Case-Control Studies for Outbreak Investigations

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Case-Control Studies for Outbreak Investigations

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Describe the basic steps of conducting a case-control study

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Discuss how to select cases and controls

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Discuss how to conduct basic data analysis (odds, odds ratios, and matched analysis)

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Provide examples of recent outbreak

investigations that have used the case-control study design

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Quick Review of

Case-Control Studies

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Analytic studies answer “what is the

relationship between exposure and disease?”

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Case-control design often conducted with relatively few diseased individuals (so is efficient)

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Case-control design useful when studying a rare disease or investigating an outbreak

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Case Selection

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Depends on how the study investigator defines a case

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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

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Mass screening programs

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Case-patients identify other persons who have similar illness

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Case Selection Example

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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

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Control Selection

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Most difficult part of a case-control study!

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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)

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Control Selection

(1)

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Controls are persons who do not have the disease in question

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Should be representative of population from which cases arose (source population)

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If a control had developed the disease, would have been included as a case in the study

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Should provide good estimate of the level of exposure one would expect in that population

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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)

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Control Selection Example

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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:

1.Had blood cultures taken during either cluster period (July 30-August 3 and August 19-30);

2.Had blood cultures that did not yield R. pickettii; and

3.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

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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

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Control Selection Example

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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

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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 patient’s village

Spun a bottle in front of village elder’s 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

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Multiple methods of control selection

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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

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Bias: distortion of relationship between exposure and disease

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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

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Bias related to the way cases or controls are chosen for a study is ‘selection bias’

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Selection Bias Example

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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

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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

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But the difference is due to where they

worked in the building, which resulted in how often they ate out

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Selection Bias Example

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Outbreak at a gym and a majority of the case-patients are females

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Majority of the controls are male

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Found an association between illness and an aerobics class

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Outbreak was caused by the steam in the sauna in the women’s locker room

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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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Validity is dependent on the similarity of

cases and controls in all respects except for exposure

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“Match” cases and controls on characteristics like age and gender

 Matching factors should be important in disease development, but not the exposure under

 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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 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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Can be time efficient, cost effective, and improve statistical power

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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

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Don’t match on anything you think might be a risk factor!

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Individual Matching Example

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Outbreak of tularemia in Sweden in 2000

(8)

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Selected two controls for each case

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Matched for age, sex, and place of residence

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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

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Outbreak of Escherichia coli associated with petting zoo at 2004 North Carolina State Fair

(9)

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Recruited 3 controls for each case

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Group-matched by age groups (1-5 years, 6-17 years, and 18 years and older)

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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

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Gather demographic information and exposure histories from cases and

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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

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

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Calculating Odds

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Odds measure occurrence of an event compared to non-occurrence of same event

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Variables with two levels (binary

variables) used to calculate an odds ratio

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Examples of binary variables: yes/no responses (disease/no disease,

exposed/not exposed)

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Calculating Odds

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Odds of exposure among cases

calculated by dividing number of

exposed cases by number of unexposed cases

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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 numberof 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

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Calculating Odds

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A 2x2 table shows distribution of cases and controls:

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Calculating Odds Ratios

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Odds ratio is odds of exposure among cases divided by odds of exposure

among controls

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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

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Odds ratio calculated by dividing odds of exposure among cases (a/c) by odds of exposure among controls (b/d)

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Numerically the same as dividing the products obtained when multiplying

diagonally across the 2x2 table (ad/bc)

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Also known as “cross-products ratio”

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Calculating Odds Ratios

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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 > 1: odds of exposure among cases is greater than among controls (a positive

association between disease and exposure)

 If odds ratio < 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

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Outbreak of Hepatitis A among patrons of a single Pennsylvania restaurant

(10)

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240 case-patients and 134 controls identified

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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

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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

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

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Example Case-Control Study:

 November 1999: children’s 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:

 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

O157:H7, 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 O157:H7 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

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Example Case-Control Study:



Controls age-matched and systematically

identified using computer-assisted telephone interviewing or residents in the same

telephone exchange area as case patients.

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Attempted 2 controls per case

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Enrolled 10 cases and 19 matched controls

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Only chain A showed statistically significant association with illness among cases and controls

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Example Case-Control Study:

 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

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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

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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.

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