{"paper_id":"41d07844-36d3-405f-b0ec-9aae0b1bb2cf","body_text":"Industrial air pollution and lung and bronchus cancer survival in New Mexico, USA | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Industrial air pollution and lung and bronchus cancer survival in New Mexico, USA Xi Gong, Yanhong Huang, Charles L. Wiggins, Angela L. W. Meisner, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7880883/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Purpose Lung and bronchus cancer (LBC) is the leading cause of cancer-related deaths. This study examines the relationship between exposure to industrial air pollution and lung and bronchus cancer survival (LBCS) in New Mexico, USA, from 1990 to 2019. Methods The analysis included data from 26,337 lung and bronchus cancer patients. Residential exposure to nine industrial air pollutants was estimated for each patient using the Emission Weighted Proximity Model (EWPM), which integrates industrial air emission data and air quality monitoring data form the U.S. Environmental Protection Agency’s (EPA). Cox proportional hazards modeling was applied to identify industrial air pollutants as decreased LBCS risk factors, adjusting for age at diagnosis, gender, cancer stage, race/ethnicity, and urbanization of residential address. Results Results show that residential exposure to 1,1,1-trichloroethane and cobalt during the survival period was significantly associated with decreased LBCS (adjusted hazard ratio [adjHR] for 1,1,1-trichloroethane: 1.15, 95% CI: 1.09, 1.21 and adjHR for cobalt: 1.14, 95% CI: 1.07, 1.21) among all patients. When exposure levels were categorized into four groups (unexposed, low, medium, high) based on annual average exposure during the survival period, the associations remained consistent. The extended Kaplan-Meier survival analysis indicated reduced survival probabilities for patients exposed to these two pollutants compared to those residing in unexposed areas during the study period. Conclusion This study underscores the negative influence of industrial air pollution on LBCS and emphasizes the need for targeted public health interventions in high-exposure areas. industrial air pollution lung and bronchus cancer survival GIS environmental health exposure assessment Figures Figure 1 Figure 2 1. Introduction Lung cancer is the leading cause of cancer-related deaths, accounting for 1.8 million deaths, or 18.7% of all cancer fatalities worldwide in 2022 [ 1 ]. It is also the third most commonly diagnosed cancer in the United States in 2024 [ 2 ]. Between 2017 and 2021, nearly 50% of all lung cancer cases in the United States were diagnosed at a distant stage, indicating that the cancer had already spread from the lungs to other parts of the body [ 3 ]. The overall five-year survival rate for lung cancer in the United States was mere 27.5% during the period from 2014 to 2020 [ 3 ]. In New Mexico, lung and bronchus cancer (LBC) had an age-adjusted incidence rate of 32.7 per 100,000 population between 2017 and 2021, ranking as the second most commonly diagnosed cancer in the state [ 4 ]. Among racial and ethnic groups, the Asian/Pacific Islander (AIAN) population had the highest incidence rate at 48.1 per 100,000 [ 5 ]. From 2018 to 2022, the age-adjusted mortality rate for LBC in New Mexico was 21.4 per 100,000, making it the leading cause of cancer-related mortality in New Mexico [ 4 ]. Lung and bronchus cancer survival (LBCS) is influenced by a range of factors, including age at diagnosis, sex, cancer stage at diagnosis, and comorbidities such as cardiovascular, hypertension, and diabetes [ 6 – 10 ]. Increasing evidence links air pollution—particularly fine particulate matter (PM), nitrogen dioxide (NO 2 ), ozone (O 3 ), sulfur dioxide (SO 2 ), and other pollutants—to reduce lung cancer survival [ 11 – 13 ]. Globally, air pollution was estimated to contribute approximately 14.1% to lung cancer deaths, making it the second leading cause after tobacco smoking in 2017 [ 14 ]. Studies in specific regions further illustrate the associations between air pollution and lung cancer survival. In northern China, significant correlations were observed between long-term exposures to PM 10 and SO 2 and decreased lung cancer survival from 1988 to 2009 [ 15 ]. In the Californian, United States, air pollution exposures to NO 2 , O 3 , PM 10 , PM 2.5 after diagnosis for lung cancer patients can significantly shorten survival from 1988 to 2009 [ 16 ]. Additionally, a strong association was observed between elevated blood lead (Pb) levels and decreased lung cancer survival among lead-exposed workers from 2003 to 2004 [ 17 ]. Similarly, in South Korea, exposure to pollutants such as SO₂, carbon monoxide (CO), NO₂, PM 2.5 , and PM 10 was linked to reduced 1-, 3-, and 5-year lung cancer survival rates from 2009 to 2022 [ 18 ]. Furthermore, residential exposure to PM 2.5 , NO₂, and black carbon (BC) was positively associated with reduced lung cancer survival across Denmark, England, Norway, and Rome (Italy) based on the corresponding cohorts during 2000 to 2017 [ 19 ]. Together, these findings highlight the urgent need to reduce air pollution as a key component of global efforts to increase lung cancer survival. The most commonly studied air pollutants linked to decreased lung cancer survival include PM, O 3 , SO 2 , CO, Pb, and NO 2 , which are the criteria air pollutants (CAPs). Industrial air pollution is a significant contributor to environmental contamination, which is generated by factories, power plants, refineries, and various other industrial facilities [ 20 ]. Common pollutants from these sources include PM, SO₂, nitrogen oxides (NOₓ), volatile organic compounds (VOCs), and heavy metals, all of which pose serious health risks [ 21 , 22 ]. Exposure to industrial air pollution has been associated with numerous adverse health outcomes, including respiratory and cardiovascular diseases, adverse birth outcomes, and premature mortality [ 23 – 25 ]. In addition, outdoor air pollution is a main contributor to lung cancer incidence globally [ 26 ]. These findings underscore the widespread effects of industrial air pollution on public health and suggest a potential link to lung cancer survival. However, the impact of many industrial air pollutants other than CAPs on lung cancer survival remains largely unexplored. Despite known health risks of industrial air pollution, a research gap exists in understanding its specific link to LBCS, especially beyond commonly studied CAPs. The gap is particularly concerning for New Mexico, a state with high LBC incidence rate, mining legacy, rural landscape, economic challenges, and diverse population. These intersecting factors may contribute to disproportionate exposure burdens and poorer health outcomes, including lower cancer survival. Therefore, this study aims to explore the relationship between industrial air pollution and LBCS in the state of New Mexico from 1990 to 2019, with the potential to identify modifiable environmental risk factors and inform the development of targeted interventions and policies. 2. Data and Methods 2.1. Lung and bronchus cancer data As a comprehensive, population-based cancer registry for New Mexico, the New Mexico Tumor Registry (NMTR) provides high-quality cancer surveillance data to support scientific research and cancer control initiatives [ 27 ]. Our study cohort includes incident cases of LBC that were diagnosed among New Mexico residents during the time period 1990 to 2019. The NMTR collects individual-level data on various demographic and clinical characteristics, including age at diagnosis, sex, race/ethnicity, date of diagnosis, cancer stage at diagnosis, survival time (months), cause of death, date of last information (i.e., date of death for deceased patients and date last known to be alive for patients who have not yet died), and residential address at the time of diagnosis. This study initially identified a cohort of 26,337 patients diagnosed with LBC in New Mexico between 1990 and 2019 using data from NMTR. We excluded cases based on the following: diagnosis reported only through autopsy or death certificate (15.75%), lack of microscopic confirmation with diagnostic sources such as laboratory, visualization, radiographic, clinical, or unknown (5.87%), missing geocoded residential coordinates (2.2%), missing or unknown cause of death (0.9%), missing survival months (6.2%), and missing cancer stage at diagnosis (3.1%) [ 28 , 29 ]. After applying these exclusions, a final sample of 18,273 LBC cases remained for analysis. Cancer site and morphology were coded according to the International Classification of Diseases for Oncology , Second Edition ( ICD-O-2 ) or Third Edition ( ICD-O-3 ), depending on the year of diagnosis [ 28 ]. According to The Surveillance, Epidemiology, and End Results (SEER) Program, cause-specific death classification variable is defined by taking into account cause of death in conjunction with sequence of tumor occurrence (i.e., only one tumor or the first of multiple tumors), site of the original cancer diagnosis, and comorbidities (e.g., AIDS and/or site-related diseases), with the aim of capturing deaths that were related to the specific cancer [ 30 ]. The SEER historic staging scheme provided information for in situ and invasive cancers, with the invasive cancers being divided into the following four stage categories: localized to the primary tumor site (localized), tumor with regional spread or metastases to regional lymph node (regional), tumor with distant metastases (distant), or unknown stage [ 28 ]. The diagnosis age (years) refers to the patient's age at the time of their first LBC diagnosis. 2.2. Estimation of exposure 2.2.1. Industrial air pollution emission and air quality monitoring data The U.S. Environmental Protection Agency’s (EPA) Toxics Release Inventory (TRI) program is a federally mandated initiative requiring industrial facilities nationwide to submit annual reports detailing chemical emissions to the environment, including the types, quantities, and geographic locations of releases [ 31 ]. The primary objective of the TRI program is to monitor the management of toxic chemicals that pose potential risks to human health and the environment [ 31 ]. For this study, emission data were sourced from TRI records of industrial facilities located in New Mexico and neighboring areas, including northwestern Texas, southern Colorado, and eastern Arizona. Between 1990 and 2019, 577 industrial facilities across these regions released 188 distinct chemicals into the atmosphere (Fig. 1 ), with annual emissions calculated by aggregating emissions from both stacks and fugitive sources. Additionally, air quality monitoring data were collected from the U.S. EPA’s Air Quality System (AQS) DataMart, which compiles data collected through the national ambient air monitoring program, including raw measurements and aggregated values (such as 8-hour averages, daily averages, and annual averages) [ 32 ]. In New Mexico, between 1990 and 2019, 112 monitoring sites recorded data on 271 different air pollutants (Fig. 1 ). To ensure consistency with the timescale of the emission data, annual average monitoring records were used for the subsequent analyses. 2.2.2. Air pollution exposure assessment This study utilized a calibrated Emission Weighted Proximity Model (EWPM) that was previously used to assess exposure to industrial air pollution at residential locations [ 33 ]. The EWPM estimates exposure intensity​ for a given pollutant at a given location by incorporating emission rate, duration, and distance from each emission source. The model has a key parameter, the effective distance, which defines the threshold beyond which emissions are excluded. Using monitoring sites as virtual exposure receptors, estimated exposure intensities at these locations were validated against monitoring data across varying effective distances. Therefore, effective distances were calibrated only for pollutants with both emission data and sufficient monitoring data (nine pollutants in this study), with each pollutant calibrated separately to minimize misclassifications of exposure assessment. The optimal effective distance for each chemical was determined by selecting the threshold that maximized the positive Spearman rank correlation between estimated and observed values. For further methodological details, please refer to [ 25 , 34 – 37 ]. For each chemical with a calibrated optimal effective distance, we used the EWPM with the corresponding distance to estimate annual average exposure intensities at each case's residential location for every year throughout the survival period. The exposure period for each patient was defined as the time from the midpoint of the diagnosis year to the midpoint of the year of death or censoring. 