Epidemiology is the study of the quantitative investigation of the factors that influence the state of health of the population. Epidemiological studies help identify those people more or less likely to have certain diseases. It also explores whether disease rates change over the years and in which areas certain diseases are particularly prevalent.
Epidemiological studies compare different populations to provide insightful results. These conclusions from epidemiological studies are very important for the management of the health system. Thanks to this research, it is possible to observe the health status of a population. Thus, based on epidemiological findings, public health programs for the early detection of diseases can be established.
Clinical medicine also benefits from epidemiology. Because epidemiology is not only concerned with infectious diseases and epidemics, but with all sorts of diseases. The epidemiological approach is often used to develop reliable guidelines and provides physicians with advice on which diagnostic tests and treatments are useful.
Epidemiology is mostly involved in designing research studies, data collection and analysis of results. The study results will be communicated to support decision makers in healthcare, clinics and the public.
The field of epidemiology includes many different aspects. Epidemiological studies usually require the expertise of
to explore the biological aspects of the disease. Many epidemiological studies are based on the experience of biostatisticians and information technologists. In this way, appropriate methods of data collection are designed to perform a statistical analysis in the fastest way possible. In addition, social scientists are often involved to identify important topics for the perception of a particular clinical picture and to assess how the population might react to it.
Since epidemiology requires many different skills and scientific knowledge, the background and training of epidemiologists is also very different. For example, some epidemiologists start their careers as laboratory scientists in chemistry or microbiology and only later begin epidemiological studies.
Many doctors and nurses are also working in clinical practice and finding their way into epidemiology and public health to treat the entire population and focus on the patient population. Other co-workers, such as biostatists, often begin their education in statistics and mathematics before later becoming involved in epidemiology. There they develop suitable study designs and methods of data analysis.
One version of the history of epidemiology is that the famous Hippocrates, whose oath is worn by doctors almost everywhere in the world, was an epidemiologist. Hippocrates already hypothesized that some diseases are caused by environmental factors and affect the population.
However, there is another variant of the story: before the 17th century, there were no comparisons between populations that considered various diseases. It was not until the seventeenth century that John Graunt conducted a series of analyzes on London’s newly introduced „Bill of Mortality,“ the weekly statistics on deaths in London. For the first time, the most frequent causes of death were systematically determined. The various causes were thus published every week as an early warning signal for the onset of a disease. From today’s perspective, the Bill of Mortality showed that overall mortality remained constant while mortality peaked at the time of the plague. This suggests that the environment has an impact on the mortality rate.
Another pioneer in epidemiology was James Lind. The Scottish doctor discovered the treatment of scurvy with lemon juice. In the Lind experiment, he divided twelve scorbut-sick sailors into groups. Two people were always given different foods for their diet:
Group 1: 1 liter of cider
Group 2: 25 drops of sulfuric acid
Group 3: 6 spoons of vinegar
Group 4: ¼ liter pint
Group 5: 2 oranges and 1 lemon
Group 6: spice paste and barley water
Patients in group 5 – oranges and Lemon – were back in action after a week and almost completely healthy.
About 200 years after Graunt, epidemiologist Jon Snow compared the mortality of cholera between clients of two London-based companies. His study confirmed the hypothesis that cholera is somehow related to contaminated drinking water. In the end, his research led to the removal of the water pump handle on Broad Street. At the time, that was a controversial move, as there were many more theories about the cause of cholera. Meanwhile, it was proven that Jon Snow was right and providing clean and safe drinking water was one of the key elements of improving health during the 19th century.
Another important example of the epidemiology of the 19th century is the study by Dr. med. Pierre Louis on the effectiveness of bloodletting, also called bloodletting, for the treatment of pneumonia. Furthermore, Ignaz Semmelweis proved the effectiveness of hand washing in the prevention of puerperal fever.
In the mid-twentieth century, several epidemiological studies attested that smoking could increase lung cancer risk. Some epidemiologists like Wynder and Graham or Doll and Hill compared the frequency of smoking in lung cancer using carefully selected controls. Others compared the occurrence of lung cancer in smokers and non-smokers. These controls were highly controversial among some scientists and the tobacco industry. However, epidemiological studies have over the years confirmed numerous negative health effects of smoking, such as heart disease and various types of cancer.
