NURS 8310 BLOG: CRITIQUING SOURCES OF ERROR IN POPULATION RESEARCH TO ADDRESS GAPS IN NURSING PRACTICE

Week 6: Main Post

          Medication adherence is the extent to which individuals take their medications as prescribed by healthcare professionals (Liu et al., 2023). The commonly accepted threshold for adherence is often considered 80% or more of prescribed medicines taken (Emadi et al., 2022). Medication nonadherence presents a significant public health challenge, particularly among older patients with multimorbidity and polypharmacy (Liu et al., 2023). Emadi et al. (2022) reported that > 50 % of elderly patients with chronic conditions are non-adherent to their medication regimen.

          The selected practice gap is the absence of a standardized staff education program for assessing and enhancing medication adherence among the elderly. This gap suggests that healthcare systems need more structured approaches to educate staff on effectively evaluating and improving medication adherence in older adults. Given the complexities associated with aging, such as cognitive decline, polypharmacy, and comorbidities, ensuring medication adherence in this demographic is crucial for managing chronic conditions and preventing adverse health outcomes (Christopher et al., 2022). Without a standardized program, healthcare providers may struggle to identify barriers to adherence and implement tailored interventions, ultimately compromising the quality of care for elderly patients.

How the Treatment of This Population/Issue Could be Affected by Having an Awareness of Bias and Confounding in the Epidemiologic Literature

          Awareness of bias and confounding in epidemiologic literature is essential when addressing medication adherence in the elderly population. Bias refers to systematic errors in study design, data collection, or analysis that can distort the results and conclusions of research (Enzenbach et al., 2019). Confounding variables are factors associated with both the exposure and outcome of interest, and if they are not accounted for, they can lead to erroneous interpretations (Friis & Sellers, 2021). 

          In medication adherence among the elderly, biases and confounding factors such as socioeconomic status, health literacy, and polypharmacy can significantly influence study findings. Socioeconomic status (SES) encompasses various aspects such as income, education level, and occupation (Navarro-Carrillo et al., 2020). Elderly individuals from lower SES backgrounds may face barriers to accessing healthcare services, affording medications, or adhering to prescribed regimens due to financial constraints. Studies that do not account for SES disparities may overestimate medication adherence rates if they disproportionately sample from higher SES populations, thus leading to biased conclusions about the effectiveness of interventions (Xie et al., 2019). Health literacy is an individual’s ability to understand and act upon health-related information (Sørensen, 2022). Limited health literacy among elderly patients can hinder their comprehension of medication instructions, dosing schedules, and potential side effects, impacting adherence. Studies that fail to assess or adjust for health literacy levels may inaccurately estimate medication adherence rates, as individuals with low health literacy are more likely to struggle with adherence but may not be adequately represented in the study population. Polypharmacy, defined as the concurrent use of multiple medications, is common among the elderly, particularly those with multiple chronic conditions (Aggarwal et al., 2020). Polypharmacy increases the complexity of medication regimens, heightens the risk of drug interactions and adverse effects, and complicates adherence (Chang et al., 2020). Studies that do not adjust for polypharmacy may underestimate medication adherence rates, as individuals taking numerous medications are more likely to experience adherence challenges due to regimen complexity.

Strategies Researchers can Employ to Minimize Bias and Confounding

1. Randomized controlled trials (RCTs): By randomly assigning participants to intervention and control groups, RCTs help mitigate selection bias and confounding variables (Curley, 2022). Researchers can implement blinding techniques to further reduce performance and detection biases (Monaghan et al., 2021). Randomization helps minimize selection bias, ensuring participant characteristics are distributed evenly between groups at baseline, allowing researchers to attribute any differences in medication adherence outcomes between groups to the intervention rather than pre-existing differences between participants.
2. Adjusting for potential confounders: Researchers can use statistical techniques such as multivariable regression analysis to control for confounding variables in observational studies (Curley, 2020). By including relevant covariates in the analysis, researchers can isolate the effect of the exposure (e.g., educational program for staff) on the outcome (medication adherence) while accounting for potential confounders. 

