Assignment: CAPS 401 Week 3 Compose a focused paper that explains and describes your healthcare issue/topic from the scientific and mathematical/analytical perspectives of inquiry.

Assignment: CAPS 401 Week 3 Compose a focused paper that explains and describes your healthcare issue/topic from the scientific and mathematical/analytical perspectives of inquiry.

Assignment: CAPS 401 Week 3 Compose a focused paper that explains and describes your healthcare issue/topic from the scientific and mathematical/analytical perspectives of inquiry.

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Compose a focused paper that explains and describes your healthcare issue/topic from the scientific and mathematical/analytical perspectives of inquiry. (You will cover two perspectives in one paper.)

Address your general topic by forming and answering two levels of research questions for each inquiry.

Choose a “Level 1 Research Question/Writing Prompt” from both of the lists below to answer in the paper.

Compose a “Level 2 Research Question/Writing Prompt” for each kind of inquiry that provides detail, specificity, and focus to your inquiry, research, and writing.

State your research questions in the introduction of your paper.

Answer each research question and support your assertions with evidence (research) to form the body of your paper.

In the conclusion of the paper, briefly review the issues, research questions, answers, and insights.

Level 1 Research Questions/Writing Prompts

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SCIENTIFIC Perspective of Inquiry

What are the anatomical, physiological, pathological, or epidemiological issues?

Which body systems are affected?

What happens at the cellular or genetic level?

Which chemical or biological issues are most important?

Level 1 Research Questions/Writing Prompts

MATHEMATICAL/ANALYTICAL Perspective of Inquiry

What are the economic issues involved?

Which economic theories or approaches best explain the issue?

What are the statistical facts related to the issue?

Which statistical processes used to study the issue provide for the best explanation or understanding?

Your paper must be five pages in length and reference four to six scholarly, peer-reviewed resources. Be sure to follow current APA Style (e.g., spacing, font, headers, titles, abstracts, page numbering).

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A Comprehensive Analysis of Healthcare Inequality Scientific and Mathematical/Analytical Perspectives

Inequality persists in the delivery and affordability of healthcare throughout the United States, especially for minorities. Given the complexity of this problem, it is crucial to approach it and study it from a scientific and mathematical/analytical viewpoint. Kurlansky (2021) elaborates that the scientific approach investigates the structural, functional, and distributional characteristics contributing to healthcare disparities. On the other hand, verifying ideas utilizes the mathematical/analytical point of view, where the economic and statistical sides of the issue are entailed (Selden & Berdahl, 2020). This paper addresses four research questions based on the two viewpoints: What epidemiological issues drive healthcare inequality? How do these epidemiological factors manifest in different body systems, particularly during public health crises like the COVID-19 pandemic? What economic issues are involved in healthcare inequality? Furthermore, which statistical processes best explain healthcare inequality, particularly concerning access to healthcare and health outcomes? This report seeks deeper insight into healthcare inequality and possible solutions from these inquiries.

Scientific Perspective

Level 1 Question: What are the epidemiological issues driving healthcare inequality?

In studying the distribution and other factors that affect the prevalence of health-related events in limited population groups, epidemiology provides basic information regarding the inequality in health care. In this context, Zelner et al. (2022) note that epidemiological issues are central to conveying disparities in the healthcare system. According to research, health dynamics include income, education, and location on healthcare’s physical and occupational dimensions (Jiang et al., 2021). These social determinants are often mutually reinforcing and accumulated in several deprived neighborhoods, thus aggravating health inequalities.

Chronic diseases such as high blood pressure, diabetes, and obesity, for instance, are more prevalent among some races and ethnic groups than in others. Caraballo et al. (2022) pointed out that these complications affect general health, especially during the pandemic. Environmental factors are also relevant for the same reason, as access to clean air and water will likely worsen among marginalized populations (Nana-Sinkam et al., 2021). These exposures will likely lead to higher rates of respiratory diseases, cancers, and other diseases.

