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American Journal of  Medical 
Science and Innovation (AJMSI) 

Socioeconomic Differential of  Cardiovascular Diseases Risk Factors in Bangladesh: 
Findings from a Nationally Representative Survey

Shimlin Jahan Khanam1*, Md Awal Kabir2

Volume 2 Issue 2, Year 2023
ISSN: 2836-8509 (Online)

DOI: https://doi.org/10.54536/ajmsi.v2i2.2135
https://journals.e-palli.com/home/index.php/ajmsi

Article Information ABSTRACT

Received: August 20, 2023
Accepted: September 23, 2023
Published: September 29, 2023

Diabetes, hypertension, and overweight/obesity are three major risk factors for cardiovascular 
diseases (CVD). However, their distributions across socioeconomic status (SES) are debated 
despite its critical importance to design healthcare system. This study aimed to examine the 
associations between SES and diabetes, hypertension, and overweight/obesity in Bangladesh. 
Data was extracted from the 2017/18 Bangladesh Demographic and Health Survey. 
Diabetes, hypertension, and overweight/obesity were outcome of  interest. Three variables 
wealth quintiles, education, and paid employment were considered as proxy of  respondents’ 
SES, as such the major explanatory variables of  interest. Age-standardised prevalence 
of  diabetes, hypertension, and overweight/obesity by SES were estimated using direct 
standardisation.  Associations between SES and CVD risk factors were examined using 
multilevel Poisson regression model with robust variance. The overall age-standardized 
prevalence of  diabetes, hypertension, and overweight/obesity in the sample were 9.82% 
(95% CI, 9.11-10.58), 27.37% (95% CI, 26.37-28.40) and 40.11% (35% CI, 38.82-41.42), 
respectively. We found a significant gradient of  diabetes, hypertension, and overweight/
obesity in Bangladesh with a higher prevalence among the respondents of  advantageous 
wealth quintiles, education, and paid employment. In the fully adjusted models improved 
wealth quintiles and higher education were found as important predictors of  diabetes and 
overweight/obesity. Prevalence ratio of  hypertension and overweight/obesity was found 
lower among respondents with no education/pre-school or primary education as compared 
to the respondents with primary education. Diabetes, hypertension, and overweight/obesity 
are not distributed proportionately by SES in Bangladesh, especially by wealth quintiles 
and levels of  education. Policies and programs to increase awareness on importance of  
controlling weight and regular screening for diabetes and hypertension are important. 
Treatment facilities for diabetes and hypertension should also need to be spread up at the 
community level. 

Keywords
Socioeconomy, Cardiovascular 
Diseases, Risk Factors, Survey 
Findings

1 Department of  Population Science, Jatiya Kabi Kazi Nazrul Islam University, Mymensingh, Bangladesh
2 Department of  Social Work, Pabna University of  Science and Technology, Pabna, Bangladesh
* Corresponding author’s e-mail: shimlinjahan2208@gmail.com

INTRODUCTION
The world is now observing a rapid transition in diseases 
pattern, where the non-communicable diseases (NCDs) 
is showing a surge increase from the previous burden 
of  infectious diseases (Dandona et al., 2017). The 
Cardiovascular disease (CVD), a group of  disorders 
of  the heart and blood vessels, is among the highest in 
this list, that includes but not limited to coronary heart 
disease, cerebrovascular disease, and rheumatic heart 
disease (WHO, 2020). Together these lead an estimated 
17.9 million deaths each year, which represents over 
31% of  all global deaths, a number which is projected 
to grow in future (WHO, 2020), [3]. Over three quarter 
(80%) of  these deaths occur in low- and middle income 
countries (LMICs), whereas late detection and less access 
to healthcare services are the major reasons (WHO, 
2020). Nearly 37% of  these deaths occur in premature 
age a figure that represents over 82% of  the total 17 
million premature deaths occur globally (WHO, 2002 & 
Assembly, 2015)[4]. Importantly, this number is rising 
rapidly instead of  the world’s level target through the 
Sustainable Development Goals (SDGs) to stabilize the 
premature deaths along with the rate reduced to 2015’s 
level by 2030 (Assembly, 2015). However, this is unlikely 

that LMICs will achieve this target unless the ongoing 
burden of  CVD has been stabilized (Vos, et al. 2020).
In Bangladesh, the CVD is an ongoing concern with 
an exponential rising of  the people suffering from it 
(Chowdhury, et al. 2018). Of  the ten major causes of  
deaths in Bangladesh,  the CVD takes the first three, 
stroke, ischemic heart disease, and Chronic Obstructive 
Pulmonary Disease (COPD), consecutively as reported 
by the global diseases burden in 2019 (Vos, et al. 2020). 
However, these three forms of  CVDs were the 5th, 4th, 3rd 
major causes of  deaths in 2010 just a 10 years back from 
now (CDCP, 2014). With time passes, Bangladesh has 
been observing a change the NCDs’ risk factors which is 
being responsible for CVDs rapid rising though evidence 
are rare.
Diabetes and hypertension are two most prominent risk 
factors of  the CVD (Khanam, et al. 2019 & Islam, et al. 
2021), whereas overweight/obesity comprises a major risk 
factor of  CVD directly and catalyst for other intermittent 
risk factors, including the diabetes and hypertension 
(Koliaki, et al. 2019). Previous studies in Bangladesh 
reported a higher prevalence of  diabetes (9.82%) and 
hypertension (27.36%) (Islam, et al. 2021 & Khan, et al. 
2021) with a rapid rise of  overweight/obesity from a 



