The Morbidity and Mortality Weekly Report (MMWR (Links to an external site.)) is an epidemiological report published by the Centers for Disease Control and Prevention (CDC). This weekly report contains data on specific diseases as reported by state and regional health departments, as well as recommendations that have been issued by the CDC. Access the MMWR (Links to an external site.) and select a report pertaining to one of the eight national practice problems to address the following.
- Describe the epidemiologic principles and measures used to address the practice problem.
- Discuss the use of descriptive and/or analytic epidemiology to address the practice problem.
- Recommend additional measures required to integrate proposed changes into practice.
- Share your professional experience related to the topic.
Reflection on Learning
Reflective inquiry allows for expansion in self-awareness, identification of knowledge gaps, and assessment of learning goals. Each week, you will reflect upon what you have learned and complete a reflective journal assignment: Reflection on Learning. Each weekly reflection is placed in one document, which will be submitted for grading at the end of Week 7. There is no weekly reflection in Week 8 because a reflection is incorporated into the discussion question. Please review the Reflection Guidelines and Rubric for complete assignment requirements. Create a document where you will keep your weekly reflection.
In your document, write 1 page reflecting on your experience of beginning your journey to achieve your DNP and becoming a practice scholar.
- As you assess your learning, provide one specific example of how you achieved the weekly objective(s):
- Share one example of descriptive or analytic epidemiology that you see applied in your practice setting.
- What do you value most about your learning this week?
Hello! I hope you enjoyed Week 1, learning about the foundations of population health and the culturally appropriate services needed to improve outcomes for populations. This week, you’ll take that knowledge a step further by examining the role of epidemiology in assessing the health—or lack of health—in populations. Your exploration will begin with an investigation into descriptive and analytic epidemiology. These concepts build on information gained in NR714, so you won’t be surprised to learn that the difference between descriptive and analytic epidemiology is the use of a control group to develop hypotheses about causal relationships. In addition, you’ll investigate the different measures of morbidity and mortality. Without a clear understanding of the data and the implication of statistics reflective to health and disease, the practice scholar cannot intervene appropriately to resolve health disparities in vulnerable populations. There’s much important work to be done to improve population health outcomes. Let’s get started!
Week 2: Student Lesson Plan for Learning Success
Outcomes, Objectives, and Concepts
|Weekly Outcomes||Weekly Objectives||Main Topics and Concepts|
|Synthesize ethical and legal principles that impact population health initiatives. (PO 1)Assimilate epidemiology principles and interventions to impact population healthcare outcomes. (PO 1)||Identify the legal and ethical implications of population health surveillence.Consider the role of the DNP-prepared nurse in reporting population health issues.Differentiate between the meaning and appropriate use of epidemiological measurements in assessing population health risks.Analyze the impact of epidemiological measurements on population health outcomes.||Epidemiologic ConceptsEpidemiologic TriangleWeb of CausationRelationships between Multiple Risk FactorsDescriptive and Analytic Epidemiology Incidences and Prevalence RatesMorbidity and Mortality RatesLife Expectancy RatesEpidemiologic Measures in Action|
Foundations for Learning
Start your learning this week by reviewing commonly used epidemiologic measures, such as morbidity and mortality rates.
Centers for Disease Control and Prevention. (2012). Lesson 3: Measures of risk, Section 2: Morbidity frequency measures. In, Principles of epidemiology in public health (3rd ed.). https://www.cdc.gov/ophss/csels/dsepd/ss1978/lesson3/section2.html (Links to an external site.)
Centers for Disease Control and Prevention. (2012). Lesson 3: Measure of risk, Section 3: Mortality frequency measures. In, Principles of epidemiology in public health (3rd ed.). https://www.cdc.gov/ophss/csels/dsepd/ss1978/lesson3/section3.html (Links to an external site.)
Student Learning Activities
|Learning Activities||This week you will complete: PrepareAssigned ReadingsExplore|
Interactive LessonTranslate to PracticeDiscussion Question ReflectReflection on Learning
|Additional Resources||Centers for Disease Control and Prevention. (2012). Lesson 2: Summarizing data, Section 2: Types of variables. In, Principles of epidemiology in public health (3rd ed.). https://www.cdc.gov/ophss/csels/dsepd/ss1978/lesson2/section2.html (Links to an external site.) Centers for Disease Control and Prevention. (2012). Lesson 2: Summarizing data, Section 3: Frequency distributions. In, Principles of epidemiology in public health (3rd ed.). https://www.cdc.gov/ophss/csels/dsepd/ss1978/lesson2/section3.html (Links to an external site.) Centers for Disease Control and Prevention. (2012). Lesson 2: Summarizing data, Section 4: Properties of frequency distributions. In, Principles of epidemiology in public health (3rd ed.). https://www.cdc.gov/ophss/csels/dsepd/ss1978/lesson2/section4.html (Links to an external site.) Centers for Disease Control and Prevention. (2012). Lesson 2: Summarizing data, Section 6: Measures of central location. In, Principles of epidemiology in public health (3rd ed.). https://www.cdc.gov/ophss/csels/dsepd/ss1978/lesson2/section6.html (Links to an external site.) Centers for Disease Control and Prevention. (2012). Lesson 2: Summarizing data, Section 7: Measures of spread. In, Principles of epidemiology in public health (3rd ed.). https://www.cdc.gov/ophss/csels/dsepd/ss1978/lesson2/section7.html (Links to an external site.) Centers for Disease Control and Prevention. (2012). Lesson 2: Summarizing data, Section 8: Choosing the right measure of central location and spread. In, Principles of epidemiology in public health (3rd ed.). https://www.cdc.gov/ophss/csels/dsepd/ss1978/lesson2/section8.html (Links to an external site.)|
Learning Success Strategies
- An optional phone call with your faculty is available to you. Please reach out to your faculty member if you would like to schedule a welcome call.
- Take time to complete the interactive lessons in the Explore section. Often by listening, watching, and reading, your understanding will grow. These interactive lessons will help you understand your role as a DNP Scholar.
- Review key terms in the chapters to ensure you understand the definitions and relate them to epidemiologic measurement.
- As you review weekly content, consider how each concept and discussion can be translated into practice at your unique setting.
- Be ready to share your thoughts through the interactive discussion. Review the discussion guidelines and rubric to optimize your performance.
- You have access to a variety of resources to support your success. Click resources on the home page to access program and project resources.
- Your course faculty are here to support your learning journey. Reach out for guidance with study strategies, time management, and course-related questions.
Bemker, M. A. & Ralyea, C. (2018). Population health and its integration into advanced nursing practice. DEStech Publications, Inc.
- Chapter 14: Infectious Diseases as a Population Health Issue
Parrish, R. G. (2010). Measuring population health outcomes. Preventing Chronic Disease, 7(4), A71. https://www.cdc.gov/pcd/issues/2010/jul/10_0005.htm (Links to an external site.)
The U.S. Burden of Disease Collaborators. (2018). The state of U.S. health, 1990-2016 burden of diseases, injuries, and risk factors among U.S. states. JAMA, 319(14), 1444-1472.
Week 2: Foundations in Epidemiology
Epidemiology is the study of the variables that determine and influence the frequency and distribution of health, disease, injury, and other health-related events and their causes (Gordis, 2013). Only with this fundamental knowledge can the DNP scholar implement effective strategies to mitigate or eliminate the risk factors associated with disease. Epidemiological concepts and terminology have a long history; Florence Nightingale used these concepts in the Crimean War to measure the rates of death and illness per 1,000 soldiers (Gammon & Hunt, 2018). Her use of epidemiological concepts brought to the forefront the devastating effect that communicable diseases have on morbidity and mortality rates. Although epidemiologic thinking has been traced throughout history, it blossomed as a discipline following World War II. Today, epidemiology is used regularly to characterize the health of communities and to solve day-to-day problems, both simple and complex.
Since the era of Florence Nightingale, a number of models of disease causation have emerged. The simplest of these models is the epidemiologic triangle, which consists of an external agent, a susceptible host, and an environment that brings the host and agent together to impact health and produce disease.
View the following diagram to examine how agent, host, and environmental factors interrelate to produce disease.
Web of Causation
Another model addressing disease causation is the web of causation. Unlike the epidemiologic triangle, this model addresses multiple factors that interact to produce disease. These many determinants make up the web of causation and underpin the population health model of multiple causation.
Now, view the following diagram to investigate how multiple factors interact to produce disease.
Relationships between Multiple Risk Factors
In addition to examining independent contributions of risk factors on disease causation, multiple risk factors may be clustered to determine associations or relationships between risk factors. In their landmark text, Shi and Stevens (2010) identified a profile of risk factors contributing to poor health based on income, insurance coverage, and a regular source of care. This profile of risks was then used to investigate the combined influences of these multiple risks on unmet health care needs due to costs of care.
Further your exploration of epidemiology by considering the combined influence of co-occurring risk factors on disease.
Despite concerns that culture is immutable, once precise mechanisms linking cultural variants and health outcomes are identified and modified, culture change and health improvements can occur. Strategies to improve health outcomes must simultaneously target co-occurring risks through integrative approaches, rather than use more fragmented approaches that address single risk factors.
