Case-Control Studies for Outbreak Investigations

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

Case-Control Studies for Outbreak Investigations

 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

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

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

 Mass screening programs

 Case-patients identify other persons who have similar illness

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

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)

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

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)

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:

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

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

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

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

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

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’

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

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 women’s 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

 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

 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

 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

 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

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

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)

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

Conducting the Investigation

 Gather demographic information and exposure histories from cases and

 After you have collected the data you need, you can begin the analysis and calculate measures of association

Analyzing the Data

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

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)

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

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

Calculating Odds

 A 2x2 table shows distribution of cases and controls:

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

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”

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 > 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)

Calculating Odds Example

 Outbreak of Hepatitis A among patrons of a single Pennsylvania restaurant (10)

 240 case-patients and 134 controls identified

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

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

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

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

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

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

 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

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

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

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.