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point of  nearly 1 in 5 adults being any of  these categories 
(Safiri, et al. 2020). Rapid urbanization, dietary changes, 
and limited physical activity are main promotors to such 
rise (Mohiuddin, et al. 2019 & Moniruzzaman, et al. 2016). 
Unawareness over these diseases lead development of  
the CVD and associated adverse consequences, including 
the premature deaths (Khan, et al., 2021). This is fuelled 
by community level cognitions in Bangladesh including 
the NCDs are kind of  rich and novel diseases occur 
among aged and highly educated people. Consequently, 
the detection, treatment, and control rates are low 
among the segments of  population outside these groups, 
though they represent a major share of  the country’s 
total population (Khan, et al., 2021). Higher burden of  
the NCDs among educated and higher aged people, that 
were found in the previous studies of  this series (Islam, 
et al. 2021 & Khan, et al. 2021) and have been reported 
from the years (Fottrell, et al. 2018 & Jafar, et al. 2018), are 
often consider to justify these cognitions. The country 
level initiatives are also somehow supportive to these 
cognitions with being the CVD’s treatment facilities are 
available on a limited basis at the government tertiary level 
hospital in Bangladesh. Consequently, the private sector 
is the major provider of  the CVD’s treatment which is 
often characterised as expensive, therefore not accessible 
for the general population of  Bangladesh (Sulaiman & 
Misha, 2016). 
Assessment of  socioeconomic distribution of  diabetes, 
hypertension, and overweight/obesity has been central to 
equity debate of  the CVD and focus of  disease priorities 
in the health sector in Bangladesh. However, this focus 
is completely absent in the available literature, though 
this was found as a significant considerable factor in 
India, a Bangladeshi neighbouring country (Corsi & 
Subramanian, 2016).
This study has been conducted to present a comprehensive 
equity analysis of  the socio-economic gradients of  the 
burden of  CVD’s risk factors in Bangladesh. Nationally 
representative men and women sample aged 18 and more 
have been analysed with a focus on diabetes, hypertension, 
and overweight/obesity. 

METHODOLOGY
Data Source 
Data originated in the 8th Bangladesh Demographic and 
Health Survey (BDHS), a nationally representative survey 
conducted between October 24, 2017, and March 15, 
2018. The National Institute of  Population Research and 
Training, as a local body, conducted this survey as part of  
the Demography and Health Survey Program, USA. The 
Ministry of  Health and Family Welfare of  Bangladesh 
provided supervision. Two-stage stratified survey 
designed was used in this survey to collect the nationally 
representative sample. A total of  675 Primary Sampling 
units (PSUs) was selected at the first stage of  sampling. 
Of  which the data collection was undertaken in 672 PSUs 
(the remaining three PSUs were excluded due to flood), 
through probability proportional to Enumeration Area’s 

(EA) size. The PSU is the census EA with an average of  
120 households that created in 2011 as part of  the 2011 
Population and Housing Census of  Bangladesh. A fixed 
number of  30 households was selected randomly at the 
second stage of  sampling from each selected PSU. This 
generated a list of  20,160 households, 19,584 of  them 
households were occupied. One fourth of  these selected 
households, 4,896 in total, were selected for further data 
collection on non-communicable diseases including 
diabetes and hypertension. The targeted respondent was 
men and women aged 18 and older.

Outcomes
The study outcomes were diabetes, hypertension, and 
overweight/obesity. Diabetes was measured based on 
the fasting blood glucose (FBGs) level collected using 
the HemoCue Glucose 201 DM system. According to 
the World Health Organization (WHO) criteria, FBG 
level ≥7 or self-reported diabetes medication use, were 
used to classify respondents with the diabetes. Systolic 
and diastolic blood pressure (in millilitres of  mercury 
[mmHg]) were considered to classify respondents with 
hypertension. For this, a digital oscillometric blood 
pressure measuring device with automatic upper-arm 
inflation and an automatic pressure release were used. 
The survey measured blood pressure in three times with 
an interval of  at least 5 minutes, whereas the average of  
the second and third time was reported. These along with 
hypertensive medication use/non-use status were used 
to classify hypertension: (i) systolic blood pressure ≥140 
mmHg and/or a diastolic blood pressure ≥90 mmHg, or 
(ii) taking any prescribed drugs to control blood pressure. 
The national guidelines for management of  hypertension 
in Bangladesh was followed for this classification (DGHS, 
2013) a guideline that is comparable with the 2018 
European Society of  Hypertension (ESH) and European 
Society of  Cardiology (ESC) HTN guidelines (Williams, 
et al. 2018). The WHO’s recommendation for Body Mass 
Index (BMI) for the Asian population were used to defined 
overweight and obesity, which is BMI≥23 to <27.5 kg/m2 
for overweight and BMI ≥ 27.5 kg/m2 for obesity.

Exposure Variables
The SES was defined based on wealth quintiles, education 
and working status. Household wealth quintile, an index 
based on indicators of  asset ownership and housing 
characteristics, developed and reported by the DHS 
as a unique measure of  income and expenditure. The 
approach has been validated in several countries. The 
principal component analysis technique was used to 
construct this index with 5 categories, ranging from 
the lowest to the highest. Education was categorized in 
four levels based on the number of  years completed: 
no education/pre-school (0), primary (0-5), secondary 
(6-10), and higher (11 and more). Respondents’ current 
working classified dichotomously as Yes vs No. We have 
reclassified these three variables to make a dichotomous 
group of  advantageous and disadvantageous 



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socioeconomic status an approach that was followed in 
previous studies in LMICs including Bangladesh [20, 23]. 
The wealth quintiles were reclassified in such a way where 
respondents in lowest and lower quintile were classified 
as disadvantageous group and remaining were classified 
as advantageous group. Respondents with no education/
preschool education were classified as disadvantageous 
group and the remaining were classified as advantageous 
group. Respondents who engaged any forms work to 
generate money were classified as advantage group.

