Short-term Effect of Ambient Air Pollutants Change on the Risk of Tuberculosis Outpatient Visits: A Time-series Study in Fuyang, China

doi:10.21203/rs.3.rs-789004/v1

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Short-term Effect of Ambient Air Pollutants Change on the Risk of Tuberculosis Outpatient Visits: A Time-series Study in Fuyang, China

https://doi.org/10.21203/rs.3.rs-789004/v1

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published 06 Jan, 2022

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There is growing evidence that air pollution plays a role in TB, but few studies have comprehensively included the six common air pollutants. Our objectives were to investigate the association between short-term exposure to six common air pollutants and the risk of tuberculosis outpatient visits in Fuyang, China, during the period 2015-2020. We combined the two models to explore the effects of exposure to six air pollutants on the risk of tuberculosis outpatient visits, including Poisson generalized linear regression model and distributed lag non-linear model (DLNM). We performed stratified analyses for season, gender and age. We used the lag-specific relative risks and cumulative relative risk obtained by increasing pollutant concentration by per 10 units to evaluate the connection between six air pollutants and TB, PM_2.5(RR=1.0018, 95%CI: 1.0004-1.0032, delay of 12 days) and (RR=1.0169,95%CI: 1.0007-1.0333, lag 0-16 days), SO₂ were 0.9549 (95%CI: 0.9389-0.9712, lag 0 days) and 0.8212 (95%CI: 0.7351-0.9173, 0-20 days lag). Stratified analyses showed that seasonal differences had a greater impact on TB, and male more likely to develop TB than female, older people more likely to develop TB than younger people. Exposure to O₃, CO, PM₁₀, PM_2.5, and NO₂ increases the risk of TB outpatient visits, except SO₂ reduces the risk. Male and elderly people should be protected. We need to pay more attention to the impact of seasonal effects on TB.

Environmental Engineering

Tuberculosis

Risk of outpatient visit

Ambient air pollutants

DLNM

Time-series study

China

Table 1. Summary statistics of air pollutants and active TB case in Fuyang, 2015–2020.
Variables	Number (%)	Mean ± SD			Centiles
			Min	25%	50%	75%	Max	IQR
Atmospheric pollutants (μg / m³)
PM_2.5	/	55.18 ± 38.22	0	29	45	73	276	44
PM₁₀	/	85.89 ± 49.42	0	52	76	110	379	58
SO₂	/	13.33 ± 11.51	2	6	9	18	237	12
CO (mg/m³)	/	0.77 ± 0.34	2	0.5	0.7	0.9	2.9	0.4
NO₂	/	32.09 ± 13.98	9	21	30	40	93	19
O₃	/	92.77 ± 42.95	0	61	88	122	226	61
TB case
Total	21591(100)	9.85 ± 5.78	0	5	9	13	41	8
Male	15207(70.43)	6.94 ± 4.35	0	4	6	9	29	5
Female	6384(29.57)	2.91 ± 2.22	0	1	2	4	15	3
Age < 15 years	201(0.94)	0.09 ± 0.31	0	0	0	0	2	0
Age15-65years	14857(68.8)	6.78 ± 4.22	0	4	6	9	29	5
Age ≥ 65 years	6533(30.26)	2.98 ± 2.38	0	1	3	4	13	3
Warm	10817(50.35)	9.80 ± 5.60	0	6	9	13	33	7
Cold	10774(49.65)	9.90 ± 5.92	0	5	9	13	41	8
Meteorology measure
Mean Temperature (°C)	/	16.19 ± 9.48	-7.8	7.6	17	24.4	33.5	16.8
Relative Humidity (%)	/	75.38 ± 13.52	33	66.5	76.8	85.5	99	19

PM2.5, particulate matter < 2.5 μm in aerodynamic diameter; PM10, particulate matter < 10 μm in aerodynamic diameter; CO, carbon monoxide; O3, ozone; SO2, sulfur dioxide; NO2, nitrogen dioxide; Min = minimum; Max = maximum; IQR = interquartile range; TB, tuberculosis.

