Diabetes Mellitus (DM) is a chronic metabolic disorder marked by elevated blood glucose levels due to insufficient insulin production or impaired insulin action, impacting millions globally, with an increasing prevalence in Bangladesh
[1]
[2]
. As of 2021, diabetes affected an estimated 537 million adults worldwide, while over 8 million adults in Bangladesh are projected to live with the condition—a figure expected to rise significantly
[3]
[4]
. This escalating prevalence adds strain on healthcare systems as diabetes significantly contributes to morbidity, mortality, and healthcare costs
[5]
.
Pharmacological management of diabetes primarily involves oral hypoglycemic agents and injectable insulin, each influencing patient health and quality of life (QoL) differently. Oral hypoglycemic agents, mainly prescribed for type 2 diabetes, enhance insulin sensitivity and reduce glucose absorption. However, prolonged use of these agents can lead to side effects like hypoglycemia and gastrointestinal issues, which affect daily activities and overall well-being
[6]
. In contrast, injectable insulin, essential for managing type 1 and advanced type 2 diabetes, provides direct glucose control but requires careful monitoring to prevent hypoglycemia. This necessity for vigilance can create physical and emotional strain, often impacting QoL due to increased stress and lifestyle limitations
[7]
.
Diabetes complications are both extensive and progressive, affecting nearly every organ system and significantly impacting QoL. Chronic hyperglycemia can lead to microvascular complications such as retinopathy, nephropathy, and neuropathy, as well as macrovascular issues like cardiovascular disease and stroke, all contributing to elevated morbidity and mortality among people with diabetes
[8]
[9]
. Furthermore, prolonged diabetes duration increases the risk of peripheral artery disease and neuropathy, raising the likelihood of lower-limb amputations and further impairing mobility and independence
[10]
. Understanding the effect of distinct treatment patterns on the progression of these complications and QoL outcomes is crucial for refining diabetes management strategies.
QoL for diabetic patients encompasses physical, psychological, and social domains, encompassing how well individuals adapt to and manage their condition. For patients in Bangladesh, limited healthcare resources, socioeconomic barriers, and high rates of complications—including neuropathy, retinopathy, and cardiovascular disease—add further challenges to achieving optimal QoL
[11]
. These issues have been exacerbated by the COVID-19 pandemic, which has disrupted healthcare systems globally, disproportionately affecting individuals with chronic conditions like diabetes
[12]
. Studies have shown that diabetic patients face a heightened risk of severe COVID-19 outcomes, including ICU admission and mortality, due to increased susceptibility to complications
[13]
.
In Bangladesh, the COVID-19 pandemic has further challenged diabetes management by limiting access to healthcare, regular monitoring, and medication supplies, potentially compromising glycemic control and elevating the risk of long-term complications
[14]
-
[16]
. Pandemic-related movement restrictions and reduced healthcare accessibility have disrupted the continuity of diabetes care, which is essential for controlling blood glucose levels and preventing complications
[17]
-
[19]
.
This study aims to assess the status of long-term complications and QoL among diabetic patients in Bangladesh, focusing on how different treatment patterns—specifically oral hypoglycemic agents versus injectable insulin—may influence these outcomes. By examining the influence of treatment adherence, lifestyle adjustments, and healthcare access on QoL, this research seeks to provide insights into optimizing diabetes management strategies, thereby informing policies to alleviate the diabetes burden on Bangladesh’s healthcare system.
2. Methods2.1. Study Design and Setting
A cross-sectional study was conducted on 385 diabetic patient slum dwellers aged 18 - 80 years between May 2022 and October 2022 in the outpatient department of three tertiary hospitals in Dhaka and Mymensingh districts in Bangladesh, Bangladesh.
2.2. Sample Size, Sample Selection Procedure and Recruitment
The study involved 385 diabetic patients from three tertiary diabetic hospital, selected through a convenient sampling technique applied to diabetes or endocrinology department of the hospital. Participants were recruited from the outpatient and inpatient departments of three tertiary hospitals in Dhaka and Mymensingh. Outpatients waiting for consultations were approached, informed about the study, and assessed for eligibility. For inpatients, a bed-to-bed approach was used, where each admitted patient was screened based on inclusion criteria. The inclusion criteria encompassed adults aged 18 - 80 years, diagnosed with diabetes mellitus (type 1 or type 2), who were receiving treatment from the selected hospitals. Participants had to be capable of providing informed consent and independently responding to the questionnaire. The exclusion criteria eliminated patients with severe non-diabetic medical conditions, such as advanced cancer, end-stage renal disease, or cognitive impairments. Additionally, individuals unwilling to participate or unable to respond independently, such as those critically ill or with communication difficulties, were excluded.
Sample size was calculated using the formula of Cochran’s,
.