Covariates 
Respondents’ age, sex, and place of  residence were 
included as covariates. Where applicable, diabetes, 
hypertension, and body mass index were also adjusted.

Statistical Analysis 
Descriptive statistics was used to describe the 
characteristics of  the respondents. Prevalence of  
diabetes, hypertension, and overweight/obesity were 
calculated using direct standardization and reported by 
SES and other covariates. Multilevel Poisson regression 
model was used to calculate prevalence ratio (PR) of  
the diabetes, hypertension, and overweight/obesity for 
three exposure variables considered adjusting with the 
confounders. We used Poisson regression because the 
odds ratio estimated using logistic regression is usually 
overestimated if  the outcome of  interest is common, and 
the study design is cross-sectional (Barros & Hirakata, 
2003). Furthermore, in the BDHS, individuals were nested 
within the household; households were nested within the 
PSU/cluster. Therefore, our multilevel mixed-effects 
Poisson regression model accounts for these multiple 
hierarchies and dependency in data and the problem of  
overestimation. Both unadjusted and adjusted model 
were run. In unadjusted models, we considered outcome 
variables separately with each explanatory variable. In the 
adjusted models, outcome variables were considered with 
the explanatory variables and covariates. We considered 
multistage survey design and sampling weights in all 
analyses. Analyses were conducted using the Stata 
software version 15.1. 

RESULTS 
Table 1 presents the basic characteristics of  the total 
respondents and the respondents who had either diabetes, 
hypertension, or overweight/obesity at the time of  the 
survey. The BDHS 2017/18 covered 11,949 respondents- 
a majority of  them were aged 18-34 years (45.11%) and 
female (57.12%). Around 60% of  the total respondents 
had have either primary (30.03%) or secondary (29.62%) 
level of  education. A three-quarter (73.4%) of  the total 
respondents analysed was resided in the rural areas. 
The overall prevalence of  diabetes was 9.82 (95% CI, 
9.11-10.58%), the prevalence of  hypertension was 
27.37% (95% CI, 26.37-28.40), and the prevalence of  
overweight/obesity was 40.11 (95% CI, 38.82-41.42). 
The differences in the rates of  diabetes and hypertension 
across sexes were not quite high as like what was reported 
for the overweight/obese while the rates were 46.19% 
and 32.56% among male and female, respectively. The 
rates of  diabetes, hypertension, and overweight/obese 
were found higher among the currently not working 
respondents than their counterparts of  not currently 
working.

Socio-Economic Difference of  Diabetes, Hypertension, 
and Overweight/Obesity 
Among the socio-economic status (SES) markers, a 
noticeable difference of  diabetes, hypertension, and 
overweight/obesity was found across household wealth 
quintile (Table 1). The prevalence of  diabetes varied 
between 5.32% among the poorest to 16.55% among the 
richest, the rate of  hypertension varied between 20.68% 
among the poorest to 32.89% among the richest, and 
the rate of  overweight/obesity varied between 23.88% 
among the poorest to 63.66% among the richest. The 
rates of  diabetes, hypertension and overweight/obesity 
were also found to be increased from the 6.89%, 
23.70%, and 28.10%, respectively with the increase 
grades of  education. The occupational gradient of  
diabetes, hypertension and overweight/obesity were not 
noticeably different, however, the prevalences were found 
higher among respondents engaged with an advantage 
occupation category.

Table 1: Age-standardised prevalence of  diabetes, hypertension, and overweight/obesity among Bangladeshi adults, 
2017-2018
Characteristics Overall 

(N=11949) na (%)
Diabetes %
(95% CI)

Hypertension % 
(95% CI)

Overweight/
obesity % (95% CI)

Average prevalence 9.82 (9.11-10.58) 27.37 (26.37-28.40) 40.11 (38.82-41.42)
Age in years
18-34 5390 (45.11) 5.12 (4.53-5.70) 12.86 (11.98-13.75) 38.58 (37.28-39.87)
35-39 1371 (11.47) 9.98 (8.41-11.55) 27.44 (25.11-29.78) 49.96 (47.35-52.58)
40-44 1047 (8.76) 11.78 (9.83-13.72) 31.72 (28.91-34.53) 48.15 (45.13-51.17)
45-49 994 (8.32) 12.88 (10.83-14.93) 37.66 (34.69-40.63) 47.71 (44.65-50.77)
50-54 672 (5.62) 16.37 (13.58-19.15) 41.59 (37.88-45.31) 41.30 (37.59-45.01)
55-59 676 (5.66) 15.90 (13.17-18.62) 46.53 (42.81-50.25) 39.88 (36.23-43.54)
60-64 673 (5.64) 15.31 (12.61-18.00) 49.70 (45.96-53.45) 33.09 (29.57-36.61)



