Influence of air temperature on number of respiratory diseases hospitalization in two counties of Liaoning Province

doi:10.11755/j.issn.1006-7639(2023)-01-0132

Journal of Arid Meteorology ›› 2023, Vol. 41 ›› Issue (1): 132-142.DOI: 10.11755/j.issn.1006-7639(2023)-01-0132

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Influence of air temperature on number of respiratory diseases hospitalization in two counties of Liaoning Province

YIN Ninglu¹^,⁵(), LI Junlin¹, HONG Ye², GUO Yong³, WANG Shigong¹^,⁴()

1. School of Atmospheric Sciences/Institute of Environmental Meteorology and Health， Chengdu University of Information Science and Technology， Chengdu 610225， China
2. Institute of Atmospheric Environment， China Meteorological Administration， Shenyang 110016， China
3. Department of Civil Affairs of Guizhou Province， Guiyang 550004， China
4. Zunyi Academician Work Center， Key Laboratory of Climatic Environment and Medical Rehabilitation， Zunyi 563000， China
5. Wuhai Meteorological Bureau of Inner Mongolia Autonomous Region， Wuhai 016000， China

Received:2021-03-19 Revised:2021-06-08 Online:2023-02-28 Published:2023-02-28

辽宁省两县域城市气温对呼吸系统疾病住院人数的影响

殷宁潞¹^,⁵(), 李俊林¹, 洪也², 郭勇³, 王式功¹^,⁴()

1.成都信息工程大学大气科学学院/环境气象与健康研究院，四川成都 610225
2.中国气象局沈阳大气环境研究所，辽宁沈阳 110016
3.贵州省民政厅，贵州贵阳 550004
4.遵义院士工作中心气候环境与医疗康养重点实验室，贵州遵义 563000
5.内蒙古自治区乌海市气象局，内蒙古乌海 016000

通讯作者: 王式功(1955—)，男，山东安丘人，教授，博士生导师，主要从事医学气象学研究。 E-mail：wangsg@cuit.edu.cn。
作者简介:殷宁潞(1992—)，女，内蒙古乌海人，硕士生，主要研究方向为天气预报、气象服务。 E-mail：387990190@qq.com。
基金资助:
中国气象局公共服务中心2020年度创新基金重点项目(K2020010);中国气象局预报预测核心业务发展专项(CMAHX 20160307);辽宁省气象局科学技术研究课题项目(202012)

Abstract

Abstract:

In order to find out the relationship between temperature and the number of hospitalized patients with respiratory diseases, relevant departments reasonably implement disease prevention and early warning in county-level cities in Liaoning Province. Based on the daily meteorological observation data and respiratory system hospitalization data of Beipiao City and Xifeng County in Liaoning Province from 2016 to 2018, the seasonal distribution characteristics and age distribution characteristics of the number of hospitalizations with respiratory system were analyzed. On this basis, the generalized additive model (GAM) and the distributed lag non-linear model (DLNM) were used to explore the effect of temperature on the number of hospitalizations for respiratory diseases, and the results were stratified by sex and age. Modeling using attributable fractions (AF) to quantify the disease risk of exposure to specific temperature ranges (eg, extreme cold temperature, moderate cold temperature, moderate hot temperature, extreme hot temperature). The results show that the annual peak of the number of hospitalizations for respiratory diseases in the two places occurs in winter and spring, and the majority of the sick people are children and the elderly. The optimal temperatures for people in Beipiao City and Xifeng County are 26.2 ℃ and 22.2 ℃, respectively. The impact of temperature on the incidence of respiratory diseases is dominated by the lag effect of cold temperature, and the immediate effect of hot temperature is not significant. In Beipiao City and Xifeng County, 27.0% (95% confidence interval: 20.3%-32.9%) and 29.0% (95% confidence interval: 22.1%-35.0%) of respiratory system hospitalizations were attributed to temperature, respectively, and the disease risk was mainly moderate cold temperature. The incidence of Beipiao City and Xifeng County was attributed to moderate cold temperature accounted for 25.9% (95% confidence interval: 19.5%-31.5%) and 28.1% (95% confidence interval: 21.5%-33.9%), respectively. In terms of age distribution, compared with the adult group, the percentage of diseases attributable to moderate cold is higher in the children group and the elderly group. In addition, the elderly group is also more sensitive to extreme low temperatures. In terms of gender stratification, women are more susceptible to low temperature than men. The temperature of the two county-level cities in Liaoning Province has different effects on different groups of people, the risk of disease caused by temperature on women and elderly residents is high.

