Long-term association of air pollution and incidence of lung cancer among older Americans: A national study in the Medicare cohort.

BACKGROUND: Despite strong evidence of the association of fine particulate matter (PM2.5) exposure with an increased risk of lung cancer mortality, few studies had investigated associations of multiple pollutants simultaneously, or with incidence, or using causal methods. Disparities were also understudied. OBJECTIVES: We investigated long-term effects of PM2.5, nitrogen dioxide (NO2), warm-season ozone, and particle radioactivity (PR) exposures on lung cancer incidence in a nationwide cohort. METHODS: We conducted a cohort study with Medicare beneficiaries (aged ≥ 65 years) continuously enrolled in the fee-for-service program in the contiguous US from 2001 to 2016. Air pollution exposure was averaged across three years and assigned based on ZIP code of residence. We fitted Cox proportional hazards models to estimate the hazard ratio (HR) for lung cancer incidence, adjusted for individual- and neighborhood-level confounders. As a sensitivity analysis, we evaluated the causal relationships using inverse probability weights. We further assessed effect modifications by individual- and neighborhood-level covariates. RESULTS: We identified 166,860 lung cancer cases of 12,429,951 studied beneficiaries. In the multi-pollutant model, PM2.5 and NO2 exposures were statistically significantly associated with increased lung cancer incidence, while PR was marginally significantly associated. Specifically, the HR was 1.008 (95% confidence interval [CI]: 1.005, 1.011) per 1-µg/m3 increase in PM2.5, 1.013 (95% CI: 1.012, 1.013) per 1-ppb increase in NO2, and 1.005 (0.999, 1.012) per 1-mBq/m3 increase in PR. At low exposure levels, all pollutants were associated with increased lung cancer incidence. Men, older individuals, Blacks, and residents of low-income neighborhoods experienced larger effects of PM2.5 and PR. DISCUSSION: Long-term PM2.5, NO2, and PR exposures were independently associated with increased lung cancer incidence among the national elderly population. Low-exposure analysis indicated that current national standards for PM2.5 and NO2 were not restrictive enough to protect public health, underscoring the need for more stringent air quality regulations.

Additive effects of 10-year exposures to PM2.5 and NO2 and primary cancer incidence in American older adults.

Epidemiologic evidence on the relationships between air pollution and the risks of primary cancers other than lung cancer remained largely lacking. We aimed to examine associations of 10-year exposures to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) with risks of breast, prostate, colorectal, and endometrial cancers. Methods: For each cancer, we constructed a separate cohort among the national Medicare beneficiaries during 2000 to 2016. We simultaneously examined the additive associations of six exposures, namely, moving average exposures to PM2.5 and NO2 over the year of diagnosis and previous 2 years, previous 3 to 5 years, and previous 6 to 10 years, with the risk of first cancer diagnosis after 10 years of follow-up, during which there was no cancer diagnosis. Results: The cohorts included 2.2 to 6.5 million subjects for different cancers. Exposures to PM2.5 and NO2 were associated with increased risks of colorectal and prostate cancers but were not associated with endometrial cancer risk. NO2 was associated with a decreased risk of breast cancer, while the association for PM2.5 remained inconclusive. At exposure levels below the newly updated World Health Organization Air Quality Guideline, we observed substantially larger associations between most exposures and the risks of all cancers, which were translated to hundreds to thousands new cancer cases per year within the cohort per unit increase in each exposure. Conclusions: These findings suggested substantial cancer burden was associated with exposures to PM2.5 and NO2, emphasizing the urgent need for strategies to mitigate air pollution levels.

Machine learning-based automated planning for hippocampal avoidance prophylactic cranial irradiation

Purpose Design and evaluate a knowledge-based model using commercially available artificial intelligence tools for automated treatment planning to efficiently generate clinically acceptable hippocampal avoidance prophylactic cranial irradiation (HA-PCI) plans in patients with small-cell lung cancer. Materials and methods Data from 44 patients with different grades of head flexion (range 45°) were used as the training datasets. A Rapid Plan knowledge-based planning (KB) routine was applied for a prescription of 25 Gy in 10 fractions using two volumetric modulated arc therapy (VMAT) arcs. The 9 plans used to validate the initial model were added to generate a second version of the RP model (Hippo-MARv2). Automated plans (AP) were compared with manual plans (MP) according to the dose-volume objectives of the PREMER trial. Optimization time and model quality were assessed using 10 patients who were not included in the first 44 datasets. Results A 55% reduction in average optimization time was observed for AP compared to MP. (15 vs 33 min; p = 0.001).Statistically significant differences in favor of AP were found for D98% (22.6 vs 20.9 Gy), Homogeneity Index (17.6 vs 23.0) and Hippocampus D mean (11.0 vs 11.7 Gy). The AP met the proposed objectives without significant deviations, while in the case of the MP, significant deviations from the proposed target values were found in 2 cases. Conclusion The KB model allows automated planning for HA-PCI. Automation of radiotherapy planning improves efficiency, safety, and quality and could facilitate access to new techniques (AU)

Machine learning-based automated planning for hippocampal avoidance prophylactic cranial irradiation.