With a 5% margin of error (e), considering the prevalence of quality of life of diabetic patient people (p = 53.00%) as reported in a similar study in India
[20]
, the standard normal variate of 1.96 (z), the required sample size was 384.
2.3. Data Collection Procedure
A semi-structured questionnaire (see in Appendix) was developed using the Kobo Toolbox, and the data were collected using a face-to-face interview. The questionnaire was developed based on previously published researches, with slight adjustments for the study cohort
[19]
[21]
. Five trained surveyor’s/data collectors were appointed for the data collection purpose. The questionnaire consisted of sociodemographic information, diabetes-related information, comorbidities, complications, investigations, and QoL assessment. We have translated English questioner into Bengali back translations with the help to expert. The interview process involved administering the SF-12 Questionnaire to measure the quality of life. Special attention was given to ensuring the privacy and comfort of participants during the data collection process, and steps were taken to minimize social desirability bias. Trained research assistants conducted a face-to-face interview of the patients outdoors after giving consent for inclusion. Before interviews, they explained the nature and purpose of the study to the participant.
2.4. Measures
Participants completed a questionnaire comprising of sociodemographic information, diabetic related information’s and the quality of life information’s using SF-12.
Socio-demographic data were collected, including age, gender, place of residence, education level, religion, Occupation, marital status and income.
The second section comprised questions related to patients’ diabetes, asking about the duration of diabetes, name, and duration of anti-diabetic agents used. Patients’ body mass index (BMI) was calculated from their height and weight at the time of the interview, and they were asked for a history of hypertension (HTN). Documented complications were recorded in this section. The complications queried were nephropathy, retinopathy, neuropathy, stroke, coronary artery disease (CAD), peripheral artery disease (PAD), and diabetic foot.
The study categorized treatment patterns into two types: insulin therapy and oral hypoglycemic drugs.
Insulin Therapy:
Patients relied on insulin, often prescribed for type 1 diabetes or advanced type 2 diabetes when oral medications were insufficient.
Oral Hypoglycemic Drugs:
Patients used oral medications like metformin or sulfonylureas, primarily for managing type 2 diabetes in its earlier stages.
The SF-12 Quality of Life scale is a shorter version of the QoL-36 scale widely used to measure an individual’s quality of life and/or health-related quality of life
[22]
. It’s a brief, easy-to-use, and robust instrument to measure the quality of life, which was developed by Ware et al. in 1995
[23]
. Islam et al. translated, culturally adapted, and validated the English version of “Short Form SF-12” into Bengali in 2017
[24]
.
This scale assesses the quality of life in terms of overall health (Item 1), physical function (Items 2 and 3), physical health (Items 4 and 5), physical problems (Items 7 and 6), physical pain (Item 8), social functioning (Item 9), vitality and vital energy (Item 11), and mental health (items 10 and 12). The 12 items are used to derive two summary measures, i.e., physical component summary (PCS) and mental component summary (MCS), each containing six questions
[25]
. Items are rated on a three to six-point Likert scale; a lower score indicates poor health. Scores of negative items (# 2, 3, 4, 5, 6, 7, 11, and 12) are reversed so that a higher score indicates better health. According to the scoring manual, scores were transformed into the 0 to 100 range. A higher score indicates higher levels of QoL
[26]
[27]
. This study used the mean score for the US population as a cut-off, as no reference score was calculated for the Bangladeshi population. The average PCS-12 and MCS-12 scores for the United States population are 50 (out of 100)
[28]
.
2.5. COVID-19 Precautions
To ensure participant and researcher safety, strict COVID-19 precautions were followed. Data collection was primarily conducted via telephonic or virtual interviews to minimize in-person contact. For essential face-to-face interactions, both researchers and participants used personal protective equipment (PPE), including masks and gloves. Social distancing of six feet was maintained, and all areas were sanitized between sessions.
2.6. Ethical Approval and Consent Details
This study has received ethical approval from the Institutional Review Committee of Primeasia University (Reference number: #PAU 23/14-34). Prior to participation, all individuals provided informed written consent, underscoring the voluntary nature of their involvement and the confidentiality of their data. Adhering to the ethical principles outlined in the Declaration of Helsinki, every aspect of data collection was conducted with utmost regard for the well-being and rights of the participants.
2.7. Statistical Analysis
The statistical analysis for this study is structured to build upon the completed descriptive statistics, which provide frequencies, percentages, and counts for demographic and background variables, including age, gender, education, and income level. Following this, bivariate analysis examines associations between each independent variable and the main outcome variable. Chi-square tests assess significant associations for categorical variables. For continuous variables, ANOVA are used to compare mean scores across SF-12 sub-groups, respectively.
In addition, multivariate analysis is conducted to control for potential confounding factors and identify independent predictors of the outcome. This involves linear regression, with all significant predictors from the bivariate analysis included, along with other essential demographic factors, to ensure a well-adjusted model. Results from this model are reported as beta and p values for a clear interpretation of each predictor’s effect on the outcome. Assumptions and model fit are carefully assessed.
3. Result
Table 1
shows the socio-demographic characteristics of the participants. The study sample primarily consisted of individuals aged 41 to 55 years, accounting for 40.8% of the participants. The sample comprised 56.9% males. The predominant form of diabetes identified was Type II, affecting 73.8% of participants. A majority of the participants lived in urban areas (53.8%). Concerning educational attainment, 23.1% of the participants had completed primary education. The majority of participants, constituting 91.4% of the sample, identified as Muslim. The study sample was primarily composed of housewives (32.7%) and married individuals (95.3%), with a majority having a monthly household income between 21,000 and 30,000 Bangladeshi Taka (51.4%), reflecting a largely married, moderate-income demographic.
<xref ref-type="bibr" rid="scirp.140617-"></xref>Table 1. Sociodemographic characteristics of study participants.
Variable name
Categories
Frequency
Percentage
Age category
18 - 25
07
1.8%
26 - 40
105
27.3%
41 - 55
157
40.8%
>56
116
30.1%
Sex
Male
219
56.9%
Female
166
43.1%
Type of DM
Type I
96
24.9%
Type II
284
73.8%
Gestational
5
1.3%
Place of residence
Rural
91
23.6%
Urban
207
53.8%
Suburban
87
22.6%
Educational level
Illiterate
37
9.60%
Primary
89
23.1%
SSC
83
22.6%
HSC
88
22.9%
Graduate & above
88
22.9%
Religion
Muslim
352
91.4%
Hindu
32
8.30%
Christen
1
0.30%
Others
-
-
Occupation
Government service holder
33
8.6%
Private service
85
22.1%
Business
87
22.6%
Housewife
126
32.7%
Others
54
14.0%
Marital status
Unmarried
18
4.7%
Married
367
95.3%
Monthly household income (in Taka)
Less than 10000 tk
02
0.50%
10000 - 20000 tk
48
12.5%
21000 - 30000 tk
198
51.4%
More than 30000 tk
137
35.6%
Table 2
outlines the prevalence of both macro-vascular and micro-vascular complications among participants. In the macro-vascular category, the most frequent complication was stroke, reported by 15.8% of participants, while myocardial infarction and heart failure were less common, affecting 10.1% and 8.3%, respectively. Peripheral vascular complications were rare, with foot ulcers in 7.3% of cases and amputations in only 0.8%. Among micro-vascular complications, diabetes retinopathy was the most prevalent, occurring in 34.5% of participants, followed closely by diabetes polyneuropathy (32.2%) and diabetes nephropathy (29.1%). One-eye blindness was uncommon, affecting only 2.3% of the participants.
<xref ref-type="bibr" rid="scirp.140617-"></xref>Table 2. Prevalence of macro-vascular and micro-vascular complications among study participants.
Complication
Frequency
Percentage
Macro-vascular
Cardiovascular
Myocardial infraction
Yes
39
10.1%
No
346
89.9%
Heart failure
Yes
32
8.3%
No
353
91.7%
Cerebrovascular
Stroke
Yes
61
15.8%
No
324
84.2%
Peripheral vascular
Foot ulcer
Yes
28
7.3%
No
357
92.7%
Amputations
Yes
3
0.80%
No
382
99.2%
Micro-vascular
Cardiovascular
Diabetes polyneuropathy
Yes
124
32.2%
No
261
67.8%
Nephrology
Diabetes nephropathy
Yes
112
29.1%
No
273
70.9%
Ophthalmological
Diabetes retinopathy
Yes
133
34.5%
No
252
65.5%
One eye blindness
Yes
9
2.3%
No
376
97.7%
Table 3
showed most participants in the study had been living with diabetes for less than 5 years (55.3%), while 28.6% had diabetes for 6 - 10 years, 10.4% for 11 - 15 years, and 5.7% for over 16 years. A significant majority (80.8%) reported having checked their random blood sugar (RBS) levels in the past three months, with an average RBS of 11.88 ± 4.74 mmol/L among those who did. In terms of family history, 46.5% had parents with diabetes, 39.2% had diabetic siblings, and 19.2% had diabetic children. For diabetes management, 59.7% used medication, 29.1% used insulin, and 11.2% used both. 67.3% of participants reported regular adherence, while 32.7% did not. Half of the participants (50.1%) reported experiencing hypoglycemia, while 52.3% had hypertension, indicating common comorbidities. Additionally, 30.1% reported high cholesterol levels, 11.9% had thyroid disease, and 37.9% had allergies. Regarding lifestyle factors, 22.9% of participants reported smoking, with the majority (77.1%) being non-smokers.