Key words: temperature, respiratory diseases, the Distributed Lay Non-linear Model, Attributable Fraction

摘要：

为了探明气温与呼吸系统疾病住院人数的关系，合理实施辽宁省县域城市疾病预防预警，基于2016—2018年辽宁省北票市和西丰县两县域城市的逐日气象观测资料和呼吸系统疾病住院病例资料，分析当地呼吸系统疾病住院就诊人数的季节分布特征及其年龄分布特征。在此基础上，采用广义相加模型（Generalized Additive Model，GAM）和分布滞后非线性模型（the Distributed Lag Non-linear Model，DLNM）探究了气温对呼吸系统疾病住院人数的影响，并按性别、年龄分层建模，使用归因分值（Attributable Fraction，AF）量化了暴露在特定气温（极端低温、中度低温、中度高温、极端高温）范围内的患病风险。结果表明，两地呼吸系统疾病住院人数全年峰值出现在冬春季，患病人群以少儿和老年人群居多。北票市、西丰县人群的最适宜气温分别为26.2、22.2 ℃；气温对呼吸系统疾病患病的影响以低温滞后效应为主，高温存在即时效应但并不显著。北票市和西丰县分别有27.0%（95%置信区间为20.3%~32.9%）和29.0%（95%置信区间为22.1%~35.0%）的呼吸系统住院人数归因于气温，且患病风险主要以中度低温为主，北票市和西丰县患病归因于中度低温分别占25.9%（95%置信区间为19.5%~31.5%）和28.1%（95%置信区间为21.5%~33.9%）。就年龄分布而言，与成年组相比，少儿组和老年组中归因于中度低温的患病百分比均较高，此外老年组对极端低温也较敏感。就性别而言，女性比男性更容易受低温影响。辽宁省两县域城市的气温对不同人群的影响不同，气温对女性与老年居民造成的发病风险较大。

关键词: 气温, 呼吸系统疾病, 分布滞后非线性模型, 归因分值

CLC Number:

P49
P423

YIN Ninglu, LI Junlin, HONG Ye, GUO Yong, WANG Shigong. Influence of air temperature on number of respiratory diseases hospitalization in two counties of Liaoning Province[J]. Journal of Arid Meteorology, 2023, 41(1): 132-142.

殷宁潞, 李俊林, 洪也, 郭勇, 王式功. 辽宁省两县域城市气温对呼吸系统疾病住院人数的影响[J]. 干旱气象, 2023, 41(1): 132-142.

Figures/Tables 9

Tab.1 The statistics characteristics of meteorological elements and pollution parameters in Beipiao and Xifeng of Liaoning Province from 2016 to 2018

地区	统计量	气象要素				污染要素/（µg·m^-3）
地区	统计量	平均气温/℃	气压/hPa	平均相对湿度/%	平均风速/（m·s^-1）	PM_2.5	SO₂	NO₂
北票	$x -$	9.5	1 016.7	50.3	3.2	38.4	30.0	22.3
	s	12.8	10.4	20.1	1.3	29.6	26.8	9.4
	P_min	-21.9	992.9	8.5	0.7	0.0	4.0	6.0
	P_2.5	-13.6	999.7	18.8	1.2	7.0	6.0	9.0
	P_50.0	11.4	1 016.2	46.8	3.0	30.0	20.0	20.0
	P_97.5	27.9	1 036.6	89.0	6.1	124.6	105.6	45.0
	P_max	32.0	1 045.2	98.8	7.5	219.0	190.0	58.0
西丰	$x -$	5.8	1 016.6	68.4	2.2	45.2	19.0	29.8
	s	14.7	10.1	14.3	0.9	32.4	13.6	11.7
	P_min	-30.4	984.5	19.3	0.6	0.0	3.0	6.0
	P_2.5	-21.8	999.8	36.1	1.0	8.0	5.0	13.0
	P_50.0	7.5	1 016.1	69.5	2.0	36.0	15.0	28.0
	P_97.5	26.9	1 035.6	93.0	4.4	133.0	55.6	56.0
	P_max	30.5	1 045.1	99.0	6.0	250.0	91.0	79.0

Tab.1 The statistics characteristics of meteorological elements and pollution parameters in Beipiao and Xifeng of Liaoning Province from 2016 to 2018

