Prevalence and risk factors of hypertension among bank employees in Dhaka city of Bangladesh

Abstract

: Jobs in the banking sector are perceived challenging and stressful; therefore, the likelihood of hypertension among the bank employees is high. However, no previous study investigated the prevalence and associated factors of hypertension among the bank employees in Bangladesh. Therefore, in this study, we estimated prevalence and risk factors of hypertension among a sample of bank employees in Dhaka city, Bangladesh. A cross-sectional design was employed to collect data from the 180 bank employees selected conveniently. Multiple logistic regression was conducted to observe the factors associated with hypertension. Prevalence of hypertension and pre-hypertension among the respondents were 24.44% and 32.22%, respectively. Furthermore, more than half of those found with hypertension were previously undiagnosed. We found that bank employees’ sex, family size, monthly salary, parental hypertension, parental diabetes, parental renal disease, and daily meat consumption were the factors significantly associated with hypertension. Our findings indicated that hypertension prevalence among the respondents was higher than that of national prevalence. Awareness build-up and healthy lifestyle is recommended to prevent hypertension associated morbidity and mortality in the future. Further studies could be carried out to identify the causal mechanisms of hypertension and appropriate interventions to address the issue.

Keywords:

• hypertension
• pre-hypertension
• bank employees
• blood pressure
• Bangladesh

1. Introduction

Hypertension is one of the most prevalent non-communicable diseases and the leading risk factor for all-cause mortality worldwide (Stanaway et al., 2018). Prevalence of hypertension is increasing globally, particularly in the low- and middle-income countries like Bangladesh (Ibrahim & Damasceno, 2012; Islam & Majumder, 2012; Basu & Millett, 2013; Lloyd-Sherlock et al., 2014). The issue is serious because hypertension not only increases the burden on socioeconomic and health systems but also a principal risk factor to kidney and cardiovascular diseases (CVDs) including, heart failure, stroke, and coronary artery diseases (He & Whelton, 1997; Naomi et al., 2018).

In Bangladesh, around one-fourth of population is affected by hypertension which was significantly associated with diabetes related complications, and the prevalence is increasing equally both in urban and rural areas (Islam & Majumder, 2012; Tabassum et al., 2010; SMS Islam et al., 2015). Studies reported that age, sex, BMI, education, economic condition, and place of residence are potential predictors of diagnosis, treatment, and control of hypertension in Bangladesh although a considerable proportion of people with hypertension are unfortunately undiagnosed and untreated (Rahman, 2013).

Hypertension is associated with human occupational efficiency. Occupational factors can lead to high blood pressure; therefore, in turn, hypertension can also cause disturbance in occupational life. Blood pressure during work or during leisure time was found to be significantly elevated with high job strain, which is measured as a combination of high job demands and low control, and with high effort-reward imbalance at work (Babu et al., 2014; Kang, 2022; Rosenthal & Alter, 2012). Employees working in banking sector are exposed to such job strains and could subsequently be susceptible to an elevated blood pressure. A study conducted in urban Puducherry, India, found that prevalence of hypertension and pre-hypertension among bank employees was 44.3% and 41.1%, respectively (Kumar & Sundaram, 2014). Another study conducted in St. Petersburg, Russia identified that hypertension was prevalent among 35.2% bank employees (Konradi et al., 2011).

In recent decades, there has been a significant expansion of the banking industry in Bangladesh. As an emerging and challenging sector in Bangladesh, bank jobs are generally perceived with high occupational stress, heavy workload, and long working hours (Rahman, 2013; Ukil & Ullah, 2016). However, building a career in banking sectors is still highly desired among the job seekers because of attractive salary, job security, and financial incentives. In Bangladesh, previous study reported the prevalence and risk factors of hypertension among employed population groups (Khan et al., 2021; Islam, Mainuddin, et al., 2015). However, the prevalence of hypertension among bank employees were never reported separately even though they are exposure to risk factors. Therefore, in this study, we will focus on the prevalence and associated factors of hypertension among the bank employees in Dhaka city of Bangladesh. Findings could be useful to have an idea about hypertension prevalence among bank employees in Dhaka and to consider preventive measures for improving their quality of life and well-being.

2. Methods

2.1. Study setting

Currently, there are 60 banks in Bangladesh and every bank has at least a branch in the capital city Dhaka. There is no recent published literature regarding the number of branches as well as employees of every individual bank in Dhaka city; therefore, we personally contacted respective bank authorities and collected the information about their number of branches and employees in Dhaka city. The data were used to calculate population proportion to estimate the sample size. Dhaka city is currently divided into two administrative areas – Dhaka south and Dhaka north. Bank branches were almost equally divided into the north and south part of the city.

