Air quality and cancer risk in the All of Us Research Program

Air quality and cancer risk in the All of Us Research Program | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Air quality and cancer risk in the All of Us Research Program Andrew Craver, Jiajun Luo, Muhammad G. Kibriya, Nina Randorf, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2489321/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Dec, 2023 Read the published version in Cancer Causes & Control → Version 1 posted 8 You are reading this latest preprint version Abstract Introduction The NIH All of Us Research Program has enrolled over 544,000 participants across the US with unprecedented racial/ethnic diversity, offering opportunities to investigate myriad exposures and diseases. This paper aims to investigate the association between PM 2.5 exposure and cancer risks. Materials and Methods This work was performed on data from 325,264 All of Us Research Program participants using the All of Us Researcher Workbench. Cancer case ascertainment was performed using data from electronic health records and the self-reported Personal Medical History questionnaire. PM 2.5 exposure was retrieved from NASA’s Earth Observing System Data and Information Center and assigned using participants’ 3-digit zip code prefixes. Multivariate logistic regression was used to estimate the odds ratio (OR) and 95% confidence interval (CI). Generalized additive models (GAMs) were used to investigate non-linear relationships. Results A total of 32,073 prevalent cancer cases were ascertained from participant EHR data, while 16,688 cases were ascertained from self-reported survey data; 7,692 cancer cases were captured in both the EHR and survey data. Average PM 2.5 level from 2006 to 2016 was 9.4 µg/m3 (min 3.0, max 15.1). In analysis of cancer cases from both sources combined (n = 41,069), each unit increase in PM 2.5 was associated with increased odds for blood cancer (OR = 1.02, 95% CI: 1.01–1.03), brain cancer (OR = 1.06, 95% CI: 1.03–1.09), breast cancer (OR = 1.03, 95% CI: 1.02–1.04), colon and rectum cancer (OR = 1.02, 95% CI: 1.00-1.04), and endometrial cancer (OR = 1.06, 95% CI: 1.03–1.10). In GAM, higher PM 2.5 concentration was associated with increased odds for blood cancer, bone cancer, brain cancer, breast cancer, colon and rectum cancer, endocrine system cancer, lung cancer, pancreatic cancer, prostate cancer, and thyroid cancer. Conclusions We found evidence of an association of PM 2.5 with brain, breast, blood, colon and rectum, and endometrial cancers. There is little to no prior evidence in the literature on the impact of PM 2.5 on risk of these cancers, warranting further investigation. cancer risk PM2.5 air pollution Figures Figure 1 Figure 2 Figure 3 1. Introduction Despite decades of improvements in ambient air quality in the United States ( 1 ), air pollution remains an environmental exposure of significant interest given disproportionate exposure ( 2 – 4 ) and the impact of even relatively low exposures on health ( 5 ) and health outcomes ( 6 – 8 ). There is ample evidence of its adverse impact on cardiovascular health ( 9 , 10 ) and excess mortality ( 9 , 11 , 12 ). The impact of poor air quality has also been extensively studied for lung cancer ( 9 , 13 – 17 ), and associations with cancer have been observed at other organ sites; however, the epidemiological evidence is limited ( 18 – 33 ). Outdoor air pollution and airborne particulate matter are classified as carcinogenic to humans for lung cancer ( 31 ), and evidence points to the need for further investigation of air quality’s impact on cancers including those of the bladder, breast, brain, liver, and kidney ( 34 – 36 ). The All of Us Research Program is enrolling a cohort of over one million participants, offering researchers an unprecedented opportunity to investigate diseases including cancers ( 37 , 38 ). Notably, All of Us includes participants from racial and ethnic minority groups that have been underrepresented in previous cancer research cohorts ( 39 ). All of Us may therefore confer sufficient statistical power to understand the burden of cancer in these populations and identify opportunities for intervention. In the era of precision prevention and precision medicine, investigating the role of the environment in cancer risk is critical ( 40 – 42 ). Realizing the potential of precision health will call for holistic measures of individual risk that take the physical environment into account. We recently conducted a preliminary investigation of cancer in the All of Us Research Program ( 43 ) as part of a demonstration project to show the quality, usefulness, validity, and diversity of the All of Us data ( 44 ). We generated descriptive statistics for the most common cancers and considered differences in cancer case ascertainment compared to what would be expected in the broader US population by data source type (self-reported cancer in survey data and/or from the electronic health record). We found that over 