Prevalence of comorbidities and its associated factors among type-2 diabetes patients: a hospital-based study in Jashore District, Bangladesh

Abstract

Objective

This study aimed to estimate the prevalence of comorbidity and its associated factors among Bangladeshi type-2 diabetes (T2D) patients.

Design

A hospital-based cross-sectional study.

Setting

This study was conducted in two specialised diabetic centres residing in the Jashore District of Bangladesh. A systematic random sampling procedure was applied to identify the T2D patients through a face-to-face interview.

Participants

A total of 1036 patients with T2D were included in this study. A structured questionnaire was administered to collect data on demographic, lifestyle, medical and healthcare access-related data through face-to-face and medical record reviews.

Outcome measures and analyses

The main outcome variable for this study was comorbidities. The prevalence of comorbidity was measured using descriptive statistics. A logistic regression model was performed to explore the factors associated with comorbidity among Bangladeshi T2D patients.

Results

The overall prevalence of comorbidity was 41.4% and the most prevalent conditions were hypertension (50.4%), retinopathy (49.6%), obesity (28.7%) and oral problem (26.2). In the regression model, the odds of comorbidities increased with gender (male: OR: 1.27, 95% CI 1.12 to 1.87), age (50–64 years: OR: 2.14, 95% CI 1.32 to 2.93; and above 65 years: OR: 2.96, 95% CI 1.83 to 4.16), occupation (unemployment: OR: 3.32, 95% CI 1.92 to 6.02 and non-manual worker: OR: 2.31, 95% CI 1.91 to 5.82), duration of diabetes (above 15 years: OR: 3.28, 95% CI 1.44 to 5.37), body mass index (obese: OR: 2.62, 95% CI 1.24 to 4.26) of patients. We also found that individuals with recommended moderate to vigorous physical activity levels (OR: 0.41, 95% CI 0.25 to 0.74) had the lowest odds of having comorbidity. Meanwhile, respondents with limited self-care practice, unaffordable medicine and financial problems had 1.82 times, 1.94 times and 1.86 times higher odds of developing comorbidities.

Conclusion

The findings could be useful in designing and implementing effective intervention strategies and programmes for people with T2D to reduce the burden of comorbidity.

STRENGTHS AND LIMITATIONS OF THIS STUDY.

• Our study used a systematic simple random sampling method with an acceptable response rate.

• Data collection included clinical measures of blood pressure, blood glucose concentration, disease status, duration of diabetes, body weight and height collected by a health technician.

• This study is limited due to its cross-sectional nature and inability to establish a causal relationship.

• Recall and reporting bias could occur as the data were collected via face-to-face interviews.

• This study provides preliminary data to help support future research.

Introduction

Diabetes mellitus (DM) is a global public health concern with an increasing prevalence, particularly among Southeast Asians.¹ The WHO lists it as the third largest cause of mortality globally² and ranked it as the seventh leading cause of mortality in 2030.³ In 2012, it was predicted that 1.5 million fatalities worldwide were caused by diabetes itself, and an additional 2.2 million deaths were caused by the increased risk of comorbidities related to hyperglycaemia.^{2 3} The prevalence rate of diabetes is increasing, especially in low and middle-income countries,^{4 5} whereas 84.5% of undiagnosed cases will lead to several diabetic cases in the future.⁴ Type-2 diabetes (T2D) patients have increased in number in Bangladesh over the past few years, increasing morbidity, mortality and healthcare expenses.⁶ Different studies suggested that the significant underdiagnosis of patients with diabetes in Southeast Asia, including Bangladesh, is aggravated by the current COVID-19 pandemic.^{7 8} According to the International Diabetes Federation, 13.1 million adults in Bangladesh had diabetes in 2021, and the prevalence of diabetes in South East Asia is expected to rise by 69% between 2021 and 2045.⁹ Unless addressed, this will result in 22.3 million diabetic cases in Bangladesh by 2045.⁹ The rising prevalence of diabetes in Bangladesh will exacerbate diabetic complications and comorbid illnesses.¹⁰ Diabetes can trigger complications that drive down the quality of life and lead to premature mortality.¹¹ Additionally, a lack of awareness and diabetic care will affect the patient’s quality of life and economic burden.¹²