Descriptive and Analytic Epidemiology
Epidemiologic research utilizes two methodologies to gather data regarding the distribution and determinants of events and diseases in groups of people: descriptive epidemiology and analytic epidemiology. Descriptive epidemiology examines the patterns of disease occurrence, with a focus on person, place, and time. Different from this approach that uses relatively accessible data, analytic epidemiology aims to quantify the association between exposures and outcomes and test hypotheses about causal relationships. Both methodologies are useful in generating evidence to promote health.
View the following activity to explore how descriptive and analytic epidemiology are used to promote population health outcomes.
[MUSIC] Like other scientists, the five Ws, what, who, where, when, and why/how provide epidemiologists with a method to collect comprehensive information regarding health event. Unlike other scientists, epidemiologists use synonyms for the five Ws. The what refers to the health issue of concern. The who would be the person. Where is the place.
The when is the time. And the why/how are the causes, risk factors, and modes of transmission. Descriptive epidemiology is concerned with organizing and analyzing data in order to understand variations in disease frequency, geographically and over time, and how disease or health varies among people based on personal characteristics. It focuses primarily on the three Ws, person, place, and time.
Let’s take a closer look at these three Ws of descriptive epidemiology. Personal determinants influence health. Measuring characteristics such as age, sex, race, marital status, and other personal data are helpful to identify health trends. Personal characteristics are helpful when evaluating population health interventions and their impact on disease. Place also influences health, measuring whether a disease affects a specific geographic region is important to determine causation.
It is also important to determine if population health intervention influences the disease cases in a given area. For example, a disease maybe specific to a small area such as a park or a building, but maybe as large as a country or continent. Diseases and other population health issues change over time.
Measuring and displaying the patterns of disease occurrence by time are critical for monitoring disease within a community. Cases are monitored chronologically to determine whether population health interventions improve outcomes by decreasing occurrences. Next, let’s consider analytic epidemiology. Analytic epidemiology is concerned with the search for causes and effects, or the why and the how.
Epidemiologists use analytic epidemiology to quantify the association between exposures and outcomes, and to test hypothesis about casual relationships. Now let’s take a look at how these approaches differ. The difference between descriptive and analytic epidemiology is the use of a controlled group to develop hypothesis about casual relationships. Descriptive epidemiology will provide the time, place, and person involved in the health issue.
But it is through an analytic approach that appropriate control and prevention measures can be developed to improve health outcomes. Consider the Salmonella outbreak in 2018 where 92 people were infected. The CDC was able to trace the determinant back to raw chicken by looking at those infected and those not infected.
Let’s take a look at some important findings using descriptive epidemiology. Illnesses were evaluated from January 19th, 2018 to September 9th, 2018. Ill people ranged in age from less than 1 year to 105, with a median age of 36. 69% of ill people were female. Of 62 people with information available, 21 of them or 34% were hospitalized.
No deaths were reported. You can view the location of people infected with the outbreak strain of Salmonella Infantis by state of residence as of October 15th, 2018. [MUSIC] Now let’s consider an example of when epidemiologists use descriptive epidemiology to test a hypothesis. Consider the large outbreak of hepatitis A that occurred in Pennsylvania in 2003.
Investigators found that most of the patients had eaten at a particular restaurant two to six weeks before the onset of illness. The investigators needed to confirm which particular food may have been contaminated. The investigators asked the patients which restaurant foods they had eaten, and enrolled and interviewed a comparison or a control group.
A group of persons who had eaten at the restaurant during the same period but who did not get sick. Of 133 items on the restaurants menu, the striking difference between the case and control groups was in the proportion of people who ate salsa. 94% of the case patients ate the salsa, compared with only 39% of the controls.
Further investigation of the ingredients in the salsa implicated green onions as the source of infection. The Food and Drug Administration issued an advisory to the public about green onions and the risk of hepatitis A due to convincing results of the analytic epidemiology. [MUSIC]
Week 2: Epidemiologic Measurements
Morbidity and Mortality Rates
Epidemiologic measurements are statistical calculations used to determine outcome data in population health. Diseases are often studied using the epidemiologic principles of morbidity and mortality rates. Morbidity is the condition of being ill, diseased, or unhealthy, and includes both acute and chronic illnesses. Mortality rates refer to the number of deaths in a population over time, either in general or due to a specific cause.
View the following activity to consider how morbidity and mortality rates are used to analyze health changes over time.
