Epidemiological Case Study Using The Internet Research A Nat

Epidemiological Case Studyusing The Internet Research A National Un

Epidemiological Case Study using The Internet, research a national (United States) or international (outside the United States) reportable disease. Locate charts and tables relating to the leading causes of death because of the disease. Based on your research and understanding, create a 2- to 3-page Microsoft Word document, which includes the following: A brief description of the disease you selected for your research A brief summary of your research and findings A detailed description of the data in terms of person, place, and time A detailed description of the potential problems relating to the completeness and quality of data A description of how the potential problems can influence the way you use data to explain the patterns of variation in a population and the causes of changes in disease trends Support your responses with examples. Cite any sources in APA format.

Paper For Above instruction

Introduction

The selected disease for this epidemiological case study is Alzheimer's disease, a progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and behavioral changes. It is one of the leading causes of death among older adults in the United States and worldwide. Understanding the patterns and trends of Alzheimer's disease is crucial for public health planning and intervention strategies.

Summary of Research and Findings

Research indicates that Alzheimer's disease has seen a rising prevalence over recent decades, partly due to increasing life expectancy and aging populations globally. Data from the Centers for Disease Control and Prevention (CDC) show that Alzheimer's is the sixth leading cause of death in the United States, with approximately 6.2 million Americans aged 65 or older living with the disease as of 2023. The disease's impact is observed predominantly among the elderly, with incidence rates increasing sharply after age 65, and especially after age 85. Geographically, higher prevalence rates are recorded in the Midwest and Southeast regions, possibly linked to socioeconomic factors, healthcare access, and demographic variables.

Data Description in Terms of Person, Place, and Time

The epidemiological data regarding Alzheimer's disease can be broken down as follows:

- Person: The disease predominantly affects older adults, with incidence and prevalence increasing with age. Females are somewhat more affected than males, attributed partly to longer life expectancy. Ethnic and racial disparities are evident; African Americans and Hispanics show higher prevalence and incidence rates compared to Whites, which may relate to genetic, socioeconomic, and healthcare disparities.

- Place: Geographic variation exists across different regions, states, and communities. Socioeconomic status and access to healthcare significantly influence diagnosis rates and reported prevalence.

- Time: Trends over time reveal an increasing prevalence attributed to aging populations, along with improved diagnosis and reporting mechanisms. Data comparing decades show a consistent upward trend in reported cases and death rates related to Alzheimer's disease.

Potential Problems Related to Data Completeness and Quality

Several challenges affect the quality and completeness of Alzheimer's disease data. Underreporting can occur due to misdiagnosis or lack of diagnosis, especially in underserved populations. Variability in diagnostic criteria and reporting practices across regions and healthcare providers can introduce inconsistencies. Additionally, changes in diagnostic technologies and increased awareness over time can lead to apparent increases in prevalence that may not solely reflect actual disease growth.

Impact of Data Problems on Analysis of Disease Trends

These potential data issues can significantly influence how epidemiologists interpret patterns and causes of change. Underreporting and misdiagnosis might underestimate the true burden of the disease, leading to insufficient resource allocation. Variability in data collection can distort geographic and demographic comparisons, making it difficult to identify at-risk populations accurately. Changes in diagnostic criteria over time could artificially inflate trend data, leading researchers to infer false increases in disease prevalence. Therefore, understanding data limitations is crucial when analyzing epidemiological patterns and planning public health interventions.

Conclusion

In summary, Alzheimer's disease presents a significant and growing public health challenge, with extensive epidemiological data providing insights into its distribution and trends. Recognizing the limitations of data quality and completeness is vital for accurate interpretation. Addressing these potential problems through improved data collection, standardized diagnostic protocols, and targeted surveillance can enhance understanding and guide effective public health responses to this complex disease.

References

Centers for Disease Control and Prevention (CDC). (2023). Alzheimer’s disease mortality data. https://www.cdc.gov/aging/aginginfo/alzheimers.htm

Alzheimer's Association. (2022). 2022 Alzheimer's disease facts and figures. Alzheimer’s & Dementia, 18(4), 700-789. https://doi.org/10.1002/alz.12328

Prince, M., Wimo, A., Guerchet, M., Ali, G. C., Wu, Y. T., & Prina, M. (2015). World Alzheimer Report 2015: The global impact of dementia. Alzheimer's Disease International.

Hebert, L. E., Weuve, J., Scherr, P. A., & Evans, D. A. (2013). Alzheimer disease in the United States (2010–2050) estimated using the 2010 Census. Neurology, 80(19), 1778-1783.

Rizzi, L., Rossetto, F., & Crippa, A. (2014). Epidemiology and risk factors for Alzheimer’s disease: A comprehensive review. Clinical Interventions in Aging, 9, 2003-2017. https://doi.org/10.2147/CIA.S134734

Brookmeyer, R., Johnson, E., Ziegler-Graham, K., & Arrighi, H. M. (2007). Forecasting the global burden of Alzheimer’s disease. Alzheimer's & Dementia, 3(3), 186-191. https://doi.org/10.1016/j.jalz.2007.04.372

Morris, J. C. (2005). The clinical dementia rating (CDR): Current version and scoring rules. Neurology, 46(8), 2268-2270.

Jack, C. R., Knopman, D. S., Jagust, W. J., et al. (2013). Tracking pathophysiological processes in Alzheimer’s disease: An updated hypothetical model of dynamic biomarkers. Lancet Neurology, 12(2), 207-216.

Shao, A., & Lin, Y. (2012). Socioeconomic and racial disparities in the epidemiology of Alzheimer’s disease. Journal of Geriatric Psychiatry and Neurology, 25(2), 118-124.

Cronk, B. L., & Lyngstad, T. H. (2019). Challenges in epidemiologic data collection and analysis. Journal of Public Health Data, 3(1).