Comorbidities are defined as the coexistence of other medical conditions with an index disease (ie, the primary medical condition being studied), even though these additional medical conditions are not side effects of the index condition.^{13 14} In the actual world, T2D almost seldom occurs on its own and is almost always accompanied by a co-occurring condition.¹⁵ People with T2D are more likely to have high blood pressure, hypertension, overweight/obesity, dyslipidaemia, chronic kidney disease, cardiovascular disease and chronic obstructive pulmonary disease (COPD).^16–18 Furthermore, while locally available services are more accessible, they currently lack the necessary infrastructure and experience to fully care for patients.¹⁹ The study suggests that the majority of adults with diabetes have at least one comorbidity, and up to 40% of them have at least three.²⁰ According to the study, among the 1.3 million population, more than 97.5% of study participants had at least one comorbid condition. However, in the case of diabetes mellitus, 88.5% of participants had at least two comorbid conditions.²¹ Another study report suggests that the prevalence of multimorbidity among adult diabetic patients is about 50%.²²

However, comorbidities accelerate illness progression, promote physical and mental disorders and can lead to premature mortality.^{23 24} They negatively affect overall health, well-being and functioning.^{24 25} Comorbidities in T2D patients worsen diabetes-related healthcare outcomes, treatment options, care needs and associated costs,^{18 25} resulting in a significant treatment burden, increased healthcare utilisation, expenses and economic productivity loss.¹⁸ Consistent evidence points to a wide range of sociodemographic characteristics being correlated to comorbidity among patients with T2D.^{11 26–28} Furthermore, many studies have also connected lifestyle behaviours like smoking, obesity, unhealthy diet and alcohol use to the development of comorbidity.^{8 29}

However, numerous researches have examined T2D patients’ comorbid depression, treatment habits and health-related quality of life.^30–32 Moreover, several cross-sectional studies have established the prevalence and risk factors associated with the propagation of T2D.^33–35 Despite nationwide survey data in Bangladesh, non-communicable diseases (NCDs) prevalence and medical and healthcare access-related risk factors of comorbidity remain unmapped. Understanding NCD prevalence and risk factors is crucial for Bangladesh to accomplish the Sustainable Development Goal (SDG) aim of reducing premature death from NCDs by one-third by 2030.³⁶ Therefore, this study primarily aimed to evaluate the prevalence of comorbidities and their associated factors among Bangladeshi patients with T2D in the Jashore district of Bangladesh. This study could be helpful among policymakers, researchers and clinicians to understand the treatment process and save both patients’ and healthcare providers’ time and cost by finding trends and common clusters of chronic diseases.

Methods

Study design and participants

The hospital-based cross-sectional study was conducted among patients with T2D attending the outpatient department of two diabetes hospitals (Ahad Diabetic and Health Complex, and Kapotakkho Lions Eye and Diabetic Hospital) residing in Jashore district of Bangladesh. All patients with T2D in the Jashore district of Bangladesh who visited these two diabetes hospitals, aged above or equal to 21 years, were included as a sampling frame. A sample size of 384 was determined based on the following parameters: 95% CI, 5% margin of error, 90% test power and 95% response rate assuming 50% prevalence. However, a total of 1036 eligible participants who were approached responded to the study, providing the overall response rate an approximation of 95%. The study period was from February to March 2022. A simple random sampling technique was followed to select the targeted number of participants from these hospitals who seeking care at diabetic hospitals. Adult patients (≥21 years old) who were diagnosed with T2D were considered for inclusion in the study. Participants who were diagnosed with gestational diabetes, type-1 diabetes, secondary diabetes or an unknown type of diabetes were excluded from the study.

Data collection procedures and tools

Data were collected through face-to-face interviews using a pretested and structured questionnaire followed by previous literature (see online supplemental file 1).^{28 29 37} Eight research assistants with experience conducting health surveys were recruited for the data collection process. Before the data collection, the research assistants participated in Zoom meetings for in-depth training. Two language expert researchers translated the original English questionnaire into Bengali and then back into English to check for inconsistencies in the content. To further refine the language in the final version, the questionnaire was piloted among a small group (n=50) of diabetic patients. The tool used in the pilot study did not receive any corrections/suggestions from the participants in relation to the contents developed in the Bengali language. All information provided by respondents was ensured to remain confidential. The respondents were informed that they could withdraw at any time throughout the interview. The corresponding author stored the data, granting access solely to the researchers within the group.

