Joint associations of short-term exposure to ambient air pollutants

Supplemental Digital Content

Joint associations of short-term exposure to ambient air pollutants

with hospital admission of ischemic stroke

Contents

1.1 Data collection and management...1

eFigure 1. The process of participant selection...3

eFigure 2. Number of air pollution monitoring station in selected Chinese cities...4

eFigure 3. Number of weather monitoring station in selected Chinese cities...5

eFigure 4. Selection of key variables closely associated with hospital admission of IS....6

eFigure 5. The correlations among air pollutants and meteorological factors...7

eFigure 6. The ERs (%, 95%CI) of IS hospital admissions for increase in each IQR of air pollutants (lag01-day) estimated by single-pollutant and multi-pollutant models...8

eTable 1. The Variance Inflation Score (VIF) of air pollutants and meteorological factors in the conditional Logistic regression model in all participants...9

eTable 2. The exposure-response associations (ER, 95%CI) between exposures to air pollutants (lag01-days) and hospital admission of IS patients based on single-pollutant model ...10

eTable 3. The ERs (%, 95%CI) of IS hospital admissions for increase in each IQR of air pollutants (lag01-day) estimated by single-pollutant models...11

eTable 4. The exposure-response associations (ER, 95%CI) between exposures to air pollutants (lag01-day)) and hospital admission of IS patients based on multi-pollutant model ...12

eTable 5. The ERs (%, 95%CI) of IS hospital admissions for increase in each IQR of air pollutants (lag01-day) estimated by multi-pollutant models...13

eTable 6. The AFs of IS hospital admission attributable to air pollutants based on multi- pollutant model...14

eTable 7. The impacts of lag days on the ERs (%, 95%CI) between exposures to air pollutants and hospital admission of IS patients...15

eTable 8. Comparisons of air pollutants estimated by Kriging interpolation between the case days and control days...16

eTable 9. Comparisons of air pollutants and meteorological factors between Northern China and Southern China...17

eTable 10. Comparisons of air pollutants and meteorological factors between warm and cold seasons...18

References...19

1.1 Data collection and management

All data in the CSCA program were collected using a web-based patient data collection and management tool, which was developed by the Medicine Innovation Research Center, Beijing, China. This tool abstracted collected data via chart review, coded, deidentified and transmitted in a secure manner to maintain patient confidentiality compliant with national privacy standards. There were two main functions in the tool to control the quality of collected data.

¹

The first function was to collect concurrent data from different sources. This tool is characterized by predefined logic features, range checks, and user alerts to identify a potentially invalid format or value entries and to optimize data quality at the time of entry. The required items were structured so that valid data must be entered before the data can be saved as a complete record and submitted to the database. Range checks were used to find inconsistent or out-of-range data and prompted users to correct or review data entries that were outside a predefined range. To create an audit trail for data entered or changed, all hospital personnel using the tool received individual passwords. Training in the use of the tool was provided online and onsite for all users.

The second function of the tool is to analyze and provide data feedback. The China National Clinical Research Center for Neurological Diseases (NCRCND) serves as the data analysis center and has an agreement to analyze the aggregate deidentified data for care quality feedback and research purposes. All hospitals using the tool received an independent account and password to view the benchmark for adherence to evidence-based performance measures and to compare their own

1

hospital’s current performance to past levels and the concurrent standards of other

regional hospitals within their purview.

eFigure 1. The process of participant selection

3

eFigure 2. Number of air pollution monitoring station in selected Chinese cities

The city in this map represents an administrative region which includes both urban

and rural areas.

eFigure 3. Number of weather monitoring station in selected Chinese cities The city in this map represents an administrative region which includes both urban and rural areas.

5

A

B

C

eFigure 4. Selection of key variables closely associated with hospital admission of IS.

Seven variables including PM

2.5

, MDA8 O

3

, NO

2

, SO

2

, CO, TM and RH were included as potential variables.

Panel A: LASSO Cox regression model (λ=1.412×10

^-5

, ln(λ)=-11.17).

Panel B: Ridge Cox regression model (λ=2.904×10

^-5

, ln(λ)=-10.87).

