Measures Used in Epidemiology
One important application of epidemiology is to identify factors that could increase the likelihood of a certain health problem occurring within a specific population. Epidemiologists use
measures of effect to examine the association or linkage in the relationship between risk factors and emergence of disease or ill health. For instance, they may use measures of effect to better understand the relationships between poverty and lead poisoning in children, smoking and heart disease, or low birth weight and future motor skills. The following are some common measures used in epidemiology:
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Odds ratio: The odds ratio quantifies the association between an independent variable (exposure) and a dependent variable (outcome). It is calculated as the odds that an effect will occur given the presence or exposure to a studied variable, compared to the odds when there is no exposure (e.g., lung cancer and smoking)
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Risk ratio (also called relative risk): Also quantifies the association between an independent variable and a dependent variable. The risk of an effect occurring in one population versus another population (e.g., preeclampsia in women <35 versus >35). Risks greater than one suggest that exposure to a given variable is associated with an increase in the risk of the outcome, and a risk ratio of less than one indicates that the exposure is associated with a decrease in the risk of the outcome.
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Mortality: Measure of deaths in a particular population during a specified time interval. If this is attributed to a specific cause, it is referred to as cause-specific mortality.
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Morbidity: Measure of instances of illness or disability in a population from a given cause (e.g., heart disease) during a specified time interval
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Incidence: The occurrence of new cases of an effect or disease in a population over a defined time period relative to the size of the population at risk (e.g., new cases of COVID-19 in a population over a 7-day period/1000 people)
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Prevalence: The number of all cases of an effect or disease, not just new ones, in a population at a given time relative to the size of the population (e.g., number of people with autism/1000)
What is the significance of these measures of effect for nursing practice? In this Discussion, you will consider this pivotal question.
Resources
Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.
· Curley, A. L. C. (Ed.). (2024).
Population-based nursing: Concepts and competencies for advanced practice (4th ed.). Springer.
· Chapter 4, “Epidemiological Methods and Measurements in Population-Based Nursing Practice: Part I” (pp. 68-105)
· Friis, R. H., & Sellers, T. A. (2021).
Epidemiology for public health practice (6th ed.). Jones & Bartlett.
· Chapter 3, “Measures of Morbidity and Mortality Used in Epidemiology”
· Chapter 9, “Measures of Effect”
To prepare:
· Select item 1, 2, or 3 to use for this Discussion. Consider the definitions, differences, and utility of the two terms listed under your item selection. Your response will need to include both terms in the item selected.
1. Odds ratio and risk ratio
2. Mortality and morbidity
3. Incidence and prevalence
· Consider how these epidemiologic measures strengthen and support nursing practice.
· Assess practice limitations of not using these measures in nursing practice.
· Conduct additional research in the Walden Library and other credible resources, and then locate two examples in the scholarly literature that support your insights.
By Day 3 of Week 5
Post a cohesive scholarly response that addresses the following:
· Explain how your selected measures of effect strengthen and support nursing practice. Provide at least two specific examples from the literature to substantiate your insights.
· Assess limitations of
not using measures of effect in nursing practice.
By Day 6 of Week 5
Respond to
at least two colleagues on
two different days in one or more of the following ways:
· Ask a probing question, substantiated with additional background information, evidence or research.
· Share an insight from having read your colleagues’ postings, synthesizing the information to provide new perspectives.
· 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.
RESPOND TO THIS DISCUSSION POST
Mortality and Morbidity
Mortality and Morbidity are two key terms used in Epidemiology to define the numbers of deaths and the frequency of illness respectively (Friis & Sellers, 2021). Mortality rate is the number of deaths in a population over a specific time, usually expressed as deaths per unit population (Curley et al., 2024). Meanwhile the morbidity rate is the frequency of disease within a population over a specific time, usually expressed as a percentage, a proportion, or a rate per unit population (Curley et al., 2024).
How Mortality and Morbidity Strengthen and Support Nursing Practice
Epidemiological surveillance, whether in a large community or within a healthcare facility, provides important information on health outcomes. Mortality and morbidity surveillance is necessary to understand the impact and find ways to improve public health.