2.3. Identification of potential risk factors We employed a Cox proportional hazards model (hereafter referred to as the Cox model) with time-dependent covariates to evaluate the associations (hazard ratios [HRs]) between residential exposure to industrial air pollution and lung and bronchus cancer survival (LBCS) in New Mexico from 1990 to 2019, adjusting for relevant covariates [ 38 ]. Annual average industrial air pollution exposure was defined as a time-dependent covariate to account for changes in exposure over the follow-up period. The selection of covariates in this analysis was guided by a directed acyclic graph (DAG), presented in Figure S1 . Covariates included age at diagnosis, sex (male or female), race/ethnicity (Non-Hispanic White, Hispanic, Non-Hispanic Black, non-Hispanic AIAN, non-Hispanic Asian/Pacific Islander (API), and Non-Hispanic Others), stage at diagnosis (localized, regional, distant, or unknown stage), and the urbanicity of the residential address (urban or rural). Urban and rural classifications were determined based on U.S. Census Bureau definitions, using census-designated criteria linked to the residential address at the time of diagnosis [ 39 ]. We assessed the proportional hazards assumption using Schoenfeld residuals and identified significant violations of the assumptions for two covariates: age at diagnosis and LBC diagnosis stage. To address this, we used a stratified Cox model, where the violating covariates were included as stratification variables (diagnosis age group: <50, 50–59, 60–69, 70–79, and > = 80 years; LBC diagnosis stage: localized, regional, distant, or unknown stage). Each industrial air pollutant was evaluated individually in a single-pollutant Cox model, where it was included as the independent variable to assess its effect on LBCS. To examine potential effect modification by stage at diagnosis, we conducted stratified analyses using the Cox model separately for localized, regional, distant, and unknown stages, adjusting for all covariates except stage to capture stage-specific effects. The extended Kaplan-Meier estimator with time-varying covariates [ 40 ] was employed to estimate the overall survival information for the cohort, focusing on the key pollutants obtained in prior analyses (Section 2.2). Median survival times and five-year survival probabilities were calculated for the entire cohort. To further examine the exposure-response relationships, patients in the exposed group were further categorized into unexposed, low, medium, and high groups based on a cutoff at 0 exposure and the tertiles of annual average exposure intensities among those with exposures greater than 0 during the survival period. These exposure groups were compared to the unexposed group (exposure intensity = 0) respectively. Additionally, a Wald test for ordinal exposure levels was conducted to assess the significance of the trend of the associations. Bonferroni correction [ 41 ] was used to correct for multiple testing (adjusted p < 0.05). 3. Results The descriptive statistics and demographic characteristics of the study cohort are summarized in Table 1 . The analysis included 18,273 patients diagnosed with LBC between 1990 and 2019. The cohort was primarily composed of males (54.99%) and non-Hispanic Whites (74.43%), with an average age of 69.50 years at diagnosis. Most patients were diagnosed at distant stage of LBC, comprising 52.86% of the cohort. Over the study period, 84.40% patients (N = 15,422) died specifically of LBC. Additionally, the majority of patients (77.94%) resided in urban areas at the time of diagnosis. The median survival time varied across cancer stages, with patients in the localized stage exhibiting median survival of 32 months, while those diagnosed at the distant stage had median survival of 4 months. Table 1 Characteristics of patients with lung and bronchus cancer (LBC) in New Mexico by stage of diagnosis, summarized across all stages (Total) and deaths, 1990–2019. Characteristics LBC Diagnosis LBC Death Localized (n = 2855) Regional (n = 4290) Distant (n = 9659) Unknown (n = 1469) Total (n = 18,273) Death (n = 15,422) Race/ethnicity (n (%)) Non-Hispanic White 2125 (74.43) 3186 (74.26) 6906 (71.50) 1001 (68.14) 13,218 (72.34) 11,198 (72.61) Non-Hispanic Black 57 (1.89) 79 (1.84) 187 (1.94) 26 (1.77) 349 (1.91) 301 (1.95) Hispanic 606 (21.23) 909 (21.19) 2294 (23.75) 398 (27.09) 4207 (23.02) 3532 (22.90) American Indian and Alaska Native 46 (1.61) 78 (1.82) 176 (1.82) 23 (1.57) 323 (1.77) 274 (1.78) Asian/Pacific Islander 11 (0.39) 34 (0.79) 79 (0.82) 9 (0.61) 133 (0.73) 101 (0.65) Non-Hispanic Others 10 (0.35) 4 (0.09) 17 (0.18) 12 (0.82) 43 (0.24) 16 (0.10) Sex (n (%)) Male 1570 (54.99) 2519 (58.72) 5584 (57.81) 854 (58.13) 10,527 (57.61) 8916 (57.81) Female 1285 (45.01) 1771 (41.28) 4075 (42.19) 615 (41.87) 7746 (42.39) 6506 (42.19) Urbanicity (n (%)) Urban 2282 (79.93) 3379 (79.52) 7481 (79.18) 1100 (74.88) 14,242 (77.94) 12,026 (77.80) Rural 573 (20.07) 911 (20.48) 2178 (20.82) 369 (25.12) 4031 (22.06) 3396 (22.20) Death (n (%)) Alive/Death (other causes) 962 (33.70) 740 (17.25) 914 (9.46) 235 (16.00) 2851 (15.60) ̶ Death (cancer cause-specific) 1893 (66.30) 3550 (82.75) 8745 (90.54) 1234 (84.00) 15,422 (84.40) ̶ Age at diagnosis (mean ± SD) 71.82 ± 9.24 69.26 ± 9.81 68.46 ± 10.42 72.54 ± 10.44 69.50 ± 10.21 69.34 ± 10.29 < 50 46 (1.61) 125 (2.91) 391 (4.05) 33 (2.25) 595 (3.26) 533 (3.46) 50–59 234 (8.20) 585 (13.64) 1493 (15.46) 131 (8.92) 2443 (13.37) 2132 (13.82) 60–69 770 (26.97) 1377 (32.10) 3138 (32.49) 358 (24.37) 5643 (30.88) 4771 (30.94) 70–79 1211 (42.42) 1554 (36.22) 3222 (33.36) 555 (37.78) 6542 (35.80) 5435 (35.24) >=80 594 (20.81) 649 (15.13) 1415 (14.65) 392 (26.68) 3050 (16.69) 2551 (16.54) Median survival months (interquartile range) 32 (120) 20 (62) 4 (19) 12 (52) 12 (30) 12 (26) SD: Standard deviation . Following the calibration of effective distances for the selected nine chemicals, exposure intensities for the nine chemicals estimated using the EWPM exhibited statistically significant positive associations (p < 0.05) with the corresponding monitoring data (Table S1 ). The optimal effective distances of these nine chemicals ranged from 6 km to 50 km, with correlation coefficients varying between 0.060 and 0.999 (Table S1 ). Table 2 Adjusted HR (95% CI) for the effect of industrial air pollution exposure on lung and bronchus cancer survival (LBCS) in New Mexico, 1990–2019, stratified by cancer stage (Localized, Regional, Distant, Unknown) and summarized across all stages (Total). Adjusted HR b (95% CIs) p -value for interaction of stratified stage and air pollution exposure Polluant (CAS number) a Localized Regional Distant Unknown Total 1,1,1-Trichloroethane (71556) 0.96 (0.79, 1.17) 1.21 (1.08, 1.34) * 1.27 (1.17, 1.37) * 0.84 (0.64, 1.05) 1.15 (1.09, 1.21) * 0.002* Cobalt (7440484) 1.10 (0.88, 1.34) 1.14 (0.95, 1.34) 1.20 (1.10, 1.30) * 0.83 (0.60, 1.07) 1.14 (1.07, 1.21) * 0.003* Chromium (7440473) 1.11 (0.93, 1.31) 1.08 (0.99, 1.18) 1.14 (1.07, 1.21) * 0.91 (0.71, 1.13) 1.08 (1.01, 1.15) 0.001* Copper (7440508) 1.10 (0.90, 1.20) 1.11 (0.96, 1.27) 1.14 (1.02, 1.27) 0.80 (0.60, 1.02) 1.04 (0.98, 1.10) 0.294 Ethylbenzene (100414) 1.05 (0.84, 1.30) 1.11 (0.95, 1.28) 0.98 (0.86, 1.11) 0.98 (0.75, 1.22) 1.00 (0.94, 1.06) 0.169 Chlorine (7782505) 0.97 (0.82, 1.13) 1.02 (0.90, 1.16) 1.08 (0.96, 1.21) 0.91 (0.71, 1.12) 1.01 (0.97, 1.05) 0.093 Manganese (7439965) 1.16 (0.89, 1.44) 1.02 (0.80, 1.24) 1.17 (0.95, 1.40) 1.09 (0.80, 1.39) 1.02 (0.93, 1.11) 0.161 1,2,4-Trimethylbenzene (95636) 1.02 (0.79, 1.28) 1.12 (0.99, 1.26) 0.97 (0.84, 1.11) 0.84 (0.60, 1.09) 0.95 (0.90, 1.01) 0.170 Mercury (7439976) 1.34 (0.97, 1.71) 1.15 (1.00, 1.31) 0.96 (0.76, 1.17) 0.74 (0.44, 1.06) 0.91 (0.82, 1.01) 0.848 *Statistically significant after Bonferroni correction for multiple comparisons at level 0.05. a A unique identification number assigned by Chemical Abstracts Service (CAS) to every chemical substance described in the open scientific literature; order in ascending p -values of the adjusted HR in total stage. b Adjusted for diagnosis age, gender, race/ethnicity, and urbanicity. c Adjusted for diagnosis age, gender, race/ethnicity, diagnosis stage, and urbanicity. Table 2 presents the adjusted HR (adjHR) for the impact of exposure to nine industrial air pollutants on LBCS in New Mexico, stratified by cancer stage (localized, regional, distant, unknown) and summarized across all stages (represented as total stage hereafter). Significant associations with decreased LBCS were observed for exposure to 1,1,1-Trichloroethane (adjHR: 1.15, 95% CI: 1.09, 1.21) and cobalt (adjHR: 1.14, 95% CI: 1.07, 1.21) in the total stage. Specifically, exposure to 1,1,1-trichloroethane and cobalt were associated with 15% and 14% higher risk of dying from LBC, respectively, after adjusting for covariates. Exposure to chromium during the survival period was associated with increased risk of dying from LBC in the total stage (adjHR: 1.08, 95% CI: 1.01, 1.15). In contrast, exposure to mercury (adjHR: 0.91, 95% CI: 0.82, 1.01) and 1,2,4-Trimethylbenzene (adjHR: 0.95, 95% CI: 0.90, 1.01) showed associations with decreased risk of dying from LBC in the analysis for all stages combined. However, all the associations are statistically insignificant after Bonferroni correction for multiple comparisons, further research is needed to investigate these effects. Specifically, exposure to 1,1,1-trichloroethane was significantly associated with increased risk of dying from LBC in the regional stage (adjHR: 1.21, 95% CI: 1.08, 1.34) and distant stage (adjHR: 1.27, 95% CI: 1.17, 1.37), though it showed no significant association in the localized and unknown stages (Table 2 ). These findings indicate that among LBC patients diagnosed at the regional stage, exposure to 1,1,1-trichloroethane during the survival period was associated with 21% higher risk of dying from LBC compared to unexposed patients; and for patients diagnosed at the distant stage, a 27% increased risk of dying from LBC was observed. Similarly, cobalt exhibited significant association with reduced LBCS in the distant stage (adjHR: 1.20, 95% CI: 1.10, 1.30), with no significant associations observed in the localized and regional stages (Table 2 ). Additionally, exposure to chromium was significantly associated with an increased risk of dying from LBC only among patients diagnosed at the distant stage (adjHR: 1.14, 95% CI: 1.07, 1.21). Table 3 presents the estimated median survival time and five-year survival rates for the cohort stratified by exposure to two significant air pollutants identified above (statistically significant adjHRs in the total stage in Table 2 ). For both pollutants, the unexposed group demonstrated a longer median survival time (15 months vs. 12 months) and higher five-year survival rates compared to the exposed group (1,1,1-trichloroethane: 28.23% vs. 21.70%; cobalt: 28.02% vs. 21.66%). Figure 2 depicts the survival curves for patients exposed and unexposed to the two air pollutants, illustrating the probability of survival over time (in months) for each group, and result of for Log-rank test (p value in the figure). The unexposed group (represented in red) consistently exhibited a significant higher survival probability than the exposed group (represented in blue) throughout the studied period. Table 3 Median overall survival time and five-year overall survival rate and air pollution exposure in New Mexico, 1990–2019. Pollutant (CAS number) a Exposure intensity b Median survival time in months (95% CIs) Five-year survival rate in percentage (95% CIs) 1,1,1-Trichloroethane (71556) Unexposed 15 (14,15) 28.23 (27.45, 29.04) 1,1,1-Trichloroethane (71556) Exposed 12 (11,13) 21.70 (19.47, 24.18) Cobalt (7440484) Unexposed 14 (14,15) 28.02 (27.25, 28.81) Cobalt (7440484) Exposed 11 (10,13) 21.66 (18.96, 24.75) a A unique identification number assigned by Chemical Abstracts Service (CAS) to every chemical substance described in the open scientific literature, order alphabetically. b Annual average residential air pollution exposure intensity during survival period. Table 4 Adjusted HR (95% CI) for the effect of exposure intensities of industrial air pollution on lung and bronchus cancer survival (LBCS) in New Mexico, 1990–2019. Pollutant (CAS number) a Exposure intensity b Mortality (LBC cause-specific) Adjusted HR c p -value for trend n % 1,1,1-Trichloroethane (71556) Unexposed (0) 16,258 90.27 1.00 (referenced) < 0.001 1,1,1-Trichloroethane (71556) Low (0–2247.86) 434 2.41 1.03 (0.94, 1.13) 1,1,1-Trichloroethane (71556) Medium (2247.87–6127.70) 523 2.90 1.17 (1.08, 1.26)* 1,1,1-Trichloroethane (71556) High (> 6127.70) 805 4.47 1.19 (1.11, 1.27)* Cobalt (7440484) Unexposed (0) 16,874 93.64 1.00 (referenced) < 0.001 Cobalt (7440484) Low (0–35.99) 390 2.16 1.08 (0.98, 1.19) Cobalt (7440484) Medium (36.00–68.21) 309 1.71 1.10 (0.98, 1.22) Cobalt (7440484) High (> 68.21) 447 2.48 1.15 (1.05, 1.25)* *Statistically significant after Bonferroni correction for multiple comparisons at level 0.05. a A unique identification number assigned by Chemical Abstracts Service (CAS) to every chemical substance described in the open scientific literature, order alphabetically. b Annual average residential air pollution exposure intensity during the survival period. c Adjusted for diagnosis age, gender, race/ethnicity, diagnosis stage, and urbanicity. Table 4 shows the effects of varying levels of exposure (unexposed, low, median, high) to two pollutants identified as having significant adjusted HRs in the total stage on LBCS. Exposure levels were defined based on the annual average exposure during the survival period. For 1,1,1-trichloroethane, medium and high exposure levels were linked to significantly elevated risks of dying from LBC, while the association for low exposure group was not statistically significant. Specifically, medium exposure increased the risk by 17% (adjHR: 1.17, 95% CI: 1.08, 1.26), and high exposure was linked to 19% (adjHR: 1.19, 95% CI: 1.11, 1.27) higher risk of dying from LBC compared to the unexposed group. For cobalt, high exposure was associated with a significant increased risk in dying from LBC (adjHR: 1.15, 95% CI: 1.05, 1.25), while both low and medium exposure levels showed no significant association with reduced LBCS. Notably, significant linear trends were observed among associations between residential exposure to both identified chemicals (1,1,1-trichloroethane and cobalt) and reduced LBCS. 4. Discussion Our study contributes to the growing body of evidence linking air pollution exposure to LBCS. By analyzing nine industrial air pollutants across New Mexico from 1990 to 2019 using the large patient-level datasets collected by the NMTR, we identified significant associations between residential air pollution exposure to 1,1,1-trichloroethane and cobalt and reduced LBCS in the total stage (all diagnose stages combined). The findings are consistent with previous research indicating that exposure to air pollutants may exacerbate respiratory conditions and decreased cancer survival [ 42 ]. 