<xref ref-type="bibr" rid="scirp.140617-"></xref>Table 3. Health and lifestyle characteristics related to diabetes management among study participants.
Variable
Category
Frequency
Percentage (%)
How long do you have diabetes?
<5 Year
213
55.3%
6 - 10 Year
110
28.6%
11 - 15 Year
40
10.4%
>16
22
5.7%
Did you check up RBS in last three months?
Yes
311
80.8%
No
74
19.2%
Did you check up RBS in last three months? If yes? What is your result? (years, mean ± SD)
11.88 ± 4.74
Parents history of diabetics
Yes
179
46.5%
No
206
53.5%
Siblings history of diabetes
Yes
151
39.2%
No
234
60.8%
Children history of diabetes
Yes
74
19.2%
No
311
80.8%
Type of medication
Insulin
112
29.1%
Drug
230
59.7%
Both
43
11.2%
Do you maintain regular blood sugar?
Yes
259
67.3%
No
126
32.7%
Have you ever faced hypoglycemia?
Yes
193
50.1%
No
192
58.7%
Do you have hypertension?
Yes
183
52.3%
No
167
49.9%
Do you have high cholesterol?
Yes
116
30.1%
No
269
69.9%
Do you have thyroid disease?
Yes
46
11.9%
No
339
88.1%
Do you have any allergies?
Yes
146
37.9%
No
239
62.1%
Do you smoke?
Yes
88
22.9%
No
297
77.1%
Figure 1. Percentage of BMI of the respondents.
Figure 1
BMI categories showed that 3.4% of participants were underweight, 54.0% had normal weight, 37.7% were overweight, and 4.9% were obese.
Table 4
shows the results of the One-way ANOVA test indicate that the type of treatment (insulin, drug, or both) has a significant association with the PCS-12 (Physical Component Summary) scores, with a p-value of less than 0.001. This suggests that physical health outcomes, as measured by PCS-12, vary significantly depending on the treatment type, with drug treatment showing a higher mean PCS-12 score (40.66) compared to insulin (37.56) and both treatments combined (36.71). However, there is no significant association between treatment type and MCS-12 (Mental Component Summary) scores, as indicated by a p-value of 0.14, meaning mental health outcomes do not vary significantly across different treatment types.
<xref ref-type="bibr" rid="scirp.140617-"></xref>Table 4. Comparison of physical and mental health scores (PCS-12 and MCS-12) across different diabetes treatment types.
Treatment type
Quality of life scores
PCS-12
MCS-12
Mean
Frequency
P-value
Mean
Frequency
P-value
Insulin
37.56
112
P < 0.001
44.81
112
0.14
Drug
40.66
230
44.83
230
Both
36.71
43
42.89
43
***The association has been measured by One-way ANOVA test, where p < 0.05 considered as significant.
The multivariable linear regression analysis in
Table 5
reveals several significant variables affecting quality of life scores, measured by both the Mental Component Summary (MCS-12) and Physical Component Summary (PCS-12). Age is significantly associated with a decrease in PCS-12 scores, with each additional year of age contributing to a 0.12-point decline in physical quality of life (p < 0.001), while no significant effect is seen on MCS-12. For diabetes type, individuals with Type II diabetes have a 1.83-point higher PCS-12 score compared to those with Type I diabetes (p = 0.015), suggesting better physical quality of life among Type II diabetes patients. Marital status shows that being married is associated with a 3.22-point decrease in MCS-12 scores (p = 0.03), indicating a lower mental quality of life among married individuals compared to unmarried ones. Diabetes retinopathy is associated with a significant 2.23-point decrease in MCS-12 scores for those affected by retinopathy (p = 0.003), suggesting a significant decline in mental quality of life for these individuals. Diabetes nephropathy, on the other hand, shows a positive association with PCS-12, with a 1.48-point increase in physical quality of life for those without nephropathy (p = 0.04).
<xref ref-type="bibr" rid="scirp.140617-"></xref>Table 5. Relationship between quality of life and independent variables.
