Epidemiology Morbidity And Mortality

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 taken advantage of by using excerpts from research papers taken out of context. For example, during the Ebola outbreak, early communication characterized the ensuing effort as lacking preparation, while later communication was mired in government mistrust.[3] During the outbreak, one of the avenues of communication that the Centers for Disease Control and Prevention used 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 situation is especially true during disease outbreaks when information regarding its determinants may not be immediately peer-reviewed.[5] Information of varying reliability and quality then leads to the potential of spreading unnecessary terror or panic that may or may not be remediable 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 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 rely on census data for their calculations, 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 play a role in the formulation of inaccuracies. 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 possess the potential to affect the accurate reporting of morbidity and mortality rates adversely, while the health event progressively becomes underreported. Ultimately, it is the cumulation of the inaccuracies mentioned above that can manifest as ill-advised decisions or interventions for health-related events. If data is not available from a census, other means of data collection 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 need to be in place to garner the most accurate picture of the health event and 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 necessary background information to effectively understand and apply the epidemiology of a health event. Educational interactions also provide an opportunity for epidemiologists to qualify their claims as well as the limitations of a study. This approach ultimately ensures that the data produced is utilized in its intended manner. 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 epidemiology department of CDC, their epidemic intelligence service (EIS) is not only responsible for research, field investigation, and surveillance, but also education via presentations on complex scientific topics, writing peer-reviewed journals and updating public health information.

Clinical Significance

Epidemiology can be broken down into two 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 start learning about the natural history, modes of transmission, risk factors, and even disparities of a health event that is present within a community. Descriptive epidemiology also utilizes information from continuous public health surveillance and ultimately initiates the process of developing hypotheses and directing field investigations to develop effective and informed analytic studies. For example, in a breast cancer study, incidence and prevalence rates may be observed in different countries and in different age groups to learn about its etiologies, risk factors, and potential preventative 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 is possible via experimentation or observation. Experimental studies usually involve clinical trials within a controlled process; this includes using randomization procedures, implementations of placebos, and counterbalancing measures to counteract any 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 three types of observational studies: cohort, case-control, and cross-sectional. Cohort studies observe both exposed and non-exposed individuals and record the number of outcomes between the two groups over a designated amount of time. In these observations, if the outcomes are higher in those exposed than non-exposed (control), then an association can be inferred. For example, in a study of obesity risk factors, over 8000 children were followed until 7 years of age. 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 were found to include: parental obesity, early body mass index rebound, more than eight hours watching television per week at age three, catch-up growth, short sleep duration, and weight gain in the first year.[13]

Case-control studies involve a more retrospective approach, wherein the rates of exposures in individuals who present with the outcome get compared with those that do not (control). If there are more individuals exposed who also have the outcome of interest in these observations, then an association can be inferred. For example, in a study of running-related injuries, it was found that people who have been active for less than 8.5 years and women with a BMI of less than 21 kg/m^2  were at a higher likelihood of developing tibial injuries.[14]

Lastly, cross-sectional studies focus more on a specific time-point than a more extended period, leading to data showing the prevalence or incidence of an outcome after an exposure. However, this format does not offer as much information as the first two types of observational studies and is usually better suited when focusing on the descriptive epidemiology of a larger population. For example, in a study of medical student mental health, students in their first, third, and sixth 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 sixth-year students rated lack of support as a stressor.[15] Ultimately, it is the combination of these analytic studies that direct epidemiologists' decision-making and responses to public health issues or involvement in policy development and law-making. By knowing these factors, interventions can be highly targeted, and the potential for unintended consequences can be limited or completely avoided.

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 within these two measures can indicate not only the severity of a health event but also serve as one of the litmus tests for the responses that epidemiologists may take. Morbidity and mortality measures can be gathered using either descriptive or analytic epidemiology and can undergo stratification into various subcategories, such as perinatal, neonatal, infant, and maternal morbidity mortalities, to name a few. Morbidity and mortality can also be stratified by age, race,  ethnicity, sex, gender, nationality, and socioeconomic status, which provide 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 four 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 these types of information that stakeholders of a community or population can make better decisions on the type, target, order, and scope of an intervention to pursue, ultimately allowing for 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 what their outcomes mean. 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.