Diabetes in Adults Data Sources

Diabetes in older adults is a growing public health concern. The condition affects approximately 16 million older adults, 25% of all cases, and 33% of the elderly population, with the figure projected to reach 27 million by 2050. The progressive urbanization and sedentary lifestyles will continue to shift the epidemiology of diabetes toward old age. The epidemiology is accompanied by several functional disabilities, comorbidities, and premature mortality (Kalyani et al., 2017). A comprehensive geriatric assessment that includes functional, cognitive, mental and social status are necessary for identifying focused measures on the elderly’s patient preference, needs, and risks. The review analyzes three data sources related to diabetes in adults, identifies variables in each data source, and assesses their validity. Furthermore, the paper explains challenges in identifying an accurate data set and securing permission to use it.


 The study “Effectiveness of a multimodal intervention in functionally impaired older people with type 2 diabetes mellitus” (1) aims to evaluate the effectiveness of the multimodal intervention on the performance of diabetic participants above the age of 70. Second, the “Mortality implications of pre-diabetes and diabetes in older adults analyses the substantial and independent effect of diabetes on short-term mortality (2). Lastly, the “Evidence-based diabetes care for older people with Type 2 diabetes: a critical review’ (3) analyses various sources to gauge the treatment methods for older adults, primarily high-risk patients, given their lowering glucose and lifestyle interventions

The first source uses cluster-randomized trials from seven European countries to compare the effectiveness of the multimodal interventions with usual care for older adults. The intervention was necessary given the spectrum of comorbidities such as cognitive and physical decline, decreased survival chances, and the need for an individualized strategic approach to management. The selected subjects were over 70 years and had type 2 diabetes for at least 2 years. After 12 months, the results indicate significant benefits for multimodal intervention compared to those who did not complete the procedure (Rodriguez‐Mañas et al., 2019). Consequentially, the intervention led to cost-effective improvement for the patients.

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The second source study population involves older adults above 65 years, a sample size of 5791, and fasted for over eight hours (Tang et al., 2020). The study compared pre-diabetes, newly diagnosed diabetes, short, medium, and long-standing diabetes. The research attempts to assess the association between the diabetes stages and cardiovascular mortality among the participants. The risk of microvascular and macrovascular tends to increase with physiologic dysfunction (Munshi et al., 2020). The study results indicate that excess mortality risk was mainly concentrated among those with long-standing diabetic conditions. Hence, long-standing diabetes has a direct and independent correlation with short term-mortality.

Finally, the third source assessed various reviews and output from national, international, and endocrine societies and professional bodies to illustrate the relationship between the interventions taken for glucose-lowering and other care outcomes for diabetic older individuals. Database medical search involved older people, diabetes glucose control, and interventions (Sinclai et al., 2019). The research results indicate various strategies for robust and high functioning of diabetic older people. Individualized care is necessary Rooney et al., 2021). All the proposed treatment requires a risk-benefit analysis. Some appropriate measures for the person with diabetes include nutritional observance, exercise involvement, pharmacy vigilance, and use of de-intensification where necessary.

The validity of the first source, which is a MID study, is vital. The research involves generalizing the findings. The study observes the feasibility of the practical implementation of the intervention. Regardless of the educational significance, the study has a legacy of benefits with high adherence to other frail older adults’ diabetic analysis. Furthermore, the study has utilized a considerable sample in the Western world to deliver a sophisticated and functional analysis. Notably, 63 other studies denote the study’s significance.

The second source strength is in the extensive, community-based sample analyses. The research has an active follow-up for all causes and cardiovascular mortality. Additionally, the researchers have measurement methods for both glaciated hemoglobin and blood glucose that allow them to assess their research’s robustness to other publications’ findings of pre-diabetes and diabetes status. Part of the research is presented as an abstract for the America Heart Association and is cited by 34 publications signifying its relevance.

The third review presents a comprehensive framework on the significance of diabetic care methods. The study uses comprehensive data sources from various countries to expand its reliability. The research is among the pioneers to analyze how diabetic older individuals should be effectively treated considering their immediate surroundings. However, further research is essential to compare the effective interventions for the patients in typical care settings. The research significance is indicated by the 45 publications citing it.

Data collection is critical for research, and if adequately implemented (Ross et al., 2018, Whang & Lee, 2020). However, there are various challenges a researcher might face in identifying the proper data sat or securing permission to use one. A researcher, mainly a novice one, might find it challenging to establish a rapport with participants or encounter unwilling participants in the interviews. In some cases, the participants may have concern for their well-being due to the need for confidential information when the researcher asks for personal information. If wrong information is presented, the validity and reliability of the study are flawed (NASEM, 2016, Sivakumar, 2021). Triangulation of the data might be challenging in secondary data if the information is unverified. Using verified data, which sources its information from multiple sources, is crucial to prevent subjective viewpoints. It might be challenging to secure permission to use a particular set in some cases. Some authors fear for their research to be undermined, misinterpreted, and find themselves accused of scientific fraud. Hence, these authors take restrictive measures to ensure the necessary access procedures are taken.


Tang, O., Matsushita, K., Coresh, J., Sharrett, A. R., McEvoy, J. W., Windham, B. G., … & Selvin, E. (2020). Mortality implications of pre-diabetes and diabetes in older adults. Diabetes Care43(2), 382-388.

Rodriguez‐Mañas, L., Laosa, O., Vellas, B., Paolisso, G., Topinkova, E., Oliva‐Moreno, J., … & European MID‐Frail Consortium. (2019). Effectiveness of a multimodal intervention in functionally impaired older people with type 2 diabetes mellitus. Journal of cachexia, sarcopenia and muscle10(4), 721-733.

Sinclair, A. J., Abdelhafiz, A. H., Forbes, A., & Munshi, M. (2019). Evidence‐based diabetes care for older people with type 2 diabetes: a critical review. Diabetic Medicine36(4), 399-413.

Kalyani, R. R., Golden, S. H., & Cefalu, W. T. (2017). Diabetes and aging: unique considerations and goals of care. Diabetes care40(4), 440-443.

Rooney, M. R., Rawlings, A. M., Pankow, J. S., Tcheugui, J. B. E., Coresh, J., Sharrett, A. R., & Selvin, E. (2021). Risk of progression to diabetes among older adults with pre-diabetes. JAMA internal medicine181(4), 511-519.

Munshi, M. N., Meneilly, G. S., Rodríguez-Mañas, L., Close, K. L., Conlin, P. R., Cukierman-Yaffe, T., … & Sinclair, A. J. (2020). Diabetes in ageing: pathways for developing the evidence base for clinical guidance. The Lancet Diabetes & Endocrinology8(10), 855-867.

Ross, M. W., Iguchi, M. Y., & Panicker, S. (2018). Ethical aspects of data sharing and research participant protections. American Psychologist73(2), 138.

Whang, S., & Lee, J. G. (2020). Data collection and quality challenges for deep learning.

National Academies of Sciences, Engineering, and Medicine. (2016). Principles and obstacles for sharing data from environmental health research: Workshop summary.

Sivakumar, V. (2021). Prison research: Challenges in securing permission and data collection. Sociological Methods & Research50(1), 348-364.