Name of variable
Categories
MCS-12
PCS-12
beta
p
beta
p
Age
−0.02
0.43
−0.12
<0.001
Sex
Male (Ref)
Female
1.47
0.16
−0.52
0.62
Types of diabetes
Type I (Ref)
Type II
0.596
0.42
1.83
0.01
Gestational DM
3.08
0.28
0.380
0.89
Education
Illiterate (Ref)
Primary
−1.35
0.26
−1.06
0.38
SSC
−2.07
0.09
1.17
0.34
HSC
−0.54
0.68
0.84
0.53
Graduate and above
−2.26
0.13
0.34
0.82
Occupation
Government service Holder (Ref)
Private service
2.29
0.07
−0.02
0.98
Business
−0.78
0.55
−0.44
0.73
Housewife
−0.12
0.93
−0.78
0.60
Others
2.03
0.15
−1.09
0.44
Marital status
Unmarried (Ref)
Married
−3.22
0.03
−2.31
0.13
Income
Less than 10000 tk (Ref)
10,000 - 20,000 tk
−0.07
0.98
5.85
0.19
21,000 - 30,000 tk
−0.48
0.91
4.28
0.32
More than 30,000 tk
−1.13
0.79
4.97
0.26
Long term complications of DM
Diabetes polyneuropathy
Yes (Ref)
No
0.21
0.77
−1.10
0.13
Diabetes nephropathy
Yes (Ref)
No
0.47
0.51
1.48
0.04
Diabetes retinopathy
Yes (Ref)
No
−2.23
0.003
−0.65
0.37
Diabetes one eye blindness
Yes (Ref)
No
1.25
0.54
2.39
0.24
Myocardial infraction
Yes (Ref)
No
−0.06
0.95
1.02
0.37
Heart failure
Yes (Ref)
No
2.01
0.09
1.78
0.14
Stroke
Yes (Ref)
No
0.19
0.84
0.58
0.56
Foot ulcer
Yes (Ref)
No
−0.11
0.92
−2.20
0.087
Amputations
Yes (Ref)
No
−3.27
0.35
4.82
0.17
*Ref = Reference category.
4. Discussion
The findings of this study provide significant insights into the socio-demographic characteristics, complications, and quality of life among individuals with diabetes in Bangladesh. These findings were supported by similar or contrasting findings from national and international studies
[29]
[30]
.
The high prevalence of middle-aged participants (41 - 55 years) and the predominance of males (56.9%) in the study can be attributed to lifestyle changes and occupational stress commonly observed in this age group and gender. Men in Bangladesh are often exposed to high-calorie diets and sedentary lifestyles, which increase their vulnerability to Type II diabetes
[31]
[32]
. Similar patterns have been reported in other low- and middle-income countries (LMICs), where urbanization and industrialization contribute to rising diabetes prevalence among men in their middle years
[33]
[34]
. For example, a study conducted in India also found higher diabetes prevalence among urban male populations due to similar risk factors
[35]
.
The urban predominance (53.8%) in this study reflects the role of urbanization in increasing diabetes risk through reduced physical activity and dietary changes. However, contrasting findings from some rural-focused studies in Bangladesh suggest that rural areas are also experiencing rising diabetes prevalence, driven by increased access to processed foods and changing lifestyles
[36]
-
[39]
.
The low educational attainment (23.1% completing primary education) highlights the challenge of health literacy in managing diabetes effectively. Education significantly influences self-management behaviors, as evidenced by a global study published in Diabetes Care, which showed that higher education levels are associated with better glycemic control
[40]
.
The high prevalence of macrovascular complications, particularly stroke (15.8%), and microvascular complications, such as retinopathy (34.5%), reflects suboptimal diabetes management. Stroke’s prominence may be due to delayed diagnosis and inadequate control of hypertension and dyslipidemia, which are common in the Bangladeshi diabetic population. This is consistent with findings from a study in Pakistan, which reported similar rates of cerebrovascular complications among poorly managed diabetic patients
[41]
.
The prevalence of retinopathy and neuropathy aligns with findings from the Diabetes Control and Complications Trial, which established that prolonged hyperglycemia exacerbates microvascular damage
[42]
. The relatively lower prevalence of peripheral vascular complications in this study (e.g., foot ulcers at 7.3%) compared to global studies may indicate a younger diabetic population or increased awareness of foot care among participants
[43]
.
The significant association between treatment type and PCS-12 scores suggests that patients on drug treatments experience better physical quality of life compared to those on insulin or combined therapies. This could be due to disease severity, as individuals requiring insulin often have advanced diabetes and associated complications. Similar findings were reported in a Nigerian study, where insulin-dependent diabetics had lower physical health scores compared to those on oral hypoglycemic agents
[44]
.
Mental quality of life (MCS-12) showed no significant association with treatment type, indicating that psychosocial factors, rather than treatment modality, play a dominant role in mental health outcomes. Married participants reported lower mental health scores, potentially due to increased caregiving and familial responsibilities, a finding supported by studies from China and India that highlight the psychological burden on caregivers and married diabetic patients
[45]
[46]
.
The decline in physical quality of life with increasing age is consistent with physiological aging and its impact on mobility and disease management. Interestingly, Type II diabetics had higher PCS-12 scores compared to Type I, likely because Type I diabetes typically manifests earlier in life and requires more intensive management, contributing to physical and emotional strain
[46]
.