The Effects Biases Could Have on the Interpretation of Study Results if not Minimized

          If not minimized, biases and confounding in studies investigating medication adherence among the elderly can lead to erroneous interpretations and flawed conclusions (Bykov et al., 2021). For example:

1. Biased study results may lead healthcare organizations to allocate resources towards interventions that are not effective or neglect interventions that could significantly improve medication adherence in the elderly population, resulting in detrimental effects on patient outcomes and healthcare system efficiency (Vela et al., 2022). For example, an intervention may appear beneficial in a study due to methodological flaws or uncontrolled confounding, leading to overestimating its effectiveness. As a result, resources may be diverted toward implementing interventions that do not yield the expected improvements in medication adherence, wasting valuable time and resources.
2. Biased research findings can inform clinical guidelines and healthcare policies, potentially leading to suboptimal practices for managing medication adherence in older adults (McMaughan et al., 2020). Without robust evidence-based recommendations, healthcare providers may struggle to deliver high-quality care to elderly patients, exacerbating health disparities and increasing healthcare costs.

Conclusion

           Addressing bias and confounding in epidemiologic literature is crucial for accurately assessing and improving medication adherence among the elderly population. Employing rigorous study designs and analytical techniques can enhance the validity and generalizability of research findings, ultimately informing evidence-based practices and optimizing patient care.

Reference

Aggarwal, P., Woolford, S. J., & Patel, H. P. (2020). Multi-morbidity and polypharmacy in older people: Challenges and opportunities for clinical practice. Geriatrics, 5(4), 85. https://doi.org/10.3390/geriatrics5040085

Bykov, K., Patorno, E., D’Andrea, E., He, M., Lee, H., Graff, J. S., & Franklin, J. M. (2021). Prevalence of avoidable and bias‐inflicting methodological pitfalls in real‐world studies of medication safety and effectiveness. Clinical Pharmacology & Therapeutics, 111(1), 209–217. https://doi.org/10.1002/cpt.2364

Chang, T., Park, H., Kim, D., Jeon, E., Rhee, C. M., Kalantar-Zadeh, K., Kang, E., Kang, S.-W., & Han, S. (2020). Polypharmacy, hospitalization, and mortality risk: A nationwide cohort study. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-75888-8

Christopher, C., Kc, B., Shrestha, S., Blebil, A., Alex, D., Mohamed Ibrahim, M., & Ismail, N. (2022). Medication use problems among older adults at a primary care: A narrative of literature review. Aging Medicine, 5(2), 126–137. https://doi.org/10.1002/agm2.12203

Curley, A. L. C. (Ed.). (2020). Population-based nursing: Concepts and competencies for advanced practice (3rd ed.). Springer.

Emadi, F., Ghanbarzadegan, A., Ghahramani, S., Bastani, P., & Baysari, M. T. (2022). Factors affecting medication adherence among older adults using tele-pharmacy services: A scoping review. Archives of Public Health, 80(1). https://doi.org/10.1186/s13690-022-00960-w

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). https://doi.org/10.1186/s12874-019-0779-8

Friis, R. H., & Sellers, T. A. (2021). Epidemiology for public health practice (6th ed.). Jones & Bartlett.

Liu, J., Yu, Y., Yan, S., Zeng, Y., Su, S., He, T., Wang, Z., Ding, Q., Zhang, R., Li, W., Wang, X., Zhang, L., & Yue, X. (2023). Risk factors for self-reported medication adherence in community-dwelling older patients with multimorbidity and polypharmacy: A multicenter cross-sectional study. BMC Geriatrics, 23(1). https://doi.org/10.1186/s12877-023-03768-7

Monaghan, T. F., Agudelo, C. W., Rahman, S. N., Wein, A. J., Lazar, J. M., Everaert, K., & Dmochowski, R. R. (2021). Blinding in clinical trials: Seeing the big picture. Medicina, 57(7), 647. https://doi.org/10.3390/medicina57070647