13,000 cancer cases were self-reported in the study population of 315,000 people and nearly 24,000 cancer cases were detected in the electronic health records collected for All of Us research participants. Researchers currently have access to data from 372,380 All of Us participants through the Researcher Workbench, including residential data for linkage to air pollution exposure. Although the program does not target enrollment by health status, the sample includes sufficient participants with a history of cancer, prevalent cancers, and incident cancers to enable initial investigation of the role of the environment on cancer in the All of Us Research Program. Here we investigate the association between ambient air pollution and any health outcome in All of Us for the first time, and we present preliminary findings on the association of air quality and cancer in this key precision medicine cohort. We focus on fine particulate matter (PM 2.5 ), but our analysis suggests that this is only a first step toward understanding the full impact of diverse environmental factors on cancer and the extensive health outcomes collected by the All of Us Research Program. 2. Materials and Methods 2.1. The All of Us Research Program Data collected from 2017 to 2022 were accessed from the All of Us Research Program, a cohort of over 544,000 adults aged 18 and over living in the United States and its territories. The goals, recruitment methods and sites, and scientific rationale for All of Us have been described previously ( 37 ). All of Us data include participants’ responses to a series of questionnaires, physical measurements collected by study staff at time of enrollment, and information from participants’ Electronic Health Records (EHR). These data are collected either at an All of Us affiliated health care provider organization (HPO) or through a “direct-volunteer” mechanism and are made available to re-searchers via the Researcher Workbench in registered, controlled, and restricted access tiers. Because zip code was required for this analysis, the data for this project were accessed at the controlled tier. 2.2. All of Us Questionnaire Data and Physical Measurements Participant-provided information for our analysis including self-reported cancer diagnoses was derived from the Basics , Lifestyle , and Personal Medical History questionnaires. The full text of these questionnaires is available in the Survey Explorer found on the All of Us Research Hub, a publicly available website designed to support both researchers and the public ( 45 ). The Basics questionnaire elicits demographic information including age, race/ethnicity, education, marital status, household income, and geography. The Lifestyle questionnaire collects data on the use of tobacco, alcohol, and other drugs. The Personal Medical History questionnaire collects self-reported cancer history. The Basics and Lifestyle questionnaires are collected at baseline. Until recently, Personal Medical History was collected during retention efforts 3 months after enrollment; participants now have the option to complete this questionnaire at the time of enrollment. Body Mass Index (BMI) was calculated using participant height and weight collected by All of Us study staff at time of enrollment; height and weight data are housed in the Physical Measurements section of the Re-searcher Workbench. 2.3. EHR-Derived Cancer Diagnoses Cancer diagnosis data were also derived from participant electronic health records linked to their All of Us data. EHR-derived diagnoses were determined using Systematized Nomenclature of Medicine -- Clinical Terms (SNOMED CT) codes and mapped to Observational Health and Medicines Outcomes Partnership (OMOP) concept ID by the All of Us Data and Research Center. EHR data include procedures, medications, laboratory tests, and health care provider visits. Our analysis used the following OMOP parent concept IDs for cancers/cancer sites: bladder: 93689003, 4095756, 4095755, 197508, 73712, 4312802; blood: 93143009, 109989006, 118601006; bone: 93725000, 78097; brain: 93727008, 4246451; breast: 372137005, 4157332, 4112853; cervix: 372024009, 198984; colon and rectum: 93761005, 36683531, 93984006, 435754, 4180790, 443382, 4180791, 4180792, 443390, 443381, 4181344, 443384; endocrine system: 4241776, 4156115, 371983001; endometrium: 4247238, 4095749; esophagus: 371984007, 4095316, 4094856, 4094854, 4181343, 4089656, 4092060, 4092059, 4094855; eye: 371986009; head, neck, and mouth: 372123001, 372001002, 4090224, 4177101, 4114222, 4089530, 25189, 4178964, 4181350, 4118989, 4090226; kidney: 93849006, 196653, 4091485; lung: 93880001, 443388, 4110587, 254591; ovary: 4116073, 4112864, 93934004, 4181351, 199752; pancreas 372003004, 4092072, 4112734, 4111024, 4178967, 4180793, 4095436; prostate: 93974005, 4163261; stomach: 372014001, 4095320, 4095319, 4149838, 4149837, 4092061, 4095317, 443387l; and thyroid: 94098005, 4178976, 36676291. 