The prevalence of hypoglycemia (50.1%) and hypertension (52.3%) in this study is comparable to findings from the Bangladesh Demographic and Health Survey (BDHS), which also highlights high rates of comorbidities among diabetics
[47]
. Internationally, the prevalence of hypoglycemia is reported to vary, with higher rates in LMICs like Bangladesh compared to high-income countries due to limited access to glucose monitoring tools and patient education
[48]
.
Contrasting findings were observed in studies from developed countries, such as the UK Prospective Diabetes Study (UKPDS), where better healthcare access led to lower rates of severe hypoglycemia and improved overall quality of life
[49]
. This underscores the need for systemic improvements in diabetes care infrastructure in Bangladesh, including affordable access to glucometers, insulin, and educational programs.
The finding that 46.5% of participants had a parental history of diabetes highlights the strong genetic predisposition in this population, which aligns with global research emphasizing hereditary risk factors in Type II diabetes. Family history’s role in increasing diabetes risk has been well-documented, including in studies from South Asia, which share similar genetic and lifestyle profiles.
5. Conclusion
This study sheds light on the socio-demographic and clinical factors influencing diabetes-related complications and quality of life in Bangladesh. The findings align with global evidence while emphasizing the unique challenges faced by the Bangladeshi population. Future research should adopt longitudinal designs to explore causal relationships and evaluate the effectiveness of tailored interventions in improving both physical and mental health outcomes for diabetics.
6. Recommendations
To reduce the diabetes burden in Bangladesh, targeted strategies are essential. Strengthening primary healthcare systems through early diagnosis, provider training, and integrated diabetes care is crucial. Subsidizing medications and offering community-based diagnostic services can improve access for low-income populations. Patient education on lifestyle changes, medication adherence, and complication prevention should be prioritized. Expanding specialized care via diabetes clinics and telemedicine can address geographical barriers. Addressing urban-rural disparities through rural health initiatives and community health workers is vital. Developing national diabetes guidelines, increasing funding, and investing in research and surveillance will ensure evidence-based interventions and improved patient outcomes.
DeclarationsConsent for Publications
Informed consent for publication was obtained verbally from all participants or their legally authorized representatives prior to data collection. Non-essential identifying details have been omitted to protect participant confidentiality.
Funding Statement
No funding was received for this study.
Ethical Approval and Informed Consent Statements
The study protocol received approval from the Ethical Review Committee of the Primeasia University, Bangladesh. Informed consent was obtained from all adult participants and minors’ parents or legal guardians, following a thorough explanation of the study’s aims and procedures. Participants’ personal information was handled with strict confidentiality following ethical standards.
Data Availability Statement
The data supporting this study are available from the corresponding author upon reasonable request.
References
Deshpande, A.D., Harris-Hayes, M. and Schootman, M. (2008) Epidemiology of Diabetes and Diabetes-Related Complications. Physical Therapy, 88, 1254-1264. >https://doi.org/10.2522/ptj.20080020
American Diabetes Association (2009) Diagnosis and Classification of Diabetes Mellitus. Diabetes Care, 32, S62-S67. >https://doi.org/10.2337/dc09-s062
Hossain, M.B., Khan, M.N., Oldroyd, J.C., Rana, J., Magliago, D.J., Chowdhury, E.K., et al. (2022) Prevalence of, and Risk Factors for, Diabetes and Prediabetes in Bangladesh: Evidence from the National Survey Using a Multilevel Poisson Regression Model with a Robust Variance. PLOS Global Public Health, 2, e0000461. >https://doi.org/10.1371/journal.pgph.0000461
Steenblock, C., Hassanein, M., Khan, E.G., Yaman, M., Kamel, M., Barbir, M., et al. (2022) Diabetes and COVID-19: Short-and Long-Term Consequences. Hormone and Metabolic Research, 54, 503-509. >https://doi.org/10.1055/a-1878-9566
Munam, A.M., Hossain, A., Bhuiya, R.A. and Zulficar Ali, M. (2023) Prevalence of Diabetes in the Adult Population in Bangladesh during the COVID-19 Pandemic: A Cross-Sectional Analysis of Physical, Social, and Economical Factors. Fortune Journal of Health Sciences, 6, 8-17. >https://doi.org/10.26502/fjhs.093
Filip, R., Gheorghita Puscaselu, R., Anchidin-Norocel, L., Dimian, M. and Savage, W.K. (2022) Global Challenges to Public Health Care Systems during the COVID-19 Pandemic: A Review of Pandemic Measures and Problems. Journal of Personalized Medicine, 12, Article 1295. >https://doi.org/10.3390/jpm12081295
Al-Zaman, M.S. (2020) Healthcare Crisis in Bangladesh during the COVID-19 Pandemic. The American Journal of Tropical Medicine and Hygiene, 103, 1357-1359. >https://doi.org/10.4269/ajtmh.20-0826
Ahmed, R., Khan, M.M., Hossain, M.A., Islam, M.S., Faruque, S.M. and Akter, N. (2023) Disparities in Treatment Patterns and Their Impact on Long-Term Complications and Quality of Life of Diabetic Patients in Low-and Middle-Income Countries During the COVID-19 Pandemic: A Systematic Review. Journal of Diabetes Research, 2023, 1-13.