2.2. Study design and sample size

In this study, STROBE was considered as reporting guideline (Supplementary file). A cross-sectional design was employed to collect information from the bank employees in Dhaka, Bangladesh. We calculated sample size using following formula, sample size (n) = Z_1-α/2² P*(1-P)/d², where Z_1-α/2 = standard normal variate considered at 90% level of confidence (1.65), P = expected proportion of bank employees with hypertension (0.146) which was considered based on the prevalence of hypertension among employed population in Dhaka city as reported by Islam et al. (2015), d = desired precision (0.05). Considering a 25% non-response, the calculation gives at least a total of 170 respondents to be interviewed. In our study, a total of 180 conveniently selected bank employees in Dhaka north area were reached for interviewing. For reaching the sampled respondents, we randomly selected 18 bank branches, and ten employees from every branch were conveniently chosen. Banks from all categories such as public, private, commercial, and specialized banks were included so that representative samples could be obtained.

2.3. Data collection

Respondents were selected conveniently at their workplace. Data was collected through face-to-face interviews. Before interviewing, authoritative approval from the respective banks was confirmed; however, the interview date was not pre-scheduled. A structured questionnaire was used for data collection and conducting face-to-face interviews from July 2019 to September 2019. Questionnaire was developed based on literature review regarding hypertension related research conducted in Bangladesh and discussion with the team members. Previous research found that factors such as age, sex, lifestyle, diet, diseases history, nutritional status, education, and wealth are associated with hypertension and pre-hypertension among the people in Bangladesh (Chowdhury et al., 2016; SM Islam et al., 2015; Islam & Majumder, 2012; Khanam et al., 2019; M Rahman et al., 2018). Therefore, in our study, we aimed to observe whether hypertension among bank employees is associated with socio-demographic, lifestyle, stress, nutrition, parental diseases, and dietary factors.

Interviews were also conducted at the workplace of participants. As bank employees are very busy during working hours, interviews were conducted during break or after working time in a separate room. To standardize the questionnaire, pre-testing was performed by five pilot interviews which were not counted in the final analysis. On each day, around 10 interviews were carried out. Stress level was assessed by Cohen’s stress scale (Cohen et al., 1994). This scale was previously used among Bangladeshi population, and good level of validity and reliability was found (Yasmin & Islam, 2018). Blood pressure (BP) was measured by sphygmomanometer. To measure the BP, respondents were requested to sit in a relaxed position, and it was ensured that they did not consume any food within 30 minutes prior to BP measurement so that the effect of dietary fluid intake and other BP regulatory factors could be minimized as much as possible (Guyton, 1991). Digital bathroom scale and height measuring tape were used to measure weight and height, respectively. Weight and height were measured at the nearest value of ±100 gm and ±1 cm, respectively. Standards were followed while conducting all types of measurements. For every different measurement such as BP, height, and weight, the reading was taken three times and average was considered.

2.4. Data processing and variables

On an everyday basis, a team meeting was arranged after data collection to confirm if the questionnaires were properly filled-up and to share the field experience. SPSS (version 22.0 SPSS Inc, Chicago, IL, USA) was used for data entry and analysis. Variables such as body height and weight were converted into body mass index (BMI) and categorized according to standard (underweight: BMI <18.5; normal: BMI 18.5 to 24.9; overweight: BMI 25–29.9; obese: BMI ≥30; Nishida et al., 2004). Values of systolic and diastolic blood pressure (BP) were categorized as normal or no hypertension (systolic BP <120 mmHg and diastolic BP <80 mmHg), prehypertension (systolic BP 120–139 mmHg and diastolic BP 80–89 mmHg), and hypertension (systolic BP >140 mmHg and diastolic BP >90 mmHg) according to the 7th report of the Joint National Committee on the Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC 7; Lenfant et al., 2003). This categorization of blood pressure is internationally used to observe hypertension and pre-hypertension among population. For the ease of the interpretation of the analysis, all the independent variables were categorized if they were continuous, and some categorical variables were further re-categorized.