The application of the epidemiological method is important in many areas as epidemiologists are diverse. Epidemiologists can be used in hospitals or local health care facilities, but also in government or commercial companies such as the pharmaceutical industry. All these areas have one thing in common: They work to examine the state of health in different groups of people, making comparisons over time and place. Laboratory scientists and research physicians often use experimental methods that focus on a specific risk factor or event. So all other risk factors can be checked in detail. This happens among other things by a random generation, which means it is decided randomly, who receives which treatment.
In contrast, epidemiologists can study multiple diseases and risk factors for a disease simultaneously. This often requires refined study designs. So epidemiology always tries to deal with many determinants of diseases in the population. Often, epidemiologists are busy isolating the link between a risk factor and a particular health outcome, while considering other important relationships in people’s daily lives. Thus, epidemiologists are not limited to factors that are measured on an individual level. Instead, they also measure community-level factors and incorporate all sorts of information into their analysis. They also take into account that some factors at different stages may have different effects and that these effects of exposure at a younger age may manifest only at a higher age.
While important and useful information from laboratory studies can be obtained for treatment, these results are not always generally applicable to the entire population. Here the epidemiology has the advantage that it examines in more detail what happens in which population. In addition, many health-related issues can not be addressed by experimental randomization in humans, such as the health effects of smoking.
Under certain circumstances, epidemiologists can apply randomization in the community. This works, for example, to evaluate the potential benefits and disadvantages of screening for cancer screening. In doing so, they can randomize parts of the population that are screened – or not – to observe subsequent experiences with cancer detection and prognosis. However, this type of study is rather unusual due to the large sample size and the long period of follow-up.
Instead, epidemiologists are more concerned with investigating the effects
the health of people, but without the ability to randomly put people on risk categories. This poses the biggest challenge for epidemiologists. Nonetheless, epidemiology is crucial for the detection and treatment of epidemics in infectious diseases such as influenza, HIV or measles.
It is an important question how epidemiologists count the number of people who are at risk for disease and those who already have a disease. These counts are used to calculate disease risks in different parts of the population or for different periods of time.
The goal of epidemiologists is to identify groups of individuals and calculate their risk of disease. Different factors are important for this: the number of people suffering from the disease and the number of people in the part of the population studied by the epidemiologists.
For example, the risk of disease is studied in men aged 50-59 years. The number of men in this age group who may have or develop the disease being studied is the denominator of a risk estimate. The number of people who actually contract the disease is the risk counter. For example, suppose that this study involves 1,000 men in the 50-59 age group and 15 of these men have lung cancer for over 10 years. In this case, the 10-year lung cancer risk in the population would be 15/1000 or 1.5%.
In order to determine and determine the causes and effects of diseases, counts must be compared. Thus, the incidence of coronary heart disease is compared between persons with different characteristics. In this way, one learns to what extent the characteristics contribute to the incidence of diseases.
There are several ways to make such comparisons. Different study designs lead to different measurements to summarize the comparisons of the counts. The widely used measurements in epidemiology are:
The study designs include cohort studies, case-control studies and cross-sectional and ecological comparisons.
Count comparisons are susceptible to error in epidemiology, as often no true value is measured. Thus, in epidemiology, different forms of bias – that is a distortion of the result of a representative survey – are known. These deviations are categorized as follows:
Selection bias: Due to the selection of study participants
Information bias: Due to the way the information is used in the study
Screening Bias: Due to the peculiarity of the screening
The most important step in epidemiology is the comparison of counts with causality statements: If a factor is considered causal, we assume that the frequency of the disease can be changed if that factor is changed. Therefore concepts and strategies are developed. The main goal is to decide if a particular factor is a cause or not. A cause causes something to happen, thus causing or preventing disease. When causes are detected, things can be changed as well.
Although philosophers have long talked about causes and are still talking about them, epidemiologists cling to an age-related pragmatic definition of a cause: „Something that reduces the incidence of disease by taking it away.“ This view points to the current dominant philosophy of thinking about causes in medical statistics and epidemiology based on potential outcomes and counterfactual thinking.
Most diseases have several causes. Sometimes potential causes are so close to one another that it is tempting to mistake one cause for another: we believe that alcohol consumption is linked to lung disease, but it may also be that smokers drink more alcohol than non-smokers. At another time, the effect of the other is increased or decreased. For example, a mutation in a gene can lead to a higher frequency of disease, an environmental factor (eg smoking) can also lead to a higher frequency of the disease. When both causes come together, the resulting burden is much higher. This means that some people only get ill if they are exposed to both factors. The task of Ep
So idiologists also have to separate the effects of mixed causes and, at another point in time, assess their common effects or interactions.