2.4. Air Pollution Exposure Data Daily PM 2.5 concentrations were estimated at a resolution of 1km×1km across the contiguous US using a well-validated ensemble-based prediction model that integrates random forest regression, gradient boosting machine, and artificial neural networking ( 46 ). Over 100 variables were used for prediction in this approach including satellite data, land-use information, weather variables, and modelled chemical transport characteristics. Output from this approach has been validated with daily PM 2.5 concentrations measured at 2,156 US EPA monitoring sites. The validation results yielded an average cross-validated R-squared value of 0.86 for daily PM 2.5 predictions, indicating outperformance compared to prior approaches ( 47 , 48 ). While residential addresses are not available in the All of Us Researcher Workbench, the dataset does contain 3-digit residential zip code prefix for each participant. We therefore used zonal statistics to calculate the daily average PM 2.5 concentration based on all 1km×1km grids within the zip code. Specifically, we identified the 1km×1km grids with centroid in one 3-digit zip code area and then averaged daily PM 2.5 concentrations across all these grids. The average concentration was thus the PM 2.5 exposure level for participants in that 3-digit zip code area. Analysis was restricted to 3-digit zip code areas with 50 or more All of Us participants (N = 379). At the time of analysis zip codes beginning in “0” were unavailable in the Researcher Workbench, and therefore participants from Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, Rhode Island, and Vermont could not be included. Figure 1 shows the distribution of All of Us participants represented in this analysis as well as the location of All of Us HPO sites. 2.5. Covariates Following a review of known risk factors for cancer, we selected appropriate variables from the All of Us Researcher Workbench data for inclusion in all analyses. Baseline measurements of socioeconomic and demographic covariates including age (19–35, 36–50, 51–65, > 65), gender (female, male, other), race/ethnicity (White, Black, Hispanic/Latino, Asian, other, multiracial, none of the above), current smoking status (yes, no), education (less than high school, high school graduate, some college, college graduate), and BMI (underweight, normal weight, overweight, obese) were included as covariates in the model. 2.6. Data Analysis Data were analyzed in the All of Us Researcher Workbench. The Researcher Workbench offers a secure environment and tools to enable users to select cohorts, create datasets for analysis, and conduct analysis using R and Python programming languages in a Jupyter Notebook. We generated descriptive statistics and prevalence for 19 cancers and conducted Chi-square tests to determine the difference in the categorical distribution of data source types (survey data, EHR, and both) across key categories. Descriptive analysis was undertaken on the prevalence of cancer as well as air pollution, and how these were distributed between the different groups of the covariates. To investigate the association between PM 2.5 and cancer, univariable and multi-variable logistic regression was performed given the rare disease assumption and ability to approximate odds ratio from relative risk for interpretation convenience. The first model introduced the unadjusted association between PM 2.5 exposure and the outcome of interest (cancer overall and by type). The second model was adjusted for age, gender, race/ethnicity, smoking status, education, and BMI. PM 2.5 concentration was analyzed as a continuous variable as well as categorical variable (quartiles) in the regression models. To evaluate the non-linear relationship between PM 2.5 exposure and cancer odds, we fitted a generalized additive model (GAM) including a spline term for the accessibility score with 3 degrees of freedom and visualized the exposure–outcome response with adjustment for other covariates. Participants with missing cancer data were excluded and missing values in covariates were treated as an independent category in the analysis. All analyses were conducted using the statistical software R version 4.2.1. 3. Results Table 1 shows the distribution of the mean annual PM 2.5 exposure and the base-line characteristics of all participants (N = 325,264), among whom 31,143 participants had at least one self-reported or EHR-derived cancer diagnosis. Differences in age, race, smoking status, education, and BMI were observed between the participants overall, with older, female, White, non-smoking, more educated, and obese participants more likely to have data on cancer history. We also note differences in cancer outcomes as captured from the EHR (N = 23,745), via self-report in the survey database (N = 14,950 completed the Personal Medical History questionnaire), and from participants with both survey and EHR data (N = 7,552). However, mean PM 2.5 did not vary across these different populations. Figure 2 shows PM 2.5 levels across the 379 3-digit zip code