Dataset Information

Relationship between exposure to PM2.5 and lung cancer incidence and mortality: A meta-analysis.

ABSTRACT: We conducted a meta-analysis to examinine the relationship between exposure to PM2.5 and lung cancer incidence and mortality. In total, 17 studies met our inclusion criteria and provided information necessary to estimate the change in lung cancer risk per 10 ?g/m3 increase in exposure to PM2.5. The random-effects model was used to estimate the relative risk (RR) for specific PM2.5 values. The meta-estimate for lung cancer risk associated with PM2.5 was 1.11 for mortality (95% CI: 1.05, 1.18) and 1.08 (95% CI: 1.03, 1.12) for incidence. Analyses by continent showed that the meta-estimate for lung cancer mortality associated with PM2.5 was greatest in North America [1.15 (95% CI: 1.07, 1.24)], followed by Asia [1.12 (95% CI: 0.94, 1.35)], and then Europe [1.05 (95% CI: 1.01, 1.10)]. Lung cancer incidence associated with PM2.5 was greatest in Asia [1.09 (95% CI: 1.03, 1.15)], followed by North America [1.06 (95% CI: 1.01, 1.11)], and then Europe [1.03 (95% CI: 0.61, 1.75)]. In subgroup analyses of country, the mortality meta-estimate for developed countries was 1.14 (95% CI: 1.06, 1.23), and for developing countries was 1.03 (95% CI: 1.00, 1.07). The incidence meta-estimate for developed countries was 1.07 (95% CI: 0.96, 1.20), and was similar to that of developing countries, 1.07 (95% CI: 1.06, 1.09). In subgroup analyses of males and females, the meta-estimate for lung cancer mortality associated with PM2.5 was greater for males [1.26 (95% CI: 1.15, 1.40)] than for females [1.17 (95% CI: 0.98, 1.39)]. The meta-estimate for lung cancer incidence associated with PM2.5 was greater for males [1.23 (95% CI: 0.83, 1.81)] than for females [1.15 (95% CI: 1.12, 1.18)]. In subgroup analyses of smoking status, the meta-estimate for lung cancer mortality associated with PM2.5 for former smokers was 1.46 (95% CI: 0.84, 2.55), for current smokers was 1.33 (95% CI: 1.20, 1.49), and for never smokers was 1.16 (95% CI: 1.02, 1.33), respectively. The meta-estimate for lung cancer incidence associated with PM2.5 for former smokers was 1.19 (95% CI: 0.95, 1.50), for never smokers was 1.10 (95% CI: 0.76, 1.59), and for current smokers was 1.03 (95% CI: 0.87, 1.21). The relative risks of a relationship between PM2.5 and lung cancer incidence and mortality were 1.08 (95% CI: 1.03, 1.12) and 1.11 (95% CI: 1.05, 1.18), respectively. These findings will provide some evidence for policy makers and public health practitioners worldwide.

SUBMITTER: Huang F

PROVIDER: S-EPMC5522148 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Relationship between exposure to PM2.5 and lung cancer incidence and mortality: A meta-analysis.

Huang Feifei F Pan Bing B Wu Jun J Chen Engeng E Chen Liying L

Oncotarget 20170601 26

We conducted a meta-analysis to examinine the relationship between exposure to PM2.5 and lung cancer incidence and mortality. In total, 17 studies met our inclusion criteria and provided information necessary to estimate the change in lung cancer risk per 10 μg/m3 increase in exposure to PM2.5. The random-effects model was used to estimate the relative risk (RR) for specific PM2.5 values. The meta-estimate for lung cancer risk associated with PM2.5 was 1.11 for mortality (95% CI: 1.05, 1.18) and ...[more]

PMID: 28487493

Similar Datasets

Project description:BackgroundExposure measurement error is a central concern in air pollution epidemiology. Given that studies have been using ambient air pollution predictions as proxy exposure measures, the potential impact of exposure error on health effect estimates needs to be comprehensively assessed.ObjectivesWe aimed to generate wide-ranging scenarios to assess direction and magnitude of bias caused by exposure errors under plausible concentration-response relationships between annual exposure to fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] and all-cause mortality.MethodsIn this simulation study, we use daily PM2.5 predictions at 1-km2 spatial resolution to estimate annual PM2.5 exposures and their uncertainties for ZIP Codes of residence across the contiguous United States between 2000 and 2016. We consider scenarios in which we vary the error type (classical or Berkson) and the true concentration-response relationship between PM2.5 exposure and mortality (linear, quadratic, or soft-threshold-i.e., a smooth approximation to the hard-threshold model). In each scenario, we generate numbers of deaths using error-free exposures and confounders of concurrent air pollutants and neighborhood-level covariates and perform epidemiological analyses using error-prone exposures under correct specification or misspecification of the concentration-response relationship between PM2.5 exposure and mortality, adjusting for the confounders.ResultsWe simulate 1,000 replicates of each of 162 scenarios investigated. In general, both classical and Berkson errors can bias the concentration-response curve toward the null. The biases remain small even when using three times the predicted uncertainty to generate errors and are relatively larger at higher exposure levels.DiscussionOur findings suggest that the causal determination for long-term PM2.5 exposure and mortality is unlikely to be undermined when using high-resolution ambient predictions given that the estimated effect is generally smaller than the truth. The small magnitude of bias suggests that epidemiological findings are relatively robust against the exposure error. In practice, the use of ambient predictions with a finer spatial resolution will result in smaller bias. https://doi.org/10.1289/EHP10389.

Dataset Information

Relationship between exposure to PM2.5 and lung cancer incidence and mortality: A meta-analysis.

Publications

Relationship between exposure to PM2.5 and lung cancer incidence and mortality: A meta-analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets