• Users Online: 810
  • Print this page
  • Email this page


 
 Table of Contents  
ORIGINAL ARTICLES
Year : 2022  |  Volume : 13  |  Issue : 3  |  Page : 262-269

Non-communicable disease risk factors among garment factory workers in Bangladesh


1 Centre for Global Health Research, Diabetic Association of Bangladesh, Dhaka, Bangladesh
2 National Healthcare Network, Diabetic Association of Bangladesh, Mirpur-10, Dhaka 1216, Bangladesh
3 Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo 0318, Norway
4 Directorate General of Health Services, Mohakhali, Dhaka 1212, Bangladesh

Date of Submission15-Apr-2022
Date of Decision06-Jun-2022
Date of Acceptance09-Jun-2022
Date of Web Publication26-Sep-2022

Correspondence Address:
Dr. Bishwajit Bhowmik
Centre for Global Health Research, Diabetic Association of Bangladesh, 122 Kazi Nazrul Avenue, Shahbagh, Dhaka 1000
Bangladesh
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jod.jod_35_22

Rights and Permissions
  Abstract 

Aim: Many studies identified numerous health problems among industrial workers, in general, and garment workers. But a health problem with a specific focus on non-communicable disease (NCD) risk factors for Bangladeshi garment workers is lacking. Objective: The aim of this article is to investigate the NCD risk factors among garment workers in Bangladesh. Materials and Methods: A total of 4736 randomly selected garment workers aged ≥15 years in the Gazipur district of Bangladesh were investigated in a cross-sectional study from April 2014 to November 2014. Sociodemographic, anthropometric, and clinical details, including type 2 diabetes mellitus (T2DM) and hypertension (HTN), and albuminuria were collected by using a structured interviewer-administered questionnaire. Results: Among the study participants, the mean age was 27.6 ± 8.0 years. About 16.1% of the participants were underweight (body mass index <18.5 kg/m2), 15.8% were overweight (≥23.0–24.9 kg/m2), and 21.2% (≥25.0 kg/m2) were obese. The prevalence of central obesity based on waist circumference (male ≥90 cm and female ≥80 cm) and waist–hip ratio (male ≥0.90 and female ≥0.80) was 24.9% and 62.1%, respectively. The prevalences of T2DM (random blood glucose: ≥11.1 mmol/L), HTN (≥140/90 mmHg), and albuminuria (≥1+) were 7.0%, 13.9%, and 5.1%, respectively. The rates of general obesity and central obesity were significantly higher in females, and smoking habits, albuminuria, T2DM, and HTN were significantly higher in males. Increased age, male gender, general obesity, and central obesity were significantly associated with T2DM and HTN. Conclusion: The prevalences of T2DM, HTN, obesity, and albuminuria were common in study participants. Long-term follow-up studies of these workers are therefore warranted.

Keywords: Bangladesh, garment workers, NCD risk


How to cite this article:
Bhowmik B, Kamruzzaman M, Siddiquee T, Samad MA, Khan FA, Rashid H, do Moreira NC, Alim A, Khan FA, Milon SU, Robed MA, Hossain AH, Hussain A, Khan AK. Non-communicable disease risk factors among garment factory workers in Bangladesh. J Diabetol 2022;13:262-9

How to cite this URL:
Bhowmik B, Kamruzzaman M, Siddiquee T, Samad MA, Khan FA, Rashid H, do Moreira NC, Alim A, Khan FA, Milon SU, Robed MA, Hossain AH, Hussain A, Khan AK. Non-communicable disease risk factors among garment factory workers in Bangladesh. J Diabetol [serial online] 2022 [cited 2022 Nov 30];13:262-9. Available from: https://www.journalofdiabetology.org/text.asp?2022/13/3/262/357126




