地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (11): 2177-2187.doi: 10.12082/dqxxkx.2020.190743

• 地理空间分析综合应用 • 上一篇    下一篇

夜间灯光数据表征的区域经济发展水平对老年人高血压与Ⅱ型糖尿病患病率分布的影响

廖书冰1(), 蔡宏1,*(), 袁艳琼2, 张蓓蓓1, 李义平1   

  1. 1.贵州大学矿业学院,贵阳 550025
    2.湖南省常宁市三角塘医院,常宁 421500
  • 收稿日期:2019-11-28 修回日期:2020-03-02 出版日期:2020-11-25 发布日期:2021-01-25
  • 通讯作者: 蔡宏 E-mail:shubingliao94@126.com;588cai@163.com
  • 作者简介:廖书冰(1994— ),男,湖南常宁人,硕士生,主要研究方向为资源环境遥感。E-mail: shubingliao94@126.com
  • 基金资助:
    贵州省科技计划项目([2016]1028);贵州大学人才引进项目([2015]28)

Impact of Regional Economic Development Represented by Nighttime Light on the Prevalence Rate of Elderly Hypertension and Type 2 Diabetes

LIAO Shubing1(), CAI Hong1,*(), YUAN Yanqiong2, ZHANG Beibei1, LI Yiping1   

  1. 1. College ofMining, Guizhou University, Guiyang 550025, China
    2. Sanjiaotang Hospital, Changning City, Changning 421500, China
  • Received:2019-11-28 Revised:2020-03-02 Online:2020-11-25 Published:2021-01-25
  • Contact: CAI Hong E-mail:shubingliao94@126.com;588cai@163.com
  • Supported by:
    Science and Technology Program of Guizhou Province([2016]1028);Talents Introduction Funding Project of Guizhou University([2015]28)

摘要:

区域社会经济发展程度与老年慢性病患病率有着极强的正相关关系,而夜间灯光强度是区域经济发展程度的直接反征;因此,使用夜间灯光数据进行老年慢性病研究具有重要的现实意义。本文以湖南省常宁市为研究对象,结合珞珈一号夜间灯光数据和研究区各乡镇老年高血压及Ⅱ型糖尿病患病率统计数据,对常宁市26个乡镇单位老年高血压和Ⅱ型糖尿病患病率的分布特征差异进行了分析,进而通过建立模型模拟了研究区2种老年慢性病患病率的空间分布。研究结果表明:① 研究区夜间灯光均值与老年高血压及Ⅱ型糖尿病患病率的相关性都强于夜间灯光总量,且老年Ⅱ型糖尿病患病率与夜间灯光强度之间的关系要强于老年高血压患病率;② 对于同种慢性病患病率的分布,夜间灯光均值的影响要大于夜间灯光总量,且夜间灯光总量和均值对糖尿病患病率分布的影响均大于老年高血压患病率;③ 居住在高夜间灯光均值地区的老年人患高血压的风险是低夜间灯光均值地区的6.493倍,患Ⅱ型糖尿病的风险为8.556倍;④研究区老年高血压及Ⅱ型糖尿病与夜间灯光均值的一元线性拟合模型精度较高,可以较为精确地在模拟研究区老年高血压和Ⅱ型糖尿病的患病率的空间分布。该研究成果可为夜间灯光数据在疾病研究中的应用和区域性老年人高血压及Ⅱ型糖尿病患病原因分析,及相似病种患病情况的调査和预测提供参考。

关键词: 珞珈一号, 夜间灯光, 高血压, Ⅱ型糖尿病, 皮尔逊相关性分析, OR值, 患病率空间分布, 模型拟合

Abstract:

The prevalence of elderly hypertension and type 2 diabetes diseases have a strong positive correlation with regional socioeconomic development. As nighttime light images can reflect the regional socio-economic development directly, the application of nighttime light data to study of diseases in the elderly become very significant. Selecting Changning City as the study area, this paper analyzed the difference in spatial distributions of the prevalence of elderly hypertension and type 2 diabetes among 26 townships based on the Luojia1-01 nighttime light data and the prevalence rate data of these two diseases in the study area. The spatial distribution of the prevalence of these two diseases in the study area was simulated by linear regression models. Results show that: (1) The correlation between mean nighttime light values and prevalence of hypertension or type 2 diabetes was stronger than that between total nighttime light values and prevalence of hypertension or type 2 diabetes. The relationship between mean or total nighttime light values and prevalence of hypertension was weaker than that between mean or total nighttime light values and prevalence of type 2 diabetes in the elderly; (2) The impacts of mean nighttime light on the distribution of the both diseases were larger than that of total nighttime value. And both the mean and total nighttime light had larger impacts on the spatial distribution of type 2 diabetes; (3) The risk of the elderly living in areas with high nighttime light was 6.493 times higher than those living in areas with low nighttime light, with the OR value for type 2 diabetes was 8.556; and (4) The linear regression model between the prevalence of either elderly hypertension or type 2 diabetes and mean nighttime light showed a high accuracy, which could accurately predict the spatial distribution of the prevalence of hypertension or type 2 diabetes of the elderly in the study area. Our research results can provide reference for the application of nighttime light data in disease researches and the analysis of the causes of regional hypertension and type 2 diabetes diseases in the elderly, as well as the investigation and prediction of similar diseases.

Key words: LJ1-01, nighttime light, hypertension, type 2 diabetes, Pearson's correlation coefficient, Odd Ratio, spatial distribution of prevalence, modeling