[MUSIC] Hello, I’m Midori, your DNP coach. Let’s begin our investigation into epidemiologic calculations by examining morbidity and mortality rates in population health. Morbidity refers to the incidence of ill health within a population. Morbidity rates help determine the risks of an illness in a population. Mortality refers to the number of deaths in a population and is usually calculated as the number of deaths per 1,000 individuals per year.
If we look at diabetes and heart disease, diseases related to the eight national practice problems both conditions have low morbidity rates. However, heart disease has a greater mortality rate than diabetes. Unlike heart disease and diabetes, obesity and Alzheimer’s disease have higher morbidity rates. However, Alzheimer’s disease has a greater mortality than obesity.
Changes in morbidity and mortality rates over time, and within specific populations provide important data regarding population health. This graph illustrating CDC statistics shows the age adjusted mortality rates for many diseases over time. Look how mortality rates related to Alzheimer’s disease have increased in the United States in the recent years.
Conversely, mortality rates related to cerebral vascular disease have decreased during the same period.
Life Expectancy Rates
Another frequently used epidemiologic calculation is life expectancy rates. Life expectancy is a measure of the average time of life of an individual, based on the year of birth, current age, and other demographic factors.
View the following activity to examine life expectancy rates and the measure of premature mortality.
[MUSIC] Hello, I’m back to guide your investigation into life expectancy rates and the measure of premature mortality. Life expectancy at birth is one of the most common ways to calculate life expectancy. It can also be calculated as the remaining life expectancy for any given age. However, if the average life expectancy at birth for one individual is 79 years, the remaining average life expectancy of that same individual at 72 years old is 7 years.
In population health, the years of potential life lost is often calculated in reference to mortality. This is useful in measuring the outcomes of population health interventions. Let’s consider the years of potential life lost, YPLL, for diabetes. Consider an intervention aimed at stabilizing A1C levels in a population of ten patients.
The patients in this study range in age from 22 to 67. If diabetes has the potential to reduce life expectancy by nine years, the total life expectancy for the group of ten patients is 233 years. The long-term goal of the intervention is to increase the life expectancy of diabetic patients through the maintenance of stable A1C levels for five years for individuals under the age of 50.
And three years for those over the age of 50. If this occurs, the years of potential life lost decreases by 42 years. The outcome associated with this long-term intervention decreases the years of potential life lost due to diabetes by 42 years for the 10 patients. [MUSIC]
Incidences and Prevalence Rates
Other commonly used epidemiologic statistical measures are incidences and prevalence rates. Incidence is a measure to determine an individual’s probability of being diagnosed with a disease during a given period of time. Prevalence is a measure to determine an individual’s likelihood of having a disease.
View the following activity to explore the incidences and prevalence rates of disease.
[MUSIC] Hello, I’m back to further your exploration of epidemiology methods. Now let’s turn our attention to the principles of prevalence and incidence. Prevalence and incidence are often confused. Prevalence is the proportion of individuals who have a condition at or during the time period being measured. It measures disease burden which you have examined in NR-701.
Incidence measures disease risk and is the proportion of individuals who develop a condition during the time period being measured. When measuring the prevalence of disease, the numerator equals all cases present during the time being measured. This includes new and existing cases of the condition. The numerator for calculating the incidence of disease is the number of new cases of the condition being reported during the time period being measured.
Let’s add the denominator next to complete the formula. For both prevalence and incidence, the denominator is the total number of individuals in the population who have the possibility of acquiring the condition. Let’s consider a scenario to apply these concepts. As a practice scholar, you are implementing a prostate cancer early detection screening for intervention.
You will measure a prostate-specific antigen or PSA Test results to determine the incidence and prevalence of prostate cancer in your patient population during the period of one year. Your total patient population is 2,000, but only 200 are men between the ages of 50 and 70, and only 100 of those individuals agreed to have the PSA Test.
Therefore, your denominator is 100. Based on past medical histories, 3 of the 100 participants have a history of prostate cancer. Based on the results of the PSA Test, two more participants are diagnosed with prostate cancer during this period. To measure the rate of incidence, divide the number of new diagnosis, 2, by the total population in the study.
And then multiply it by 100 to find the incidence rate, which equals 2%. [MUSIC] For the prevalence rate, add up the number of individuals with the currents diagnoses, 3 to the number of individuals with a new diagnoses, 2. Divide that number by the population of the study, 100, and then multiply by 100 to find the prevalence rate, which equals 5%.
Epidemiologic Measures in Action
Epidemiologic measures are foundational to obtaining and using data to drive health. The role of the DNP scholar hinges on health advocacy for all people. Applying epidemiologic concepts paves the way for the scholar and other healthcare providers to realize greater possibilities in advancing the health of our nation and beyond.