  Introduction Top


Both communicable and non-communicable diseases (NCDs) are still prevalent in Bangladesh.[1] However, during the last two decades, disease patterns are shifting toward NCDs. Major NCDs, including type 2 diabetes mellitus (T2DM), hypertension (HTN), and coronary artery disease (CAD), contribute to 67% of mortality in Bangladesh.[2] The National 2018 Stepwise Approach to NCD Risk Factor Surveillance (STEP) survey also reported that 97% of participating people had at least one NCD risk factor, including less than five servings of fruits/vegetables per day, smoking, physical inactivity, overweight/obesity, T2DM, and HTN. The prevalences of T2DM, HTN, overweight, physical inactivity, and tobacco use (any form) are 8.4%, 25.2%, 20.3%, 29.1%, and 43.7%, respectively.[2] In addition, the prevalence of nutritional deficiency, including undernutrition and anemia, was also found to be high in Bangladeshi people.[2],[3],[4] All these will create a substantial financial burden on health care in a low-middle income country like Bangladesh. Therefore, the government needs to strengthen early screening and develop appropriate prevention strategies to address these chronic disease burdens and their related risk factors.

The readymade garment (RMG) industry is one of Bangladesh’s most important catalysts for economic development. The RMG sector, with more than 4.4 million workers, represents around 83% of Bangladesh’s total export.[5] Many unskilled laborers, predominantly women from rural locations, work in this sector. Many studies identified numerous health problems among industrial workers, in general, and garment workers.[6],[7],[8],[9],[10],[11] A systematic review also reported that RMG workers of South and Southeast Asian countries (including Bangladesh, India, Cambodia, Sri Lanka, China, and Thailand) are vulnerable to several health challenges, including physical and psychological issues.[12] These make them less productive, more prone to injury, and have higher claim costs. But a health problem with a specific focus on non-communicable risk factors for Bangladeshi garment workers is lacking. Therefore, the present study with a large sample size was designed to provide an overview of health problems, focussing on non-communicable risk factors among garment workers in Bangladesh.


  Materials and Methods Top


Study design, location, and participants

This cross-sectional observational study was conducted over 8 months from April 2014 to November 2014 in six randomly selected readymade garment (RMG) factories located in the Gazipur district of Bangladesh. The current study is reported in compliance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for observational research.[13] A total of 5100 individuals (850 participants from each factory) were invited following a simple random procedure, and among them, 4736 (92%) participated in the study.

Sampling method

The student’s formula was used for infinite population size to determine the required sample size for this study, in which n is the sample size and d is the allowable error and Z = z-score, and P is the prevalence (which is 0.079) in this study[14]:



This basic sample size was adjusted for a design effect of 1.5, and the required sample size was, therefore, n = 2795 × 1.5 = 4192. However, assuming a non-response rate of 20%, the final sample size was 5038. A total of 5100 participants were finally invited.

Questionnaire and data collection

Both written and verbal permissions were obtained from all participants after explaining the rationale of the study. Permission from the factory authorities was received before the enrollment of the participants. The factory authorities were informed in detail about the objectives and procedure of the study. Two trained data collectors per factory with a background in health education and phlebotomy were responsible for administering the questionnaires, conducting physical examinations, and collecting laboratory samples. The completed questionnaires were collected from the participants on the same day. Participation in the study was voluntary. The inclusion criteria were: (i) age ≥15 years; (ii) willingness to participate; and (iii) providing informed consent. The exclusion criteria were: (i) any acute illness and (ii) pregnancy at the time of screening.

The survey procedures included completing a pretested questionnaire on sociodemographic information, anthropometry, blood pressure, and random blood glucose (RBG) measurement. Anthropometric measurements (including height, weight, waist, and hip circumferences) were taken with the participants wearing light clothes, without shoes. Body mass index (BMI) was calculated as the weight in kilogram divided by the height in meter squares. Waist circumference (WC) was measured between the lower margin of the last palpable rib and iliac crest on the mid-axillary line by placing a tape horizontally midway. Hip circumference was determined at the largest circumference of the buttocks, at a level parallel to the floor, and then waist–hip ratio (WHR) was calculated from the waist and hip circumference (cm). After a 10-min rest, and with standard procedure, the blood pressure (BP) was measured on the left arm in a sitting position by an aneroid sphygmomanometer. RBG was measured using the portable glucometer (One Touch II, LifeScan, Milpitas, CA, USA) in capillary blood glucose obtained by finger prick in the middle finger.[3] The blood glucose measurements were adjusted to obtain equivalent plasma glucose levels.[15] In addition, a urine dipstick test was used for detecting urine albumin.