2.5. Data analysis and association measurement

Difference in the prevalence of hypertension and prehypertension among bank employees by their socio-demographic and lifestyle characteristics were observed using chi-square and Fisher’s exact test. As the outcome variable was binary (hypertension and not hypertension), logistic regression was used. Both bivariate and multivariate association between independent (status of hypertension) and dependent variables were observed. Variables which showed an association level of p ≤ 0.1 in bivariate analysis were considered to include in the multivariable logistic regression model. All the statistical tests were carried out at 90% confidence interval or α = 0.1.

3. Results

3.1. Distribution of the prevalence of hypertension and prehypertension

Among the participants, the prevalence of hypertension and prehypertension was 24.44% (n = 44) and 32.22% (n = 58), respectively (Table 1). Among individuals with hypertension, 47.73% (n = 21) were previously diagnosed and 52.27% (n = 23) were undiagnosed with hypertension. Furthermore, 32.22% (n = 58) participants were in pre-hypertensive condition and were undiagnosed (Table 1).

Table 1. Distribution of pre-diagnosed, undiagnosed and total hypertension among interviewed bank employees

Category	Pre-diagnosed	Undiagnosed	Total
Hypertension	11.67 (21)	12.78 (23)	24.44 (44)
Pre-hypertension	0 (0)	32.22 (58)	32.22 (58)

Data is presented as % (n), n = number of samples

The distribution of the prevalence of hypertension and pre-hypertension among bank employees by respondents’ characteristics has been presented in Table 2. Analysis found prevalence of hypertension was higher among male (30.41%), 41–50 year age group (35.29%), managerial or directorial staffs (30.56%), Muslim employees (25.49%), married employees (26.06%), respondents with family member 1–5 (27.14%), employees with education level graduation or above (55.13%), employees with job duration 1–3 years (28.13%), and those who had the highest monthly salary (40.48%) than those of their respective counterparts. On the other hand, prevalence of pre-hypertension was high among female (32.14%), 41–50-year age group (37.37%), managerial or directorial staffs (33.33%), other religion group (48.15%), married employees (33.10%), respondents with family member between 1–5 (32.86%), undergraduate employee (50%), employees with job duration more than 3 years (34.46%), and with salary range BDT 61k-80k (39.29%).

Table 2. Distribution of the prevalence of hypertension and pre-hypertension among bank employees by respondents’ characteristics

