Treatment of Population Issue can be Affected by Awareness of Bias and Confounding in Epidemiologic Literature

Selected Practice Gap

            Prescription drug abuse is one of the fastest-growing public health crises classified by the Centers for Disease Control and Prevention [CDC] (2022) as an epidemic. It refers to using prescription medications outside the prescribed use (Schepis et al., 2020). Despite some decrease in the use of illegal drugs, prescription drug abuse has increased, especially among young people (Schepis et al., 2020). Prescription drug abuse is multifaceted and complex and requires a clear understanding to propose an effective response in clinical practice (Shupp et al., 2020). The prevalence of prescription drug misuse and abuse is highest among young adults compared to the rest of the population (McCabe et al., 2022). This public health issue is significant because prescription drug abuse can have detrimental immediate and long-term physical and mental health consequences, including morbidity and mortality (Al Rawwad et al., 2023; McCabe et al., 2022).

Treatment of Population Issue can be Affected by Awareness of Bias and Confounding in Epidemiologic Literature

           There are various forms of bias that exist in epidemiologic literature and that can affect understanding how to address this practice gap. Jager et al. (2020) write that sampling bias can occur in epidemiological research. This is a form of selection bias where the study does not use representative samples which can lead to distortion of the information collected. Specifically with this issue of prescription drug abuse in young adults, Hudgins et al. (2020) believe that the data underestimates the true prevalence due to inadequate screening for substance use and the misunderstanding among youth that prescription drug misuse is safer than using illicit drugs. There is also evidence of focusing disproportionately on male and white participants in some of the research which may skew the data collected on interventions to address this problem (Crowley et al., 2019).

           Another form of bias is confounding. This is where there is bias that occurs because of variables that are not being accounted for in the study. Confounder bias occurs when an analysis fails to adequately control for a variable that may affect the exposure and outcome. In the research by Tattan-Birch et al. (2021), the researchers note that there are many confounding variables in substance use and substance abuse research that there are confounders such as personality traits that are not incorporated into the research on this topic. Failure to address these variables affects the value of the information obtained after the benefit of interventions to address substance use or even the potential risk factors that influence the condition.

           Information bias is the type of bias that arises from obtaining or confirming study measurements. Under information bias, there are three categories which are self-reporting bias, measurement error bias, and confirmation bias. Self-reporting is an important approach used in epidemiological research to gather data (Althubaiti, 2019). However, self-reporting can introduce issues with recall as well as social desirability bias where individuals want to provide what they perceive to be the right or socially acceptable answers to the questions (Althubaiti, 2019).

           The final category of bias present in this type of research is attrition bias. Some epidemiological studies take time, and it is possible for there to be withdrawals or dropouts especially in longitudinal studies (Jager et al., 2020). Imagine a study where you are evaluating an intervention or treatment to reduce prescription drug use. Those with compliance issues are more likely to leave the study leaving the more determined and motivated individuals or those with support in the study which may possibly lead to overestimation of the effect of the treatment on reducing prescription drug abuse.

Strategies to Minimize Bias in Studies (Design or Analysis Considerations)

           One of the approaches that can be used to overcome self-reporting bias is to validate the self-reporting instrument before implementing data collection. Internal validation may include collecting responses from a self-reporting instrument and comparing it with other data collection methods such as laboratory measurements (Althubaiti, 2019). If laboratory data is not possible due to cost and time, external validation may be used to examine the validity of the self-reporting instruments. If the issue is recall bias, some options to address the problem include selecting