Keywords
glycemic control - diabetes - diabetes centers - assessment - Sudan
Introduction
Type 2 diabetes (T2D) accounts for ∼90% of all cases of diabetes.[1] In T2D, the insulin response is diminished, which is defined as insulin resistance.[2] There is growing evidence that insulin is ineffective and is initially countered
by an increase in insulin production to maintain glucose homeostasis, but over time,
insulin production decreases, resulting in T2D. T2D is most common in people older
than 45 years.[3] Diabetes is a major public health problem locally, regionally, and internationally.
According to the International Diabetes Federation (IDF), ∼415 million adults between
20 and 79 years old have DM.[1] T2D is a global public health burden, which is expected to increase to another 200
million by 2040.[1]
[2] In Sudan, one publication has shown that the prevalence of T2D among adults has
increased to 19%.[4]
Patients with an HbA1c greater than 6.5% (48 mmol/mol) are diagnosed with DM. HbA1c
is a convenient, rapid, standardized test that shows less variation due to the use
of preanalytical variables. It is not strongly affected by acute illness or stress.[5]
Studies have shown that many factors interact to control blood sugar, including factors
related to the patient, the doctor, and the health system.
Concerning patients, self-management behavior appears to influence glycemic control,
and diabetic patients should be consistently advised to restrict sugar intake, exercise,
stop smoking, and adhere to medication instructions. In addition to health insurance,
the affordability of health care services, the availability of health professionals,
and good insulin storage influence glycemic control.[5]
[6]
Several factors can contribute to poor glycemic control, including a lack of access
to healthcare, inadequate diabetes education, noncompliance with treatment plans,
medication side effects, psychological factors, socioeconomic conditions, and cultural
beliefs. Addressing these factors is crucial in optimizing diabetes management.[7] Intensive medical treatment combined with diabetes self-management education has
been demonstrated to significantly reduce the risk of diabetes-related complications
by 50 to 75%. Furthermore, this approach is associated with cost savings of approximately
$79,280 per individual and can extend life expectancy by an average of 6 years.[8] In Saudi Arabia, Alshahri et al assessed self-management care and HbA1c levels among
T2D patients and the factors that may be associated with poor control. HbA1c data
were extracted from patients' records. Most of the participants (65%) were found to
have poor glycemic control. Glucose management was better in patients with T2D for
more than 5 years.[9]
A Sudanese study by Omar and his colleagues showed that the prevalence rates of T2D,
newly diagnosed T2DM, and uncontrolled T2D were 20.8, 10.0, and 80.0%, respectively.
Logistic regression analysis showed no significant association between education,
marital status, body mass index, waist circumference, and diabetes control. However,
older age and a family history of DM were associated with T2D. They concluded that
the prevalence of T2D is high among the Sudanese population, especially in older people
and those with a family history of DM. The high prevalence of uncontrolled DM in this
setting is another hidden burden.[10]
Although many international studies have examined factors contributing to poor glycemic
control, limited research has been conducted in Northern Sudan, where unique cultural,
dietary, and healthcare access factors may influence diabetes management. Understanding
these local dynamics is essential for designing effective, context-specific interventions.
Thus, this study aims to identify the factors contributing to poor glycemic control,
informing the necessary interventions to improve glycemic control and prevent related
complications.
Patients and Methods
Study Design and Setting
This study was an observational, cross-sectional, multicenter-based study. The study
was conducted in selected diabetes care centers in Atbara, River Nile State, Sudan.
In general, these diabetes care centers provide comprehensive care and support for
individuals with diabetes. These centers are dedicated to promoting proper management
of diabetes through various functions. They offer regular medical check-ups and consultations
to monitor blood sugar levels and provide necessary medications. They also provide
education and guidance on healthy eating habits, exercise routines, and self-care
practices to manage the condition effectively.
Additionally, they offer specialized services such as diabetic foot care, eye examinations,
and counseling to prevent diabetes-related complications. Ultimately, they aim to
increase the quality of life of individuals with diabetes by providing holistic care
and empowering them with the knowledge and tools to live well with their condition.
The study was conducted from June to December 2023.
