Definition/Introduction

From an etymological perspective, the word “epidemiology” can be divided into the Greek roots “epi,” “demos,” and “logos,” which respectively mean “upon,” “people,” and “the study of.” Historically, epidemiology has focused on population-level factors related to communicable diseases. Still, it has evolved to include non-communicable infectious diseases, chronic diseases, infant health, and environmental and behavioral health. Today, it is a broad umbrella that encompasses any health-related issues that may influence a population's overall health, such as environmental exposures, injuries, natural disasters, and terrorism, to name a few. It is a multifaceted branch of medicine, fundamentally guided by systematic scientific inquiry using ratios, probabilities, and other statistical calculations, focusing on the incidence, distribution, and factors related to diseases and health outcomes within a specific population.

Epidemiologists work with other health professionals to study patterns, modes of transmission, and determinants of specific health events within a particular population. They also work proactively to learn about diseases that have not previously been studied, such as Legionnaires disease and severe acute respiratory syndrome. For example, the World Health Organization met in Geneva, Switzerland, in 2003 to discuss the epidemiology of severe acute respiratory syndrome and to learn about its risk factors, modes of transmission, and reservoirs. These proactive studies better equip health professionals with the knowledge necessary for a robust response in the event of a health-related event, such as a disease outbreak. Other notable instances in which epidemiologists have played a role include historical, biological warfare, eradicating smallpox, relief efforts to victims of hurricanes and terrorist attacks, and, most recently, Ebola and Zika virus outbreaks. When responding to these health events, epidemiologists use previously collected, analyzed, and verified data to develop, advise on, and implement informed, targeted solutions to monitor and prevent the occurrence, worsening, or recurrence of population-level health events. Epidemiologists play an integral role in maintaining the overall health and wellness of specific populations.

Lastly, epidemiology works closely with public health surveillance, creating a system in which patterns and health outcomes of health-related events are continuously monitored; this is especially important in fields of medicine focused on upstream factors and preventive health issues. Legionnaires disease, for example, was surveilled by the Chinese Center for Disease Control and Prevention in 18 Chinese hospitals between 2014 and 2016. This study showed correlational data identifying cities, gender, age group, and seasons with the highest prevalence rates. It also identified L. pneumophila as the pathogen of interest in pneumonia-causing Legionnaires disease in China. The team acknowledged the lack of Legionnaires' disease reporting in China and suggested establishing routine diagnostic methods to reduce the likelihood of misdiagnosis and underreporting.[1]

Morbidity and mortality are 2 key measures used in epidemiological surveillance to describe how a health event progresses and how severe its impact becomes. These measures help identify disease risk factors and allow meaningful comparisons across different populations. Although related, morbidity and mortality are distinct concepts. Morbidity refers to the state of being symptomatic or unhealthy due to a disease or condition and is typically expressed through prevalence or incidence. Prevalence reflects the proportion of a population affected at a given time, while incidence captures the number of new cases arising within a specific period. Calculating incidence requires subtracting those already affected from the total population to determine the population at risk.

On the other hand, mortality is related to the number of deaths caused by the health event under investigation. It can be communicated as a rate or as an absolute number. Mortality is usually represented as a rate per 1000 individuals, also called the death rate. The calculation for this rate is to divide the number of deaths in a given time for a given population by the total population. To keep these values concise and for ease of comparison to other health events, this number can be multiplied by 1000 to reflect the “per 1000” rate of the target population.

Morbidity and mortality are 2 types of retrospective information that allow continuous evaluation of the efficacy of a specific health care system or an implemented intervention. For example, the use of maternal morbidity and mortality to gauge the risks of pregnancy and childbirth, as well as the efficacy of the health care they receive, is of vital importance.[2] In a related vein, accurate assessment of these measures is crucial to understanding and evaluating their impact and trajectory. Ultimately, mortality and morbidity allow epidemiologists to study further the burdens that a health event may place on a population. These metrics also allow stakeholders to more effectively prioritize which health events to tackle and allocate resources toward, and proactively manage the potential onset of a health event.

