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NCPD Article

COVID-19 Pandemic 101: An Epidemiology and Public Health Primer for Oncology Nurses

Susan Yackzan
Suzanne M. Mahon
CJON 2021, 25(1), 33-40 DOI: 10.1188/21.CJON.33-40

Background: Understanding basic epidemiology and public health concepts is essential to the provision of safe care during a pandemic. These basic concepts and terms include containment, mitigation, predictive modeling, latent period, incubation period, reproduction number, case fatality rate, and test sensitivity and specificity.

Objectives: Public health concepts and terms are defined, described in the context of the COVID-19 pandemic, and specific implications for oncology nursing practice are discussed.

Methods: A review of public health literature and reputable websites with a focus on COVID-19 data. This article defines epidemiologic and public health concepts and uses examples from the pandemic to illustrate oncology nursing implications.

Findings: The COVID-19 pandemic is changing oncology nursing care delivery. Oncology nurses need to understand these concepts to anticipate and advocate for optimal oncology care.

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Epidemiology and public health initiatives are familiar topics to oncology nurses. The familiar perspective is one of prevention or mitigation of the exposures contributing to cancer, such as environmental carcinogens. With the onset of the COVID-19 pandemic, the epidemiology and public health focus in oncology quickly shifted to a perspective focused on an airborne infectious disease. During this time, oncology nurses have been expected to understand broad epidemiology concepts and to apply the concepts in practice for the benefit and safety of clinicians, patients, and communities. To that end, this article will define relevant public health concepts and terms in the context of the current COVID-19 pandemic. A discussion of the concept or term significance and application to oncology nursing practice will follow each definition.

Public Health Concepts

Epidemiology, Public Health, Outbreak, Epidemic, and Pandemic

Epidemiology is a broad field and includes the study of the distribution and determinants of health or disease in a specified location or population. Public health focuses on populations as opposed to individuals. Outbreak, epidemic, and pandemic are all relative terms. No criteria or established number of cases is required for a condition to fall into any one of those categories. Instead, the terms are a relative comparison of how “common a condition is at a point in time relative to how common it was at an earlier time” (Grennan, 2019, p. 910). In general, an epidemic is an increase of more than an expected number of cases in a regional population; a pandemic is an epidemic spread across multiple regions or countries (Centers for Disease Control and Prevention [CDC], 2012). The terms are a way to describe what is expected versus what is observed. In the case of COVID-19, a cluster of unexplained pneumonia cases in Wuhan, China, was initially reported in late December 2019 (Sun et al., 2020; Zhu et al., 2020). The cluster of cases grew into an outbreak, which was described as a public health emergency on January 30, 2020 (Sun et al., 2020). It was then identified as a global pandemic on March 11, 2020, with cases in more than 100 countries (World Health Organization, 2020).

Implications: The application to nurses revolves around awareness and readiness. Identifying reputable sources of information is essential. The CDC provides information and links to resources for a broad range of pandemic topics (CDC, 2020d), including guidance for healthcare facilities, communication tools, and clinical care. Professional oncology groups, such as the Oncology Nursing Society (2020), the American Society of Clinical Oncology (2020), and the American Society for Therapeutic Radiology and Oncology (2020), provide oncology-specific information and links to additional resources. State and local community health departments are important sources of information that may be more focused on the areas in which nurses live and work.

Containment and Mitigation

Containment is an initial strategy aimed at controlling the spread of disease. Containment measures are vital in the early phases of a pandemic and include identification of cases, contact tracing, isolation or quarantine, and social distancing. As part of these efforts, persons under investigation (PUIs) are identified. PUIs are those who have been in close contact with an infected person or who have been to an area with an identified outbreak. This definition adjusts over time as different areas experience outbreaks. In the case of a disease transmitted by aerosolization, such as COVID-19, containment measures also may include airborne isolation precautions.

Mitigation begins when positive cases are identified that provide evidence of community transmission (i.e., occurring without identifiable exposure, travel history, or in those not previously identified as a PUI). Mitigation relies on nonpharmaceutical interventions such as hand hygiene, travel restrictions, school closures, and social distancing (Walensky & Del Rio, 2020).