areas included in this analysis. Table 1 Distribution of cancer types by ascertainment source All participants w/ zip code* Participants with cancer any source Participants with cancer in EHR Participants with cancer in survey Participants with cancer in survey and EHR N % N % N % N % N % Total 325,264 31,143 23,745 14,950 7,552 Mean PM2.5 9.42 µg/m3 9.41 µg/m3 9.52 µg/m3 9.30 µg/m3 9.30 µg/m3 Age 19–35 61,777 19.0% 809 2.6% 598 2.5% 335 2.2% 124 1.6% 36–50 72,891 22.4% 2,941 9.4% 2,171 9.1% 1,392 9.3% 622 8.2% 51–65 96,652 29.7% 8,682 27.9% 6,655 28.0% 3,990 26.7% 1,963 26.0% 66–100 93,888 28.9% 18,705 60.1% 14,316 60.3% 9,232 61.8% 4,843 64.1% Gender Female 190,033 58.4% 18,448 59.2% 13,792 58.1% 9,240 61.8% 4,584 60.7% Male 119,760 36.8% 11,584 37.2% 9,257 39.0% 4,985 33.3% 2,658 35.2% Other gender 1,457 0.5% 62 0.2% 39 0.2% 39 0.3% < 20 ~ Missing 10,014 3.1% 1,049 3.4% 657 2.8% 686 4.6% 294 3.9% Race/Ethnicity White 169,313 52.1% 20,962 67.3% 15,250 64.2% 11,730 78.5% 6,018 79.7% Black/AA 68,611 21.1% 4,123 13.2% 3,600 15.2% 1,015 6.8% 492 6.5% Hispanic/Latino 60,491 18.6% 3,703 11.9% 3,250 13.7% 921 6.2% 468 6.2% Asian 10,969 3.4% 602 1.9% 469 2.0% 250 1.7% 117 1.6% Other 2,335 0.7% 177 0.6% 152 0.6% 56 0.4% 31 0.4% > 1 population 5,914 1.8% 382 1.2% 275 1.2% 196 1.3% 89 1.2% None of these 3,328 1.0% 275 0.9% 209 0.9% 119 0.8% 53 0.7% Missing 64,794 19.9% 4,622 14.8% 3,790 16.0% 1,584 10.6% 752 10.0% Current Smoker No 252,178 77.53% 27,541 88.43% 20,866 87.88% 13,751 91.98% 7,076 93.7% Yes 54,534 16.77% 2,854 9.16% 2,265 9.54% 938 6.27% 349 4.62% Missing 18,552 5.7% 748 2.4% 614 2.59% 261 1.75% 127 1.68% Education < High School 31,089 9.6% 1,754 5.6% 1,611 6.8% 264 1.8% 121 1.6% High School 63,695 19.6% 4,357 14.0% 3,731 15.7% 1,335 8.9% 709 9.4% Some College 82,776 25.4% 7,896 25.4% 6,091 25.7% 3,612 24.2% 1,807 23.9% Finished College 134,546 41.4% 15,912 51.1% 11,499 48.4% 9,039 60.5% 4,626 61.3% Missing 13,158 4.0% 1,224 3.9% 813 3.4% 700 4.7% 289 3.8% BMI Underweight 3,865 1.19% 368 1.18% 302 1.27% 132 0.88% 66 0.87% Normal Weight 69,811 21.46% 6,981 22.42% 5591 23.55% 3333 22.29% 1943 25.73% Overweight 80,505 24.75% 9,261 29.74% 7605 32.03% 4137 27.67% 2481 32.85% Obese 111,030 34.14% 11,407 36.63% 9256 38.98% 4979 33.30% 2828 37.45% Missing 60,053 18.46% 3,126 10.04% 991 4.17% 2,369 15.85% 234 3.10% *≥ 50 participants (excl. AK, HI CT, MA, ME, NH, NJ, RI, VT) Table 2 shows that All of Us participants’ EHR data indicate a history of breast cancer most frequently (N = 6,785; 21.7% of cases) followed by prostate cancer (N = 5,422; 13.2%), and blood cancers (N = 4,605; 11.2%). More cancers were detected in the EHR passively as opposed to self-reported in the surveys, and the total case numbers are much lower (N = 7,692) for cancers cross-referenced in both the EHR and survey data. For the analysis of PM 2.5 and cancer risk, the case population includes cases detected in either the EHR or in survey data (N = 41,069). The number of cancer cases per participant is summarized in the supplemental table. Table 2 Cancer type distribution by data source EHR or Survey (Total Cases) EHR Survey Data EHR + Survey N % dist N % dist N % dist N % dist Total Cancers 41,069 32,073 16,688 7,692 Bladder 1,377 3.4% 1,064 3.3% 608 3.6% 295 3.8% Blood 4,605 11.2% 3,833 12.0% 1,386 8.3% 614 8.0% Bone 1,710 4.2% 1,522 4.8% 269 1.6% 81 1.1% Brain 1,144 2.8% 1,028 3.2% 216 1.3% 100 1.3% Breast 8,901 21.7% 6,785 21.2% 4,934 29.6% 2,818 36.6% Cervix 1,735 4.2% 577 1.8% 1,283 7.7% 125 1.6% Colon & Rectum 2,339 5.7% 1,802 5.6% 945 5.7% 408 5.3% Endocrine System 2,018 4.9% 1,900 5.9% 148 0.9% 30 0.4% Endometrium 997 2.4% 632 2.0% 555 3.3% 190 2.5% Esophagus 306 0.8% 219 0.7% 123 0.7% 36 0.5% Eye 254 0.6% 188 0.6% 94 0.6% 28 0.4% Head & Neck 2,846 6.9% 2,610 8.1% 411 2.5% 175 2.3% Kidney 1,570 3.8% 1,284 4.0% 627 3.8% 341 4.4% Lung 1,535 3.7% 1,100 3.4% 561 3.4% 126 1.6% Ovary 1,122 2.7% 875 2.7% 434 2.6% 187 2.4% Pancreas 655 1.6% 578 1.8% 139 0.8% 62 0.8% Prostate 5,422 13.2% 4,156 13.0% 2,721 16.3% 1,455 18.9% Stomach 392 1.0% 312 1.0% 106 0.6% 26 0.3% Thyroid 2,141 5.2% 1,608 5.0% 1,128 6.8% 595 7.7% Table 3 presents cancer type distribution across the quartile distribution of PM 2.5 exposure. More than 25% of blood, breast, endometrium, and stomach cancers are observed in the highest exposure quartile (11.22–15.08 µg/m 3 ). Table 3 Cancer type distribution by mean annual outdoor PM 2.5 µg/m 3 quartiles Q1 (3–7.91 µg/m 3 ) Q2 (7.91–10.07 µg/m 3 ) Q3 (10.07–11.22 µg/m 3 ) Q4 (11.22–15.08 µg/m 3 ) total N % N % N % N % Overall 41,069 11,025 26.8% 10,070 24.5% 10,299 25.1% 9,675 23.6% Bladder 1,377 397 28.8% 348 25.3% 329 23.9% 303 22.0% Blood 4,605 1,195 26.0% 1,112 24.1% 1,132 24.6% 1,166 25.3% Bone 1,710 531 31.1% 387 22.6% 373 21.8% 419 24.5% Brain 1,144 243 21.2% 320 28.0% 303 26.5% 278 24.3% Breast 8,901 2,202 24.7% 2,068 23.2% 2,344 26.3% 2,287 25.7% Cervix 1,735 486 28.0% 427 24.6% 457 26.3% 365 21.0% Colon & Rectum 2,339 607 26.0% 590 25.2% 558 23.9% 584 