Table 2. RR and 95% CI for cumulative association between a 10 μg/m³increase in concentration of ambient air pollutants and TB risk in Fuyang, 2015–2020.

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
A: PM_2.5
All cases	/	/	/	1.027 (1.0051, 1.0493) *	1.027 (1.0051, 1.0494) *	1.025 (1.003, 1.0475) *
Male	/	/	/	1.023 (0.9972, 1.0495)	1.023 (0.9969, 1.494)	1.021 (0.9952, 1.0479)
Female	/	/	/	1.036 (0.9961, 1.0784)	1.037 (0.9964, 1.0791)	1.034 (0.9934, 1.0765)
<15 years old	/	/	/	0.844 (0.6683, 1.0669)	0839 (0.6629, 1.0625)	0.838 (0.6626, 1.06)
15-65 years old	/	/	/	1.023 (0.9966, 1.0496)	1.022 (0.9962, 1.0493)	1.02 (0.9941, 1.0474)
>65 years old	/	/	/	1.042 (1.0023, 1.0841) *	1.043 (1.0025 1.0847) *	1.041 (1.0007, 1.0832) *
Cold season	/	/	/	1.034 (1.0088, 1.0605) *	1.035 (1.009, 1.061) *	1.033 (1.0073, 1.0595) *
Warm season	/	/	/	1.004 (0.9626 1.0473)	1.002 (0.9609 1.0457)	1.000 (0.9585, 1.0436)
B: PM₁₀
All cases	/	/	1.0148 (0.9999, 1.299)	1.0141 (0.9992, 1.0293)	1.0149 (0.9999, 1.0342)	1.0153 (1.0003, 1.031) *
Male	/	/	1.0123 (0.9947, 1.0303)	1.0116 (0.9939, 1.0296)	1.0123 (0.9945, 1.0304)	1.0127 (0.9948, 1.0308)
Female	/	/	1.0209 (0.9934, 1.0491)	1.0202 (0.9926, 1.0486)	1.0212 (0.9934, 1.0497)	1.0216 (0.9938, 1.0502)
<15 years old	/	/	0.9937 (0.8496, 1.1623)	0.9799 (0.8371, 1.147)	0.9754 (0.8326, 1.1425)	0.9603 (0.8194, 1.1254)
15-65 years old	/	/	1.0126 (0.9947, 1.0308)	1.0111 (0.9932, 1.0294)	1.0117 (0.9936, 1.03)	1.012 (0.994, 1.0305)
>65 years old	/	/	1.0226 (0.9956, 1.0504)	1.0241 (0.9969, 1.0521)	1.0253 (0.99, 1.0533)	1.026 (0.9985, 1.0541)
Cold season	/	/	1.0188 (1.0014, 1.036) *	1.0179 (1.0004, 1.0356) *	1.0191 (1.0015, 1.0369) *	1.0197 (1.002, 1.0376)*
Warm season	/	/	1.0019 (0.9735, 1.0311)	1.0022 (0.9737, 1.0315)	1.0014 (0.9729, 1.0308)	1.0018 (0.9732, 1.0313)
C: CO
All cases	/	1.0062(10222,1.1121) *	/	1.0639(1.0197,1.1099) *	1.066(1.0215,1.1124) *	1.0615 (1.017, 1.108) *
Male	/	1.085(1.0322,1.1412) *	/	1.0831(1.0298,1.1391) *	1.0856(1.0319,1.1421) *	1.0811(1.0273,1.137) *
Female	/	1.0218 (0.9453, 1.1045)	/	1.0192 (0.9425, 1.1021)	1.0191 (0.9419, 1.1027)	1.0152 (0.9379, 1.0989)
<15 years old	/	1.1191 (0.6868, 1.8233)	/	1.0709 (0.6545, 1.7522)	1.0638 (0.6479, 1.7469)	1.0471 (0.6383, 1.7176)
15-65 years old	/	1.073(1.0199, 1.1288) *	/	1.0685(1.0155,1.1244) *	1.0674(1.014,1.1237) *	1.0625 (1.009,1.1188) *
