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

doi:10.12082/dqxxkx.2020.190743

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.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. 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/Abstract

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

廖书冰, 蔡宏, 袁艳琼, 张蓓蓓, 李义平. 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[J]. 地球信息科学学报, 2020, 22(11): 2177-2187.DOI:10.12082/dqxxkx.2020.190743

LIAO Shubing, CAI Hong, YUAN Yanqiong, ZHANG Beibei, LI Yiping. Impact of Regional Economic Development Represented by Nighttime Light on the Prevalence Rate of Elderly Hypertension and Type 2 Diabetes[J]. Journal of Geo-information Science, 2020, 22(11): 2177-2187.DOI:10.12082/dqxxkx.2020.190743

图/表 11

图1

图2

表1

表2

表3

表4

表5

常宁市各乡镇夜间灯光均值与老年高血压和Ⅱ型糖尿病患病率的一元线性拟合方程"

名称	拟合方程	R²
老年高血压患病率	$y = 0.0262 x + 0.1624$	0.6822
老年二型糖尿病患病率	$y = 0.0148 x - 0.0087$	0.8996

表5

表6

修正后的常宁市各乡镇夜间灯光均值与老年高血压和Ⅱ型糖尿病患病率的一元线性拟合方程"

慢性病	拟合方程	R²
老年高血压患病率	$y = 0.0282 x + 0.1280$	0.8623
老年Ⅱ型糖尿病患病率	$y = 0.0150 x - 0.0133$	0.9129

