Additional file 1 of The role of lifestyle in the association between long-term ambient air pollution exposure and cardiovascular disease: a national cohort study in China

Additional file 1: Method S1. Ambient air pollution exposure acquisition. Figure S1. Sampling procedure. Figure S2. Study flowchart. Figure S3. The association of different lifestyle factors. Figure S4. (a) The proportion of single ideal factor in different lifestyle groups. (b) The proportion of ideal factors in different lifestyle groups. Figure S5. Directed acyclic graph. Figure S6. The marginal effect of lifestyle on CVD and in the relationship between ambient air pollutant exposure and CVD. Table S1. The score criteria of different lifestyle factors. Table S2. The exposure level of different air pollutants among the study population. Table S3. The exposure level by quintile of air pollutant. Table S4. The HRs (95% CIs) of the associations between lifestyle and CVD with and without adjustment for ambient air pollutant exposure. Table S5. Joint effects of lifestyle and air pollutant exposure on the incidence of CVD. Table S6. The HRs (95% CIs) of incident CVD associated with each lifestyle factor at different levels of air pollutant exposure. Table S7. Subgroup analysis of the additive interactions analysis of the effect of dichotomized lifestyle on the association between ambient air pollutant exposure and CVD in high air pollutant exposure levels (Q2–Q5). Table S8. The HRs (95% CIs) of associations between air pollutant exposure (per 10 μg/m3 increase) and incident CVD, and the mediation effect of lifestyle categories on air pollution and CVD in different sensitivity analysis models. Table S9. The HRs (95% CIs) of the association between ambient air pollutant exposure (per 10 μg/m3 increase) and CVD in different lifestyle categories in different sensitivity analysis models. Table S10. Multiplicative and additive interaction analysis of the effect of dichotomized lifestyle on the association between time-varying ambient air pollutant exposure and CVD. Table S11. Multiplicative and additive interaction analysis of the effect of dichotomized lifestyle on the association between 3 years of ambient air pollutant exposure and CVD. Table S12. Multiplicative and additive interaction analysis of the effect of dichotomized lifestyle considering new categories and nighttime sleep duration on the association between ambient air pollutant exposure and CVD. Table S13. Multiplicative and additive interaction analysis of the effect of dichotomized lifestyle considering new assignment of lifestyle categories on the association between ambient air pollutant exposure and CVD. Table S14. The subdistribution HRs (sHRs, 95% CI) of the associations between ambient air pollutant exposure (per 10 μg/m3) and CVD in different lifestyle categories. Table S15. Baseline characteristics of included and excluded participants. Table S16. Baseline characteristics of included participants and those without lifestyle scores

Correlation coefficients of air pollutants across 25 districts.

<p>Abbreviations: PM₁₀, particles with aerodynamic diameter 10 µm or less; SO₂, sulfur dioxide; NO₂, nitrogen dioxide; CO, carbon monoxide; O₃, ozone.</p><p>*<i>p</i><0.05.</p

Adjusted OR and 95% CIs of respiratory diseases with respect to ambient air pollutants (2006–2008) among children with allergic predisposition (n = 4135)^†.

†<p>Models were adjusted for the variables with asterisks in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0022470#pone-0022470-t004" target="_blank">table 4</a>.</p>‡<p>OR were scaled to the interquartile range for each pollutant (31 µg/m³ for PM₁₀, 21 µg/m³ for SO₂, 10 µg/m³ for NO₂, 1001 µg/m³ for CO, and 23 µg/m³ for O₃).</p

Adjusted odds ratios (OR) for personal and household covariates associated with respiratory morbidity.

<p>*<i>p</i><0.15;</p><p>**<i>p</i><0.05.</p><p>Items with asterisks are included in the final adjustment model for this measurement. These items are adjusted for each other; remaining variables are adjusted only for the footnoted items, as well as for districts.</p

Adjusted OR and 95% CIs of respiratory diseases with respect to ambient air pollutants (2006–2008) among children without allergic predisposition (n = 26004)^†.

†<p>Models were adjusted for the variables with asterisks in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0022470#pone-0022470-t004" target="_blank">table 4</a>.</p>‡<p>OR were scaled to the interquartile range for each pollutant (31 µg/m³ for PM₁₀, 21 µg/m³ for SO₂, 10 µg/m³ for NO₂, 1001 µg/m³ for CO, and 23 µg/m³ for O₃).</p

Predictive Equations Using Regression Analysis for the Developed and other Reference Equations Based on the Training Subset.

<p>Predictive Equations Using Regression Analysis for the Developed and other Reference Equations Based on the Training Subset.</p

Pearson's correlation coefficients between spirometric parameters and height, weight, age in the training subset^*.

*<p><i>P</i><0.001 for all correlation coefficients. FVC, forced vital capacity; FEV1, forced expiratory volume in one second; PEF, peak expiratory flow; MMEF, maximal mid-expiratory flow.</p

Difference Between Measured and Predicted Values Based on the Validation Subset.

*<p>Data are presented as mean of difference (predicted-measured) ±95% CI (confidence interval) of difference, and <i>P</i> value.</p

The frequency and percentage for age stratified by gender.

<p>The frequency and percentage for age stratified by gender.</p

Association between maternal family history of hypertension and preterm birth: modification by noise exposure and multivitamin intake

To measure the effect of maternal family history of hypertension on preterm birth (PTB) and to identify factors that modified this association. A case-control study was nested in a prospective cohort of the entire pregnant population in Wuhan, China, from 2011 to 2013. Home-visit interviews were scheduled for all PTBs and their controls, to collect extensive information on maternal exposures to behavioral, environmental, and intergenerational risk factors of PTB. The effects of maternal family history of hypertension on PTB were measured by logistic regression analyses, controlling for potential confounders. Potential effect modifiers were examined using stratified analyses. There were 2393 PTBs and 4263 full-term births out of all eligible births. A positive association was observed between maternal family history of hypertension and PTB, after adjusted for potential confounders (adjusted odds ratio: 1.17 [1.03, 1.33]). A higher effect was observed when mothers were exposed to certain noise during pregnancy (adjusted odds ratio: 1.37 [1.14, 1.65]) and/or when they did not take multivitamins during pregnancy (adjusted odds ratio: 1.46 [1.20, 1.78]), whereas, this association was weaker and no longer significant when mothers took multivitamins during pregnancy (adjusted odds ratio: 1.00 [0.84, 1.19]) and/or when they were not exposed to certain noise during pregnancy (adjusted odds ratio: 1.01 [0.85, 1.12]). The modification effect from maternal multivitamin intake was significant on both spontaneous and medically indicated PTBs, and the modification effect from maternal exposure to certain noise was only significant on spontaneous PTB. Increased PTB risk was observed for pregnant women with a family history of hypertension in Wuhan, China. This effect was stronger when pregnant women did not take multivitamin and/or exposed to certain noise during pregnancy, than those who took multivitamin and/or unexposed to certain noise.</p