1,1,1-trichloroethane (TCA), also known as methyl chloroform, is a volatile organic compound (VOC) that has been one of the most widely used cleaning and degreasing solvents in the United States [ 43 , 44 ]. There is study found that ambient concentrations of VOCs were associated with increased risk of cancer-specific death (HR: 1.06, 95% CI: 1.02, 1.11) in Toronto, Canada in 1982 to 2004 [ 45 ]. Exposure to certain metals are strongly associated with increased risk of lung cancer mortality due to their toxic, carcinogenic, and oxidative properties [ 46 , 47 ]. For instance, elevated mortality rates from various cancers and respiratory diseases have been observed among members of the International Union of Bricklayers and Allied Craftworkers, potentially due to occupational exposure to cobalt and nickel [ 48 ]. In addition, study have reported elevated lung cancer mortality among workers exposed to hexavalent chromium, with a standardized mortality ratio of 1.39 (95% CI: 1.17, 1.63) in Burbank, California, USA [ 49 ]. Figure S2 shows average air pollution exposure intensities in New Mexico and nearby regions for the two identified chemicals during 1990–2019 estimated using the EWPM model. In New Mexico, emissions were mainly concentrated in the Albuquerque metropolitan area (Fig.S2). Additionally, many industrial facilities in northern and western Texas (including the El Paso area) and eastern Arizona also released these chemicals into the air (Fig.S2). Future research may focus more specifically on these areas, with particular attention to Albuquerque, New Mexico. Based on the results presented in Table 4 , a significant trend is observed in the adjHRs for increasing exposure intensities to 1,1,1-trichloroethane and cobalt. This suggests a positive association between higher exposure levels and higher risks of dying from LBC. Interestingly, residential exposure to low levels of 1,1,1-trichloroethane and low or medium levels of cobalt are insignificantly associated with dying from LBC. This finding may be due to complex biological interactions, where low-level exposure may enhance antioxidant activity, while higher exposure levels likely surpass the body’s defensive capacity, resulting in oxidative stress, metabolic dysfunction, and an immunosuppressive environment that exacerbates cancer progression [ 50 ]. Further studies are essential to explore these potential explanations and to determine whether this association reflects an actual effect under specific conditions. To explore potential interaction effects among the identified chemicals, we incorporated each pair of the nine chemicals into our Cox model, one pair at a time. For each pair, covariates were adjusted as outlined previously, ensuring that the variance inflation factor (VIF) for each variable in the model remained below 5 to avoid multicollinearity. Compared to the Cox models with exposure to only a single pollutant, the adjHR showed slight variations when accounting for residential exposure to an additional pollutant (Fig.S3). Notably, 1,1,1-trichloroethane consistently demonstrated a significant positive association with decreased LBCS, even after adjusting for any of the other pollutants. In contrast, cobalt’s association became insignificant when adjusted for 1,1,1-trichloroethane (Fig.S3). These findings suggest that 1,1,1-trichloroethane may act as an independent risk factor for decreased LBCS, while cobalt may not. In this study, individual air pollution exposure was assessed based on each participant's residential location. To account for uncertainties in potential exposure due to mobility beyond residential locations, we also conducted exposure assessments across different spatial scales, including census tract (2010) and zip code levels. Subsequently, individual-based exposure estimates were replaced with average exposure levels at the census tract and zip code levels corresponding to the residential location in the Cox models. This approach sought to provide a more comprehensive assessment of individual exposure by integrating spatial mobility factors. As shown in Table S2, results of the two identified chemicals at the census tract and zip code levels remained consistent with the individual-based estimates. This consistency implies that the identified associations between air pollution exposure and LBCS are stable and robust to exposure estimation at finer or coarser spatial resolutions. The alignment of results across scales may indicate small variability in exposure levels within these larger geographic units or that the exposure contrast within residential neighborhoods is preserved across scales. Additionally, it reinforces confidence in the use of these alternative spatial aggregations, particularly when finer-scale data may be unavailable or when examining populations that may have spatial mobility. This study also has a few limitations. First, individual-level variables such as smoking history and dietary factors were not available in this analysis. These factors can influence the risk of decreased of LBCS and potentially introduce additional confounding but were not collected for the cohort during the data collection stage. Future studies should aim to collect more detailed individual-level data to better account for confounding factors and provide a more comprehensive understanding of the relationship between air pollution and lower LBCS risk. Second, residential air pollution exposure was estimated based on residential locations which may not accurately reflect individual exposure levels. Variations in indoor pollution sources and time spent away from the residence (e.g., for work or travel) are not captured in this study, possibly leading to potential bias in the estimation of actual exposure. But the analysis conducted across different spatial scales indicates that the individual-based estimation is an appropriate scale for capturing residential air pollution exposure among patients. However, future studies could improve exposure assessment precision by incorporating mobility patterns and personal exposure monitoring. Third, our study used air monitoring data from New Mexico to calibrate the air pollution exposure model, where monitors are unevenly distributed across regions. This uneven distribution can introduce spatial bias, as monitoring sites are often concentrated in urban areas with higher population densities, leaving rural areas with limited coverage. Consequently, individuals residing in less monitored or rural regions may have less accurate exposure estimates. Future studies could address this limitation by incorporating advanced spatial interpolation techniques to enhance monitor coverage in under-monitored areas. 5. Conclusion This study investigated the effect of industrial air pollution exposure on LBCS in New Mexico, USA, from 1990 to 2019. It is the first study in New Mexico to examine the relationship between air pollution and LBCS using individual-level data, which is particularly significant given that LBC is the leading cause of cancer-related mortality in the state. The analysis employed a Cox model with time-varying covariates, enabling the modeling of dynamic exposure-response relationships over the survival period. The study identified significant associations between residential exposure to 1,1,1-trichloroethane and cobalt and a decreased LBCS. When exposure levels were categorized into four groups (unexposed, low, medium, high) based on annual average exposure during the survival period, the positive associations of the two air pollutants persisted. The extended Kaplan-Meier survival analysis indicated reduced survival probabilities for patients exposed to these two pollutants compared to those residing in unexposed areas during the study period. Additionally, chromium exposure has been found to be significantly associated with reduced LBCS among patients diagnosed at the distant stage. The findings underscore the urgent need for targeted public health interventions on reducing chemical exposures to improve patient survival outcomes. It is recommended that these findings be confirmed by further epidemiological, biological, and toxicological research. Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Ethics approval This study was conducted in accordance with the Declaration of Helsinki and was approved by the University of New Mexico Health Sciences Office of Research Human Protections Program (IRB). Consent to publish This study analyzed secondary, de-identified datasets only; no direct contact with individuals occurred and no identifiable information was used. Funding This work was supported by the National Cancer Institutes and the Cancer Center Support Grant through grant number P30CA118100; Contract HHSN2612018000014I, Task Order HHSN26100001 from the National Cancer Institute; the National Institute on Minority Health and Health Disparities (NIMHD) of the NIH under award number P50MD015706; and the National Institute of Environmental Health Sciences (NIEHS) of NIH under award numbers P42ES025589 and 1P30ES032755; and National Institute of Nursing Research of NIH under award number 1P20NR021824-01. We also acknowledge assistance provided by the Population Research Institute at Penn State University, which is supported by an infrastructure grant by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (P2CHD041025). The data used in the analyses were obtained from the New Mexico Tumor Registry (NMTR) and the U.S. Environmental Protection Agency (U.S. EPA). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding sources and data providers. This manuscript is the result of funding in whole or in part by the National Institutes of Health (NIH). It is subject to the NIH Public Access Policy. Through acceptance of this federal funding, NIH has been given a right to make this manuscript publicly available in PubMed Central upon the Official Date of Publication, as defined by NIH. Author Contribution **Xi Gong:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project Administration, Resources, Supervision, Validation, Visualization, Writing – review and editing.**Yanhong Huang:** Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing - Original Draft, Writing – review and editing.**Charles L Wiggins:** Data Curation, Funding Acquisition, Methodology, Resources, Validation, Writing – review and editing.**Angela L Meisner** Data Curation, Methodology, Resources, Validation, Writing – review and editing.**Yan Lin:** Funding acquisition, Methodology, Validation, Writing - Review & Editing.**Li Luo:** Funding acquisition, Methodology, Project Administration, Software, Supervision, Validation, Writing - Review & Editing. Acknowledgement This work was supported by the National Cancer Institutes and the Cancer Center Support Grant through grant number P30CA118100; Contract HHSN2612018000014I, Task Order HHSN26100001 from the National Cancer Institute; the National Institute on Minority Health and Health Disparities (NIMHD) of the NIH under award number P50MD015706; and the National Institute of Environmental Health Sciences (NIEHS) of NIH under award numbers P42ES025589 and 1P30ES032755; and National Institute of Nursing Research of NIH under award number 1P20NR021824-01. We also acknowledge assistance provided by the Population Research Institute at Penn State University, which is supported by an infrastructure grant by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (P2CHD041025). Data Availability The datasets generated during and/or analyzed during the current study are not publicly available due to Health Insurance Portability and Accountability Act. 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01:41:21\",\"extension\":\"html\",\"order_by\":9,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":163172,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"earlyproof.html\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7880883/v1/b05e249d653ba1eaf8e2e6c1.html\"},{\"id\":95877155,\"identity\":\"6501907e-0b85-4d9c-9044-0287360f7a8e\",\"added_by\":\"auto\",\"created_at\":\"2025-11-14 01:41:21\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":36182,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eGeographic distribution of emission sources and monitoring sites in New Mexico and its surrounding areas during 1990–2019.