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≥65 1126 (9.42) 14.85 (12.80-16.90) 56.64 (53.79-59.50) 27.55 (24.97-30.12)
Sex
Male 5124 (42.88) 8.84 (8.09-9.59) 23.52 (22.42-24.61) 32.56 (31.26-33.86)
Female 6825 (57.12) 9.58 (8.89-10.28) 29.17 (28.17-30.17) 46.19 (45.02-47.36)
Educational status
No education/preschool 3033 (25.38) 6.89 (5.72-8.05) 23.70 (21.85-25.55) 28.10 (25.71-30.49)
Primary education 3588 (30.03) 9.30 (8.38-10.23) 25.52 (24.21-26.83) 35.22 (33.67-36.77)
Secondary education 3539 (29.62) 11.67 (10.49-12.85) 28.98 (27.45-30.51) 49.03 (47.30-50.75)
Higher education 1789 (14.98) 13.15 (11.40-14.89) 31.07 (28.93-33.20) 57.42 (55.16-59.68)
Working status 
No 4620 (38.66) 11.85 (10.86-12.84) 30.30 (29.00-31.59) 48.43 (46.96-49.90)
Yes 7329 (61.34) 8.10 (7.49-8.72) 24.31 (23.38-25.24) 36.78 (35.67-37.90)
Socio-economic status 
Poorest 2311 (19.34) 5.32 (4.42-6.22) 20.68 (19.17-22.18) 23.88 (22.13-25.63)
Poorer 2354 (19.7) 5.65 (4.71-6.58) 23.38 (21.74-25.01) 28.52 (26.66-30.38)
Middle 2465 (20.63) 7.58 (6.54-8.62) 25.65 (24.02-27.27) 38.38 (36.43-40.33)
Richer 2378 (19.90) 10.36 (9.15-11.56) 28.44 (26.73-30.14) 45.08 (43.10-47.07)
Richest 2441 (20.43) 16.55 (15.19-17.90) 32.89 (31.26-34.53) 63.66 (61.85-65.46)
Place of  residence 
Urban 3178 (26.60) 11.85 (10.86-12.84) 28.99 (27.73-30.25) 48.76 (47.28-50.24)
Rural 8771 (73.40) 8.10 (7.49-8.72) 25.00 (24.10-25.90) 36.11 (35.03-37.18)
Administrative division
Barishal 659 (5.51) 9.23 (7.67-10.81) 28.40 (26.10-30.70) 43.24 (40.46-46.02)
Chattogram 2051 (17.17) 10.86 (9.39-12.33) 29.60 (27.51-31.71) 47.03 (44.62-49.43)
Dhaka 2767 (23.16) 14.99 (13.25-16.73) 24.07 (22.09-26.04) 46.84 (44.41-49.27)
Khulna 1488 (12.45) 8.00 (6/76-9.24) 27.16 (25.18-29.14) 46.53 (44.11-48.94)
Mymensingh 973 (8.15) 7.76 (6.35-9.18) 21.27 (19.22-23.32) 31.32 (28.83-33.82)
Rajshahi 1728 (14.46) 8.16 (6.86-9.48) 26.11 (24.11-28.11) 38.22 (35.83-40.63)
Rangpur 1503 (12.58) 5.61 (4.50-6.72) 28.90 (26.77-31.02) 36.44 (34.03-38.84)
Sylhet 780 (6.53) 9.62 (8.09-11.13) 25.16 (23.08-27.25) 32.99 (30.55-35.43)

Note: All counts are weighted. a Column percentage

Figure 1: Distribution of  Diabetes, Hypertension, and overweight/obesity by socioeconomic status, Bangladesh, 
2017/18



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We also examined the distribution of  the socio-
economic markers among the individuals with diabetes, 
hypertension, and overweight/obesity (Figure 1). The 
proportion of  diabetes, hypertension, overweight/obese 
were 77%, 65% and 82%, respectively among respondents 
of  high SES. Majority of  the high SES and educated 
people had either diabetes (81%), hypertension (74%), 
or overweight/obese (86%). A similar result was reported 
for the respondents with high SES and currently working 
respondents- around 84% of  them had either diabetes, 
73% had hypertension, and 83% overweight/obesity.

Multivariable Analyses of  Socioeconomic Gradients 
in CVD Risk Factors  
The socioeconomic gradients in CVD risk factors were 

determined using unadjusted and adjusted multilevel 
passion regression models. Models were adjusted with 
potential covariates. Their results are presented in Table 
2. The prevalence ratio of  diabetes and overweight/
obesity were found significantly higher among richer and 
richest respondents. Respondents’ education levels were 
not found associated with diabetes and hypertension, 
however, prevalence ratios of  overweight/obesity were 
found lower among respondents with no education/
preschool (PR, 0.68, 95% CI, 0.63-0.74) and primary 
education (PR, 0.86, 95% CI, 0.80-0.91) as compared to 
the respondents with secondary education.  Respondents’ 
current working status was found negatively associated 
with diabetes (PR, 0.81, 95% CI, 0.69-0.94).

Table 2: Associations between socio-economic status and diabetes, hypertension and overweight/obesity among 
Bangladeshi adults
Characteristics Diabetes Prevalence 

Ratio± 95% CI
Hypertension Prevalence 
Ratio± 95% CI

Overweight/Obesity 
Prevalence Ratio± 95% CI

Unadjusted Adjusted Unadjusted Adjusted Unadjusted Adjusted
Wealth quintile
Poorest 

0.
70

 (0
.5

4-
0.

90
)**

*

0.
81

 (0
.6

2-
1.

04
)

0.
89

 (0
.7

9-
0.

99
)**

0.
96

 (0
.8

5-
1.

07
)

0.
62

 (0
.5

6-
0.

69
)**

*

0.
68

 (0
.6

1-
0.

76
)**

*

Poorer 

0.
77

 (0
.6

0-
0.

99
)**

0.
83

 (0
.6

5-
1.

07
)

0.
93

 (0
.8

4-
1.

04
)

0.
99

 (0
.8

9-
1.

10
)

0.
74

 (0
.6

7-
0.

81
)**

*

0.
76

 (0
.6

9-
0.

84
)**

*

Middle 

1.
00

1.
00

1.
00

1.
00

1.
00

1.
00

Richer 

1.
38

 (1
.1

3-
1.

69
)**

*

1.
29

 (1
.0

5-
1.

59
)**

1.
08

 (0
.9

7-
1.

20
)

1.
04

 (0
.9

3-
1.

15
)

1.
19

 (1
.0

9-
1.

30
)**

*

1.
16

 (1
.0

7-
1.

