Respond to 
two colleagues  in one or more of the following ways:


· Offer and support an alternative perspective using readings from the classroom or from your own research in the Walden Library.

· Validate an idea with your own experience and additional research.

· Make a suggestion based on additional evidence drawn from readings or after synthesizing multiple postings.

· Expand on your colleagues’ postings by providing additional insights or contrasting perspectives based on readings and evidence.



Delee Ellwinger

Initial Post

Odds Ratio and Risk Ratio

Confusing odds ratio (OR) and risk ratio (RR) are common, and the terms are sometimes incorrectly used interchangeably (Labrecque et al., 2021). The findings from the odds and risk ratios are presented in the same way, leading to misunderstandings in interpretation. To add to the confusion, if the outcome is rare, the results of OR and RR are very similar. However, they do not provide the same analysis and cannot be used interchangeably (Norton et al., 2018).

Odds Ratio

The odds ratio determines if an exposure is protective or causative but does not determine risk (Curley, 2020). OR is not a predictor of the outcome but instead calculates the probability of an outcome in a stated population by dividing the occurrence number by the non-event number (Chatterjee et al., 2021) (Norton et al., 2018). Odds ratios are notorious for misinterpretation and should only be used in case-control studies (Labrecque et al., 2021). However, when used correctly, results greater than 1 point to the dependent variable connecting to the outcome (Curley, 2020).

Risk Ratio

The risk ratio determines the benefits or risks of exposure in cohort studies (Curley, 2020). RR tells us how likely the result is depending on exposure. It answers the question 
of how many times higher/lower the outcome’s risk depends on exposure. The risk ratio calculates the chance of it happening for the group by dividing the incident number by the total group population (Norton et al., 2018). Greater than one means there is an increased risk between exposure and disease. Less than one means benefits may increase from exposure.

Strengthen and Support Nursing Practice

Depending on the study, the odds ratio and risk ratio support nursing practice by ensuring the data collected is applicable to support best practice. The odds ratio is calculated in case-control studies; the risk ratio is calculated through cohort studies. Evidence-based practice must have confirmation of the benefit of the intervention (Schloemer et al., 2021). OR and RR provide information to help clinicians make informed decisions (Pantaleon, 2019). Health promotion and prevention programs also need accurate data to support recommendations and policies (Schloemer et al., 2021). Statistics influence program funding and implementation.  

Limitations of not Using Measures of Effect

While OR and RR have limitations, the absence of data restricts nursing practice (Su, 2021). Odds ratios may overestimate risk and cannot be used to compare the results of different studies because of the arbitrary nature of the variables (Norton et al., 2018). The risk ratio is easily understood, but without accurate primary data, RR can identify correlations where there are none (Dattani, 2023). Despite limitations, statistics and measures of effect must be used to ensure interventions have scientific backing. Arbitrary policies and recommendations confuse the public and ensure chaos and noncompliance ensue (Su, 2021).


Chatterjee, A., Woodruff, H., Wu, G., & Lambin, P. (2021). Limitations of only reporting the odds ratio in the age of precision medicine: A deterministic simulation study. 
Frontiers in Medicine, 8

to an external site.

Curley, A. L. C. (Ed.). (2020). 
Population-based nursing: Concepts and competencies for advanced practice (3rd ed.). Springer.

Dattani, S. (2023). Risk ratios, odds ratios, risk differences: How do researchers calculate the risk from a risk factor? 
Our World in Data. 

to an external site.

Labrecque, J. A., Hunink, M. M. G., Ikram, M. A., & Ikram, M. K. (2021). Do case-control studies always estimate odds ratios? 
American Journal of Epidemiology
190(2), 318–321. 

to an external site.

Norton, E. C., Dowd, B. E., & Maciejewski, M. L. (2018). Odds ratios–Current best practice and use. 
JAMA, 320(1), 84-85. 

to an external site.

Schloemer, T., Bock, F., Schroder-Back, P. (2021). Implementation of evidence-based health promotion and disease prevention interventions: theoretical and practical implications of the concept of transferability for decision-making and the transfer process. 
Springer, 64(5), 534-543. 

to an external site.

Su, Z. (2021). Rigorous policy-making amid COVID-19 and beyond: Literature review and critical insights. 
International Journal of Environmental Research and Public Health, 18(23). 

to an external site.



 ReplyReply to Comment



Miriam Edouard

                                        Epidemiologic Measures in Population Health

Mortality and morbidity

In caring for the homeless population mortality and morbidity measures play a crucial role in strengthening nursing practice, particularly in the context of caring for homeless individuals. Here’s how these epidemiological measures can support homeless healthcare and the limitations of not using them.

Support Nurse Practice:

Targeted Intervention: Understanding mortality and morbidity in my practice of homeless individuals, allows the practice to identify health issues and tailor interventions accordingly. For instance, in my role as a Nurse Practitioner (NP) working with the homeless population, leveraging such epidemiological measures enables me to identify prevalent health issues and tailor interventions accordingly.

For instance, drawing insights from the study “Mortality, Morbidity, and Health in Developed Societies: a Review of Data Sources” by Wunsch et al. (2018), if morbidity measures reveal a high prevalence of chronic conditions such as cardiovascular disease (CVD) among homeless individuals, I can prioritize screening, medication education, and treatment options accordingly. This proactive approach not only addresses immediate health needs but also contributes to preventive care and overall health promotion among this vulnerable population.

Limitations of Not Using Measures of Effect:

Neglecting to utilize measures of effect in nursing practice presents significant limitations, particularly in terms of missed opportunities for prevention and intervention. For example, mortality and morbidity data are vital for identifying health risks, trends, and opportunities for early prevention and intervention.

For instance, drawing insights from the study “Quality Improvement Focused Morbidity and Mortality Rounds: An Integrative Review” by Churchill et al. (2020), the absence of mortality and morbidity data can lead NPs to overlook critical health trends and patterns. This oversight may result in delayed diagnosis or disease progression, ultimately compromising patient outcomes and exacerbating healthcare disparities

Examples from Scholarly Literature:

2. The article “Mortality, Morbidity, and Health in Developed Societies: a Review of Data Sources” by Wunsch et al. (2018) is an example of how the mortality and morbidity data strengthen homeless intervention practices by providing insight into chronic disease among homeless individuals.

2. The article “Quality Improvement Focused Morbidity and Mortality Rounds: An Integrative Review” by Churchill et al. (2020) is another example of the limitations of not using  measures of effect can negatively impact promotion and engagement in quality improvement (QI) and patient safety,




Churchill, K. P., Murphy, J., & Smith, N. (2020). Quality improvement focused morbidity and mortality                       rounds: An integrative review. 

to an external site.

Curley, A. L., Ed. (2020). Measures of Morbidity and Mortality Used in Epidemiology. In 
Population-based                nursing, third edition: Concepts and competencies for advanced practice (3rd ed., pp. 91–131). Springer Publishing Company.

Wunsch, G., & Gourbin, C. (2018). Mortality, morbidity, and health in developed societies: A review of                    data sources. 

to an external site.


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