Abstract
The worldwide incidence of diabetes mellitus has been increasing, notwithstanding geographical variations. Along with adhering to prescribed medications, key strategies for preventing and managing diabetes include following a healthy diet, regularly monitoring blood glucose levels, staying physically active, and practicing proper foot care.
Introduction
Individuals with inadequately regulated blood glucose levels are at an elevated risk of developing complications related to their diabetes. In individuals with type 2 diabetes, maintaining reasonable glycemic control enhances health-related quality of life, lowers healthcare expenditures, slows the progression of complications, and helps prevent or postpone their development.
Disease Statistics
Rural areas are no longer immune to diabetes; the disease is more common and deadly in less developed regions. Despite geographical differences, the global prevalence level of diabetes mellitus has been on the rise. Compared to Europe’s 15% increase, Africa would see a whopping 134% increase in diabetes cases by 2045.
Type 2 diabetes mellitus (T2DM) constitutes nearly 95 percent of the cases associated with diabetes, affecting 537 million people worldwide. In 2021 alone, diabetes was responsible for the deaths of over 6.7 million people; more than 80% of these casualties were in underdeveloped nations (Alor et al., 2023). In 2021, the cost of diabetes accounted for at least USD 966 billion, or 9% of all adult health expenditures.
Study Summary
The purpose of the article by Aloret et al. (2023) was to determine the level of glycemic control among patients with T2DM and the factors contributing to it. Managing one’s nutrition, monitoring one’s blood glucose levels, engaging in regular physical activity, and taking good care of one’s feet are all essential parts of preventing and managing diabetes, in addition to taking one’s medication as prescribed.
The study used a hospital-based, cross-sectional, descriptive research design. From this, it was deduced that effective glycemic control in type 2 diabetes promotes health-related quality of life, reduces healthcare costs, delays the progression of complications, and prevents or delays their onset. Imprecise glycemic control was more common among patients who used a combination of oral medications and insulin (Adjusted odds ratio (AOR) = 3.67, 95% confidence interval (CI): 1.34-8.74).
It was also the case among those with diabetes for 16 years or longer (AOR = 4.67, 95% CI: 2.44-9.29), those who did not engage in diabetes self-care, and those with complications (Alor et al., 2023). Poor glycemic control was substantially linked with age, comorbidities, job position, diabetes knowledge, diabetes self-care routines, treatment approach, complications, residence, and duration of effect. The results suggest that healthcare providers, including nurses, doctors, nutritionists, and pharmacists, should prioritize helping people with diabetes better adhere to their self-care routines to achieve adequate glycemic control.
Target Population
Type 2 diabetes patients regularly seeking healthcare services at Ho Municipal and Teaching Hospitals, 1161, formed the target population for this study. Glycemic control should be prioritized immediately to reduce the percentage of patients with inadequate glucose control and effectively prevent and manage diabetes. Maintaining tight control of blood sugar levels is critical for halting disease progression and avoiding complications (Alor et al., 2023). This study enrolled patients with T2DM to determine their level of glycemic control and the factors contributing to it. Three hundred and twenty-six patients diagnosed with type 2 diabetes at Ho Municipal and Teaching Hospitals participated in this descriptive cross-sectional study.
Using the Yamane method, the researcher found that a sample of 326 people would be sufficient, with a 95% confidence interval, a 5% margin of error, and a 10% non-response rate. More than two-thirds (76.1%) of the 310 patients enrolled in the study had inadequate glycemic control (Alor et al., 2023). The Ghana Health Service (GHS) has worked with the World Health Organization (WHO) for many years to establish a national strategy for non-communicable diseases. Diabetes is one of the primary focuses and necessitates extensive education and counseling. Diabetes type 2represents a high proportion of all diabetes cases, and the disease’s prevalence and consequences are both on the rise.
Sampling Procedure
Participants were selected using a systematic random sampling procedure, with the sampling frame consisting of patients’ charts. Fasting blood glucose (FBG) levels were used when measuring glycemic levels. Blood glucose levels exceeding 130 mg/dl (7 mm/L) on average over three months indicate inadequate glycemic management; STATA version 15.0 was used for data analysis (Alor et al., 2023).
A systematic random sampling technique is an excellent procedure for the study since it reduces the likelihood of biased samples and unsatisfactory survey results. Systematic samples are straightforward in design, implementation, comparison, and comprehension. This is of the utmost importance for surveys or research with little funding.
Researchers and statisticians might gain a feeling of control and process by employing a systematic approach. Presuming the sampling is appropriately designed to capture specific characteristics might be especially useful for investigations with precise parameters or a carefully formulated hypothesis. Systematic sampling removes clustered selection, wherein randomly selected samples are abnormally densely packed within a population. The only way random samples can handle this is to conduct multiple surveys or increase the sample size, which can be costly.