27
)**

*

Richest 

2.
24

 (1
.8

6-
2.

70
)**

*

1.
78

 (1
.4

5-
2.

19
)**

*

1.
26

 (1
.1

4-
1.

39
)**

*

1.
03

 (0
.9

2-
1.

14
)

1.
65

 (1
.5

4-
1.

77
)**

*

1.
52

 (1
.4

1-
1.

65
)**

*

Respondents’ education 
No education, preschool 

1.
13

 (0
.9

6-
1.

34
)

0.
86

 (0
.7

2-
1.

04
)

1.
58

 (1
.4

5-
1.

72
)**

*

0.
97

 (0
.8

9-
1.

06
)

0.
61

 (0
.5

6-
0.

66
)**

*

0.
68

 (0
.6

3-
0.

74
)**

*



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Unadjusted Models
Models are adjusted for age, sex, place of  residence 
and place of  residence Beside these, age of  the 
respondents was found as most important predictor 
of  diabetes, hypertensive, and overweight/obese with a 
general increase of  the likelihoods with increasing years 
(Supplementary table 1). We reported a similar trend in 
our subgroup analysis for diabetes, hypertension and 
overweight/obese across age and specific to the wealth 
quintiles (Supplementary Figures S1 to S3).
We also explored prevalence ratios of  diabetes, 
hypertension, and overweight/obesity across respondents’ 
place of  region (Supplementary table 2). Prevalence ratio 
of  diabetes was found higher among richer and richest 
respondents resided in the Dhaka division.  In Khulna 
division, prevalence ratio of  diabetes was found higher 
among the poorer and richest respondents as compared to 
the respondents with middle wealth quintile. Prevalence 
of  diabetes was also found lower among no educated/
pre-school respondents in the Rajshahi division and 
higher educated respondents in Sylhet division. 

DISCUSSION 
Our analysis provides strong evidence of  socio-economic 
gradients of  CVDs’ risks factors, diabetes, hypertension, 
and overweight/obesity, in Bangladesh where the 
key determinants were wealth quintile and education. 
Prevalence ratio of  diabetes were found higher among 
middle to richest wealth quintile. We also reported 
a gradual increase in the prevalence ratio of  being 
overweight/obesity among poorest to richest quintile. 
Prevalence ratios of  hypertension and overweight/

obesity were found lower among no education/preschool 
and primary educated respondents as compared to 
the secondary educated respondents. In addition, we 
reported around 18% (95% CI, 0.73-0.92) likelihood of  
diabetes among respondents reported they were formally 
employed than the respondents who were not formally 
employed.
We reported a higher SES gradient of  diabetes, 
hypertension, and overweight/obesity whereas the 
previous studies in Bangladesh reported their rising 
prevalence regardless of  the SES (Islam, et al. 2021, 
Khan, et al. 2021). The likelihoods were even higher for 
specific regions, including Dhaka and Khulna. This calls 
for policies and programs to ensure universal availability 
of  the NCDs treatments. However, current governmental 
level NCDs focus is opposite, governmental NCDs 
treatment facility is yet to available in the urban areas only, 
on tertiary health facilities (e.g district hospitals, medical 
colleges) on limited basis, therefore, not accessible for all 
(DAO, 2020) . Therefore, the private health facilities is 
major provider of  NCDs treatment in Bangladesh (Khan, 
et al., 2021), which is also located in the urban areas and 
treatment available there is expensive (NIPORT, 2017). 
Consequently, NCDs services provides by private health 
facilities could not be accessible for lower SES population 
and population from rural areas, though they cover over 
70% of  the country’s total population (NIPORT, 2017). 
This leads detection of  NCDs at its optimal stage which 
increase the risk of  pre-mature mortality as well as 
difficulties in control due to concurrent comorbidities.
Challenges are also exit in awareing people about the 
diabetes, hypertension and risk of  becoming overweight/

Primary education 

1.
13

 (0
.9

5-
1.

33
)

1.
07

 (0
.9

2-
1.

26
)

1.
15

 (1
.0

5-
1.

26
)**

*

0.
96

 (0
.8

8-
1.

04
)

0.
78

 (0
.7

3-
0.

84
)**

*

0.
86

 (0
.8

0-
0.

91
)**

*

Secondary education

1.
00

1.
00

1.
00

1.
00

1.
00

1.
00

Higher 
0.

97
 (0

.8
1-

1.
17

)

0.
92

 (0
.7

7-
1.

11
)

0.
96

 (0
.8

6-
1.

07
)

1.
01

 (0
.9

1-
1.

12
)

1.
08

 (1
.0

1-
1.

15
)**

1.
01

 (0
.9

5-
1.

08
)

Paid employment 
No

1.
00

1.
00

1.
00

1.
00

1.
00

1.
00

Yes

0.
82

 (0
.7

3-
0.

92
)**

*

0.
81

 (0
.6

9-
0.

94
)**

*

0.
82

 (0
.7

7-
0.

87
)**

*

0.
94

 (0
.8

8-
1.

02
)

0.
84

 (0
.8

0-
0.

89
)**

*

1.
01

 (0
.9

6-
1.

