Skip to main content

Table 4 Extreme influence analysis of different air pollution factors from 2008 to 2014 using two-pollutant models. Relative risk (RR) and 95% confidence interval (CI) were used to estimate the cumulative influence of air pollution factors in total IS cases

From: Association between short-term exposure to air pollution and ischemic stroke onset: a time-stratified case-crossover analysis using a distributed lag nonlinear model in Shenzhen, China

pollutant

extreme-low influence

extreme-high influence

lag0–6

lag0–8

lag0–10

lag0–12

lag0–13

lag0–14

lag0–6

lag0–8

lag0–10

lag0–12

lag0–13

lag0–14

SO2

0.98 (0.87,1.10)

0.99 (0.87,1.13)

1.00 (0.86,1.16)

0.99 (0.85,1.17)

0.99 (0.84,1.17)

0.98 (0.82,1.17)

1.14 (0.98,1.32)

1.29 (1.09,1.52)

1.42 (1.18,1.71)

1.50 (1.22,1.84)

1.50 (1.21,1.86)

1.48 (1.17,1.87)

NO2

0.88 (0.77,1.00)

0.87 (0.75,1.01)

0.86 (0.73,1.03)

0.86 (0.71,1.04)

0.85 (0.70,1.04)

0.85 (0.68,1.05)

1.32 (1.13,1.54)

1.36 (1.14,1.62)

1.37 (1.13,1.67)

1.37 (1.10,1.70)

1.36 (1.08,1.71)

1.34 (1.05,1.72)

PM10

0.81 (0.71,0.92)

0.79 (0.68,0.92)

0.79 (0.66,0.94)

0.81 (0.67,0.97)

0.82 (0.67,1.00)

0.84 (0.68,1.04)

1.09 (0.95,1.26)

1.17 (1.00,1.37)

1.24 (1.04,1.47)

1.26 (1.04,1.53)

1.25 (1.02,1.54)

1.23 (0.98,1.53)

O3

1.13 (0.97,1.32)

1.17 (0.99,1.38)

1.21 (1.00,1.45)

1.24 (1.02,1.52)

1.27 (1.03,1.56)

1.29 (1.03,1.61)

1.07 (0.94,1.22)

1.11 (0.96,1.28)

1.15 (0.99,1.35)

1.20 (1.01,1.42)

1.22 (1.03,1.45)

1.25 (1.04,1.49)

  1. Note: Estimates were generated using a quasi-Poisson regression model combined with time-stratified case-crossover design and distributed lag non-linear model (DLNM), adjusting for meteorological factors, holiday, and time stratum. The extreme-high influence was estimated by the RR of ischemic stroke by comparing the 99th percentile of daily air pollution value to the median value, whereas the extreme-low influence was estimated by comparing the 1st percentile of daily air pollution value to the median value