Supplementary Tables Table S1. the Number Observation Days With

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Supplementary tables

Table S1. The number observation days with missing values of air pollution in the

11 cities in Shanxi Province, 2017-2019

City PM2.5 SO2 NO2 O3

Datong 6 6 6 7

Jincheng 5 3 3 7

Jinzhong 2 2 2 2

Linfen 10 10 10 12

Lvliang 0 0 0 0

Shuozhou 2 23 23 23

Taiyuan 23 23 23 23

Xinzhou 0 0 0 0

Yangquan 1 1 1 1

Yuncheng 1 1 1 1

Changzhi 2 2 2 2

Table S2. Spearman correlation between air pollutants and meteorological factors

in Shanxi, China, 2017-2019

PM2.5 SO2 NO2 O3 Temperature humidity

PM2.5 1.00 0.59* 0.59* -0.29* -0.31* 0.06*

SO2 1.00 0.65* -0.48* -0.55* -0.37*

NO2 1.00 -0.55* -0.34* -0.16*

Cao D, et al. Thorax 2021;0:1–6. doi: 10.1136/thoraxjnl-2020-215838 BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) Thorax

O3 1.00 0.78* 0.12*

Temperature 1.00 0.33*

Humidity 1.00

*p<0.05

Table S3. The estimated avoidable number of hospital admissions for respiratory diseases in the 11 cities in Shanxi, China

China’s standard (75 μg/m3) WHO’s guideline (25 μg/m3)

Overall Population attributable Population attributable City Avoidable value Avoidable value (in thousand) fraction fraction (95% CI) (95% CI) (%, 95% CI) (%, 95% CI)

Datong 88 196 (60, 346) 0.22 (0.07, 0.39) 1642 (513, 2827) 1.87 (0.59, 3.23)

Jincheng 57 160 (-96, 426) 0.28 (-0.17, 0.75) 514 (-309, 1371) 0.91 (-0.55, 2.42)

Jinzhong 93 379 (-204, 984) 0.41 (-0.22, 1.06) 975 (-525, 2532) 1.05 (-0.56, 2.73)

Linfen 75 688 (371, 1019) 0.92 (0.50, 1.36) 2387 (1289, 3527) 3.19 (1.72, 4.71)

Lvliang 67 110 (-228, 463) 0.17 (-0.34, 0.70) 398 (-824, 1675) 0.60 (-1.24, 2.52)

Shuozhou 29 204 (21, 399) 0.71 (0.07, 1.38) 561 (58, 1092) 1.94 (0.20, 3.77)

Taiyuan 75 225 (-206, 675) 0.30 (-0.27, 0.89) 614 (-565, 1842) 0.81 (-0.75, 2.44)

Xinzhou 37 181 (-40, 415) 0.48 (-0.11, 1.11) 612 (-136, 1405) 1.64 (-0.36, 3.76)

Yangquan 33 70 (-125, 274) 0.21 (-0.37, 0.82) 271 (-483, 1061) 0.81 (-1.45, 3.19)

Yuncheng 49 511 (179, 859) 1.04 (0.37, 1.75) 644 (226, 1080) 1.31 (0.46, 2.20)

Changzhi 80 93 (-204, 400) 0.12 (-0.26, 0.50) 420 (-923, 1811) 0.53 (-1.16, 2.27)

Table S4. The estimated avoidable hospital cost for respiratory diseases in the 11 cities in Shanxi, China

China’s standard (75 μg/m3) WHO’s guideline (25 μg/m3)

Overall Population attributable Avoidable value Population attributable City Avoidable value (million RMB) fraction (million RMB, 95% fraction (million RMB, 95% CI) (%, 95% CI) CI) (%, 95% CI)

Datong 696 3.11 (1.52, 4.70) 0.45 (0.22, 0.68) 24.82 (12.15, 37.48) 3.57 (1.75, 5.39)

Jincheng 320 1.39 (-1.27, 4.04) 0.43 (-0.40, 1.26) 5.30 (-4.83, 15.44) 1.66 (-1.51, 4.82)

Jinzhong 559 4.46 (0.53, 8.38) 0.80 (0.10, 1.50) 21.07 (2.52, 39.62) 3.77 (0.45, 7.08)

Linfen 489 9.75 (5.06, 14.43) 2.00 (1.04, 2.95) 29.40 (15.27, 43.53) 6.02 (3.13, 8.91)

Lvliang 414 3.62 (0.49, 6.75) 0.87 (0.12, 1.63) 17.86 (2.40, 33.32) 4.31 (0.58, 8.04)

Shuozhou 154 0.27 (-0.59, 1.13) 0.18 (-0.38, 0.74) 1.48 (-3.24, 6.20) 0.96 (-2.11, 4.03)