Definition of variables

According to the International Association for the Study of Obesity and the International Obesity Task Force guidelines, underweight was defined as <18.5 kg/m2, normal weight 18.5–22.9 kg/m2, overweight 23.0–24.9 kg/m2, and obesity ≥25 kg/m2.[16],[17] Central obesity was defined as a WC for men and women ≥90 and ≥80 cm, respectively, and WHR for males ≥0.90 and for females ≥0.80.[18] Diabetes was diagnosed if the RBG value was ≥11.1 mmol/L,[19] self-reported T2DM, or use of diabetes medication, and hypertension (HTN) was defined as systolic blood pressure (SBP) of ≥140 mmHg and/or diastolic blood pressure (DBP) of ≥90 mmHg[20] or if they were receiving treatment for HTN. Based on the monthly expenditure, socio-economic condition was classified as low [<10,000 Bangladeshi Taka (BDT, 1 USD = 84 BDT)], medium (10,000–15,000 BDT), and high (>15,000 BDT). The smoking habit was classified as either current or non-/ex-smoker. Urine albumin was reported as the following six values: negative, trace (±), 1+, 2+, 3+, and 4+ (corresponding to albumin levels of undetectable or <10, 10–29, 30–99, 100–299, 300–999, and 1000 mg/dL or greater, respectively).[21]

Ethical approval

The protocol was officially approved by the Ethical Review Committee of the Diabetic Association of Bangladesh. Research participation, confidentiality, and consent are followed as per Helsinki declaration, with local adaptation to allow both verbal and written instructions.

Data analysis

All the continuous variables in this study were presented as mean ± standard deviation (SD) and categorical data as number (percentage). Both independent t-test and one-way analysis of variance were used to compare differences in means and the χ2 test for categorical covariates, respectively. In addition, pairwise comparisons between the groups were performed and corrected for multiple testing using the Bonferroni method. A trend analysis test was used to determine the differences in proportions and means across the groups. Multivariable logistic regression analysis assessed the association between predictors and dependent variables. Both STATA 14 for Windows (STATA Co., College Station, TX, USA) and PASW statistics version 21 for Windows (SPSS Inc., Chicago, IL, USA) were used as needed. A P-value of less than 0.05 was deemed statistically significant.


  Results Top


[Table 1] shows the characteristics of the study participants by gender. The mean (± SD) age of study participants was 27.6 ± 7.9 years ranging from 15 to 60. About 39.9% of the participants were less than 25 years of age, 46.3% were between 25 and 35 years, and the remaining 13.8% were at and above 35 years. Among 4736 study participants, 2825 (59.6%) were males, 3503 (74.6%) of the participants belonged to the low-income group, and 966 (21.9%) had a smoking habit (cigarette and bidi). About 16.1% of the participants were underweight (BMI <18.5 kg/m2), 46.9% had normal weight (18.5–22.9 kg/m2), 15.8% overweight (≥23.0–24.9 kg/m2) and 21.2% were obese (≥25.0 kg/m2). The prevalence of central obesity for WC (males ≥90 cm and females ≥80 cm) was 24.9%, and for WHR (males ≥0.90 and females ≥0.80) it was 62.1%. The prevalence of albuminuria (≥1+), HTN (≥140/90 mmHg), and T2DM (RBG ≥11.1 mmol/L) were 5.1%, 13.9%, and 7.0%, respectively. Rates of general obesity (19.3% vs. 24.0%) and central obesity (WC: 16.4% vs. 36.3% and WHR: 49.4% vs. 79.1%) were significantly higher in females, whereas smoking habit (34.8% vs. 0.3%), albuminuria (7.1% vs. 2.2%), HTN (17.4% vs. 8.7%), and T2DM (8.8% vs. 4.3%) were significantly higher in males.
Table 1: Characteristics of study participants by gender