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Onlinefloatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7880883/v1/efb4008cd5c98e86c50848f4.png\"},{\"id\":95877163,\"identity\":\"e426f670-0ee9-48ec-adfd-ed886f404512\",\"added_by\":\"auto\",\"created_at\":\"2025-11-14 01:41:21\",\"extension\":\"jpeg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":215798,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eKaplan-Meier Survival Curve for lung and bronchus cancer (LBC) patients of exposed and unexposed groups to 1,1,1-trichloroethane and cobalt.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7880883/v1/6a56e8666165a5f27a5d26cb.jpeg\"},{\"id\":96453437,\"identity\":\"9aee4eaf-5419-494d-9531-868f0637eb04\",\"added_by\":\"auto\",\"created_at\":\"2025-11-21 09:59:51\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1330424,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7880883/v1/3fc9acd3-82c1-4efd-b7f7-0d23be505303.pdf\"},{\"id\":95877157,\"identity\":\"419482c0-7773-44f2-ac32-0c36aa5fadbf\",\"added_by\":\"auto\",\"created_at\":\"2025-11-14 01:41:21\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":990557,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplement.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7880883/v1/4bec1d88856d6b5ef5994579.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Industrial air pollution and lung and bronchus cancer survival in New Mexico, USA\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eLung cancer is the leading cause of cancer-related deaths, accounting for 1.8\\u0026nbsp;million deaths, or 18.7% of all cancer fatalities worldwide in 2022 [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. It is also the third most commonly diagnosed cancer in the United States in 2024 [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Between 2017 and 2021, nearly 50% of all lung cancer cases in the United States were diagnosed at a distant stage, indicating that the cancer had already spread from the lungs to other parts of the body [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. The overall five-year survival rate for lung cancer in the United States was mere 27.5% during the period from 2014 to 2020 [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. In New Mexico, lung and bronchus cancer (LBC) had an age-adjusted incidence rate of 32.7 per 100,000 population between 2017 and 2021, ranking as the second most commonly diagnosed cancer in the state [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. Among racial and ethnic groups, the Asian/Pacific Islander (AIAN) population had the highest incidence rate at 48.1 per 100,000 [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. From 2018 to 2022, the age-adjusted mortality rate for LBC in New Mexico was 21.4 per 100,000, making it the leading cause of cancer-related mortality in New Mexico [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eLung and bronchus cancer survival (LBCS) is influenced by a range of factors, including age at diagnosis, sex, cancer stage at diagnosis, and comorbidities such as cardiovascular, hypertension, and diabetes [\\u003cspan additionalcitationids=\\\"CR7 CR8 CR9\\\" citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. Increasing evidence links air pollution\\u0026mdash;particularly fine particulate matter (PM), nitrogen dioxide (NO\\u003csub\\u003e2\\u003c/sub\\u003e), ozone (O\\u003csub\\u003e3\\u003c/sub\\u003e), sulfur dioxide (SO\\u003csub\\u003e2\\u003c/sub\\u003e), and other pollutants\\u0026mdash;to reduce lung cancer survival [\\u003cspan additionalcitationids=\\\"CR12\\\" citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. Globally, air pollution was estimated to contribute approximately 14.1% to lung cancer deaths, making it the second leading cause after tobacco smoking in 2017 [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Studies in specific regions further illustrate the associations between air pollution and lung cancer survival. In northern China, significant correlations were observed between long-term exposures to PM\\u003csub\\u003e10\\u003c/sub\\u003e and SO\\u003csub\\u003e2\\u003c/sub\\u003e and decreased lung cancer survival from 1988 to 2009 [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. In the Californian, United States, air pollution exposures to NO\\u003csub\\u003e2\\u003c/sub\\u003e, O\\u003csub\\u003e3\\u003c/sub\\u003e, PM\\u003csub\\u003e10\\u003c/sub\\u003e, PM\\u003csub\\u003e2.5\\u003c/sub\\u003e after diagnosis for lung cancer patients can significantly shorten survival from 1988 to 2009 [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. Additionally, a strong association was observed between elevated blood lead (Pb) levels and decreased lung cancer survival among lead-exposed workers from 2003 to 2004 [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. Similarly, in South Korea, exposure to pollutants such as SO₂, carbon monoxide (CO), NO₂, PM\\u003csub\\u003e2.5\\u003c/sub\\u003e, and PM\\u003csub\\u003e10\\u003c/sub\\u003e was linked to reduced 1-, 3-, and 5-year lung cancer survival rates from 2009 to 2022 [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Furthermore, residential exposure to PM\\u003csub\\u003e2.5\\u003c/sub\\u003e, NO₂, and black carbon (BC) was positively associated with reduced lung cancer survival across Denmark, England, Norway, and Rome (Italy) based on the corresponding cohorts during 2000 to 2017 [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. Together, these findings highlight the urgent need to reduce air pollution as a key component of global efforts to increase lung cancer survival.\\u003c/p\\u003e\\u003cp\\u003eThe most commonly studied air pollutants linked to decreased lung cancer survival include PM, O\\u003csub\\u003e3\\u003c/sub\\u003e, SO\\u003csub\\u003e2\\u003c/sub\\u003e, CO, Pb, and NO\\u003csub\\u003e2\\u003c/sub\\u003e, which are the criteria air pollutants (CAPs). Industrial air pollution is a significant contributor to environmental contamination, which is generated by factories, power plants, refineries, and various other industrial facilities [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. Common pollutants from these sources include PM, SO₂, nitrogen oxides (NOₓ), volatile organic compounds (VOCs), and heavy metals, all of which pose serious health risks [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Exposure to industrial air pollution has been associated with numerous adverse health outcomes, including respiratory and cardiovascular diseases, adverse birth outcomes, and premature mortality [\\u003cspan additionalcitationids=\\\"CR24\\\" citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. In addition, outdoor air pollution is a main contributor to lung cancer incidence globally [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. These findings underscore the widespread effects of industrial air pollution on public health and suggest a potential link to lung cancer survival. However, the impact of many industrial air pollutants other than CAPs on lung cancer survival remains largely unexplored.\\u003c/p\\u003e\\u003cp\\u003eDespite known health risks of industrial air pollution, a research gap exists in understanding its specific link to LBCS, especially beyond commonly studied CAPs. The gap is particularly concerning for New Mexico, a state with high LBC incidence rate, mining legacy, rural landscape, economic challenges, and diverse population. These intersecting factors may contribute to disproportionate exposure burdens and poorer health outcomes, including lower cancer survival. Therefore, this study aims to explore the relationship between industrial air pollution and LBCS in the state of New Mexico from 1990 to 2019, with the potential to identify modifiable environmental risk factors and inform the development of targeted interventions and policies.\\u003c/p\\u003e\"},{\"header\":\"2. Data and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.1. Lung and bronchus cancer data\\u003c/h2\\u003e\\u003cp\\u003eAs a comprehensive, population-based cancer registry for New Mexico, the New Mexico Tumor Registry (NMTR) provides high-quality cancer surveillance data to support scientific research and cancer control initiatives [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. Our study cohort includes incident cases of LBC that were diagnosed among New Mexico residents during the time period 1990 to 2019. The NMTR collects individual-level data on various demographic and clinical characteristics, including age at diagnosis, sex, race/ethnicity, date of diagnosis, cancer stage at diagnosis, survival time (months), cause of death, date of last information (i.e., date of death for deceased patients and date last known to be alive for patients who have not yet died), and residential address at the time of diagnosis.\\u003c/p\\u003e\\u003cp\\u003eThis study initially identified a cohort of 26,337 patients diagnosed with LBC in New Mexico between 1990 and 2019 using data from NMTR. We excluded cases based on the following: diagnosis reported only through autopsy or death certificate (15.75%), lack of microscopic confirmation with diagnostic sources such as laboratory, visualization, radiographic, clinical, or unknown (5.87%), missing geocoded residential coordinates (2.2%), missing or unknown cause of death (0.9%), missing survival months (6.2%), and missing cancer stage at diagnosis (3.1%) [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. After applying these exclusions, a final sample of 18,273 LBC cases remained for analysis.\\u003c/p\\u003e\\u003cp\\u003eCancer site and morphology were coded according to the \\u003cem\\u003eInternational Classification of Diseases for Oncology\\u003c/em\\u003e, Second Edition (\\u003cem\\u003eICD-O-2\\u003c/em\\u003e) or Third Edition (\\u003cem\\u003eICD-O-3\\u003c/em\\u003e), depending on the year of diagnosis [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. According to The Surveillance, Epidemiology, and End Results (SEER) Program, cause-specific death classification variable is defined by taking into account cause of death in conjunction with sequence of tumor occurrence (i.e., only one tumor or the first of multiple tumors), site of the original cancer diagnosis, and comorbidities (e.g., AIDS and/or site-related diseases), with the aim of capturing deaths that were related to the specific cancer [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. The SEER historic staging scheme provided information for in situ and invasive cancers, with the invasive cancers being divided into the following four stage categories: localized to the primary tumor site (localized), tumor with regional spread or metastases to regional lymph node (regional), tumor with distant metastases (distant), or unknown stage [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. The diagnosis age (years) refers to the patient's age at the time of their first LBC diagnosis.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.2. Estimation of exposure\\u003c/h2\\u003e\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.2.1. Industrial air pollution emission and air quality monitoring data\\u003c/h2\\u003e\\u003cp\\u003eThe U.S. Environmental Protection Agency\\u0026rsquo;s (EPA) Toxics Release Inventory (TRI) program is a federally mandated initiative requiring industrial facilities nationwide to submit annual reports detailing chemical emissions to the environment, including the types, quantities, and geographic locations of releases [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. The primary objective of the TRI program is to monitor the management of toxic chemicals that pose potential risks to human health and the environment [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. For this study, emission data were sourced from TRI records of industrial facilities located in New Mexico and neighboring areas, including northwestern Texas, southern Colorado, and eastern Arizona. Between 1990 and 2019, 577 industrial facilities across these regions released 188 distinct chemicals into the atmosphere (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e), with annual emissions calculated by aggregating emissions from both stacks and fugitive sources. Additionally, air quality monitoring data were collected from the U.S. EPA\\u0026rsquo;s Air Quality System (AQS) DataMart, which compiles data collected through the national ambient air monitoring program, including raw measurements and aggregated values (such as 8-hour averages, daily averages, and annual averages) [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. In New Mexico, between 1990 and 2019, 112 monitoring sites recorded data on 271 different air pollutants (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). To ensure consistency with the timescale of the emission data, annual average monitoring records were used for the subsequent analyses.