Measurement of Health Outcomes Assignment

Measurement of Health Outcomes Assignment

Health Outcome Measured at the Nominal Level

In public health, one often assessed outcome at the nominal level is the individual’s vaccination status. Nominal variables classify data into discrete groups without any inherent ordinality or numerical magnitude (Mishra et al., 2018). Regarding vaccination status, the variable indicates whether an individual has obtained a certain vaccine or not. Vaccination status is commonly assessed using self-reporting or electronic health data. People can be classified into two categories: those who have received a vaccination and those who have not. The process of documenting this nominal variable entails the allocation of codes or labels, such as “1” to indicate vaccination and “0” to indicate non-vaccination.

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Descriptive statistics suitable for nominal data comprise frequency counts and percentages (Kaliyadan & Kulkarni, 2019). These metrics provide information about the number or percentage of individuals in each group, providing a concise summary of the distribution of vaccination status within the community or population of interest. A bar or pie chart might be appropriate for visually depicting immunization status. According to Zhao and Gaschler (2022), a bar chart can be used to illustrate the frequency rate (for instance, vaccinated and unvaccinated persons), while a pie chart can visually indicate the relative proportion of each category in relation to the entire population.

Health Outcome Measured at the Ordinal Level

In healthcare settings, there are several outcomes that are assessed using an ordinal scale, such as the severity of pain. Ordinal variables classify data based on a meaningful sequence but do not have exact intervals between groups (Kaliyadan & Kulkarni, 2019). Regarding pain intensity, individuals have the option to express their pain levels on a scale ranging from 0 to 10, with higher numbers indicating greater severity of pain. Pain intensity is typically measured by self-reporting when individuals subjectively evaluate and convey their levels of pain. Typically, this process involves utilizing standardized pain scales, enabling individuals to assess and rate their pain levels using a graduated scale corresponding to their perceived intensity.

Measuring pain intensity at the ordinal level requires assigning numerical values according to the hierarchy of severity. For example, a pain scale ranging from 0 to 10 would include ratings such as “1” to indicate mild pain, “5” to indicate moderate pain, and “10” to indicate severe pain. This arrangement preserves the significant order while recognizing the lack of precise intervals between the values. Descriptive statistics appropriate for ordinal data encompass metrics that represent both the central tendency and the dispersion of the variable. The concepts of median and interquartile range are specifically applicable to the measurement of pain intensity. According to Bensken et al. (2021), the median represents the middle value of the data, while the interquartile range indicates the spread of the pain intensity scores within the middle 50% of the data, providing a strong description of the distribution of the data.

A box plot is appropriate for visually representing pain intensity assessed at the ordinal level. It graphically represents the median, quartiles, and any outliers (Park et al., 2022), offering a summary of the distribution of pain intensity scores. This visual representation facilitates a rapid comprehension of the central tendency and variability within the ordinal variable.

Health Outcome Measured at the Ratio Level

In healthcare, “blood pressure” is a health outcome that is monitored at the interval or ratio level and is of crucial significance to both my community and area of expertise. Blood pressure is a physiological parameter that signifies the intensity of blood pushing against the arterial walls, serving as a crucial marker for cardiovascular well-being. Healthcare workers use a sphygmomanometer, consisting of an upper arm cuff and a pressure gauge, to assess blood pressure. The assessment entails two parameters: systolic pressure (the pressure in the arteries during cardiac contractions) and diastolic pressure (the pressure in the arteries during the resting phase between contractions) (Rehman et al., 2024). The outcome is denoted in millimeters of mercury (mmHg), establishing an interval or ratio scale that assigns significance to the differences between values and permits the use of ratios due to the existence of a genuine zero point (lack of pressure).

Recording blood pressure entails capturing both the systolic and diastolic measurements. For instance, a blood pressure measurement of 120/80 mmHg indicates a systolic pressure of 120 and a diastolic pressure of 80. This simultaneous recording depicts the all-encompassing nature of blood pressure evaluation. Descriptive statistics suitable for blood pressure data at the interval or ratio level encompass measures of central tendency, such as the mean, which represents the average of a given parameter (such as blood pressure) in a specific population (Hurley & Tenny, 2021). Furthermore, metrics such as the standard deviation or range can provide valuable information about the spread of blood pressure values, assisting in the detection of possible health hazards.

A box-and-whisker plot is a highly effective graphical representation for displaying blood pressure data. This map graphically illustrates the distribution of blood pressure data, including the median, quartiles, and any outliers. These visualizations provide a thorough summary of the average and spread of data, making it easier to identify trends or patterns that could be important for making healthcare decisions (Hu, 2020).

