References
- Chobanian AV, Bakris GL, Black HR, 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(19):2560–2571. doi:10.1001/jama.289.19.256012748199
- Fuster V, Kelly BB. Promoting Cardiovascular Health in the Developing World: A Critical Challenge to Achieve Global Health. Washington, DC: National Academies Press; 2010.
- Cooper RS, Amoah AG, Mensah GA. High blood pressure: the foundation for epidemic cardiovascular disease in African populations. Ethn Dis. 2003;13(2):S2–48.
- Ezzati M, Lopez AD, Rodgers A, Vander Hoorn S, Murray CJ; Comparative Risk Assessment Collaborating Group. Selected major risk factors and global and regional burden of disease. Lancet. 2002;360(9343):1347–1360. doi:10.1016/S0140-6736(02)11403-612423980
- Kearney PM, Whelton M, Reynolds K, Muntner P, Whelton PK, He J. Global burden of hypertension: analysis of worldwide data. lancet. 2005;365(9455):217–223. doi:10.1016/S0140-6736(05)17741-115652604
- Opie LH, Seedat YK. Hypertension in Sub-Saharan African populations. Circulation. 2005;112(23):3562–3568. doi:10.1161/CIRCULATIONAHA.105.53956916330697
- Tesfaye F, Byass P, Wall S. Population based prevalence of high blood pressure among adults in Addis Ababa: uncovering a silent epidemic. BMC Cardiovasc Disord. 2009;9(1):39. doi:10.1186/1471-2261-9-3919698178
- Tran A, Gelaye B, Girma B, et al. Prevalence of metabolic syndrome among working adults in Ethiopia. Int J Hypertens. 2011;2011. doi:10.4061/2011/193719
- Henderson R, Diggle P, Dobson A. Joint modeling of longitudinal measurements and event time data. Biostatistics. 2000;4:465–480. doi:10.1093/biostatistics/1.4.465
- Guo X, Carlin BP. Separate and joint modeling of longitudinal and event time data using standard computer packages. Am Stat. 2004;58(1):16–24. doi:10.1198/0003130042854
- Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics. 1982;38:963–974. doi:10.2307/25298767168798
- Wu L. Mixed Effects Models for Complex Data. Boca Raton: CRC Press; 2009. doi:10.1201/9781420074086
- Erango MA, Goshu AT, Buta GB, Dessiso AH. Bayesian joint modeling of survival of HIV/AIDS patients using AFT data and longitudinal CD4 cell counts. Br J Med Med Res. 2017;20:1–12. doi:10.9734/BJMMR/2017/32123
- Klein JP, Moeschberger ML. Survival Analysis Techniques for Censored and Truncated Data. 2nd ed. Berlin: Statistics for Biology and Health, Springer; 2003.
- Spiegelhalter D, Thomas A, Best N, Lunn D. WinBUGS version 1.4 user manual; 2003.
- Spiegelhalter DJ, Best NG, Carlin BP. Bayesian Deviance, the Effective Number of Parameters and Comparison of Arbitrarily Complex Models. MRC Biostatistics Unit, Cambridge University; 1998.
- Cappuccio FP, Micah FB, Emmett L, et al. Prevalence, detection, management and control of hypertension in Ashanti, West Africa: P2. 400. J Hypertens. 2004;22:263–264. doi:10.1097/00004872-200406002-00927
- Seifu W, Hussein M, Ibrahim M, Sigale A. Behavioral risk factors of hypertension among pastoral and agro pastoral adult communities, Eastern Ethiopia, Somali Regional State, 2016. J Trop Dis. 2017;5(234). doi:10.4172/2329-891X.1000234
- Meshram II, Laxmaiah A, Mallikharjun RK, Arlappa N, Balkrishna N. Prevalence of hypertension and its correlates among adult tribal population (≥ 20 Years) of Maharashtra State, India. Int J Health Sci Res. 2014;4(1):130–139.
- Khan RJ, Stewart CP, Christian P. A cross-sectional study of the prevalence and risk factors for hypertension in rural Nepali women. BMC Public Health. 2013;13(1):55. doi:10.1186/1471-2458-13-5523336578
- Andualem MA, Hassen TS, Yemane T. Effect of khat (Catha edulis Forsk) on blood pressure & heart rate, a community based study. Ethiop J Health Dev. 2002;16(3):325–334.