# Prediction of the Prevalence of Hypertension and Associated Risk Factors in Rwanda Using Gibbs Sampling Method

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## Abstract

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## 1. Introduction

## 2. Material and Methods

- Initial values $\left({x}_{0},{y}_{0}\right);$
- Sample: ${x}_{1}~p\left(x|{y}_{0}\right)\mathrm{i}.\mathrm{e}.,\left(X|Y={y}_{0}\right);$State $\left({x}_{1},{y}_{0}\right);$Sample: ${y}_{1}~p\left(y|{x}_{1}\right)\mathrm{i}.\mathrm{e}.,(Y|X={x}_{1})$;Convert a random variable $\left({x}_{1},{y}_{1}\right);$
- Sample: ${x}_{2}~p\left(x|{y}_{1}\right)\mathrm{i}.\mathrm{e}.,(X|Y={y}_{1};$State $\left({x}_{2},{y}_{1}\right)$;Sample: ${y}_{2}~p\left(y|{x}_{2}\right)\mathrm{i}.\mathrm{e}.,\left(Y|X={x}_{2}\right);$Convert a random variable $\left({x}_{2},{y}_{2}\right);$
- Repeat 2 and 3 as many times as $k.$

## 3. Results

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Muhimpundu, M.A.; Binagwaho, A.E. Republic of Rwanda-Rwanda Non-Communicable Diseases Risk Factors Report; Ministry of Health: Kigali, Rwanda, 2015.
- Unger, T.; Borghi, C.; Charchar, F.; Khan, N.A.; Poulter, N.R.; Prabhakaran, D.; Ramirez, A.; Schlaich, M.; Stergiou, G.S.; Tomaszewski, M.; et al. 2020 International Society of Hypertension Global Hypertension Practice Guidelines. Hypertension
**2020**, 75, 1334–1357. [Google Scholar] [CrossRef] [PubMed] - Jones, D.W.; Whelton, P.K.; Allen, N.; Clark, D., 3rd; Gidding, S.S.; Muntner, P.; Nesbitt, S.; Mitchell, N.S.; Townsend, R.; Falkner, B. Management of stage 1 hypertension in adults with a low 10-year risk for cardiovascular disease: Filling a guidance gap: A scientific statement from the American Heart Association. Hypertension
**2021**, 77, e58–e67. [Google Scholar] [CrossRef] [PubMed] - World Health Organization. Clinical Guidelines for the Management of Hypertension. 2005. Available online: https://apps.who.int/iris/handle/10665/119738 (accessed on 17 November 2021).
- National Institutes of Health. Fact Sheet, Hypertension (High Blood Pressure); National Institutes of Health U.S., 2010. Available online: https://www.who.int/news-room/fact-sheets/detail/hypertension (accessed on 2 December 2021).
- Pandey, N. The Lifestyle of Hypertensive People and its Health Effects. Madhyabindu J.
**2022**, 7, 55–65. [Google Scholar] [CrossRef] - Gabb, G.M.; Mangoni, A.A.; Anderson, C.S.; Cowley, D.; Dowden, J.S.; Golledge, J.; Hankey, G.J.; Howes, F.S.; Leckie, L.; Perkovic, V.; et al. Guideline for the diagnosis and management of hypertension in adults—2016. Med. J. Aust.
**2016**, 205, 85–89. [Google Scholar] [CrossRef] [PubMed] - World Health Organization. Global Diffusion of Ehealth: Making Universal Health Coverage Achievable: Report of the Third Global Survey on Ehealth; World Health Organization: Geneva, Switzerland, 2017. [Google Scholar]
- Writes, S. Common Causes of Hypertension in Senior Adults. Health Sci. J.
**2017**, 11, 1. [Google Scholar] [CrossRef] [Green Version] - World Health Organization. Noncommunicable Diseases Country Profiles 2018; World Health Organization: Geneva, Switzerland, 2018. [Google Scholar]
- Peberdy, V. Hypertension: Putting the pressure on the silent killer. Int. Fed. Pharm. Manuf. Assoc.
**2016**, 2, 1–19. [Google Scholar] - Mufunda, J.; Mebrahtu, G.; Usman, A.; Nyarango, P.; Kosia, A.; Ghebrat, Y.; Ogbamariam, A.; Masjuan, M.; Gebremichael, A. The prevalence of hypertension and its relationship with obesity: Results from a national blood pressure survey in Eritrea. J. Hum. Hypertens.
**2006**, 20, 59–65. [Google Scholar] [CrossRef] [PubMed] - Ndabarora, E.; Nishimwe, C.; Ndikumasabo, C.; Twahirwa, J.C.; Muvandimwe, J.D.L.C.; Hitayezu, E.; Bizimana, E. Prevalence of hypertension and factors associated with screening uptake in Kanjongo, Nyamasheke District, Rwanda. KIBOGORA Polytech. Sci. J.
**2018**, 1, 15–19. [Google Scholar] [CrossRef] - Kibret, K.T.; Mesfin, Y.M. Prevalence of hypertension in Ethiopia: A systematic meta-analysis. Public Health Rev.
**2015**, 36, 14. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Adeloye, D.; Basquill, C. Estimating the Prevalence and Awareness Rates of Hypertension in Africa: A Systematic Analysis. PLoS ONE
**2014**, 9, e104300. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Mosha, N.R.; Mahande, M.; Juma, A.; Mboya, I.; Peck, R.; Urassa, M.; Michael, D.; Todd, J. Prevalence, awareness and factors associated with hypertension in North West Tanzania. Glob. Health Action
**2017**, 10, 1321279. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Owolabi, E.O.; Ter Goon, D.; Adeniyi, O.V.; Adedokun, A.O.; Seekoe, E. Prevalence and Correlates of Metabolic Syndrome Among Adults Attending Healthcare Facilities in Eastern Cape, South Africa. Open Public Health J.
**2017**, 10, 148–159. [Google Scholar] [CrossRef] - Nahimana, M.R.; Nyandwi, A.; Muhimpundu, M.A.; Olu, O.; Condo, J.U.; Rusanganwa, A.; Okeibunor, J.C. A population-based national estimate of the prevalence and risk factors associated with hypertension in Rwanda: Implications for prevention and control. BMC Public Health
**2018**, 18, 2. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Nyirahabimana, M.V. Knowledge and Preventive Practice Regarding Cardiovascular Diseases Risk Factors among Rural Population at a Selected District in Rwanda. Doctoral Dissertation, 2017. Available online: http://dr.ur.ac.rw/handle/123456789/390 (accessed on 18 April 2022).
- Gilks, W.R.; Wild, P. Adaptive rejection sampling for Gibbs sampling. J. R. Stat. Soc. Ser. C (Appl. Stat.)
**1992**, 41, 337–348. [Google Scholar] - Geyer, C.J. Introduction to markov chain monte carlo. In Handbook of Markov Chain Monte Carlo; T&FeBooks: New York, NY, USA, 2011; Volume 20116022, p. 45. [Google Scholar]
- van Ravenzwaaij, D.; Cassey, P.; Brown, S.D. A simple introduction to Markov Chain Monte–Carlo sampling. Psychon. Bull. Rev.
**2018**, 25, 143–154. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Bai, J.; del Campo, C.; Keller, L.R. Markov chain models in practice: A review of low-cost software options. Investig. Oper.
**2018**, 38, 56–62. [Google Scholar] - Apenteng, O.O.; Ismail, N.A. A Markov chain Monte Carlo approach to estimate AIDS after HIV infection. PLoS ONE
**2015**, 10, e0131950. [Google Scholar] [CrossRef] [PubMed] - Čiegis, R.; Meilūnas, M. Some algorithms in mathematical modelling of diabetes mellitus. Informatica
**1995**, 6, 15–33. [Google Scholar] - Pande, R.; Niyonzima, J.P. Prevalence and Clinical Features of Arterial Hypertension in Ruhengeri District Hospital, Musanze, Rwanda. Rwanda Med. J.
**2013**, 69, 9–13. [Google Scholar] - Gilks, W.R. Full conditional distributions. In Markov Chain Monte Carlo in Practice; T&FeBooks: New York, NY, USA, 1996; pp. 75–88. [Google Scholar]
- Lee, M.D.; Wagenmakers, E.-J. Bayesian Cognitive Modeling: A Practical Course; Cambridge University Press: Cambridge, UK, 2014. [Google Scholar]
- Kelly, A. Proof of Bayes’s theorem. In Decision Making Using Game Theory: An Introduction for Managers; Cambridge University Press: Cambridge, UK, 2003; pp. 190–191. [Google Scholar] [CrossRef]
- Kabakambira, J.D.; Bitwayiki, R.N.; Mujawamariya, G.; Lucero-Prisno, D.E., III; Mucumbitsi, J. Kigali Car Free Day: An innovative model in the fight against non-communicable disease pandemics. Rwanda Med. J.
**2019**, 76, 1–5. [Google Scholar]