 

 

Epidemiologic Data on Health Issue

Epidemiological information regarding the COVID-19 outbreak is provided in the first article, “Epidemiological data from the COVID-19 outbreak, real-time case information” by Xu et al. (2020). To present current case information regarding the outbreak, the article compiles data from a wide range of data sources. The writers extend appreciation to people and institutions everywhere who have demonstrated the ability and willingness to disclose data promptly and transparently. The paper emphasizes how critical high-quality data is to decision-making and proactive planning of subsequent actions and future directions. Regarding COVID-19 in the United States, the second article, “Missing science: A scoping study of COVID-19 epidemiological data in the United States” by Bhatia et al. (2022), focuses on the data that are currently lacking. The paper emphasizes the need for more thorough and uniform data collection and reporting to better understand how the epidemic has affected various communities. The authors stress that data-driven decision-making is crucial to combat the epidemic and lessen its effects.

These articles highlight the value of epidemiological data in comprehending and resolving health-related problems. The COVID-19 pandemic has highlighted how crucial data is for guiding decisions and lessening the pandemic’s effects. The articles stress the importance of high-quality data reporting and gathering to better understand how health issues affect various groups and to guide evidence-based practice. This data-driven approach can help nurses practice more effectively, enhance patient outcomes, and raise awareness of the importance of nursing care in general.

 

References

Bensken, W. P., Pieracci, F. M., & Ho, V. P. (2021). Basic Introduction to Statistics in Medicine, Part 1: Describing Data. Surgical Infections, 22(6), 590–596. https://doi.org/10.1089/sur.2020.429

Bhatia, R., Sledge, I., & Baral, S. (2022). Missing science: A scoping study of COVID-19 epidemiological data in the United States. PLOS ONE, 17(10), e0248793. https://doi.org/10.1371/journal.pone.0248793

Hu, K. (2020). Become Competent within One Day in Generating Boxplots and Violin Plots for a Novice without Prior R Experience. Methods and Protocols, 3(4). https://doi.org/10.3390/mps3040064

Hurley, M., & Tenny, S. (2021). Mean. PubMed; StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK546702/

Kaliyadan, F., & Kulkarni, V. (2019). Types of variables, descriptive statistics, and sample size. Indian Dermatology Online Journal, 10(1), 82–86. https://doi.org/10.4103/idoj.IDOJ_468_18

Mishra, P., Pandey, C., Singh, U., & Gupta, A. (2018). Scales of measurement and presentation of statistical data. Annals of Cardiac Anaesthesia, 21(4), 419–422. ncbi. https://doi.org/10.4103/aca.aca_131_18

Park, J. H., Lee, D. K., Kang, H., Kim, J. H., Nahm, F. S., Ahn, E., In, J., Kwak, S. G., & Lim, C.-Y. (2022). The principles of presenting statistical results: Figures. Korean Journal of Anesthesiology. https://doi.org/10.4097/kja.21508

Rehman, S., Hashmi, M. F., & Nelson, V. L. (2024). Blood Pressure Measurement. PubMed; StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK482189/#:~:text=%5B5%5D%20This%20method%20utilizes%20a

Xu, B., Gutierrez, B., Mekaru, S., Sewalk, K., Goodwin, L., Loskill, A., Cohn, E. L., Hswen, Y., Hill, S. C., Cobo, M. M., Zarebski, A. E., Li, S., Wu, C.-H., Hulland, E., Morgan, J. D., Wang, L., O’Brien, K., Scarpino, S. V., Brownstein, J. S., & Pybus, O. G. (2020). Epidemiological data from the COVID-19 outbreak, real-time case information. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-0448-0

Zhao, F., & Gaschler, R. (2022). Graph schema and best graph type to compare discrete groups: Bar, line, and pie. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.991420

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Unit 2

You will write a 3 page Microsoft Word document (excluding the title and reference pages) which contains:

  1. Select a health outcome of interest to your community or area of practice that is measured at the nominal level.
    1. Briefly define the variable.
    2. Specify how the measurement is obtained.
    3. Describe how the measurement should be recorded.
    4. Specify what descriptive statistics should be used to describe the variable.
    5. Select an appropriate graphical display to depict the variable.
  2. Select a health outcome of interest to your community or area of practice that is measured at the ordinal level.
    1. Briefly define the variable.
    2. Specify how the measurement is obtained.
    3. Describe how the measurement should be recorded.
    4. Specify what descriptive statistics should be used to describe the variable.
    5. Select an appropriate graphical display to depict the variable.
  1. Select a health outcome of interest to your community or area of practice that is measured at the interval or ratio level.
    1. Briefly define the variable.
    2. Specify how the measurement is obtained.
    3. Describe how the measurement should be recorded.
    4. Specify what descriptive statistics should be used to describe the variable.
    5. Select an appropriate graphical display to depict the variable.
  1. Find two articles that provide epidemiologic data about a health issue. Then discuss how decisions are made based on the data and how the data advances the practice, understanding, and value of nursing.

Your writing assignment should:

  • follow the conventions of Standard English (correct grammar, punctuation, etc.)
  • be well ordered, logical, and unified, as well as original and insightful
  • display superior content, organization, style, and mechanics; and;
  • use APA 7th edition formatting and citation style.
  • a minimum of five peer-reviewed evidence-based journal articles with findings relevant to levels of measure and epidemiologic data decision-making.

 

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