Panel C: Elastic Net Cox regression model (α=0.5, λ=5.946×10

^-5

, ln(λ)=-9.73).

7

eFigure 5. The correlations among air pollutants and meteorological factors

eFigure 6. The ERs (%, 95%CI) of IS hospital admissions for increase in each IQR of air pollutants (lag01-day) estimated by single- pollutant and multi-pollutant models

All ERs were adjusted for ambient temperature, relative humidity, and holiday.

9

eTable 1. The Variance Inflation Score (VIF) of air pollutants and meteorological factors in the conditional Logistic regression model in all

participants

Variables VIF

PM

2.5

2.83

MDA8 O

3

2.50

NO

2

2.26

SO

2

1.85

CO 2.91

TM 3.21

RH 2.18

eTable 2. The exposure-response associations (ER, 95%CI) between exposures to air pollutants (lag01-days) and hospital admission of IS patients based on single-

pollutant model

ER (%, 95%CI)

PM2.5 MDA8 O3 NO2 SO2 CO

Nationwide 0.38 (0.29, 0.47) 0.29 (0.18, 0.40) 1.15 (0.92, 1.39) 0.82 (0.53, 1.11) 3.47 (2.70, 4.26) Gender

Males 0.40 (0.28, 0.52) 0.33 (0.19, 0.47) 1.20 (0.89, 1.50) 0.94 (0.58, 1.30) 3.66 (2.67, 4.65) Females 0.35 (0.20, 0.51) 0.23 (0.04, 0.41) 1.08 (0.70, 1.47) 0.60 (0.13, 1.08) 3.18 (1.91, 4.46)

P for heterogeneity 0.617 0.398 0.632 0.264 0.560

Age (years)

<65 0.35 (0.21, 0.49) 0.26 (0.09, 0.43) 1.10 (0.74, 1.46) 0.72 (0.31, 1.13) 3.03 (1.91, 4.17)

≥65 0.41 (0.28, 0.53) 0.31 (0.17, 0.46) 1.19 (0.88, 1.51) 0.92 (0.52, 1.32) 3.87 (2.79, 4.95)

P for heterogeneity 0.531 0.661 0.712 0.494 0.292

Season

Warm 0.36 (0.12, 0.61) 0.27 (0.14, 0.41) 1.06 (0.58, 1.53) 0.97 (0.21, 1.74) 4.28 (2.43, 6.16) Cold 0.36 (0.25, 0.46) 0.35 (0.15, 0.54) 1.13 (0.85, 1.41) 0.78 (0.47, 1.09) 3.16 (2.30, 4.03)

P for heterogeneity 1.000 0.508 0.804 0.652 0.284

Region

Southern China 0.32 (0.11, 0.54) 0.13 (-0.03, 0.29) 0.95 (0.56, 1.34) 1.28 (0.39, 2.18) 5.99 (3.71, 8.32) Northern China 0.40 (0.30, 0.51) 0.51 (0.35, 0.67) 1.27 (0.97, 1.58) 0.76 (0.45, 1.06) 3.20 (2.37, 4.04)

P for heterogeneity 0.512 0.001 0.205 0.280 0.024

The ERs for PM

2.5

, MDA8 O

3

, NO

2

and SO

2

were for each 10μg/m

³

increase in their concentrations, and the ERs for CO were for each 1mg/m

³

increase in the concentration.

All ERs were adjusted for ambient temperature, relative humidity, and holiday.

11

eTable 3. The ERs (%, 95%CI) of IS hospital admissions for increase in each IQR of air pollutants (lag01-day) estimated by single-pollutant models

ER (%, 95%CI)

PM2.5 MDA8 O3 NO2 SO2 CO

Nationwide 1.30 (0.99, 1.61) 1.84 (1.14, 2.55) 2.64 (2.10, 3.19) 1.01 (0.65, 1.37) 0.16 (0.13, 0.20) Gender

Males 1.36 (0.95, 1.77) 2.11 (1.21, 3.02) 2.74 (2.04, 3.45) 1.16 (0.72, 1.60) 0.17 (0.13, 0.22) Females 1.22 (0.68, 1.76) 1.47 (0.29, 2.67) 2.51 (1.61, 3.41) 0.74 (0.15, 1.32) 0.15 (0.09, 0.21)

