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Step 1: Prepare for Field Work Step 2: Establish the Existence of an Outbreak
Step 3: Verify the Diagnosis Step 4: Define and Identify Cases
Step 5: Describe and Orient the Data Step 6: Develop Hypotheses
Step 7: Evaluate Hypotheses Step 8: Refine Hypotheses
Step 9: Implement Control and Prevention Measures Step 10: Communicate Findings

Step 7: Evaluate Hypotheses

The next step is to evaluate the credibility of your hypotheses. There are two approaches you can use, depending on the nature of your data: 1) comparison of the hypotheses with the established facts and 2) analytic epidemiology, which allows you to test your hypotheses.

You would use the first method when your evidence is so strong that the hypothesis does not need to be tested. A 1991 investigation of an outbreak of vitamin D intoxication in Massachusetts is a good example. All of the people affected drank milk delivered to their homes by a local dairy. Investigators hypothesized that the dairy was the source, and the milk was the vehicle of excess vitamin D. When they visited the dairy, they quickly recognized that far more than the recommended dose of vitamin D was inadvertently being adding to the milk. No further analysis was necessary.

The second method, analytic epidemiology, is used when the cause is less clear. With this method, you test your hypotheses by using a comparison group to quantify relationships between various exposures and the disease. There are two types of analytic studies: cohort studies and case-control studies. Cohort studies compare groups of people who have been exposed to suspected risk factors with groups who have not been exposed. Case-control studies compare people with a disease (case-patients) with a group of people without the disease (controls). The nature of the outbreak determines which of these studies you will use.

Cohort studies
A cohort study is the best technique for analyzing an outbreak in a small, well-defined population. For example, you would use a cohort study if an outbreak of gastroenteritis occurred among people who attended a social function, such as a wedding, and a complete list of wedding guests was available. In this situation, you would ask each attendee the same set of questions about potential exposures (e.g., what foods and beverages he or she had consumed at the wedding) and whether he or she had become ill with gastroenteritis.

After collecting this information from each guests, you would be able to calculate an attack rate for people who ate a particular item (were exposed) and an attack rate for those who did not eat that item (were not exposed). For the exposed group, the attack rate is found by dividing the number of people who ate the item and became ill by the total number of people who ate that item. For those who were not exposed, the attack rate is found by dividing the number of people who did not eat the item but still became ill by the total number of people who did not eat that item.

To identify the source of the outbreak from this information, you would look for an item with:

  • a high attack rate among those exposed and
  • a low attack rate among those not exposed (so the difference or ratio between attack rates for the two exposure groups is high); in addition
  • most of the people who became ill should have consumed the item, so that the exposure could explain most, if not all, of the cases.

Usually, you would also calculate the mathematical association between exposure (consuming the food or beverage item) and illness for each food and beverage. This is called the relative risk and is produced by dividing the attack rate for people who were exposed to the item by the attack rate for those who were not exposed.

The table on the next page is based on a famous outbreak of gastroenteritis following a church supper in Oswego, New York, in 1940 and illustrates the use of a cohort study (9). Of the 80 people who attended the supper, 75 were interviewed. Forty-six people met the case definition. Attack rates for those who did and did not eat each of 14 items are presented in the table. Scan the column of attack rates among those who ate the specified items. Which item shows the highest attack rate? Did most of the 46 people who met the case definition eat that food item? Is the attack rate low among people who did not eat that item? You should have identified vanilla ice cream as the implicated vehicle, or source. The relative risk is calculated as 80 / 14, or 5.7. This relative risk indicates that people who ate the vanilla ice cream were 5.7 times more likely to become ill than were those who did not eat the vanilla ice cream.

