Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-22T16:15:39.742Z Has data issue: false hasContentIssue false
  • English
  • Français

Differences in prevalence and determinants of hypertension according to rural–urban place of residence among adults in Bangladesh

Published online by Cambridge University Press:  19 December 2018

Gulam Muhammed Al Kibria Open the ORCID record for Gulam Muhammed Al Kibria [Opens in a new window] ,
Krystal Swasey ,
Rajat Das Gupta ,
Allysha Choudhury ,
Jannatun Nayeem ,
Atia Sharmeen  and
Vanessa Burrowes
Gulam Muhammed Al Kibria*
Affiliation:
Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, USA Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
Krystal Swasey
Affiliation:
Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, USA
Rajat Das Gupta
Affiliation:
James P Grant School of Public Health, Brac University, Dhaka, Bangladesh
Allysha Choudhury
Affiliation:
Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
Jannatun Nayeem
Affiliation:
Chattagram International Dental College and Hospital, Chittagong, Bangladesh
Atia Sharmeen
Affiliation:
School of Community Health and Policy, Morgan State University, Baltimore, MD, USA
Vanessa Burrowes
Affiliation:
Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
*
*Corresponding author. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This cross-sectional study analysed Bangladesh Demographic and Health Survey 2011 data with the aim of investigating the prevalence of, and risk factors for, hypertension in individuals aged over 35 by rural–urban place of residence. After estimation of the stratified prevalence of hypertension by background characteristics, multivariable logistic regression analysis was conducted to calculate the adjusted odds (AORs) and 95% confidence intervals (CIs) for selected factors. Of the 7839 participants, 1830 were from urban areas and 6009 from rural areas. The overall prevalence of hypertension was 32.6% (95% CI: 30.5–34.8) in urban areas and 23.6% (95% CI: 22.5–24.7) in rural areas. The prevalence and odds of hypertension increased with increasing age, female sex, concomitant diabetes and overweight/obesity and richer wealth status in both urban and rural regions. Although residence in Khulna and Rangpur divisions and higher education level were associated with increased odds of hypertension in urban regions, this was not the case in rural regions (p>0.05). Residence in Sylhet and Chittagong divisions had lower odds of hypertension in rural regions. Furthermore, the proportions of overweight/obese, diabetic and higher wealth status participants were higher in urban than in rural regions. The prevalence and odds of hypertension were found to be associated with several common factors after stratifying by place of residence. Some of these factors are more concentrated in urban regions, so urban residents with these risk factors need to be made more aware of these in order to control hypertension in Bangladesh. Public health programmes also need to be tailored differently for urban and rural regions, based on the different distribution of these significant factors in the two areas.

