Long-term Residential Exposure to Air Pollution and Lung Cancer Risk

Hystad, Perry; Demers, Paul A.; Johnson, Kenneth C.; Carpiano, Richard M.; Bräuer, Michael

doi:10.1097/ede.0b013e3182949ae7

Cited by 158 publications

(140 citation statements)

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“…Fixed-site monitor measurements from urban centers were used to conduct a sub-analysis. Results show positive associations of lung cancer incidence with both exposure metrics, with varying degrees of magnitude and significance (Hystad et al 2013). Heck et al examined associations between childhood cancers and traffic-related exposures to PM 2.5 and CO, both in utero, and during the child's first year.…”

Section: Studies Of Chronic Health Effects Associated With Long-term mentioning

confidence: 99%

The importance of the exposure metric in air pollution epidemiology studies: When does it matter, and why?

Dionisio

Baxter

Burke

et al. 2015

Air Qual Atmos Health

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Exposure error in ambient air pollution epidemiologic studies may introduce bias and/or attenuation of the health risk estimate, reduce statistical significance, and lower statistical power. Alternative exposure metrics are increasingly being used in place of central-site measurements, with the intent of reducing exposure error. Dependent on the study design, health outcome, and pollutant of interest, these metrics may provide a means of reducing error (leading to less bias and uncertainty in health risk estimates) if they capture variability in exposure which is not represented when central-site measurements are used. We examine the current evidence for answering the question of when the choice of exposure metric matters and why, focusing on studies which examined multiple exposure metrics in the same epidemiologic study. We conclude that for timeseries and case-crossover studies, central-site measurements may be sufficient, especially for homogeneous pollutants, and in cohort and panel studies, approaches that increase spatial resolution of the exposure metrics do impact the health effect estimates. We note that while the current literature is widely dispersed across exposure metrics and health outcomes, the largest collective, common body of literature is focused on birth/pregnancy outcomes and traffic-related pollution. Also additional discussion and agreement is needed on how to classify metrics as Bdifferent^and Bbetter.^Primary recommendations are to provide context for the reasons behind selection of exposure metrics and to encourage collaboration between exposure scientists and epidemiologists when designing and implementing a study, as results can have important implications for the development of policies and regulations.

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Section: Studies Of Chronic Health Effects Associated With Long-term mentioning

confidence: 99%

The importance of the exposure metric in air pollution epidemiology studies: When does it matter, and why?

Dionisio

Baxter

Burke

et al. 2015

Air Qual Atmos Health

View full text Add to dashboard Cite

show abstract

“…Currently, using the average concentration of PM2.5/per year reflected environmental risk factors (Pongpiachan et al, 2013). When PM2.5 air concentrations increased each additional 10μg/m3, the incidence of lung cancer would increase 1.29-fold (Hystad et al, 2013). Since 2013, fog haze was frequently in China and PM2.5 average concentration was high over 150μg/ m3, greatly harmed the health of population, in particular caused lung cancer.…”

Section: Environmental Risk Factorsmentioning

confidence: 99%

Study on Theoretical Models of Regional Humanity Lung Cancer Hazards Assessment

Zhang

Gao²

2015

Asian Pacific Journal of Cancer Prevention

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Purpose: To establish the concept of lung cancer hazard assessment theoretical models, evaluating the degree of lung cancer risk of Beijing for regional population lung cancer hazard assessment to provide a basis for technical support. Materials and Methods: ISO standards were used to classify stratified analysis for the entire population, life cycle, processes and socioeconomic management. Associated risk factors were evaluated as lung cancer hazard risk assessment first class indicators. Study design: Using the above materials, indicators were given the weight coefficients, building lung cancer risk assessment theoretical models. Regional data for Beijing were entered into the theoretical model to calculate the parameters of each indicator and evaluate the degree of local lung cancer risk. Results: Adopting the concept of lung cancer hazard assessment and theoretical models for regional populations, we established a lung cancer hazard risk assessment system, including 2 first indicators, 8 secondary indicators and 18 third indicators. All indicators were given weight coefficients and used as information sources. Score of hazard for lung cancer was 84.4 in Beijing. Conclusions: Comprehensively and systematically building a lung cancer risk assessment theoretical model for regional populations in conceivable, evaluating the degree of lung cancer risk of Beijing, providing technical support and scientific basis for interventions for prevention.

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“…Table 1 demonstrates the pronounced impact of smoking and socioeconomic status on lung cancer incidence within the study. Table 3 shows that the estimated effect of PM 2.5 depends considerably on the kind of adjustment chosen by Hystad et al (2013). Interestingly, Table 4 shows effect estimates among never smokers.…”

mentioning

confidence: 99%

“…Interestingly, Table 4 shows effect estimates among never smokers. Although of low precision (only 1,381 never smokers could be studied, among them 120 lung cancer cases), Table 4 in Hystad et al (2013) reported an unexceptional odds ratio of 0.95 for PM 2.5 (per 10 μg/m 3 increase). Similar observations were made in a large pooled case-control study (Olsson et al 2011) on workers exposed to diesel motor emissions (DME).…”

mentioning

confidence: 99%

“…Another study that may be of relevance at the IARC meeting in October reported on lung cancer incidence in relation to long-term exposure to three ambient air pollutants and proximity to major roads, using a Canadian population-based case-control study (Hystad et al 2013). The estimated increase in lung cancer incidence (expressed as an odds ratio) was 1.29 (95 % CI 0.95-1.76) with a ten unit increase in PM 2.5 (μg/m 3 ).…”

mentioning

confidence: 99%

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