Click here to view


[Table 2] shows the characteristics of the study participants by age group. The mean income level, height, weight, BMI, WC, hip, WHR, SBP, DBP, and RBS increased with the increasing age categories. Significant linear trends were observed for these risk variables (P-value for trend <0.001).
Table 2: Characteristics of study participants by age groups

Click here to view


The prevalence of male participants, smoking habits, general obesity, central obesity, albuminuria, HTN, and T2DM increased with increasing age (P-value for trend <0.001). The prevalence of the following according to <25, 25–35, and >35 year age groups, respectively, was male participants: 48.6%, 62.7%, and 81.4%; smoking habit: 14.4%, 24.3%, and 35.1%; general obesity: 13.6%, 25.7%, and 28%; central obesity defined by WC: 16.0%, 29.7%, and 36.0%; central obesity defined by WHR: 52.9%, 66.8%, and 74.5%; albuminuria: 3.4%, 5.1%, and 10.2%; HTN: 7.3%, 14.8%, and 29.8%, and T2DM: 2.1%, 6.8%, and 12.7%. However, the prevalence of undernutrition (23.1%, 12.0%, and 9.4%) decreased significantly with increasing age (P-value for trend <0.001).

Male gender [odds ratio (OR): 1.72, P < 0.001], participants’ age between 25 and 35 years (OR: 2.96, P < 0.001), age more than 35 years (OR: 9.84, P < 0.001), belong to middle-income groups (OR: 1.67, P = 0.003), high-income groups (OR: 1.66, P = 0.005), overweight (OR: 1.58, P = 0.016), obesity (OR: 2.32, P < 0.001), abdominal obesity defined by WC (OR: 1.84, P = 0.001), and HTN (OR: 1.63, P = 0.003) were significant risk indicators for the development of T2DM. However, male gender (OR: 6.20, P < 0.001), age more than 35 years (OR: 2.70, P < 0.001), overweight (OR: 2.70, P < 0.001), obesity (OR: 5.77, P < 0.001), abdominal obesity defined by WC (OR: 1.77, P = 0.001), and T2DM (OR: 1.71, P = 0.012) were significant risk indicators for the development of HTN [Table 3].
Table 3: Association of diabetes (T2DM) and hypertension (HTN) by the following characteristics

Click here to view


[Figure 1] shows the study participants’ non-communicable risk factors (including general obesity, abdominal obesity, smoking habits, T2DM, and HTN). Regarding combined risk factors, 51.7% (male 45.3% and female 60.7%, P < 0.001) had no risk factor, whereas 28.2% (male 35.9% and female 17.1%, P < 0.001) had one, 13.1% (10.6% vs. 17.0%, P < 0.001) had two, and 7% (8.2% vs. 5.2%, P = 0.001) had three or more risk factors, respectively [Figure 1](a).
Figure 1: Non-communicable risk factors among the study participants, by gender (a) and age groups (b)

Click here to view


About 68.2% of the participants aged <25 years, 44.3% of the participants aged between 25 and 35 years, and 26.1% of the participants aged >35 years had no risk factor (P for trend <0.001), whereas 22.2% of the participants aged <25 years, 31.1% participants aged between 25 and 35 years, and 36.2% of the participants aged >35 years had one risk factor (P for trend <0.001), 7.3% of the participants aged <25 years, 16.1% of the participants aged between 25 and 35 years, and 21.1% of the participants aged >35 years had two risk factors (P for trend <0.001), and 2.3% of the participants aged <25 years, 8.5% of the participants aged between 25 and 35 years, and 16.6% of the participants aged >35 years had three and more risk factors (P for trend <0.001) [Figure 1](b).


  Discussion Top


This study aimed to investigate the health problems of garment workers in Bangladesh, with a particular focus on non-communicable risk factors. In this study, the prevalence of combined non-communicable risk factors (including general obesity, abdominal obesity, smoking habits, T2DM, and HTN) was high among relatively young garment workers of low socio-economic backgrounds.

