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The relevant methods for mortality standardization in the GBD database have been introduced in previous studies [16, 17]. To assess the magnitude and direction of trends in the mortality rate of lung cancer over time, we used JoinPoint software (Version to calculate the average annual percentage change (AAPC) and the corresponding 95% CIs by joinpoint regression analysis. The JoinPoint software took the trend data and fitted the simplest possible joinpoint model to the data, with the natural logarithm of the age-standardized mortality rates as the dependent variable and the calendar year as the independent variable. The significance tests used a Monte Carlo permutation method, and the overall asymptotic significance level was obtained through a Bonferroni correction.

To assess the mortality rate of lung cancer in the population in a particular year and the accumulation of health risks since birth, we used an age-period-cohort model to analyse the temporal trends in lung cancer mortality rate attributable to PM2.5 exposure by age, period, and cohort. The age-period-cohort model provides a useful parametric framework that complements standard nonparametric descriptive methods. The longitudinal age curve represents the fitted longitudinal age-specific rates relative to the reference cohorts adjusted for period deviations. The age effect refers to age-related physiological and pathological changes that affect disease mortality rates. The period rate ratios (period RRs) are the ratios of age-specific rates in a given period compared to the reference period. The period effect refers to changes in disease mortality rate caused by various events over time, such as the introduction of effective treatments, the implementation of screening procedures, and the increasingly stringent air pollution control policies introduced by the Chinese government over the past 30 years. The cohort rate ratios (cohort RRs) are the ratios of age-specific rates in a given cohort compared to the reference cohort. The cohort effects refer to differences in disease mortality rates between generations as a consequence of lifestyle changes over time or different exposure to risk factors, such as through changes in dietary structures, cooking habits, or living and kitchen environments. Local drifts represent the annual percentage change in the expected age-specific rates over time. Net drift represents the annual percentage change in the expected age-adjusted rates over time. And we added the definitions of APC model parameters in the Supplementary 2.

In our model, we needed to convert the collected data into successive 5-year age groups and consecutive 5-year periods. Because the GBD dataset does not provide successive 5-year age groups for those over 85 years old in the related mortality data on lung cancer attributable to PM2.5 exposure and the ASMRs of lung cancer attributable to PM2.5 for those under the age of 24 in GBD are equal to 0, the related mortality data on lung cancer attributable to PM2.5 exposure were recoded into successive 5-year age groups for those aged from 25 to 29 years to 80–84 years and one group for those aged over 85 years and consecutive 5-year periods (1990–1994 to 2015–2019). A general linear model was used to analyse the slope of the period/cohort RRs. The statistical analysis was performed by R statistical software (R version 3.5.1), and p < 0.05 was considered significant.

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