Epidemiology And Public Health

The Journal

Descriptive epidemiology deals with the spread and occurrence of diseases in a population. For such epidemiologists, it is essential to find out which social strata get sick, when, and where. The underlying causes of diseases are clarified by analytical epidemiology, but descriptive epidemiology does not allow assumptions about the etiology and possible risk factors. Social groups are heterogeneous; depending on the education level, workplace, and residence, different diseases are characteristic and often other life expectancies.

A critical aspect of descriptive epidemiology is predicting disease outbreaks over time. I think it is vital to use descriptive epidemiology for public health. Outbreak forecasting will allow regulating the forces of doctors and administrators, which will be rationally spent. Finding the connection between diseases with places of residence or work is also significant. The results of this search help doctors and administrators, and ergonomics specialists. Detailed research in this area will make it possible to reorganize the premises conveniently for workers or change their daily routine: add a walk, exercise, and eat in a particular room. In addition, by examining social groups directly, it is possible to identify who needs a detailed medical examination, more or less frequently and allows the government (even at the local level) to allocate funds for those in particular need.

Assessment #1

The main elements (variables) of descriptive epidemiology, including person, time, and place, have been briefly outlined above. Each of these elements helps in an orderly way to find out the most characteristic diseases for certain social groups and also allows you to make logical and reasonable forecasts. The first element is a person, and following it, this variable corresponds to the question ‘Who is sick?’. Epidemiologists need to find out who is more susceptible and who is less (Frérot et al., 2018). It will help people develop behavioral patterns that, over time, will help them avoid serious illnesses. Activities for sex education and the promotion of a healthy lifestyle are often the responsibility of public health professionals. Time is the second variable that helps professionals investigate seasonal illnesses such as the flu. People at risk are advised to get vaccinated, knowing about possible outbreaks in the future. Now, focusing on mass events, experts can make predictions about new episodes of COVID-19.

The third variable is location, which includes where you live or work and any places where you often spend time. Particular risks and diseases characterize people living in poor ecology (close to factories or landfills). Factory workers and IT specialists are characterized by their conditions related to diseases of the joints or back problems. In addition to local diseases, epidemiologists often study the global geographies of diseases such as malaria, dysentery, and a host of others transmitted in tropical climates.

Assessment #2

The concepts of population risk difference and risk difference are very similar since they answer the same questions. Both concepts in epidemiology refer to the difference between groups that have been exposed and groups that have not. However, population risk is considered by research models very broadly. It is necessary to find only a tiny proportion of people who are disposed to a particular disease from a large population to detect it (Frérot et al., 2018). Typically, population risk differences apply the condition in question to one specific population.

Investigators can usually find the population risk by dividing the attributable risk by the prevalence of the risk factor in the population, expressed as a fraction of a unit. Subsequently, in this formula, the result must be multiplied by 100 to obtain a percentage. Thus, specialists can apply an attributable risk not tied to a particular population to a specific disease in a specific group. A good example is comparing illnesses in groups that went through a period of homelessness with groups that never had such a period and generally lived well. Another frequently encountered instance corresponds to smokers and non-smokers and their susceptibility to lung diseases such as cancer. In absolute terms, the likelihood of lung cancer in smokers does not significantly exceed that of non-smokers since non-smokers often smoke passively. It affects the lungs, while smokers often suffer from emphysema or heart disease. However, population risks show much higher risks for smokers in a closed population. I can use this example in a real scenario investigating the difference between smokers and non-smokers to campaign against smoking.

Assessment #3

The described scenario well represents the situation with kidney diseases and the number of deaths in the defined period in only one place. All data is crucial for epidemiologists and specialists in medical statistics, as well as for administrators. First, the prevalence of Hispanics was found in the total study population, which was in the region of 6% of the total number of people participating. In order to solve this problem, it was necessary to take the number of participants from the general population, both dead and now living: 1230. This number had to be divided by 22735, that is, by the total population. Investigators can conclude that Hispanics were not particularly common in this study. One can also note that, along with the Other line, their rates of death from liver disease are significantly lower than Blacks and Whites.

The ratio of males to females in the total study population is calculated by dividing the male total by females, i.e., 15748/6987. Then the result of the calculations will be 225; this is the resulting ratio. Additionally, investigators should study the percentage of males and females in the cohort since all the details will be helpful to epidemiologists. Some data could support researchers if scientists made errors in calculating other data. Thus, detailed calculations of even the most minor and noticeable data can help researchers quickly spot errors and create meticulous studies. Estimates of dividing the general population by the number of first men and then women show that men make up 69% of the participants, while women make up 31%.

More precisely, the following values ​​were related to the deceased Black and White patients’ proportions. Investigators can find these proportions by multiplying by 100 the original data. Thus, 523 (the number of Black deaths) was divided by the total number of deaths, 2965. The resulting proportion is 17%, the second-largest result in the entire scenario. The number of White deaths is 1951, the highest mark in the whole system. The value of White deaths is also divided by the total number, 2965. The total is 65%, which is the majority of the overall deaths recorded at the Idaho Kidney Center. The resulting percentages among the Other and Hispanic populations are significantly smaller and do not differ considerably from each other. Both lines end up with about 8-10%; however, Hispanics died more often during the described period.

The presented values ​​play an essential role for epidemiologists interested in local infections and deaths from certain diseases. In particular, for epidemiologists, it may be of specific interest to compare Whites with other races and minorities and how large the gap is between them. Researchers may be interested in differences in disease outcomes between men and women. It is generally accepted that women are most willing to go to the doctor and hospitals for examination. Men are more likely to ignore their symptoms and wait until they start attracting the attention of their next of kin or until the pain becomes very uncomfortable.


Frérot, M., Lefebvre, A., Aho, S., Callier, P., Astruc, K., & Aho Glélé, L. S. (2018). What is epidemiology? Changing definitions of epidemiology 1978–2017. Plos One, 13(12).