表6

图3

表7

图4

参考文献 28

[1]	胡秀静, 吴小亚, 王家骥, 等. 慢性病防治视角下的我国医养结合与健康管理发展回顾[J]. 中国慢性病预防与控制, 2019,27(8):561-564.
	[ Hu X J, Wu X Y, Wang J J, et al. Review on the development of medical integration and health management in China from the perspective of prevention and treatment of chronic diseases[J]. Chinese Journal of Prevention and Control of Chronic Diseases, 2019,27(8):561-564. ]
[2]	吴文军, 徐蕾, 黄丽梅, 等. 2016-2017年上海市社区糖尿病高危人群筛查结果分析[J]. 中华疾病控制杂志, 2019,23(7):802-806,817.
	[ Wu W J, Xu L, Huang L M, et al. Analysis of diabetes screening in high-risk population in different communities in Shanghai from 2016 to 2017[J]. Chinese Journal of Disease Control & Prevention, 2019,23(7):802-806,817. ]
[3]	李枝萍, 刘军, 徐婷婷, 等. 我国2型糖尿病的流行病学及危险因素研究现状[J]. 西南军医, 2010,12(4):754-756.
	[ Li Z P, Liu J, Xu T T, et al. Current status of epidemiology and risk factors for type 2 diabetes in China[J]. Journal of Military Surgeon in Southwest China, 2010,12(4):754-756. ]
[4]	孙丽娜. 西安市养老机构老年人慢性病与生活方式的流行病学调查[D]. 西安:第四军医大学, 2012.
	[ Sun L N. Study on the prevalence of chronic diseases and health-promoting lifestyle in aged people living in nursing homes of Xi'an[D]. Xi'an: Fourth Military Medical University, 2012. ]
[5]	Bird Y, Lemstra M, Rogers M, et al. The relationship between socioeconomic status/income and prevalence of diabetes and associated conditions: A cross-sectional population-based study in Saskatchewan, Canada[J]. International Journal for Equity in Health, 2015,14(1):93.
[6]	Lee T C, Glynn R J, Peña J M, et al. Socioeconomic status and incident type 2 diabetes mellitus: Data from the Women's Health Study[J]. Plos One, 2011,6(12):e27670.
[7]	何静, 洪忻, 王志勇, 等. 南京市城区居民家庭人均收入与自我报告2型糖尿病关系[J]. 中国公共卫生, 2018,34(3):354-357.
	[ He J, Hong X, Wang Z Y, et al. Relationship between family average income and self-reported type 2 diabetes in residents of urban Nanjing[J] Chinese Journal of Public Health, 2018,34(3):354-357. ]
[8]	栗洋, 詹中群. 深圳市光明新区SES与高血压关系调查与研究[J]. 中国实用医药, 2019,14(22):3-6.
	[ Li Y, Zhang Z Q. Investigation and study on the relationship between SES and hypertension in Guangming New District of Shenzhen City[J]. China Practical Medicine, 2019,14(22):3-6. ]
[9]	何静. 南京城区居民家庭人均收入与自我报告2型糖尿病之间关系的研究[D]. 南京:南京医科大学, 2018.
	[ He J. The association between family average income and self-reported type 2 diabetes among urban adults in Nanjing[D]. Nanjing: Nanjing Medical University, 2018. ]
[10]	李德仁, 余涵若, 李熙. 基于夜光遥感影像的“一带一路”沿线国家城市发展时空格局分析[J]. 武汉大学学报·信息科学版, 2017,42(6):711-720.
	[ Li D R, Yu H R, Li X. The spatial-temporal pattern analysis of city development in countries along the belt and road initiative base on nighttime light data[J]. Geomatics and Information Science of Wuhan University, 2017,42(6):711-720. ]
[11]	李德仁, 李熙. 论夜光遥感数据挖掘[J]. 测绘学报, 2015,44(6):591-601.
	[ Li D R, Li X. An overview on data mining of nighttime light remote sensing[J]. Acta Geodaetica et Cartographica, 2015,44(6):591-601. ]
[12]	王利伟, 冯长春. 转型期京津冀城市群空间扩展格局及其动力机制—基于夜间灯光数据方法[J]. 地理学报, 2016,71(12):2155-2169.
	[ Wang L W, Feng C C. Spatial expansion pattern and its driving dynamics of Beijing-Tianjin-Hebei metropolitan region: Based on nighttime light data[J]. Acta Geographica Sinica, 2016,71(12):2155-2169. ]
[13]	柏中强, 王卷乐, 杨雅萍, 等. 基于乡镇尺度的中国25省区人口分布特征及影响因素[J]. 地理学报, 2015,70(8):1229-1242.
	[ Bai Z Q, Wang J L, Yang Y P, et al. Characterizing spatial patterns of population distribution at township level across the 25 provinces in China[J]. Acta Geographica Sinica, 2015,70(8):1229-1242. ]
[14]	李伟, 郑新奇. 结合VIIRS和监测数据插值的北京雾霾监测方法[J]. 测绘学报, 2015,44(S1):123-128.
	[ Li W, Zheng X Q. A haze monitoring method combined viirs images with real-time observation data interpolation in Beijing[J]. Acta Geodaetica et Cartographica, 2015,44(S1):123-128. ]
[15]	Kloog I, Haim A, Stevens R G, et al. Global Co-distribution of Light at Night (LAN) and Cancers of Prostate, Colon, and Lung in Men[J]. Chronobiology International, 2009,26(1):108-125.
[16]	Bauer S E, Wagner S E, Burch J, et al. A case-referent study: light at night and breast cancer risk in Georgia[J]. International Journal of Health Geographics, 2013,12:23.
[17]	Kloog I, Haim A, Stevens R G, et al. Light at night co-distributes with incident breast but not lung cancer in the female population of Israel[J]. Chronobiology International, 2009,25(1):65-81. pmid: 18293150
[18]	Laohasiriwong W, Nattapong P, Atthawit S. Prevalence of hypertension in Thailand: Hotspot clustering detected by spatial analysis[J]. Geospatial Health, 2018,13:608. pmid: 29772873
[19]	钟亮, 刘小生. 珞珈一号新型夜间灯光数据应用潜力分析[J].测绘通报,2019(7):132-137.
	[ Zhong L, Liu X S. Application potential analysis of LJ1-01 new nighttime light data[J]. Bulletin of Surveying and Mapping, 2019(7):132-137. ]
[20]	Zhao M, Cheng W, Zhou C, et al. GDP spatialization and economic differences in south China Based on NPP-VIIRS nighttime light imagery[J]. Remote Sensing, 2017,9(7):673.
[21]	秦永, 刘凯敏. 中国地级市GDP数据的真实性:基于NPP-VIIRS夜间灯光数据的检验[J]. 统计与决策, 2019,35(13):19-23.
	[ Qin Y, Liu K M. Authenticity of China's prefecture-level city GDP data: Test based on NPP-VIIRS night light data[J]. Statistics & Decision, 2019,35(13):19-23. ]
[22]	李翔. 基于夜光遥感数据的中国2005-2015年居民收入时空变化与驱动力研究[D]. 南京:南京大学, 2018.
	[ Li X. Study on spatiotemporal changes and driving force of resident income of China from 2005 to 2015 based on night light remote sensing data[D]. Nanjing: Nanjing University, 2018. ]
[23]	黄益修. 基于夜间灯光遥感影像和社会感知数据的人口空间化研究[D]. 上海:华东师范大学, 2016.
	[ Huang X Y. Spatialization of population using nighttime light remote sensing images and social sensing data[D]. Shanghai: East China Normal University, 2016. ]
[24]	张秋媛, 彭明春, 王崇云, 等. 基于DMSP/OLS夜间灯光数据的贵州省人口分布及影响因子分析[J]. 云南大学学报(自然科学版), 2019,41(5):992-1000.
	[ Zhang Q Y, Peng M C, Wang C Y, et al. Population distribution of Guizhou province based on DMSP/OLS night lighting data[J]. Journal of Yunnan University: Natural Sciences Edition, 2019,41(5):992-1000. ]
[25]	武娜, 沈镭, 钟帅. 基于夜间灯光数据的晋陕蒙能源消费碳排放时空格局[J]. 地球信息科学学报, 2019,21(7):1040-1050.
	[ Wu N, Shen L, Zhong S. Spatiotemporal pattern of carbon emissions based on nightlight data of Shanxi-Shaanxi-Inner Mongolia region of China[J]. Journal of Geo-information Science, 2019,21(7):1040-1050. ]
[26]	王劲峰, 徐成东. 地理探测器:原理与展望[J]. 地理学报, 2017,72(1):116-134.
	[ Wang J F, Xu C D. Geodetector: Principle and prospective[J]. Acta Geographica Sinica, 2017,72(1):116-134. ]
[27]	石国宁, 张涛, 王无为. 中国非洲猪瘟疫情的时空演化特征及影响因素[J]. 干旱区资源与环境, 2020,34(3):137-142.
	[ Shi G N, Zhang T, Wang W W. Spatio-temporal evolution characteristics and causes of African swine fever inChina[J]. Journal of Arid Land Resources and Environment, 2020,34(3):137-142. ]
[28]	张晴晴, 徐子淇, 孙俞莉, 等. 长春市18~79岁居民高血压患病状况及影响因素城乡对比分析[J]. 中华疾病控制杂志, 2014,18(1):5-9.
	[ Zhang Q Q, Xu Z Q, Sun Y L, et al.Comparison of the prevalence of hypertension and its influencing factors among residents aged from 18 to 79 betweenurban and rural areas of Changchun City[J]. Chinese Journal of Disease Control & Prevention, 2014,18(1):5-9. ]