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.2.2. Air pollution exposure assessment\\u003c/h2\\u003e\\u003cp\\u003eThis study utilized a calibrated Emission Weighted Proximity Model (EWPM) that was previously used to assess exposure to industrial air pollution at residential locations [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]. The EWPM estimates exposure intensity​ for a given pollutant at a given location by incorporating emission rate, duration, and distance from each emission source. The model has a key parameter, the effective distance, which defines the threshold beyond which emissions are excluded. Using monitoring sites as virtual exposure receptors, estimated exposure intensities at these locations were validated against monitoring data across varying effective distances. Therefore, effective distances were calibrated only for pollutants with both emission data and sufficient monitoring data (nine pollutants in this study), with each pollutant calibrated separately to minimize misclassifications of exposure assessment. The optimal effective distance for each chemical was determined by selecting the threshold that maximized the positive Spearman rank correlation between estimated and observed values. For further methodological details, please refer to [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR35 CR36\\\" citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]. For each chemical with a calibrated optimal effective distance, we used the EWPM with the corresponding distance to estimate annual average exposure intensities at each case's residential location for every year throughout the survival period. The exposure period for each patient was defined as the time from the midpoint of the diagnosis year to the midpoint of the year of death or censoring.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.3. Identification of potential risk factors\\u003c/h2\\u003e\\u003cp\\u003eWe employed a Cox proportional hazards model (hereafter referred to as the Cox model) with time-dependent covariates to evaluate the associations (hazard ratios [HRs]) between residential exposure to industrial air pollution and lung and bronchus cancer survival (LBCS) in New Mexico from 1990 to 2019, adjusting for relevant covariates [\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]. Annual average industrial air pollution exposure was defined as a time-dependent covariate to account for changes in exposure over the follow-up period. The selection of covariates in this analysis was guided by a directed acyclic graph (DAG), presented in Figure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e. Covariates included age at diagnosis, sex (male or female), race/ethnicity (Non-Hispanic White, Hispanic, Non-Hispanic Black, non-Hispanic AIAN, non-Hispanic Asian/Pacific Islander (API), and Non-Hispanic Others), stage at diagnosis (localized, regional, distant, or unknown stage), and the urbanicity of the residential address (urban or rural). Urban and rural classifications were determined based on U.S. Census Bureau definitions, using census-designated criteria linked to the residential address at the time of diagnosis [\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e]. We assessed the proportional hazards assumption using Schoenfeld residuals and identified significant violations of the assumptions for two covariates: age at diagnosis and LBC diagnosis stage. To address this, we used a stratified Cox model, where the violating covariates were included as stratification variables (diagnosis age group: \\u0026lt;50, 50\\u0026ndash;59, 60\\u0026ndash;69, 70\\u0026ndash;79, and \\u0026gt;\\u0026thinsp;=\\u0026thinsp;80 years; LBC diagnosis stage: localized, regional, distant, or unknown stage). Each industrial air pollutant was evaluated individually in a single-pollutant Cox model, where it was included as the independent variable to assess its effect on LBCS. To examine potential effect modification by stage at diagnosis, we conducted stratified analyses using the Cox model separately for localized, regional, distant, and unknown stages, adjusting for all covariates except stage to capture stage-specific effects.\\u003c/p\\u003e\\u003cp\\u003eThe extended Kaplan-Meier estimator with time-varying covariates [\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e] was employed to estimate the overall survival information for the cohort, focusing on the key pollutants obtained in prior analyses (Section 2.2). Median survival times and five-year survival probabilities were calculated for the entire cohort. To further examine the exposure-response relationships, patients in the exposed group were further categorized into unexposed, low, medium, and high groups based on a cutoff at 0 exposure and the tertiles of annual average exposure intensities among those with exposures greater than 0 during the survival period. These exposure groups were compared to the unexposed group (exposure intensity\\u0026thinsp;=\\u0026thinsp;0) respectively. Additionally, a Wald test for ordinal exposure levels was conducted to assess the significance of the trend of the associations. Bonferroni correction [\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e] was used to correct for multiple testing (adjusted \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05).\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cp\\u003eThe descriptive statistics and demographic characteristics of the study cohort are summarized in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. The analysis included 18,273 patients diagnosed with LBC between 1990 and 2019. The cohort was primarily composed of males (54.99%) and non-Hispanic Whites (74.43%), with an average age of 69.50 years at diagnosis. Most patients were diagnosed at distant stage of LBC, comprising 52.86% of the cohort. Over the study period, 84.40% patients (N\\u0026thinsp;=\\u0026thinsp;15,422) died specifically of LBC. Additionally, the majority of patients (77.94%) resided in urban areas at the time of diagnosis. The median survival time varied across cancer stages, with patients in the localized stage exhibiting median survival of 32 months, while those diagnosed at the distant stage had median survival of 4 months.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eCharacteristics of patients with lung and bronchus cancer (LBC) in New Mexico by stage of diagnosis, summarized across all stages (Total) and deaths, 1990\\u0026ndash;2019.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"11\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eCharacteristics\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"8\\\" nameend=\\\"c9\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003eLBC Diagnosis\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003eLBC Death\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eLocalized (n\\u0026thinsp;=\\u0026thinsp;2855)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003eRegional (n\\u0026thinsp;=\\u0026thinsp;4290)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003eDistant (n\\u0026thinsp;=\\u0026thinsp;9659)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003eUnknown\\u003c/p\\u003e\\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;1469)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003eTotal (n\\u0026thinsp;=\\u0026thinsp;18,273)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003eDeath (n\\u0026thinsp;=\\u0026thinsp;15,422)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eRace/ethnicity (n (%))\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNon-Hispanic White\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2125 (74.43)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003e3186 (74.26)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003e6906 (71.50)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003e1001 (68.14)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e13,218 (72.34)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e11,198 (72.61)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNon-Hispanic Black\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e57 (1.89)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003e79 (1.84)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003e187 (1.94)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003e26 (1.77)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e349 (1.91)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e301 (1.95)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHispanic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e606 (21.23)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003e909 (21.19)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003e2294 (23.75)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003e398 (27.09)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e4207 (23.02)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e3532 (22.90)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAmerican Indian and Alaska Native\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e46 (1.61)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003e78 (1.82)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003e176 (1.82)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003e23 (1.57)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e323 (1.77)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e274 (1.78)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAsian/Pacific Islander\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e11 (0.39)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003e34 (0.79)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003e79 (0.82)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003e9 (0.61)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e133 (0.73)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e101 (0.65)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNon-Hispanic Others\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e10 (0.35)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003e4 (0.09)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003e17 (0.18)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003e12 (0.82)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e43 (0.24)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e16 (0.10)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eSex (n (%))\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1570 (54.99)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003e2519 (58.72)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003e5584 (57.81)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003e854 (58.13)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e10,527 (57.61)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e8916 (57.81)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFemale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1285 (45.01)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003e1771 (41.28)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003e4075 (42.19)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003e615 (41.87)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e7746 (42.39)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e6506 (42.19)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eUrbanicity (n (%))\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eUrban\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2282 (79.93)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003e3379 (79.52)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003e7481 (79.18)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003e1100 (74.88)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e14,242 (77.94)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e12,026 (77.80)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRural\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e573 (20.07)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003e911 (20.48)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003e2178 (20.82)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003e369 (25.12)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e4031 (22.06)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e3396 (22.20)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eDeath (n (%))\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAlive/Death (other causes)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e962 (33.70)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003e740 (17.25)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003e914 (9.46)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003e235 (16.00)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e2851 (15.60)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e̶\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDeath (cancer cause-specific)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1893 (66.30)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003e3550 (82.75)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003e8745 (90.54)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003e1234 (84.00)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e15,422 (84.40)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e̶\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eAge at diagnosis (mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e71.82\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.24\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003e69.26\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.81\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003e68.46\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;10.42\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003e72.54\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;10.44\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e69.50\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;10.21\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e69.34\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;10.29\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;50\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e46 (1.61)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003e125 (2.91)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003e391 (4.05)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003e33 (2.25)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e595 (3.26)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e533 (3.46)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e50\\u0026ndash;59\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e234 (8.20)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003e585 (13.64)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003e1493 (15.46)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003e131 (8.92)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e2443 (13.37)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e2132 (13.82)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e60\\u0026ndash;69\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e770 (26.97)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003e1377 (32.10)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003e3138 (32.49)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003e358 (24.37)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e5643 (30.88)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e4771 (30.94)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e70\\u0026ndash;79\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1211 (42.42)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003e1554 (36.22)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003e3222 (33.36)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003e555 (37.78)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e6542 (35.80)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e5435 (35.24)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026gt;=80\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e594 (20.81)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003e649 (15.13)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003e1415 (14.65)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003e392 (26.68)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e3050 (16.69)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e2551 (16.54)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eMedian survival months (interquartile range)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e32 (120)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003e20 (62)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003e4 (19)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003e12 (52)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e12 (30)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e12 (26)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003csup\\u003eSD: Standard deviation\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003eFollowing the calibration of effective distances for the selected nine chemicals, exposure intensities for the nine chemicals estimated using the EWPM exhibited statistically significant positive associations (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) with the corresponding monitoring data (Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). The optimal effective distances of these nine chemicals ranged from 6 km to 50 km, with correlation coefficients varying between 0.060 and 0.999 (Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eAdjusted HR (95% CI) for the effect of industrial air pollution exposure on lung and bronchus cancer survival (LBCS) in New Mexico, 1990\\u0026ndash;2019, stratified by cancer stage (Localized, Regional, Distant, Unknown) and summarized across all stages (Total).\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"8\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c7\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003eAdjusted HR\\u003csup\\u003eb\\u003c/sup\\u003e (95% CIs)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003ep\\u003c/em\\u003e-value for interaction of stratified stage and air pollution exposure\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePolluant (CAS number)\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eLocalized\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eRegional\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eDistant\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eUnknown\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eTotal\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e1,1,1-Trichloroethane (71556)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.96 (0.79, 1.17)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.21 (1.08, 1.34) *\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.27 (1.17, 1.37) *\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.84 (0.64, 1.05)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.15 (1.09, 1.21) *\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.002*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCobalt (7440484)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.10 (0.88, 1.34)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.14 (0.95, 1.34)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.20 (1.10, 1.30) *\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.83 (0.60, 1.07)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.14 (1.07, 1.21) *\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.003*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eChromium (7440473)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.11 (0.93, 1.31)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.08 (0.99, 1.18)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.14 (1.07, 1.21) *\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.91 (0.71, 1.13)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.08 (1.01, 1.15)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.001*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCopper (7440508)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.10 (0.90, 1.20)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.11 (0.96, 1.27)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.14 (1.02, 1.27)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.80 (0.60, 1.02)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.04 (0.98, 1.10)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.294\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEthylbenzene (100414)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.05 (0.84, 1.30)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.11 (0.95, 1.28)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.98 (0.86, 1.11)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.98 (0.75, 1.22)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.00 (0.94, 1.06)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.169\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eChlorine (7782505)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.97 (0.82, 1.13)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.02 (0.90, 1.16)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.08 (0.96, 1.21)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.91 (0.71, 1.12)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.01 (0.97, 1.05)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.093\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eManganese (7439965)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.16 (0.89, 1.44)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.02 (0.80, 1.24)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.17 (0.95, 1.40)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.09 (0.80, 1.39)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.02 (0.93, 1.11)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.161\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e1,2,4-Trimethylbenzene (95636)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.02 (0.79, 1.28)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.12 (0.99, 1.26)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.97 (0.84, 1.11)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.84 (0.60, 1.09)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.95 (0.90, 1.01)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.170\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMercury (7439976)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.34 (0.97, 1.71)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.15 (1.00, 1.31)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.96 (0.76, 1.17)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.74 (0.44, 1.06)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.91 (0.82, 1.01)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.848\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e*Statistically significant after Bonferroni correction for multiple comparisons at level 0.05.\\u003c/p\\u003e\\u003cp\\u003e\\u003csup\\u003ea\\u003c/sup\\u003e A unique identification number assigned by Chemical Abstracts Service (CAS) to every chemical substance described in the open scientific literature; order in ascending \\u003cem\\u003ep\\u003c/em\\u003e-values of the adjusted HR in total stage.\\u003c/p\\u003e\\u003cp\\u003e\\u003csup\\u003eb\\u003c/sup\\u003e Adjusted for diagnosis age, gender, race/ethnicity, and urbanicity.\\u003c/p\\u003e\\u003cp\\u003e\\u003csup\\u003ec\\u003c/sup\\u003e Adjusted for diagnosis age, gender, race/ethnicity, diagnosis stage, and urbanicity.\\u003c/p\\u003e\\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e presents the adjusted HR (adjHR) for the impact of exposure to nine industrial air pollutants on LBCS in New Mexico, stratified by cancer stage (localized, regional, distant, unknown) and summarized across all stages (represented as total stage hereafter). Significant associations with decreased LBCS were observed for exposure to 1,1,1-Trichloroethane (adjHR: 1.15, 95% CI: 1.09, 1.21) and cobalt (adjHR: 1.14, 95% CI: 1.07, 1.21) in the total stage. Specifically, exposure to 1,1,1-trichloroethane and cobalt were associated with 15% and 14% higher risk of dying from LBC, respectively, after adjusting for covariates. Exposure to chromium during the survival period was associated with increased risk of dying from LBC in the total stage (adjHR: 1.08, 95% CI: 1.01, 1.15). In contrast, exposure to mercury (adjHR: 0.91, 95% CI: 0.82, 1.01) and 1,2,4-Trimethylbenzene (adjHR: 0.95, 95% CI: 0.90, 1.01) showed associations with decreased risk of dying from LBC in the analysis for all stages combined. However, all the associations are statistically insignificant after Bonferroni correction for multiple comparisons, further research is needed to investigate these effects.\\u003c/p\\u003e\\u003cp\\u003eSpecifically, exposure to 1,1,1-trichloroethane was significantly associated with increased risk of dying from LBC in the regional stage (adjHR: 1.21, 95% CI: 1.08, 1.34) and distant stage (adjHR: 1.27, 95% CI: 1.17, 1.37), though it showed no significant association in the localized and unknown stages (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). These findings indicate that among LBC patients diagnosed at the regional stage, exposure to 1,1,1-trichloroethane during the survival period was associated with 21% higher risk of dying from LBC compared to unexposed patients; and for patients diagnosed at the distant stage, a 27% increased risk of dying from LBC was observed. Similarly, cobalt exhibited significant association with reduced LBCS in the distant stage (adjHR: 1.20, 95% CI: 1.10, 1.30), with no significant associations observed in the localized and regional stages (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Additionally, exposure to chromium was significantly associated with an increased risk of dying from LBC only among patients diagnosed at the distant stage (adjHR: 1.14, 95% CI: 1.07, 1.21).\\u003c/p\\u003e\\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e presents the estimated median survival time and five-year survival rates for the cohort stratified by exposure to two significant air pollutants identified above (statistically significant adjHRs in the total stage in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). For both pollutants, the unexposed group demonstrated a longer median survival time (15 months vs. 12 months) and higher five-year survival rates compared to the exposed group (1,1,1-trichloroethane: 28.23% vs. 21.70%; cobalt: 28.02% vs. 21.66%). Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e depicts the survival curves for patients exposed and unexposed to the two air pollutants, illustrating the probability of survival over time (in months) for each group, and result of for Log-rank test (p value in the figure). The unexposed group (represented in red) consistently exhibited a significant higher survival probability than the exposed group (represented in blue) throughout the studied period.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eMedian overall survival time and five-year overall survival rate and air pollution exposure in New Mexico, 1990\\u0026ndash;2019.