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Epidemiology of Infectious Diseases: Epidemiological Approaches to Disease Control


Whether it is an oil spill or an earthquake, the what, who, where, when and why/how of an incident are all essential details that every prospective newspaper writer must provide. Epidemiologists strive for comparable comprehensiveness when defining an epidemiological event, regardless of whether it is a SARS-CoV2 pandemic or a local increase in chemical pollutants in drinking water. However, epidemiologists often use scientific terms for the five "W's" described above: Person (who), case definition (what), place (where), time (when), and causes/risk factors/modes of transmission (why). Simply said, an epidemiologist measures occurrences of health events, defines them in terms of time, location, and people, and produces rates—a measure of the frequency with which an event happens in a defined population during a specific time period. Such rates are subsequently investigated over time or among various groups of people to establish whether or not there is a relationship between risk factors and health outcomes (Dicker et al., 2006).


It All Starts With a Case: Fundamentals of Epidemiology

Like other scientific endeavors, epidemiology is founded on a systematic approach. Before epidemiologists can count cases and calculate rates, they must first decide what constitutes a case. This is done by creating a case definition, which is a collection of established standards used in epidemiology to determine whether or not a person has a certain illness, syndrome, or health condition. The use of case definitions in epidemiology is essential for establishing uniform standards for identifying occurrences (Dicker, 2008). These often rely on individual medical diagnoses, registers and reports, clinical summaries, general population surveys and screenings. This is of paramount importance to closely monitor trends in reported diseases, detect their unusual occurrence and consequently evaluate the effectiveness of public health interventions (Wharton et al., 2001). After defining a case, epidemiologists collect and evaluate data from field surveys, surveillance systems, statistics, or other sources. This process, known as descriptive epidemiology, enables cases of illness to be categorized according to time, place and person. Analytical epidemiological techniques are then employed to compare the incidence of disease in the target population to that of an appropriate reference population and assess whether this rate is higher than normally predicted and, if so, to discover variables that contribute to this increase (Lakna, 2019). Subsequent tasks, such as reporting the results and suggesting how these might be used for public health action, are also essential.

Figure 1: Epidemiology is an integral part of public health and serves as the basis for guiding effective health interventions ("Is the World Ready", 2020).
From Traditional to Modern Epidemiology: Writing a 5 W's Story

Epidemiology is the study of the distribution and determinants of health-related conditions or events in specific populations, and its application to the control of health problems. Twenty years before the microscope was invented, John Snow studied cholera outbreaks to identify the underlying cause of the disease and avert future outbreaks. Two of his investigations are presented in depth because they illustrate the conventional epidemiological method from descriptive epidemiology (the "who", "where", and "when") to analytical epidemiology (the "why"/"how") and their use in public health. Snow's cholera epidemic studies, including his renowned 1854 inquiry, entailed the careful gathering of data to discover where cholera-infected individuals resided. For several years, he knocked on doors, surveyed those affected by cholera to map cases and fatalities, compared cities and neighbors, and so founded field epidemiology. A map that depicts the geographical distribution of cases has come to be referred to as a spot map (Dean, 1976; Rogers, 2013). He believed that cesspools would transport contaminants into drinking water, but his hypothesis was first received with skepticism. Believing that water was a source of infection for cholera, Snow highlighted the locations of water pumps on his spot map and then then searched for a link between the distribution of cholera cases and the location of pumps. The iconic Broad Street water pump in Soho was the link between all these cases (Shiode et al., 2015; Stanwell-Smith, 2003).


To further test his theory, Snow gathered data on the sources of water used by cholera-infected individuals. Snow came to the conclusion that the Lambeth Company, as well as the Southwark and Vauxhall Companies, served the areas with the greatest fatality rates (Shiode et al., 2015; Snow, 1856a). Both firms drew water from the Thames River at intake locations downstream from London sewage, which was discharged straight into the Thames, rendering them vulnerable to pollution. To avoid pollution from London sewage, the Lambeth Company moved their waterworks to a location on the River Thames well upstream from London in 1852. In the summer of 1854, Snow studied cholera death rates over a seven-week period in regions that received water from either or both water companies. Snow found that the cholera death rate in districts served exclusively by the Southwark and Vauxhall Company (downstream of London) was more than five times higher than in regions served solely by the Lambeth Company (upstream of London), thereby confirming his premise (Snow, 1856a). Snow's study of cholera disease outlined the processes that epidemiologists use today to study disease outbreaks. By identifying at-risk individuals and populations according to time and location, Snow put forth a testable hypothesis. Following this investigation, attempts to control the outbreak focused on removing the Broad Street water pump and relocating the Southwark and Vauxhall Company water intake to prevent sources of contamination (Snow, 1856a, 1856b). Unaware of the existence of microorganisms, Snow concluded via epidemiological research that water may act as a carrier for cholera transmission and that these data could be utilized to implement quick and appropriate public health interventions.

Figure 2: As a result of Snow's studies, the Broad Street water pump was removed (Wyk, 2003).