Keywords

Hypertension Risk FactorsRural-Urban ResidenceBangladesh
Type
Research Article
Information
Journal of Biosocial Science , Volume 51 , Issue 4 , July 2019 , pp. 578 - 590
Copyright
© Cambridge University Press, 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Barnes, AS (2012) Obesity and sedentary lifestyles: risk for cardiovascular disease in women. Texas Heart Institute Journal 39 (2), 224–227.Google Scholar
Benneyworth, L, Gilligan, J, Ayers, JC, Goodbred, S, George, G, Carrico, A et al. (2016) Drinking water insecurity: water quality and access in coastal south-western Bangladesh. International Journal of Environmental Health Research 26(5–6), 508–524.Google Scholar
Bernabe-Ortiz, A, Benziger, CP, Gilman, RH, Smeeth, L and Miranda, JJ (2012) Sex differences in risk factors for cardiovascular disease: the PERU MIGRANT study. PloS One 7 (4), e35127.Google Scholar
Bhurosy, T and Jeewon, R (2014) Overweight and obesity epidemic in developing countries: a problem with diet, physical activity, or socioeconomic status? Scientific World Journal 2014, 964236.Google Scholar
Biswas, T, Garnett, SP, Pervin, S and Rawal, LB (2017) The prevalence of underweight, overweight and obesity in Bangladeshi adults: data from a national survey. PloS One 12(5), e0177395.Google Scholar
Biswas, T, Islam, MS, Linton, N and Rawal, LB (2016) Socio-economic inequality of chronic non-communicable diseases in Bangladesh. PloS One 11(11), e0167140.Google Scholar
Chobanian, AV, Bakris, GL, Black, HR, Cushman, WC, Green, LA, Izzo, JL Jr et al. (2003) The seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA 289(19), 2560–2572.Google Scholar
Chowdhury, MA, Uddin, MJ, Haque, MR and Ibrahimou, B (2016) Hypertension among adults in Bangladesh: evidence from a national cross-sectional survey. BMC Cardiovascular Disorders 16(22).Google Scholar
Chowdhury, MA, Uddin, MJ, Khan, HM and Haque, MR (2015) Type 2 diabetes and its correlates among adults in Bangladesh: a population based study. BMC Public Health 15(1070).Google Scholar
Chowdhury, MAB, Adnan, MM and Hassan, MZ (2018) Trends, prevalence and risk factors of overweight and obesity among women of reproductive age in Bangladesh: a pooled analysis of five national cross-sectional surveys. BMJ Open 8, e018468-2017–018468.Google Scholar
Christiani, Y, Byles, JE, Tavener, M and Dugdale, P (2015) Assessing socioeconomic inequalities of hypertension among women in Indonesia’s major cities. Journal of Human Hypertension 29(11), 683–688.Google Scholar
Costa, FV, Ambrosioni, E, Montebugnoli, L, Paccaloni, L, Vasconi, L and Magnani, B (1981) Effects of a low-salt diet and of acute salt loading on blood pressure and intralymphocytic sodium concentration in young subjects with borderline hypertension. Clinical Science (London, England) 61 (Supplement 7), 21s–23s.Google Scholar
da Costa, JS, Barcellos, FC, Sclowitz, ML, Sclowitz, IK, Castanheira, M, Olinto, MT et al. (2007) Hypertension prevalence and its associated risk factors in adults: a population-based study in Pelotas. Arquivos Brasileiros de Cardiologia 88(1), 59–65.Google Scholar
Doak, CM, Adair, LS, Bentley, M, Monteiro, C and Popkin, BM (2005) The dual burden household and the nutrition transition paradox. International Journal of Obesity 29(1), 129.Google Scholar
Erwteman, TM, Nagelkerke, N, Lubsen, J, Koster, M and Dunning, AJ (1984) Beta blockade, diuretics, and salt restriction for the management of mild hypertension: a randomised double blind trial. BMJ (Clinical Research Edition) 289(6442), 406–409.Google Scholar
Fan, AZ, Strasser, SM, Zhang, X, Fang, J and Crawford, CG (2015) State socioeconomic indicators and self-reported hypertension among US adults, 2011 behavioral risk factor surveillance system. Preventing Chronic Disease 12, E27.Google Scholar
Fateh, M, Emamian, MH, Asgari, F, Alami, A and Fotouhi, A (2014) Socioeconomic inequality in hypertension in Iran. Journal of Hypertension 32(9), 1782–1788.Google Scholar
Feng, W, Dell’Italia, LJ and Sanders, PW (2017) Novel paradigms of salt and hypertension. Journal of the American Society of Nephrology 28(5), 1362–1369.Google Scholar
GBD 2015 DALYs and HALE Collaborators (2016) Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet 388 (10053), 1603–1658.Google Scholar
GBD 2015 Disease and Injury Incidence and Prevalence Collaborators (2016) Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet 388 (10053), 1545–1602.Google Scholar
GBD 2015 Mortality and Causes of Death Collaborators (2016) Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet 388 (10053), 1459–1544.Google Scholar
GBD 2015 Risk Factors Collaborators (2016) Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet 388 (10053), 1659–1724.Google Scholar
Global Burden of Metabolic Risk Factors for Chronic Diseases Collaboration (2014) Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: a comparative risk assessment. The Lancet. Diabetes and Endocrinology 2(8), 634–647.Google Scholar