Characteristics	Total sample n (%)	Hypertension		Prehypertension
		n1 (% of n)	p value	n2 (% of n)	p value
Gender
Male	112 (62.22)	34 (30.36)	0.018^a	36 (32.14)	0.977^a
Female	68 (37.78)	10 (14.71)		22 (32.35)
Age (year)
≤ 30	57(31.67)	15 (26.32)	0.642^a	14 (24.56)	0.422^a
31–40	99 (55.00)	21 (21.21)		37 (37.37)
41–50	17 (9.44)	6 (35.29)		5 (29.41)
≥ 51	7 (3.88)	2 (28.58)		2 (28.59)
Employee type
Managerial or directorial	36 (20.00)	11 (30.56)	0.595^a	12(33.33)	0.808^a
Officer or executive	137 (76.11)	31 (22.63)		43(3199)
Support staff	7 (3.89)	2 (28.57)		3(42.89)
Religion
Muslim	153 (85.00)	39 (25.49)	0.437^a	45 (29.41)	0.055^a
Others	27(15.00)	5(18.51)		13 (48.15)
Marital status
Married	142 (78.89)	37 (26.06)	0.331^a	47 (33.10)	0.627^a
Unmarried	38 (21.11)	7 (18.42)		11 (29.05)
Number of family members
1–5	140 (77.78)	38 (27.14)	0.115^a	46 (32.86)	0.733^a
More than 5	40 (22.22)	6 (15.0)		12 (30.00)
Education
Below undergraduate level	2 (1.11)	1 (50.00)	0.398^b	1 (50.0)	0.589^b
Graduate or above	178 (98.89)	43 (55.13)		57 (32.02)
Job duration (years)
1–3	32 (17.78)	9 (28.13)	0.593^a	7 (21.88)	0.167^a
More than 3	148 (82.22)	35 (23.65)		51 (34.46)
Monthly salary (BDT)
≤ 40k	56 (31.11)	13 (23.21)	0.019^a	15 (28.57)	0.081^a
41–60k	54 (30.00)	11 (20.37)		18 (33.33)
61–80k	28 (15.56)	3 (10.71)		11 (39.29)
≥ 81k	42 (23.33)	17 (40.48)		14 (33.33)
Stress level
Low stress	17 (9.44)	3 (17.65)	0.655^a	3(17.65)	0.094^a
Moderate stress	109 (60.56)	29 (26.61)		32 (29.36)
High stress	54 (30.00)	12 (22.22)		23 (42.59)
Routine health check-up
Never	6 (3.33)	2(33.33)	0.007^a	0 (0)	0.011^a
Once a year	37 (20.56)	12 (32.43)		9 (24.32)
More than once a year	23 (12.78)	11 (47.83)		3 (13.04)
Only when emergency	114 (63.33)	19 (16.67)		46 (40.35)
Smoking habit
Yes	36 (20.00)	11 (30.56)	0.340^a	15 (41.67)	0.175^a
No	144 (80.00)	33 (28.95)		43 (29.86)
Regular intake of betel or tobacco leaf
Yes	6 (3.33)	2 (33.33)	0.606^b	1 (16.67)	0.407^b
No	174 (96.67)	42 (24.14)		57 (32.76)
Intake of alcohol
Yes	45 (25.00)	11 (24.44)	1.000^a	16 (35.56)	0.006^a
No	135 (75.00)	33 (24.44)		22 (16.30)
Junk food consumption
Daily	17 (9.44)	5 (29.41)	0.617^b	3 (17.65)	0.633^a
Not daily	163(90.56)	39 (23.93)		56(34.36)
Meat consumption
Daily	9 (5.00)	6(66.67)	0.009^b	2 (22.22)	0.510^b
Weekly	171 (95.00)	38(22.23)		56(33.03)
Antidepressant drug intake
Yes	21 (11.67)	5 (23.81)	0.943^a	1 (4.76)	0.004^a
No	159 (83.33)	39 (24.53)		57 (35.85)
Steroid drug intake
Yes	2 (1.11)	2 (100.00)	0.012^b	0 (0)	0.327^b
No	178 (98.89)	42 (23.60)		58 (32.58)
Anti-hypertensive drug intake
Yes	19 (10.56)	19 (10.55)	0.001^b	0 (0)	0.001^a
No	161 (89.44)	25 (13.88)		58 (36.02)
Diabetic
Yes	8 (4.44)	2 (1.11)	0.970^b	3 (37.50)	0.744^b
No/doesn’t know	172 (95.56)	42 (24.14)		55 (31.98)
Renal diseases
Yes	1 (0.56)	1 (100.00)	0.078^b	0 (0)	0.489^b
No/doesn’t know	179 (99.44)	43 (23.88)		58 (32.40)
Cardiovascular diseases
Yes	2 (1.11)	1 (50.00)	0.398^b	0 (0)	0.327^b
No/doesn’t know	178 (98.89)	43 (24.16)		58 (32.58)
Respiratory diseases
Yes	2 (1.11)	1 (50.00)	0.398^b	0 (0)	0.327^b
No	178 (98.89)	43 (24.16)		58 (32.58)
Parental hypertension
Yes	77 (42.78)	29 (37.66)	0.001^a	19 (24.68)	0.061^a
No	103 (57.22)	15 (14.56)		39 (37.87)
Parental diabetes
Yes	78 (43.33)	26 (33.33)	0.015^a	23 (29.49.)	0.492^a
No	102 (56.67)	18 (17.65)		35 (34.31)
Parental renal diseases
Yes	15 (8.33)	7 (46.67)	0.036^b	2 (13.33)	0.102^b
No	165 (91.67)	37 (22.42)		56 (33.94)
Parental cardiovascular diseases
Yes	41 (22.78)	16 (39.02)	0.013^a	8 (19.51)	0.048^a
No	139 (77.22)	28 (20.14)		50 (35.97)
Parental respiratory diseases
Yes	14 (7.78)	3 (21.43)	0.784^b	1 (7.14)	0.037^b
No	166 (92.22)	41 (24.70)		57 (34.34)
Body mass intake
Normal	83 (46.11)	20 (24.10)	0.932^a	28 (33.73)	0.658^a
Overweight	83 (46.11)	20 (24.10)		27 (32.53)
Obese	14 (7.78)	4 (28.57)		3 (21.43)

Note: ^AP-value calculated from Chi square test. ^bP-value calculated from Fisher’s exact test. P-values ≤0.000 were converted into 0.001