地区	统计量	气象要素				污染要素/（µg·m^-3）
地区	统计量	平均气温/℃	气压/hPa	平均相对湿度/%	平均风速/（m·s^-1）	PM_2.5	SO₂	NO₂
北票	$x -$	9.5	1 016.7	50.3	3.2	38.4	30.0	22.3
	s	12.8	10.4	20.1	1.3	29.6	26.8	9.4
	P_min	-21.9	992.9	8.5	0.7	0.0	4.0	6.0
	P_2.5	-13.6	999.7	18.8	1.2	7.0	6.0	9.0
	P_50.0	11.4	1 016.2	46.8	3.0	30.0	20.0	20.0
	P_97.5	27.9	1 036.6	89.0	6.1	124.6	105.6	45.0
	P_max	32.0	1 045.2	98.8	7.5	219.0	190.0	58.0
西丰	$x -$	5.8	1 016.6	68.4	2.2	45.2	19.0	29.8
	s	14.7	10.1	14.3	0.9	32.4	13.6	11.7
	P_min	-30.4	984.5	19.3	0.6	0.0	3.0	6.0
	P_2.5	-21.8	999.8	36.1	1.0	8.0	5.0	13.0
	P_50.0	7.5	1 016.1	69.5	2.0	36.0	15.0	28.0
	P_97.5	26.9	1 035.6	93.0	4.4	133.0	55.6	56.0
	P_max	30.5	1 045.1	99.0	6.0	250.0	91.0	79.0

Tab.2 The statistics characteristics of the number of inpatients with respiratory diseases in Beipiao and Xifeng of Liaoning Province from 2016 to 2018

地区	统计量	不同人群			不同年龄段
地区	统计量	总数	男性	女性	<15岁	15~59岁	≥60岁
北票	$x -$	116.9	82.6	34.2	35.6	11.1	70.2
	s	35.4	24.1	13.9	16.9	4.5	23.3
	P_min	0.0	0.0	0.0	0.0	0.0	0.0
	P_2.5	63.4	39.4	16.4	13.0	4.0	33.4
	P_50.0	110.0	81.0	31.0	31.0	10.0	67.0
	P_97.5	199.6	137.0	70.0	74.0	22.6	126.6
	P_max	237.0	158.0	83.0	99.0	26.0	150.0
西丰	$x -$	62.0	32.0	30.0	22.2	9.3	30.6
	s	22.2	11.4	12.2	13.1	4.0	11.7
	P_min	0.0	0.0	0.0	0.0	0.0	0.0
	P_2.5	27.4	14.4	11.0	4.0	1.0	13.0
	P_50.0	58.0	30.0	28.0	20.0	9.0	29.0
	P_97.5	111.6	59.6	56.0	56.0	17.0	56.0
	P_max	131.0	75.0	72.0	69.0	22.0	89.0

Tab.2 The statistics characteristics of the number of inpatients with respiratory diseases in Beipiao and Xifeng of Liaoning Province from 2016 to 2018

地区	统计量	不同人群			不同年龄段
地区	统计量	总数	男性	女性	<15岁	15~59岁	≥60岁
北票	$x -$	116.9	82.6	34.2	35.6	11.1	70.2
	s	35.4	24.1	13.9	16.9	4.5	23.3
	P_min	0.0	0.0	0.0	0.0	0.0	0.0
	P_2.5	63.4	39.4	16.4	13.0	4.0	33.4
	P_50.0	110.0	81.0	31.0	31.0	10.0	67.0
	P_97.5	199.6	137.0	70.0	74.0	22.6	126.6
	P_max	237.0	158.0	83.0	99.0	26.0	150.0
西丰	$x -$	62.0	32.0	30.0	22.2	9.3	30.6
	s	22.2	11.4	12.2	13.1	4.0	11.7
	P_min	0.0	0.0	0.0	0.0	0.0	0.0
	P_2.5	27.4	14.4	11.0	4.0	1.0	13.0
	P_50.0	58.0	30.0	28.0	20.0	9.0	29.0
	P_97.5	111.6	59.6	56.0	56.0	17.0	56.0
	P_max	131.0	75.0	72.0	69.0	22.0	89.0

Fig.1 The distribution of inpatients with respiratory diseases in different ages in Beipiao (a) and Xifeng (b) of Liaoning Province from 2016 to 2018

Fig.2 Monthly variation of inpatients with respiratory diseases by gender （a， b） and age （c， d） in Beipiao （a， c） and Xifeng （b， d） of Liaoning Province from 2016 to 2018

Tab.3 Statistics of the number of hospitalized patients with respiratory diseases in different seasons in Beipiao and Xifeng of Liaoning Province from 2016 to 2018