25.0% Endocrine 2,018 548 27.2% 463 22.9% 526 26.1% 481 23.8% Endometrium 997 241 24.2% 214 21.5% 275 27.6% 267 26.8% Esophagus 306 104 34.0% 68 22.2% 69 22.5% 65 21.2% Eye 254 72 28.3% 61 24.0% 78 30.7% 43 16.9% Head & Neck 2,846 823 28.9% 737 25.9% 750 26.4% 536 18.8% Kidney 1,570 454 28.9% 393 25.0% 357 22.7% 366 23.3% Lung 1,535 473 30.8% 365 23.8% 342 22.3% 355 23.1% Ovary 1,122 270 24.06% 256 22.82% 315 28.07% 281 25.04% Pancreas 655 201 30.7% 190 29.0% 137 20.9% 127 19.4% Prostate 5,422 1,493 27.5% 1,446 26.7% 1,303 24.0% 1,180 21.8% Stomach 392 96 24.5% 104 26.5% 93 23.7% 99 25.3% Thyroid 2,141 589 27.5% 521 24.3% 558 26.1% 473 22.1% Table 4 reports the odds ratio (OR) and 95% confidence interval (CI) for air pollution with all cancers. The ORs are reported per unit increase in PM 2.5 or using the first quartile as the reference group. We observed increased odds for blood cancer (per unit: OR = 1.02, 95% CI: 1.01–1.03), brain cancer (per unit: OR = 1.06, 95% CI: 1.03–1.09), breast cancer (per unit: OR = 1.03, 95% CI: 1.02–1.04), colon and rectum cancer (per unit: OR = 1.02, 95% CI: 1.00-1.04), and endometrial cancer (per unit: OR = 1.06, 95% CI: 1.03–1.10). Comparing the highest quartile and lowest quartile of PM 2.5 , strong associations were observed for brain cancer (OR = 1.24, 95% CI: 1.03–1.49), breast cancer (OR = 1.12, 95% CI: 1.04–1.19), and endometrial cancer (OR = 1.30, 95% CI: 1.08–1.58). However, some inverse associations were also observed for bone cancer (2nd vs. 1st quartile: OR = 0.80, 95% CI: 0.69–0.92; 3rd vs. 1st quartile: OR = 0.76, 95% CI: 0.66–0.87; 4th vs. 1st quartile: OR = 0.86, 95% CI: 0.75–0.99), eye cancer (4th vs. 1st quartile: OR = 0.64. 95% CI: 0.43–0.97), head and neck cancer (4th vs. 1st quartile: OR = 0.79. 95% CI: 0.70–0.88), lung cancer (2nd vs. 1st quartile: OR = 0.81, 95% CI: 0.70–0.94), pancreatic cancer (3rd vs. 1st quartile: per unit: OR = 0.74, 95% CI: 0.59–0.94; 4th vs. 1st quartile: OR = 0.69. 95% CI: 0.55–0.94), and prostate cancer (4th vs. 1st quartile: OR = 0.91. 95% CI: 0.84–0.99). When we restrict to EHR as source of the cancer report, the ORs become significant for blood cancer (OR = 1.03. 95% CI: 1.02–1.05), brain cancer (OR = 1.07. 95% CI: 1.04–1.11), breast cancer (OR = 1.06. 95% CI: 1.05–1.07), colon & rectum cancer (OR = 1.04, 95% CI: 1.02–1.07), endocrine system cancer (OR = 1.02, 95% CI: 1.00-1.04), endometrial cancer OR = 1.14. 95% CI: 1.09–1.19), ovarian cancer (OR = 1.04, 95% CI: 1.01–1.08), prostate cancer (OR = 1.02, 95% CI: 1.01–1.04), and thyroid cancer (OR = 1.03, 95% CI: 1.00-1.05). There are significant differences in effect when comparing the effect of air quality on cancer from EHR versus from survey data. Gender and race stratified results are presented in Supplementary Tables 2 and 3 . Table 4 Cancer odds by increasing mean annual PM 2.5 exposure overall and restricted to EHR and survey source Combined Sources Q2 (7.91–10.07 µg/m3) Q3 (10.07–11.22 µg/m3) Q4 (11.22–15.08 µg/m3) EHR Survey Data OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) Bladder 1.01 (0.98, 1.03) 1.09 (0.93, 1.28) 1.03 (0.88, 1.21) 0.99 (0.85, 1.17) 1.02 (0.99, 1.05) 0.99 (0.95, 1.03) Blood 1.02 (1.01, 1.03) 0.99 (0.91, 1.08) 1.01 (0.93, 1.10) 1.08 (0.99, 1.18) 1.03 (1.02, 1.05) 0.98 (0.95, 1.00) Bone 0.98 (0.96, 1.00) 0.80 (0.69, 0.92) 0.77 (0.67, 0.89) 0.86 (0.75, 0.99) 0.98 (0.96, 1.01) 0.98 (0.92, 1.04) Brain 1.06 (1.03, 1.09) 1.41 ( 1.18, 1.68 ) 1.33 (1.11, 1.59) 1.24 (1.03, 1.49) 1.07 (1.04, 1.11) 1.06 (0.99, 1.14) Breast 1.03 (1.02, 1.04) 0.98 (0.92, 1.05) 1.05 (0.98, 1.12) 1.12 (1.04, 1.19) 1.06 (1.05, 1.07 ) 1.02 (1.00, 1.03) Cervix 0.99 (0.96, 1.01) 0.95 (0.82, 1.10) 1.00 (0.87, 1.16) 0.87 (0.75, 1.01) 1.03 (0.99, 1.08) 0.97 (0.94, 0.99) Colon & Rectum 1.02 (1.00, 1.04) 1.09 (0.96, 1.22) 1.03 (0.92, 1.18) 1.12 (0.99, 1.26) 1.04 (1.02, 1.07 ) 0.99 (0.96, 1.02) Endocrine System 1.02 (0.99, 1.04) 0.90 (0.79, 1.02) 1.03 (0.91, 1.17) 1.00 (0.88, 1.14) 1.02 (1.00, 1.04) 0.96 (0.89, 1.04) Endometrium 1.06 (1.03, 1.10) 0.99 (0.82, 1.22) 1.23 (1.02, 1.48) 1.30 (1.08, 1.58 ) 1.14 (1.09, 1.19) 1.00 (0.96, 1.05) Esophagus 0.97 (0.92, 1.03) 0.82 (0.59, 1.13) 0.80 (0.57, 1.11) 0.83 (0.59, 1.16) 0.97 (0.92, 1.03) 0.97 (0.89, 1.07) Eye 0.98 (0.92, 1.03) 0.87 (0.61, 1.24) 1.16 (0.83, 1.62) 0.64 (0.43, 0.97) 1.07 (0.99, 1.15) 0.87 (0.79, 0.95) Head & Neck 1.01 (0.99, 1.03) 0.98 (0.88, 1.09) 1.00 (0.90, 1.12) 0.79 (0.70, 0.88) 1.01 (0.99, 1.03) 0.99 (0.94, 1.03) Kidney 0.99 (0.98, 1.02) 0.98 (0.84, 1.13) 0.90 (0.77, 1.04) 0.97 (0.83, 1.12) 0.99 (0.97, 1.02) 0.99 (0.95, 1.03) Lung 0.98 (0.95, 1.00) 0.91 (0.79, 1.05) 0.81 (0.70, 0.94) 0.88 (0.76, 1.02) 0.98 (0.95, 1.00) 1.00 (0.96, 1.04) Ovary 1.03 (0.99, 1.06) 0.95 (0.79, 1.14) 1.13 (0.95, 1.34) 1.11 (0.93, 1.33) 1.04 (1.01, 1.08) 1.01 (0.96, 1.06) Pancreas 0.96 (0.92, 0.99) 1.07 (0.87, 1.32) 0.74 (0.59, 0.94) 0.69 ( 0.55, 0.88 ) 0.97 ( 0.93, 1.01 ) 0.94 (0.86, 1.02) Prostate 1.01 (0.99, 1.02) 1.07 (0.98, 1.16) 1.01 (0.93, 1.10) 0.91 (0.84, 0.99) 1.02 (1.01, 1.04) 0.99 (0.98, 1.02) Stomach 1.03 (0.98, 1.08) 1.16 (0.86, 1.57) 1.09 (0.80, 1.48) 1.15 (0.85, 1.56) 1.04 (0.98, 1.09) 0.98 (0.89, 1.09) Thyroid 1.01 (0.99, 1.03) 0.94 (0.82, 1.06) 1.01 (0.89, 1.15) 0.98 (0.86, 1.11) 1.03 (1.00, 1.05) 1.01 (0.98, 1.04) * adjusted for gender, race/ethnicity, age, smoking status, education, and BMI Figure 3 presents the non-linear relationship between PM 2.5 and cancers with a p-value for spline less than 0.10. A non-linear relationship was observed for blood cancer, bone cancer, brain cancer, breast cancer, colon & rectum cancer, endocrine system cancer, lung cancer, pancreatic cancer, prostate cancer, and thyroid cancer. Notably, although we observed inverse associations for bone cancer, lung cancer, and pancreatic cancer in Table 4 , results from GAM suggest that high PM 2.5 concentrations increase the odds for these cancers. 4. Discussion In this study, the median PM 2.5 concentration was 10 µg/m 3 , in line with the WHO health-based world air-quality guideline ( 49 , 50 ). The highest concentration of 15 µg/m 3 was observed in California, while prior review reported an annual average PM 2.5 concentration of 7 µg/m 3 in the US ( 33 ). The difference can be explained by the spatial distribution of our study population. At present, because urban residents have easier access to All of Us HPOs, most participants are concentrated in large cities such as New York City, Chicago, and Los Angeles where the level of air pollution is generally higher than rural areas. However, even the highest PM 2.5 concentration in this study indicates a recent reduction in average PM 2.5 exposure level across the US. For instance, a US-wide cohort study based on the American Cancer Society (ACS) Cancer Prevention Study II (CPS-II) reported a median PM 2.5 concentration of 12.5 µg/m 3 between 1999 and 2008, with the highest concentration of 28 µg/m 3 ( 19 ) . Outdoor air pollution and PM in outdoor air pollution have been classified as Group 1 human carcinogens for lung cancer by the IARC since 2013 ( 31 ), a determination based largely on findings from outdoor air pollution exposure analysis in population cohort studies ( 14 , 15 ). Similarly, a recent meta-analysis reported a 9% increase in risk for lung cancer incidence or mortality per each 10 µg/m 3 increase in PM 2.5 concentration as well as an 8% (95% CI, 0%-17%) increase in risk per 10 µg/m 3 for PM 10 ( 16 ). Our study observed an inverse association between PM 2.5 and lung cancer. However, this inverse association was only manifest when the exposure level was low, which may reflect measurement error. In our analysis of the variables’ non-linear relationship, the odds for lung cancer increased drastically when PM 2.5 level exceeded a certain threshold. Therefore, our observation is still consistent with prior conclusions. While the IARC has reported adverse associations between outdoor air pollution and bladder cancer ( 31 , 51 ), this association was not observed in our study. Systemic inflammation, oxidative stress, and epigenetic changes induced by PM exposure ( 52 – 55 ) are thought to play a role in the progression of breast tumors ( 56 – 60 ), and studies from a variety of settings demonstrate an association between PM 2.5 levels and breast cancer mortality rates as well as all-cause mortality ( 56 , 61 ). A recent analysis of 47,433 women in the US Sister Study found adverse associations between PM 2.5 (HR per 3.6 µg/m 3 , 1.05; 95% CI, 0.99–1.11) and breast cancer incidence overall (n = 2848) ( 22 ). An analysis of 57,589 women in the Multiethnic Cohort observed adverse associations of NO x , NO 2 , PM 2.5 , and PM 10 and breast cancer incidence among those living within 500 meters of major roads ( 26 ). The Canadian National Breast Screening Study (n = 89,247) found adverse associations of both PM 2.5 (HR per 10 µg/m 3 , 1.26; 95% CI, 0.99–1.61) and NO 2 (HRs per 9.7 ppb, range 1.13–1.17) and the risk of incident premenopausal disease ( 62 , 63 ). However, no other recent studies have reported clear associations with incident breast cancer risk ( 23 , 64 , 65 ). In our study, we did observe increased risk for breast cancer associated with PM 2.5 exposure. This association was more evident when the PM 2.5 level was high. The finding is generally consistent with previous studies that present suggestive associations for breast cancer. The larger number of breast cancer cases in this study yielded larger statistical power and may explain why we could observe associations in this study. Additionally, we also observed significant increased odds for blood and brain cancers. Previous studies have reported associations between air pollution and blood cancer ( 66 ), while other cancers have rarely been studied. We report some of these associations for the first time and the findings warrant further investigation of these cancers in air pollution studies. A limitation of this study is that we only examined the association of PM 2.5 with cancers while other