We found hypertension prevalence was the highest (26.61%) among those who were in moderate stress. On the contrary, pre-hypertension prevalence was high (42.59%) among respondents who had high stress levels. Furthermore, hypertension and pre-hypertension prevalence was higher among those who had their health checked-up more than once a year (47.83%) and only during emergency (40.35%), respectively. According to our analysis, proportion of hypertension was higher among participants with status of smoking (30.56%), tobacco and betel leaf intake (33.33%), junk food consumption (29.41%), meat consumption (66.67%), and obesity (28.57%). Furthermore, proportion of hypertension was higher among participants with status of steroid drug intake (100%), renal disease (100%) cardiovascular disease (50%), respiratory disease (50%), parental hypertension (37.66%), parental diabetes (33.33%), parental renal diseases (46.67%), and parental cardiovascular diseases (39.02%).

We also found that hypertension status among the participants significantly differed by sex, monthly salary, routine health check-up, meat consumption, steroid intake, anti-hypertensive drug intake, parental hypertension, parental diabetes, and parental cardiovascular diseases.

3.2. Factors associated with hypertension

Factors associated with hypertension among the bank employees according to the adjusted logistic regression model have been portrayed in Table 3. According to our analysis, being male (AOR: 3.92, 90% CI: 1.75–8.75), a family size of ≥ 6 persons (AOR: 0.19, 90% CI: 0.07, 0.53), a monthly salary of BDT ≥ 81k (AOR: 2.65, 90% CI: 1.07–6.59), parental hypertension (AOR: 4.64, 90% CI: 2.16–9.97), parental diabetes (AOR: 1.93 90% CI: 0.92–4.03), parental renal disease (AOR: 5.48 90% CI: 1.62–18.50), and weekly meat consumption (AOR: 0.07, 90% CI: 0.01–0.37) were significantly associated with having hypertension among bank employees.

Table 3. Factors associated with hypertension among the interviewed bank employees

Hypertension factors	Unadjusted OR (90% CI)	Adjusted OR (90% CI)
Sex of the respondents
Female	1.00	1.00
Male	2.53 (1.31–4.87)**	3.92 (1.75–8.75)***
Size of the family
1–5 persons	1.00	1.00
≥ 6 persons	0.47 (0.21–1.05)	0.19 (0.07–0.53)***
Salary (thousand taka)
≤ 40	1.00	1.00
41–60	0.85 (0.39–1.81)	0.73 (0.30–1.78)
61–80	3.9 (0.17–1.23)	0.13 (0.03–0.51)**
≥ 81	2.25 (1.08–4.68)	2.65 (1.07–6.59)*
Parental hypertension
No	1.00	1.00
Yes	3.54 (1.94–6.46)***	4.64 (2.16–9.97)***
Parental diabetes
No	1.00	1.00
Yes	2.33 (1.30–4.17) **	1.93 (0.92–4.03)
Parental renal diseases
No	1.00	1.00
Yes	3.03 (1.22–7.48)**	5.48 (1.62–18.50)**
Frequency of meat consumption
Daily	1.00	1.00
Weekly	0.14 (0.04–0.47)***	0.07 (0.01–0.37)***
Constant		0.57 (0.09–3.39)
LR		54.28
P-value		0.001
Number of observations		180

Note: *p < 0.1; **p < 0.05; ***p < 0.01

4. Discussion

According to our study, prevalence of hypertension and prehypertension among the participant bank employees were 24.44% (n = 44) and 32.22% (n = 58), respectively.

We found respondents’ sex, higher monthly salary, parental hypertension, parental renal disease, family size, and daily meat consumption were significantly associated with hypertension among participants. There was no previous study that investigated the prevalence and risk factors of hypertension among bank employees in Bangladesh. Using Bangladesh Demographic and Health Survey 2011 data, MM Rahman et al. (2013) investigated hypertension among the adult (age more than 35 years) population in Bangladesh and found the prevalence of hypertension and pre-hypertension were respectively 24.4% and 27.1%. This finding seems to be similar with that of our study where one third of the respondents above 40 years old were found with hypertension.

Awareness is a key issue associated with hypertension. Many of the hypertension patients do not know their actual condition. Undiagnosed hypertension may lead to serious health problems. Different studies portrayed only half of the respondents were aware of their hypertension status (Alam et al., 2014; Rahman, 2013). Surprisingly, despite being educated, only 47.72% respondents in our study were aware of their hypertension status, and the rest 52.28% were unaware of their conditions. This could be due to lack of routine health check-up and comparatively younger age, which probably made them unaware of developing hypertension, of the participants. Routine health check-up could improve awareness, but our findings show only 20.56% bank employees do routine health check-ups annually, and 63.33% do only during emergency health conditions.

