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

doi:10.19982/j.issn.1000-6621.20220488

Abstract

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

CLC Number:

R521
R825.2

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

Figures/Tables 5

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)

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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)