Disadvantages of this study method include the fact that systematic random sampling, for instance, relies on knowing the population size, which is not always the case. The systematic approach presupposes that the population size is known or may be approximately projected. Take the hypothetical case of scientists interested in determining the average size of mice in a specific region. It is impossible to systematically choose an initial basis or interval size without knowing the number of mice.
Furthermore, systematic random sampling does not provide the necessary natural measure of randomness for the study (Stratton, 2023). A population should display an inherent level of unpredictability along the selected metric. It is more likely that widespread examples will be chosen by chance if the population follows some trend. Systematic random sampling also increases the possibility of data manipulation. Researchers may design their systems to maximize the likelihood of attaining a desired outcome rather than relying on random data to produce a representative solution, thereby avoiding unreliable statistics.
Researchers could have opted for non-probability sampling rather than systematic random sampling; this method involves the researcher making subjective decisions about which samples to include in the final data set. In addition, non-probability approaches are usually more cost-effective to implement (Kim & Son, 2024). Even if the sample method is not the primary source of time and money savings, the many delivery options help. Since the results from interviewing the entire population can be achieved with a sample of 326, this study’s sample reflects the target population of 1161.
Assessment of Sample Size
With 28% of the population represented, the sample size is sufficient for the study. A sample that is too large would be impractical and unethical, whereas one that is too small would be scientifically flawed and unethical; thus, it is vital to estimate the sample size when the study is presented. Under specific assumptions, the Yamane technique in the statistical software was used to determine the required sample size.
Using a huge sample would be unethical, as it would cause unnecessary suffering to more patients than necessary to achieve the study’s aims (Andrade, 2020). A statistically nonsignificant result could be due to an inadequate sample size if the sample is insufficient to support the primary research question. Therefore, it is unethical to use a small sample, as it could cause unnecessary suffering to the study’s participants without helping future patients or advancing scientific knowledge.
Researchers use the term ‘sample size’ to describe the total number of people who comprise a study’s sample. To ensure that the sample accurately reflects the population at large, researchers frequently divide the overall total participants into subgroups based on demographic variables like age and gender; this process is known as sampling. Determining the correct sample size is one of the most crucial aspects of statistical analysis (Ji et al., 2023). Valid results and an accurate reflection of the investigated population cannot be achieved with a sample size that is too small. While larger, more representative samples have narrower margins of error, conducting research with a vast sample can be prohibitively expensive and time-consuming.
Margin of Error and Confidence Level
The margin of error is a statistical measure that indicates how confident a sampling process is and how uncertain a given statistic is. Alor et al. (2023) reported a 90% response rate, a 5% margin of error, and a 95% confidence interval in their study. The margin of error, in its simplest form, informs readers of the degree to which they can trust that the results are consistent with what they would expect if the entire population under study could be surveyed.
The standard way to represent the margin of error is as a figure marked with a±s sig±. In this study, 90% of the participants chose an answer, and the margin of error was 5%. Therefore, it is safe to assume that between 85% (90-5) and 95% (90+5) of the public would have chosen that option if the researchers had polled everyone.
When researchers pick a random sample multiple times, the margin of error would include the actual population parameter. The probability percentage is called the confidence level, a proportion that shows how often the number of people who would choose an answer falls within the margin of error. Taking the study by Alor et al. (2023) as an example, with a confidence level of 95%, it means that if researchers were to replicate the experiment, the results would match the population data 95 percent of the time. Researchers can have greater confidence that their results are representative of the population as a whole when they use a larger sample size. Therefore, a narrower margin of error is obtained with a greater sample size for each given degree of confidence.
Conclusion
An association is found between inadequate glycemic control and factors such as age, occupation, diabetes knowledge, comorbidities, self-care practices, treatment approach, complications, place of residence, and duration of diabetes. Confidence level refers to the probability percentage; it indicates the frequency with which the proportion of individuals selecting a particular answer falls within the margin of error.
References
Alor, S. K., Kretchy, I. M. A., Glozah, F. N., & Adongo, P. B. (2023). Factors associated with glycemic control among patients with type 2 diabetes mellitus in Ho, Ghana: A cross-sectional study. Metabolism Open, 20.
Andrade, C. (2020). Sample size and its importance in research. Indian Journal of Psychological Medicine, 42(1), 102-103.
Ji, L., Ahmann, A. J., Ahrén, B., Capehorn, M. S., Hu, P., Lingvay, I., Liu, W., Rodbard, H., Shen, Z., & Sorli, C. (2023). Proportion of participants with type 2 diabetes achieving a metabolic composite endpoint with once‐weekly semaglutide treatment versus comparators: Post hoc pooled analysis from SUSTAIN 1‐5, 7‐10 and SUSTAIN China. Diabetes, Obesity and Metabolism, 26(1), 233-241.
Kim, H., & Son, H. (2024). Multidimensional behavioral factors for diabetes management among middle-aged adults: A population-based study. Journal of Public Health, 1(1), 1-15.
Stratton, S. J. (2023). Population sampling: Probability and non-probability techniques. Prehospital and Disaster Medicine, 38(2), 147-148.