P031 SPATIAL ASSOCIATION BETWEEN HYPERTENSION AND AIR POLLUTION IN ULAANBAATAR, MONGOLIA

Background and Objective: This study aims to assess the spatial association between hypertension prevalence and PM2.5 levels in Ulaanbaatar. Methods: To estimate the hypertension prevalence, we conducted a complex survey with a two-stage cluster sampling method in 2018. For PM2.5 level estimation, we used data collected during cold weather months (October to March) from 2015 to 2019. These estimates were used to predict PM2.5 levels across the entire city, using the Inverse Distance Weighted (IDW) interpolation method. The same approach was applied to predict the hypertension prevalence. We fitted a multiple linear regression model to estimate the association between PM2.5 and hypertension prevalence with adjustment for income, a proxy of socioeconomic factors to examine effect modification. In exploratory analysis, we estimated the association between PM2.5 level and income level. We used RStudio 1.3.1093 and ArcMap 10.8.2 for data analysis. Results: A total of 4515 participants were included in the sample. The hypertension prevalence was 46.5% with a threshold of 130/80 mm Hg. Adjusting for other covariates, a 10-unit increase in PM2.5 level is associated with a 6.66 (95% CIs;[10.23,10.23]) point increase in hypertension prevalence (p<0.001). With each 100,000 (MNT) increase in income level, the impact of 10-unit increases in PM2.5 level on hypertension decreases by 2.98 (95% CIs;[-5.48,-0.47]) points (p=0.02). In the partial F-test, an increase in income level was associated with a lower hypertension prevalence (p=0.002). In the exploratory analysis, a 100,000 (MNT) increase in income level was associated with a 0.23 (95% CIs;[-0.41,-0.05]) point decrease in PM2.5 level (p=0.011). Figure 1. Spatial association between hypertension prevalence. A).Hypertension prevalence (%); B).Income level (MNT) PM2.5 level; C).PM 2.5 (μg/m 3 ); D).Linear regression model. Note: Inverse Distance Weighted method is used for predictions. Green points indicate the PM2.5-measuring site locations. Conclusions: The people who live in lower-income areas experience a stronger influence of air pollution on hypertension prevalence compared to others in Ulaanbaatar.