Taiyuan 740 6.17 (0.50, 11.84) 0.83 (0.07, 1.60) 24.24 (1.95, 46.52) 3.28 (0.26, 6.29)

Xinzhou 306 1.93 (-0.79, 4.64) 0.63 (-0.26, 1.51) 8.04 (-3.28, 19.35) 2.63 (-1.07, 6.32)

Yangquan 280 -0.04 (-2.75, 2.67) -0.01 (-0.98, 0.95) -0.17 (-10.90, 10.57) -0.06 (-3.89, 3.78)

Yuncheng 246 4.92 (2.26, 7.58) 2.00 (0.92, 3.08) 15.28 (7.01, 23.55) 6.21 (2.85, 9.57)

Changzhi 472 1.09 (-1.68, 3.86) 0.23 (-0.36, 0.82) 5.23 (-8.07, 18.53) 1.11 (-1.71, 3.93)

Table S5. The estimated avoidable length of hospital stays (LOS) for respiratory diseases in the 11 cities in Shanxi, China

China’s standard (75 μg/m3) WHO’s guideline (25 μg/m3) Overall Avoidable value Population attributable Avoidable value Population attributable City (thousand (thousand days, fraction (thousand days, fraction days) 95% CI) (%, 95% CI) 95% CI) (%, 95% CI)

Datong 885 2.53 (0.67, 4.40) 0.29 (0.08, 0.50) 20.20 (5.35, 35.05) 2.28 (0.60, 3.96)

Jincheng 438 2.84 (0.16, 5.52) 0.65 (0.04, 1.26) 10.85 (0.63, 21.08) 2.48 (0.14, 4.81)

Jinzhong 702 3.81 (0.21, 7.41) 0.54 (0.03, 1.06) 18.00 (0.98, 35.02) 2.56 (0.14, 4.99)

Linfen 592 10.62 (6.30, 14.94) 1.80 (1.06, 2.53) 32.03 (18.99, 45.07) 5.41 (3.21, 7.62)

Lvliang 503 1.37 (-1.54, 4.27) 0.27 (-0.31, 0.85) 6.75 (-7.58, 21.08) 1.34 (-1.51, 4.19)

Shuozhou 239 0.98 (-0.16, 2.13) 0.41 (-0.07, 0.89) 5.40 (-0.88, 11.68) 2.26 (-0.37, 4.89)

Taiyuan 680 4.01 (-0.44, 8.46) 0.59 (-0.07, 1.25) 15.75 (-1.74, 33.24) 2.32 (-0.26, 4.89)

Xinzhou 316 2.67 (0.45, 4.89) 0.85 (0.14, 1.55) 11.16 (1.89, 20.43) 3.53 (0.60, 6.46)

Yangquan 310 1.16 (-0.94, 3.26) 0.37 (-0.30, 1.05) 4.59 (-3.73, 12.90) 1.48 (-1.20, 4.16)

Yuncheng 392 9.93 (5.87, 13.99) 2.53 (1.50, 3.56) 30.88 (18.26, 43.49) 7.87 (4.65, 11.08)

Changzhi 611 0.70 (-2.30, 3.70) 0.11 (-0.38, 0.61) 3.35 (-11.05, 17.74) 0.55 (-1.81, 2.90)

Table S6. The estimated avoidable hospital cost and hospital stays for each respiratory patient in the 11 Chinese cities

Hospital cost Avoidable cost (RMB, 95% CI) Avoidable hospital stays (days, 95% CI) Hospital stays City per case China’s standard WHO’s guideline China’s standard WHO’s guideline per case (thousand RMB) (75 μg/m3) (25 μg/m3) (75 μg/m3) (25 μg/m3)

Datong 7.94 35.52 (17.39, 283.19 (138.65, 10.10 0.03 (0.01, 0.05) 0.23 (0.06, 0.40)

53.65) 427.72)

Jincheng 5.65 24.53 (-22.37, 93.63 (-85.38, 7.74 0.05 (0.00, 0.10) 0.19 (0.01, 0.37)

71.43) 272.64)

Jinzhong 6.02 47.99 (5.73, 90.25) 226.80 (27.10, 7.56 0.04 (0.00, 0.08) 0.19 (0.01, 0.38)

426.50)

Linfen 6.53 130.31 (67.69, 393.01 (204.15, 7.91 0.14 (0.08, 0.20) 0.43 (0.25, 0.60)

192.93) 581.86)

Lvliang 6.23 54.42 (7.32, 268.51 (36.09, 7.56 0.02 (-0.02, 0.06) 0.10 (-0.11, 0.32)

101.52) 500.92)

