Determinants of Hypertension Among Transport Workers in Ibadan, Nigeria: A Structural Equation Modeling Approach.

Introduction Hypertension is one of the most potent risk factors for cardiovascular disease (CVD). Transport workers are frequently exposed to multiple health risks, and undiagnosed hypertension among commercial drivers may result in severe road accidents. Objective The study aimed at assessing the multidimensional relationship between risk factors for hypertension among transport workers in Nigeria. Methodology A cross-sectional study was conducted among 682 transport workers (≥18 years) recruited from motor parks across the Ibadan metropolis. Data were extracted from the Community-Based Investigation of the Risk Factors for Cardiovascular Diseases (COMBAT-CVDs) study. The WHO STEPwise instrument was used to collect information on sociodemographic, lifestyle, and body fat distribution characteristics. Blood pressure and anthropometric measurements were recorded. Hypertension was defined as systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg. Data were analyzed using descriptive statistics, chi-square test (α = 5%), and structural equation modeling (SEM). Results The mean age of respondents was 42.4 ± 7.9 years. The overall prevalence of hypertension was 33.3% (227, 95% confidence interval (CI): 29.8%-36.8%). The highest prevalence was observed among participants aged 45-64 years (123, 45.2%), followed by those aged ≥65 years (16, 45.7%). Married participants had a higher prevalence of hypertension (198, 37.4%) compared with unmarried participants (29, 19.0%). Obesity, based on body mass index (BMI) classification, was present in 324 (47.5%) participants; individuals with abnormal BMI exhibited a higher prevalence of hypertension (129, 39.8%) than those with normal BMI (98, 27.4%). A history of smoking was reported in 72 (41.1%) hypertensive participants, while 115 (40.5%) reported alcohol consumption. Poor sleep quality was associated with hypertension in 93 (30.5%) participants, compared with 134 (35.5%) among those with good sleep quality. The SEM demonstrated a good overall fit (root mean square error of approximation (RMSEA) = 0.04, Goodness of Fit Index (GFI) = 0.90, Tucker-Lewis Index (TLI) = 0.90, and Comparative Fit Index (CFI) = 0.91). Among the latent constructs, age exerted the strongest total effect on hypertension (β = 0.214), followed by body-fat distribution (0.114), lifestyle (-0.147), sociodemographic characteristics (0.069), and physical inactivity (0.032). Conclusions Hypertension was common among transport workers and was influenced by sociodemographic, lifestyle, and body fat distribution factors. Age and central obesity were the strongest predictors, while modifiable lifestyle behaviors such as smoking, alcohol intake, and physical inactivity also contributed significantly. These findings highlight the need for targeted interventions and workplace health programs to reduce hypertension risk in this group.