Mitigation efforts are unique to a community. The more population in an area, the higher the risk of transmission. Cities with dense urban living conditions (such as Chicago or New York City) or temporarily dense population influxes (such as for Mardi Gras in New Orleans or the Super Bowl) require different mitigation strategies. In the case of a densely populated urban area, face masks could be provided to city residents as a mitigation effort. In the case of a mass gathering for Mardi Gras or the Super Bowl, mitigation efforts may include cancellation of those events. These mitigation efforts are undertaken to slow the spread of disease to protect those at increased risk and to protect healthcare system capacity (CDC, 2020a). Another example of a mitigation strategy might be a strong campaign to increase the awareness and availability of the vaccines to help decrease cases and subsequent admissions to hospitals, with the goal of conserving resources.

From an oncology perspective, deferral of some surgical procedures may be recommended to ensure availability of ventilators and personal protective equipment (PPE). Recommendations for cancer surgery deferral and ways to make the distinction between elective versus essential cancer surgery were developed and promulgated by professional organizations and groups to help guide clinical practice (American College of Surgeons, 2020; Bartlett et al., 2020; Dietz et al., 2020).

Implications: Nurses have a long history of public health advocacy in disease prevention and health promotion. Education about potential infections, risk, and avoidance behaviors to patients or communities is necessary during a pandemic. In the case of a pandemic from a novel virus, nurses may seek information to educate themselves in order to become better educators and serve as role models for others. Reputable and current sources of information provide background information and data so the best possible decisions about how to render care can be made. In this pandemic, the CDC (2020c) provided links to scholarly publications, and many journals have allowed online access to submitted manuscripts.

An understanding of basic epidemiologic concepts, such as containment and mitigation, allows nurses to anticipate and pivot quickly to the implementation of appropriate measures. Measures might include limiting visitors in the inpatient setting, screening all employees and patients as they enter the cancer center, and requiring a negative COVID-19 test result prior to any scheduled procedure or surgery.

Recognition of high-risk status related to social determinants of health and inequities is an important concept that varies in each practice setting and community and drives tailored mitigation efforts (Patel et al., 2020). People who live in poverty may live with extended family in smaller homes, making quarantine isolation difficult logistically. In addition, some may be essential workers who cannot work from home and may encounter exposure while working or while commuting via public transportation. These individuals may be less likely to seek out testing because a period of forced quarantine could result in lost wages—wages that they and their family members rely on for food and housing costs (Walensky & Del Rio, 2020).

Figure 1 illustrates the differences in case rates in the city of St. Louis, Missouri. In three of the four areas with greatest case rates, 25%–34% of the population live below the poverty line (City of St. Louis, 2020). Mitigation efforts in this region included targeted placement of testing sites to better identify affected individuals and those who should be quarantined. Development of drive-through food pantries in impoverished neighborhoods can assist with short-term nutritional needs if employment is restricted by quarantine. Another strategy in these areas is the implementation of telehealth hubs in neighborhood churches and religious centers to allow individuals without Internet to have access to telehealth visits in a private area in their own neighborhood. Through telehealth, patients can receive ongoing care for chronic conditions and less serious acute conditions in an effort to prevent more serious problems and exacerbations of existing health conditions. In addition, telehealth removes the use of public transportation as an additional exposure opportunity (Rotermund, 2020).

Identified positive cases will require education about quarantine rationale and procedures. Mitigation depends on truly quarantining affected individuals. The identification of barriers to successful quarantine allows nurses and public health officials to plan interventions to decrease those barriers. An example of this would be strategizing to assist an individual with obtaining groceries or oral cancer medications through home delivery (Segelov et al., 2020).

Nurses also need to role model containment and mitigation behaviors at work and in the community. This might include wearing a mask when entering the workplace and when walking to and from parked cars, careful handwashing and hand sanitizing, practicing social distancing, and avoiding large crowds.