Measurement of variables

Outcome variables

The primary outcome variable for this study is comorbidities. Previous research was used to define the array of comorbidities that are concordant with T2D.^{38 39} Comorbidities included were: hypertension, obesity, hyperlipidaemia, coronary artery disease, cerebrovascular disease, cardiovascular problem, kidney disease, asthma/COPD, stroke, cancer, chronic lung disease, thyroid disease, neuropathy, retinopathy, nephropathy, diabetic foot, and oral problem. At first, data were obtained from the participants, and subsequently, prescriptions were reviewed by a registered physician for the validity of the diagnosis. Participants with a history of hypertension, current use of anti-hypertensive medication or a high blood pressure level during the interview were all considered. Systolic blood pressure (SBP) 140 mm Hg and diastolic blood pressure (DBP) 90 mm Hg were used to diagnose hypertension.⁴⁰

Explanatory variables

The list of potential risk factors of comorbidities was based on literature about the risk factors of different chronic diseases.^{28 29} The assumption was that the risk factors would be similar for multiple chronic diseases combined. Explanatory variables considered in this study include socio-demographic characteristics (sex, age, education, occupation, economic status, family history of diabetes), as well as health and lifestyle-related factors (physical activity, smoking habit, diabetes duration, body mass index (BMI), fasting blood glucose (FBG) level and dietary diversity). Participants were asked about their physical activity levels with the question, ‘How frequently do you engage in moderate or intense physical activity for at least 30 min?’ The responses were categorised into six preassigned options: ‘not at all’, ‘less than once a week’, ‘one or two times a week’, ‘three times a week’, ‘more than three times a week but not every day’ and ‘every day’. This study used the gathered information to assess the respondents' moderate to vigorous physical activity (MVPA) based on a previous study.⁴¹ The responses were then grouped into two levels: meeting the recommended level of physical activity (more than three times a week but not every day, and every day) and falling below the recommended level of physical activity (not at all, less than once a week, one or two times a week and three times a week). This variable was initially taken as a continuous variable, such as what is your monthly income (in US dollars (USD)). Then, it was further classified into ≤96.5 USD, 96.6–144.7 USD, 144.8–192.9 USD and ≥193 USD. The participant’s occupation was also assessed as a categorical variable and categorised as manual worker, non-manual worker and unemployment. Data on education were collected as the total number of years of a full education, which were then categorised into five categories: illiterate, primary, secondary and college or higher education. BMI was calculated as weight (kg)/height (m²). We used Asian-specific BMI cut-offs to define underweight (˂18.5 kg/m²), normal (18.5–22.9 kg/m²), overweight (23.0–27.5 kg/m²) and obese (≥27.6 kg/m²).⁴² Regarding diabetes, our inclusion criteria encompassed individuals who had received a T2D diagnosis from medical professionals were actively taking medication, or had tested positive on a urinary strip test. Although the HbA1C test is recognised as the gold standard for diagnosing T2D, many patients cannot afford this test according to the hospital’s medical officer and pathology department report. Instead, the hospital uses conventional FBG tests to confirm diabetes in admitted patients. Diabetes mellitus was defined as FBG level greater than or equal to 7.0 mmol/L (126 mg/dL) or self-reported DM medication use.³⁹ The presence of other diseases was confirmed by checking the available prescriptions and diagnosed by a registered physician as well. According to FAO and the FANTA Project, the Dietary Diversity Scores (DDS) of the respondents were determined by using data from the 24-hour dietary recall.⁴³ The DDS considered 12 different food groups, and one point was given for each group’s consumption during the reference period. Individuals were given a maximum dietary diversity score of 12 points if respondents reported consuming from all the food groups during the reference period. The total dietary diversity score (DDS) ranges from 0 to 12 and is classified into three groups: low (scores of 0–3), moderate (scores of 4–6) and high.^{7–12 43}

Statistical analysis

Variables were precoded and data were entered and analysed in STATA V.14.0. The prevalence of comorbidities was estimated in the form of frequencies and percentages using descriptive statistics. Participants' demographic and lifestyle characteristics were cross-tabulated using Pearson’s χ² test to determine their association with comorbidities. A binary logistic regression model was used to assess the factors associated with comorbidities. All sociodemographic, lifestyle, medical and healthcare access-related characteristics were entered into the same model. The model was adjusted for potential confounding by gender, age, education, occupation, economic status, family history of diabetes, MVPA, smoking habit, diabetes duration, body mass index, FBG level, and dietary diversity score, transport difficulties, limited self-care practice, delayed care seeking, unaffordable medicine, medical staff shortage and financial problem which influenced the risk of comorbidities. ORs and their 95% CIs were also calculated. We included variables in the final logistic model that were significant at the 5% significance level. The statistical significance of the association was considered for p values<0.05.

