The concept of descriptive epidemiology is crucial in health care since it searches for patterns to describe the outbreak of diseases in terms of place, time, and person. Diseases occur and change over time, and the changes can be regular or unpredictable. For diseases that occur seasonally, health practitioners can take effective preventive and control measures to limit them from spreading. For instance, influenza is predictable as it is especially present during winter, and therefore, vaccination campaigns can be conducted in time. On the other hand, sporadic diseases pose a challenge since they have to be first analyzed to find appropriate control measures.
Describing the presence of diseases by place provides efficient information about the geographic extent and variation of the problem. It involves employment units, residences, diagnosis places, birthplaces, or travel destinations (Wiemken & Kelley, 2020). The place could also refer to regional areas, whether urban or rural. Communities most likely to be affected can be identified by looking into their genetic characteristics, diet, or exposure to contaminated substances. Communicable and air-borne diseases may require patients to be quarantined for treatment.
Personal characteristics may affect illness organization, and the data analysis is done considering sex, age, ethnicity, and race, among other features. Some patients may have weak immune systems or may have inherently acquired the disease. The social-economic status of people may also be used to explain the state of an illness. The conditions under which they live and their accessibility to medical care are critical. Age is mostly analyzed since many health-related events vary with age (Goodman et al., 2019). Age groups are used to narrow down the population to make accurate records.
Categorizing diseases in aspects of time, person and place assist health administrators in healthcare planning. Knowing which populations are likely to be most or least affected enables them to target specific areas and develop educational programs. Moreover, there is efficient allocation of resources to the most affected units. The studies also make hypothesis generation easier since officials are able to examine the risk factors that can be altered or eliminated to reduce or prevent diseases (Wiemken & Kelley, 2020). Researching the various trends of illnesses helps to encourage innovation in vaccines and treatment.
Thorough assessment and monitoring of cases make market analysis in the health industry simple. Due to new and recurring health problems, the industry is dynamic, and continuous investigation is necessary (Goodman et al., 2019). More drugs can be developed to handle widespread diseases, and the records kept can be useful in managing future occurrences. Additionally, the information acquired from the surveillance is useful in assessing the effectiveness of control measures.
A sample case carried out recently showed that the rates of gastric cancer in Japan were on the rise compared to the ones in the United States population. This proves that the extent of health problems varies among nations (Thrift & Hashem, 2020). However, when the Japanese people relocate to the United States, the rates decrease, and their offspring are less likely to acquire the illness. A possible explanation is that a change in the diet lowers the chances of gastric cancer occurring. Through this observation, health officials have been able to handle the situation of gastric cancer more efficiently. Various programs have been developed to advise the Japanese population on managing their diet (Thrift & Hashem, 2020). In this way, their health conditions have significantly improved, leading to a more productive population. In conclusion, descriptive epidemiology continues to be the basis of numerous strategies applied in health care.
References
Goodman, K., Qifang, B., Kaminsky, J., Lessler, J. (2019). “What is Machine Learning? A Primer for the Epidemiologist”. American Journal of Epidemiology. 188(12): 2222-2239.
Thrift, A. & Hashem, B. (2020). Burden of Gastric Cancer. Clinical Gastroenterology and Hepatology. 18(3): 534-542.
Wiemken, T. & Kelley, R. (2020). Machine Learning in Epidemiology and Health Outcomes Research. Annual Review of Public Health. (41): 21-36.