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Depression as Public Health Population-Based Issue Essay

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Summary

This epidemiology assignment is dedicated to exploring depression and associated health determinants. According to Villarroel and Terlizzi (2020), depression is characterized by feelings of sadness, emptiness, or irritability accompanied by significant bodily and cognitive changes. Depression is associated with substantial societal costs and greater functional impairment than many other chronic diseases, such as diabetes or arthritis (Brody et al., 2018). In 2019, approximately 18,5% of adult Americans experienced either mild, moderate, or severe symptoms of depression (Villarroel & Terlizzi, 2020). Additionally, women were more likely to experience depression, with 21,8% of adult American women having the symptoms as opposed to 15% of men (Villarroel & Terlizzi, 2020). Given this information, this paper focuses on explaining the possible health and social determinants of depression in women.

The issue of depression has a massive impact on American society if one considers the fact that depression affects almost 20% of the total U.S. population. According to Brody et al. (2018), depression created some difficulties in work and social life for 48,4% of adult men and 51,2% of adult women diagnosed with that mental health disorder. Moreover, 31,5% of adult men and 29,2% of adult women with depression reported moderate and extreme difficulties in work, home, or social activities (Brody et al., 2018). In this regard, depression poses a serious threat to the overall health of the American nation and the normal functioning of American society. Lastly, depression demands extensive studying in regard to associated health and social determinants since certain population groups, such as women and racial minorities, might be at greater risk. As such, the issue of depression in women has an additional incentive for exploration.

Data Interpretation and Social Determinants of Health

The data for interpretation and understanding of depression determinants in women was mainly acquired from the Centers for Disease Control and Prevention (CDC) databases. In addition, the adapted data from the U.S. Department of Health Office of Minority Health (OMH) was utilized for creating an overview of the data. Whereas the data was sufficient to create the overall health picture of depression in women, it is necessary to note that CDC conducted most analyses in four years from 2014 to 2018. Therefore, the latest information updates will likely occur at the end of 2022; the 2018 data is the latest available in most metrics.

First of all, the issue of depression in women was connected to a dangerous disease that might lead to depression — female breast cancer. Depression is one of the most common psychiatric symptoms in patients with breast cancer (Pilevarzadeh et al., 2019). In regard to mortality, female breast cancer took the second spot in the top 10 cancer types, with 20,1 deaths per 100,000 women (CDC, 2018). In absolute numbers, in the 2014-2018 span, 1.238,159 new cases of female breast cancer were reported, and 208,686 women died of this cancer type (CDC, 2018). In regard to particular races and ethnicities, CDC (2018) provided the following breakdown of female breast cancer cases and deaths:

  • White women: 128 new cases and 20 deaths per 100.000 women;
  • Black women: 124 new cases and 27 deaths per 100.000 women;
  • American Indian and Alaska Native women: 73 new cases and 12 deaths per 100.000 women;
  • Asian and Pacific Islander women: 98 new cases and 12 deaths per 100.000 women;
  • Hispanic women: 96 new cases and 14 deaths per 100.000 women;

Judging from this data, a health picture of increased Black women’s vulnerability to female breast cancer emerges. The social determinants of health, such as inequality and racial disparities, explain this issue. Firstly, Black women face a significantly higher risk of death in female breast cancer cases. For instance, the death ratio between Black and White women equals 1,35, or 1,4 if rounded. As such, Black women are almost 40% more likely to die from breast cancer than White women (OMH, 2021). The significantly higher risk of death may be associated with the development of depression symptoms in Black women. Secondly, Black women are at increased risk of depression due to the social factor manifested in family income level. According to Brody et al. (2018), women with family income below the federal poverty level (FPL) had the highest prevalence of depression — 19,8%. For reference, in 2020, Black households had an annual median income of $45,870; meanwhile, the average median household income came out to $67,521 (Statista, 2021). Overall, such social justice and health inequities likely result in increased depression rates in Black women.

Tables 1, 2, and 3: A Detailed Explanation

The two additional determinants contributing to the greater risk of depression in women are represented in Tables 1 and 2, respectively. Table 1 presents the data adopted from the 2014-2018 female breast cancer in the United States and the 2021 data compilation by OMH. CDC (2018) divided the total cases into categories based on ethnicity. As a result, it became possible to highlight specifically vulnerable groups among the U.S. female population. In total, the biggest absolute number of new cases were registered among White women — 128 per 100,000; however, Black women suffered significantly greater mortality. Breast cancer death ratios were calculated for women of all races via a simple method used by the OMH. The data on White women were taken as a benchmark, and the number of deaths per 100,000 women of other ethnicities was divided by that benchmark. Overall, a brief analysis allowed confirming the vulnerability of Black women to breast cancer.