One of the most significant measures of healthcare inequalities is providing preventive services. Yao et al. (2021) affirm that disparities in basic physical examinations, cancer-related screening, and preventive care lead to a more advanced stage of diagnosis and worse states when diagnosed. However, they mention that variation in health literacy gives individuals different opportunities to seek health care, understand health information, and make proper health decisions.

Level 2 Question: How do these epidemiological factors manifest in different body systems, particularly during public health crises like the COVID-19 pandemic?

The impact of healthcare inequalities when applied to various body systems was brought to light, especially during the COVID-19 pandemic. Statistics by Alcendor (2020) show that minorities were worse affected by the severe complications of COVID-19, like acute respiratory distress syndrome (ARDS). Further studies reveal that this progression is also partly attributable to higher rates of chronic respiratory diseases and a generally higher propensity of these minority populations to suffer from air pollution (Sundaram et al., 2022). Based on these studies, some pre-existing health conditions, including cardiovascular diseases, which are prevalent among minorities, made patients more susceptible to the severe manifestations of COVID-19.

The other system that was interfered with was the endocrine system, as there were elevated rates of negative outcomes in people with diabetes during COVID-19. Puig-Domingo et al. (2021) point out that the pandemic showed that stress experienced from low socioeconomic status negatively impacts the endocrine system and potentially exacerbates diseases like diabetes and obesity. These researchers further argue that the immune system is also one of the most crucial factors contributing to health status variations. On the other hand, Sundaram et al. (2022) explain that stress resulting from systematic racism and socioeconomic factors can be chronic, making it possible for the immune system to be dysregulated. This progression leads to susceptibility to infections and reduced capacity to respond to infections, such as those that have preventive vaccines.

Emerging data point to the neurological signs of COVID-19 arguably being compounded by health disparity, which has consequent implications for higher neurological morbidity in underprivileged populations. Selden and Berdahl (2020) pointed out that minorities were overrepresented on the frontlines during the pandemic and thus were more vulnerable to the virus and its subsequent impact on various body systems. This occupational divergence is a quintessential example of how socioeconomic discrepancies translate into biological vulnerabilities.

Mathematical/Analytical Perspective

Level 1 Question: What economic issues are involved in healthcare inequality?

When broken down mathematically and analytically, some multifaceted economic factors intensify healthcare inequality. McMaughan et al. (2020) clarify that such a financial perspective is valid because some individuals may be unable to afford proper healthcare, nutritious foods, and even decent housing, all of which influence health. Essentially, a person who earns less income has poor health; hence, their health will limit them from gaining more, forming a cycle.

Another economic factor increasing healthcare inequality is the American insurance policy based on employer payments. In Wang et al.’s (2023) view, it is inconvenient that American policies equate health insurance with employment since it does not shield those who are either part-time employees, gig workers, or have no job. This provision particularly affects minorities because they are more inclined to work in positions that do not provide sufficient benefits (Berthelot & Bornstein, 2024). In a similar study, Tang (2024) adds that the condition further increases healthcare inequality because even those with insurance have to dig deep into their pockets to pay other medical bills. Deductibles, copayments, and out-of-pocket maximums are also hurdles to receiving care, particularly for preventive or chronic illnesses.

Geographical imbalance is also a driving factor in healthcare inequality. Flinterman et al. (2023) show that investment in healthcare facilities is not evenly distributed, leading to “healthcare deserts” in rural and some urban areas. This inequitable distribution of healthcare resources and supportive infrastructure could make it difficult for the inhabitants of these regions to access health facilities, forcing them to spend more money on their medical bills.

The emerging intergenerational wealth gap is the most disturbing long-term effect of economic policies and practices that have produced wealth stratification by race. Thakur et al. (2020) mentioned that such economic disparities can be attributed to structural and social characteristics such as separation and unsuitable schooling amenities. This relative wealth distribution determines the ability of families to obtain health-improving assets and to manage health risks and shocks. These factors lead to a cycle of disfavor perpetuating the tradition of injustice in healthcare from one generation to the next.

Level 2 Question: Which statistical processes best explain healthcare inequality, particularly concerning access to healthcare and health outcomes?