Study Population
Adult patients with T2D who were attending diabetes care centers in Atbara within
the study period and agreed to participate in the study were included. The sample
size of the study was determined through the following formula: n
= (
Z
2 × (
P
× q))/
e
2, where n is the sample size required by the study, Z is the determined area under the normal curve by the desired confidence interval
(CI: 95%), and P is the proportion of the main attribute of the study (the expected proportion of
diabetes poor control [based on HbA1c] is unknown in the state, p = 0.5, p = 1–p = 1–0.5 = 0. 5 and e = the desired precision (e = 0.05). n
= 385 study participants. Convenience sampling was used.
Data Collection and Analysis
Data were collected via direct face-to-face interviews with the participants using
a comprehensive, structured, closed-ended questionnaire covering sociodemographic
characteristics, clinical characteristics, patients, and service-related factors that
affect glycemic control and diabetes control data.
The data were entered, and analyzed via SPSS version 28.0. Descriptive statistics
regarding frequency tables, graphs, and means and standard deviations were used. Bivariable
analysis was performed to determine the associations between the risk factors for
diabetes control via the chi-square test, and a p-value of 0.05 or less was considered statistically significant. A logistic regression
test was performed to determine the predictors of glycemic control.
Results
Sociodemographic Characteristics of the Participants
Among the participants, 47.3% were aged between 40 and 60, and 42.1% were above 60
years. A total of 56.4% were females, 74.3% were married, and 74.8% were from urban
areas. The socioeconomic class varied: 19.5% were classified as low, 71.4% as middle,
and 9.1% as high. 15.3% were illiterate, 34.8% had a primary education, and 26.2%
had a bachelor's degree. 42.3% were homemakers. Among the participants, 39.5% had
no other chronic diseases, and 35.3% had hypertension ([Table 1]).
Table 1
Sociodemographic characteristics and comorbidities of the participants
|
Characteristics
|
Frequency (%)
|
|
Age (years)
|
< 40
|
41 (10.6)
|
|
40–60
|
182 (47.3)
|
|
> 60
|
162 (42.1)
|
|
Sex
|
Male
|
168 (43.6)
|
|
Female
|
217 (56.4)
|
|
Marital status
|
Single
|
29 (7.5)
|
|
Married
|
286 (74.3)
|
|
Divorced
|
9 (2.4)
|
|
Widowed
|
61 (15.8)
|
|
Residence
|
Rural
|
97 (25.2)
|
|
Urban
|
288 (74.8)
|
|
Socioeconomic class
|
Low
|
75 (19.5)
|
|
Middle
|
275 (71.4)
|
|
High
|
35 (9.1)
|
|
Education
|
Illiterate
|
59 (15.4)
|
|
Primary
|
134 (34.8)
|
|
Secondary
|
91 (23.6)
|
|
University
|
101 (26.2)
|
|
Occupation
|
Housewife
|
163 (42.3)
|
|
Freelancer
|
86 (22.3)
|
|
Laborer
|
43 (11.2)
|
|
Employee
|
57 (14.8)
|
|
Others
|
36 (9.4)
|
|
Health insurance
|
Insured
|
286 (74.3)
|
|
Not insured
|
99 (25.7)
|
|
Comorbidities and complications
|
None
|
152 (39.5)
|
|
Hypertension
|
136 (35.3)
|
|
Retinopathy
|
11 (2.9)
|
|
Diabetic septic foot
|
10 (2.6)
|
|
Numbness
|
10 (2.6)
|
|
Benign prostatic hypertrophy
|
6 (1.6)
|
|
Ischemic heart disease
|
6 (1.6)
|
|
Asthma
|
6 (1.6)
|
|
Arrhythmia
|
4 (1.0)
|
|
Kidney transplant
|
4 (1.0)
|
|
Thyroid disorders
|
4 (1.0)
|
|
Others
|
19 (5.2)
|
Patient-Related Factors that Affect Diabetes Control
A total of 20.8% of the participants were obese, 13.5% were smokers, 1.3% consumed
alcohol, 15.1% had a sedentary lifestyle, and 30.4% had a high intake of unhealthy
food. More than two-thirds of the participants (68.6%) had good medication adherence.
Concerning awareness of diabetes control, 53.0% had good awareness. It is worth noting
that medication adherence and diabetes awareness were evaluated using a structured
questionnaire that categorized participants' responses into “good,” “average,” or
“poor.” These classifications were based on participants' subjective self-assessments
rather than objective or validated scoring criteria. While practical in the study
context, this approach may limit precision and replicability across different settings.
The most common form of diabetes treatment was oral hypoglycemic agents (55.6%), followed
by insulin (45.2%) and diet control (11.9%). Moreover, 52.5% reported experiencing
a stressful lifestyle ([Table 2]).