>65 years old	/	1.049 (0.9713, 1.1329)	/	1.0523 (0.9741, 1.1368)	1.0578 (0.9788, 1.1432)	1.0551 (0.9759, 1.1407)
Cold season	/	1.086(1.0344,1.1405) *	/	1.0828(1.0378,1.1372) *	1.0844 (1.032,1.1394) *	1.0804 (1.028,1.1355) *
Warm season	/	1.0047 (0.924, 1.0926)	/	1.007 (0.9256, 1.0956)	1.0058 (0.9242, 1.0947)	1.0018 (0.9201, 1.0907)
D: O₃
All cases	1.0084 (0.9993, 1.0176)	1.0084 (0.9991, 1.0177)	1.0084 (0.992, 1.0177)	/	1.0103 (1.001, 1.0197) *	1.011 (1.0017, 1.005) *
Male	1.0081 (0.9973, 1.0191)	1.0074 (0.9964, 1.0185)	1.007(1.0006,1.0166) *	/	1.0049 (1.0003, 1.0096) *	1.005(1.0006,1.0099) *
Female	1.0092 (0.9926, 1.026)	1.0108 (0.9939, 1.0279)	1.014(1.0003,1.0286) *	/	1.0154 (1.0013, 1.0297) *	1.0163 (1.002,1.0308) *
<15 years old	1.03 (0.9375, 1.1317)	1.0202 (0.9269, 1.1229)	1.0188 (0.9255, 1.1215)	/	1.0243 (0.9307, 1.1273)	1.0248 (0.9304, 1.1287)
15-65 years old	1.013(1.0024,1.0245) *	1.0131 (1.002,1.0244) *	1.013(1.0019,1.0244) *	/	1.0143 (1.0031, 1.0257) *	1.015 (1.0037,1.0264) *
>65 years old	0.9962 (0.98, 1.0128)	0.9967 (0.9802, 1.0135)	0.9971 (0.9805, 1.0139)	/	1.0001 (0.9833, 1.0171)	1.0009 (0.9841, 1.018)
Cold season	1.0124 (1.0018,1.023) *	1.0121 (1.0014,1.023) *	1.0121 (1.0014,1.023) *	/	1.0144 (1.0036, 1.0254) *	1.015(1.0043,1.0262) *
Warm season	0.9971 (0.9795, 1.015)	0.9975 (0.9797, 1.0157)	0.9978 (0.9799, 1.016)	/	0.9983 (0.9802, 1.0166)	0.9992 (0.981, 1.0176)
E: SO₂
All cases	0.820(0.7341,0.9631) *	0.8196 (0.7333, 0.9161)	0.816 (0.7298, 0.9124)	0.8163 (0.7299, 0.9129)	/	0.812(0.7265,0.9093) *
Male	0.809(0.7093,0.9235) *	0.8042 (0.7044, 0.9182)	0.802 (07022, 0.9161)	0.8016 (0.7016, 0.9158)	/	0.797 (0.6973,0.9109) *
Female	0.8375 (0.9825, 1.0282)	08481 (0.6904, 1.0417)	0.84 (0.6832, 1.0328)	0.8419 (0.6845, 1.0355)	/	0.841 (0.6834, 1.035)
<15 years old	0.476 (0.0773, 2.9299)	0.4576 (0.0717, 2.9214)	0.4599 (0.0708, 2.9876)	0.4479 (0.0687, 2.9216)	/	0.41 (1.2499, 2.688) *
15-65 years old	0.823(0.7206,0.9417) *	0.824(0.7206,0.9425) *	0.8188 (0.7156,0.937) *	0.8202 (0.7167, 0.9388) *	/	0.816(0.7129,0.9346) *
>65 years old	0.812(0.6647,0.9923) *	0.811(0.6636,0.9922) *	0.812(0.6641,0.9942) *	0.8118 (0.6633, 0.9937) *	/	0.7692 (0.96, 0.8742) *
Cold season	0.7806 (0.688,0.8857) *	0.779 (0.6863,0.8844) *	0.7746 (0.682,0.8798) *	0.7743 (0.6815, 0.8796) *	/	0.769(0.6768,0.8742) *
