Essay on Epidemiology in Public Health

Epidemiology is a long-existing scientific approach that gained prominence in the Second World War to understand disease occurrence. Its conception was primarily required to abolish the supernatural viewpoint peddled by various scholars in conducting diagnosis and treatment. Recognizable contributors include John Raunt, whose publication elaborate on mortality data and its quantification in terms of disease occurrence, birth and death patterns in 1662. He established urban-rural influences, female-male disparities, seasonal variations, and infant mortality rates from his epidemiological approach (Friis & Sellers, 2020). Farr added further to this contribution through his data collection of mortality statistics in Britain which earned him immense recognition globally in creating basic protocols applied in disease classification and vital statistics. Other contributors include John Snow in understanding disease outbreaks. In the modern-day setting, this concept is utilized in medical and public health fields in various ways, including conducting community or population health evaluations, providing a clear clinical picture, influencing individual health decisions and establish ing causes of disease occurrence. Studying descriptive epidemiology provides a profound outlook on the intricacies of this concept, further highlighting its relevance in the medical and public health domains.

Discussion

• Concept of Descriptive Epidemiology

The concept of descriptive epidemiology focuses on the organization and summarization of data according to the epidemiological variables. These parameters include time, person and place. This action is desirable in population studies and applied based on several justifications. First, it allows the researcher to establish familiarity with the available data and its correlation to the population under investigation. Second, there is accessible communication on the health status of the assessed group presenting a detailed description of the same. The third justification involves the identification of high-risk groups affected by the disease (Friis & Sellers, 2020). Finally, the information derived from the above-stated actions is pertinent in determining disease causes, thus generating verifiable hypotheses.

Time, as a parameter, is considered based on the changing nature of disease rates. These modifications are predictable and regular. For example, during the cold season, there is increased reporting of influenza demonstrating it is seasonal. With this information, health departments are better prepared in dealing with the bouts of an outbreak. Also, they can conduct adequate campaigns aimed at educating the public. However, it is noteworthy to mention some maladies are unpredictable (Friis & Sellers, 2020). This unpredictability warrants the importance of assessing time-based events occurring before the decrease or increase in disease rate. The following data is elemental in identifying its causative factors and implement appropriate strategies in avoiding further occurrence or managing its impact on the community. Time-related information is presented in a graph versus the disease rates or events of death to visually portray the severity of the problem, future direction, and past trends (Ghouth, 2020). In addition, it illustrates the effect of other events on the disease manifestations. This information provides impeccable insights into the causative factors.

A health occurrence is described by a place that allows quantification of its geographical extent. In this description, scholars consider aspects such as employment location, resident, school district, birthplace, hospital unit, amongst others determined by the intricacies of the event (Ghouth, 2020). Other larger geographical units can be applied, including census tract, country, map coordinates, country etc. However, more studies use rural or urban locations to analyze data in some other cases. An analysis considerate of this parameter presents several advantages. First, it identifies the site where the disease-causing agents reside and the preferred place of multiplication. Second, from this deduced data, the researcher can recognize factors influencing escalating the disease risk in individuals located within the same area also considered risk factors (Zaidi & Cherif, 2017). For instance, if the disease is contagious from one person to another with higher occurrence in urban compared to rural, a contributing risk factor derived from this observation is overcrowding present in the urban areas. Third, located based data is easier to comprehend if presented in a pictorial map because of colour, different shadings and colour to signify different rates in varied locations (Zaidi & Cherif, 2017). Also, a spot map is applicable in depicting an outbreak that identifies the focal point of the disease event further display the event’s orientation.

The third measurement, person, consider various categories and their inherent characteristics, activities, acquired traits and activities, and living conditions to determine the risk posed to different groups and their predisposition to experience more devastating effects of the disease. Its presentation is effectively displayed in graphs or tables (Mohini & Agarwal, 2019). The implication of applying this parameter varies depending on this trait is analyzed. Farr added further to this contribution through his data collection of mortality statistics in Britain which earned him immense recognition globally in creating basic protocols applied in disease classification and vital statistics (Mohini & Agarwal, 2019). Other contributors include John Snow in understanding disease outbreaks. In the modern-day setting, this concept is utilized in medical and public health fields in various ways, including conducting community or population health evaluations, providing a clear clinical picture, influencing individual health decisions, and establishing causes of disease occurrence (Ghouth, 2020). Studying descriptive epidemiology provides a profound outlook on the intricacies of this concept, further highlighting its relevance in the medical and public health domains.