Previous studies reported that hypertension in Bangladesh was significantly associated with gender, age, educational level, body mass index (BMI), overweight, and higher wealth index (Ahmed et al., 2019; Rahman, 2013; Talukder & Ali, 2020). In our study, although pre-hypertension prevalence was almost equal among male and female bank employees (32.14% among male and 32.35% among female), we identified prevalence of hypertension among male employees (30.36%) was more than twice that of female employees (14.71%). We did not find a significant association between hypertension and education level and BMI; however, the association was significant for sex and income level. The possible reasons for non-association with education could be that most of the respondents had almost the same education level. Obese and elder respondents had higher hypertension prevalence than others, but the association was not significant which could be possibly because majority of the respondents in this study were below 40 years old who were not high at risk of developing hypertension. Crosstab between age versus BMI represented that 81.4% (n = 79) of total overweight and obese respondents had age below 40 years.

Globally, there is little literature regarding hypertension among bank employees. Hinson et al. (2019) investigated hypertension factors among bank employees in Cotonou (Benin) and found age, lack of knowledge about preventive measures of hypertension, work under pressure, and obesity were significant factors of hypertension. In our study, only 23.33% (n = 42) respondents were able to respond to the actual definition of hypertension. In India, some researchers investigated hypertension among bank employees and found hypertension was associated with age, job position, smoking, alcohol consumption, and BMI (Maroof et al., 2007; Momin et al., 2012). Our study found only 20% (n = 36) respondents used to smoke and 25% (n = 45) respondents used to drink alcohol, and no significant association was found with hypertension. The possible reason may be that the smokers and drinkers were equally distributed in hypertension and non-hypertension categories. Another study in India shows that hypertension was not associated with stress level (Kumar & Sundaram, 2014). We also found that hypertension was not significantly associated with overall stress score. The possible factor behind the insignificant association between overall stress score and hypertension in our study is age, for 84.66% (n = 138) of those who were in moderate or high stress were below 40 years old and probably had high coping mechanism.

Regarding preventing hypertension in Bangladesh, research shows awareness and simple health messages increase provider visit, reduce blood pressure, and improve BP control in hypertensive Bangladeshis (Alam et al., 2014). Considering the messages, bank employees who were in pre-hypertension stage may be cautious about their health and should follow routine check-ups. Appropriate knowledge and healthy lifestyle practice may control hypertension induced morbidity and mortality in the future.

5. Strength and limitations

This is the first study to investigate hypertension among bank employees in Bangladesh. In this study respondents were educated; thus, they were more likely to easily understand the questions, and the likelihood of respondent bias might be low. This study also had some limitations. The association between hypertension and other variables doesn’t indicate causal relationship due to cross-sectional nature of the study. Undiagnosed hypertension, body weight, and stress of the respondents may be influenced by recent food intake, workload, and other factors on the day of interview; therefore, it might lead to potential misclassification. The sample size estimation and statistical analysis were conducted at 90% confidence level which provides less strength of the study. The small sample size led to low power of the study. Finally, there were limitations of resource, time, and workforce for which it was not possible to consider other issues such as analysing biological samples (blood, serum etc.) and to implement the study more smoothly at field level.

6. Conclusion

In accordance with the overall Bangladeshi population, hypertension prevalence among bank employees was also high. A big proportion of bank employees was in pre-hypertension stage and was also un-diagnosed which was alarming. Both diagnosed and undiagnosed hypertension may also lead to serious health problems; thus, awareness building and preventive measures are important. Further research could be conducted to observe the prevalence of hypertension among bank employees in other settings in Bangladesh and to identify the causal mechanism of how the associated factors may lead to hypertension.

Ethical issue

This study was conducted for the partial fulfilment of the Bachelor of Public Health Nutrition Program from the University of Primeasia, Bangladesh. The study protocol was reviewed by the review committee of the University of Primeasia, Bangladesh (PAU/IEAC/22/114). Ethical guidelines of the Bangladesh Medical Research Council (BMRC) were followed in this study. No invasive technique was used; no biological sample was collected and analysed. Scope of the research was clearly explained to the participants before interviewing and written consent was received.

Acknowledgements

Authors would like to thank University of Primeasia, Bangladesh for providing technical support and to respective bank authorities for allowing us to conduct the interviews.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.