Navarro-Carrillo, G., Alonso-Ferres, M., Moya, M., & Valor-Segura, I. (2020). Socioeconomic status and psychological well-being: Revisiting the role of subjective socioeconomic status. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.01303

Sørensen, K. (2022). Defining health literacy: Exploring differences and commonalities. In International handbook of health literacy (pp. 5–20). Policy Press. https://doi.org/10.51952/9781447344520.ch001

Vela, M. B., Erondu, A. I., Smith, N. A., Peek, M. E., Woodruff, J. N., & Chin, M. H. (2022). Eliminating explicit and implicit biases in health care: Evidence and research needs. Annual Review of Public Health, 43(1), 477–501. https://doi.org/10.1146/annurev-publhealth-052620-103528

Xie, Z., St. Clair, P., Goldman, D. P., & Joyce, G. (2019). Racial and ethnic disparities in medication adherence among privately insured patients in the united states. PLOS ONE, 14(2), e0212117. https://doi.org/10.1371/journal.pone.0212117

SAMPLE 2

Practice Gap

The primary deficiency in addressing the issue of malaria in the United States lies in the lack of comprehensive and focused interventions for prevention and control. Although the risk of malaria transmission within the country is comparatively low, localized instances, particularly in regions like parts of Hawaii and the southern states, persist due to imported cases from travelers and migrants. To effectively combat malaria, there is a need for enhanced monitoring, diagnosis, and treatment methods. This entails swift case identification and reporting, ensuring access to reliable diagnostic tools, implementing preventive measures for high-risk groups, and fostering awareness among healthcare providers and the public. To bridge this gap, a collaborative approach involving public health authorities, healthcare providers, researchers, and community organizations is imperative. Customized interventions tailored to the unique challenges of malaria transmission in the United States are crucial, considering factors such as travel history, immigrant populations, vector control initiatives, and healthcare infrastructure. By addressing this deficiency, healthcare professionals and public health entities can enhance their ability to diagnose, prevent, and treat malaria cases more efficiently, ultimately leading to a reduction in the disease’s prevalence nationwide (Gaertner et al., 2023).

Awareness of Bias and Confounding in Epidemiologic Literature

In epidemiological research, bias and confounding are persistent challenges. Bias refers to discrepancies between observed findings and the true association between exposure and outcome, often stemming from flaws in study design, execution, or interpretation. Common types of bias include recollection bias, information bias, and selection bias. Confounding occurs when a third variable related to both the exposure and outcome distorts their connection, leading to false relationships or obscured correlations. Awareness of bias and confounding is vital for assessing the validity and applicability of epidemiological findings, especially in the context of malaria in the United States. Given that malaria is not endemic to the US, studies often rely on data from patients originating from other regions with higher transmission rates. Researchers must consider potential biases and confounding introduced by these factors when analyzing data and determining appropriate treatment strategies (Odo et al., 2021).

To develop evidence-based treatment recommendations for malaria in the United States, it is essential to acknowledge the presence of bias and confounding in epidemiological research, as highlighted by Tirumalaraju et al. (2020). Failure to address these issues adequately can lead to flawed or ineffective treatment strategies. For example, if studies assessing the effectiveness of a specific antimalarial drug do not properly consider confounding factors such as age, immunological status, or underlying health conditions, the apparent association between treatment and improved outcomes may be distorted. Incorrectly attributing therapeutic benefits to the medication due to unaddressed confounders can result in individuals receiving treatments that are not optimal for their condition, as discussed by Hawbani (2021). Therefore, a comprehensive understanding and meticulous consideration of bias and confounding are imperative for the development of reliable and beneficial malaria treatment guidelines for the US population (Odo et al., 2021).

Strategies to Minimize Bias and Confounding

Randomized controlled trials (RCTs) are considered the gold standard for minimizing bias and confounding factors in research studies. In an RCT, participants are randomly assigned to different treatment groups, which helps to evenly distribute confounding variables among the groups. By creating comparable groups through randomization, the likelihood of confounding is reduced. RCTs offer an objective and systematic approach for evaluating the effectiveness of interventions and therapies for malaria in the United States (Gaertner et al., 2023).