地区	季节	日数/d	日数占全年百分比%	住院人数/人
地区	季节	日数/d	日数占全年百分比%	总人数	男性	女性	<15岁	15~59岁	≥60岁
北票市	春季	79	21.6	106.0	74.9	31.0	34.8	9.1	62.1
	夏季	71	19.4	88.9	65.1	23.9	25.2	8.4	55.3
	秋季	57	15.6	101.3	76.2	25.0	22.3	11.1	67.9
	冬季	159	43.4	139.9	96.3	43.6	45.0	13.3	81.6
西丰县	春季	68	18.6	54.4	27.8	26.7	20.4	8.0	26.1
	夏季	53	14.5	48.1	25.8	22.3	17.3	7.1	23.7
	秋季	47	12.8	44.4	25.2	19.2	10.2	8.8	25.5
	冬季	198	54.1	72.6	36.8	35.9	27.0	10.5	35.2

Fig.3 The lag effect of the low temperature and the high temperature on the incidence of respiratory diseases in Beipiao (a) and Xifeng (b) of Liaoning Province (The color shaded areas represent the 95% confidence interval)

Fig.4 Cumulative effect of 15 days temperature lag on the total population of respiratory diseases and occurrence times of different temperature in Beipiao (a) and Xifeng (b) of Liaoning Province (The dotted lines on both sides are 2.5% and 97.5% quantiles of temperature distribution, respectively, while the middle dotted line is the optimal temperature, blue and red curves represent exposure curves of cold effect and hot effect respectively, and the grey area represents the 95% confidence interval. the same as below)

Fig.5 Cumulative effect of 15 d lag of mean temperature on respiratory diseases in different gender and age groups and occurrence times of different temperature in Beipiao （the left） and Xifeng （the righ） of Liaoning Province

Tab.4 Attribution fraction and its 95% confidence interval of respiratory diseases caused by ambient temperature in Beipiao and Xifeng of Liaoning Province from 2016 to 2018

地区	不同人群	总体气温（95%CI）	极端低温（95%CI）	中度低温（95%CI）	中度高温（95%CI）	极端高温（95%CI）
北票市	总人群	27.0（20.3~32.9）*	1.5（0.9~2.0）*	25.9（19.5~31.5）*	0.0（0.0~0.1）	0.1（-0.2~0.4）
	少儿组	36.3（30.0~41.7）*	0.6（-0.6~1.4）	34.3（28.7~39.3）*	0.8（-0.2~1.8）	0.8（0.4~1.2）*
	中青年组	10.4（-1.7~20.2）	0.2（-1.1~1.2）	13.7（2.8~22.8）*	-1.1（-1.7~0.5）	-2.6（-3.7~-1.7）
	老年组	25.7（17.1~32.9）*	1.8（1.2~2.4）*	24.2（16.2~31.2）*	0.0（-0.1~0.1）	0.2（-0.3~0.5）
	男性	17.8（10.2~24.6）*	0.9（0.3~1.4）*	16.7（9.6~23.1）*	0.1（0.0~0.2）*	0.4（0.0~0.6）*
	女性	19.4（7.2~30.4）*	1.5（0.7~2.2）*	18.1（6.3~28.2）*	0.0（0.0~0.0）	0.2（-0.3~0.5）
西丰县	总人群	29.0（22.1~35.0）*	0.9（0.2~1.4）*	28.1（21.5~33.9）*	0.2（-0.1~0.4）	0.2（-0.1~0.4）
	少儿组	37.0（25.5~45.4）*	-0.5（-1.6~0.3）	36.1（24.8~44.6）*	0.8（0.2~1.4）*	0.4（0.0~0.8）*
	中青年组	11.4（-2.5~22.4）	1.0（-0.1~1.9）	11.7（-1.3~21.9）	-0.5（-0.9~0.1）	-0.8（-1.4~-0.2）
	老年组	25.3（17.1~32.3）*	1.6（1.0~2.2）*	23.9（16.0~30.7）*	0.1（-0.2~0.3）	0.1（-0.2~0.4）
	男性	24.7（18.0~30.6）*	0.6（0.0~1.1）*	23.9（17.2~29.4）*	0.3（-0.2~0.7）	0.1（-0.2~0.4）
	女性	34.5（26.6~41.1）*	1.2（0.5~1.8）*	33.4（25.8~39.6）*	0.2（-0.1~0.4）	0.2（-0.1~0.5）

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Influence of air temperature on number of respiratory diseases hospitalization in two counties of Liaoning Province

辽宁省两县域城市气温对呼吸系统疾病住院人数的影响

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