Introduction Cardiovascular diseases (CVDs) are a leading cause of death in low-income and middle-income countries. This study aimed to estimate the 10-year CVD risk in Bangladeshi adults using the updated 2019 WHO laboratory-based and non-laboratory-based models, identify key CVD risk determinants and assess agreement between the models. Methods We conducted a cross-sectional analysis of nationally representative the WHO STEPwise approach to NCD risk factor surveillance (STEPS) 2018 Bangladesh survey data, involving 2782 adults aged 40–69 years without prior CVDs, using both versions of the updated 2019 WHO 10-year CVD risk prediction models. Logistic regression identified determinants of elevated predicted 10-year CVD risk (≥10%) and model agreement was assessed using Bland-Altman plots, Lin’s Concordance Correlation Coefficient (LCCC) and kappa statistics. Results In the laboratory-based model, 10.3% (95% CI 8.7% to 12.0%) of the population was at elevated risk (≥10%), compared with 9.2% (95% CI 7.7% to 11.0%) in the non-laboratory-based model. Risk was higher in males (p<0.001) in both models. Elevated risk in the laboratory-based model was associated with residing in Dhaka division (adjusted OR (aOR)=2.21, 95% CI 1.25 to 3.93), homemakers (aOR=0.29, 95% CI 0.16 to 0.51), unemployed/retired (aOR=7.18, 95% CI 3.84 to 13.44), high waist-hip ratio (aOR=2.10, 95% CI 1.36 to 3.25) and elevated low-density lipoprotein cholesterol (aOR=1.94, 95% CI 1.22 to 3.09). The non-laboratory-based model showed similar associations. Bland-Altman analysis (laboratory minus non-laboratory) showed a small mean difference of 0.18 with 95% limits of agreement from −3.15 to 3.51, with proportional bias (males: y=−0.43 + 1.11x; females: y=−0.43 + 1.16x). LCCC indicated high concordance (0.90, 95% CI 0.89 to 0.91), and the kappa indicated substantial agreement (0.69, 95% CI 0.67 to 0.72). Conclusion A substantial portion of Bangladeshi adults had elevated predicted 10-year CVD risk, with higher risk in males. Sociodemographic and metabolic factors indicate potential target groups for intervention. The non-laboratory-based model showed high concordance and substantial categorical agreement with the laboratory-based model and may support population screening where laboratory testing is limited, although caution is warranted at higher risk levels.
Monower et al. (Wed,) studied this question.