multistate outbreak of Escherichia coli O157H7
  infection linked to consumption of beef tacos at
  a fast-food restaurant chain. Clin Infect Dis.
  2004391-7.
• 12.Gottlieb SL, Newbern EC, Griffin PM, et al and
  the Listeriosis Working Group. Multistate
  outbreak of listeriosis linked to turkey deli
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