Patient and public involvement

Prior to the development of this study, we conducted a pilot study among T2D patients who were seeking care at diabetic hospitals.

Results

Participants’ characteristics

Table 1 illustrates the prevalence of comorbidities with different demographic, lifestyle and health-related characteristics among T2D patients. Almost two-thirds of the respondents were women, 22.4% of respondents had no education and very few had higher education (18.1%). The majority of respondents were aged 50–64 years (44.5%) and non-manual employment (65.1%). Nearly, 32% of the respondents had a monthly family income between 144.8 and 192.9 USD, whereas 39.5% of the respondents had a family history of diabetes. The majority of participants had recommended levels of MVPA (71.7%) and non-smokers (82.2%), respectively. In addition, 30.8% reported that they suffered a 6–15-year duration of diabetes, while 60.6% claimed below 6 years’ duration of diabetes.

Table 1.

Prevalence of comorbidities among T2D patients following their demographic, lifestyle, and health-related characteristics (n=1036)

Characteristics		Total	Prevalence of comorbidities N (%)
			No	Yes	P value
Gender	Female	658 (63.5)	383 (58.2)	275 (41.8)	0.741
	Male	378 (36.5)	224 (59.3)	154 (40.7)
Education	College or Higher	188 (18.1)	121 (64.4)	67 (35.6)
	Secondary	293 (28.3)	161 (54.9)	132 (45.1)	0.007
	Primary	323 (31.2)	205 (63.5)	118 (36.5)
	Illiterate	232 (22.4)	120 (51.7)	112 (48.3)
Age	Below 35 years	90 (8.7)	61 (67.8)	29 (32.2)
	35–49 years	318 (30.7)	201 (63.2)	117 (36.8)	0.001
	50–64 years	461 (44.5)	267 (57.9)	194 (42.1)
	Above 65 years	167 (16.1)	78 (46.7)	89 (53.3)
Occupation	Non-manual worker	676 (65.1)	408 (60.3)	268 (29.7)
	Manual worker	168 (9.2)	110 (65.5)	58 (34.5)	0.001
	Unemployment/retired	192 (18.5)	89 (46.4)	103 (53.6)
Economic status	≥193 USD	213 (20.6)	129 (60.6)	84 (39.4)
	144.8–192.9 USD	330 (31.8)	194 (58.8)	136 (41.2)	0.613
	96.6–144.7 USD	318 (30.7)	178 (55.9)	140 (44.1)
	≤96.5 USD	140 (13.5)	82 (58.6)	58 (41.4)
	Depend on other	35 (3.4)	24 (68.6)	11 (31.4)
Residence	Rural	416 (40.1)	232 (55.7)	184 (44.3)	0.002
	Urban	620 (59.9)	242 (39.1)	378 (60.9)
Family history of diabetes	No	627 (60.5)	356 (56.8)	271 (43.2)	0.143
	Yes	409 (39.5)	251 (61.4)	158 (38.6)
Moderate to vigorous physical activity (MVPA)	Less than the recommended level	295 (28.3)	138 (47.1)	155 (52.9)	0.001
	Recommended level	741 (71.7)	469 (63.3)	272 (36.7)
Smoking habit	Non-smoker	852 (82.2)	506 (59.4)	346 (40.6)	0.035
	Ex-smoker	114 (11.0)	55 (48.3)	59 (51.7)
	Current-smoker	70 (6.8)	46 (65.7)	24 (34.3)
Duration of diabetes	Below≤5 years	628 (60.6)	390 (62.1)	238 (37.9)
	6–15 years	319 (30.8)	173 (54.2)	146 (45.8)	0.033
	Above 15 years	89 (8.6)	44 (49.4)	45 (50.6)
BMI	Healthy weight (18.5–22.9)	261 (25.2)	157 (60.1)	104 (39.9)
	Underweight (<18.5)	38 (3.7)	19 (50.0)	19 (50.0)
	Overweight (23–27.5)	439 (42.4)	280 (63.8)	159 (36.2)	0.003
	Obese (>27.5)	298 (28.7)	151 (50.7)	147 (49.3)
Fasting blood glucose level	Controlled (<7.0 mmol/L)	311 (30.1)	193 (62.1)	118 (37.9)
	Uncontrolled (≥7.0 mmol/L)	725 (69.9)	414 (57.1)	311 (42.9)	0.013
DDS category	High (≥6)	318 (30.7)	204 (64.1)	114 (35.9)	0.050
	Moderate (4-5)	477 (46.0)	270 (56.6)	207 (43.4)
	Low (≤3)	241 (23.3)	133 (55.2)	108 (44.8)
Challenges of getting routine medical and healthcare
Transport difficulties	No	886 (85.5)	529 (59.7)	357 (40.3)	0.07
	Yes	150 (14.5)	78 (52.0)	72 (48.0)
Limited self-care practice	No	908 (87.7)	558 (61.5)	350 (38.5)	<0.001
	Yes	128 (12.3)	49 (38.8)	79 (61.2)
Delayed care seeking	No	837 (81.0)	506 (60.5)	331 (39.5)	0.006
	Yes	199 (19.0)	97 (49.5)	102 (50.5)
Unaffordable medicine	No	658 (63.5)	395 (60.0)	263 (40.0)	0.215
	Yes	378 (36.5)	212 (56.1)	166 (43.9)
Medical staff shortage	No	937 (90.4)	559 (59.6)	378 (40.4)	0.033
	Yes	99 (9.6)	48 (48.4)	51 (51.6)
Financial problem	No	553 (53.4)	350 (63.3)	203 (36.7)	0.001
	Yes	483 (46.6)	255 (52.9)	228 (47.1)