Scholars use diverse statistical procedures and techniques to analyze and respond to healthcare inequity. Key methodologies include regression analyses, multilevel modeling, and geospatial techniques, which collectively enhance understanding of these inequities. Multiple regression analysis enables the researcher to control other variables in the study, which makes it easier to establish the effect of certain factors on health status (Zadeh et al., 2024). For instance, the statistical logistic regression technique enabled the prediction of the probability of getting preventive care, given socioeconomic characteristics.

Also, multilevel modeling considers the nested nature of health data at the individual, community, and system levels. Sua et al. (2024) elaborate that it is most useful in demonstrating how neighborhood characteristics moderate the effects of certain traits on health. On the other hand, time series is important when analyzing trends in health disparities and establishing the impact of policy changes or health crises.

Mapping, a form of geospatial analysis, can show geographic disparities in healthcare accessibility and outcomes to target resources to the right areas. Moreover, propensity score matching is one statistical method that can minimize confounding factors when comparing health outcomes between groups to determine disparities (Obeidat & Alourd, 2024). Generally, applying complex forms of analysis, such as structural equation modeling, is valuable to understanding the relationship between the identified social determinants and health outcomes. It enables researchers to examine the theoretical propositions on how different factors give rise to health inequality.

Conclusion

This paper has considered healthcare inequality from scientific and mathematical/analytical perspectives to answer four research questions. From the scientific point of view, the study revealed important epidemiological factors that determine the inequality in access to healthcare services, such as social demographic factors, distribution of chronic diseases, and environmental factors. The study also examined how these factors present themselves across the body systems, especially during the COVID-19 pandemic, and how social, economic, and biological factors interact to determine health. From a mathematical and analytical point of view, the research focused on the financial aspects of healthcare inequity, such as income differences, the healthcare system, and intergenerational mobility. It also expounded on different statistical procedures important in describing and analyzing healthcare disparity, ranging from regression analysis to geographical information systems. This approach to studying healthcare disparity is holistic and points to the fact that the problem is not simple and requires a multifaceted solution. Subsequent studies and policies should also persist in applying scientific information and sophisticated statistical approaches in formulating sound strategies for minimizing healthcare disparity and optimizing the health of all people.

References

Alcendor, D. J. (2020). Racial disparities-associated COVID-19 mortality among minority populations in the U.S. Journal of Clinical Medicine, 9(8), 2442. https://doi.org/10.3390/jcm9082442

Berthelot, E. R., & Bornstein, S. G. (2024). Inequality in healthcare. Oxford University Press EBooks, 322–337. https://doi.org/10.1093/oso/9780197615133.003.0025

Caraballo, C., Ndumele, C. D., Roy, B., Lu, Y., Riley, C., Herrin, J., & Krumholz, H. M. (2022). Trends in racial and ethnic disparities in barriers to timely medical care among adults in the U.S., 1999 to 2018. JAMA Health Forum, 3(10), e223856. https://doi.org/10.1001/jamahealthforum.2022.3856

Flinterman, L. E., González-González, A. I., Seils, L., Bes, J., Ballester, M., Bañeres, J., Dan, S., Domagała, A., Dubas-Jakóbczyk, K., Likic, R., Kroezen, M., & Batenburg, R. (2023). Characteristics of medical deserts and approaches to mitigate their health workforce issues: A scoping review of empirical studies in Western countries. International Journal of Health Policy and Management. https://doi.org/10.34172/ijhpm.2023.7454

Jiang, X., Liu, J., Sun, Y., & Wang, X. (2021). The concealed fact: Relationship of healthcare performance and income inequality in the United States based on factor analysis. Advances in Economics, Business and Management Research/Advances in Economics, Business and Management Research. https://doi.org/10.2991/assehr.k.211209.082

Kurlansky, P. (2021). Commentary: Race and medicine: Not all differences are disparities. The Journal of Thoracic and Cardiovascular Surgery, 165(5), 1826–1827. https://doi.org/10.1016/j.jtcvs.2021.11.060