P for heterogeneity 0.617 0.398 0.632 0.264 0.560

Age (years)

<65 1.21 (0.73, 1.70) 1.66 (0.57, 2.76) 2.54 (1.71, 3.38) 0.93 (0.40, 1.46) 0.15 (0.09, 0.20)

≥65 1.39 (0.97, 1.82) 1.97 (1.04, 2.90) 2.73 (2.00, 3.46) 1.09 (0.62, 1.56) 0.18 (0.13, 0.23)

P for heterogeneity 0.531 0.661 0.712 0.494 0.292

Season

Warm 0.78 (0.25, 1.31) 1.80 (0.90, 2.70) 1.83 (1.01, 2.65) 0.80 (0.17, 1.42) 0.15 (0.09, 0.21) Cold 1.67 (1.18, 2.17) 1.67 (0.74, 2.62) 2.95 (2.22, 3.69) 1.31 (0.79, 1.83) 0.18 (0.13, 0.23)

P for heterogeneity 1.000 0.508 0.804 0.652 0.284

Region

Southern China 0.84 (0.28, 1.41) 0.70 (-0.16, 1.56) 1.91 (1.13, 2.70) 0.92 (0.28, 1.57) 0.19 (0.12, 0.26) Northern China 1.63 (1.20, 2.06) 3.73 (2.55, 4.93) 3.08 (2.33, 3.83) 1.34 (0.80, 1.88) 0.20 (0.15, 0.26)

P for heterogeneity 0.512 0.001 0.205 0.280 0.024

All ERs were adjusted for ambient temperature, relative humidity, and holiday.

eTable 4. The exposure-response associations (ER, 95%CI) between exposures to air pollutants (lag01-day)) and hospital admission of IS patients based on multi-

pollutant model

ER (%, 95%CI)

PM2.5 MDA8 O3 NO2 SO2 CO

Nationwide 0.01 (-0.13,0.15) 0.25 (0.14, 0.36) 0.74 (0.41,1.07) 0.04 (-0.30,0.38) 1.77 (0.59, 2.96) Gender

Males -0.01 (-0.18, 0.17) 0.29 (0.15, 0.43) 0.72 (0.30, 1.15) 0.18 (-0.24, 0.61) 1.90 (0.42, 3.41) Females 0.05 (-0.18, 0.27) 0.18 (0.00, 0.37) 0.76 (0.22, 1.31) -0.22 (-0.78, 0.35) 1.55 (-0.37, 3.51)

P for heterogeneity

0.704 0.373 0.912 0.268 0.778

Age (years)

<65 0.02 (-0.18, 0.23) 0.22 (0.05, 0.39) 0.75 (0.25, 1.26) 0.02 (-0.47, 0.51) 1.37 (-0.33, 3.10)

≥65 0.01 (-0.18, 0.20) 0.27 (0.12, 0.42) 0.72 (0.28, 1.16) 0.07 (-0.41, 0.54) 2.13 (0.50, 3.78)

P for heterogeneity

0.919 0.665 0.920 0.885 0.532

Season

Warm -0.12 (-0.42, 0.19) 0.20 (0.05, 0.35) 0.64 (0.03, 1.25) -0.15 (-1.04, 0.75) 2.88 (0.49, 5.33) Cold 0.01 (-0.15, 0.17) 0.41 (0.21, 0.61) 0.77 (0.37, 1.18) 0.11 (-0.26, 0.48) 1.57 (0.20, 2.96)

P for heterogeneity

0.494 0.103 0.713 0.608 0.355

Region

Southern China -0.22 (-0.52, 0.08) 0.09 (-0.08,0.25) 0.62 (0.09,1.15) 0.17 (-0.85,1.21) 5.20 (2.12, 8.37) Northern China 0.08 (-0.08,0.23) 0.49 (0.32, 0.65) 0.78 (0.33,1.24) 0.03 (-0.33,0.40) 1.24 (-0.08,2.57)

P for heterogeneity 0.088 0.001 0.644 0.802 0.020

The ERs for PM

2.5

, MDA8 O

3

, NO

2

and SO

2

were for each 10μg/m

³

increase in their concentrations, and the ERs for CO were for each 1mg/m

³

increase in the concentration.

