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Assessing the true association between hypertension status and stature of individuals in Bangladesh: propensity score analysis

Journal of Human Hypertension volume 35, pages 250–256 (2021)Cite this article

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Abstract

Inconsistent evidence is found in the literature regarding the association between individuals’ stature and hypertension status. In this study, an attempt has been made to investigate the true association between height and occurrence of hypertension. For analysis purpose, this study considers Bangladesh Demographic and Health Survey (BDHS) 2011 data obtained from an observational study. By dividing height (tall/normal/short) based on 25th and 75th percentile points separately for females and males, a binary logistic regression model was fitted to the weighted data, where weights were calculated from propensity scores (PS). From the PS-based weighted data, we did not find any significant association between height and hypertension (p > 0.05). Besides the respondent’s height, logistic regression analyses of a balanced data set have revolved around some well-known factors that are associated with the occurrence of hypertension: gender of the respondent, higher wealth index status, as well as overweight. This study also found higher odds of occurring hypertension among the residents of Khulna and Rangpur divisions, whereas lower likelihood of hypertension is reported for the individual living in Chittagong and Sylhet districts. The findings of this paper indicate that human stature is not a risk factor for hypertension. Apart from height, this study uncovers some important risk factors for developing hypertension. By considering these factors, awareness should be raised among male, wealthier, and overweighted individuals in Bangladesh. However, why the prevalence of hypertension is higher in Khulna and Rangpur, as well as lower in Chittagong and Sylhet, demands further research.

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Data availability

The secondary data set, BDHS 2011, is freely available in the following website: http://dhsprogram.com/data/available-datasets.cfm.

References

  1. World Health Organization. Global Status Report on Non communicable Disease. Geneva: WHO; 2014. http://apps.who.int/iris/bitstream/10665/148114/1/9789241564854_eng.pdf. Accessed 2 May 2016.

  2. Alwan A, World Health Organization. Global status report on non-communicable diseases 2010. 2011. http://apps.who.int/iris/bitstream/10665/44579/1/9789240686458_eng.pdf.

  3. He J, Whelton PK. Epidemiology and prevention of hypertension. Med Clin N Am. 1997;81:1077–97.

    Article  CAS  Google Scholar 

  4. Whelton PK. Epidemiology of hypertension. Lancet. 1994;344:101–6.

    Article  CAS  Google Scholar 

  5. Zaman MM, Ahmed J, Chowdhury SR, Numan SM, Parvin K, Islam MS. Prevalence of ischemic heart disease in a rural population of Bangladesh. Indian Heart J. 2007;59:239–41.

    PubMed  Google Scholar 

  6. Karar ZA, Alam N, Streatfield PK. Epidemiological transition in rural Bangladesh, 1986–2006. North America: Glob Health Action; 2009. http://www.globalhealthaction.net/index.php/gha/article/view/1904.

  7. Sicree RA, Zimmet PZ, Dunstan DW, Cameron AJ, Wel-born TA, Shaw JE. Differences in height explain gender differences in the response to the oral glucose tolerance test- the AusDiab study. Diabet Med. 2008;25:296–302.

    Article  CAS  Google Scholar 

  8. Singh RB, Suh IL, Singh VP, Chaithiraphan S, Laothavorn P, Sy RG, et al. Hypertension and stroke in Asia: prevalence, control and strategies in developing countries for prevention. J Hum Hypertens. 2000;14:749–64.

    Article  CAS  Google Scholar 

  9. Van Minh H, Byass P, Chuc NTK, Wall S. Gender differences in prevalence and socioeconomic determinants of hypertension: findings from the WHO STEPs survey in a rural community of Vietnam. J Hum Hypertens. 2005;20:109–15.

    Article  Google Scholar 

  10. Hoang VM, Byass P, Dao LH, Nguyen TK, Wall S. Risk factors for chronic disease among rural Vietnamese adults and the association of these factors with sociodemographic variables: findings from the WHO STEPS survey in rural Vietnam, 2005. Prev Chronic Dis. 2007;4:A22–2.

    PubMed  Google Scholar 

  11. Neupane D, McLachlan CS, Sharma R, Gyawali B, Khanal V, Mishra SR, et al. Prevalence of hypertension in member countries of South Asian Association for Regional Cooperation (SAARC): systematic review and meta-analysis. Medicine. 2014;93:e74.

    Article  Google Scholar 

  12. Joshi P, Islam S, Pais P, Reddy S, Dorairaj P, Kazmi K, et al. Risk factors for early myocardial infarction in South Asians compared with individuals in other countries. JAMA. 2007;297:286–94.

    Article  CAS  Google Scholar 

  13. Lee SH, Kim YS, Sunwoo S, Huh BY. A retrospective cohort study on obesity and hypertension risk among Korean adults. J Korean Med Sci. 2005;20:188–195. https://doi.org/10.3346/jkms.2005.20.2.188.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Chowdhury MAB, Uddin MJ, Haque MR, Ibrahimou B. Hypertension among adults in Bangladesh: evidence from a national cross‐sectional survey. BMC Cardiovasc Disord. 2016;16:22. https://doi.org/10.1186/s12872-016-0197-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Babu GR, Murthy GVS, Ana Y, Patel P, Deepa R, Neelon SEB, et al. Association of obesity with hypertension and type 2 diabetes mellitus in India: a meta‐analysis of observational studies. World J Diabetes. 2018;9:40–52. https://doi.org/10.4239/wjd.v9.i1.40.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Das Gupta R, Haider SS, Hashan MR, Rahman MA, Sarker M. Association between height and hypertension in the adult Nepalese population: findings from a nationally representative survey. Health Sci Rep. 2019;e141. https://doi.org/10.1002/hsr2.141.

