评估肺癌发病率和空气污染物的空间变化:空间回归建模方法
Sruthi S1, Aleyamma Mathew1, Jagathnath Krishna K M1
1Division of Cancer Epidemiology & Biostatistics, Regional Cancer Centre, Thiruvananthapuram, Kerala, India.
概括
三重市肺癌发病率正在上升,与空气污染有关. 这项研究使用空间分析来确定高风险区域,并确认PM2.5暴露与肺癌之间的关联.
科学领域:
- 环境健康 环境健康
- 流行病学 流行病学
- 空间分析 空间分析
背景情况:
- 肺癌 (LC) 发病率在喀拉拉邦的蒂鲁万纳塔普勒姆呈现越来越高的趋势.
- 长期暴露于空气污染是肺癌的公认环境风险因素.
- 了解空气污染物和液化碳之间的空间关系对于公共卫生干预至关重要.
研究的目的:
- 为了调查肺癌发病率和三重洲空气污染物暴露之间的空间关联.
- 确定肺癌发病率高的特定地区及其与环境因素的相关性.
- 评估空间回归模型在分析这些关系中的有效性.
主要方法:
- 使用空间滞后模型 (SLM),空间错误模型 (SEM) 和地理加权回归 (GWR) 进行空间分析.
- 分析了肺癌发病率 (每10^5男性,年龄>60岁) 和它们的地理分布.
- 评估了特定空气污染物,特别是PM2.5和肺癌发病率之间的联系.
主要成果:
- 整体肺癌发病率为每10^5名男性 (年龄>60岁) 的111人.
- 空间分布显示,该地区48%的发病率超过150.
- 在PM2.5暴露和肺癌发病率之间发现了显著的关联,SLM解释了62%的变化.
结论:
- 空间回归技术有效地解决了空间效应,并确定了肺癌高风险区域.
- 地理加权回归 (GWR) 增强了模型性能,提供了更好的本地预测,特别是在东南地区.
- 这些发现强调了空气污染 (PM2.5) 和肺癌之间的联系,强调了三重省有针对性的环境健康战略的必要性.
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