Table 2 elucidates the prevalence of individual comorbid conditions in participants with T2DM. In terms of comorbidities, 41.4% of the study population presented two or more of the aforementioned diseases. The prevalence of individual diseases among study participants was as follows: hypertension (50.4%), retinopathy (49.6%), obesity (28.7%), oral problem (26.2%), neuropathy (25.1%), coronary artery disease (20.4%) and diabetic foot (19.0%).

Table 2.

Prevalence of individual comorbid conditions in participants with T2D

Diseases	No n (%)	Yes n (%)
Hypertension	509 (49.6)	527 (50.4)
Obesity	738 (71.3)	298 (28.7)
Hyperlipidaemia	910 (87.8)	126 (12.2)
Coronary artery disease	825 (79.6)	211 (20.4)
Cerebrovascular disease	991 (95.6)	45 (4.4)
Cardiovascular disease	890 (85.9)	146 (14.1)
Kidney disease	892 (86.1)	144 (13.9)
Asthma/COPD (chronic obstructive pulmonary disease)	942 (90.9)	94 (9.1)
Neuropathy	776 (74.9)	260 (25.1)
Retinopathy	522 (50.4)	514 (49.6)
Nephropathy	939 (90.6)	97 (9.4)
Diabetic foot	839 (81.0)	197 (19.0)
Oral problem	765 (73.8)	271 (26.2)
Number of chronic diseases
<2		607 (58.6)
2–3		388 (37.4)
≥4		41 (4.0)
Comorbidities (having two or more physical conditions)	607 (58.6)	429 (41.4)

COPD, chronic obstructive pulmonary disease; T2D, type-2 diabetes.

Factors associated with comorbidities

The logistic model revealed that male diabetes patients had 1.27 times increased odds of being comorbidities (OR: 1.27, 95% CI 1.12 to 1.87), as compared with female diabetes patients (table 3). Moreover, the odds of having comorbidities were 2.96 times and 2.14 times higher among ages above 65 years and 50–64 years, as compared with those below 35 years’ participants (OR: 2.96, 95% CI 1.83 to 4.16 and OR: 2.14, 95% CI 1.32 to 2.93), and the odds of having comorbidities were 2.78 times higher among participants who had no formal education (OR:2.78, 95% CI 1.76 to 3.85), as compared with participants who had college or higher education. Furthermore, the odds of being comorbidities were 3.32 times and 2.31 times higher among unemployment and non-manual worker participants as compared with participants who were doing labour work (OR: 3.32, 95% CI 1.92 to 6.02 and OR: 2.31, 95% CI 1.91 to 5.82). Also, participants who had monthly family income ≤96.5 USD were at 1.89 times increased odds (OR: 1.89, 95% CI 1.55 to 2.97) of having comorbidities, as compared with participants who had had monthly family income ≥193 USD (table 3).

Table 3.

