ABSTRACT
Background Epidemiological evidence indicates an association of particulate matter with depression and cognitive performance. From 2013 to 2017, China implemented the Air Pollution Prevention and Control Action Plan to reduce particulate matter concentration. There are few studies on the relationship between the decrease of particulate matter concentration and the improvement of mental health in middle-aged and elderly people. Objective To analyze the relationship between the decrease of city-level particulate matter concentration and the improvement of depression and cognitive function in the middle-aged and elderly population after the implementation of the Air Pollution Prevention and Control Action Plan. Methods Using the China Health and Retirement Longitudinal Study (CHARLS) data in 2011 and 2018, this study applied longitudinal data clustering technology to group cities based on the actual response of each city to the policy (the dynamic change trajectory of PM2.5 in each city during the study period); the higher the degree of response, the greater the reduction of PM2.5 concentration in the city. We assigned participants to three groups with different degrees of response to the policy, including low-response group A as the control group, medium-response intervention group B, and high-response intervention group C. A difference-in-differences (DID) model was used to estimate the influence of PM2.5 decline on the depression and cognitive function among middle-aged and elderly people in China by considering potential individual and city-level time-varying confounders. Depression was measured using the 10-item Center for the Epidemiological Studies of Depression Scale (CES-D10) (10 questions, total score range 0-30) with higher score representing higher severity of depression. Cognitive function was evaluated with reference to the international cognitive function test questionnaire for the middle-aged and the elderly which was further categorized into two dimensions of memory and cognitive status and included 31 questions with a score range of 0-31; the higher the score, the better the cognitive function. Samples with relatively complete outcomes were selected for analysis, including 10729 people in depression analysis and 4510 people in cognitive analysis. Results The longitudinal clustering results indicated that the urban groups with the highest decline of PM2.5 concentrations (high-response group C) had the highest baseline PM2.5 concentrations, mainly in Beijing-Tianjin-Hebei region, Chengdu-Chongqing metropolitan area, and Wuhan metropolitan area. In 2011, no significant differences were observed in depression and cognitive function among the three groups of middle-aged and elderly populations (Kruskal Wallis test: Pdepression=0.864, Pcognition=0.239). Significant differences were found in depression and cognitive function in both low-response group A and medium-response group B in 2018 compared to 2011 (paired Wilcox test, all P<0.001). However, in the high-response group C, there was no significant difference in depression in 2018 compared to 2011 (P=0.195), while a significant difference was detected in cognitive function (P=0.006). As PM2.5 concentrations decreased, the DID model showed that the depression of the middle-aged and elderly people in the high-response group C decreased by 7.55% (95%CI: 2.83%-12.03%), and the cognitive function improved by 2.70% (95%CI: 0.25%-5.22%) compared with the low-response group A. However, no intervention effect was observed in group B with moderate response level compared with group A with low response level. Conclusion After the implementation of the Air Pollution Prevention and Control Action Plan policy, the decrease of PM2.5 concentration has an ameliorative effect on the depression and cognitive function of middle-aged and elderly people in China. Given the aging population and the increasing burden of mental-related diseases in China, the promotion of environmental air pollution control has important public health implications.
ABSTRACT
We developed a user-friendly, interactive program to simultaneously cluster and visualize omics data, such as DNA and protein array profiles. This program provides diverse algorithms for the hierarchical clustering of two-dimensional data. The clustering results can be interactively visualized and optimized on a heatmap. The present tool does not require any prior knowledge of scripting languages to carry out the data clustering and visualization. Furthermore, the heatmaps allow the selective display of data points satisfying user-defined criteria. For example, a clustered heatmap of experimental values can be differentially visualized based on statistical values, such as p-values. Including diverse menu-based display options, QCanvas provides a convenient graphical user interface for pattern analysis and visualization with high-quality graphics.