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePollutant (CAS number)\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eExposure intensity\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eMedian survival time in months (95% CIs)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eFive-year survival rate in percentage (95% CIs)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e1,1,1-Trichloroethane (71556)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eUnexposed\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e15 (14,15)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e28.23 (27.45, 29.04)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e1,1,1-Trichloroethane (71556)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eExposed\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e12 (11,13)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e21.70 (19.47, 24.18)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCobalt (7440484)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eUnexposed\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e14 (14,15)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e28.02 (27.25, 28.81)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCobalt (7440484)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eExposed\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e11 (10,13)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e21.66 (18.96, 24.75)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003csup\\u003ea\\u003c/sup\\u003e A unique identification number assigned by Chemical Abstracts Service (CAS) to every chemical substance described in the open scientific literature, order alphabetically.\\u003c/p\\u003e\\u003cp\\u003e\\u003csup\\u003eb\\u003c/sup\\u003e Annual average residential air pollution exposure intensity during survival period.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eAdjusted HR (95% CI) for the effect of exposure intensities of industrial air pollution on lung and bronchus cancer survival (LBCS) in New Mexico, 1990\\u0026ndash;2019.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"7\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003ePollutant (CAS number)\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" morerows=\\\"1\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eExposure intensity\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e\\u003cp\\u003eMortality (LBC cause-specific)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eAdjusted HR\\u003csup\\u003ec\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003ep\\u003c/em\\u003e-value for trend\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003en\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e%\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e1,1,1-Trichloroethane (71556)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eUnexposed (0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e16,258\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e90.27\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.00 (referenced)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e1,1,1-Trichloroethane (71556)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eLow (0\\u0026ndash;2247.86)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e434\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e2.41\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.03 (0.94, 1.13)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e1,1,1-Trichloroethane (71556)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eMedium (2247.87\\u0026ndash;6127.70)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e523\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e2.90\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.17 (1.08, 1.26)*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e1,1,1-Trichloroethane (71556)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eHigh (\\u0026gt;\\u0026thinsp;6127.70)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e805\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4.47\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.19 (1.11, 1.27)*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eCobalt (7440484)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eUnexposed (0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e16,874\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e93.64\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.00 (referenced)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eCobalt (7440484)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eLow (0\\u0026ndash;35.99)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e390\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e2.16\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.08 (0.98, 1.19)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eCobalt (7440484)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eMedium (36.00\\u0026ndash;68.21)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e309\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.71\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.10 (0.98, 1.22)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eCobalt (7440484)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eHigh (\\u0026gt;\\u0026thinsp;68.21)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e447\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e2.48\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.15 (1.05, 1.25)*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e*Statistically significant after Bonferroni correction for multiple comparisons at level 0.05.\\u003c/p\\u003e\\u003cp\\u003e\\u003csup\\u003ea\\u003c/sup\\u003e A unique identification number assigned by Chemical Abstracts Service (CAS) to every chemical substance described in the open scientific literature, order alphabetically.\\u003c/p\\u003e\\u003cp\\u003e\\u003csup\\u003eb\\u003c/sup\\u003e Annual average residential air pollution exposure intensity during the survival period.\\u003c/p\\u003e\\u003cp\\u003e\\u003csup\\u003ec\\u003c/sup\\u003e Adjusted for diagnosis age, gender, race/ethnicity, diagnosis stage, and urbanicity.\\u003c/p\\u003e\\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e shows the effects of varying levels of exposure (unexposed, low, median, high) to two pollutants identified as having significant adjusted HRs in the total stage on LBCS. Exposure levels were defined based on the annual average exposure during the survival period. For 1,1,1-trichloroethane, medium and high exposure levels were linked to significantly elevated risks of dying from LBC, while the association for low exposure group was not statistically significant. Specifically, medium exposure increased the risk by 17% (adjHR: 1.17, 95% CI: 1.08, 1.26), and high exposure was linked to 19% (adjHR: 1.19, 95% CI: 1.11, 1.27) higher risk of dying from LBC compared to the unexposed group. For cobalt, high exposure was associated with a significant increased risk in dying from LBC (adjHR: 1.15, 95% CI: 1.05, 1.25), while both low and medium exposure levels showed no significant association with reduced LBCS. Notably, significant linear trends were observed among associations between residential exposure to both identified chemicals (1,1,1-trichloroethane and cobalt) and reduced LBCS.\\u003c/p\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eOur study contributes to the growing body of evidence linking air pollution exposure to LBCS. By analyzing nine industrial air pollutants across New Mexico from 1990 to 2019 using the large patient-level datasets collected by the NMTR, we identified significant associations between residential air pollution exposure to 1,1,1-trichloroethane and cobalt and reduced LBCS in the total stage (all diagnose stages combined).\\u003c/p\\u003e\\u003cp\\u003eThe findings are consistent with previous research indicating that exposure to air pollutants may exacerbate respiratory conditions and decreased cancer survival [\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]. 1,1,1-trichloroethane (TCA), also known as methyl chloroform, is a volatile organic compound (VOC) that has been one of the most widely used cleaning and degreasing solvents in the United States [\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e]. There is study found that ambient concentrations of VOCs were associated with increased risk of cancer-specific death (HR: 1.06, 95% CI: 1.02, 1.11) in Toronto, Canada in 1982 to 2004 [\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]. Exposure to certain metals are strongly associated with increased risk of lung cancer mortality due to their toxic, carcinogenic, and oxidative properties [\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e]. For instance, elevated mortality rates from various cancers and respiratory diseases have been observed among members of the International Union of Bricklayers and Allied Craftworkers, potentially due to occupational exposure to cobalt and nickel [\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e]. In addition, study have reported elevated lung cancer mortality among workers exposed to hexavalent chromium, with a standardized mortality ratio of 1.39 (95% CI: 1.17, 1.63) in Burbank, California, USA [\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eFigure S2 shows average air pollution exposure intensities in New Mexico and nearby regions for the two identified chemicals during 1990\\u0026ndash;2019 estimated using the EWPM model. In New Mexico, emissions were mainly concentrated in the Albuquerque metropolitan area (Fig.S2). Additionally, many industrial facilities in northern and western Texas (including the El Paso area) and eastern Arizona also released these chemicals into the air (Fig.S2). Future research may focus more specifically on these areas, with particular attention to Albuquerque, New Mexico.\\u003c/p\\u003e\\u003cp\\u003eBased on the results presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, a significant trend is observed in the adjHRs for increasing exposure intensities to 1,1,1-trichloroethane and cobalt. This suggests a positive association between higher exposure levels and higher risks of dying from LBC. Interestingly, residential exposure to low levels of 1,1,1-trichloroethane and low or medium levels of cobalt are insignificantly associated with dying from LBC. This finding may be due to complex biological interactions, where low-level exposure may enhance antioxidant activity, while higher exposure levels likely surpass the body\\u0026rsquo;s defensive capacity, resulting in oxidative stress, metabolic dysfunction, and an immunosuppressive environment that exacerbates cancer progression [\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e]. Further studies are essential to explore these potential explanations and to determine whether this association reflects an actual effect under specific conditions.\\u003c/p\\u003e\\u003cp\\u003eTo explore potential interaction effects among the identified chemicals, we incorporated each pair of the nine chemicals into our Cox model, one pair at a time. For each pair, covariates were adjusted as outlined previously, ensuring that the variance inflation factor (VIF) for each variable in the model remained below 5 to avoid multicollinearity. Compared to the Cox models with exposure to only a single pollutant, the adjHR showed slight variations when accounting for residential exposure to an additional pollutant (Fig.S3). Notably, 1,1,1-trichloroethane consistently demonstrated a significant positive association with decreased LBCS, even after adjusting for any of the other pollutants. In contrast, cobalt\\u0026rsquo;s association became insignificant when adjusted for 1,1,1-trichloroethane (Fig.S3). These findings suggest that 1,1,1-trichloroethane may act as an independent risk factor for decreased LBCS, while cobalt may not.\\u003c/p\\u003e\\u003cp\\u003eIn this study, individual air pollution exposure was assessed based on each participant's residential location. To account for uncertainties in potential exposure due to mobility beyond residential locations, we also conducted exposure assessments across different spatial scales, including census tract (2010) and zip code levels. Subsequently, individual-based exposure estimates were replaced with average exposure levels at the census tract and zip code levels corresponding to the residential location in the Cox models. This approach sought to provide a more comprehensive assessment of individual exposure by integrating spatial mobility factors. As shown in Table S2, results of the two identified chemicals at the census tract and zip code levels remained consistent with the individual-based estimates. This consistency implies that the identified associations between air pollution exposure and LBCS are stable and robust to exposure estimation at finer or coarser spatial resolutions. The alignment of results across scales may indicate small variability in exposure levels within these larger geographic units or that the exposure contrast within residential neighborhoods is preserved across scales. Additionally, it reinforces confidence in the use of these alternative spatial aggregations, particularly when finer-scale data may be unavailable or when examining populations that may have spatial mobility.