Issues of Concern

Epidemiology has undoubtedly been a critical player in the continued wellness of today's society. There are, however, some potential concerns with this discipline, namely in the application or misuse of epidemiological data. Information intended to help make informed decisions, prepare for future adverse health events, or advance the general population's knowledge can be otherwise used for propaganda or scare tactics, especially in today's heavily connected society. The knowledge of laypeople can be exploited by using excerpts from research papers taken out of context. For example, during the Ebola outbreak, early communication portrayed the ensuing effort as unprepared, while later communication was mired in government mistrust.[3] During the outbreak, 1 of the Centers for Disease Control and Prevention's communication channels was Twitter.[4] While the original intention was to increase transparency and rapidly provide the public with information about the outbreak, it instead increased the likelihood of acting on data that had not been fully verified. This is especially true during disease outbreaks, when information about its determinants may not be peer-reviewed immediately.[5] Information of varying reliability and quality can lead to the potential for spreading unnecessary terror or panic, which may or may not be remedied by expert opinions.

Misinformation or incomplete information can also complicate the interpretation and application of epidemiological research. Morbidity and mortality face many challenges similar to those faced by other population-based statistical measures. These include language barriers, variations in methodologies and definitions, and sampling and reporting biases.[2][6][7][8] Often, morbidity and mortality calculations rely on census data, but this data is not always available, especially in less developed countries or those affected by humanitarian crises or natural disasters.[8] 

Government mistrust can also contribute to inaccuracies in the formulation. For example, research has shown that about 40% of the undocumented Latino immigrants in the United States report a fear of accessing care due to concerns about being deported, which are only made worse by the limited health services and financial resources to which undocumented immigrants legally have access.[9][10] These factors have the potential to adversely affect the accurate reporting of morbidity and mortality rates as the health event becomes progressively underreported. Ultimately, the accumulation of the inaccuracies mentioned above can manifest as ill-advised decisions or interventions in health-related events. If data are not available from a census, other data collection methods should be employed. Their advantages and disadvantages merit consideration, and their limitations require immediate action. The anticipation of foreseeable pitfalls, sound study designs, and effective countermeasures must be in place to yield the most accurate picture of the health event and the population in focus.

Furthermore, continued education from epidemiologists needs to be a priority. Through educational activism, epidemiologists can present their research and equip their audiences with the background information needed to understand and apply the epidemiology of a health event. Educational interactions also provide an opportunity for epidemiologists to qualify their claims and to discuss the limitations of a study. This approach ultimately ensures that the data produced is used as intended. Continued educational activism not only empowers the general population, but it also keeps organizations, people in power, and other epidemiologists in check. In fact, within the CDC's epidemiology department, the epidemic intelligence service is not only responsible for research, field investigations, and surveillance, but also for education through presentations on complex scientific topics, publishing in peer-reviewed journals, and updating public health information.

Clinical Significance

Epidemiology can be broken down into 2 types: descriptive and analytic. Typically, descriptive epidemiology precedes analytic epidemiology. Descriptive epidemiology aims to develop foundational knowledge regarding the health event in focus. This data can include rates of occurrences, populations affected, timing, and geographic-specific presentations of a health event. By studying population-specific characteristics, epidemiologists can begin to learn about the natural history, modes of transmission, risk factors, and even disparities of a health event within a community. Descriptive epidemiology also uses information from continuous public health surveillance and ultimately initiates the process of developing hypotheses and directing field investigations to produce effective, informed analytic studies. For example, in a breast cancer study, incidence and prevalence rates may be observed across countries and age groups to learn about its etiologies, risk factors, and potential preventive measures.[11]

Analytic epidemiology, on the other hand, builds from descriptive epidemiology. As hypotheses arise in descriptive epidemiology, analytic epidemiology aims to test its validity. It seeks to uncover potential associations and any other contributors between factors and outcomes using a control group. This hypothesis testing can be done through experimentation or observation. Experimental studies usually involve clinical trials conducted under controlled conditions; this includes randomization procedures, placebo administration, and counterbalancing measures to control for potential confounding variables or bias. The overarching goal of experimental studies is to establish a causal relationship between an exposure and an outcome. For example, in a parasitic infection study, the exposure would be the parasites, and the outcome would be host survival or host extinction.[12]

