基于分布滞后非线性模型研究新疆疏附县气温对肺结核发病的影响

doi:10.19982/j.issn.1000-6621.20220488

中国防痨杂志 ›› 2023, Vol. 45 ›› Issue (5): 507-513.doi: 10.19982/j.issn.1000-6621.20220488

基于分布滞后非线性模型研究新疆疏附县气温对肺结核发病的影响

美合日班·买买提¹(), 买吾拉江·依马木²(), 卢冬梅³, 郑彦玲⁴, 张学良⁴(), 彭孝旺²()

¹ 新疆医科大学公共卫生学院, 乌鲁木齐 830017
² 新疆维吾尔自治区喀什地区疾病预防控制中心, 喀什 844000
³ 新疆维吾尔自治区人民医院呼吸与危重症医学中心, 乌鲁木齐 830002
⁴ 新疆医科大学医学工程技术学院, 乌鲁木齐 830017

收稿日期:2022-12-14 出版日期:2023-05-10 发布日期:2023-04-25
通信作者: 张学良,彭孝旺 E-mail:shuxue2456@126.com;410388299@qq.com
基金资助:
国家自然科学基金(72064036);国家自然科学基金(72174175);国家自然科学基金(72163033);新疆自治区天山优秀青年项目(2020Q020)

Study on the effect of daily average temperature on the incidence of tuberculosis in Shufu County at Xinjiang, based on a distributed lag nonlinear model

Meiheriban·Maimaiti ¹(), Mawlanjan·Emam ²(), Lu Dongmei³, Zheng Yanling⁴, Zhang Xueliang⁴(), Peng Xiaowang²()

¹ School of Public Health, Xinjiang Medical University, Urumqi 830017, China
² Kashgar Regional Center for Disease Control and Prevention, Kashgar 844000, China
³ Center of Respiratory and Critical Care Medicine, Xinjiang Uygur Autonomous Region People’s Hospital, Urumqi 830002, China
⁴ College of Medical Engineering Technology, Xinjiang Medical University, Urumqi 830017, China

Received:2022-12-14 Online:2023-05-10 Published:2023-04-25
Contact: Zhang Xueliang,Peng Xiaowang E-mail:shuxue2456@126.com;410388299@qq.com
Supported by:
National Natural Science Foundation of China(72064036);National Natural Science Foundation of China(72174175);National Natural Science Foundation of China(72163033);Xinjiang Autonomous Region Tianshan Excellent Youth Project(2020Q020)

摘要/Abstract

摘要： 目的探讨新疆维吾尔自治区喀什地区疏附县气温对肺结核发病的影响。方法新疆维吾尔自治区喀什地区疏附县2017年1月1日至2021年12月31日的肺结核报告发病数来源于中国传染病网络报告系统。该地区同期气象数据,包括日平均气温、日平均相对湿度、日平均风速,来源于中国气象数据网。对日平均气温与肺结核日发病例数进行Spearman相关性分析和分布滞后非线性模型分析,研究疏附县气温与肺结核每日发病数的关联效应。结果 2017—2021年疏附县共报告肺结核发病数为5212例,其中,男性2669例,女性2543例;较高发于年龄≥65岁人群(3269例);最高日发病例数为76例;日平均气温的中位数(四分位数)为14.7(2.30,22.50)℃。Spearman相关性分析结果显示,肺结核日发病例数与日平均气温呈正相关(相关系数为0.070,P<0.01)。分布滞后非线性模型分析结果显示,气温与肺结核发病呈非线性关系。低温(-4℃)对36~64岁人群肺结核日发病风险影响效应较明显,随着滞后时间增加,RR值呈现先下降后上升的趋势,在滞后28d时对36~64岁人群肺结核日发病风险RR值达到最大值[RR(95%CI)=2.833(1.581~5.078)]。低温(-4℃)对女性肺结核日发病风险影响效应较明显,随着滞后时间增加,RR值呈现先下降后上升的趋势,也是在滞后28d时对女性肺结核日发病风险RR值达到最大值[RR(95%CI)=2.377(1.334~4.234)]。结论新疆维吾尔自治区喀什地区疏附县气温与人群肺结核发病风险相关,低温环境下女性和36~64岁人群肺结核发病风险增加。

关键词: 结核, 肺, 温度, 危险因素, 模型,统计学

Abstract:

Objective: To investigate the effect of temperature on the incidence of pulmonary tuberculosis in Shufu County, Kashgar Region, Xinjiang Uygur Autonomous Region. Methods: The number of reported cases of pulmonary tuberculosis in Shufu County, Kashgar Region, Xinjiang Uygur Autonomous Region from January 1, 2017 to December 31, 2021 was sourced from the Chinese Infectious Disease Reporting System. Meteorological data for the same period in the region, including average daily temperature, average daily relative humidity, and average daily wind speed, were obtained from the China Meteorological Data Network. Spearman correlation analysis and distribution lag nonlinear model analysis were performed on the daily mean air temperature and daily incidence of tuberculosis cases to study the association effect of air temperature and daily incidence of pulmonary tuberculosis in Shufu County. Results: From 2017 to 2021, a total of 5212 cases of pulmonary tuberculosis were reported in Shufu County, including 2669 males and 2543 females; higher incidence in people aged ≥65 years old (3269 cases); the highest number of daily onset cases was 76; the median (quartile) of daily average temperature was 14.7 (2.30, 22.50) ℃. The Spearman correlation analysis showed a positive correlation between the daily incidence of tuberculosis and the daily average temperature (correlation coefficient 0.070, P<0.01). The results of the distributed lagged nonlinear model analysis showed a nonlinear relationship between air temperature and pulmonary tuberculosis. The effect of low temperature (-4 ℃) on the daily risk of pulmonary tuberculosis incidence in the 36-64-year-old population was more pronounced, and the RR values showed a trend of decreasing and then increasing with increasing lag time, reaching a maximum RR value at a lag of 28 d on the daily risk of pulmonary tuberculosis incidence in the 36-64-year-old population (RR (95%CI)=2.833 (1.581-5.078)). The effect of low temperature (-4 ℃) on the number of daily incidences of pulmonary tuberculosis in women was more pronounced, with a trend of decreasing and then increasing RR values as the lag time increased, also reaching a maximum RR value at a lag of 28 d on the risk of daily incidences of pulmonary tuberculosis in women (RR (95%CI)=2.377 (1.334-4.234)). Conclusion: Temperature was associated with the risk of pulmonary tuberculosis incidence in the population in Shufu County, Kashgar Region, Xinjiang Uygur Autonomous Region, and the risk of pulmonary tuberculosis incidence was increased in women and people aged 36-64 years in a low-temperature environment.

Key words: Tuberculosis, pulmonary, Temperature, Risk factors, Models, statistical

中图分类号:

R521
R825.2

美合日班·买买提, 买吾拉江·依马木, 卢冬梅, 郑彦玲, 张学良, 彭孝旺. 基于分布滞后非线性模型研究新疆疏附县气温对肺结核发病的影响[J]. 中国防痨杂志, 2023, 45(5): 507-513. doi: 10.19982/j.issn.1000-6621.20220488

Meiheriban·Maimaiti , Mawlanjan·Emam , Lu Dongmei, Zheng Yanling, Zhang Xueliang, Peng Xiaowang. Study on the effect of daily average temperature on the incidence of tuberculosis in Shufu County at Xinjiang, based on a distributed lag nonlinear model[J]. Chinese Journal of Antituberculosis, 2023, 45(5): 507-513. doi: 10.19982/j.issn.1000-6621.20220488

图/表 5

表1

图1

图2~10

表2

不同滞后时长下冷/热效应对疏附县肺结核日发病风险的影响 [RR(95%CI)值]

滞后时长 (d)	冷效应(P₁₀=-4℃)			热效应(P₉₀=26.1℃)
滞后时长 (d)	全人群	男性	女性	全人群	男性	女性
0	1.957(1.048~ 3.654)	1.725(0.875~ 3.402)	2.237(1.193~ 4.195)	1.140(0.860~ 1.510)	1.247(0.917~ 1.696)	1.042(0.786~ 1.381)
1	1.323(0.897~ 1.952)	1.204(0.789~ 1.838)	1.463(0.988~ 2.164)	1.081(0.896~ 1.303)	1.155(0.942~ 1.417)	1.010(0.837~ 1.219)
2	1.012(0.787~ 1.302)	0.944(0.718~ 1.241)	1.091(0.846~ 1.405)	1.040(0.912~ 1.185)	1.090(0.946~ 1.257)	0.990(0.868~ 1.129)
3	0.857(0.699~ 1.050)	0.813(0.652~ 1.015)	0.906(0.738~ 1.112)	1.012(0.910~ 1.127)	1.046(0.931~ 1.174)	0.979(0.878~ 1.091)
4	0.787(0.642~ 0.964)	0.756(0.606~ 0.944)	0.821(0.669~ 1.007)	0.995(0.898~ 1.102)	1.016(0.910~ 1.135)	0.974(0.878~ 1.080)
5	0.769(0.625~ 0.946)	0.746(0.595~ 0.935)	0.796(0.646~ 0.980)	0.986(0.890~ 1.092)	0.997(0.893~ 1.114)	0.973(0.878~ 1.080)
6	0.787(0.644~ 0.963)	0.767(0.616~ 0.956)	0.811(0.662~ 0.992)	0.982(0.888~ 1.086)	0.987(0.885~ 1.101)	0.976(0.881~ 1.081)
7	0.832(0.690~ 1.003)	0.813(0.663~ 0.997)	0.854(0.708~ 1.031)	0.982(0.891~ 1.082)	0.982(0.884~ 1.091)	0.980(0.889~ 1.081)
8	0.895(0.754~ 1.063)	0.875(0.726~ 1.055)	0.919(0.773~ 1.091)	0.984(0.897~ 1.079)	0.982(0.888~ 1.085)	0.985(0.897~ 1.082)
9	0.970(0.825~ 1.140)	0.948(0.795~ 1.130)	0.996(0.846~ 1.172)	0.987(0.903~ 1.078)	0.983(0.893~ 1.082)	0.989(0.904~ 1.083)
10	1.049(0.892~ 1.233)	1.024(0.859~ 1.221)	1.077(0.915~ 1.268)	0.990(0.907~ 1.081)	0.986(0.897~ 1.084)	0.993(0.908~ 1.085)
11	1.121(0.946~ 1.330)	1.095(0.910~ 1.316)	1.154(0.972~ 1.370)	0.992(0.908~ 1.084)	0.989(0.899~ 1.089)	0.994(0.908~ 1.088)
12	1.181(0.985~ 1.416)	1.151(0.946~ 1.402)	1.216(1.012~ 1.461)	0.993(0.906~ 1.088)	0.991(0.898~ 1.094)	0.994(0.905~ 1.090)
13	1.218(1.007~ 1.473)	1.187(0.966~ 1.459)	1.254(1.035~ 1.521)	0.992(0.902~ 1.089)	0.992(0.896~ 1.097)	0.991(0.901~ 1.090)
14	1.228(1.012~ 1.490)	1.197(0.971~ 1.476)	1.264(1.039~ 1.537)	0.989(0.898~ 1.087)	0.990(0.894~ 1.097)	0.985(0.895~ 1.085)
15	1.209(0.999~ 1.462)	1.180(0.961~ 1.450)	1.243(1.026~ 1.507)	0.983(0.895~ 1.081)	0.988(0.892~ 1.093)	0.978(0.889~ 1.077)
16	1.164(0.971~ 1.395)	1.138(0.935~ 1.385)	1.195(0.994~ 1.436)	0.977(0.891~ 1.071)	0.983(0.890~ 1.086)	0.970(0.883~ 1.065)
17	1.099(0.925~ 1.305)	1.077(0.895~ 1.297)	1.125(0.945~ 1.340)	0.969(0.886~ 1.060)	0.977(0.888~ 1.076)	0.960(0.877~ 1.051)
18	1.022(0.865~ 1.207)	1.005(0.839~ 1.204)	1.044(0.881~ 1.236)	0.961(0.881~ 1.048)	0.971(0.884~ 1.066)	0.950(0.870~ 1.038)
19	0.943(0.794~ 1.119)	0.930(0.772~ 1.119)	0.960(0.807~ 1.141)	0.961(0.876~ 1.040)	0.965(0.880~ 1.059)	0.942(0.863~ 1.027)
20	0.869(0.722~ 1.046)	0.859(0.702~ 1.051)	0.883(0.732~ 1.064)	0.948(0.869~ 1.035)	0.961(0.874~ 1.056)	0.935(0.856~ 1.022)
21	0.809(0.660~ 0.992)	0.801(0.642~ 1.000)	0.821(0.669~ 1.008)	0.946(0.864~ 1.036)	0.959(0.869~ 1.058)	0.932(0.850~ 1.021)
22	0.770(0.619~ 0.958)	0.762(0.601~ 0.967)	0.781(0.627~ 0.973)	0.949(0.863~ 1.043)	0.962(0.869~ 1.066)	0.933(0.848~ 1.027)
23	0.759(0.608~ 0.947)	0.749(0.588~ 0.954)	0.771(0.617~ 0.963)	0.957(0.870~ 1.054)	0.972(0.876~ 1.078)	0.941(0.854~ 1.038)
24	0.786(0.637~ 0.969)	0.772(0.615~ 0.970)	0.803(0.651~ 0.991)	0.975(0.886~ 1.073)	0.990 (0.893~ 1.098)	0.958(0.870~ 1.056)
25	0.871(0.720~ 1.055)	0.848(0.688~ 1.044)	0.899(0.742~ 1.088)	1.004(0.911~ 1.106)	1.020(0.919~ 1.133)	0.986(0.894~ 1.088)
26	1.052(0.850~ 1.304)	1.009(0.799~ 1.274)	1.101(0.888~ 1.366)	1.048(0.938~ 1.172)	1.066(0.945~ 1.203)	1.030(0.920~ 1.152)
27	1.416(1.007~ 1.989)	1.329(0.918~ 1.924)	1.511(1.073~ 2.127)	1.113(0.954~ 1.299)	1.133(0.958~ 1.341)	1.092(0.935~ 1.277)
28	2.166(1.220~ 3.846)	1.978(1.060~ 3.692)	2.377(1.334~ 4.234)	1.205(0.953~ 1.523)	1.229(0.953~ 1.585)	1.182(0.934~ 1.496)