\\u003c/p\\u003e\\u003cp\\u003eThis study also has a few limitations. First, individual-level variables such as smoking history and dietary factors were not available in this analysis. These factors can influence the risk of decreased of LBCS and potentially introduce additional confounding but were not collected for the cohort during the data collection stage. Future studies should aim to collect more detailed individual-level data to better account for confounding factors and provide a more comprehensive understanding of the relationship between air pollution and lower LBCS risk.\\u003c/p\\u003e\\u003cp\\u003eSecond, residential air pollution exposure was estimated based on residential locations which may not accurately reflect individual exposure levels. Variations in indoor pollution sources and time spent away from the residence (e.g., for work or travel) are not captured in this study, possibly leading to potential bias in the estimation of actual exposure. But the analysis conducted across different spatial scales indicates that the individual-based estimation is an appropriate scale for capturing residential air pollution exposure among patients. However, future studies could improve exposure assessment precision by incorporating mobility patterns and personal exposure monitoring.\\u003c/p\\u003e\\u003cp\\u003eThird, our study used air monitoring data from New Mexico to calibrate the air pollution exposure model, where monitors are unevenly distributed across regions. This uneven distribution can introduce spatial bias, as monitoring sites are often concentrated in urban areas with higher population densities, leaving rural areas with limited coverage. Consequently, individuals residing in less monitored or rural regions may have less accurate exposure estimates. Future studies could address this limitation by incorporating advanced spatial interpolation techniques to enhance monitor coverage in under-monitored areas.\\u003c/p\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eThis study investigated the effect of industrial air pollution exposure on LBCS in New Mexico, USA, from 1990 to 2019. It is the first study in New Mexico to examine the relationship between air pollution and LBCS using individual-level data, which is particularly significant given that LBC is the leading cause of cancer-related mortality in the state. The analysis employed a Cox model with time-varying covariates, enabling the modeling of dynamic exposure-response relationships over the survival period. The study identified significant associations between residential exposure to 1,1,1-trichloroethane and cobalt and a decreased LBCS. When exposure levels were categorized into four groups (unexposed, low, medium, high) based on annual average exposure during the survival period, the positive associations of the two air pollutants persisted. The extended Kaplan-Meier survival analysis indicated reduced survival probabilities for patients exposed to these two pollutants compared to those residing in unexposed areas during the study period. Additionally, chromium exposure has been found to be significantly associated with reduced LBCS among patients diagnosed at the distant stage. The findings underscore the urgent need for targeted public health interventions on reducing chemical exposures to improve patient survival outcomes. It is recommended that these findings be confirmed by further epidemiological, biological, and toxicological research.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eCompeting Interests\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors have no relevant financial or non-financial interests to disclose.\\u003c/p\\u003e\\n\\u003ch2\\u003eEthics approval\\u003c/h2\\u003e\\n\\u003cp\\u003eThis study was conducted in accordance with the Declaration of Helsinki and was approved by the University of New Mexico Health Sciences Office of Research Human Protections Program (IRB).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to publish\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study analyzed secondary, de-identified datasets only; no direct contact with individuals occurred and no identifiable information was used.\\u003c/p\\u003e\\n\\u003ch2\\u003eFunding\\u003c/h2\\u003e\\n\\u003cp\\u003eThis work was supported by the National Cancer Institutes and the Cancer Center Support Grant through grant number P30CA118100; Contract HHSN2612018000014I, Task Order HHSN26100001 from the National Cancer Institute; the National Institute on Minority Health and Health Disparities (NIMHD) of the NIH under award number P50MD015706; and the National Institute of Environmental Health Sciences (NIEHS) of NIH under award numbers P42ES025589 and 1P30ES032755; and National Institute of Nursing Research of NIH under award number 1P20NR021824-01. We also acknowledge assistance provided by the Population Research Institute at Penn State University, which is supported by an infrastructure grant by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (P2CHD041025). The data used in the analyses were obtained from the New Mexico Tumor Registry (NMTR) and the U.S. Environmental Protection Agency (U.S. EPA). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding sources and data providers. This manuscript is the result of funding in whole or in part by the National Institutes of Health (NIH). It is subject to the NIH Public Access Policy. Through acceptance of this federal funding, NIH has been given a right to make this manuscript publicly available in PubMed Central upon the Official Date of Publication, as defined by NIH.\\u003c/p\\u003e\\n\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\n\\u003cp\\u003e**Xi Gong:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project Administration, Resources, Supervision, Validation, Visualization, Writing \\u0026ndash; review and editing.**Yanhong Huang:** Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing - Original Draft, Writing \\u0026ndash; review and editing.**Charles L Wiggins:** Data Curation, Funding Acquisition, Methodology, Resources, Validation, Writing \\u0026ndash; review and editing.**Angela L Meisner** Data Curation, Methodology, Resources, Validation, Writing \\u0026ndash; review and editing.**Yan Lin:** Funding acquisition, Methodology, Validation, Writing - Review \\u0026amp; Editing.**Li Luo:** Funding acquisition, Methodology, Project Administration, Software, Supervision, Validation, Writing - Review \\u0026amp; Editing.\\u003c/p\\u003e\\n\\u003ch2\\u003eAcknowledgement\\u003c/h2\\u003e\\n\\u003cp\\u003eThis work was supported by the National Cancer Institutes and the Cancer Center Support Grant through grant number P30CA118100; Contract HHSN2612018000014I, Task Order HHSN26100001 from the National Cancer Institute; the National Institute on Minority Health and Health Disparities (NIMHD) of the NIH under award number P50MD015706; and the National Institute of Environmental Health Sciences (NIEHS) of NIH under award numbers P42ES025589 and 1P30ES032755; and National Institute of Nursing Research of NIH under award number 1P20NR021824-01. We also acknowledge assistance provided by the Population Research Institute at Penn State University, which is supported by an infrastructure grant by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (P2CHD041025).\\u003c/p\\u003e\\n\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\n\\u003cp\\u003eThe datasets generated during and/or analyzed during the current study are not publicly available due to Health Insurance Portability and Accountability Act.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eWHO, Lung (2023) cancer \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.who.int/news-room/fact-sheets/detail/lung-cancer\\u003c/span\\u003e\\u003cspan address=\\\"https://www.who.int/news-room/fact-sheets/detail/lung-cancer\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eNCI. 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Am J Ind Med 47:10\\u0026ndash;19. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1002/ajim.20115\\u003c/span\\u003e\\u003cspan address=\\\"10.1002/ajim.20115\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLipworth L, Panko JM, Allen BC, Mumma MT, Vincent MJ, Bare JL et al (2025) Lung cancer mortality among aircraft manufacturing workers with long-term, low-level, hexavalent chromium exposure. J Occup Environ Hyg 22:214\\u0026ndash;227. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1080/15459624.2024.2439817\\u003c/span\\u003e\\u003cspan address=\\\"10.1080/15459624.2024.2439817\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eU.S. EPA (2011) TOXICOLOGICAL REVIEW OF\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"cancer-causes-and-control\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"caco\",\"sideBox\":\"Learn more about [Cancer Causes \\u0026 Control](https://www.springer.com/journal/10552)\",\"snPcode\":\"10552\",\"submissionUrl\":\"https://submission.nature.com/new-submission/10552/3\",\"title\":\"Cancer Causes \\u0026 Control\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"industrial air pollution, lung and bronchus cancer, survival, GIS, environmental health, exposure assessment\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7880883/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7880883/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003ePurpose\\u003c/h2\\u003e\\u003cp\\u003eLung and bronchus cancer (LBC) is the leading cause of cancer-related deaths. This study examines the relationship between exposure to industrial air pollution and lung and bronchus cancer survival (LBCS) in New Mexico, USA, from 1990 to 2019.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e\\u003cp\\u003eThe analysis included data from 26,337 lung and bronchus cancer patients. Residential exposure to nine industrial air pollutants was estimated for each patient using the Emission Weighted Proximity Model (EWPM), which integrates industrial air emission data and air quality monitoring data form the U.S. Environmental Protection Agency\\u0026rsquo;s (EPA). Cox proportional hazards modeling was applied to identify industrial air pollutants as decreased LBCS risk factors, adjusting for age at diagnosis, gender, cancer stage, race/ethnicity, and urbanization of residential address.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e\\u003cp\\u003eResults show that residential exposure to 1,1,1-trichloroethane and cobalt during the survival period was significantly associated with decreased LBCS (adjusted hazard ratio [adjHR] for 1,1,1-trichloroethane: 1.15, 95% CI: 1.09, 1.21 and adjHR for cobalt: 1.14, 95% CI: 1.07, 1.21) among all patients. When exposure levels were categorized into four groups (unexposed, low, medium, high) based on annual average exposure during the survival period, the associations remained consistent. The extended Kaplan-Meier survival analysis indicated reduced survival probabilities for patients exposed to these two pollutants compared to those residing in unexposed areas during the study period.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e\\u003cp\\u003eThis study underscores the negative influence of industrial air pollution on LBCS and emphasizes the need for targeted public health interventions in high-exposure areas.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Industrial air pollution and lung and bronchus cancer survival in New Mexico, USA\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-11-14 01:41:16\",\"doi\":\"10.21203/rs.3.rs-7880883/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-12-19T18:44:04+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-12-10T02:32:49+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-12-09T00:21:28+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-11-27T04:55:16+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"37343605644458423833721817565231333160\",\"date\":\"2025-11-20T13:08:20+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"339747558832494847965566999273007406588\",\"date\":\"2025-11-17T17:51:23+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"215451292945361728329343693259755714435\",\"date\":\"2025-11-15T19:28:14+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"91437357764981823958944012353373114522\",\"date\":\"2025-11-14T19:38:04+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"291191132349330586956015663029253927814\",\"date\":\"2025-11-13T17:49:11+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-11-03T08:46:42+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-10-25T02:29:19+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-10-25T02:29:04+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Cancer Causes \\u0026 Control\",\"date\":\"2025-10-16T20:42:23+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"cancer-causes-and-control\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"caco\",\"sideBox\":\"Learn more about [Cancer Causes \\u0026 Control](https://www.springer.com/journal/10552)\",\"snPcode\":\"10552\",\"submissionUrl\":\"https://submission.nature.com/new-submission/10552/3\",\"title\":\"Cancer Causes \\u0026 Control\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"d48035e4-5f42-4058-adab-c13d6fbb402c\",\"owner\":[],\"postedDate\":\"November 14th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-02T13:09:50+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-11-14 01:41:16\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7880883\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7880883\",\"identity\":\"rs-7880883\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}