Harshfield, E, Chowdhury, R, Harhay, MN, Bergquist, H and Harhay, MO (2015) Association of hypertension and hyperglycaemia with socioeconomic contexts in resource-poor settings: the Bangladesh Demographic and Health Survey. International Journal of Epidemiology 44(5), 1625–1636.Google Scholar
Heck, JE, Marcotte, EL, Argos, M, Parvez, F, Ahmed, A, Islam, T et al. (2012) Betel quid chewing in rural Bangladesh: prevalence, predictors and relationship to blood pressure. International Journal of Epidemiology 41(2), 462–471.Google Scholar
Hossain, MG, Bharati, P, Aik, S, Lestrel, PE, Abeer, A and Kamarul, T (2012) Body mass index of married Bangladeshi women: trends and association with socio-demographic factors. Journal of Biosocial Science 44(4), 385–399.Google Scholar
Hu, FB (2011) Globalization of diabetes: the role of diet, lifestyle, and genes. Diabetes Care 34(6), 1249–1257.Google Scholar
Hypertension Prevention Trial Research Group (1990) The Hypertension Prevention Trial: three-year effects of dietary changes on blood pressure. Archives of Internal Medicine 150(1), 153–162.Google Scholar
Jiang, S, Lu, W, Zong, X, Ruan, H and Liu, Y (2016) Obesity and hypertension. Experimental and Therapeutic Medicine 12(4), 2395–2399.Google Scholar
Khanam, MA, Lindeboom, W, Razzaque, A, Niessen, L and Milton, AH (2015) Prevalence and determinants of pre-hypertension and hypertension among the adults in rural Bangladesh: findings from a community-based study. BMC Public Health 15(203).Google Scholar
Langford, HG, Blaufox, MD, Oberman, A, Hawkins, CM, Curb, JD, Cutter, GR et al. (1985) Dietary therapy slows the return of hypertension after stopping prolonged medication. JAMA 253, 657–664.Google Scholar
Longo, GZ, Neves, J, Luciano, VM and Peres, MA (2009) Prevalence of high blood pressure levels and associated factors among adults in Southern Brazil. Arquivos Brasileiros de Cardiologia 93(4), 387–394, 380–386.Google Scholar
Maldonado, G and Greenland, S (1993) Simulation study of confounder-selection strategies. American Journal of Epidemiology 138(11), 923–936.Google Scholar
NCD Risk Factor Collaboration (2017) Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19.1 million participants. The Lancet 389, 37–55.Google Scholar
Neuman, M, Kawachi, I, Gortmaker, S and Subramanian, SV (2013) Urban–rural differences in BMI in low- and middle-income countries: the role of socioeconomic status. American Journal of Clinical Nutrition 97(2), 428–436.Google Scholar
NIPORT, Mitra and Associates and ICF International (2013) National Institute of Population Research and Training Bangladesh Demographic and Health Survey 2011. Dhaka, Bangladesh and Calverton, MD, USA.Google Scholar
Osei, K (2003) Global epidemic of type 2 diabetes: implications for developing countries. Ethnicity and Disease 13, S102–106.Google Scholar
Popkin, BM, Adair, LS and Ng, SW (2012) Global nutrition transition and the pandemic of obesity in developing countries. Nutrition Reviews 70(1), 3–21.Google Scholar
Rahman, MHSE, Islam, MJ, Mostofa, MG and Saadat, KA (2015a) Association of socioeconomic status with diagnosis, treatment and control of hypertension in diabetic hypertensive individuals in Bangladesh: a population-based cross-sectional study. JRSM Open 6(10), 2054270415608118.Google Scholar
Rahman, MM, Gilmour, S, Akter, S, Abe, SK Saito, E and Shibuya, K (2015b) Prevalence and control of hypertension in Bangladesh: a multilevel analysis of a nationwide population-based survey. Journal of Hypertension 33(3), 465–472.Google Scholar
Sampson, UK, Edwards, TL, Jahangir, E, Munro, H, Wariboko, M and Wassef, MG (2014) Factors associated with the prevalence of hypertension in the southeastern United States: insights from 69,211 blacks and whites in the Southern Community Cohort Study. Circulation. Cardiovascular Quality and Outcomes 7(1), 33–54.Google Scholar
Scheelbeek, PFD, Chowdhury, MAH, Haines, A, Alam, DS, Hoque, MA and Butler, AP (2017) Drinking water salinity and raised blood pressure: evidence from a cohort study in coastal Bangladesh. Environmental Health Perspectives 125(5), 057007.Google Scholar
Scholes, S, Bajekal, M, Love, H, Hawkins, N, Raine, R, O’Flaherty, M and Capewell, S (2012) Persistent socioeconomic inequalities in cardiovascular risk factors in England over 1994–2008: a time-trend analysis of repeated cross-sectional data. BMC Public Health 12(129).Google Scholar
Talukder, MRR, Rutherford, S, Phung, D, Islam, MZ and Chu, C (2016) The effect of drinking water salinity on blood pressure in young adults of coastal Bangladesh. Environmental Pollution 214, 248–254.Google Scholar
Tareque, MI, Koshio, A, Tiedt, AD and Hasegawa, T (2015) Are the rates of hypertension and diabetes higher in people from lower socioeconomic status in Bangladesh? Results from a nationally representative survey. PloS One 10(5), e0127954.Google Scholar
WHO (2017) About 9 Voluntary Targets 2017. URL: http://www.who.int/nmh/ncd-tools/definition-targets/en/ (accessed 25th May 2018).Google Scholar
WHO (2009) Global Health Risks. WHO Mortality and Burden of Disease Attributable to Selected Major Risks. URL: http://www.who.int/healthinfo/global_burden_disease/GlobalHealthRisks_report_full.pdf (accessed 25th May 2018).Google Scholar