NURS 8310 Week 2: Discussion Descriptive Epidemiology

NURS 8310 Week 2 Assignment

By Day 3
Post a cohesive response that addresses the following:

  • Evaluate your selected health problem in the population you identified by describing three to five characteristics related to person, place, and time. NURS 8310 Week 2 Discussion Descriptive Epidemiology. NURS 8310 Week 2 Discussion Descriptive Epidemiology.
  • Appraise the data sources you utilized by outlining the strengths and limitations of each.
  • Discuss two methods you could use to collect raw data to determine the descriptive epidemiology of your health problem, Determine how these methods would influence the completeness of case identification as well as the case definition/diagnostic criteria used.
    Read a selection of your colleagues’ responses. NURS 8310 Week 2 Discussion Descriptive Epidemiology.

Sample Solution

Descriptive Epidemiology Discussion in Week 2

Descriptive epidemiology provides valuable information for disease prevention, intervention design, and further research. Person, place, and time are central to epidemiology research, implying the individuals affected by a specific condition, where that specific incidence occurred, and when it occurred (Beghi et al., 2020). These three concepts attempt to analyze any existing patterns to find a possible solution. As a result, this discussion aims to apply epidemiologic concepts to Tuberculosis in African Americans.

Evaluation of the Health Problem

Tuberculosis is a disease that disproportionately affects populations, which can be attributed to various health disparities. According to the CDC (2020), African Americans or non-Hispanic blacks are eight times more likely to have the condition than non-Hispanic whites. A person’s characteristics include race, gender, age, socioeconomic status, and biological characteristics such as immune status. Age is taken into account because health-related events vary with age. Age groups are considered for TB and other diseases to detect data patterns related to age. In some cases, females have higher rates of illness than males and vice versa. As previously stated, race influences TB rates and this population is more susceptible to the disease.

The characteristics of time include whether or not the frequency of the condition has changed over time. Indeed, the prevalence of tuberculosis among African Americans has decreased from 7 to 3 cases per 100,000 people in the last decade (Marks et al., 2019). Another time characteristic is whether or not the disease frequency varies seasonally; diabetes, however, does not vary seasonally. The third characteristic is whether the condition changes over time, as in the case of an outbreak. The relevant place characteristic includes whether the problem occurs in a specific geographical location and a location relevant to the condition’s occurrence, such as the location of the report or diagnosis. The other distinguishing feature is a location category such as non-institutional or institutional, foreign or domestic, rural or urban. NURS 8310 Week 2 Discussion Descriptive Epidemiology.

Data Source Appraisal

Data on tuberculosis was obtained from a variety of sources. The Centers for Disease Control and Prevention’s National Health Interview Survey was one of the sources (NHIS). This data source has several benefits and drawbacks. One of the benefits is the continuous collection of data and availability. It also provides prevalence and incidence data for various health conditions (Blumberg, 2020). This source also includes a nationwide sample and data on the condition’s associated risk factors. However, this source has drawbacks, such as a five-year delay in data availability and the possibility of huge sampling errors when estimating small populations. The National Notifiable Diseases Surveillance System (NNDSS) and the Morbidity and Mortality Weekly Report were also sources (MMWR). One advantage is that data is constantly available and collected. However, it may be incompletely reported and only represents the number of events. NURS 8310 Week 2 Discussion Descriptive Epidemiology.

Data Collection

Some methods I would use to collect raw data include a survey asking participants if they had any TB cases in their families. While this data collection can be effective, it may be incomplete because some people will not tell the truth and lie about their condition (Yap et al., 2018). The implication is that it will also impact case definition because fewer cases will be reported. I would also look for trends in data from patient records. Because electronic health record data is usually accurate, this method would make the data more complete. As a result, it will improve case definition.

Conclusion

Descriptive epidemiology is essential for a comprehensive discussion of the nature of a condition in populations. As a result, descriptive epidemiology concepts were applied in the case of tuberculosis among African Americans. Aspects related to people, place, and time have all been investigated.

References

Beghi, E., Giussani, G., & Poloni, M. (2020). Descriptive epidemiology and related neurobiology. Oxford Textbook of Neurologic and Neuropsychiatric Epidemiology, 331.

Blumberg, S. (2020). An Overview of the Redesigned National Health Interview Survey. APHA’s 2020 VIRTUAL Annual Meeting and Expo (Oct. 24-28). American Public Health Association.

CDC. (2020). TB and Black or African American Persons. https://www.cdc.gov/tb/topic/populations/tbinafricanamericans/default.htm

Marks, S. M., Katz, D. J., Davidow, A. L., Pagaoa, M. A., Teeter, L. D., & Graviss, E. A. (2019). The impact of HIV infection on TB disparities among US-born Black and White tuberculosis patients in the United States. Journal of public health management and practice: JPHMP. https://dx.doi.org/10.1097%2FPHH.0000000000000949.

Yap, P., Tan, K. H. X., Lim, W. Y., Barkham, T., Tan, L. W. L., Mark, I., … & Chee, C. B. E. (2018). Prevalence of and risk factors associated with latent tuberculosis in Singapore: a cross-sectional survey. International Journal of Infectious Diseases, 72, 55-62. https://doi.org/10.1016/j.ijid.2018.05.004