Table 2
Patient- and service-related factors that affect diabetes control
|
Factors affecting glycemic control
|
Frequency (%)
|
|
Patient-related factors
|
Social habits and lifestyle
|
High intake of unhealthy food
|
117 (30.4)
|
|
Obesity
|
80 (20.8)
|
|
Sedentary/Inactive lifestyle
|
58 (15.1)
|
|
Smoking
|
52 (13.5)
|
|
Alcohol
|
5 (1.3)
|
|
Other relevant
|
2 (0.5)
|
|
None
|
174 (45.2)
|
|
Medication adherence
|
Good
|
264 (68.6)
|
|
Average
|
87 (22.6)
|
|
Poor
|
34 (8.8)
|
|
Relevant awareness of diabetes and its control
|
Good
|
204 (53.0)
|
|
Average
|
149 (38.7)
|
|
Poor
|
32 (8.3)
|
|
Diabetes treatment
|
Oral hypoglycemic agents
|
214 (55.6)
|
|
Insulin
|
174 (45.2)
|
|
Diet control
|
46 (11.9
|
|
Others
|
8 (2.1)
|
|
Stressful lifestyle
|
Yes
|
202 (52.5)
|
|
No
|
183 (47.5)
|
|
Service-related factors that can affect diabetes control
|
Received counseling on diabetes care during the visit
|
Yes
|
354 (91.9)
|
|
No
|
31 (8.1)
|
|
Regular monitoring and testing
|
Yes
|
251 (65.2)
|
|
No
|
134 (34.8)
|
|
Medication and service availability
|
Available
|
286 (74.3)
|
|
Partially available
|
78 (20.3)
|
|
Unavailable
|
21 (5.4)
|
|
Affordability of medications and relevant health services
|
Affordable
|
167 (43.4)
|
|
Partially affordable
|
153 (39.7)
|
|
Not affordable
|
65 (16.9)
|
|
Accessibility of treatment and health services
|
Accessible
|
279 (72.5)
|
|
Partially accessible
|
79 (20.5)
|
|
Inaccessible
|
27 (7.0)
|
Service-Related Factors that Can Affect Diabetes Control
During the visit, 91.9% of the participants reported receiving counseling on diabetes
care, and nearly two-thirds reported that the physicians did regular monitoring and
testing for them. A total of 72.5% of them reported that they had accessible treatment
and health services ([Table 2]).
Glycemic Control
The mean (± SD) HbA1c level was 8.8% (± 2.5). The minimum HbA1c level was 3.5%, whereas
the maximum was 15%. In terms of glycemic control, only 51.9% of the participants
had well-controlled diabetes, while the remaining 48.1% had uncontrolled diabetes.
Cross-tabulation and chi-square tests were performed to determine the associations
between demographic and patient-related factors and glycemic control among the participants.
The analysis revealed that glycemic control was significantly associated with age,
medication adherence, relevant awareness of diabetes and the control, receiving counseling
on diabetes, and receiving regular monitoring and testing (p = 0.037, < 0.001, < 0.001, 0.008, < 0.001, respectively; [Table 3]).