指标		乡、镇、街道办事处
夜间灯光总值	高 (SUM_LAT≥10×10⁶)	培元街道,泉峰街道
	较高 (3×10⁶≤SUM_LAT≤7×10⁶)	水口山镇,宜阳街道,三角塘镇
	低 (SUM_LAT≤1.2×10⁶)	原宜潭乡,板桥镇,洋泉镇,罗桥镇,原兰江乡,官岭镇,西岭镇,塔山瑶族乡,柏坊镇,新河镇,原江河乡,胜桥镇,蓬塘乡,大堡乡,庙前镇,原盐湖镇,荫田镇,白沙镇,烟洲镇,原弥泉乡,天堂山办事处
夜间灯光均值	高 (MEAN_LAT≥17000)	培元街道,宜阳街道,泉峰街道
	较高 (8000≤MEAN_LAT≤11000)	水口山镇,三角塘镇
	低 (MEAN_LAT≤6000)	板桥镇,宜潭乡,江河乡,官岭镇,原盐湖镇,原兰江乡,柏坊镇,罗桥镇,洋泉镇,塔山瑶族乡,弥泉乡,胜桥镇,西岭镇,白沙镇,天堂山办事处,庙前镇,蓬塘乡,烟洲镇,荫田镇,新河镇,大堡乡
老年高血压患病率	高 (患病率≥60%)	培元街道,泉峰街道,宜阳街道,塔山瑶族乡
	较高 (30%≤患病率≤50%)	原兰江乡,原盐湖镇,三角塘镇,水口山镇
	低 (患病率≤30%)	烟洲镇,天堂山办事处,庙前镇,官岭镇,新河镇,罗桥镇,白沙镇,柏坊镇,洋泉镇,大堡乡,江河乡,蓬塘乡,西岭镇,荫田镇,宜潭乡,胜桥镇,板桥镇,弥泉乡
老年Ⅱ型糖尿病患病率	高 (患病率≥20%)	培元街道,泉峰街道,宜阳街道
	较高 (7%≤患病率≤10%)	三角塘镇,水口山镇,塔山瑶族乡,原盐湖镇,原兰江乡
	低 (患病率≤6%)	西岭镇,白沙镇,大堡乡,烟洲镇,新河镇,庙前镇,罗桥镇,柏坊镇,天堂山办事处,官岭镇,胜桥镇,荫田镇,宜潭乡,蓬塘乡,洋泉镇,板桥镇,江河乡,弥泉乡

	皮尔逊相关系数(r)
	高血压患病率	Ⅱ型糖尿病患病率
夜间灯光总量	0.777^**	0.826^**
夜间灯光均值	0.880^**	0.948^**

	夜间灯光总量		夜间灯光均值
	q	p	q	p
高血压患病率	0.663	0.037	0.759	0.000
Ⅱ型糖尿病患病率	0.845	0.000	0.963	0.000

老年高血压
分组	患病率/%	OR值	95%CI
高夜间灯光均值	68.50	6.493	6.150~6.855
较高夜间灯光均值	30.58	1.315	1.263~1.370
低夜间灯光均值	25.08	1.000
老年Ⅱ型糖尿病
分组	患病率/%	OR值	95%CI
高夜间灯光均值	27.95	8.556	8.031~9.155
较高夜间灯光均值	8.41	2.025	1.887~2.173
低夜间灯光均值	4.34	1.000

拟合方程	MRE/%	RMSE
老年高血压患病率	17.68	0.0589
老年Ⅱ型糖尿病患病率	41.30	0.0243

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

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

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