Logistic regression analysis of factors associated with comorbidities among the participants with T2D (N=1036)

Characteristics		Comorbidities (yes)
		OR	95% CI	P value
Gender	Female	Ref.	Ref.
	Male	1.27	(1.12 to 1.87)	0.009
Age category	Below 35 years	Ref.	Ref.
	35–49 years	1.61	(0.91 to 2.83)	0.167
	50–64 years	2.14	(1.32 to 2.93)	0.034
	Above 65 years	2.96	(1.83 to 4.16)	0.040
Education	College or Higher	Ref.	Ref.
	Secondary	2.14	(1.23 to 3.52)	0.009
	Primary	2.17	(1.14 to 4.03)	0.031
	Illiterate/no formal education	2.78	(1.76 to 3.85)	0.012
Occupation	Manual worker	Ref.	Ref.
	Non-manual worker	2.31	(1.91 to 5.82)	<0.001
	Unemployment or retired	3.32	(1.92 to 6.02)	0.001
Economic status	≥193 USD	Ref.	Ref.
	144.8–192.9 USD	1.13	(0.72 to 1.64)	0.747
	96.6–144.7 USD	1.24	(1.12 to 1.70)	0.045
	≤96.5 USD	1.89	(1.55 to 2.97)	0.019
	Depend on other	0.53	(0.22 to 1.13)	0.097
Residence	Rural	Ref.	Ref.
	Urban	2.41	(1.13 to 3.81)	<0.001
Family history of diabetes	No	Ref.	Ref.
	Yes	0.81	(0.61 to 1.03)	0.091
Moderate to vigorous physical activity (MVPA)	Less than the recommended level	Ref.	Ref.
	Recommended level	0.41	(0.25 to 0.74)	0.026
Smoking habit	Non-smoker	Ref.	Ref.
	Ex-smoker	1.23	(0.62 to 2.40)	0.549
	Current-smoker	1.68	(1.27 to 2.74)	0.032
Duration of diabetes	Below≤5 years	Ref.	Ref.
	6–15 years	1.32	(0.91 to 1.75)	0.144
	Above 15 years	3.28	(1.44 to 5.37)	0.028
BMI	Healthy weight (18.5–22.9)	Ref.	Ref.
	Underweight (<18.5)	0.82	(0.61 to 1.25)	0.276
	Overweight (23–27.5)	1.20	(0.64 to 2.53)	0.606
	Obese (>27.5)	2.62	(1.24 to 4.26)	0.038
Fasting blood glucose level	Controlled (<7.0 mmol/L)	Ref.	Ref.
	Uncontrolled (≥7.0 mmol/L)	1.19	(1.12 to 1.57)	0.05
Dietary Diversity Score (DDS)	High (≥6)	Ref.	Ref.
	Moderate (4-5)	1.42	(1.13 to 1.64)	0.029
	Low (≤3)	2.52	(1.19 to 2.24)	0.032
Challenges of getting routine medical and healthcare
Transport difficulties	No	Ref.	Ref.
	Yes	1.79	(0.89 to 2.12)	0.144
Limited self-care practice	No	Ref.	Ref.
	Yes	1.82	1.11 to 2.98	0.017
Delayed care seeking	No	Ref.	Ref.
	Yes	1.77	0.85 to 2.79	0.562
Unaffordable medicine	No	Ref.	Ref.
	Yes	1.94	1.15 to 2.76	0.006
Medical staff shortage	No	Ref.	Ref.
	Yes	1.44	0.85 to 2.42	0.170
Financial problem	No	Ref.	Ref.
	Yes	1.86	1.28 to 2.60	0.001

BMI, body mass index; T2D, type-2 diabetes.

The findings also indicate that recommended level of MVPA was 0.41 times (OR: 0.41, 95% CI 0.25 to 0.74) reduced odds of being comorbidities compared with their less than recommended level physical activity counterparts, respectively. In addition, in subjects who were current-smoker, the odds of exhibiting comorbidities were 1.68 times (OR: 1.68, 95% CI 1.27 to 2.74), as compared with non-smoker participants. Furthermore, the rate of comorbidities was 3.2 times (OR: 3.28, 95% CI 1.44 to 5.27) higher among patients who had diabetes above 15 years as compared with participants who had diabetes below 5 years, respectively. The rate of comorbidities was 2.62 times (OR: 2.62, 95% CI 1.24 to 4.26) higher among obese diabetes patients than their normal weight counterparts, respectively. Participants with uncontrolled blood glucose levels, low DDS and moderate DDS had 1.19 times (OR: 1.19, 95% CI 1.12 to 1.57), 2.52 times (OR: 2.52, 95% CI 1.19 to 2.24) and 1.42 times (OR: 1.42, 95% CI 1.13 to 1.64) higher odds of comorbidities than their counterparts. Meanwhile, participants who had limited self-care practice, unaffordable medicine and financial problems had 1.82 times, 1.94 times and 1.86 times higher odds of comorbidities compared with their peers (table 3).