Table 3
Association between diabetes control and related factors via cross-tabulation and
the chi-square test
|
Factors
|
Glycemic control [N (%)]
|
p-Value
|
|
Good
|
Poor
|
All
|
|
N = 200
|
N = 185
|
N = 385
|
|
Age (years)
|
< 40
|
28 (14.0)
|
13 (7.0)
|
41 (10.6)
|
0.037
|
|
40–60
|
85 (42.5)
|
97 (52.4)
|
182 (47.3)
|
|
> 60
|
87 (43.5)
|
75 (40.5)
|
162 (42
|
|
Sex
|
Male
|
78 (39.0)
|
90 (48.6)
|
168 (43.6)
|
0.056
|
|
Female
|
122 (61.0)
|
95 (51.4)
|
217 (56.4)
|
|
Residence
|
Rural
|
52 (26.0)
|
45 (24.3)
|
97 (25.2)
|
0.705
|
|
Urban
|
148 (74.0)
|
140 (75.7)
|
288 (74.8)
|
|
Socioeconomic class
|
Low
|
32 (16.0)
|
43 (23.2)
|
75 (19.5)
|
0.187
|
|
Middle
|
148 (74.0)
|
127 (68.6)
|
275 (71.4)
|
|
High
|
20 (10.0)
|
15 (8.1)
|
35 (9.1)
|
|
Education
|
Illiterate
|
27 (13.5)
|
32 (17.3)
|
59 (15.3)
|
0.532
|
|
Primary
|
68 (34.0)
|
66 (35.7)
|
134 (34.8)
|
|
Secondary
|
47 (23.5)
|
44 (23.8)
|
91 (23.6)
|
|
University
|
58 (29.0)
|
43 (23.2)
|
101 (26.2)
|
|
Health insurance
|
Insured
|
144 (72.0)
|
142 (76.8)
|
286 (74.3)
|
0.286
|
|
Not insured
|
56 (28.0)
|
43 (23.2)
|
99 (25.7)
|
|
Medication adherence
|
Good
|
168 (84.0)
|
96 (51.9)
|
264 (68.6)
|
< 0.001
|
|
Average
|
27 (13.5)
|
60 (32.4)
|
87 (22.6)
|
|
Poor
|
5 (2.5)
|
29 (15.7)
|
34 (8.8)
|
|
Relevant awareness toward diabetes control
|
Good
|
149 (74.5)
|
55 (29.7)
|
204 (53.0)
|
< 0.001
|
|
Average
|
49 (24.5)
|
100 (54.1)
|
149 (38.7)
|
|
Poor
|
2 (1.0)
|
30 (16.2)
|
32 (8.3)
|
|
Stressful lifestyle
|
Yes
|
104 (52.0)
|
98 (53.0)
|
202 (52.5)
|
0.849
|
|
No
|
96 (48.0)
|
87 (47.0)
|
183 (47.5)
|
|
Received diabetes education
|
Yes
|
191 (95.5)
|
163 (88.1)
|
354 (91.9)
|
0.008
|
|
No
|
9 (4.5)
|
22 (11.9)
|
31 (8.1)
|
|
Regular monitoring
|
Yes
|
163 (81.5)
|
88 (47.6)
|
251 (65.2)
|
< 0.001
|
|
No
|
37 (18.5)
|
97 (52.4)
|
134 (34.8)
|
|
Access to medication and service
|
Yes
|
155 (77.5)
|
131 (70.8)
|
286 (74.3)
|
0.243
|
|
Partial
|
37 (18.5)
|
41 (22.2)
|
78 (20.3)
|
|
No
|
8 (4.0)
|
13 (7.0)
|
21 (5.5)
|
|
Affordability of medications
|
Yes
|
93 (46.5)
|
74 (40.0)
|
167 (43.4)
|
0.422
|
|
Partial
|
76 (38.0)
|
77 (41.6)
|
153 (39.7)
|
|
No
|
31 (15.5)
|
34 (18.4)
|
65 (16.9)
|
|
Accessibility for treatment and health services
|
Yes
|
154 (77.0)
|
125 (67.6)
|
279 (72.5)
|
0.070
|
|
Partial
|
32 (16.0)
|
47 (25.4)
|
79 (20.5)
|
|
No
|
14 (7.0)
|
13 (7.0)
|
27 (7.0)
|
Multivariate Analysis
Logistic regression analysis was conducted to determine the main predictors of poor
glycemic control among the participants. The predictors included age, relevant awareness
of diabetes, and regular monitoring and testing. Participants older than 60 years
had greater odds of having poor glycemic control than those younger than 40 years
(OR = 2.79, p = 0.014). Participants with average (OR = 4.00, p = 0.000) and poor (OR = 15.78, p = 0.001) diabetes awareness had higher odds of poor glycemic control than those with
good awareness. Not undergoing regular monitoring and testing was significantly associated
with increased odds of poor glycemic control (OR = 2.66, p = 0.000). Other demographic, patient, and service-related factors were not significantly
related to glycemic control ([Table 4]).