In situations where randomization is not feasible in observational studies, researchers may employ matching or stratification techniques to address confounding variables, as outlined by Gaertner et al. (2023). Matching involves selecting participants in a way that ensures comparable characteristics between the exposed and control groups. On the other hand, stratification involves dividing the study population into subgroups based on relevant confounding factors and then analyzing the treatment effect within each subgroup. Both matching and stratification aim to balance potential confounding factors across groups, thus facilitating a more precise assessment of therapy impact. These strategies enhance the accuracy of determining the effects of various factors in observational studies (Tirumalaraju et al., 2020).

Effects of Unminimized Bias and Confounding

Failure to appropriately address biases and confounding in epidemiological research on malaria in the United States can lead to several adverse outcomes, as discussed by Odo, Yang, & Knibbs (2021):

Incorrect conclusions: Biases and confounding can result in erroneous conclusions regarding the effectiveness of interventions or treatments. This may lead to the implementation of inefficient or unsuitable measures for preventing and managing malaria in the United States.

Over- or underestimation of treatment effects: When confounding factors are not accounted for, there is a risk of overestimating or underestimating the effects of therapy. Underestimation may cause potentially beneficial interventions to be overlooked, while overestimation may lead to the acceptance of therapies that are less effective than perceived. Both scenarios are undesirable and can impact patient outcomes negatively.

Limited generalizability: Biases and confounding can hinder the generalizability of research findings. If certain groups or characteristics are consistently over- or underrepresented due to biases, the conclusions drawn from the research may not be applicable to the broader population in the United States affected by malaria. This limitation makes it challenging to provide accurate and relevant recommendations for treatment and prevention methods.

Bias and confounding factors significantly compromise the validity and reliability of research outcomes. Systematic errors, such as selection bias and information bias, threaten the internal validity of studies. If confounding is not adequately managed, it can introduce noise or distortion in the data, undermining the reliability of observed relationships. This erosion of trust in research conclusions hampers the translation of evidence into practice (Odo et al., 2021).

Furthermore, failure to minimize biases and confounders poses a risk of inefficient resource allocation for malaria prevention and treatment. Decision-makers rely on credible evidence for prioritizing actions and allocating resources effectively. Biased or confounded studies may lead to misallocation or underutilization of funds, potentially resulting in wasteful spending or neglect of critical malaria control initiatives in the United States (Tirumalaraju et al., 2020).

References

Gaertner, K., von Ammon, K., Fibert, P., Frass, M., Frei-Erb, M., Klein-Laansma, C., … & Weiermayer, P. (2023). Recommendations in the design and conduction of randomised controlled trials in human and veterinary homeopathic medicine. Complementary Therapies in Medicine, 102961. https://doi.org/10.1016/j.ctim.2023.102961Links to an external site.

Hawbani, Y. A. (2021). Impacts of Antimalarial Drugs on Malarial Management Outcome of African Regions. Saudi Journal of Medical and Pharmaceutical Sciences, 7(12), 609-636. https://saudijournals.com/media/articles/SJMPS_712_609-636.pdfLinks to an external site.

Odo, D. B., Yang, I. A., & Knibbs, L. D. (2021). A systematic review and appraisal of epidemiological studies on household fuel use and its health effects using demographic and health surveys. International journal of environmental research and public health, 18(4), 1411. https://doi.org/10.3390/ijerph18041411Links to an external site.

Tirumalaraju, V., Suchting, R., Evans, J., Goetzl, L., Refuerzo, J., Neumann, A., … & Selvaraj, S. (2020). Risk of depression in the adolescent and adult offspring of mothers with perinatal depression: a systematic review and meta-analysis. JAMA network open, 3(6), e208783-e208783. https://jamanetwork.com/journals/jamanetworkopen/article-abstract/2767744Links to an external site.

World Health Organization. (2020). Health policy and system support to optimize community health worker programmes for HIV, TB and malaria services: an evidence guide. https://apps.who.int/iris/bitstream/handle/10665/340078/9789240018082-eng.pdf?sequence=1Links to an external site.