pollutants such as SO 2 , NO 2 , NO x , and O 3 were not included. PM 2.5 is the most investigated pollutant and is often used as an indicator of overall air quality. However, the sole investigation of PM 2.5 may lead to an underestimation of the association between air pollution and cancer risks. For instance, a recent review found that a higher risk of breast cancer was associated with NO 2 and NO x , but not PM 2.5 ( 60 ). Another meta-analysis on leukemia concluded that higher exposure to NO 2 , but not PM 2.5 , was associated with higher leukemia risk. Additionally, this study only includes ambient PM 2.5 exposure level and relies on historical data. Current indoor air pollution exposure may pose greater health threats, as people spend most of their time indoors and indoor air pollution is generally more complicated than outdoor pollution. Therefore, results from this study only reveal the partial impacts of air pollution. A multi-level approach accounts for multiple pollutants and sources is warranted in future studies. To preserve participant privacy, the All of Us Researcher Workbench only offers participant data at the 3-digit zip code prefix level, rather than at the full 6-digit level which would confer higher spatial resolution for exposure estimates. As the first three digits of a zip code designate a city or a larger rural area, exposure assessment in this study may underestimate geospatial variations in air pollution. Recent epidemiological research has demonstrated the importance of within-city variability in air pollution concentration ( 67 , 68 ). However, the current resolution in this study is not sufficient to account for this within-city variability and thus may overlook exposure inequalities faced by urban minorities and underestimate the true associations. Another notable limitation is that we relied on the self-report and electronic health record capture of both incident and prevalent cancers and did not distinguish between primary and secondary cancers. We report differences in the effect based on the source of cancer report. The degree of impact of multiple cancers is illustrated in Supplemental Table 1 . Likewise, self-report data are not sufficiently detailed to allow for finer-grained analysis including reproductive or menopausal factors for breast cancer. We also found significant disparity by race in the self-reported survey data. For example, while Black/African American participants comprised 21% of the overall sample population, they accounted for only 6.8% of self-reported cancers. Similarly, participants identifying as Hispanic/Latino comprised 18.6% of our sample, yet they accounted for only 6.2% of self-reported cancers. This disparity is consistent with our previous analysis of All of Us data and highlights the importance of continued engagement with populations historically underrepresented in biomedical research by both incentivizing and removing barriers to follow up data collection ( 43 ). The difference in association between cancer risk and PM 2.5 based on data source is clearly illustrated in our report. Furthermore, the representativeness of this work is limited given the sampling plan; as illustrated in Figs. 1 and 2 , the health provider organizations that account for the greatest share of participant recruitment are generally located in metropolitan areas. Furthermore, at the current stage, the All of Us data used for this analysis are cross-sectional in nature as we relied on baseline data and limited longitudinal transfer of EHR. It is therefore difficult to establish temporality between air pollution and cancer outcomes and it is impossible to investigate cancer progression in relation to air pollution. However, reverse causation - the greatest concern in cross-sectional studies - is not likely in this study as higher cancer prevalence does not cause higher air pollution. The association between air pollution and cancer prevalence observed in this study still supports the adverse impact of air pollution on cancer outcomes. Likewise, the cross-sectional nature of the current data also presents the limitation of a lack of “latency” or “lag” of exposure. To address this limitation our analysis used the 10-year PM 2.5 average from 2006 to 2016, aiming to cover the cancer progression stages before the study enrollment period. However, we understand that these efforts cannot completely offset the limitation induced by the study design. Some inverse associations observed in this study may be the consequence of this limitation. The study has several notable strengths. First, All of Us is a nationwide cohort that can be representative of the general population in the US. While previous studies have been limited by small numbers of cancer cases, the sample size of this study, with more than 300,000 participants, entails the largest investigation of the association between air pollution and cancer to date. Second, research on the carcinogenicity of air pollution has long focused nearly exclusively on lung