Warm season	0.9574 (0.7584, 1.2087)	0.9601 (0.7599, 1.213)	0.9574 (0.7572, 1.2105)	0.9605 (0.7593, 1.215)	/	0.9609 (0.7591, 1.2162)
F: NO₂
All cases	1.041(1.0184,1.0643) *	1.043(1.0202,1.0669) *	1.041(1.0181,1.0661) *	1.0413 (1.0174, 1.0657) *	1.0464 (1.0219, 1.0714) *	/
Male	1.043(1.0167,1.0715) *	1.047(1.0198,1.0756) *	1.046(1.0178,1.0753) *	1.0456 (1.0171, 1.075) *	1.051 (1.0218, 1.081) *	/
Female	1.0344 (0.9931, 1.0773)	1.0334 (0.9916, 1.077)	1.0314 (0.9884, 1.0762)	1.0307 (0.9875, 1.0757)	1.0341 (0.99, 1.0803)	/
<15 years old	0.861 (0.6731, 1.1014)	0.8953 (0.6967, 1.1505)	0.8639 (0.67, 1.114)	0.8514 (0.6604, 1.0977)	0.8513 (0.6549, 1.1067)	/
15-65 years old	1.047(1.0148,1.0757) *	1.049(1.0215,1.0781) *	1.047(1.0185,1.0768) *	1.0458 (1.017, 1.0755) *	1.0782 (1.0184, 1.0782) *	/
>65 years old	1.0322 (0.9918, 1.0743)	1.0344 (0.9933, 1.0772)	1.0355 (0.9931, 1.0798)	1.0377 (0.9948, 1.0824)	1.0474 (1.0035, 1.0934) *	/
Cold season	1.043 (1.0168,1.0699) *	1.0457 (1.0191,1.073) *	1.042(1.0143,1.0697) *	1.0403 (1.0129, 10686) *	1.0444 (1.0162, 1.0734) *	/
Warm season	1.0347 (0.9901, 1.0813)	1.0348 (0.9894, 1.0824)	1.0405 (0.9935, 1.0898)	1.0421 (0.9946, 1.0919)	1.045 (0.9967, 1.0957)	/

RR: relative risk; CI: confidence interval; TB, tuberculosis; PM2.5, particulate matter < 2.5 μm in aerodynamic diameter; PM10, particulate matter < 10 μm in aerodynamic diameter; CO, carbon monoxide; O3, ozone; SO2, sulfur dioxide; NO2, nitrogen dioxide.

*: P < 0.05.

A The model is based on a maximum lag of 24 days; B The model is based on a maximum lag of 24 days; C The model is based on a maximum lag of 21 days; D The model is based on a maximum lag of 21 days; E The model is based on a maximum lag of 20 days; F The model is based on a maximum lag of 4 days.

model 1 adjusted with PM2.5 based on the original model, model 2 adjusted with PM2.5 and PM10 based on model 1; model 3 adjusted with PM2.5, PM10 and CO based on model 1; model 4 adjusted with PM2.5, PM10, CO and O3 based on model 1; model 5 adjusted with PM2.5, PM10, CO, O3 and SO2based on model 1; model 6 adjusted with PM2.5, PM10, CO, O3 SO2 and NO2 based on model 1.

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Short-term Effect of Ambient Air Pollutants Change on the Risk of Tuberculosis Outpatient Visits: A Time-series Study in Fuyang, China

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