Further your exploration of epidemiologic measures by applying these concepts in the below case study.
Epidemiologic Measures in Action
Epidemiologic Measures in Action
Working at the Texas Department of Health (TDH), you have received a telephone call from a student at a nearby university. The student reported that he and his roommate, a fraternity brother, were suffering from nausea, vomiting, and diarrhea. Both had become ill during the night. The roommate had taken an over-the-counter medication with some relief of his symptoms. Neither the student nor his roommate had seen a physician or had gone to the emergency room.
The students believed their illness was due to food they had eaten at a local pizzeria the previous night. They asked if they should attend classes and take a biology midterm exam that was scheduled that afternoon.
Click continue to begin the case study.
What questions (or types of questions) would you ask the student? Select all answers below that would apply, and review the information provided by the population health expert.
In recording a complaint about a possible foodborne illness, it is important to systematically collect the following information:
- WHAT is the person’s problem? (e.g., clinical description of the illness, whether a physician was consulted, whether any tests were performed or any treatments were provided)
- WHO else became ill, their characteristics (e.g., age, sex, occupation), and the nature of their illnesses (e.g., symptoms, whether any persons were hospitalized or died)?
- WHEN did the affected person(s) become ill?
- WHERE are the affected persons located? (including names and telephone numbers)
- WHY (and HOW) do they think they became ill? (e.g., risk factors, suspected exposures, suspected modes of transmission, hints from who else did and did not become ill)
What would you advise the student about attending classes that day?
Click to review the expert’s notes
While symptomatic, the students would probably be most comfortable staying in their dorm room. Persons involved in food-handling and direct care of high-risk persons should not return to work until 48-72 hours after symptoms have resolved. One should check local isolation and quarantine policies for clarification.
TDH staff were skeptical of the student’s report but felt that a minimal amount of exploration was necessary. They began by making a few telephone calls to establish the facts and determine if other persons were similarly affected. The pizzeria, where the student and his roommate had eaten, was closed until 11:00 a.m. There was no answer at the University Student Health Center, so a message was left on its answering machine.
A call to the emergency room at a local hospital (Hospital A) revealed that 23 university students had been seen for acute gastroenteritis in the last 24 hours. In contrast, only three patients had been seen at the emergency room for similar symptoms from March 5-9, none of whom were associated with the university.
At 10:30 a.m., the physician from the University Student Health Center returned the call from TDH and reported that 20 students with vomiting and diarrhea had been seen the previous day. He believed only 1-2 students typically would have been seen for these symptoms in a week. The Health Center had not collected stool specimens from any of the ill students.
TDH experts are reviewing their findings to determine if this classifies as an outbreak. Do you think these cases of gastroenteritis represent an outbreak at the university? Why or why not?
When you are ready to review their initial assessment, open their e-mail below.
An outbreak is the occurrence of more cases of a disease than expected for a particular place and time. In a 2-day period, over 40 cases of gastroenteritis occurred among students at the university (assuming that individual students did not visit both the Student Health Center and the emergency room). This compares with the handful of students that would normally have been seen for these symptoms at the two facilities in a week. Therefore, it is highly likely that these cases represent an outbreak.
What is not clear is whether the outbreak is limited to the university or if the wider community may also be affected. Case finding methods to this point, a hospital near the university and the University Student Health Center, are more likely to pick up cases among students than in the community. NOTE: The terms “outbreak” and “epidemic” are used interchangeably by most epidemiologists. The term “outbreak” is sometimes preferred, particularly when talking to the press or public, because it is not as frightening as “epidemic”.
On the afternoon of March 11, TDH staff visited the emergency room at Hospital A and reviewed medical records of patients seen at the facility for vomiting and/or diarrhea since March 5. Based on these records, symptoms among the 23 students included:
vomiting (91%), diarrhea (85%), abdominal cramping (68%), headache (66%), muscle aches (49%), and bloody diarrhea (5%).
Oral temperatures ranged from 98.8/F (37.1/C) to 102.4/F (39.1/C) (median: 100/F [37.8/C]). Complete blood counts, performed on 10 students, showed an increase in white blood cells (median count: 13.7 per cubic mm with 82% polymorphonuclear cells, 6% lymphocytes, and 7% bands). Stool specimens had been submitted for routine bacterial pathogens, but no results were available.
