Urbanization, socioeconomic status, and exposure to PM2.5, associated with township-based cerebrovascular disease (CBD) mortality.

BACKGROUND/OBJECTIVE: Previous research has shown an association between socioeconomic status (SES) and mortality, particularly in chronic diseases. However, limited studies simultaneously examined the relationship between urbanization, SES, exposure to PM2.5, and cerebrovascular disease (CBD) mortality at a township level from 2011 to 2020 in Taiwan.

METHODS

Township-level SES data (percentages of low-income and education with college and above) and seven levels of urbanization from 2011 to 2020 were obtained from data sources in Taiwan's central government. Age-standardized CBD mortality rates in 358 townships were calculated using the Geographic Information System (GIS) provided by the Research Center for the Humanities and Social Sciences (RCHSS) at Academia Sinica. Exposure to PM2.5 concentration was estimated using a combination of land-use regression and Ordinary Kriging to enhance the robustness of PM2.5 concentration estimates at the township level. Panel regression and structural equation modeling (SEM) was employed to analyze the association between urbanization, SES, exposure to PM2.5, and township-based CBD mortality rates.

RESULTS

There are significant differences in SES variables and exposure to PM2.5 among townships with seven levels of urbanization (P < 0.001). Even after controlling for other covariates (SES and PM2.5 concentration) through multivariate analysis, the associations between CBD mortality rates and urbanization areas persisted. SEM analysis revealed a negative correlation between age-standardized CBD mortality rate and education levels (β = -0.22), but a positive correlation with the proportion of low-income individuals (β = 0.41). There was no significant association between exposure to PM2.5 and CBD mortality. The panel regression analysis revealed that socioeconomic variables had different effects on CBD mortality rates across the three models (pooled ordinary least squares, fixed-effects, and random-effects) in both urban and rural areas. Notably, the level of urbanization was observed to modify the relationship between socioeconomic variables and CBD mortality rates.

CONCLUSION

Our findings suggest that township-based CBD mortality is significantly associated with SES variables and levels of urbanization, despite a reduction in CBD mortality from 2011 to 2020. Therefore, targeted intervention programs should be implemented to reduce CBD mortality in different levels of urbanization, particularly in remote townships. It is necessary to assess the disparities in socioeconomic status to achieve a fair allocation of resources at the township level.