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. 2018 Aug 31;126(8):087004. doi: 10.1289/EHP2732

Table 2.

Estimated percentage change in daily mortality with an increase in the mean value of the instrumental variable (10μg/m3), PM2.5 (10μg/m3), or NO2 (10ppb), respectively, on the day of and day before death (pooled city-specific estimates derived by random effects meta-analysis).

Regression Model % Change 95% CI
Instrumental Variable (10μg/m3)a 1.54% 1.12%, 1.97%
Instrumental Variable (10μg/m3) With Negative Controla,b 1.54% 1.12%, 1.97%
Marginal Structural Modelsc    
PM2.5 (10μg/m3) 0.75% 0.35%, 1.15%
PM2.5 with Negative Control (10μg/m3)b 0.79% 0.36%, 1.23%
PM2.5<25μg/m3d 0.83% 0.39%, 1.27%
NO2 (10ppb) 2.59% 1.78%, 3.40%
NO2 with Negative Control (10ppb)b 2.62% 1.81%, 3.43%
Conventional Time Seriese    
PM2.5(10μg/m3) 0.60% 0.34%, 0.85%
NO2 (10ppb) 0.38% 0.08%, 0.69%
PM2.5<25μg/m3 0.62% 0.32%, 0.93%
a

Instrumental Variable models: quasi-Poisson regression models stratified on month-by-year.

b

Negative Controls: Models with negative controls are adjusted for mean IV, PM2.5, or NO2, respectively, on the second and third day after death, in addition to the exposure on the day of and day before death.

c

Marginal Structural Models: Fit with city-specific inverse probability weights based on month, day-of-the-week, temperature, previous day’s temperature, and, for each pollutant, the other pollutant.

d

PM2.5<25μg/m3: Percentage change in daily mortality with a 10μg/m3 increase in PM2.5 on the day of and day before death, restricted to days with PM2.5 below the 25μg/m3.

e

Conventional Time Series: Models of PM2.5 or NO2 with penalized splines for temperature (same day and previous day) and indicator variables for the month-of-year and day-of-week.