Attack Rates by Items Served at a Church Supper,
Oswego, New York, April 1940

Number of people who
ate specified item
Number of people who 
did not eat
specified item
Food Ill Well Total Attack Rate % Ill Well Total Attack Rate %
Baked Ham 29 17    46 63 17 12 29 59
Spinach 26  17    43 60 20 12 32 62
Mashed potatoes* 23  14    37 62 23 14 37 62
Cabbage salad 18  10    28 64 28 19 47 60
Jell-O 16  7    23 70 30 22 52 58
Rolls 21 16    37 57 25 13 38 66
Brown bread 18 9    27 67 28 20 48 58
Milk 2 2      4 50 44 27 71 62
Coffee 19 12    31 61 27 17 44 61
Water 13 11    24 54 33 18 51 65
Cakes 27 13    40 67 19 16 35 54
Ice Cream (van) 43 11    54 80 3 18 21 14
Ice Cream (choc)* 25 22    47 53 20 7 27 74
Fruit Salad 4 2      6 67 42 27 69 61

*Excludes 1 person with indefinite history of consumption of that food.  Source: 9

Case-control studies
In most outbreaks the population is not well defined, and so cohort studies are not feasible. In these instances, you would use the case-control study design. In a case-control study, you ask both case-patients and controls about their exposures. You then can calculate a simple mathematical measure of association—called an odds ratio—to quantify the relationship between exposure and disease. This method does not prove that a particular exposure caused a disease, but it is very helpful and effective in evaluating possible vehicles of disease.

When you design a case-control study, your first, and perhaps most important, decision is who the controls should be. Conceptually, the controls must not have the disease in question, but should be from the same population as the case-patients. In other words, they should be similar to the case-patients except that they do not have the disease. Common control groups consist of neighbors and friends of case-patients and people from the same physician practice or hospital as case-patients.

In general, the more case-patients and controls you have, the easier it will be to find an association. Often, however, you are limited because the outbreak is small. For example, in a hospital, 4 or 5 cases may constitute an outbreak. Fortunately, the number of potential controls will usually be more than you need. In an outbreak of 50 or more cases, 1 control per case-patient will usually suffice. In smaller outbreaks, you might use 2, 3, or 4 controls per case-patient. More than 4 controls per case-patient will rarely be worth your effort.

In a case-control study, you cannot calculate attack rates because you do not know the total number of people in the community who were and were not exposed to the source of the disease under study. Without attack rates, you cannot calculate relative risk; instead, the measure of association you use in a case study is an odds ratio. When preparing to calculate an odds ratio, it is helpful to look at your data in a 2×2 table. For instance, suppose you were investigating an outbreak of hepatitis A in a small town, and you suspected that the source was a favorite restaurant of the townspeople. After questioning case-patients and controls about whether they had eaten at that restaurant, your data might look like this:

    Case Patients Controls Total
Ate at Restaurant A? Yes a = 30 b = 36 66
No c = 10 d = 70 80
Total:   40 106 146

The odds ratio is calculated as ad/bc. The odds ratio for Restaurant A is thus 30 × 70 / 36 × 10, or 5.8. This means that people who ate at Restaurant A were 5.8 times more likely to develop hepatitis A than were people who did not eat there. Even so, you could not conclude that Restaurant A was the source without comparing its odds ratio with the odds ratios for other possible sources. It could be that the source is elsewhere and that it just so happens that many of the people who were exposed also ate at Restaurant A.

Testing statistical significance
The final step in testing your hypothesis is to determine how likely it is that your study results could have occurred by chance alone. In other words, how likely is it that the exposure your study results point to as the source of the outbreak was not related to the disease after all? A test of statistical significance is used to evaluate this likelihood. Statistical significance is a broad area of study, and we will include only a brief overview here.

The first step in testing for statistical significance is to assume that the exposure is not related to disease. This assumption is known as the null hypothesis. Next, you compute a measure of association, such as a relative risk or an odds ratio. These measures are then used in calculating a chi-square test (the statistical test most commonly used in studying an outbreak) or other statistical test. Once you have a value for chi-square, you look up its corresponding p-value (or probability value) in a table of chi-squares.

In interpreting p-values, you set in advance a cutoff point beyond which you will consider that chance is a factor. A common cutoff point is .05. When a p-value is below the predetermined cutoff point, the finding is considered "statistically significant," and you may reject the null hypothesis in favor of the alternative hypothesis, that is you may conclude that the exposure is associated with disease. The smaller the p-value, the stronger the evidence that your finding is statistically significant.

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Step 8: Refine Hypotheses and Carry Out Additional Studies

Additional epidemiological studies
When analytic epidemiological studies do not confirm your hypotheses, you need to reconsider your hypotheses and look for new vehicles or modes of transmission. This is the time to meet with case-patients to look for common links and to visit their homes to look at the products on their shelves.