  17. Hoque ME, Khokan MR, Bari W. Impact of stature on non-communicable diseases: evidence based on Bangladesh Demographic and Health Survey, 2011 data. BMC Public Health. 2014;14:1007. https://doi.org/10.1186/1471-2458-14-1007.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Olatunbosun ST, Bella AF. Relationship between height, glucose intolerance, and hypertension in an urban African black adult population: a case for the “thrifty phenotype” hypothesis? J Natl Med Assoc. 2000;92:265–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Schooling CM, Thomas GN, Leung GM, Ho SY, Janus ED, Lam TH. Is height associated with cardiovascular risk in Chinese adults? Epidemiology. 2007;18:274–8. https://doi.org/10.1097/01.ede.0000254656.02400.27.

    Article  PubMed  Google Scholar 

  20. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70:41–55.

    Article  Google Scholar 

  21. Rosenbaum PR, Rubin DB. Reducing bias in observational studies using sub-classification on the propensity score. J Am Stat Assoc. 1984;79:516–24.

    Article  Google Scholar 

  22. Rosenbaum PR, Rubin DB. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. J Am Stat Assoc. 1985;39:33–38.

    Google Scholar 

  23. Austin PC. An introduction to propensity-score methods for reducing the effects of confounding in observational studies. Multivar Behavio Res. 2011;46:399–424.

    Article  Google Scholar 

  24. Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat. 2011;10:150–61.

    Article  Google Scholar 

  25. Talukder A, Akter N, Mallick TS. Exploring association between individuals’ stature and type 2 diabetes status: propensity score analysis. Environ Health Insights. 2019;13:1178630219836975.

    Article  Google Scholar 

  26. Robins JM. Marginal structural models. In: 1997 Proceedings of the Section on Bayesian Statistical Science, Alexandria, VA: Am Stat Assoc; 1998. p. 1–10.

  27. Robins JM, Hernan M, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11:550–60.

    Article  CAS  Google Scholar 

  28. Agresti A. Analysis of ordinal categorical data. Hoboken, NJ: John Wiley & Sons, Inc.; 2010.

  29. Curtis LH, Hammill BG, Eisenstein EL, Kramer JM, Anstrom KJ. Using inverse probability-weighted estimators in comparative effectiveness analyses with observational databases. Med Care. 2007;45:103–7.

    Article  Google Scholar 

  30. Spreeuwenberg MD, Bartak A, Croon MA, Hagenaars JA, Busschbach JJ, Andrea H, et al. The multiple propensity score as control for bias in the comparison of more than two treatment arms: an introduction from a case study in mental health. Med Care. 2010;48:166–74.

    Article  Google Scholar 

  31. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr, et al. The Seventh Report of the Joint National Committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC 7 report. JAMA. 2003;289:2560–72. https://doi.org/10.1161/01.HYP.00.00075790.33892.AE.

    Article  CAS  PubMed  Google Scholar 

  32. Ranasinghe P, Cooray DN, Jayawardena R, Katulanda P. The influence of family history of hypertension on disease prevalence and associated metabolic risk factors among Sri Lankan adults. BMC Public Health. 2015;15:576. https://doi.org/10.1186/s12889-015-1927-7.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Hegde SM, Solomon SD. Influence of physical activity on hypertension and cardiac structure and function. Curr Hypertens Rep. 2015;17:77. https://doi.org/10.1007/s11906-015-0588-3.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Yu M, Xu CX, Zhu HH, Hu RY, Zhang J, Wang H, et al. Associations of cigarette smoking and alcohol consumption with metabolic syndrome in a male Chinese population: a cross‐sectional study. J Epidemiol. 2014;24:361–9. https://doi.org/10.2188/jea.je20130112.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We would like to thank the National Institute of Population Research and Training (NIPORT), Bangladesh, for allowing us to use BDHS, 2011 data for the analysis. We also thank the reviewers and editor for their valuable comments and suggestions that helped us to improve the previous version of this paper.

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There was no financial support for the research, authorship, and/or publication of this paper.

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Authors and Affiliations

  1. Statistics Discipline, Khulna University, Khulna, 9208, Bangladesh

    Ashis Talukder & Mohammad Ali

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  1. Ashis Talukder
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  2. Mohammad Ali
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Correspondence to Ashis Talukder.

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The authors declare that they have no conflict of interest.

Ethical approval

This study used a secondary data collected by NIPORT, Bangladesh, and MEASURE DHS. All procedures performed in this study involving human participants were in accordance with the ethical standards of the national research committee, and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Talukder, A., Ali, M. Assessing the true association between hypertension status and stature of individuals in Bangladesh: propensity score analysis. J Hum Hypertens 35, 250–256 (2021). https://doi.org/10.1038/s41371-020-0328-2

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