Diabetes Treatment and Poor Community

Diabetes Treatment and Poor Community

Treatment of diabetes is influenced by several factors, both from healthcare professionals and clients. For instance, from the clients, financial factors, religious affiliations, level of education, lifestyle habits, and personal perception regarding formal health services are key determinants of health outcomes. From healthcare professionals, passion, level of knowledge, resilience, and ethical principles influence the health outcomes of patients. The interaction of the factors above culminates in a positive or negative influence on the health of individuals (Pourhabibi et al., 2022).

Notably, the focus in this discussion will be on the treatment of diabetes among the economically unprivileged community. As such, one objective is to elucidate the existing relationship between poverty and poor prognosis of diabetic patients in these communities. This is justified by the evidence of a high prevalence of diabetic-associated complications, barriers to seeking healthcare, and poor glycemic control associated with non-compliance with diabetic management strategies such as nutrition secondary to poverty.

Healthcare provision services are prone to biases and errors that eventually result in inequalities and disparities. To evade these inequalities and disparities, healthcare professionals must be aware of the possible sources of bias and errors that may affect the results of their intervention, and establish effective ways to solve them. For instance, selection bias may cause inappropriate representation across all the demographics of the entire population. This type of bias may result in the implementation of strategies that may not be inclusive of all members of a community. To navigate this challenge, multi-sectoral collaboration with other departments, such as socio-workers, will identify the genuine cases seeking representation. Secondly, information bias is a key problem in the management of diabetes. It is more common in the reporting of compliance with treatment strategies like dietary intake, random blood sugar monitoring, and medication compliance. These sources of information bias may alter the type of data collected, resulting in wrong interventions. Nonetheless, the problem may be mitigated by establishing more objective data collection strategies such as electronic health records. Confounding factors are a source of error in the data regarding diabetes management in the community. For instance, low-income levels and illiteracy may have a confounding influence on the relationship between intervention and outcomes. Lastly, random errors are also important to note when designing interventions for the management of diabetes (Popovic & Huecker, 2023).

Recognizing the existence of these biases and errors while doing research is critical and helps the researcher to come up with more inclusive, sustainable, evidence-based, and effective interventions. Also, strategies to mitigate these errors and biases significantly improve resource allocation to all members of a population, and this mitigates disparities and inequities in health. This results from ensuring data reliability and validity of findings. The two common methods used to mitigate these biases and errors are randomization and blinding. For instance, randomization involves assigning participants to intervention and control groups that minimize selection bias and confounding. This ensures equality of being assigned to any group, hence reducing the influence of external variables. Lastly, blinding the research where no one knows who is receiving the intervention will reduce information bias (Vaidyanathan, 2022).

When these biases are left unattended, they can distort the interpretation of study findings in different ways, as follows. To begin with, misleading conclusions can lead to interventions that are not inclusive, hence reducing their effectiveness due to selective bias. Secondly, information bias can distort data collection by healthcare professionals, leading to misinformed decision-making. This can lead to harmful interventions of substandard service delivery. Thirdly, confounding factors may lead to false associations of some health problems. This, in turn, can lead to wrong guidance of public health policies and interventions. Lastly, as supported by Popovic and Huecker (2023), random errors can reduce data reliability and validity, which may affect decision-making and, at times, lead to inequalities in service provision and resource allocation.

References

Popovic, A., & Huecker, M. R. (2023, June 20). Study bias. PubMed; StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK574513/

Pourhabibi, N., Mohebbi, B., Sadeghi, R., Shakibazadeh, E., Sanjari, M., Tol, A., & Yaseri, M. (2022). Factors associated with treatment adherence to treatment among patients with type 2 diabetes in Iran: A cross-sectional study. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.976888

Vaidyanathan, A. (2022). Controlling bias in research. The Journal of Indian Prosthodontic Society, 22(4), 311. https://doi.org/10.4103/jips.jips_405_22

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Post a cohesive scholarly response that addresses the following:

Describe your selected practice gap.

Diabetes Treatment and Poor Community

Explain how your treatment of this population/issue could be affected by having awareness of bias and confounding in epidemiologic literature.
Explain two strategies researchers can use to minimize these types of bias in studies, either through study design or analysis considerations.
Finally, explain the effects these biases could have on the interpretation of study results if not minimized.