Discussion

Comorbidity has received comparatively less attention compared with research on specific diseases, which normally focuses on both clinical and epidemiological aspects of healthcare research. SDG Target 3.8 aspires to attain universal health coverage, suggesting that a focus on “health in all age will have a significant impact on the post-2015 agenda”. Multiple morbidities must be addressed to achieve development goals.⁴⁴ As a result, treating those who have various chronic diseases is becoming more important as the burden of chronic diseases increases globally.^{45 46} In light of this, our study investigated to assess the prevalence and correlates of comorbidity among T2D patients.

According to the study’s findings, 41.4% of T2D patients had comorbid conditions that included two or more of the aforementioned diseases. Several studies were conducted in Bangladesh on multimorbidity and reported a relatively higher level of multimorbidity among adults.30 31 33–35 Numerous studies have found that hypertension is the most prevalent comorbidity among diabetic patients, and the results are similar to the findings of the current study, where 50.4% of patients also had hypertension.^{11 16 47} We found that the odds of comorbidities were higher in male patients with T2D compared with those in their female counterparts. The findings of this study are in line with those of prior research.^{16 28 47} Potential reasons could be the reporting differences at the biological level and gender-specific health conditions. For instance, female patients who have T2D frequently appear with symptoms that are not characteristic of cardiovascular illnesses.⁴⁸ We found that older adults had higher odds of comorbidities compared with younger respondents. Evidently, most of the previous studies have shown that the prevalence of comorbidity increased markedly with age and was more prevalent among older people.^{11 47} Another two studies also revealed that age is one of the most well-studied and consistent determinants of comorbidities.^{16 22} The degeneration of organs that occurs naturally with ageing is a factor that can have a role in the development of a wide variety of diseases.⁴⁹

Furthermore, our study found that participants with lower levels of education than those with graduate levels had a higher chance of developing comorbidities. Improvements in education levels were found to lower the probability of multimorbidity.¹² This is more likely to occur as people’s socioeconomic status (SES) and educational levels increase since they will be more aware of healthy living, improve health literacy and lower their risk.⁵⁰ Additionally, the odds of comorbidities were higher among unemployed and non-manual T2D patients, respectively. These findings were similar to previous studies.^{28 29} Moreover, we also found that people with lower socioeconomic conditions had higher individual comorbidity prevalence. Similar findings have been found in other low and middle-income countries.^{47 50} Low-income people may have fewer diagnoses of comorbid conditions due to a lack of access to healthcare.^{26 29}

In addition, the odds of comorbidities were higher in patients with T2D residing in urban subjects. In agreement with that, several studies reported that patients with T2D from rural areas have a lower risk of comorbidities than those in urban areas.^{11 16 29} The possible explanation could be economic growth, urbanisation and its negative consequences, such as an unhealthy lifestyle.⁵¹ Significantly, there is a well-established association between an unhealthy lifestyle and chronic conditions, especially NCDs.⁵² Additionally, the limited access to healthcare in rural areas of Bangladesh may lead to fewer diagnostic tests and confirmations of health conditions for patients.²⁹

The lower risk of multimorbidity among physically active individuals is consistent with other research.⁵³ According to the current study, a longer period of diabetes treatment is associated with T2D comorbidity. This finding is consistent with the findings of a study conducted in China²⁸ Malaysia⁴⁷ and Ethiopia,²⁷ which noted and emphasised that a longer duration of T2D treatment contributes to an increased risk of diabetes complications. This finding is in line with current studies carried out in northwest and southwest Ethiopia that glycaemic control is another decisive factor for T2D patients’ experience of comorbidities.^{16 27} Excess glucose chemically attaches to free amino groups of the proteins collagen and other long-lived proteins in blood vessel walls, trapping low-density lipoprotein may contribute to promote cholesterol deposition in the intima and, thus accelerates atherogenesis.¹¹