TDH staff asked healthcare providers from the University Student Health Center, the Hospital A emergency room, and the emergency departments at six other hospitals located in the general vicinity to report cases of vomiting or diarrhea seen since March 5. A TDH staff person was designated to help the facilities identify and report cases. The healthcare providers were also asked to collect stool specimens from any new cases. Bacterial cultures from patients seen in the emergency rooms were to be performed at the hospital at which they were collected and confirmed at the TDH Laboratory. Specimens collected by the Student Health Center were to be cultured at the TDH Laboratory.
By March 12, seventy-five persons with vomiting or diarrhea had been reported to TDH. All were students who lived on the university campus. No cases were identified among university faculty or staff or from the local community. Except for one case, the dates of illness onset were March 9-12. (Figure 1) The median age of patients was 19 years (range: 18-22 years), 69% were freshman, and 62% were female.
TDH staff met with the Student Health Center physician and nurse, and several university administrators including the Provost. City Health Department staff participated in the meeting.
Below, one of our experts has compiled a few notes on topics he wants to include in discussions with university officials. Click the arrow buttons below to review all his notes.
Topics to discuss…
Why it is important to investigate the outbreak (especially, to the university, itself) and to do so in a timely fashion?
What has already been done and found to date?
What the health department can provide to the university?
What kind of cooperation will be needed from the university (food samples, menus, time and place to interview foodhandlers and managers, etc.)?
How press inquiries will be handled?
How the university will be kept informed of progress in the investigation?
TDH and City Health Department staff gathered the following information. Click the arrows on the left and right to review all findings.
The university is located in a small Texas town with a population of 27,354. For the spring semester, the university had an enrollment of approximately 12,000 students; 2,386 students live on campus at one of the 36 residential halls scattered across the 200+ acres of the main campus. About 75% of the students are Texas residents.
The university uses municipal water and sewage services. There have been no breaks or work on water or sewage lines in the past year. There has been no recent road work or digging around campus.
The campus dining service includes two cafeterias managed by the same company and about half a dozen fast food establishments; about 2,000 students belong to the university meal plan which is limited to persons living on campus. Most on-campus students dine at the main cafeteria which serves hot entrees, as well as items from the grill, deli bar, and a salad bar. A second smaller cafeteria on campus offers menu selections with a per item cost and is also accessible to meal plan members. In contrast to the main cafeteria, the smaller cafeteria tends to be used by students who live off campus and university staff. The smaller cafeteria also offers hot entrees, grilled foods, and a salad bar, but has no deli bar.
Spring break is to begin on March 13 at which time all dining services will cease until March 23. Although many students will leave town during the break, it is anticipated that about a quarter of those living on campus will remain.
Hypothesis-generating interviews were undertaken with seven of the earliest cases reported by the emergency rooms and the Student Health Center; all of the cases had onset of illness on March 10. Four were male and three were female; all but one was a freshman. Two students were psychology majors; one was majoring in English and one in animal husbandry. Three students were undecided about their major.
The students were from five different residential halls and all reported eating most of their meals at the university’s main cafeteria. During the past week, all but one student had eaten food from the deli bar; two had eaten food from the salad bar, and three from the grill. Seven-day food histories revealed no particular food item that was common to all or most of the students.
Except for the psychology majors, none of the other students shared any classes; only one student had a roommate with a similar illness. Five students belonged to a sorority or a fraternity. Three students had attended an all-school mixer on March 6, the Friday before the outbreak began; two students went to an all-night science fiction film festival at one of the dorms on March 7. Students reported attendance at no other special events; most had been studying for midterm exams for most of the weekend.
Using information available to you at this point, take a moment to reflect what your leading hypotheses on the mode of transmission and the source of the outbreak might be.
When you have the hypotheses determined, click on each item below to review our expert’s hypotheses.
Mode of transmission: Illness is limited to students living on campus. The campus uses city water supplies. If city water supplies were contaminated, one would also expect to see cases in the community. It is possible that there are isolated problems with water and sewer lines on campus, but students from at least five residential halls were affected, so a break would have to affect water distribution over a wide area. (And if campus water was widely contaminated, one might expect to see illness in faculty, staff, and off-campus students who consumed water while on campus.) Cases occurred in a number of different residential facilities and, among hypothesis generating interviews, did not cluster by dorm rooms (i.e., roommates were not affected), or classes. This pattern is not consistent with person-to-person spread.
A large proportion of students living on campus are part of the meal plan; most on-campus students eat at the main dining room. University staff and off-campus students rarely eat at the main cafeteria. All students in the hypothesis generating interviews ate at the main cafeteria and most also ate at the deli bar suggesting contaminated food or drink from this site might be the mode of transmission.
Source: No common food items were identified through hypothesis generating interviews.
However, viral agents are commonly transmitted through raw or poorly cooked shellfish, sandwiches, and salads.