The study found that about half of the participants had different components of non-communicable risk factors. The rate was higher in males than in females. A significantly higher rate of smoking habits (cigarette and bidi) in males might play a significant role in widening gender differences. In addition, the prevalence of non-communicable risk factors increased significantly with increasing age. The prevalence of total participants with combined non-communicable risks was found 2.3 times higher in participants aged more than 35 years. The findings align with previous studies on garment workers[8] and the National STEP 2018 NCD Risk Factors Survey.[2] Yet, different variables were used for defining combined non-communicable risks in those studies.

Among the non-communicable risk factors, the overall prevalence of T2DM was 7.0%. The rate was found to be higher in males and participants aged more than 35 years. The study finding was in line with previous studies conducted on garment workers in Bangladesh and India.[8],[10],[22] However, the prevalence was found to be lower in two nationally representative surveys, including the STEP NCD Risk Factor Survey 2018[2] and Bangladesh Demographic Health Survey (BDHS).[23] Using different blood tests might be the cause of differences in the prevalence rates. The study found a similar prevalence while including participants aged more than 35 years. Both agreement and disagreement related to gender prevalence were also observed in previous studies.[2],[7],[8],[10],[14],[23],[24] Like the earlier studies conducted at different time points in rural and urban Bangladesh, increased age, middle-high-income groups, obesity (both general and abdominal obesity), and HTN were significant risk indicators for the development of T2DM.[2],[7],[8],[10],[14],[23],[24]

The overall prevalence of HTN was 13.9%. Like T2DM, the rate was also found to be significantly higher in males and participants aged more than 35 years. The study findings were also like previous studies conducted among garment workers in Bangladesh and India.[8],[10],[22] Like T2DM, the prevalence of HTN was found to be lower in two nationally representative surveys, but the rate found a similar prevalence while including participants aged more than 35 years.[2],[23] Increased age, obesity (both general and abdominal obesity), and T2DM were found significant risk indicators for the development of HTN. These findings also align with the previous studies conducted at different time points in Bangladesh.[3],[23],[24],[25] Agreement and disagreement related to gender prevalence were also observed.[3],[23],[24],[25]

In this study, the prevalence of malnutrition (including undernutrition, overweight, and obesity) and albuminuria was found high. Underweight, overweight, obesity, and abdominal obesity were found higher in women, and albuminuria was higher in men. In addition, increased parity, high rate of oral contraceptive pill intake, less physical activity, and low level of cigarette smoking contribute to the high level of obesity in females.[26] On the contrary, the high rate of T2DM, HTN, and smoking in men might contribute to the high albuminuria rate. These findings support urgent health education and prevention programs for those at risk within occupational health.

The study has several unique strengths. It adopted a random sampling procedure. The study covered a large sample to estimate cardiometabolic risk factors among garment workers in Bangladesh. Trained and experienced personnel used standardized protocols to obtain data and had a high response rate, which increased the validity of these results. Lastly, this study reported the gender- and age-specific non-communicable risk factors, which will help policymakers design gender- and age-specific healthcare schemes related to garment workers in Bangladesh.

Limitations of our study also warrant discussion. First, this was a cross-sectional study. Therefore, the stated associations may not be causal. The measures (including anthropometric, clinical, and biochemical parameters) were recorded on a single day, and this may lead to measurement error. It also needs further follow-up. Other known risk factors (e.g., physical activity and dietary habits) related to cardiometabolic risk factors were not recorded. We could not follow the standardized fasting or oral glucose tolerance test for collecting blood glucose samples; due to the nature of the jobs, participants could not come in a fasting condition. In addition, information gathered was based on the recall of the participated workers, thus subjected to recall bias.


  Conclusion Top


The prevalence of non-communicable risk factors has been found high among garment workers in Bangladesh. Therefore, the provision of annual health checkups including all the non-communicable risk parameters should be introduced in garment factories to reduce the disease burden. To address the scientific issues, we need a large-scale prospective study to identify the determinants of non-communicable risk factors.

Acknowledgements

We express our admiration to the authority of the BADAS and New Asia Group for their unrestricted support. We acknowledge the contribution of our survey team members for their endless effort in the collection of data. We are grateful to all participants in this study for their active co-operation.

Financial support and sponsorship

Diabetic Association of Bangladesh.