Moreover, mortality rates compare death rates among and within populations therefore providing evaluation and comparing data within the populations (Curley et al., 2024). Likewise, morbidity rates can help us to compare risk factors, prevent and manage modifiable risk factors, and decrease mortality rates. These measures strengthen nursing practices such as surveillance systems to reduce hospital-acquired infections and conditions, find innovation, develop policies, and protocols, and evaluate the system.
For example, about 70% of urinary tract infections (UTIs) developed in the hospital are associated with a urinary catheter, and catheter-associated urinary tract infections (CAUTIs) are associated with increased mortality and morbidity as well as healthcare costs (The Centers for Disease Control and Prevention, n.d.). Mitchell et al. (2016) evaluated incidence, mortality, and length of hospital-acquired UTIs, and hospital stay (Mitchell et al., 2016). In this large multi- state model, the authors found that the length of stay was up to four days due to UTIs, and the incidence of CAUTIs was related to antimicrobial resistance organisms. The study further suggested that surveillance and intervention to reduce CAUTIs are necessary. Likewise, Van Decker et al. (2021) implemented a bundle care approach to reduce CAUTIs over five years in an acute care hospital (Van Decker et al., 2021). Their bundle included processes for insertion and maintenance of Foley catheters, catheter indications, appropriate testing for CAUTIs, alternatives to indwelling devices, and sterilization techniques. With these interventions, the number of CAUTIs decreased from 83% to 33% over the five years. Therefore, mortality and morbidity surveillance of HAIs such as CAUTIs can strengthen nursing practice to reduce such incidences.
Practice Limitation of Not Using These Measures in Nursing Practice
It is important to have accuracy in reporting data for mortality and morbidity, as healthcare workers should know what to report, provide diagnosis correctly, and report requirements (Curley et al., 2024). Not having surveillance data or the lack of available data makes it harder to find ways for improvement in nursing practice, and to assess the measures of success. The Centers for Medicare and Medicaid (CMS) penalizes acute general care hospitals applicable to HAI reduction programs based on quality and safety care scoring system (Vsevolozhskaya et al., 2021). The CDC calculates a standardized infection rate for each facility based on the facility’s presumed characteristics and types of patients. Furthermore, if mitigation or risk adjustment is not calculated or is not adjusted, it can lead to inaccuracy. Therefore, hospitals submit their observed HAI cases, as well as data on the population at risk (i.e., the denominator) for risk adjustment, through the National Healthcare Safety Network, both can be misleading if not taken carefully. Researchers, quality leaders, and bedside caregivers play key roles in the success of quality measurement by observing and recording the events, vigilance, surveillance, compliance, and adherence to evidence-based guidelines and documentation.
References
Curley, A. L. C., Niedz, B. A., & Erikson, A. E. (2024).
Population-based nursing: Concepts and competencies for advanced practice (4th ed.). Springer
.
Friis, R. H., & Sellers, T. A. (2021).
Epidemiology for public health practice (6th ed.). Jones & Bartlett.
Mitchell, B. G., Ferguson, J. K., Anderson, M., Sear, J., & Barnett, A. (2016). Length of stay and mortality associated with healthcare-associated urinary tract infections: a multi-state model.
J Hosp Infect,
93(1), 92-99.
to an external site.
The Centers for Disease Control and Prevention. (n.d.).
Urinary Tract Infection.
https://www.cdc.gov/uti/hcp/clinical-safety/Links to an external site.
Van Decker, S. G., Bosch, N., & Murphy, J. (2021). Catheter-associated urinary tract infection reduction in critical care units: a bundled care model.
BMJ Open Qual,
10(4).
to an external site.
Vsevolozhskaya, O. A., Manz, K. C., Zephyr, P. M., & Waters, T. M. (2021). Measurement matters: changing penalty calculations under the hospital acquired condition reduction program (HACRP) cost hospitals millions.
BMC Health Serv Res,
21(1), 131.
to an external site.
