Measure of Effect
The importance of the measure of effect in nursing practice cannot be underestimated. It enables health practitioners to understand the association that exists between the outcomes and the prevailing risk factors that are key in prognostic and etiological medical research. Measures of effect calculated in absolute and relative terms are useful in making informed decisions (Friss and Seller, 2014). The significance of measuring effect gives the strength of the relationship observed between risk factors like blood pressure and a specific outcome like stroke (Krethong et al., 2008). Bothe relative terms for instance incidence, risk, and odds ratio and absolute terms like risk difference attribute rate are useful in decision making (Alexander et al, 2018). The measure of effect is useful in the practice of nursing as it improves service delivery to patients as the health personnel can make judgments based on calculated facts and evidence.
Tripepi et al. (2010) give examples of measures of effect. The first example is cohort research that was conducted in end-stage patients with renal disease, investigating the relationship between the high norepinephrine levels with the 3 years’ cardiovascular events that are fatal and non-fatal. The measure applied the calculation of the ratio of patients with cardiovascular on 75th percentile and below the minimum. It was found that the risk of cardiovascular was at the ratio of 1.61 within the probability of 95% thus the risk was found to be statistically significant.
A second example pointed out is an investigation of the effect of rosuvastatin on cardiovascular risk evens in patients under hemodialysis (Vinels and Kriebel, 2006). The investigation used a control and treatment group that is patients treated with rosuvastatin and the ones untreated using incidence rate ration to determine relative risk. The incidence rate of cardiovascular events was 4% less in patients treated with rosuvastatin compare to those on placebo (Schmidt and Kohlmann, 2008). The small risk reduction was thus statistically significant. The measure of effects, therefore, is very useful in making informed decisions.
Reference
Alexander, L., Lopes, B., Ricchetti-Masterson, K., & Yeatts, K. B. (2018). Common measures and statistics in the epidemiological literature. ERIC Notebook Periodical Second Edition No. 3. Retrieved from https://nciph.sph.unc.edu/tws/HEP_ERIC3/certificate.php
Friis, R. H., & Sellers, T. A. (2014). Measures of morbidity and mortality used in epidemiology. In Epidemiology for public health practice (5th ed.).
Jones & Bartlett. Review this chapter. Friis, R. H., & Sellers, T. A. (2014). Measures of effect. In Epidemiology for public health practice (5th ed.). Jones & Bartlett.Chapter 9 extends the discussion that began with Chapter 6 (which looked at ecologic, cross-sectional, and case-control study designs) by introducing additional measures that are useful in evaluating the potential implications of an exposure-disease association.
Krethong, P., Jirapaet, V., Jitpanya, C., & Sloan, R. (2008). A causal model of health-related quality of life in Thai patients with heart failure. Journal of Nursing Scholarship, 40(3), 254–260.
Schmidt, C. O., & Kohlmann, T. (2008). When to use the odds ratio or the relative risk? International Journal of Public Health, 53(3), 165–167.
Tripepi, G. Jager, K. J., Dekker, F. W. & Zoccali, C. (2010). Measures of effect in epidemiological research. Nephron Clinical Practice, 115(2), c91–c93.
Vineis, P., & Kriebel, D. (2006). Causal models in epidemiology: Past inheritance and genetic future. Environmental Health: A Global Access Science Source, 5, p. 2