What actions would you take?
- Gather more data.
- Close the cafeteria.
- Quarantine the campus.
- Cancel all classes.
- Prescribe prophylaxis to all students, faculty, and staff.
Incorrect Feedback –
The correct answer is A. At this point, we do not have enough information to make any decisions. Next steps in this investigation include a controlled epidemiologic study, an environmental investigation of the main cafeteria and deli bar (e.g., inspection of operations and interviews with staff), viral testing of stool specimens from ill persons, and collection and testing of leftover food, water, and ice from the main cafeteria and deli bar.
Correct Feedback –
Correct! At this point, we do not have enough information to make any decisions. Next steps in this investigation include a controlled epidemiologic study, an environmental investigation of the main cafeteria and deli bar (e.g., inspection of operations and interviews with staff), viral testing of stool specimens from ill persons, and collection and testing of leftover food, water, and ice from the main cafeteria and deli bar.
Cafeteria staff were questioned about their responsibilities in the cafeteria, such as the foods they handled, which meals they served, and where they usually worked (e.g., deli bar, grill). They were also asked about use of gloves, handwashing practices, their work schedule during the week before the outbreak, and if they had been ill at that time.
In the cafeteria, the deli bar had its own preparation area and refrigerator. During mealtimes, sandwiches were made to order by a food-handler. Each day, newly prepared deli meats, cheeses, and condiments were added to partially depleted deli bar items from the day before (i.e., without discarding leftover food items). While the deli was open for service, sandwich ingredients were not kept refrigerated or on ice. The deli bar containers were not routinely cleaned. Samples of leftover food, water, and ice were collected.
None of the food-handlers interviewed reported being ill in the last two weeks. Stool cultures were requested from all cafeteria staff.
Before dinner on March 12, the City Health Department closed the deli bar.
Do you agree with the decision to close the deli bar? What actions would you take now?
- Yes, I agree with the closing of the deli bar because the evidence is strong enough to implicate it as the source of the outbreak.
- No, I do not agree with the closing of the deli bar because there is no solid evidence to implicate it as the source of the outbreak.
The action is based on the most likely hypothesis and circumstantial evidence (i.e., other foodhandling problems identified in the deli bar). Because there were multiple serious problems identified at the deli bar, closing it down until safer practices can be assured would seem reasonable. Furthermore, closure will be a minimal burden to the university and its students and could prevent additional cases from occurring. Although the suspected source of the outbreak has seemingly been addressed, it is important to undertake a more definitive epidemiologic study for the following reasons: • the source may not be the deli bar and cases may continue to occur • more specific information is needed on the source to determine when it is safe to reopen the deli bar • more specific information is needed on the source to prevent the problem from recurring in the future.
Twenty-nine cases and controls were interviewed over the telephone. Investigators tabulated the most notable results in the table below.
|Exposure||Ill exposed/Total ill*(%)||Well exposed/total well*(%)||Matched odds ratio **||95% confidence interval||P-value|
|Ate at deli bar – lunch on March 9||11/28 (39)||1/29 (3)||11.0||1.6-473||<0.01|
|Ate at deli bar – dinner on March 9||7/27 (26)||2/29 (7)||6.0||0.73-275||0.06|
|Ate at deli bar – lunch on March 10||8/29 (28)||1/28 (4)||8.0||1.1-354||0.02|
|Ate at deli bar – dinner on March 10||2/29 (7)||2/28 (7)||1.0||0.01-79||0.75|
|Ate at deli bar – lunch or dinner on March 9 or lunch on March 10||15/27 (56)||3/28 (11)||7.0||1.61-63.5||<0.01|
*Denominator does not always total to 29 because several subjects could not remember where they ate the indicated meal.
**The data prvided for cases and controls cannot be used to calculate the matched odds ratio, which is based on an analysis of discordant pairs.
Reviewing large amounts of data can at times be tricky. How would you interpret this data? When you have your interpretation in mind, click on the button below to open our expert’s interpretation.
Cases were more likely than controls to have eaten at the deli bar for lunch or dinner on March 9 (11 and six times more likely, respectively) or lunch on March 10 (8 times more likely). Cases were not more likely than controls to have eaten at the deli bar for dinner on March 10. The differences were statistically significant for lunch on March 9 and lunch on March 10. Fifty-six percent of cases were exposed to the deli bar during at least one of the implicated meals.
Below are three elements of this case-control study that might affect the validity of the measured association. Click on each icon below to review the element.