Case in point, age is considered the most elemental attribute as it contributes to the health impact of any disease event because it influences latency, exposure opportunity, susceptibility, a physiological response, and incubation duration.

• Application of Descriptive Epidemiology

An analysis considerate of this parameter presents several advantages. First, it identifies the site where the disease-causing agents reside and the preferred place of multiplication. Second, from this deduced data, the researcher can recognize factors influencing escalating the disease risk in individuals located within the same area also considered risk factors (Zaidi & Cherif, 2017). For instance, if the disease is contagious from one person to another with higher occurrence in urban compared to rural, a contributing risk factor derived from this observation is overcrowding present in the urban areas. Third, located based data is easier to comprehend if presented in a pictorial map because of colour, different shadings and colour to signify different rates in varied locations (Zaidi & Cherif, 2017). Also, a spot map is applicable in depicting an outbreak that identifies the focal point of the disease event further display the event’s orientation.

This unpredictability warrants the importance of assessing time-based events occurring before the decrease or increase in disease rate. The following data is elemental in identifying its causative factors and implement appropriate strategies in avoiding further occurrence or managing its impact on the community. Time-related information is presented in a graph versus the disease rates or events of death to visually portray the severity of the problem, future direction, and past trends (Ghouth, 2020). In addition, it illustrates the effect of other events on the disease manifestations. This information provides impeccable insights into the causative factors.

• Health Disparities and Contributive Factors

In the public health domain, health populations possess different characteristics that create contrasts in responding to a disease event. Socially, several factors are highlighted in explaining the variances and justifying the vulnerability evident in some groups. First, individuals with chronic conditions are at high risk and experience a more significant impact in the onset of any disease (Haveman-Nies et al., 2017). Second, studies show these groups also present a more negative diagnosis and symptoms compared to others. Additionally, disabled people have a challenge accessing healthcare, limiting their quality of life as service delivery is hindered.

The second group constituted of individuals with low income and diagnosed with chronic illnesses. The primary difficulty cited by this unit is the severe impact their medical condition has on their physical, mental, and financial wellbeing. Additionally, this group has a disproportionate evidential distribution when analyzing varied ethnic and racial communities, with minorities having the most significant percentage. Low incomes present issues such as limit cover to healthcare, reducing interactions with healthcare facilties and professionals necessary in receiving appropriate medication or treatment (Haveman-Nies et al., 2017). It is also noteworthy to mention this unit also is placed at a higher risk of undergoing recurring medical conditions (Josney, 2018). This recurrence promotes the onset of secondary health problems such as diabetes or obesity. An exemplary case is a homeless community, where there is a high risk of contracting diseases from daily interactions and their subsequent adverse effects and outcomes. According to a US Department of Housing and Urban Development report, 550,00 people are reported to be homeless each night (Mohini & Agarwal, 2019). These statistics demonstrate the extent of the problem associated with low income on public health, which causes disparities when evaluating a particular health condition.

References

Friis, R. H., & Sellers, T. (2020). Epidemiology for public health practice. Jones & Bartlett Learning.

Ghouth, A. (2020). Descriptive and clinical epidemiology of cholera in al-hudaydah, Yemen. Journal of Health, Medicine and Nursing, 7(4), 23-30. https://doi.org/10.7176/jhmn/80-04

Haveman-Nies, A., Jansen, M., Oers, J. A., & Veer, P. V. (2017). Epidemiology in public health practice. Springer.

Josney, M. (2018). Descriptive epidemiology. A Clinician’s Guide to Progressive Supranuclear Palsy, 1(45), 6-12. https://doi.org/10.2307/j.ctvbtzpj2.5

Mohini, S., & Agarwal, A. (2019). Epidemiology of gallbladder cancer in North India – a radiological descriptive study. International Journal of Contemporary Medical Research [IJCMR], 6(8). https://doi.org/10.21276/ijcmr.2019.6.8.1

Zaidi, Z., & Cherif, M. H. (2017). Abstract 269: The descriptive epidemiology of gynecologic cancers: an international comparison of incidence, survival and mortality. Epidemiology, 1(2), 123-453. https://doi.org/10.1158/1538-7445.am2017-269