Epidemiological studies are critical in providing insights into carrying out evidence-based healthcare practices. However, such studies may sometimes be vulnerable to errors, such as information bias, selection bias, confounding, and random errors that can negatively affect the finding’s validity (Boutron et al., 2019). Researchers should know how to address the biases and confounding aspects likely to be experienced for reliable generalizability and validity of study findings. Based on the information obtained from this week’s Learning Resources, a health issue regarding diabetes management and a population of elderly patients in a nursing home have been used to determine a practice gap, how the population treatment could be affected by having an awareness of bias and confounding in epidemiologic literature, two strategies researchers can use to minimize these types of bias in a study through study design or analysis considerations, and the effects these biases on the interpretation of results if not minimized.
The Selected Practice Gap
In the context of diabetes management among older adults in a nursing home, the practice gap considered involves the lack of a comprehensive, personalized diabetes management plan. Although diabetes is highly prevalent among older adults, the patients are mostly provided with generic treatments that do not consider each patient’s individual needs (Boutron et al., 2019). This contributes to poor glycemic control and a high risk of developing diabetes-related complications. Therefore, it is critical to consider comprehensive, personalized management plans among diabetic patients, especially older adults, to improve their outcomes and life quality.
The Impact of Awareness of Bias and Confounding in Epidemiologic Literature
The knowledge of bias and confounding is critical in interpreting epidemiological studies, especially in the case of diabetes management among elderly patients in a nursing home. For instance, it is necessary to understand that selection bias is usually experienced when the sample in a study is not accurately representing the targeted population, resulting in an inaccurate measure of association (Boutron et al., 2019). Therefore, based on the issue and population considered in this case, selection bias may be experienced if the study considers relatively healthier patients or individuals with better healthcare access. This can contribute to overestimating the effectiveness of various interventions, leading to wrong treatment decisions for individuals experiencing complex diabetic conditions.
Besides, information bias is experienced when errors are made during data collection, resulting in inaccurate information utilized in data analysis. As per the context of diabetes management among older adults in a nursing home, information bias can be experienced if the patients give wrong information regarding their medication compliance, dietary intake, and their level of glucose monitoring. There are several aspects that may contribute to the provision of inaccurate information among the selected patients, such as social desirability and memory impairment (Chen et al., 2023). The bias experienced in such a case can contribute to wrong findings during analysis, leading to incorrect conclusions.
Moreover, confounding is experienced when a third variable is associated with observations between two variables. As per the context regarding diabetes management among older adults in a nursing home, confounding can be experienced if the selected population has polypharmacy and comorbidities. For instance, if it is noted that there is a positive relationship between glycemic control and a particular diabetes medication, it can be considered confounding because a patient taking such medication is at a higher risk of experiencing comorbidities, thus affecting the outcomes (Chen et al., 2023). If such confounders are not accounted for, they may cause inaccurate conclusions, leading to ineffective treatment decisions.
Two Strategies Researchers can use to minimize these Types of Bias in Studies
There are numerous strategies that can be applied to minimize bias in a study. However, one of them is randomized controlled trials (RCTs). The study designs applied in RCTs are robust, thus minimizing selection bias. RCTs ensure that a particular sample represents the target population by allocating various interventions randomly and using rigorous inclusion criteria (Chen et al., 2023). This approach can be considered in older adults with diabetes in a nursing home to minimize selection bias, thus enhancing the study findings’ generalizability.
The other strategy that can be used to minimize bias is by using stratified sampling. Stratifying the study participants based on their functional status, comorbidities, or age can assist in addressing selection bias. This is achieved by ensuring that the sample used is representative of the target population. Therefore, a researcher can stratify older adults with diabetes in a nursing home based on their diverse characteristics to prevent selection bias.
The Effects These Biases on the Interpretation of Results if not minimized
If the biases discussed are not minimized, they can significantly impact the interpretation of the study results. One of the effects of such biases is misguided treatment decisions (Jordan & Troth, 2020). If biases are not addressed, they can result in wrong interpretations, leading to making ineffective decisions in treating patients, especially those with diabetes, as per the context made. This is because bias may lead to overestimation or underestimation of the effectiveness of treatment interventions. Secondly, if biases are not minimized, they may lead to poor allocation of resources (Jordan & Troth, 2020). For instance, if the biases lead to a wrong interpretation of the study results, more or a few resources, such as healthcare professionals or medications, may be allocated ineffectively.
Boutron, I., Page, M. J., Higgins, J. P., Altman, D. G., Lundh, A., Hróbjartsson, A., & Cochrane Bias Methods Group. (2019). Considering bias and conflicts of interest among the included studies. Cochrane Handbook for systematic reviews of Interventions, pp. 177–204. https://doi.org/10.1002/9781119536604.ch7
Chen, J., Dong, H., Wang, X., Feng, F., Wang, M., & He, X. (2023). Bias and debias in recommender system: A survey and future directions. ACM Transactions on Information Systems, 41(3), 1-39. https://doi.org/10.1145/3564284
Jordan, P. J., & Troth, A. C. (2020). Common method bias in applied settings: The dilemma of researching in organizations. Australian Journal of Management, 45(1), 3–14. https://doi.org/10.1177/0312896219871976