Predictive Modeling and Flattening the Curve

A key contribution of epidemiology during a pandemic is the use of predictive modeling. This statistical method of forecasting is produced by making assumptions about variables included in the model. Examples of assumption categories include wearing face masks, shelter-in-place decrees, population density, and population risk status. These assumptions are included in different predictive models and can be manipulated to forecast various outcomes. Both the model forecast and the manipulation of assumptions can be useful to those making decisions about public health policy. They can also be used by hospital leadership when making decisions about ongoing services in specific geographic locations based on model trends (Institute for Healthcare Metrics and Evaluation, 2020). Models can perform differently depending on the model assumptions and calculations. For example, changing the demographics of the exposed population may change the projection. Multiple models have been used by national and state government as well as public health officials during the COVID-19 pandemic.

The phrase “flattening the curve” entered the mainstream media in the early days of the pandemic in the United States (Roberts, 2020). The curve refers to part of the predictive model showing the number of cases occurring over a specific period of time, with time projected into the future. Many cases happening in a short period of time can create a curve with a tall peak, whereas a slower spread of disease, resulting from the use of containment and mitigation practices, creates a flatter, shortened peak (see Figure 2). A flattened curve is desirable in a public health crisis to spread out the utilization of healthcare resources and capacity, including staff and equipment.

Implications: No model is perfectly accurate. Models are affected by the extent to which individuals and the community engage in containment and mitigation strategies included in the model. Nurses need to educate patients and the public that models are used to estimate risk and provide insight into the best means to control a pandemic, but that every model has limitations. Looking at trends, particularly in the local community, can be critical in making the best possible decisions and recommendations.

The importance of flattening the curve should not be underestimated. Patients and families need education as to why shelter in place, social distancing, and other containment and mitigation strategies are important, and how even small deviations can have significant negative consequences. The importance of these strategies are not only to protect a single individual or family from contracting a disease, but also to keep the healthcare system from becoming overwhelmed.

If there are too many infected individuals in addition to the patients using the healthcare system for other care, such as trauma or cancer, there will not be enough providers or equipment, leaving patients to receive care from an overly fatigued and stressed healthcare system. Flattening the curve reduces the numbers of people needing healthcare at one time, allowing healthcare systems to maintain capacity over time. This may directly impact the ability of a cancer center to remain open or to be adequately staffed.

Incubation Period

The time from exposure to manifestation of symptoms is referred to as the incubation period. Incubation periods include a period of latency (see Figure 3) when there are no symptoms but communicability of disease is possible (Liu et al., 2020). During this time, an infected individual is asymptomatic, and the disease may or may not be detectable with laboratory testing (Celentano & Szklo, 2019). Despite a lack of symptoms, infected individuals may become contagious at some point during this time. The incubation period of the SARS-CoV-2 virus is relatively long, with a mean incubation period of five to six days (Hussain et al., 2020; Xie & Chen, 2020). The median latency period is currently estimated at approximately three days (Bar-On et al., 2020). Both the long incubation period and a latency period shorter than the incubation period contribute to a higher degree of contagion, making the identification and quarantine of infected individuals more difficult until symptoms are evident.

Implications: Nurses need to be aware of the incubation period and the degree of contagion to appropriately plan and intervene. In patient care, this knowledge can be used in education and triage. PUIs should isolate for the designated period of time, based on current recommendations and inclusive of the anticipated incubation period; this is true for patients and healthcare workers. Patients who have been exposed may have treatment interrupted or deferred to allow for a period of quarantine (Segelov et al., 2020).

Plans should be made for safe staffing in anticipation of incubation periods and quarantine. Nurse leaders can review staff competency and look for cross-coverage options. During a pandemic, paused services in one department may supply staff to another, but only if staff are already competent or can become competent to provide cross coverage. In oncology treatment settings, competency to administer chemotherapy/immunotherapy or competency to care for the specialized needs of inpatient or ambulatory patients with cancer is of particular concern. Plans for alternative means of care delivery in the COVID-19 pandemic include widespread implementation of telehealth services, drive-through laboratory testing, online registration and consent, and identifying cohort groups of patients and designated teams of licensed and unlicensed staff to limit exposure and potential viral spread across cohorts.

