Determinants of Mean Blood Pressure and Hypertension among Workers in West Africa

Abstract

Background. This review was undertaken to estimate the mean blood pressure and evaluate its determinants as well as the determinants of hypertension among workers in West Africa. Methods. In a follow-up to an earlier study, a systematic search for articles published between 1980 and August 2015 was undertaken using major databases. Results. A total of 55 articles involving 34,919 different cadres of workers from six countries were retrieved. The mean systolic blood pressure (BP) ranged from 116.6 ± 1.3 mmHg to 151.7 ± 13.6 mmHg while the mean diastolic BP ranged from 69.6 ± 11.0 mmHg to 97.1 ± 9.1 mmHg. Population-wide prehypertension was common. The major determinants of mean BP and hypertension were similar and included male sex, older age group, higher socioeconomic status, obesity, alcohol consumption, plasma glucose, and sodium excretion. Ethnicity and educational level were inconsistently associated with hypertension. Workers at higher risk of cardiovascular event did not perceive themselves as such. Conclusion. The prevailing mean prehypertensive BP, low perception of risk, and clustering of risk factors call for interventions such as healthy diets, improved physical activity, and a favourable work environment. Successful models for improving the cardiovascular health of sedentary informal sector workers in Africa are urgently needed.

Sedentary Lifestyle and Hypertension in a Periurban Area of Mbarara, South Western Uganda: A Population Based Cross Sectional Survey

“…Our data show the factors significantly associated with hypertension were older age, obesity, and sedentary lifestyle. The findings about these risk factors for hypertension are not surprising and are in agreement with several other studies where age, obesity, and sedentary lifestyle were significant factors for hypertension [ 28 , 33 , 38 – 40 ]. Of all these factors, obesity and a sedentary lifestyle are modifiable and hence may be used as a basis for interventions to prevent hypertension.…”

Predictors of Hypertension in a Population of Undergraduate Students in Sierra Leone

“…In a small sample of undergraduate students at Benin University in Nigeria, blood pressure measurements results were similar to studies of Black Americans [ 34 ], despite the observation that levels of smoking and alcohol intake among the Nigerian students were lower. While alcohol consumption has been found to be significantly related to higher blood pressure [ 22 ], no significant relationship was found in our study population, although men who drank energy drinks had a higher BP than women.…”

“…In Caucasian populations, a strong association has been depicted between BMI and mortality [ 17 ], and positive relationship between BMI and BP has also been reported among African and Asian populations [ 18 – 21 ]. In West Africa, a meta-analysis conducted on 55 articles from 5 countries includes obesity, age, and gender as significant predictors [ 22 ] and is similar to studies of adults in Sierra Leone [ 2 , 3 ]. Significant relationships have also been reported between age and BMI among adolescents in northeast Nigeria [ 23 , 24 ], in which BMI was found to be a significant factor in predicting hypertension in rural Nigeria.…”

“…Although high blood pressure is common in SSA, a study from Brazil [ 29 ] supports a view that it is socioeconomic factors (such as low levels of education and income) and not genetic composition (African, European, or Native American) that are the important predictors. No significant difference between adults in Sierra Leone and the Gambia was found [ 4 ], and a meta-analysis also failed to find a consistent relationship with ethnicity [ 22 ].…”

Association between demand–control model components and blood pressure in the ELSA-Brasil study: exploring heterogeneity using quantile regression analyses

“…Although blood pressure is a continuously distrib-uted cardiovascular risk factor, studies of its determinants have focused on the central portion or the extremes of the distribution, using conventional regression methods that estimate effects for the population mean or that model high blood pressure as a dependent variable (4)(5)(6). These analytical strategies are very useful, but they do not permit an understanding of how such determinants affect the whole blood pressure distribution.…”