Geographical Disparities in Hypertension Incidence Rate in Iran 2004-2016: Bayesian Spatial Analysis
-
Roghaye Zare
,
Erfan Ghasemi
,
Shirin Djalalinia
,
Masoud Alimardi
,
Moein Yoosefi
,
Abbas Rahimi-Foroushani
Abstract
Introduction: Cardiovascular diseases such as coronary heart disease, heart failure, arrhythmia, and cardiomyopathy all include hypertension as a key risk factor. Research has shown that the early detection and treatment of hypertension and its risk factors, as well as public health policies to reduce behavioral risk factors, have led to a gradual reduction in mortality caused by heart disease and stroke in high-income countries in the past three decades. Trends in hypertension incidence have been monitored at the national level in Iran. The aim of this study examine province-level disparities in Hypertension incidence from 2004 to 2016.
Methods: Use the Non-Communicable Diseases Risk-Factors Surveillance in the Islamic Republic of Iran STEPs registry data. to estimate the incidence rate of hypertension for all provinces in 2004, 2006-2009, 2011, and 2016 using a Bayesian spatial model with Markov chain Monte Carlo algorithm in OpenBUGS version 3.2.3 and R version 4.2.2.
Results: The estimated Hypertension incidence rate in total increased from 19.87 per 1000 people (95% credible interval 14.28, 25.48) in 2004 to 193.02 (171.92, 220.48) in 2016. According to the estimates of 2016, we found that the provinces of Markazi, Ardabil, and Semnan had the highest rate of hypertension, and the provinces of Hormozgan, and Sistan-Baluchistan had the lowest rate. Our findings show that Khorasan, North, Alborz, and Semnan have the most significant percentage change in incidence rate from 2004-2016.
Conclusion: To reduce the prevalence of hypertension in Iranian regions, it is crucial to develop regular hypertension screening programs, especially among the elderly.
2. Mills KT, Stefanescu A, He J. The global epidemiology of hypertension. Nature Reviews Nephrology. 2020;16(4):223-37.
3. Mirzaei M, Mirzaei M, Gholami S, Abolhosseini H. Prevalence of hypertension and related risk factors in central Iran: Results from Yazd Health Study. ARYA atherosclerosis. 2021;17(1):1.
4. Tabrizi JS, Sadeghi-Bazargani H, Farahbakhsh M, Nikniaz L, Nikniaz Z. Prevalence and associated factors of prehypertension and hypertension in Iranian population: the lifestyle promotion project (LPP). PloS one. 2016;11(10):e0165264.
5. Malekzadeh MM, Etemadi A, Kamangar F, Khademi H, Golozar A, Islami F, et al. Prevalence, awareness and risk factors of hypertension in a large cohort of Iranian adult population. Journal of hypertension. 2013;31(7):1364.
6. Organization WH. A global brief on hypertension: silent killer, global public health crisis [Internet]. Geneva: WHO; 2013 [cited 2018 Jun 4].
7. Farzadfar F, Delavari A, Malekzadeh R, Mesdaghinia A, Jamshidi HR, Sayyari A, et al. NASBOD 2013: design, definitions, and metrics. Archives of Iranian medicine. 2014;17(1):0-.
8. Asgari F, Haghazali M, Heydarian H. Non-communicable diseases risk factors surveillance in Iran. 2009.
9. Djalalinia S, Modirian M, Sheidaei A, Yoosefi M, Zokaiee H, Damirchilu B, et al. Protocol design for large–scale cross–sectional studies of surveillance of risk factors of non–communicable diseases in Iran: STEPs 2016. Archives of Iranian medicine. 2017;20(9):-.
10. Rahimzadeh S, Burczynska B, Ahmadvand A, Sheidaei A, Khademioureh S, Pazhuheian F, et al. Geographical and socioeconomic inequalities in female breast cancer incidence and mortality in Iran: A Bayesian spatial analysis of registry data. PloS one. 2021;16(3):e0248723.
11. Besag J, York J, Mollié A. Bayesian image restoration, with two applications in spatial statistics. Annals of the institute of statistical mathematics. 1991;43(1):1-20.
12. Ayubi E, Mansournia MA, Motlagh AG, Mosavi-Jarrahi A, Hosseini A, Yazdani K. Exploring neighborhood inequality in female breast cancer incidence in Tehran using Bayesian spatial models and a spatial scan statistic. Epidemiology and health. 2017;39.
13. Edwards W, Lindman H, Savage LJ. Bayesian statistical inference for psychological research. Psychological review. 1963;70(3):193.
14. Xiong Y, Bingham D, Braun W, Hu X. Moran's I statistic-based nonparametric test with spatio-temporal observations. Journal of Nonparametric Statistics. 2019;31(1):244-67.
15. David JS, Nicola G, Bradley PC, Angelika vdL. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2002;64(4):583-639.
16. Liang Y, Liu R, Du S, Qiu C. Trends in incidence of hypertension in Chinese adults, 1991–2009: the China Health and Nutrition Survey. International journal of cardiology. 2014;175(1):96-101.
17. Sinnott S-J, Smeeth L, Williamson E, Douglas IJ. Trends for prevalence and incidence of resistant hypertension: population based cohort study in the UK 1995-2015. Bmj. 2017;358.
18. Dorans KS, Mills KT, Liu Y, He J. Trends in prevalence and control of hypertension according to the 2017 American College of Cardiology/American Heart Association (ACC/AHA) guideline. Journal of the American Heart Association. 2018;7(11):e008888.
19. Oori MJ, Mohammadi F, Norozi K, Fallahi-Khoshknab M, Ebadi A, Gheshlagh RG. Prevalence of HTN in Iran: Meta-analysis of Published Studies in 2004-2018. Current hypertension reviews. 2019;15(2):113-22.
| Files | ||
| Issue | Vol 9 No 3 (2023) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/jbe.v9i3.15445 | |
| Keywords | ||
| Hypertension incidence rate Bayesian spatial analysis | ||
| Rights and permissions | |
|
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