Conflicts of interest

There are no conflicts of interest.

Authors’ contribution

BB planned the study, designed the protocol, was involved in statistical analysis, and wrote the manuscript. MK, TS, and FAK conceived and carried out the fieldwork, data management, and helped in writing the manuscript. HR carried out the fieldwork. SMA, MNC, FAK, AA, and SUM helped in writing the manuscript. MRA, EH, AH, and AKA edited the manuscript. All authors have read and approved the manuscript.

Data availability statement

Data are available on request from the authors.

Declarations

Ethical approval to report this study was obtained from the Ethical Review Committee of the Diabetic Association of Bangladesh.

Consent for publication

Not applicable as this contains only de-identified information.



 
  References Top

1.
Joarder T, Chaudhury TZ, Mannan I Universal health coverage in Bangladesh: Activities, challenges, and suggestions. Psyche (Camb Mass) 2019;2019:4954095.  Back to cited text no. 1
    
2.
Riaz BK, Islam MZ, Islam ANMS, Zaman MM, Hossain MA, Rahman MM, et al. Risk factors for non-communicable diseases in Bangladesh: Findings of the Population-based Cross-Sectional National Survey 2018. BMJ Open 2020;10:e041334.  Back to cited text no. 2
    
3.
National Institute of Population Research and Training - NIPORT, Ministry of Health and Family Welfare, and ICF. 2020. Bangladesh Demographic and Health Survey 2017-18Dhaka, Bangladesh: NIPORT/ICF. Available from: https://www.dhsprogram.com/pubs/pdf/FR344/FR344.pdf.  Back to cited text no. 3
    
4.
Anik AI, Rahman MM, Rahman MM, Tareque MI, Khan MN, Alam MM Double burden of malnutrition at household level: A comparative study among Bangladesh, Nepal, Pakistan, and Myanmar. PLoS One 2019;14:e0221274.  Back to cited text no. 4
    
5.
Bangladesh Garment Manufacturers and Exporters Association: Toward a Sustainable Garment Industry (BGMEA). Trade Information. Available from: http://www.bgmea.com.bd/home/pages/TradeInformation [Last accessed on September 23, 2020].  Back to cited text no. 5
    
6.
Bhowmik B, Afsana F, Ahmed T, Akhter S, Choudhury HA, Rahman A, et al. Obesity and associated type 2 diabetes and hypertension in factory workers of Bangladesh. BMC Res Notes 2015;8:460.  Back to cited text no. 6
    
7.
Bhowmik B, Afsana F, Ahmed T, Akhter S, Choudhury HA, Ahmed T, et al. Rising prevalence of diabetes and prediabetes among Bangladeshi factory workers. J Diabetol 2016;1:3.  Back to cited text no. 7
    
8.
Fatema K, Natasha K, Ali L Cardiovascular risk factors among Bangladeshi ready-made garment workers. J Public Health Afr 2014;5:373.  Back to cited text no. 8
    
9.
Hossain M, Islam Z, Sultana S, Rahman AS, Hotz C, Haque MA, et al. Effectiveness of workplace nutrition programs on anemia status among female readymade garment workers in Bangladesh: A program evaluation. Nutrients 2019;11:1259.  Back to cited text no. 9
    
10.
Solinap G, Wawrzynski J, Chowdhury N, Zaman H, Abid T, Hoque TA, et al. A disease burden analysis of garment factory workers in Bangladesh: Proposal for annual health screening. Int Health 2019;11:42–51.  Back to cited text no. 10
    
11.
Moran J, Fitch TJ, Villanueva G, Quadir MM, Chien LC, Alamgir H Urinary symptoms and infections among female garment factory workers in Bangladesh. Work 2020;65:847–56.  Back to cited text no. 11
    
12.
Kabir H, Maple M, Usher K, Islam MS Health vulnerabilities of readymade garment (RMG) workers: A systematic review. BMC Public Health 2019;19:70.  Back to cited text no. 12
    