07
)

Note: *** p<0.01, ** p<0.05



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obesity in Bangladesh. Studies in Bangladesh have been 
reported poor awareness over these in all segments of  
the population, however, higher among who are not 
higher educated, resided in rural areas, and belongs to 
poor wealth quintile (Islam, et al. 2021 & Khan, et al. 
2021). Those findings reflect the social level taboos on 
NCDs in Bangladesh, such as, urban educated and late 
aged population are mainly in the risk of  developing the 
NCDs. However, this study reports primary educated 
people than their higher educated counterpart is even 
in the higher risk of  becoming overweight/obesity (the 
most important risk factor of  developing NCDs) and 
diabetic. Hypertension prevalence is also higher among 
respondents of  no/preschool education than respondents 
of  higher education, though the relevant likelihoods did 
not bring significant level once other socio-demographic 
factors adjusted.  Lower to moderate aged population 
are even more prone to these risk factors, contributing 
NCDs are becoming popularized among young and 
middle-aged population day by day. This is addition to 
the well documented higher likelihood of  NCDs among 
the higher aged population. The underlaying reasons of  
such association are increasing urbanization, westernized 
lifestyle, including excess dietary sodium intake, and 
physical inactivity (Mills, et al. 2016). This calls for 
awareness building programs on NCDs for all population, 
regardless of  their SES and age. However, this is still 
limited in Bangladesh, mostly in the urban centres and 
small in scale (DAO, 2020), though their effectiveness 
has been questioned Wakefield, (2010). Consequently, 
use of  poor quality of  drugs or the use of  traditional 
medicines to control NCDs are common, contributing to 
the lower control of  NCDs in Bangladesh among those 
who are treated (Khan, et al. 2021). Therefore, awareness 
building programs on the importance of  monitoring 
and controlling NCDs as well as body weight should be 
considered in future policies and programs. Technology 
based programs, such as use of  social media and mobile 
phone text messaging, could be cost effective intervention 
to control NCDs in Bangladesh, therefore, should be 
adopted in future policies and programs.
The present study has several strengths. The main 
strengths are the analyses of  nationally representative 
population-based survey data and the consideration of  
diabetes, hypertension, and overweight/obesity. Their 
distributions were accessed across all major makers used 
to measure socio-economic status, including wealth 
quintile, education, occupation and place of  residence. 
Therefore, the findings provide a comprehensive picture 
of  three major CVDs’ risk factors, as such, would be 
helpful for developing evidence-based policies and 
programs. However, the major limitation of  this study 
There is no option in the BDHS to distinguish between 
type-1 and type-2 diabetes, as such we did not consider 
this issue in this study. Dietary intake, smoking status, 
lifestyle behaviours, and level of  physical exercise are 
important factors of  becoming diabetic, hypertensive, 
and overweight/obese, therefore, essential to be included 

in the model. However, this was not done because of  the 
lack of  data.

CONCLUSIONS
We found a significant gradient of  diabetes, hypertension, 
and overweight/obesity in Bangladesh with a very higher 
prevalence among the respondents of  advantage wealth 
quintile, education, occupation, and place of  residence. 
However, wealth quintile and education were found 
important predictors of  becoming diabetic, hypertensive, 
and overweighted/obese. These suggest need for policies 
and programs on NCDs management in Bangladesh 
regardless SES and place of  residence. However, higher 
focus should be given increased aged people with 
improved SES in respect to wealth quintile and education.
 
Declaration of  Interests
The authors declare that they have no known competing 
financial interests or personal relationships that could 
have appeared to influence the work reported in this 
paper.

Acknowledgement
The authors thank the MEASURE DHS for granting 
access to the 2017/18 BDHS data.

Funding
This research did not receive any specific grant from 
funding agencies in the public, commercial, or not-for-
profit sectors.

Authors’ Contributions
Khanam SJ designed the study, performed the data 
analysis, and wrote the first draft of  this manuscript. 
Kabir MA critically reviewed and edited the previous 
versions of  this manuscript. All authors approved this 
final version of  the manuscript.

Data Availability
The datasets used and analysed in this study are available 
from the Measure DHS website: https://dhsprogram.
com/data/available-datasets.cfm
 
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Supplementary Table and Figures

Table 3: Age distribution of  the survey respondents by household wealth quintile, Bangladesh Demographic and 
Health Survey, 2017/18
Age 
(years)

Poorest Poorer Middle Richer Richest 
Number % Number % Number % Number % Number %

18-34 972 42.06 1014 43.08 1109 44.99 1165 49.02 1130 46.27
35-39 290 12.56 276 11.73 278 11.28 219 9.20 307 12.59
40-44 216 9.34 227 9.66 204 8.29 208 8.77 191 7.83
45-49 178 7.70 199 8.45 206 8.36 194 8.18 216 8.85
50-54 127 5.50 144 6.11 145 5.90 132 5.57 123 5.03



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Table 4: Mutually adjusted prevalence ratios and 95% confidence intervals from Multilevel poison regression analyses 
across respondents place of  residence 
Characteristics Barishal Chattogram Dhaka Khulna Mymensingh Rajshahi Rangpur Sylhet 
Diabetes 
Wealth quintile (ref: middle) 
  Poorest 

0.
83

 (0
.4

9-
1.

40
)

0.
72

 (0
.3

2-
1.

59
)

0.
93

 (0
.4

9-
1.

77
)

0.
86

 (0
.3

4-
2.

23
)

0.
61

 (0
.3

5-
1.

05
)

0.
96

 (0
.4

8-
1.

92
)

0.
50

 (0
.2

7-
0.

93
)**

1.
13

 (0
.5

4-
2.

40
)

  Poorer 

0.
75

 (0
.4

4-
1.

28
)

0.
70

 (0
.3

7-
1.

34
)

1.
11

 (0
.5

6-
2.

18
)

2.
14

 (1
.2

1-
3.

81
)**

*

0.
68

 (0
.3

9-
1.

19
)

0.
54

 (0
.2

8-
1.

02
)

0.
46

 (0
.2

2-
0.

96
)**

0.
59

 (0
.2

7-
1.

32
)

  Richer 

0.
81

 (0
.4

6-
1.

42
)

1.
15

 (0
.7

0-
1.