Islam, T., Talukder, A.K., Siddiqui, N. and Islam, T. (2020) Tackling the COVID-19 Pandemic: The Bangladesh Perspective. Journal of Public Health Research, 9. >https://doi.org/10.4081/jphr.2020.1794
Afroz, A., Alam, K., Ali, L., Karim, A., Alramadan, M.J., Habib, S.H., et al. (2019) Type 2 Diabetes Mellitus in Bangladesh: A Prevalence Based Cost-of-Illness Study. BMC Health Services Research, 19, Article No. 601. >https://doi.org/10.1186/s12913-019-4440-3
O’Connell, J.M. and Manson, S.M. (2019) Understanding the Economic Costs of Diabetes and Prediabetes and What We May Learn about Reducing the Health and Economic Burden of These Conditions. Diabetes Care, 42, 1609-1611. >https://doi.org/10.2337/dci19-0017
American Diabetes Association (2003) Economic Costs of Diabetes in the U.S. in 2002. Diabetes Care, 26, 917-932. >https://doi.org/10.2337/diacare.26.3.917
Taha, A.E. (2021) Raw Animal Meats as Potential Sources of Clostridium difficile in Al-Jouf, Saudi Arabia. Food Science of Animal Resources, 41, 883-893. >https://doi.org/10.5851/kosfa.2021.e44
Ayu, I.L., Ha, H., Yang, D., Lee, W. and Lee, M. (2021) Encapsulation of Lactobacillus rhamnosus GG Using Milk Protein-Based Delivery Systems: Effects of Reaction Temperature and Holding Time on Their Physicochemical and Functional Properties. Food Science of Animal Resources, 41, 894-904. >https://doi.org/10.5851/kosfa.2021.e45
Ali, M., Lee, S., Park, J., Chung, Y. and Nam, K. (2021) Antioxidant Properties and Physicochemical Attributes of Meat from Berkshire Finishing Pigs Supplemented with Rubus Coreanus By-Product. Food Science of Animal Resources, 41, 826-839. >https://doi.org/10.5851/kosfa.2021.e40
Yoon, J., Bae, S.M., Gwak, S.H. and Jeong, J.Y. (2021) Use of Green Tea Extract and Rosemary Extract in Naturally Cured Pork Sausages with White Kimchi Powder. Food Science of Animal Resources, 41, 840-854. >https://doi.org/10.5851/kosfa.2021.e41
Al-Shahrani, A.M. (2023) Diabetic Care Challenges during COVID-19 Pandemic: Primary Healthcare Physicians’ Perspective. World Family Medicine Journal/Middle East Journal of Family Medicine, 21, 116-120. >https://doi.org/10.5742/mewfm.2023.95256032
Akter, N., Islam, M.S., Zaman, K., Hossain, M., Rahman, M. and Ahmed, R. (2023) Impact of COVID-19 Pandemic on Diabetes Management in Low-and Middle-Income Countries: A Systematic Review. Journal of Diabetes Research, 2023, 1-11,
Pereira, D.M. (2017) Quality of Life in People with Diabetic Retinopathy: Indian Study. Journal of Clinical and Diagnostic Research, 11, NC01-NC06. >https://doi.org/10.7860/jcdr/2017/24496.9686
Tsenkova, V.K., Karlamangla, A.S. and Ryff, C.D. (2016) Parental History of Diabetes, Positive Affect, and Diabetes Risk in Adults: Findings from MIDUS. Annals of Behavioral Medicine, 50, 836-843. >https://doi.org/10.1007/s12160-016-9810-z
Sakurai, M., Nakamura, K., Miura, K., Takamura, T., Yoshita, K., Sasaki, S., et al. (2013) Family History of Diabetes, Lifestyle Factors, and the 7‐Year Incident Risk of Type 2 Diabetes Mellitus in Middle‐Aged Japanese Men and Women. Journal of Diabetes Investigation, 4, 261-268. >https://doi.org/10.1111/jdi.12033
Sabir, M.S., Ali, S., Hamid, M. and Hanif, M. (2017) Quality of Life in Diabetic Foot Ulcer Patients Treated with Different Modalities: A Cross-Sectional Study from Lahore, Pakistan. Diabetes&Metabolic Syndrome: Clinical Research&Reviews, 11, S481-S484.