13.
Vandenbroucke JP, von Elm E, Altman DG, Gøtzsche PC, Mulrow CD, Pocock SJ, et al; STROBE Initiative. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and elaboration. Plos Med 2007; 4:e297.  Back to cited text no. 13
    
14.
Bhowmik B, Binte Munir S, Ara Hossain I, Siddiquee T, Diep LM, Mahmood S, et al. Prevalence of type 2 diabetes and impaired glucose regulation with associated cardiometabolic risk factors and depression in an urbanizing rural community in Bangladesh: A population-based cross-sectional study. Diabetes Metab J 2012;36:422–32.  Back to cited text no. 14
    
15.
D’Orazio P, Burnett RW, Fogh-Andersen N, Jacobs E, Kuwa K, Külpmann WR, et al; International Federation of Clinical Chemistry Scientific Division Working Group on Selective Electrodes and Point of Care Testing. Approved IFCC recommendation on reporting results for blood glucose (abbreviated). Clin Chem 2005;51:1573–6.  Back to cited text no. 15
    
16.
Choo V WHO reassesses appropriate body-mass index for Asian populations. Lancet 2002;360:235.  Back to cited text no. 16
    
17.
World Health Organization, Western Pacific Region. The International Association for the Study of Obesity and the International Obesity Task Force. The Asia–Pacific perspective: Redefining obesity and its treatment. Sydney, Australia: Health Communications Australia Pty Limited; 2000. Available from: http://www.diabetes.com.au/pdf/obesity_report.pdf. [Last accessed on June 23, 2021].  Back to cited text no. 17
    
18.
Alberti KG, Zimmet P, Shaw J; IDF Epidemiology Task Force Consensus Group. The metabolic syndrome—A new worldwide definition. Lancet 2005;366:1059–62.  Back to cited text no. 18
    
19.
World Health Organization (WHO). Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia: Report of a WHO/IDF Consultation. Geneva: WHO; 2006.  Back to cited text no. 19
    
20.
Anderson MJ, Agarwal R Urinalysis. In: Lerma EV, Nisenson AR, editors. Nephrology Secrets. 3rd ed. Chicago, IL: Elsevier Mosby; 2012.  Back to cited text no. 20
    
21.
National Kidney Foundation: K/DOQI Clinical Practice Guidelines for Chronic Kidney Disease: Evaluation, classification, and stratification. Am J Kidney Dis 2002;39:S1-266.  Back to cited text no. 21
    
22.
Ravichandran SP, Pankaj BS Health problems and risk factors prevailing among garment workers in Tirupur, Tamil Nadu. Int J Community Med Public Health 2018;5:2400-5.  Back to cited text no. 22
    
23.
Akter S, Rahman MM, Abe SK, Sultana P Prevalence of diabetes and prediabetes and their risk factors among Bangladeshi adults: A nationwide survey. Bull World Health Organ 2014;92:204–13, 213A.  Back to cited text no. 23
    
24.
Iqbal A, Ahsan KZ, Jamil K, Haider MM, Khan SH, Chakraborty N, et al. Demographic, socioeconomic, and biological correlates of hypertension in an adult population: Evidence from the Bangladesh demographic and health survey 2017-18. BMC Public Health 2021;21:1229.  Back to cited text no. 24
    
25.
Saber S, Yasmin N, Alam MT, Hossain MM, Alam RF . Study on urinary tract infection among females of reproductive age group in Tertiary Care Teaching Hospital, Dhaka, Bangladesh. EJMED 2021;3:85-9.  Back to cited text no. 25
    
26.
Siddiquee T, Bhowmik B, Da Vale Moreira NC, Mujumder A, Mahtab H, Khan AK, et al. Prevalence of obesity in a rural Asian Indian (Bangladeshi) population and its determinants. BMC Public Health 2015;15:860.  Back to cited text no. 26
    


    Figures

  [Figure 1]
 
 
    Tables

  [Table 1], [Table 2], [Table 3]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
Abstract
Introduction
Materials and Me...
Results
Discussion
Conclusion
References
Article Figures
Article Tables

 Article Access Statistics
    Viewed334    
    Printed34    
    Emailed0    
    PDF Downloaded39    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]