Influence of meteorological factors on number of upper respiratory tract infection visits in Luohu of Shenzhen

doi:10.11755/j.issn.1006-7639(2021)-06-0995

Abstract

Abstract:

The daily number of patients with upper respiratory tract infection (URI) in 49 community health service centers in Luohu of Shenzhen from 2014 to 2018 and meteorological data in the same period were collected to analyze the changing characteristics of the number of patients with URI at different time scales. The distributed lag non-linear model (DLNM) and generalized linear model (GLM) were used to study the relationship between different meteorological factors and the number of patients with URI in Shenzhen. The results show that there was a significant seasonal change for the number of patients with URI in Luohu of Shenzhen. The peak periods of cases were March to April in spring, July in summer and December to next January in winter, corresponding to Pure Brightness, Lesser Heat and Lesser Cold of the 24 solar terms, respectively. The DLNM model showed that air temperature was the main impact factor, and its effect on the number of patients with URI presented mainly cold effect, the relative risk (RR) reached the peak after 4 days lag (RR was equal to 1.041, the 95% confidence interval was between 1.022 and 1.060). Women were more affected by cold effect than men, and middle-aged and elderly people were more affected by cold effect than children. Another important factors were thermal effect in summer and the variable temperature in spring. The influence of humidity presented mainly low humidity effect, and the relative risk reached the peak (RR was equal to 1.058, the 95% confidence interval was between 1.049 and 1.068) on the same day. Pressure and wind speed showed high pressure effect and strong wind effect, and RR reached the highest after 1 day lag. In conclusion, cold air activities in winter and spring and their cold effects such as low temperature, low humidity and strong wind were the key factors to induce URI, followed by the impact of continuous high temperature in summer, both of them should be focused on timely prevention.

Key words: upper respiratory tract infection, meteorological element, seasonal variation, cold effect, hysteresis effect, 24 solar terms

摘要：

基于2014—2018年深圳市罗湖区49家社区健康服务中心上呼吸道感染(简称“上感”)的逐日就诊人数和同期气象要素资料,利用不同时间尺度分析上感就诊人数的变化特征,并采用分布滞后非线性模型(DLNM)、广义线性模型(GLM)等研究不同气象要素与深圳地区上感就诊人数的关系。结果表明:深圳市罗湖区上感就诊人数存在明显的季节性变化,春季3—4月、夏季7月和冬季12月至次年1月为发病高峰时段,分别对应24节气中的清明、小暑、小寒节气。DLNM反映气温为主控因素,它对上感就诊人数的影响以冷效应为主,相对风险(RR)在滞后4 d达到峰值(RR为1.041,95%置信区间为1.022~1.060),且女性较男性、中老年人较少儿更易受冷效应的影响;其次是暑期的热效应和春季温度多变的影响。湿度影响主要表现为低湿效应,其相对风险在当天达到峰值(RR为1.058,95%置信区间为1.049~1.068)。气压和风速影响则表现为高压效应和大风效应,RR在滞后1 d达最高。总之,深圳市冬、春季冷空气活动及其所反映的低温、低湿、大风等产生的冷效应是诱发上呼吸道感染的关键因素,其次是夏季持续高温的影响,两者都应予以重点及时防范。

关键词: 上呼吸道感染, 气象要素, 季节性变化, 冷效应, 滞后效应, 24节气

CLC Number:

HUANG Kailong, LIN Jinchun, MA Pan, HUANG Wenjing, LU Junxiang, TANG Xiaoxin, WANG Shigong. Influence of meteorological factors on number of upper respiratory tract infection visits in Luohu of Shenzhen[J]. Journal of Arid Meteorology, 2021, 39(06): 995-1005.

黄开龙, 林锦春, 马盼, 黄文静, 陆俊翔, 唐小新, 王式功. 气象条件对深圳市罗湖区上呼吸道感染就诊人数的影响[J]. 干旱气象, 2021, 39(06): 995-1005.

Figures/Tables 14

References 44

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	日平均气温/ ℃	日最高气温/ ℃	日最低气温/ ℃	日平均相对湿度/%	日平均气压/ hPa	日平均风速/ (m·s^-1)	日降水量/ mm	气温日较差/ ℃	日均水汽压/ hPa
X±s	23.5±5.5	27.1±5.6	21.1±5.6	75.4±12.6	1005.6±6.6	1.9±0.8	5.3±17.0	6.0±2.0	23.2±8.3
P_min	3.5	6.5	1.7	19	983.1	0.4	0	0.9	2.7
P₂₅	19.4	23.2	16.8	70	1000.8	1.4	0	4.6	16.4
P₅₀	24.8	28.4	22.5	77	1005.6	1.8	0	5.9	24
P₇₅	28.2	31.7	25.9	84	1010.4	2.3	1.2	7.3	31
P_max	33	37	29.9	100	1027.3	6.1	187.8	14	38

	日平均气温/ ℃	日最高气温/ ℃	日最低气温/ ℃	日平均相对湿度/%	日平均气压/ hPa	日平均风速/ (m·s^-1)	日降水量/ mm	气温日较差/ ℃	日均水汽压/ hPa
X±s	23.5±5.5	27.1±5.6	21.1±5.6	75.4±12.6	1005.6±6.6	1.9±0.8	5.3±17.0	6.0±2.0	23.2±8.3
P_min	3.5	6.5	1.7	19	983.1	0.4	0	0.9	2.7
P₂₅	19.4	23.2	16.8	70	1000.8	1.4	0	4.6	16.4
P₅₀	24.8	28.4	22.5	77	1005.6	1.8	0	5.9	24
P₇₅	28.2	31.7	25.9	84	1010.4	2.3	1.2	7.3	31
P_max	33	37	29.9	100	1027.3	6.1	187.8	14	38

人群	X±s	P_min	P₂₅	P₅₀	P₇₅	P_max
总人群	764.3±258.5	6	629	741	875	2792
男性	391.2±131.9	2	321	379	450	1384
女性	373.0±128.8	1	305	361	430	1408
0~18岁	231.9±106.2	1	172	219	280	1047
19~59岁	470.1±153.7	4	392	455	539	1546
大于60岁	62.3±23.0	0	48	60	75	199

人群	X±s	P_min	P₂₅	P₅₀	P₇₅	P_max
总人群	764.3±258.5	6	629	741	875	2792
男性	391.2±131.9	2	321	379	450	1384
女性	373.0±128.8	1	305	361	430	1408
0~18岁	231.9±106.2	1	172	219	280	1047
19~59岁	470.1±153.7	4	392	455	539	1546
大于60岁	62.3±23.0	0	48	60	75	199

气象要素	人群
气象要素	全人群	男性	女性	0~18岁	19~59岁	≥60岁
日均气温	-0.120^**	-0.101^**	-0.135^**	0.025	-0.179^**	-0.187^**
日最高气温	-0.117^**	-0.098^**	-0.132^**	0.024	-0.175^**	-0.182^**
日最低气温	-0.137^**	-0.119^**	-0.151^**	0.013	-0.197^**	-0.203^**
日均相对湿度	-0.131^**	-0.129^**	-0.131^**	-0.032	-0.158^**	-0.159^**
气压	0.126^**	0.107^**	0.142^**	0.010	0.167^**	0.182^**
日均风速	-0.020	-0.021	-0.018	-0.067^**	0.010	0.013
日累计降水	-0.155^**	-0.150^**	-0.156^**	-0.124^**	-0.136^**	-0.111^**
日较差	0.098^**	0.100^**	0.093^**	0.055^*	0.101^**	0.094^**
水汽压	-0.147^**	-0.128^**	-0.161^**	0.009	-0.205^**	-0.212^**