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

Association of antenatal and early childhood air pollution and greenspace exposures with respiratory pathogen upper airway acquisitions and respiratory health outcomes

The association of air pollution and greenspace with respiratory pathogen acquisition and respiratory health was investigated in a community-based birth-cohort of 158 Australian children. Weekly nasal swabs and daily symptom-diaries were collected for 2-years, with annual reviews from ages 3-7-years. Annual exposure to fine-particulate-matter (PM2.5), nitrogen-dioxide (NO2), and normalised-difference-vegetation-index (NDVI) was estimated for pregnancy and the first 2-years-of-life. We examined rhinovirus, any respiratory virus, Streptococcus pneumoniae, Moraxella catarrhalis, and Haemophilus influenzae detections in the first 3-months-of-life, age at initial pathogen detection, wheezing in the first 2-years, and asthma at ages 5-7-years. Our findings suggest that higher NDVI was associated with fewer viral and M. catarrhalis detections in the first 3-months, while increased PM2.5 and NO2 were linked to earlier symptomatic rhinovirus and H. influenzae detections, respectively. However, no associations were observed with wheezing or asthma. Early-life exposure to air pollution and greenspace may influence early-life respiratory pathogen acquisition and illness.  </p

Mean trips per week (weekdays only) for baseline and travel targets scenario, by age and sex.

<p>Mean trips per week (weekdays only) for baseline and travel targets scenario, by age and sex.</p

Change in prevalent cases and mortality over the life course of the Brisbane adult population (95% uncertainty interval).

<p>Change in prevalent cases and mortality over the life course of the Brisbane adult population (95% uncertainty interval).</p

Analytical framework.

<p>Achieving the travel targets results in increased cycling, walking and use of public transport at the expense of private car travel (thick solid lines), which leads to gains in HALYs, gained life years, reduced health care costs, prevented/increased prevalent cases (diseases) and changes in death rates (thick lines at the bottom of the graph). Averted years lived with disability were estimated for road trauma. The effect of PA and PM_2.5 were modelled via their impact on incidence of diseases (thin lines) and road trauma via its impacts on disability and mortality (captured by HALYs and YLDs) (interrupted thick lines). The effect of less private car use was quantified as improvements in ambient PM_2.5, which benefits the population as a whole (interrupted thin lines).</p

Mode-specific mean (95% uncertainty interval (UI)) trips per weekday in 2009, by age and sex.

<p>Mode-specific mean (95% uncertainty interval (UI)) trips per weekday in 2009, by age and sex.</p

Additional mean minutes per week of transport physical activity undertaken in the travel targets scenario compared to the baseline scenario (statu-quo), by age and sex.

<p>Additional mean minutes per week of transport physical activity undertaken in the travel targets scenario compared to the baseline scenario (statu-quo), by age and sex.</p

HALYs by risk factor over the life course of the Brisbane adult population (95% uncertainty interval).

<p>HALYs by risk factor over the life course of the Brisbane adult population (95% uncertainty interval).</p

Proportional multi-state life table Markov model input parameters.

<p>Proportional multi-state life table Markov model input parameters.</p

Health care costs and health outcomes for base case by sex over the life course of the Brisbane adult population (95% uncertainty interval).

<p>Health care costs and health outcomes for base case by sex over the life course of the Brisbane adult population (95% uncertainty interval).</p