Table 4
Prediction of the factors affecting diabetes control according to logistic regression
|
Factors (predictors)
|
Categories
|
Odds ratio
|
p-Value
|
[95% CI]
|
|
From
|
To
|
|
Age (years)
|
< 40
|
1.00
|
–
|
–
|
–
|
|
40–60
|
1.73
|
0.191
|
0.76
|
3.96
|
|
>60
|
2.79
|
0.014
|
1.23
|
6.34
|
|
Medication adherence
|
Good
|
1.00
|
–
|
–
|
–
|
|
Average
|
1.69
|
0.089
|
0.92
|
3.11
|
|
Poor
|
2.18
|
0.210
|
0.64
|
7.36
|
|
Relevant awareness of diabetes
|
Good
|
1.00
|
–
|
–
|
–
|
|
Average
|
4.00
|
<0.001
|
2.38
|
6.72
|
|
Poor
|
15.78
|
0.001
|
3.05
|
81.59
|
|
Received counseling on diabetes
|
Yes
|
1.00
|
–
|
–
|
–
|
|
No
|
1.39
|
0.495
|
0.54
|
3.59
|
|
Regular monitoring and testing
|
Yes
|
1.00
|
–
|
–
|
–
|
|
No
|
2.66
|
<0.001
|
1.58
|
4.49
|
Discussion
Diabetes has become a global epidemic, affecting millions of individuals worldwide.
In Sudan, the prevalence of diabetes has been steadily increasing, posing significant
challenges to the healthcare system. Glycemic control is a cornerstone in managing
diabetes, as it directly impacts the overall health outcomes and quality of life of
individuals with this condition. Despite advancements in treatment options and medical
interventions, a considerable proportion of individuals with diabetes still struggle
to achieve optimal glycemic control. By understanding the factors influencing poor
glycemic control, healthcare professionals and policymakers can gain insights into
developing tailored interventions and strategies to improve outcomes and lower the
burden of complications associated with diabetes. The findings from this assessment
will contribute to the body of knowledge surrounding diabetes management in Sudan
and provide a basis for further research and intervention development.
The mean HbA1c level was 8.8%, with a standard deviation of 2.5%. Moreover, only 51.9%
of the participants demonstrated well-controlled diabetes, while the remaining 48.1%
had uncontrolled diabetes. This finding aligns with earlier research that has reported
suboptimal glycemic control in individuals with diabetes. For example, Babaniamansour
et al[11] reported an average HbA1c of 8.5% among diabetic patients, indicating similar patterns
of poor control. However, their study was conducted in a different healthcare context,
and variations in healthcare delivery, access to medications, and patient education
programs may influence such outcomes. While our findings are comparable, the systemic
and cultural differences between settings should be considered when interpreting these
similarities.
In our study, HbA1c levels ranged from a minimum of 3.5 to a maximum of 15, reflecting
significant variability in glycemic control. This broad range suggests that a considerable
proportion of individuals are living with poorly managed diabetes. Adham et al[12] reported a similar distribution in their cohort. Although their results parallel
ours, their study was based on a different regional and clinical setting, which may
have distinct approaches to diabetes screening, patient follow-up, and management
guidelines. Therefore, such similarities in HbA1c distribution may not necessarily
reflect identical underlying causes or patient behaviors.
Further analysis showed that 51.9% of participants had well-controlled diabetes, while
48.1% had poor glycemic control. This pattern is consistent with findings from studies
by McBrien et al[13] and Yan et al,[14] which also reported that less than half of the individuals with diabetes achieved
recommended glycemic targets. Though consistent in outcome, these studies were conducted
across varying health systems and cultural environments, where factors such as the
cost of diabetes care, insurance coverage, and societal norms related to health may
significantly influence glycemic outcomes. Thus, while these findings suggest a widespread
challenge, the degree to which these patterns are generalizable remains uncertain
and should be considered in light of contextual differences.
A significant association was observed between age and glycemic control (p = 0.037), where participants older than 60 years had a greater proportion of poor
glycemic control than those younger than 40 years. This finding is supported by previous
studies, such as that of Sinclair et al,[15] who reported higher HbA1c levels among older adults with diabetes. Munshi et al[16] also noted that older individuals demonstrated greater glycemic variability and
increased rates of both hypo- and hyperglycemia. These findings are biologically plausible,
as aging is associated with decreased β-cell function, insulin resistance, comorbidities,
and functional limitations. However, it is important to recognize that aging populations
in different countries may not experience these challenges similarly. In some health
systems, older adults have better access to structured care, while in others, limited
support and complex medication regimens may compound the problem. Thus, while the
association between age and poor glycemic control appears consistent across studies,
the underlying contributing factors may vary by context.
An interesting observation was the relationship between sex and glycemic control.