表2

表3

参考文献 15

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变量	日发病例数						气象因素
变量	合计	男性	女性	≤35 岁组	36~64 岁组	≥65 岁组	日平均气温 (℃)	日平均相对湿度(%)	日平均风速 (m/s)
最大值	76	43	34	9	22	60	31.40	93.00	11.20
最小值	0	0	0	0	0	0	-15.40	7.00	0.40
P₁	0	0	0	0	0	0	-10.30	15.00	0.70
P₅	0	0	0	0	0	0	-6.37	20.00	1.10
P₁₀	0	0	0	0	0	0	-4.20	23.00	1.30
P₂₅	0	0	0	0	0	0	2.30	31.00	1.70
P₅₀	0	0	0	0	0	0	14.70	42.00	2.20
P₇₅	2	2	1	0	1	1	22.50	57.00	2.90
P₉₀	7	4	4	1	2	4	26.00	69.00	4.00
P₉₅	16	9	8	1	6	10	27.40	76.00	4.80
P₉₉	40	22	20	3	12	30	29.50	83.73	6.77

日平均气温(℃)	滞后时长(d)	≤35岁组	36~64岁组	≥65岁组
-4	0	1.053(0.412~2.692)	2.244(1.177~4.278)	1.924(0.977~3.791)
	7	0.930(0.685~1.264)	0.779(0.642~0.945)	0.847(0.692~1.036)
	14	1.036(0.773~1.388)	1.257(1.030~1.535)	1.231(0.998~1.520)
	21	0.838(0.619~1.134)	0.741(0.601~0.913)	0.843(0.675~1.052)
	28	1.481(0.632~3.471)	2.833(1.581~5.078)	1.956(1.041~3.675)
26.1	0	0.899(0.621~1.301)	1.118(0.838~1.492)	1.193(0.874~1.628)
	7	0.926(0.820~1.046)	0.984(0.895~1.087)	0.996(0.894~1.110)
	14	0.911(0.808~1.028)	0.999(0.904~1.103)	1.001(0.901~1.112)
	21	0.957(0.853~1.074)	0.995(0.905~1.095)	0.931(0.841~1.030)
	28	1.120(0.824~1.521)	1.110(0.871~1.416)	1.272(0.983~1.645)

基于分布滞后非线性模型研究新疆疏附县气温对肺结核发病的影响

Study on the effect of daily average temperature on the incidence of tuberculosis in Shufu County at Xinjiang, based on a distributed lag nonlinear model

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