Reproduction Number

R0 (R naught) is the reproduction number that describes how contagious an infectious disease can be (Celentano & Szklo, 2019). It is a way of expressing the average number of new infections one infected person can generate. A virus with an R0 of one would spread from one person to one person while an R0 of two would spread from one person to two and, from those two people, to four, and from those four to eight, and so on (see Figure 4). An R0 of greater than or less than one is an important demarcation; numbers greater than one indicate a growing pandemic; numbers less than one indicate slowing. In January 2020, the R0 of COVID-19 was estimated between 2.2 and 2.7, but may have been as high as 4.7 and 6.6 in the very early phases of the epidemic (Sanche et al., 2020). R0 can vary over time related to public health strategies and the availability of susceptible hosts who can become infected. Mutations in a virus may also effect the reproduction number. In December 2020, a SARS-CoV-2 variant with increased transmissability was reported. This variant strain was estimated to increase the reproduction number by 0.4 or greater (European Center for Disease Prevention and Control, 2020).

Implications: R0 can be decreased by employing measures that slow the transmission of a virus, such as universal masking and employing physical barriers between chairs in an infusion department or waiting room. The use of televisits and restricting visitors in the cancer center whenever possible could limit potential exposure and spread. In a radiation oncology department, the appointment schedule could be extended to allow more time between patients and decrease the opportunity for patients to be in contact with each other. R0 may also be decreased when there are less susceptible hosts available, such as when vaccination is available. It is important to understand that, in a pandemic, the R0 is fluid and changing over time. It may also be different in different locations. Countries may exhibit differing reproduction numbers, as may differing states, counties, cities, neighborhoods, or social groups. Understanding this variance is important when considering levels of risk locally. One hospital system with cancer centers spread out in various states or counties may be relaxing or enacting guidelines based on local R0 trends. This is also important to understand when educating patients about travel or quarantine.

Case Fatality Rate

Case fatality rate (CFR) is a generalized measure to describe the risk of death from a disease. CFR is calculated by dividing the number of confirmed deaths by the number of confirmed cases of a disease. In the United States, the COVID-19 CFR was reported to be as high as 5.9% in May 2020 and decreased to 1.75% in December 2020 (Roser et al., 2020). The CFR is affected by several factors. If there is increased testing, the resulting increased number of cases will create an inflated denominator and, ultimately, a smaller CFR. Availability and expertise of health care can decrease CFR, as could discovery of effective treatments. As with many other concepts in epidemiology, the CFR represents a moment in time (Hauser et al., 2020).

The CFR for COVID-19 can be quite different for specific groups. Throughout the pandemic, the highest CFR has been seen in older adults. Data from South Korea, Spain, China, and Italy illustrate a consistent trend in higher CFR among those older than age 70 years (see Figure 5). CFR can vary between countries, within a country, and within different regions of a country. These differences may reflect disparities and various cohort populations at higher or lower risk of severe illness. A cancer diagnosis increases the risk of severe illness (CDC, 2020b), and severe illness increases the risk of fatality. The impact of a cancer diagnosis on CFR has been difficult to ascertain given other conditions common in patients with cancer, such as older age and comorbid conditions, but an increased risk of death among those with leukemia and among those with other hematologic malignancies who had recent chemotherapy has been reported (Lee et al., 2020; Robinson et al., 2020).

Implications: Nurses need to be aware of CFR because it is a measure of disease severity and comparison, reflecting the impact of disease in a specific location. In an oncology practice, patients who are older and those who have underlying cardiovascular disease, diabetes, or renal disease are at higher risk from dying if they become infected. These patients, in particular, may benefit from tailored education regarding mitigation measures. Because many patients with cancer in active treatment are immunosuppressed, there is a need for proactive strategies to reduce likelihood of infection and improve early identification in this vulnerable patient population. If they are coming for a clinic visit, efforts should be made to schedule them at a time when there are fewer patients and to minimize the time in waiting rooms and other crowded areas.

