Prevalence and factors associated with hypertension among rural community dwellers in a local government area, South West Nigeria

Abstract

Background: In Uganda, 12% of previously treated TB cases and 1.6% of new cases have MDR-TB and require specialized treatment and care. Adherence is crucial for improving MDR-TB treatment outcomes. There is paucity of information on the extent to which these patients adhere to treatment and what the drivers of non-adherence are.
 Methods: We conducted a cohort study using retrospectively collected routine program data for patients treated for MDR- TB between January 2012 – May 2016 at Mulago Hospital. We extracted anonymized data on non-adherence (missing 10% or more of DOT), socio-economic, demographic, and treatment characteristics of the patients. All participants were sen- sitive to MDR-TB drugs after second line Drug Susceptible Testing (DST) at entry into the study. Factors associated with non-adherence to MDR-TB treatment were determined using generalized linear models for the binomial family with log link and robust standard errors. We considered a p- value less than 0.05 as statistically significant.
 Results: The records of 227 MDR- TB patients met the inclusion criteria, 39.4% of whom were female, 32.6% aged be- tween 25 – 34 years, and 54.6% living with HIV/AIDS. About 11.9% of the patients were non-adherent. The main driver for non-adherence was history of previous DR-TB treatment; previously treated DR-TB patients were 3.46 (Adjusted prev- alence ratio: 3.46, 95 % CI: 1.68 - 7.14) times more likely to be non-adherent.
 Conclusion: One in 10 MDR-TB patients treated at Mulago hospital is non-adherent to treatment. History of previous DR- TB treatment was significantly associated with non-adherence in this study. MDR-TB program should strengthen adherence counselling, strengthen DST surveillance, and close monitoring for previously treated DR-TB patients.
 Keywords: Non-adherence; multi-drug resistant tuberculosis; treatment.

Modelling of South African Hypertension: Comparative Analysis of the Classical and Bayesian Quantile Regression Approaches

“…The age effect on both SBP and DBP is bigger on the 95 th quantile than the 75 th quantile, implying that the effect of age on SBP is stronger at the most extreme quantile of both BP measures. These results are in line with past studies that suggest that the prevalence of arterial stiffening and hypertension increases with age, 7 , 9 , 28 , 29 even though all these studies have utilised multiple logistic regression models in deriving their findings.…”

“…A study conducted among hypertensive patients on treatment in Lupane District, Zimbabwe, supported the current findings of this study. 9,30 Cigarette consumption displayed positive statistically significant associations with both DBP and SBP across the higher quantiles. The highest proportions of elevated blood pressure recorded on respondents who do smoke supported this finding.…”

“…A study conducted among hypertensive patients on treatment in Lupane District, Zimbabwe, supported the current findings of this study. 9 , 30 …”

“…Most studies have performed modelling of hypertension using binary and multiple logistic regression. 4 - 9 …”

“…Most studies have performed modelling of hypertension using binary and multiple logistic regression. [4][5][6][7][8][9] Quantile linear regression has emanated as a useful additional technique to either binary logistic regression or classical linear regression. Basically, quantile regression is a natural extension of classical linear regression.…”