2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 |
---|---|---|---|---|---|---|---|---|---|---|

0.1500 | 0.1899 | 0.1737 | 0.1803 | 0.1772 | 0.1787 | 0.1779 | 0.1784 | 0.1781 | 0.1783 | 0.1782 |

0.2620 | 0.02789 | 0.2531 | 0.2686 | 0.2594 | 0.2645 | 0.2617 | 0.2632 | 0.2624 | 0.2628 | 0.2626 |

0.1430 | 0.1824 | 0.1671 | 0.1733 | 0.1703 | 0.1718 | 0.1710 | 0.1714 | 0.1712 | 0.1713 | 0.1713 |

0.0280 | 0.0456 | 0.0471 | 0.0481 | 0.0479 | 0.0481 | 0.0480 | 0.0481 | 0.0480 | 0.0481 | 0.0480 |

0.4170 | 0.3032 | 0.3591 | 0.3297 | 0.3452 | 0.3369 | 0.3413 | 0.3390 | 0.3402 | 0.3396 | 0.33999 |

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**MDPI and ACS Style**

Dukunde, A.; Ntaganda, J.M.; Kasozi, J.; Nzabanita, J.
Prediction of the Prevalence of Hypertension and Associated Risk Factors in Rwanda Using Gibbs Sampling Method. *Diseases* **2023**, *11*, 87.
https://doi.org/10.3390/diseases11020087

**AMA Style**

Dukunde A, Ntaganda JM, Kasozi J, Nzabanita J.
Prediction of the Prevalence of Hypertension and Associated Risk Factors in Rwanda Using Gibbs Sampling Method. *Diseases*. 2023; 11(2):87.
https://doi.org/10.3390/diseases11020087

**Chicago/Turabian Style**

Dukunde, Angélique, Jean Marie Ntaganda, Juma Kasozi, and Joseph Nzabanita.
2023. "Prediction of the Prevalence of Hypertension and Associated Risk Factors in Rwanda Using Gibbs Sampling Method" *Diseases* 11, no. 2: 87.
https://doi.org/10.3390/diseases11020087