Table 1. Health Determinant of Depression in Women: Female Breast Cancer

Race/EthnicityBreast Cancer New Cases, per 100 womenBreast Cancer Deaths, per 100 womenBreast Cancer New Cases RatioBreast Cancer Death Ratio
White128201.01.0
Black124270,971,35
American Indian and Alaska Native73120,570,6
Asian and Pacific Islander98120,760,6
Hispanic96140,750,7

The small Table 2 showcases the influence of such social determinants as income level on the prevalence of depression. The pattern is obvious — the wealthier the household, the lower the probability of developing depression. The influence of social determinants was massive — women who had an income below the 100% of FPL were more than four times vulnerable to depression compared to those who made over 400% FPL.

Table 2. Social Determinant of Depression in Women: Level of Income

Income, % from FPL (Federal Poverty Level)Depression Prevalence, %Risk Probability
Less than 100%19,8%4.125
100% to less than 200%13,9%2,90
200% to less than 400%9,4%1,96
More than 400%4,8%1,0

Finally, Table 3 provides the flat rates of depression prevalence in adult women of different ethnicities. In general, White, Black, and Hispanic women were equally predisposed to depression, whereas Asian women were much more resilient. Black women were 5% more likely to develop depression than White or Hispanic; meanwhile, Asian women were 63% less susceptible to depressive symptoms. This data corresponds with the social determinant of income and health determinant of breast cancer — Asian households are the richest by a large margin. In contrast, Black households are significantly poorer than White and Hispanic. In this regard, Black women belong to the highest depression risk group both from a health and social determinants perspective.

Table 3. Depression Prevalence in Adult Women (Aged 20 and Over) and Median Household Income.

Race/EthnicityDepression Prevalence, %RatioMedian Household Income in 2020
White10,51,0$74,912
Black11,01,05$45,870
Hispanic10,51,0$55,321
Non-Hispanic Asian3.90,37$94,903

Figure 1: Epidemiologic Model

Causal Pie Epidemiologic Model of Depression in Women
Figure 1. Causal Pie Epidemiologic Model of Depression in Women

Given the information from the databases, depression in women can be represented in a causal pie model. This epidemiologic model is the most convenient for showcasing risk factors or component causes that may contribute to the development of depression. Furthermore, the casual pie or sufficient-component cause model highlights the multifactorial nature of the disease (Shimonovich et al., 2020). Regarding the explored data, it is possible to reveal three-component causes of depression.

Firstly, gender (A) contributes to the risk of developing depressive symptoms. For instance, in 2019, 21,8% of adult American women aged 18 and over reported depression. In contrast, only 15% of men of the same age group reported depression (Villarroel & Terlizzi, 2020). In addition, women are significantly more vulnerable to breast cancer — a serious disease with high mortality rates that has a potential for causing or worsening depressive symptoms.

Secondly, depression is catalyzed by the component cause of income level. Income below the FPL resulted in a significantly higher prevalence of depression in women (Brody et al., 2018). Moreover, difficult socio-economic conditions can amplify other risk factors of depression. For example, neighborhood disadvantages and low socio-economic status were associated with an approximately 25% increased risk of estrogen receptor negative breast cancer in U.S. Black women (Barber et al., 2021). Therefore, income disparity serves as a strong and negative health determinant of depression.

Lastly, ethnicity can also act as a risk or resilience factor in depression. Whereas women of most ethnicities and races showed approximately equal resilience to depression, the institutionalized social and racial disparities may negatively impact other component causes. For instance, Black women were particularly vulnerable to breast cancer, which can also act as a risk factor for depression development. As such, it is important to realize that physical and mental diseases can often have underlying social determinants. In this regard, targeting these determinants, such as poverty, social, gender, or racial inequities, is necessary for improving the nation’s health.

References

Barber, L. E., Zirpoli, G. R., Cozier, Y. C., Rosenberg, L., Petrick, J. L., Bertrand, K. A., & Palmer, J. R. (2021). Breast Cancer Research, 23(1), 1-12. Web.

Brody, D.J., Pratt, L.A., & Hughes, J.P. (2018). CDC. Web.

Centers for Disease Control and Prevention. (2018). Web.

Office of Minority Health. (2021). Web.

Pilevarzadeh, M., Amirshahi, M., Afsargharehbagh, R., Rafiemanesh, H., Hashemi, S. M., & Balouchi, A. (2019). Breast Cancer Research and Treatment, 176(3), 519-533. Web.

Shimonovich, M., Pearce, A., Thomson, H., Keyes, K., & Katikireddi, S. V. (2021). Assessing causality in epidemiology: Revisiting Bradford Hill to incorporate developments in causal thinking. European Journal of Epidemiology, 36(9), 873-887. Web.

Statista. (2021). Median household income in the United States 2020, by race or ethnic group. Web.

Villarroel, M.A., & Terlizzi, E.P. (2020). CDC. Web.

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