All ERs were adjusted for ambient temperature, relative humidity, and holiday.

13

eTable 5. The ERs (%, 95%CI) of IS hospital admissions for increase in each IQR of air pollutants (lag01-day) estimated by multi-pollutant models

ER (%, 95%CI)

PM2.5 MDA8 O3 NO2 SO2 CO

Nationwide 0.03 (-0.44, 0.51) 1.59 (0.89, 2.29) 1.70 (0.94, 2.46) 0.05 (-0.37, 0.47) 0.08 (0.03, 0.14) Gender

Males -0.03 (-0.62, 0.56) 1.86 (0.96, 2.76) 1.64 (0.67, 2.62) 0.22 (-0.30, 0.75) 0.09 (0.02, 0.16) Females 0.17 (-0.60, 0.96) 1.15 (-0.03, 2.34) 1.76 (0.50, 3.04) -0.27 (-0.96, 0.43) 0.07 (-0.02, 0.17)

P for heterogeneity 0.704 0.373 0.912 0.268 0.778

Age (years)

<65 0.07 (-0.64, 0.78) 1.41 (0.32, 2.50) 1.73 (0.57, 2.90) 0.03 (-0.61, 0.66) 0.07 (-0.02, 0.15)

≥65 0.03 (-0.61, 0.68) 1.71 (0.76, 2.68) 1.65 (0.64, 2.66) 0.08 (-0.48, 0.65) 0.10 (0.02, 0.17)

P for heterogeneity 0.919 0.665 0.920 0.885 0.532

Season

Warm -0.26 (-0.91, 0.40) 1.33 (0.33, 2.33) 1.10 (0.05, 2.16) -0.12 (-0.86, 0.61) 0.10 (0.02, 0.19) Cold 0.05 (-0.69, 0.79) 1.96 (1.00, 2.93) 2.01 (0.95, 3.07) 0.18 (-0.43, 0.81) 0.09 (0.01, 0.17)

P for heterogeneity 0.494 0.103 0.713 0.608 0.355

Region

Southern China -0.57 (-1.35, 0.21) 0.48 (-0.40, 1.37) 1.24 (0.18, 2.32) 0.12 (-0.62, 0.87) 0.17 (0.07, 0.26) Northern China 0.32 (-0.30, 0.96) 3.58 (2.36, 4.81) 1.88 (0.78, 2.99) 0.05 (-0.59, 0.70) 0.08 (0.00, 0.16)

P for heterogeneity 0.088 0.001 0.644 0.802 0.020

All ERs were adjusted for ambient temperature, relative humidity, and holiday.

eTable 6. The AFs of IS hospital admission attributable to air pollutants based on multi-pollutant model

ER (%, 95%CI)

PM2.5 MDA8 O3 NO2 SO2 CO

Nationwide

0 (-1,1) 2 (1,4) 3 (1,4) 0 (-1,1) 2 (1,3)

Gender

Males

0 (-1, 1) 3 (1, 4) 3 (1, 4) 0 (0, 1) 2 (0, 3)

Females

0 (-1, 1) 2 (0, 4) 3 (1, 2) 0 (-1, 1) 2 (0, 3)

P for heterogeneity 0.694 0.363 0.905 0.267 0.778

Age (years)

<65

0 (-1, 1) 2 (0, 4) 3 (1, 5) 0 (-1, 1) 1 (0, 3)

≥65

0 (-1, 1) 3 (1, 4) 3 (1, 4) 0 (-1, 1) 2 (0, 4)

P for heterogeneity 0.919 0.670 0.903 0.883 0.567

Season

Warm

0 (-1, 1) 2 (1, 4) 2 (0, 4) 0 (-1, 1) 2 (0, 4)

Cold

0 (-1, 1) 3 (2, 5) 3 (2, 5) 0 (-1, 1) 2 (0, 3)

P for heterogeneity 0.577 0.574 0.253 0.555 0.673

Region

Southern China -1 (-2, 0) 1 (-1, 2) 2 (0, 4) 0 (-1, 1) 4 (2, 7)

