Population Health Problem
The population health problem I selected is depression. This is a severe psychiatric disorder with a reduced emotional background, loss of pleasure from life, low energy balance, and psychomotor retardation. With depression, this condition lasts at least two weeks. At the same time, depression has different subtypes and degrees of severity. Since this mental illness is widespread, a lot of attention is paid to it when writing scientific papers, and many different types of data concerning the disease are collected annually.
First Data Set
Variables of Interest
The first dataset selected for study was a dataset published in A Blog of the National Center for Health Statistics (a blog) on May 26, 2023. The variables that were investigated in the work were various factors affecting the course of depression in adults 18 years and older (NCHS, 2023). To such factors, scientists attributed gender, race, and, in particular, Hispanic origin, as well as taking medications for depression prescribed by a medical specialist.
Data Set Validity
This dataset can be considered reliable, as the information accurately reflects real-world objects. The confidence probability of the required accuracy measures the reliability of information. In this dataset, the probability that the parameter value reflected by the information differs from the actual value of this parameter within the required accuracy is small. Therefore, the information presented in this publication can be considered reliable.
Use in Prior Studies
This dataset has been used in studies where examining the relationship between depression and various factors is essential. For example, this NCHS data set has been utilized in research where the aspect of race is crucial. Roundtree (2023) investigates the course of depression in black males. The author gives an example of a dataset obtained by NCHS to support his point of view regarding the influence of gender and race on mental illness.
Second Data Set
Variables of Interest
The second data set chosen for analysis was one published on May 25, 2023, in a blog. The specific variables in it are the symptoms of depression and the frequency with which they occur (NCHS, 2020). Their influence was assessed through the lens of various exogenous variables, including work situation, spending patterns, housing, food security, disruptions in education, and aspects of physical and mental well-being.
Data Set Validity
This dataset can be considered valuable, since the information presented objectively, accurately, and correctly reflects the characteristics and signs of depression. It is adequate for the given parameter of the object, namely, the symptoms of mental illness observed in patients during the week. We can say that this dataset is accurate, as it shows the degree of proximity between the displayed value of the symptomatic parameter and its actual value, expressed in terms of the disease’s severity.
Use in Prior Studies
This dataset explores the manifestations of depression during the COVID-19 pandemic. Therefore, most of the papers using it are devoted to the negative impact of quarantine on mental health. For example, Czeisler et al. (2021) are investigating the phenomenon known as “survivor syndrome” in relation to the pandemic. Scientists use the dataset to show how the COVID-19 pandemic has led to an increase in the number of mental illnesses and depression in particular.
Third Data Set
Variables of Interest
The third data set selected for study was a data set published by the World Health Organization on October 5, 2022. In this work, the specific variables are the place of work of a person suffering from depression (WHO, 2022). This is due to the purpose of collecting data to substantiate the influence of the nature of the profession on the development of mental illnesses in an employee.
Data Set Validity
Determining the accuracy of information or calculating the mathematical expectation of the absolute value of the deviation of the indicator value from the objectively existing actual value of the displayed parameter of the dependence of depression on the profession is insignificant. Therefore, we can conclude that the information presented in the dataset is generally reliable.
Use in Prior Studies
This dataset is cited in papers where it is required to establish a relationship between depression and the patient’s work. For example, Muntean et al. (2023) utilize the data to explore the question of what factors contribute to depression in employees more deeply. Since scientists conduct this research by examining the specifics of work activities, this dataset is suitable as a basis for their studies.
Challenges to Identifying the Proper Data Set & Securing Permission to Use It
To find a proper dataset, it is necessary to conduct a preliminary study on the topic of work. Challenges in this process are related to determining the vector in which the reliable information is located. Theoretically reasonable expectations about the results of practical research will allow you to assess how unbiased the collected data is critically. Difficulties may also arise when obtaining permission to use this data. In the absence of official access by an educational institution, it is sometimes impossible to access scientific research data as part of a personal initiative.
References
Czeisler, M. E., Wiley, J. F., Czeisler, C. A., Rajaratnam, M. W., & Howard, M. E. (2021). Uncovering survivorship bias in longitudinal mental health surveys during the COVID-19 pandemic. Epidemiology and Psychiatric Sciences, 30(1), e45.
Muntean, M., Colcear, D., Briciu, V., Lupse, M., & Radulescu, A. (2023). Exploring psychological factors linking burnout and depression among COVID-19 healthcare workers two years post-pandemic. International Journal of Science and Research, 12(8), 994–1004.
NCHS. (2023). Quick stats: Age-adjusted percentage of adults aged ≥18 years who take prescription medication for depression, by sex and race and Hispanic origin — National Health Interview Survey, United States, 2021. CDC.
NCHS. (2020). Indicators of anxiety or depression based on reported frequency of symptoms during last 7 days. CDC.
Roundtree, P. J. (2023). Liberating self: An autoethnographic inquiry into black male mental health. Social Work Doctoral Dissertations, 26(1), 1–110.