McMaughan, D. J., Oloruntoba, O., & Smith, M. L. (2020). Socioeconomic status and access to healthcare: Interrelated drivers for healthy aging. Frontiers in Public Health, 8(231), 1–9. https://doi.org/10.3389/fpubh.2020.00231

Nana-Sinkam, P., Kraschnewski, J., Sacco, R., Chavez, J., Fouad, M., Gal, T., AuYoung, M., Namoos, A., Winn, R., Sheppard, V., Corbie-Smith, G., & Behar-Zusman, V. (2021). Health disparities and equity in the era of COVID-19. Journal of Clinical and Translational Science, 5(1). https://doi.org/10.1017/cts.2021.23

Obeidat, B., & Alourd, S. (2024). Healthcare equity in focus: Bridging gaps through a spatial analysis of healthcare facilities in Irbid, Jordan. International Journal for Equity in Health, 23(1). https://doi.org/10.1186/s12939-024-02120-8

Puig-Domingo, M., Marazuela, M., Yildiz, B. O., & Giustina, A. (2021). COVID-19 and endocrine and metabolic diseases. An updated statement from the European Society of Endocrinology. Endocrine, 72(2), 301–316. https://doi.org/10.1007/s12020-021-02734-w

Selden, T. M., & Berdahl, T. A. (2020). COVID-19 and racial/ethnic disparities in health risk, employment, and household composition. Health Affairs, 39(9), 1624–1632. https://doi.org/10.1377/hlthaff.2020.00897

Sua, R., Huang, X., Chen, R., & Guo, X. (2024). Spatial and social inequality of hierarchical healthcare accessibility in urban system: A case study in Shanghai, China. Sustainable Cities and Society, 109, 105540–105540. https://doi.org/10.1016/j.scs.2024.105540

Sundaram, S. S., Melquist, S., Kalgotra, P., Srinivasan, S., Parasa, S., Desai, M., & Sharma, P. (2022). Impact of age, sex, race, and regionality on major clinical outcomes of COVID-19 in hospitalized patients in the United States. BMC Infectious Diseases, 22(1). https://doi.org/10.1186/s12879-022-07611-z

Tang, L. (2024). The impact of inequality in socioeconomic status on healthcare services utilization. Journal of Education, Humanities and Social Sciences, 28, 455–458. https://doi.org/10.54097/zxymv497

Thakur, N., Lovinsky-Desir, S., Bime, C., Wisnivesky, J. P., & Celedón, J. C. (2020). The structural and social determinants of the racial/ethnic disparities in the U.S. COVID-19 pandemic: What’s our role? American Journal of Respiratory and Critical Care Medicine, 202(7). https://doi.org/10.1164/rccm.202005-1523pp

Wang, J., Pei, Z. K., Wang, Y., & Qin, Z. (2023). An investigation of income inequality through Autoregressive integrated moving average and regression analysis. Healthcare Analytics, 5, 100287. https://doi.org/10.1016/j.health.2023.100287

Yao, R., Zhang, W., Evans, R., Cao, G., Rui, T., & Shen, L. (2021). Inequities in healthcare services caused by the adoption of digital health technologies: A scoping review (preprint). Journal of Medical Internet Research, 24(3). https://doi.org/10.2196/34144

Zadeh, H., Curran, M., Castillo, N. D., Morales, C., Dukes, K., Martinez, D., Salinas, J. L., Bryant, R., Bojang, M., & Carvour, M. L. (2024). Epidemiological approaches to multivariable models of health inequity: A study of race, rurality, and occupation during the COVID-19 pandemic. Annals of Epidemiology, 94, 42–48. https://doi.org/10.1016/j.annepidem.2024.04.008

Zelner, J., Masters, N. B., Naraharisetti, R., Mojola, S. A., Chowkwanyun, M., & Malosh, R. (2022). There are no equal opportunity infectors: Epidemiological modelers must rethink our approach to inequality in infection risk. PLOS Computational Biology, 18(2), e1009795. https://doi.org/10.1371/journal.pcbi.1009795