Northern China 0 (0, 1) 5 (3, 7) 3 (1, 5) 0 (-1, 1) 1 (0, 3)

P for heterogeneity 0.086 <0.001 0.349 0.861 0.058

15

eTable 7. The impacts of lag days on the ERs (%, 95%CI) between exposures to air pollutants and hospital admission of IS patients

ER (%, 95%CI)

PM

2.5

MDA8 O

3

NO

2

SO

2

CO

Lag0 0.30 (0.22, 0.39) 0.20 (0.11, 0.30) 1.04 (0.83, 1.25) 0.52 (0.28, 0.77) 2.83 (2.15, 3.51) Lag01 0.38 (0.29, 0.47) 0.29 (0.18, 0.40) 1.15 (0.92, 1.39) 0.82 (0.53, 1.11) 3.47 (2.70, 4.26) Lag02 0.42 (0.31, 0.52) 0.29 (0.16, 0.41) 1.15 (0.88, 1.41) 0.91 (0.59, 1.23) 3.62 (2.75, 4.48) Lag03 0.48 (0.37, 0.59) 0.26 (0.13, 0.40) 1.11 (0.83, 1.40) 0.93 (0.58, 1.28) 3.79 (2.85, 4.74)

The ERs for PM

2.5

, MDA8 O

3

, NO

2

and SO

2

were for each 10μg/m

³

increase in their concentrations, and the ERs for CO were for each 1mg/m

³

increase in the concentration.

All ERs were adjusted for ambient temperature, relative humidity, and holiday.

eTable 8. Comparisons of air pollutants estimated by Kriging interpolation between the case days and control days

Case days Control days

Mean SD IQR Mean SD IQR

PM2.5 (μg/m³) 47.1 37.5 33.2 46.6 37.2 32.8

MDA8 O3 (μg/m³) 97.3 44.9 60.7 97.1 44.8 60.7

NO2 (μg/m³) 31.5 16.3 20.2 31.4 16.2 20.1

SO2 (μg/m³) 16.9 16.4 10.9 16.8 116.3 10.9

CO (mg/m³) 0.964 0.504 0.448 0.959 0.502 0.445

*: Paired t test for the mean values.

SD: standard deviation IQR: interquartile range

17

eTable 9. Comparisons of air pollutants and meteorological factors between Northern China and Southern China

Northern China (Mean ± SD)

Southern China (Mean ± SD)

t value P

PM

2.5

(μg/m

³

)

57.5±45.9 38.1±25.2 241.65 <0.001

MDA8 O

3

(μg/m

³

)

101.1±50.3 88.6±40.3 124.97 <0.001

NO2 (μg/m³) 38.4±18.5 30.4±15.7 211.84 <0.001

SO2 (μg/m³) 22.4±23.3 11.7±7.0 293.69 <0.001

CO (mg/m³) 1.13±0.68 0.82±0.27 277.37 <0.001

TM (℃) 11.3±12.2 17.9±8.0 292.88 <0.001

RH (%) 60.2±16.6 78.3±10.7 595.58 <0.001

eTable 10. Comparisons of air pollutants and meteorological factors between warm and cold seasons

Warm season (Mean ± SD)

Cold season (Mean ± SD)

t value P

PM2.5 (μg/m³) 33.3±19.4 63.3±46.8 382.98 <0.001

MDA8 O

3 (μg/m³) 117.1±46.8 74.4±34.9 467.05 <0.001

NO2 (μg/m³) 28.2±13.1 41.0±19.3 354.04 <0.001

SO2 (μg/m³) 12.5±8.6 22.3±23.6 251.79 <0.001

CO (mg/m³) 0.82±0.35 1.15±0.66 285.91 <0.001

TM (℃) 22.2±5.8 6.8±9.3 911.79 <0.001

RH (%) 72.1±14.9 65.1±17.7 194.83 <0.001

19

References

1. Wang Y, Li Z, Wang Y, et al. Chinese Stroke Center Alliance: a national effort to improve healthcare quality for acute stroke and transient ischaemic attack:

rationale, design and preliminary findings. Stroke Vasc Neurol. 2018;3:256-262.

https://doi.org/10.1136/svn-2018-000154.