Shuozhou 5.31 9.33 (-20.38, 51.21 (-111.87, 8.26 0.03 (-0.01, 0.07) 0.19 (-0.03, 0.40)

39.05) 214.29)

Taiyuan 9.80 81.75 (6.59, 321.10 (25.88, 9.00 0.05 (-0.01, 0.11) 0.21 (-0.02, 0.44)

156.91) 616.32)

Xinzhou 8.19 51.50 (-21.00, 214.96 (-87.67, 8.46 0.07 (0.01, 0.13) 0.30 (0.05, 0.55)

124.01) 517.59)

Yangquan 8.42 -1.26 (-82.77, -4.98 (-327.98, 9.32 0.03 (-0.03, 0.10) 0.14 (-0.11, 0.39)

80.26) 318.02)

Yuncheng 5.02 100.29 (46.03, 311.84 (143.10, 8.01 0.20 (0.12, 0.29) 0.63 (0.37, 0.89)

154.56) 480.57)

Changzhi 5.93 13.70 (-21.14, 65.69 (-101.37, 7.68 0.01 (-0.03, 0.05) 0.04 (-0.14, 0.22)

48.54) 232.74)

Table S7. Sensitivity analyses for the association of daily PM2.5 and hospital

admission, hospital cost and length of hospital stay at lag03 in two-pollutant

models.

Hospitalization Daily respiratory Length of Hospital Models cost (thousand hospitalization stay (days) RMB)

Two-pollutant model

+SO2 0.47 (0.29, 0.65) 3.47 (1.86, 5.09) 3.87 (2.43, 5.32)

+NO2 0.47 (0.33, 0.61) 2.18(0.79, 3.56) 2.35 (1.08, 3.62)

+O3 0.53 (0.33, 0.73) 3.83 (1.90, 5.76) 4.18 (2.56, 5.81)

Note: Percentage change for daily hospital admission count and absolute change for

hospital cost and hospital stays.

Table S8. Sensitivity analyses for the association of daily PM2.5 and hospital

admission, hospital cost and length of hospital stay by changing the degree of

freedom in the smoothing functions

Daily respiratory Hospitalization cost Length of Hospital Models hospitalization (thousand RMB) stay (days)

Varying df for temporal trend

df=5 0.57 (0.34, 0.81) 4.22 (2.35, 6.10) 4.31 (2.48, 6.14)

df=7 0.38 (0.20, 0.57) 3.90 (1.92, 5.88) 4.32 (2.51, 6.12)

df=8 0.39 (0.21, 0.56) 3.19 (1.38, 5.00) 3.27 (1.99, 4.55)

Varying df for temperature

df=5 0.56 (0.35, 0.78) 3.91 (1.94, 5.88) 4.31 (2.52, 6.09)

df=7 0.57 (0.35, 0.79) 2.30 (0.90, 3.70) 2.67 (1.40, 3.95)

df=8 0.57 (0.35, 0.79) 3.91 (1.91, 5.90) 4.36 (2.53, 6.19)

Varying df for relative humidity

df=4 0.56 (0.37, 0.76) 3.87 (1.89, 5.85) 4.27 (2.50, 6.04)

df=5 0.56 (0.37, 0.75) 3.82 (1.89, 5.75) 4.31 (2.56, 6.07)

Note: Percentage change for daily hospital admission count and absolute change for

hospital cost and hospital stays.

Table S9. Sensitivity analyses for the association of daily PM2.5 and hospital

admission, hospital cost and length of hospital stay by controlling for the

longitude and latitude of the cities in the smoothing functions

Outcome Estimate 95% CI

Hospital admissions 0.54 0.32, 0.76

Hospital cost (thousand RMB) 3.11 1.74, 4.49

Hospital stays (days) 4.47 3.10, 5.85

Note: Percentage change for daily hospital admission count and absolute change for

hospital cost and hospital stays.

Table S10. Avoidable and attributable fraction of respiratory hospitalization,

hospital cost and length of hospital stay by improving ambient PM2.5 to the

background concentration (15 μg/m3) level in Shanxi Province, 2017-2019

Avoidable value Population attributable fraction Variable (95% CI) (%, 95% CI)

Hospitalization 12.08 (7.53, 16.70) 1.77 (1.10, 2.45) count (thousand)

Hospital cost 172 (84, 260) 3.68 (1.80, 5.55) (million RMB)

Hospital stay 189 (115, 263) 3.34(2.03, 4.65) (thousand days)

Based on the effects of moving averaged concentration of lag 0 to lag 3 (lag03) of

daily PM2.5;

Abbreviations: PM2.5=particulate matter with an aerodynamic diameter smaller than or

equal to 2.5 μm;

Cao D, et al. Thorax 2021;0:1–6. doi: 10.1136/thoraxjnl-2020-215838