Modern epidemiology comes a century later with a more systematized set of tenets for the design and assessment of epidemiological studies. Since their inception in the 1940s, several extensive epidemiological investigations have had a significant impact on public health. Prospective studies on the addition of fluoride to water, for instance, resulted in widespread primary prevention of tooth decay in the 1940s (Ast et al., 1951). Prospective studies track outcomes over time, such as the progression of an illness, and compare them to other variables, such as potential risks or protective factors. Typically, it entails assembling a group of people, also referred to as cohort, and following them over an extended period of time (Phillips & Smith, 1993). The Framingham Heart Study is one of several long-term follow-up studies of cardiovascular disease that have considerably enhanced our comprehension of the underlying causes of this important public health issue. This study began in 1949, prompted by President Franklin D. Roosevelt's cardiovascular health and his premature death in 1945 from hypertensive heart disease and stroke (Kannel et al., 1961; Sytkowski et al., 1990). According to this study, individuals with high blood pressure exhibited a higher risk of coronary heart disease (Dawber et al., 1957). A few years later, this study established a link between stroke and high blood pressure (Kannel et al., 1965). Noteworthy, more than 60 years later, this one-of-a-kind study still provides crucial data that allow researchers to correlate comorbidities such as diabetes to greater cardiovascular risk (Fox et al., 2007). The field trial of the Salk vaccine was the biggest and priciest medical trial in history, testing the effectiveness of the Salk vaccines against paralysis or poliomyelitis-related mortality. The study, which comprised more than a million young children, demonstrated that the vaccine was 80 to 90% effective in avoiding paralytic polio. The Salk vaccination trial was successful in part because of the study's extensive use of placebo controls and double-blind analyses. Randomized double-blind placebo-controlled trials are the gold standard in epidemiology research. Double-blind implies that neither the patients nor the researchers know who is getting the treatment and who is receiving a placebo (an inactive substance, typically a sugar pill) (Misra, 2012). This enables for the removal of confounding factors, which may otherwise lead to a biased and erroneous estimation of the treatment impact. This triumph, however, did not signal the end of the polio vaccine effort, but it was an important in proclaiming victory against poliomyelitis.


Epidemiological Approach: Constructing Hypothesis

Epidemiology's core focus is on the patterns, underlying causes, and effects of health problems and diseases among particular demographic groups. By identifying illness risk factors and disease preventive objectives, it serves as the cornerstone of public health, directing policy choices and evidence-based treatments. The epidemiologists assist in study planning, data gathering, statistical analysis, result interpretation, and dissemination. Epidemiological studies can be observational or experimental, and they are typically classified as descriptive studies (which involve the formulation of hypotheses), analytical studies (which examine pre-existing correlations or test hypotheses in greater detail), and experimental studies, which are frequently interchangeable with clinical trials (Mortimer & Borenstein, 2006). Although all three can be used to analyze disease progression, descriptive epidemiology is the holy grail of epidemiological methods. The key difference between descriptive and analytical epidemiology is that the former develops hypotheses about risk factors and disease causation, while the latter tests them by exploring the determinants of disease, focusing on risk factors and causality, and the distribution pattern of exposures and disease emergence (Naito, 2014; Tulchinsky et al., 2023).

Figure 3: Epidemiologists gather and analyze data to monitor infectious illnesses, bioterrorism threats and other areas of concern in order to prevent and reverse detrimental health outcomes (Frieden, 2023).

Descriptive epidemiology calculates number of individuals affected by a particular disease or exhibiting related health symptoms and signs at the population level. The two main metrics used in descriptive epidemiology are prevalence and incidence, as they provide the foundational information that directs public health recommendations and policy decisions. While prevalence is the percentage of a population exhibiting a specific symptom or condition (current cases) in a given period of time, regardless of when the symptoms or condition originally appeared; incidence is a measure of the number of new cases in a population. Clinical information, people, place, and time are prioritized in descriptive studies, which evaluate disease incidence patterns in light of the geographic location and temporal trends (Boyle, 1996; Tulchinsky et al., 2023). Examples of clinical information include disease signs and symptoms, test results, hospitalization records, and live or death rates. The socio-demographic makeup and habits of individuals are also evaluated for their potential to promote or deter disease's risk. For instance, the risk of bacterial and viral infections is often higher in the elderly and the very young. Importantly, since diseases are not bound by or controlled by governmental boundaries, tracing the geographic relationships between cases can be crucial to discovering where the epidemic first started (Fontaine, 2018). Spot maps could be helpful for outlining these spatial connections. Time is also essential when describing a disease since it allows epidemiologists to ascertain whether incidence rates or case numbers have increased or decreased over time, as well as whether there is a seasonal trend (Dean, 1976b; Stevenson, 1965). When a disease outbreak occurs, the relationship between time and the number of cases is represented by an epidemic curve. Whether an event is brought on by a single point source, an ongoing widespread source, or a sporadic source can be determined by the shape of the epidemic curve. The incubation period of a particular infectious agent in any given outbreak, i.e., the period between infection and symptom appearance, is another crucial epidemiological element influencing the shape of an epidemic curve (Bhadauria & Dhungana, 2022; Wilson & Burke, 1942). Thus, descriptive studies make use of easily accessible data to create programs, gauge case numbers, estimate the amount of public health resources required, or pinpoint high-risk groups.


