week6 epi

paraphrase with no plagiarism or AI use: 

The practice gap

The identified practice gap is a lack of routine depression screening among the elderly in primary care settings. According to Garcia et al. (2022), depression remains one of the leading causes of disability in the US and is largely associated with poor chronic disease management, functional disability, increased healthcare costs, and elevated mortality. In the primary care settings, depression among older Americans is undiagnosed in more than half of patients presenting with depressive symptoms. Older adults, particularly men, racial minorities, and non-English speakers with depressive symptoms, are more likely to be undiagnosed with depression at primary care settings, increasing the risk of adverse outcomes (Garcia et al., 2022). Therefore, routine screening of depression among older adults in primary care could strengthen depression detection and treatment, reducing disparities in mental healthcare access.

The impact of bias and confounding in epidemiologic research

1. Selection Bias 

Selection bias happens when participants selected for a study are not representative of the target population, resulting in skewed results. In depression screening among older Americans in primary care, male participants, including ethnic minorities and non-English speakers, are less likely to participate in this study due to selection bias, as discussed by Enzenbach et al. (2019), where low participation could possibly affect the generalizability of the findings. Thus, if an epidemiological investigation on depression screening excludes high-risk groups like non-English speakers and those who are less likely to visit primary care clinics, then the findings would underestimate depression prevalence and overestimate screening effectiveness in real-world settings.

1. Information Bias

Information bias emerges when data collection procedures and interpretation are influenced by pre-determined and preconceived beliefs (Althubaiti, 2016). For instance, in primary care settings, practitioners may overlook depressive symptoms in older adults, associating them with aging instead of mental disorders. Also, if a study on depression exclusively depends on a self-reported survey without structured screening and interviews, underreporting of depression is more likely to occur, especially in populations affected by stigma and socioeconomic status. Information may usually result in an underestimation of depression prevalence and an overestimation of the effectiveness of diagnostic practices at primary care settings.

1. Confounding

Confounding happens when an external factor or variable influences depression and its outcomes. For instance, socioeconomic status (SES) might affect depression rates and healthcare access. If researchers fail to adjust for SES, they might incorrectly attribute reduced mental health outcomes to a lack of screening at primary settings instead of structural barriers. At the same time, comorbid conditions like chronic pain and dementia can mask depression, resulting in misclassification bias if not properly controlled.

Strategies to minimize bias

1. Study design: Randomized sampling and standardized tools

To minimize selection bias, researchers should use randomized sampling strategies that include diverse populations, such as older adults from varying socioeconomic and ethnic backgrounds. As well, employing standardized depression screening tools like PHQ-9 or GDS rather than relying on clinician judgment alone can reduce information bias by ensuring consistent and objective symptom assessment (Siniscalchi et al., 2020).

1. Analytical adjustments: Multivariable regression and stratification

During analysis, researchers can control for confounding by using multivariable regression models to adjust for factors like age, SES, and comorbidities. Stratifying results by demographic subgroups, such as race and gender, can also reveal disparities in screening effectiveness. Propensity score matching may further reduce bias by balancing comparison groups based on observed characteristics (Lalani et al., 2020).

Effects of Unaddressed Bias on Study Interpretation

Finally, if researchers do not minimize bias and confounding, study results may misrepresent depression prevalence and screening efficacy. Selection bias could lead to overestimating screening success in healthier populations, while information bias may result in underdiagnosis in high-risk groups. Unadjusted confounders like SES or comorbidities may falsely attribute outcomes to screening practices rather than systemic barriers. These distortions could lead to ineffective policies, perpetuating gaps in mental healthcare for vulnerable older adults. Addressing bias is essential for generating reliable and valid evidence to guide clinical practice (Rahmani et al., 2021).

References

Althubaiti, A. (2016). Information bias in health research: Definition, pitfalls, and adjustment methods. Journal of Multidisciplinary Healthcare, 211. https://doi.org/10.2147/jmdh.s104807Links to an external site.

Enzenbach, C., Wicklein, B., Wirkner, K., & Loeffler, M. (2019). Evaluating selection bias in a population-based cohort study with low baseline participation: The LIFE-Adult-Study. BMC Medical Research Methodology, 19 (1), Article 135. https://doi.org/10.1186/s12874-019-0779-8Links to an external site.

Garcia, M. E., Hinton, L., Neuhaus, J., Feldman, M., Livaudais-Toman, J., & Karliner, L. S. (2022). Equitability of depression screening after implementation of general adult screening in primary care. JAMA Network Open, 5(8), e2227658. https://doi.org/10.1001/jamanetworkopen.2022.27658Links to an external site.

Khalili, P., Nadimi, A. E., Baradaran, H. R., Janani, L., Rahimi-Movaghar, A., Rajabi, Z., Rahmani, A., Hojati, Z., Khalagi, K., & Motevalian, S. A. (2021). Validity of self-reported substance use: Research setting versus primary health care setting. Substance Abuse Treatment, Prevention, and Policy, 16 (1), Article 66. https://doi.org/10.1186/s13011-021-00398-3Links to an external site.

Lalani, N., Jimenez, R. B., & Yeap, B. (2020). Understanding propensity score analyses. International Journal of Radiation Oncology, Biology, Physics, 107(3), 404-407. https://doi.org/10.1016/j.ijrobp.2020.02.638Links to an external site.

Siniscalchi, K. A., Broome, M. E., Fish, J., Ventimiglia, J., Thompson, J., Roy, P., Pipes, R., & Trivedi, M. (2020). Depression screening and measurement-based care in primary care. Journal of Primary Care & Community Health, 11. https://doi.org/10.1177/2150132720931261Links to an external site.