89
)

1.
71

 (1
.1

1-
2.

65
)**

1.
73

 (0
.9

0-
3.

34
)

1.
08

 (0
.4

2-
2.

78
)

1.
22

 (0
.6

8-
2.

20
)

1.
07

 (0
.5

6-
2.

05
)

0.
85

 (0
.4

2-
1.

72
)

  Richest 

0.
94

 (0
.5

2-
1.

73
)

1.
50

 (0
.9

9-
2.

29
)

2.
61

 (1
.6

3-
4.

16
)**

*

2.
72

 (1
.4

1-
5.

29
)**

*

1.
30

 (0
.7

3-
2.

33
)

1.
15

 (0
.5

6-
2.

35
)

0.
99

 (0
.5

2-
1.

89
)

2.
12

 (1
.0

3-
4.

37
)**

Respondents’ education (ref: secondary education)
No education, 
preschool 

1.
21

 (0
.6

2-
2.

35
)

0.
66

 (0
.4

1-
1.

06
)

1.
10

 (0
.7

6-
1.

59
)

1.
06

 (0
.7

0-
1.

62
)

1.
32

 (0
.6

6-
2.

63
)

0.
46

 (0
.2

7-
0.

79
)**

*

0.
66

 (0
.3

3-
1.

30
)

0.
93

 (0
.5

3-
1.

63
)

 Primary education 

1.
42

 (0
.8

7-
2.

30
)

0.
88

 (0
.6

2-
1.

23
)

1.
29

 (0
.9

5-
1.

75
)

1.
23

 (0
.8

4-
1.

79
)

1.
15

 (0
.6

3-
2.

10
)

0.
85

 (0
.5

0-
1.

44
)

0.
93

 (0
.5

2-
1.

68
)

1.
18

 (0
.7

6-
1.

82
)

  Higher 

2.
03

 (1
.1

8-
3.

49
)

0.
88

 (0
.6

4-
1.

22
)

0.
98

 (0
.6

6-
1.

43
)

0.
70

 (0
.4

2-
1.

19
)

0.
74

 (0
.3

5-
1.

54
)

0.
93

 (0
.5

3-
1.

65
)

1.
11

 (0
.5

7-
2.

15
)

0.
53

 (0
.2

9-
0.

97
)**

55-59 138 5.99 127 5.39 144 5.86 118 4.96 149 6.09
60-64 140 6.07 152 6.44 134 5.43 131 5.49 117 4.81
≥65 249 10.79 215 9.13 244 9.89 244 9.89 208 8.53



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Paid employment 
(ref: no)

0.
86

 (0
.5

7-
1.

28
)

0.
83

 (0
.5

8-
1.

18
)

0.
72

 (0
.5

3-
0.

99
)

1.
05

 (0
.6

8-
1.

62
)

0.
91

 (0
.5

5-
1.

51
)

0.
51

 (0
.3

2-
0.

79
)**

*

1.
32

 (0
.8

2-
2.

13
)

0.
89

 (0
.6

0-
1.

34
)

Hypertension 
Wealth quintile (ref: middle) 
Poorest 

0.
73

 (0
.5

9-
0.

90
)**

*

1.
15

 (0
.8

1-
1.

64
)

1.
09

 (0
.7

2-
1.

67
)

0.
90

 (0
.6

4-
1.

25
)

0.
85

 (0
.6

1-
1.

18
)

0.
93

 (0
.7

1-
1.

23
)

0.
95

 (0
.7

9-
1.

15
)

0.
78

 (0
.5

6-
1.

10
)

Poorer 

0.
92

 (0
.7

1-
1.

19
)

1.
01

 (0
.7

6-
1.

35
)

0.
95

 (0
.6

3-
1.

41
)

1.
05

 (0
.8

5-
1.

30
)

1.
13

 (0
.8

1-
1.

56
)

0.
90

 (0
.7

3-
1.

12
)

1.
03

 (0
.8

2-
1.

30
)

0.
82

 (0
.5

7-
1.

18
)

Richer 

1.
10

 (0
.8

9-
1.

36
)

1.
22

 (0
.9

4-
1.

60
)

0.
97

 (0
.7

3-
1.

29
)

1.
25

 (1
.0

1-
1.

58
)**

1.
12

 (0
.8

0-
1.

57
)

0.
72

 (0
.5

3-
0.

97
)**

1.
12

 (0
.8

6-
1.

45
)

0.
92

 (0
.6

8-
1.

27
)

Richest 

0.
92

 (0
.6

7-
1.

27
)

1.
05

 (0
.8

5-
1.

30
)

0.
91

 (0
.6

8-
1.

23
)

1.
15

 (0
.8

9-
1.

48
)

1.
25

 (0
.8

7-
1.

79
)

0.
96

 (0
.7

5-
1.

24
)

1.
34

 (0
.9

8-
1.

84
)

1.
02

 (0
.7

9-
1.

32
)

Respondents’ education (ref: secondary education)
No education, 
preschool 

0.
86

 (0
.6

5-
1.

13
)

0.
94

 (0
.7

8-
1.

14
)

0.
73

 (0
.5

8-
0.

93
)**

1.
08

 (0
.8

6-
1.

35
)

1.
06

 (0
.7

6-
1.

48
)

0.
97

 (0
.7

7-
1.

21
)

1.
27

 (0
.9

7-
1.

66
)

1.
25

 (0
.9

4-
1.

67
)

Primary education 

0.
90

 (0
.7

2-
1.

12
)

0.
99

 (0
.8

2-
1.

21
)

0.
86

 (0
.7

0-
1.

07
)

0.
99

 (0
.7

8-
1.

26
)

1.
14

 (0
.8

8-
1.

49
)

0.
85

 (0
.6

8-
1.