- selection bias – less than half of the cases reported at the time of the study (and less than a quarter of the 125 cases ultimately reported to TDH) were included in the study. We are not told how the 29 cases were selected, but since the study was limited to students who could be reached at their dormitory rooms on the night before spring break, it seems likely that the selection process may not have been random. Cases who were more severely affected or ill later in the course of the outbreak (and, therefore, not well enough to participate in the study) may also have been excluded from the study. These cases may have had different exposures compared to less severely affected cases or cases that were sick earlier in the outbreak. The impact of this potential selection bias on the resulting odds ratio cannot be determined.
- possible overmatching – university roommates often share exposures. Many eat meals together and attend special functions together. As a result, case-control roommates are more likely to be similar (i.e., concordant) with respect to exposures compared with other case-control pairs, and will ultimately not contribute to the analysis in a matched study design (i.e. a matched analysis focuses on discordant pairs). This will weaken the association between an exposure and illness (i.e., decrease the estimated odds ratio).
- matching on a possible risk factor – diarrheal diseases can be spread by person-to person transmission. Although unlikely to be the predominant mode of transmission in this outbreak (based on earlier information), matching of well and ill roommates will inhibit examination of this risk factor.
Eating at the main cafeteria, in general, was not associated with illness; however, eating from the deli bar during lunch on March 9 or March 10 was significantly associated with illness. Because such a small number of controls ate at the deli bar, individual food items from the deli bar could not be examined.
The case-control study was undertaken among students who ate at the main cafeteria. A case was defined as vomiting or diarrhea (≥3 loose bowel movements during a 24-hour period) with onset on or after March 5, 2019, in a student who was a member of the university meal plan. Cases were selected from those reported to TDH by one of the local emergency rooms or the Student Health Center. Controls were students enrolled in the university meal plan who did not have nausea, vomiting, or diarrhea since March 5.
Forty cases were randomly selected from the 125 reported through March 13. One hundred and sixty controls were randomly selected from the university meal plan database.
Below are several individuals you might consult with to develop actions/policies for the campus food service to prevent a recurrence of this problem in the future. Click on each person below to view his or her role.
- university administrators or their representatives – they will be legally responsible for the policy and dealing with union or employee grievances; they must serve the assurance function
- supervisors in the cafeteria – they will have to implement the policy and answer employee questions
- >foodhandlers – they can provide their perspectives and insights (including hardships resulting from the policy); they can also help review the policy and provide feedback on ability to be understood by other employees
- union representatives (if applicable) – they can provide information on legal implications
- local health department staff- they can provide expertise on foodborne diseases and control measures; they may be able to provide an objective viewpoint and moderate discussions
Let’s recap what you learned in Week 2. Epidemiologic principles and measurements were the basis of your exploration this week. You discovered that epidemiology is about identifying associations between exposures and outcomes. You learned that successes in treating and preventing disease have shifted emphasis to understanding the mechanisms underlying disease and its prevention. A comprehensive assessment of risk factors is now considered foundational in improving population health outcomes. The week culminated in an opportunity for you to apply epidemiological measurements to one of the eight national practice problems. What a strong foundation you’re gaining to improve healthcare outcomes at the population level!
Centers for Disease Control and Prevention. (2012). Lesson 1: Introduction to epidemiology, Section 6: Descriptive epidemiology. In, Principles of epidemiology in public health (3rd ed.). https://www.cdc.gov/ophss/csels/dsepd/ss1978/lesson1/section6.html
Centers for Disease Control and Prevention. (2012). Lesson 1: Introduction to epidemiology, Section 7: Analytic epidemiology. In, Principles of epidemiology in public health (3rd ed.). https://www.cdc.gov/ophss/csels/dsepd/ss1978/lesson1/section7.html
Centers for Disease Control and Prevention. (2012). Lesson 3: Measures of risk, Section 2: Morbidity frequency measures. In, Principles of epidemiology in public health (3rd ed.). https://www.cdc.gov/ophss/csels/dsepd/ss1978/lesson3/section2.html
Centers for Disease Control and Prevention. (2012). Lesson 3: Measure of risk, Section 3: Mortality frequency measures. In, Principles of epidemiology in public health (3rd ed.). https://www.cdc.gov/ophss/csels/dsepd/ss1978/lesson3/section3.html
Gammon, J. & Hunt, J. (2018). Source isolation and patient wellbeing in healthcare settings. British Journal of Nursing, 27(2), 88-91. https://doi.org/10.12968/bjon.2018.27.2.88
Gordis, L. (2013). Epidemiology (5th ed.). Elsevier Saunders.
Shi, L. & Stevens, G. D. (2010). Vulnerable populations in the United States. Jossey-Bass.
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