PURPOSE: Design and evaluate a knowledge-based model using commercially available artificial intelligence tools for automated treatment planning to efficiently generate clinically acceptable hippocampal avoidance prophylactic cranial irradiation (HA-PCI) plans in patients with small-cell lung cancer. MATERIALS AND METHODS: Data from 44 patients with different grades of head flexion (range 45°) were used as the training datasets. A Rapid Plan knowledge-based planning (KB) routine was applied for a prescription of 25 Gy in 10 fractions using two volumetric modulated arc therapy (VMAT) arcs. The 9 plans used to validate the initial model were added to generate a second version of the RP model (Hippo-MARv2). Automated plans (AP) were compared with manual plans (MP) according to the dose-volume objectives of the PREMER trial. Optimization time and model quality were assessed using 10 patients who were not included in the first 44 datasets. RESULTS: A 55% reduction in average optimization time was observed for AP compared to MP. (15 vs 33 min; p = 0.001).Statistically significant differences in favor of AP were found for D98% (22.6 vs 20.9 Gy), Homogeneity Index (17.6 vs 23.0) and Hippocampus D mean (11.0 vs 11.7 Gy). The AP met the proposed objectives without significant deviations, while in the case of the MP, significant deviations from the proposed target values were found in 2 cases. CONCLUSION: The KB model allows automated planning for HA-PCI. Automation of radiotherapy planning improves efficiency, safety, and quality and could facilitate access to new techniques.

Occurrence, spatiotemporal distribution, seasonal and annual variation, and source apportionment of poly- and perfluoroalkyl substances (PFASs) in the northwest of Tai Lake Basin, China.

Poly- and perfluoroalkyl substances (PFASs) have attracted mounting attention due to their potential harmful effects and degradation-resistant property. This study continuously monitored the concentration of PFASs for four seasons in two years in the northwest of Tai Lake Basin. The occurrence, spatiotemporal distribution, seasonal and annual variation, and source apportionment of 13 PFASs were investigated in 60 surface water sampling sites and 33 emission sources. The average concentrations of the total PFASs were 205.6 ng L-1 and 171.9 ng L-1 in 2018 and 2019, respectively. This improvement could be mainly attributed to the local industrial restructuring. Furthermore, principal component analysis and heat map-hierarchical cluster analysis were employed to analyze distribution characteristics and the possible sources of PFASs pollution. It showed that perfluorooctane sulfonate (PFOA) mainly originated from the effluents of chemical plants, while the potential source of perfluorohexane sulfonate (PFHxS) included all the three types of emission sources. Besides, two indicators were adopted to evaluate the impact of non-point sources and the result showed the effect of runoff was obvious while the effect of atmospheric deposition was weak. A systematic mass balance calculation showed that the total riverine input flux from Wujin District to Tai Lake was 126.5 kg/a.

Efficient multiresidue determination method for 168 pharmaceuticals and metabolites: Optimization and application to raw wastewater, wastewater effluent, and surface water in Beijing, China.

New analytical methods are needed to efficiently measure the growing list of priority pharmaceuticals in environmental samples. In this regard, a rapid, sensitive, and robust method was developed for quantitation of 168 pharmaceuticals and pharmaceutical metabolites using solid-phase extraction (SPE) and liquid chromatography with tandem mass spectrometry. The extraction protocol and instrumental efficiency were specifically addressed to increase analytical workload and throughput. The optimized protocols, which are five times more efficient than US EPA Method 1694, enabled analyte recoveries that ranged from 77% to 117% for 162 analytes with method quantitation limits (MQLs) as low as 0.1 ng L-1. To verify the suitability of the improved analytical method for environmental samples, 24-h composite samples of raw wastewater and wastewater effluent, along with downstream surface water, were analyzed. Overall, 143/168 target compounds were identified in at least one of the samples, and 130/168 analytes were present at concentrations above their MQLs. The total mass concentration of the measured analytes decreased by 93% during wastewater treatment. The analyte concentrations in the wastewater effluent were comparable to those measured in surface water 1 km downstream of the wastewater discharge point. Ultimately, the comprehensive method will serve as an important tool to inform the occurrence, fate, transport, and toxicity of a large suite of priority pharmaceuticals and pharmaceutical metabolites in natural and engineered systems.