Abstract

Introduction: Cardiovascular disease (CVD) is the leading cause of morbidity and mortality globally accounting for 30% of total deaths worldwide with 82% of these deaths occurring in low/middle income countries (LMIC). WHO targeted 4 behavioral risk factors (physical inactivity, unhealthy diet, tobacco use, harmful use of alcohol) as the foundation of the WHO framework on prevention and control of CVD in LMIC. The purpose of this study was to examine the prevalence, distribution and association of these 4 risk factors in selected countries of Sub-Saharan Africa (SSA). Methods: Secondary analysis was conducted on data from the population-based WHO World Health Survey using data from 4 countries in SSA to reflect low (Senegal), middle (Ghana, Kenya), and high (South Africa) GDP. The data was weighted to account for country-specific sample sizes and pooled to provide regional prevalence estimates. The analysis controlled for age, gender, living location (rural/urban) and education. Analysis included Chi-square and logistic regression. Results: The pooled data among 13,850 subjects revealed high prevalence of unhealthy diet (67%), with lower prevalence of physical inactivity, tobacco use and harmful alcohol (12%, 14%, 6%, respectively). Country-specific analysis revealed that prevalence of behavioral CVD risk factors varied by socio-demographic variables within the countries of interest. Physical inactivity and unhealthy diet was higher among older individuals (p<.05) and female (p<.05). Tobacco use and harmful use of alcohol was higher in middle age men (p<.001) with a trend for higher use in rural locations. Among the selected countries significant differences include: Kenya had the highest prevalence of unhealthy diet (OR=12.5, 95%CI=11.1-14.3). South Africa had the highest prevalence of physical inactivity (OR=7.8, 95%CI=6.8-9.1) and tobacco use (OR=2.8, 95%CI=2.4-3.3). Ghana had the highest prevalence of harmful use of alcohol (OR=3.8, 95%CI=3.1-4.6). Conclusions: This study demonstrates within and between-country differences in the prevalence and distribution of the 4 CVD behavioral risk factors. These results highlight the necessity of local and region-specific data to tailor policy, resource allocation and prevention measures.

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