Similar to other studies, the odds of comorbidities were higher in obese T2D patients than in normal-weight patients.¹¹ Furthermore, participants with low DDS and moderate DDS had higher odds of having comorbidities, respectively. These findings were similar to or higher than those of previous studies.^{11 29} We found that the odds of comorbidity were higher among respondents who had limited care practice. Our study findings are also in line with prior studies that found a strong association between care practice and comorbidities.²⁹ Moreover, the odds of comorbidities had higher among T2D patients who had unaffordable medicine, and financial difficulties. According to a recent study, the rate of direct costs was greater for diabetic patients with comorbidities.⁵⁴ Socioeconomic disparities and a lack of access to preventive and primary healthcare services are important contributors to the onset and progression of such chronic diseases, particularly in LMICs.^{18 19 50} As a developing country, low and moderate-income people were unable to purchase medicine, and our study found a shortage of health facilities. Poverty has an impact on health by limiting access to things like healthy food, shelter, safe places to learn, live and work, clean air and water, utilities and other things that determine a person’s standard of life.⁴⁹

Policy implications

Based on our findings, we propose moving away from focusing on treating and managing individual illnesses and towards an integrated strategy in which individuals’ needs are better addressed. We found that there is a significant correlation between comorbidities and levels of physical activity as well as BMI and challenges of getting routine medical and healthcare. In addition to aiding in the preparation of a larger study design to address the difficulties identified here, the findings of this research into prevalent comorbidities and their associated predictors could be useful in the prevention of diabetic complications. There is mounting evidence that suggests education and self-management are essential components of diabetes treatment. Finally, the most productive avenue for aiding in early detection, reducing complications and managing T2D is patient healthcare education. Our research has important implications for healthcare professionals at all levels, from primary care to the highest tiers of treatment, where they can evaluate health services and adjust measures to prevent further health complications, particularly in economically disadvantaged populations. Healthcare access and healthy lifestyle promotion should be integrated into community health programmes at all levels. A national population-based database on NCDs is needed to quantify their burden on Bangladeshi society and guide T2D and comorbidities complications policy.

Strengths and limitations

The strengths of our study include a high response rate and data collection including clinical measures of blood pressure, blood glucose concentration, disease status, duration of diabetes, body weight and height collected by a health technician. Comorbidity is a less-explored area in Bangladesh, and our study’s strength also lies in the fact that we used physician-diagnosed cases of chronic disease rather than relying entirely on patient self-reported information. However, this study has not beyond some limitations. First, the cross-sectional nature, meaning that only associations can be inferred and causality could not be determined. Second, clinical measures of DM, hypertension, and overweight/obesity were taken, but measurements of blood lipids were not taken in the survey, meaning that metabolic syndrome could not be investigated. Third, waist and hip circumferences were also not collected, limiting the analysis that could be performed. Finally, as our study was based on outpatients in two hospitals, the results may not be typical of all diabetic patients in Bangladesh. This hospital-based study’s results have the potential to be generalised to other similar situations. We recommend gathering data from both population-based and hospital-based, as this would be more comprehensive and could reveal a greater prevalence rate. Qualitative research is also essential to understand individual, community/family, and organisational issues that affect comorbidities and inform more comprehensive interventions to address them.

Conclusion

Our findings highlighted a moderate prevalence of comorbidities and their associated correlates that need to be addressed by integrating social programmes with health prevention and management at multiple levels. The findings could improve outcomes for T2D patients with comorbidities by designing, evaluating and implementing interventions. Understanding the burden of comorbidity in people with diabetes in low and middle-income countries like Bangladesh would require future longitudinal studies into areas connected to the severity in the context of comorbidity management. Considering the risk posed by fragmented diabetes care in the region, comprehensive, integrated care for diabetes with comorbidities is necessary. Consequently, the findings will help policymakers and stakeholders identify needs and develop comprehensive multisectoral strategies to meet the requirements of an increasing adult population with comorbidities.

Data availability statement

Data are available upon reasonable request. Data are available upon reasonable request. Data are available from the corresponding author.

Ethics statements

Patient consent for publication

Not applicable.

Ethics approval

This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving research study participants were approved by the Ethical Review Committee of the Faculty of Biological Science and Technology, Jashore University of Science and Technology (Ref: ERC/FBST/JUST/2022-107). Written and verbal informed consent was obtained from all patients. All information provided by respondents was ensured to remain confidential. The respondents were informed that they could withdraw at any time throughout the interview. Participants gave informed consent to participate in the study before taking part.