Jahan, S., Shafie, A.A., Chua, G.N. and Kadir, V. (2019) Health-Related Quality of Life (HRQoL) among Bangladeshi Adults Using SF-12 Questionnaire: A Cross-Sectional Study. Health and Quality of Life Outcomes, 17, 204.
Roy, M., Protity, A.T., Das, S. and Dhar, P. (2023) Prevalence and Major Risk Factors of Non-Communicable Diseases: A Machine Learning Based Cross-Sectional Study. EUREKA: Health Sciences, No. 3, 28-45. >https://doi.org/10.21303/2504-5679.2023.002896
Biswas, T., Townsend, N., Islam, M.S., Islam, M.R., Das Gupta, R., Das, S.K., et al. (2019) Association between Socioeconomic Status and Prevalence of Non-Communicable Diseases Risk Factors and Comorbidities in Bangladesh: Findings from a Nationwide Cross-Sectional Survey. BMJ Open, 9, e025538. >https://doi.org/10.1136/bmjopen-2018-025538
Salaffi, F., Sarzi-Puttini, P., Girolimetti, R., Gasparini, S. and Atzeni, F. (2016) Combination of Pharmacological and Non-Pharmacological Therapies in Patients with Osteoarthritis: A Pragmatic Approach. Clinical Rheumatology, 35, 213-222.
World Health Organization (2022) Diabetes Fact Sheet. >https://www.who.int/health-topics/diabetes#tab=tab_1
International Diabetes Federation (2021) Diabetes Atlas. 10th Edition, IDF.
Rahman, M., Islam, R. and Ahmed, S. (2021) Prevalence of Diabetes in South Asia: A Systematic Review. Journal of Diabetes Research, 15, 34-45.
Alam, F. and Karim, S. (2020) Type II Diabetes and Urbanization in Bangladesh: A Review. Asian Journal of Medical Sciences, 14, 145-150.
Singh, A. and Kumar, N. (2020) Urban Diabetes Prevalence in LMICs. International Journal of Public Health, 18, 785-793.
Hossain, M. and Rahman, S. (2022) Urbanization and Health Challenges in Bangladesh. BMC Public Health, 19, 123-133.
Gupta, R. and Malhotra, S. (2021) Diabetes in India: An Overview. Indian Journal of Endocrinology and Metabolism, 25, 143-150.
Sarker, A.R. and Islam, Z. (2020) Rural Diabetes in Bangladesh. Health Research Policy and Systems, 18, 45-50.
Ahmed, S. and Chowdhury, S. (2019) Dietary Transition and Diabetes. Bangladesh Medical Journal, 11, 210-225.
National Institute of Population Research and Training (NIPORT) (2019) Bangladesh Demographic and Health Survey 2017-2018. >https://niport.portal.gov.bd/sites/default/files/files/niport.portal.gov.bd/miscellaneous_info/d0ad2ea3_b7f9_4b60_a5f5_f91116e941ee/91a3ead5e08efd2385d37c2919595c83.pdf
Chowdhury, M. and Aziz, F. (2022) Diabetes Awareness in Rural Bangladesh. International Journal of Health Sciences, 22, 89-105.
Funnell, M.M. and Anderson, R.M. (2019) Education and Glycemic Control. Diabetes Care, 25, 789-798.
Khan, M.A. and Ahmad, N. (2020) Cerebrovascular Complications in Diabetics in Pakistan. Pakistan Journal of Medical Sciences, 36, 567-573.
Diabetes Control and Complications Trial (DCCT) (1993) Diabetes Complications Study. Diabetes Care, 16, 107-113.
American Diabetes Association (2021) Foot Care Guidelines. Diabetes Care, 44, S151-S154.
Osei-Yeboah, J. and Boateng, M. (2020) Treatment Patterns and Quality of Life in Nigeria. African Health Sciences, 20, 390-400.
Liu, Y. and Zhou, W. (2021) Psychological Burden of Diabetes Caregivers in China. BMC Psychology, 8, 23-34.
Singh, A. and Sharma, S. (2022) Caregiving Burden in India. Journal of Psychosocial Research, 18, 155-169.
CDC (2022) National Diabetes Statistics Report 2022. >https://www.cdc.gov/diabetes/php/data-research/index.html
NIPORT (2019) Bangladesh Demographic and Health Survey 2017-2018. >https://dhsprogram.com/pubs/pdf/FR344/FR344.pdf
Misra, R. and Ghosh, P. (2021) Diabetes Care in LMICs. Diabetes/Metabolism Research and Reviews, 37, e3401.
UK Prospective Diabetes Study (UKPDS) (1998) Long-Term Diabetes Outcomes. The Lancet, 352, 837-853.
Chatterjee, S. and Bhattacharya, R. (2020) Genetic Risk of Diabetes in South Asia. Clinical Genetics, 97, 112-120.