Sensitivity and Specificity

The sensitivity of a test is the ability of that test to correctly identify those with a condition, which is referred to as the true positive rate (Celentano & Szklo, 2019). The specificity of a test is the ability of that test to correctly identify those who do not have the condition, which is a true negative rate. The ideal test has both a sensitivity and specificity of 100%, meaning all who test positive truly have the condition, with no false negatives, and all who test negative truly do not have the condition, with no false positives (see Figure 6). As of this writing, the perfect test for COVID-19 is not available. In the United States, the COVID-19 reverse transcriptase polymerase chain reaction (RT-PCR) test is estimated to have 71%–98% sensitivity, with specificity possibly as high as 90% (Watson et al., 2020). Although that number might seem high, it needs to be placed in perspective. For example, the state of California has a population of approximately 40 million people. Assuming a 90% sensitivity and infections in 100% of the population, 4 million false negatives would be estimated to occur if everyone was tested. If only 1% of the population were tested, there would still be 40,000 false negatives, which has significant implications for disease prevention and mitigation (West et al., 2020).

Implications: In the case of COVID-19, false negative results among patients with cancer are potentially very serious and dangerous (West et al., 2020). Individuals with false negative results may not be quarantined and may not be as vigilant with social distancing and other mitigation measures. Among those with cancer, a false negative for a patient admitted for a stem cell transplantation or a thoracotomy, for example, could be devastating. Considering healthcare professionals, those with a false negative may be allowed to work and could potentially expose others to the virus, especially if not wearing a mask. A test result, either positive or negative, is considered in the context of sensitivity and specificity and assessment of potential exposure. Nurses should remember to always assess the patient’s signs and symptoms, and not just rely on the test results in the assessment of the patient’s condition, and to intervene accordingly.

An understanding of these concepts is important as nurses work to establish policies and procedures about patient testing and screening prior to treatment. Oncology nurse leaders working on employee safety and employee health policies need to consider the best available evidence in consultation with infectious disease and public health experts. False positives can also result in poor planning and increased transmission of disease. For example, a family caregiver with a previous false positive test result may not employ recommended mitigation measures, putting themselves at risk for infection and transmission of COVID-19.


Prelicensure programs provide nurses with education about some basic public health concepts, but they may not include the terms and concepts that are important to understand in a pandemic. The COVID-19 pandemic is a reminder that we exist in a global community and are linked to local communities. Disease outbreaks around the globe may become an infectious disease in our own city or cancer center. Awareness of the importance of the local conditions, the local community, and a specific patient population is vital. Patients with cancer may be more or less at risk based on a cancer diagnosis, age, or comorbid condition, or based on their race, socioeconomic status, or the zip code where they live.

Oncology nurses are challenged to understand these principles before a crisis occurs and to know where to obtain reliable information in order to mobilize quickly and efficiently in a crisis. In some practice settings, there may be advance practice nurses who have additional education regarding these concepts and may be able to inform others in the application of concepts. Understanding these concepts is also necessary to help educate about why approaches to care are altered and how nurses can best protect themselves and others.

About the Author(s)

Susan Yackzan, PhD, APRN, MSN, AOCN®, is the director of clinical oncology practice and a nurse scientist at Baptist Health, Cancer Service Line, in Lexington, KY; and Suzanne M. Mahon, DNS, RN, AOCN®, AGN-BC, FAAN, is a professor in the Division of Hematology/Oncology and a professor of adult nursing in the School of Nursing, both at Saint Louis University in Missouri. The authors take full responsibility for this content and did not receive honoraria or disclose any relevant financial relationships. The article has been reviewed by independent peer reviewers to ensure that it is objective and free from bias. Yackzan can be reached at syackzan@bhsi.com, with copy to CJONEditor@ons.org. (Submitted August 2020. Accepted October 3, 2020.)



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