An investigation of an outbreak of Salmonella muenchen in Ohio during 1981 illustrates this point. A case-control study failed to turn up a food source as a common vehicle. Interestingly, people 15 to 35 years of age lived in all of the households with cases, but in only 41% of control households. This difference caused the investigators to consider vehicles of transmission to which young adults might be exposed. By asking about drug use in a second case-control study, the investigators found that illegal use of marijuana was the likely vehicle. Laboratory analysts subsequently isolated the outbreak strain of S. muenchen from several samples of marijuana provided by case-patients (10).

Even when your analytic study identifies an association between an exposure and a disease, you often will need to refine your hypotheses. Sometimes you will need to obtain more specific exposure histories or a more specific control group. For example, in a large community outbreak of botulism in Illinois, investigators used three sequential case-control studies to identify the vehicle. In the first study, investigators compared exposures of case-patients and controls from the general public and implicated a restaurant. In a second study, they compared the menu items eaten by the case-patients with those eaten by healthy restaurant patrons and identified a specific menu item, a meat and cheese sandwich. In a third study, appeals were broadcast over radio to identify healthy restaurant patrons who had eaten the sandwich. It turned out that controls were less likely than case-patients to have eaten the onions that came with the sandwich. Type A Clostridium botulinum was then identified from a pan of leftover sautéed onions used only to make that particular sandwich (11).

When an outbreak occurs, whether it is routine or unusual, you should consider what questions remain unanswered about the disease and what kind of study you might use in the particular setting to answer some of these questions. The circumstances may allow you to learn more about the disease, its modes of transmission, the characteristics of the agent, and host factors.

Laboratory and environmental studies
While epidemiology can implicate vehicles and guide appropriate public health action, laboratory evidence can clinch the findings. The laboratory was essential in the outbreak of salmonellosis linked to use of contaminated marijuana. The investigation of the outbreak of Legionnaires' disease in Philadelphia mentioned earlier was not considered complete until the new organism was isolated in the laboratory over 6 months after the outbreak actually had occurred (12). Environmental studies often help explain why an outbreak occurred and may be very important in some settings. For example, in an investigation of an outbreak of shigellosis among swimmers in the Mississippi River, a local sewage plant was identified as the cause of the outbreak (13).

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Step 9: Implementing Control and Prevention Measures

Even though implementing control and prevention measures is listed as Step 9, in a real investigation you should do this as soon as possible. Control measures, which can be implemented early if you know the source of an outbreak, should be aimed at specific links in the chain of infection, the agent, the source, or the reservoir. For example, an outbreak might be controlled by destroying contaminated foods, sterilizing contaminated water, destroying mosquito breeding sites, or requiring an infectious food handler to stay away from work until he or she is well.

In other situations, you might direct control measures at interrupting transmission or exposure. For example, to limit the airborne spread of an infectious agent among residents of a nursing home, you could use the method of "cohorting" by putting infected people together in a separate area to prevent exposure to others. You could instruct people wishing to reduce their risk of acquiring Lyme disease to avoid wooded areas or to wear insect repellent and protective clothing. Finally, in some outbreaks, you would direct control measures at reducing susceptibility. Two such examples are immunization against rubella and malaria chemoprophylaxis (prevention by taking antimalarial medications) for travelers.

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Step 10: Communicate Findings

Your final task in an investigation is to communicate your findings to others who need to know. This communication usually takes two forms: 1) an oral briefing for local health authorities and 2) a written report.

Your oral briefing should be attended by the local health authorities and people responsible for implementing control and prevention measures. This presentation is an opportunity for you to describe what you did, what you found, and what you think should be done about it. You should present your findings in scientifically objective fashion, and you should be able to defend your conclusions and recommendations.

You should also provide a written report that follows the usual scientific format of introduction, background, methods, results, discussion, and recommendations. By formally presenting recommendations, the report provides a blueprint for action. It also serves as a record of performance, a document for potential legal issues, and a reference if the health department encounters a similar situation in the future. Finally, a report that finds its way into the public health literature serves the broader purpose of contributing to the scientific knowledge base of epidemiology and public health.

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