Although not statistically significant (p = 0.056), more females had well-controlled glycemic levels than males. Several studies
support this trend. For example, Jones et al[17] and Almobarak et al[18] found no significant difference in glycemic control between sexes, which aligns
with our findings. However, contrary to ours, Johnson et al[19] reported better glycemic control among males. These inconsistencies may be due to
variations in study populations, sampling methods, and sociocultural factors influencing
health behavior. For instance, sex roles and expectations, psychological stress, and
access to healthcare services can differ markedly across societies. Additionally,
hormonal influences, such as the effects of estrogen on insulin sensitivity, as noted
by Mauvais-Jarvis et al,[20] may also contribute to differences. However, these effects could be modulated by
genetic, nutritional, and environmental factors that vary between countries.
The lack of a significant relationship between health insurance and glycemic control
(p = 0.286) is consistent with the findings of Bakris et al,[21] who also observed no clear difference between insured and uninsured individuals.
While health insurance is typically assumed to enhance access to healthcare services,
its impact on glycemic control is not always straightforward. Access alone may not
ensure proper disease management; treatment adherence, availability of medications,
patient literacy, and quality of care also play critical roles. In lower-resource
settings, even insured patients may face barriers such as limited drug availability
or overburdened clinics, which can compromise glycemic outcomes.
Logistic regression analysis identified several key predictors of poor glycemic control.
Participants older than 60 years had significantly higher odds of poor control than
those younger than 40 years, reinforcing the age-related challenges previously discussed
and supporting findings from Huo et al,[22] who similarly identified age as a determinant of glycemic control. Medication adherence
also emerged as a strong predictor; individuals with average or poor adherence had
markedly higher odds of poor glycemic control than those with good adherence. Additionally,
lower levels of diabetes awareness were associated with poorer glycemic outcomes.
Interestingly, while lack of counseling was not a significant factor, infrequent monitoring
and testing were significantly associated with worse control, again in line with the
findings by Huo et al.[22] These results emphasize that, beyond demographic factors, behavioral and system-level
variables play a crucial role in glycemic management. However, the strength and nature
of these associations may differ depending on local healthcare infrastructure, patient
support systems, and cultural attitudes toward chronic disease, suggesting a need
for context-specific strategies in diabetes care. Notably, some odd ratios reported
wide confidence intervals, indicating statistical imprecision. This may be attributed
to the relatively small sample size or low event frequency, and these estimates should
be interpreted cautiously.
Our study also found a strong association between medication adherence and glycemic
control (p < 0.001). Participants with good adherence were significantly more likely to have
well-controlled diabetes. These results align with previous research, including a
study published in the Journal of Diabetes Research
[23] and another by McKnight et al,[24] highlighting the importance of medication adherence in achieving glycemic targets.
While adherence is a universal factor in diabetes control, cultural attitudes toward
medication, understanding of treatment plans, and trust in healthcare providers can
differ across regions. These factors should be considered when comparing findings
across diverse populations.
Awareness of diabetes also emerged as a key predictor of glycemic control. Participants
with average or poor awareness had higher odds of poor control than those with good
awareness. This echoes the findings by McKnight et al,[24] who linked a lack of self-monitoring and awareness to suboptimal glycemic outcomes.
Again, while the association holds across studies, awareness is shaped by local education
systems, public health campaigns, and cultural perceptions of chronic disease, which
vary by context and may influence the degree to which these findings are applicable
elsewhere.
The findings of our study must be seen in the light of some limitations. First, it
was a cross-sectional study that targeted individuals who attended diabetes centers
in Atbara. Consequently, the sample may not be fully representative of all diabetic
patients within the broader population, as those who seek care at these centers may
have different characteristics and management experiences compared with those who
do not. Moreover, convenience sampling further limits the generalizability of our
findings, as it may introduce selection bias and reduce the sample's representativeness.
Furthermore, using interview-based questionnaires introduces the potential for recall
bias, as participants may not accurately remember or report their behaviors and health
information. Similarly, medication adherence was self-reported and not validated through
objective methods such as pharmacy refill records, which may affect the reliability
of adherence data.
Additionally, our analysis did not include various modalities of therapies that could
influence glycemic control. The exclusion of certain treatment options limits the
comprehensiveness of our findings and may overlook important variables that contribute
to diabetes management in this population.
Conclusion
This study provides valuable insights into the factors influencing glycemic control
among individuals with diabetes. These findings underscore the importance of patient
education and regular monitoring in managing diabetes effectively. Future research
should focus on developing interventions targeting these areas to improve glycemic
control among individuals with diabetes.