On the other hand, observational studies detect the onset of an outcome between those exposed and those not exposed, as well as any potentially related variables, with the hopes of determining associations. There are 3 types of observational studies: cohort, case-control, and cross-sectional. Cohort studies observe both exposed and unexposed individuals and record the number of outcomes in each group over a designated period of time. In these observations, if outcomes are higher among those exposed than among the non-exposed (controls), then an association can be inferred. For example, in a study of obesity risk factors, over 8000 children were followed until age 7. The primary outcome observed was the development of obesity (BMI over the 95th percentile). In this study, risk factors associated with an increased likelihood of developing obesity included: parental obesity, early body mass index rebound, more than 8 hours of television per week at age 3, catch-up growth, short sleep duration, and weight gain in the first year.[13]

Case-control studies involve a more retrospective approach, in which the rates of exposure among individuals who present with the outcome are compared with those among individuals who do not (controls). If more individuals exposed to these observations also have the outcome of interest, an association can be inferred. For example, in a study of running-related injuries, it was found that people who had been active for less than 8.5 years and women with a BMI of less than 21 kg/m² were at a higher risk of developing tibial injuries.[14]

Lastly, cross-sectional studies focus more on a specific time point than on a longer period, leading to data on the prevalence or incidence of an outcome after an exposure. However, this format provides less information than the first 2 types of observational studies and is usually better suited for descriptive epidemiology of a larger population. For example, in a survey of medical students' mental health, students in their first, third, and 6th years of education were surveyed. The study showed that first-year students identified workload and lack of feedback as stressors. Third-year students identified "competence worries" as a stressor, and 6th-year students rated lack of support as a stressor.[15] Ultimately, it is the combination of these analytic studies that guides epidemiologists' decision-making and responses to public health issues, as well as their involvement in policy development and law-making. By understanding these factors, interventions can be highly targeted, and the potential for unintended consequences can be limited or avoided altogether.

Interventions are also monitored during implementation and evaluated for efficacy, efficiency, impact, cost-effectiveness, and potential for improvement. Two important outcome measures are morbidity and mortality. Changes in these 2 measures can indicate not only the severity of a health event but also serve as a litmus test for the responses epidemiologists may take. Morbidity and mortality measures can be gathered using descriptive or analytic epidemiology and stratified into subcategories, such as perinatal, neonatal, infant, and maternal morbidity and mortality, to name a few. Morbidity and mortality can also be stratified by age, race, ethnicity, sex, gender, nationality, and socioeconomic status, providing an opportunity to uncover group-specific susceptibilities or exposures within a population.

These subcategories provide great insight into the health of the population and highlight any group that may be disproportionately affected. For example, a review of infant mortality in the United States from 1950 to 2010 showed that while infant mortality has substantially decreased in the past 4 decades, the disparity between African Americans and Whites has progressively increased, further worsened by education and income inequities.[16] Other disparities uncovered include the indirect relationship between socioeconomic status and cancer mortalities, the prevalence of asthma morbidities in inner-city areas, and the unethical targeting of smoking advertisements in low-income schools.[17][18][19] It is by using this type of information that stakeholders in a community or population can make better decisions about the type, target, order, and scope of an intervention to pursue, ultimately allowing communities to properly allocate their time, money, and other resources toward the most impactful and cost-effective interventions.

Nursing, Allied Health, and Interprofessional Team Interventions

Nurses need to be aware of research study methodology and the meaning of the outcomes. Nurses also play a vital role in infection prevention and patient education.

Nursing, Allied Health, and Interprofessional Team Monitoring

When epidemics occur, nurses are the key staff who look after patients. Thus, nurses must be fully aware of disease transmission modes and how to limit spread to others.

Review Questions

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Disclosure: Jose Bien Hernandez declares no relevant financial relationships with ineligible companies.

Disclosure: Peggy Kim declares no relevant financial relationships with ineligible companies.