Testing Hypotheses for Disease Control

The theories drawn from descriptive epidemiology must be further validated by analytical epidemiology. For instance, early descriptive studies found that young urban men made up the vast majority of AIDS cases that were confirmed in the United States, implying that specific sexual practices served as the disease's primary source (Centers for Disease Control and Prevention, 2007; Hall et al., 2008). Analytical studies are used to evaluate assumptions like this. In contrast to descriptive epidemiology, analytical epidemiology seeks to understand, identify, and quantify the relationship between an exposure and a health outcome. The standard approach to collecting this data is through group comparisons to determine whether health outcomes differ by exposure status (Roberts et al., 2019). The case-control (or case-comparison) approach and the cohort method are the two fundamental analytical methodologies. The case-control approach investigates the etiology of a disease by initially categorizing individuals based on their outcome status. As a result, individuals who experience the outcome are first chosen (referred to as cases) and then contrasted with a control group, or a group that is devoid of the condition but similar in terms of gender, age, and socioeconomic level, to mention a few. Potential outcomes of interest include participants' surgical histories, histories of complications, or diagnoses of illnesses. Then, information is acquired retrospectively about prior exposure to one or more risk factors, often through an interview or survey (Song & Chung, 2010). Case-control studies are well suited to investigate rare diseases or illnesses with an extended latency period (time period between being infected and becoming contagious), since participants are first selected based on their outcome status (Browner et al., 2022; Elwood, 2017). This research methodology was initially acknowledged in Janet Lane-Claypon's breast cancer study from 1926, which showed that a low fertility rate increases the risk of breast cancer (Cole, 1979; Lane-Claypon, 1926). Case-control study approach gained traction later in the 1950s with the seminal study linking smoking and lung cancer (Doll & Hill, 1950).

Figure 4: Epidemiological studies help researchers identify risk factors for certain diseases, such as smoking and lung cancer (Zambon, 2020).

The term "cohort" was first used in 1935 by early 20th-century epidemiologist W.H. Frost in a study assessing age-specific death rates and tuberculosis (Morabia, 2004). In a cohort study, an outcome- or disease-free participant population is first selected. This population is then categorized according to their exposure status to an event of interest, and then tracked through time until the illness or result of interest emerges. Cohort studies are more longitudinal in nature and do not necessarily require a control group because exposure is established before disease/symptoms emergence (Horton, 2011). Either a prospective or a retrospective study design can be used for cohort studies. Prospective studies are conducted from the present into the future, i.e., a sample of individuals without the outcome of interest is selected and followed over time to determine whether they develop the desired outcome. This method, however, has a significant risk of loss to follow-up and is ineffective for analyzing disorders with prolonged latency periods. The 1948-launched and still-running Framingham Heart Study is one example of a fruitful prospective cohort study (NIH, 2021). Retrospective cohort studies, also known as historical cohort studies, are conducted in the present and investigate medical occurrences or outcomes from the past. In other words, outcome data (such as illness status) that were previously measured are reconstituted for analysis using a cohort of participants chosen depending on their exposure status at the present moment. For instance, by retrospectively assessing 200 patients who had been interviewed over a 10-year period, Spear and colleagues looked at the relationship between obesity and the likelihood of problems following flap reconstruction, i.e., the surgical reconstruction of breast using a woman's own tissue (skin, muscle or fat) (Spear et al., 2007). The patients who underwent surgical correction were divided into three groups based on their body weight status: normal/underweight, overweight and obese. The main elements to study were the multiple flap failures and losses as well as donor site complications. Because of the immediate availability of data, the main advantage of a retrospective research approach is the ease and speed with which medical events can be investigated.


Conclusions

Epidemiology is the study of how and why diseases develop in certain population groups. Measuring the impact of disease on a population at risk is a crucial aspect of epidemiology. Like the clinical findings and pathology, the epidemiology of a disease is an integral part of its fundamental description. Comparing disease rates in populations with different levels of exposure can provide information about the etiology of a disease. However, if comparisons are skewed by inadequate ascertainment of cases or exposure levels or status, data will be overlooked or false clues generated, highlighting the need of a well-defined hypothesis so that subsequent epidemiological tests are best planned and suitable public health interventions are designed. In fact, epidemiological differences would go unnoticed if everyone is similarly exposed: epidemiology thrives on heterogeneity (exposed vs non-exposed individuals). Tracking or surveillance of temporal trends to demonstrate which diseases have undergone shifts in their distribution and which are rising or dropping in incidence is an additional task of epidemiology. This data is necessary for both identifying newly emerging diseases and evaluating how well existing issues are being managed. It is important to note that criteria for diagnosis and data collection are subject to modifications, and hence inferences from historical patterns should be handled with special caution. For this reason, the data on which epidemiology relies to draw conclusions are almost always collected from large numbers of people, mostly from different nations, over long periods of time. This in turn helps to strengthen the robustness and reliability of the data. Therefore, to ensure adequate public health policies and actions, epidemiology requires rigorous methodological standardization and quality assurance.


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