Cargando…
Spatial analysis of overweight prevalence in China: exploring the association with air pollution
BACKGROUND: Overweight is a known risk factor for various chronic diseases and poses a significant threat to middle-aged and elderly adults. Previous studies have reported a strong association between overweight and air pollution. However, the spatial relationship between the two remains unclear due...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10463435/ https://www.ncbi.nlm.nih.gov/pubmed/37608324 http://dx.doi.org/10.1186/s12889-023-16518-6 |
Sumario: | BACKGROUND: Overweight is a known risk factor for various chronic diseases and poses a significant threat to middle-aged and elderly adults. Previous studies have reported a strong association between overweight and air pollution. However, the spatial relationship between the two remains unclear due to the confounding effects of spatial heterogeneity. METHODS: We gathered height and weight data from the 2015 China Health and Retirement Long-term Survey (CHARLS), comprising 16,171 middle-aged and elderly individuals. We also collected regional air pollution data. We then analyzed the spatial pattern of overweight prevalence using Moran's I and Getis-Ord Gi* statistics. To quantify the explanatory power of distinct air pollutants for spatial differences in overweight prevalence across Southern and Northern China, as well as across different age groups, we utilized Geodetector's q-statistic. RESULTS: The average prevalence of overweight among middle-aged and elderly individuals in each city was 67.27% and 57.39%, respectively. In general, the q-statistic in southern China was higher than that in northern China. In the north, the prevalence was significantly higher at 54.86% compared to the prevalence of 38.75% in the south. SO(2) exhibited a relatively higher q-statistic in middle-aged individuals in both the north and south, while for the elderly in the south, NO(2) was the most crucial factor (q = 0.24, p < 0.01). Moreover, fine particulate matter (PM(2.5) and PM(10)) also demonstrated an important effect on overweight. Furthermore, we found that the pairwise interaction between various risk factors improved the explanatory power of the prevalence of overweight, with different effects for different age groups and regions. In northern China, the strongest interaction was found between NO(2) and SO(2) (q = 0.55) for middle-aged individuals and PM(2.5) and SO(2) (q = 0.27) for the elderly. Conversely, in southern China, middle-aged individuals demonstrated the strongest interaction between SO(2) and PM(10) (q = 0.60), while the elderly showed the highest interaction between NO(2) and O(3) (q = 0.42). CONCLUSION: Significant spatial heterogeneity was observed in the effects of air pollution on overweight. Specifically, air pollution in southern China was found to have a greater impact on overweight than that in northern China. And, the impact of air pollution on middle-aged individuals was more pronounced than on the elderly, with distinct pollutants demonstrating significant variation in their impact. Moreover, we found that SO(2) had a greater impact on overweight prevalence among middle-aged individuals, while NO(2) had a greater impact on the elderly. Additionally, we identified significant statistically interactions between O(3) and other pollutants. |
---|