05
)

1.
11

 (0
.8

7-
1.

42
)

1.
18

 (0
.8

9-
1.

57
)

Higher 

0.
77

 (0
.5

8-
1.

03
)

1.
14

 (0
.9

0-
1.

43
)

0.
90

 (0
.6

8-
1.

20
)

1.
03

 (0
.8

1-
1.

32
)

1.
43

 (1
.0

1-
2.

04
)**

1.
03

 (0
.7

9-
1.

35
)

0.
94

 (0
.7

2-
1.

21
)

1.
04

 (0
.7

7-
1.

40
)

Paid employment 
(ref: no)

1.
04

 (0
.8

4-
1.

28
)

0.
97

 (0
.8

1-
1.

16
)

1.
02

 (0
.8

5-
1.

23
)

0.
84

 (0
.6

8-
1.

03
)

0.
90

 (0
.7

2-
1.

12
)

0.
94

 (0
.7

5-
1.

19
)

0.
90

 (0
.7

6-
1.

07
)

0.
88

 (0
.6

8-
1.

14
)



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Overweight/obesity 
Wealth quintile (ref: middle) 
Poorest 

0.
78

 (0
.5

8-
1.

03
)

0.
70

 (0
.5

4-
0.

90
)**

0.
68

 (0
.4

4-
1.

03
)

0.
70

 (0
.5

4-
0.

92
)**

0.
47

 (0
.3

3-
0.

68
)**

0.
89

 (0
.7

0-
1.

14
)

0.
56

 (0
.4

4-
0.

71
)**

*

0.
85

 (0
.6

0-
1.

22
)

Poorer 

0.
84

 (0
.6

3-
1.

12
)

0.
78

 (0
.5

9-
1.

03
)

0.
69

 (0
.5

1-
0.

93
)**

0.
77

 (0
.6

3-
0.

93
)**

*

0.
79

 (0
.5

7-
1.

09
)

0.
82

 (0
.6

7-
1.

00
)**

0.
73

 (0
.5

8-
0.

93
)**

*

0.
84

 (0
.6

1-
1.

16
)

Richer 

1.
21

 (0
.9

4-
1.

57
)

1.
11

 (0
.9

4-
1.

31
)

1.
22

 (0
.9

7-
1.

53
)

1.
15

 (0
.9

9-
1.

33
)

1.
30

 (0
.9

4-
1.

80
)

1.
17

 (0
.9

2-
1.

49
)

1.
12

 (0
.8

5-
1.

47
)

1.
25

 (0
.9

1-
1.

74
)**

Richest 

1.
40

 (1
.1

3-
1.

75
)**

*

1.
54

 (1
.3

3-
1.

79
)**

1.
43

 (1
.1

5-
1.

77
)

1.
50

 (1
.2

9-
1.

74
)**

1.
70

 (1
.3

0-
2.

22
)**

*

1.
76

 (1
.4

3-
2.

16
)**

1.
48

 (1
.1

3-
1.

93
)**

*

2.
18

 (1
.6

6-
2.

87
)**

*

Respondents’ education (ref: secondary education)
No education, 
preschool 

0.
73

 (0
.5

5-
0.

96
)**

0.
64

 (0
.5

3-
0.

79
)**

0.
70

 (0
.6

0-
0.

81
)**

*

0.
76

 (0
.6

2-
0.

95
)**

0.
60

 (0
.4

4-
0.

81
)**

*

0.
79

 (0
.6

1-
1.

00
)**

0.
68

 (0
.5

2-
0.

90
)**

*

0.
48

 (0
.3

8-
0.

62
)**

*

Primary education 

0.
79

 (0
.6

8-
0.

93
)**

*

0.
86

 (0
.7

5-
0.

98
)**

0.
89

 (0
.7

6-
1.

05
)**

0.
79

 (0
.6

8-
0.

92
)**

0.
70

 (0
.5

8-
0.

86
)**

0.
81

 (0
.6

8-
0.

97
)**

1.
09

 (0
.9

2-
1.

29
)

0.
83

 (0
.6

9-
0.

99
)**

Higher 

1.
17

 (0
.9

8-
1.

39
)

0.
85

 (0
.7

4-
0.

97
)**

1.
15

 (1
.0

1-
1.

32
)**

1.
04

 (0
.9

0-
1.

21
)

1.
08

 (0
.8

7-
1.

35
)

0.
93

 (0
.7

7-
1.

13
)

0.
99

 (0
.8

1-
1.

21
)

0.
73

 (0
.6

0-
0.

91
)**

Paid employment 
(ref: no)

1.
18

 (1
.0

2-
1.

36
)**

0.
96

 (0
.8

5-
1.

09
)

0.
89

 (0
.7

9-
1.

00
)**

1.
14

 (1
.0

1-
1.

30
)**

1.
08

 (0
.8

8-
1.

32
)

1.
05

 (0
.9

0-
1.

24
)

1.
11

 (0
.9

4-
1.

31
)

1.
06

 (0
.8

6-
1.

30
)



Pa
ge

 
12

1

https://journals.e-palli.com/home/index.php/ajmsi

Am. J. Med. Sci. Innov. 2(2) 110-121, 2023

Figure 2: Adjusted prevalence ratio of  Hypertension by age for quintiles of  household wealth, BDHS, 2017/18
Note: Adjusted prevalence calculated from logistic regression model where the factors adjusted were age, sex, occupation, body mass index, 
residence, region and status of  Diabetes

Figure 3: Adjusted prevalence ratio of  Obesity by age for quintiles of  household wealth, BDHS, 2017/18
Note: Adjusted prevalence calculated from logistic regression model where the factors adjusted were age, sex, occupation, residence, and region


