Government drivers of breast cancer prevention
A spatiotemporal analysis based on the association between breast cancer and macro factors
Bibliographic Data
| ID | 22075970 |
|---|---|
| Authors | Xiaodan Bai (0000-0002-0897-5836, Minzu University of China), Xiyu Zhang (0009-0001-6001-133X, Harbin Medical University), Hongping Shi (Heze Municipal Hospital), Guihong Geng (0000-0001-9974-8833, Minzu University of China), Bing Wu (0000-0003-2458-0700, Harbin Medical University), Yongqiang Lai (0009-0007-3523-9002, Harbin Medical University), Wenjing Xiang (0009-0003-3407-389X, Minzu University of China), Yanjie Wang (0009-0008-9186-6382, Minzu University of China), Yu Cao (0000-0003-0624-7648, Minzu University of China), Baoguo Shi (0000-0003-3577-3890, Minzu University of China, corresponding author), Ye Li (0000-0002-5043-9797, Harbin Medical University, corresponding author) |
| Year | 2022 |
| Volume | 10 |
| Pages | 954247-954247 |
| Publication date | 2022-10-04 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2022.954247 |
| PMID | 36268002 |
| OpenAlex | W4301396677 |
| Language | EN |
| Citations received | 4 |
| References cited | 68 |
Background: Currently, breast cancer (BC) is ranked among the top malignant tumors in the world, and has attracted widespread attention. Compared with the traditional analysis on biological determinants of BC, this study focused on macro factors, including light at night (LAN), PM2.5, per capita consumption expenditure, economic density, population density, and number of medical beds, to provide targets for the government to implement BC interventions. Methods: A total of 182 prefecture-level cities in China from 2013 to 2016 were selected as the sample of the study. The geographically and temporally weighted regression (GTWR) model was adopted to describe the spatiotemporal correlation between the scale of BC and macro factors. Results: The results showed that the GTWR model can better reveal the spatiotemporal variation. In the temporal dimension, the fluctuations of the regression coefficients of each variable were significant. In the spatial dimension, the positive impacts of LAN, per capita consumption expenditure, population density and number of medical beds gradually increased from west to east, and the positive coefficient of PM2.5 gradually increased from north to south. The negative impact of economic density gradually increased from west to east. Conclusion: The fact that the degree of effect of each variable fluctuates over time reminds the government to pay continuous attention to BC prevention. The spatial heterogeneity features also urge the government to focus on different macro indicators in eastern and western China or southern and northern China. In other words, our research helps drive the government to center on key regions and take targeted measures to curb the rapid growth of BC
Breast cancer · Cancer · Cancer prevention · Environmental health · Macro · Air Quality and Health Impacts · Computer Science · Data-Driven Disease Surveillance · Impact of Light on Environment and Health · Medicine · Psychology · Internal Medicine · Oncology
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| Citations per year | 4 |
|---|---|
| Citation span | 2025 - 2026 (2) |
| Highly cited | No |