Primarily, in understanding and applying certain nursing practices, various approaches are used. The methods applied may have different features; however, despite different procedures being used, they yield the expected results. For instance, both confounding and effect modification are models that influence epidemiologic studies. Meanwhile, for better medical experience, investigators should try and minimize their effects.
Confounding and effect modification are diversified. Generally, confounding refers to the inaccuracy in the expected measure of association that develops when the primary exposure of interest mingles with the other elements of the outcome. The exposure influences the unexpected variable, also disease outcome. On the contrary, effect modification is a situation where a variable is either positively or negatively affects the observed effect of a peril factor on the disease status. Various groups exhibit different risks of exposure (Holmes et al., 2011). Apparently, despite confounding and effect modification related to a population, they have unique characteristics. Confounding is a subject to how the treatment was assigned, while effect modification does not consider this.
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Both concepts have implications for epidemiologic studies. Putting into consideration that an independent variable adversely affects the dependent one, the existence of a confounding variable is a threat. There is a risk of imposing a hidden effect. This variable is associated with introducing bias and increasing variance in experiments (Knol & VanderWeele, 2012). Nevertheless, investigators can use several ways to curb these effects. They can do random samples, apply control variables, and counterbalance the confounding variable. In the presence of an effect modifier, the estimator shows an excellent correlation with the average weighted specific estimators (Munzel et al., 2017). Ultimately, the particular estimators will show a significant variation. Investigators can overcome their adverse effects by carefully examining the relationship separately for each level of the third variable.
In conclusion, confounding and effect modification concepts have distinct features that give them their identity in epidemiology. Despite their significance in determining various associations, they still pose side effects during studies. Investigators should identify the impact and diagnose them to enhance better control of diseases.
References
Holmes, M. V., Newcombe, P., Hubacek, J. A., Sofat, R., Ricketts, S. L., Cooper, J., ... & Smeeth, L. (2011). Effect modification by population dietary folate on the association between MTHFR genotype, homocysteine, and stroke risk: a meta-analysis of genetic studies and randomised trials. The Lancet , 378 (9791), 584-594.
Knol, M. J., & VanderWeele, T. J. (2012). Recommendations for presenting analyses of effect modification and interaction. International journal of epidemiology , 41 (2), 514-520.
Münzel, T., Sørensen, M., Gori, T., Schmidt, F. P., Rao, X., Brook, J., ... & Rajagopalan, S. (2017). Environmental stressors and cardio-metabolic disease: part I–epidemiologic evidence supporting a role for noise and air pollution and effects of mitigation strategies. European heart journal , 38 (8), 550-556.