cancer, however outdoor air pollution might cause cancer at sites other than the lung through absorption, metabolism, and distribution of inhaled carcinogens. Other cancer types, including leukemia and breast cancer, have been also investigated in relation to air pollution. However, to our knowledge no study has simultaneously investigated as many cancer types as in this one. Third, the study design of All of Us enables researchers to analyze cancer risk longitudinally, thus providing evidence for the role of air pollution in cancer occurrence and development. Many prior studies have only been able to use cancer mortality as the outcome, thus may underestimate the true odds for some cancers. In summary, the All of Us Research Program presents significant opportunities to further evaluate the role of the environment and air pollution in cancer odds and outcomes. We have observed associations of PM 2.5 exposure with several types of cancer including blood cancer, bone cancer, brain cancer, breast cancer, colon and rectum cancer, endocrine system cancer, endometrial cancer, lung cancer, ovarian cancer, pancreatic cancer, prostate cancer, and thyroid cancer. This preliminary investigation suggests that some previous work on cancer and PM 2.5 is also observed in All of Us ; for instance, our breast cancer results. Given the large and diverse All of Us study population, it may be possible to further consider the role of the environment on cancer disparities in addition to cancer risk in general. In the coming years, All of Us may confer sufficient study power to research the role of the environment in cancers that have historically been infeasible to investigate due to small sample size. This project should provide some preliminary insight and direction for future investigation. Declarations Data Sharing Statement : Data is owned by a third party, the All of Us Research Program. The data underlying this article were provided by the All of Us Research Program by permission. Data will be shared on request to the corresponding author with permission of All of Us . More information on data access can be found in the All of Us Research Hub (https://www.researchallofus.org) Conflict of Interest Statement : The authors declare no potential conflicts of interest. Funding : The All of Us Research Program is supported by grants through the National Institutes of Health Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA #: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205; 3 OT2 OD023206; Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276; and the All of Us Pilot: 1 OT2 OD023132. This work was also supported by the NIEHS funded Chicago Center for Health and the Environment (P30 ES027792-05A1). Author Contributions : All authors contributed to the study conception and design. Data management was performed by AC. Analysis was performed by AC and overseen by JL. Maps were generated by NR. The first draft of the manuscript was written by BAK, JL, and AC and all authors reviewed and edited subsequent versions of the manuscript. All authors read and approved the final manuscript. Informed Consent : Informed consent was obtained from all individual participants included in the study, and the All of Us Research Program protocol was approved by the NIH All of Us Institutional Review Board. Acknowledgements: Past and Present All of Us Research Program Principal Investigators : Brian Ahmedani, PhD, MSW 1 ; Christine D Cole Johnson, PhD, MPH 1 ; Habib Ahsan, MD, MMedSc 2 ; Donna Antoine-LaVigne, PhD, MPH, MSEd* 3 ; Glendora Singleton* 3 ; Pamelia Watson-McGee 3 ; Arnita Ford Norwood, PhD, MPH, RDN 3 ; Hoda Anton-Culver, PhD 4 ; Eric Topol, MD 5 ; Katie Baca-Motes, MBA 5 ; Julia Moore-Vogel, PhD, MBA 5 ; Steven Steinhubl, MD* 5 ; Praduman Jain, MSEE 6 ; Mark Begale 6 ; Neeta Jain 6 ; David Klein, MBA 6 ; Scott Sutherland 6 ; James Wade, MD* 6 ; Bruce Korf, MD, PhD 7 ; Mona Fouad, MD, PhD 7 ; Beth Lewis 7 ; David B Goldstein, PhD 8 ; Louise Bier, MS 8 ; Ali G Gharavi, MD 8 ; George Hripcsak, MD, MS 8 ; Eric Boerwinkle, PhD, MS, MA 9 ; Murray H Brilliant, PhD* 10 ; Narayana Murali 10 ; Scott Joseph Hebbring 10 ; Elizabeth Burnside 11 ; Dorothy Farrar-Edwards, PhD 11 ; Yashoda Sharma, PhD 12 ; Amy Taylor 12 ; Carmen Chinea, MD, MPH* 13 ; Liliana Lombardi Desa 13 ; Nancy Jenks, MS, CFNP, FAANP 13 ; Steve Thibodeau 14 ; Mine Cicek, PhD 14 ; Eric Schlueter, MD 15 ; Beverly Wilson Holmes, MSW 15 ; Martha Daviglus, MD, PhD 16 ; Robert Winn, MD* 16 ; Paul Harris, PhD+ 17 ; Consuelo Wilkins, MD, MSCI 17 ; Dan Roden, MD, CM 17 ; Joshua Denny, MD, MS* 17 ; Kim Doheny 18 ; Debbie Nickerson, PhD 19 ; Evan Eichler 19 ; Gail Jarvik, MD, PhD 19 ; Gretchen Funk 20 ; Sallie Hussey 20 ; Anthony Philippakis, MD, PhD 21 ; Heidi Rehm, PhD, MMSc, FACMG 21 ; Stacey Gabriel, PhD 21 ; Richard Gibbs 22 ; Edgar M Gil Rico, MBA, MSc 23 ; David Glazer 24 ; Jessica Burke, MBA 25 ; Philip Greenland, MD 26 ; Elizabeth Shenkman, PhD 27 ; William R Hogan, MD, MS 27 ; Priscilla Igho-Pemu, MD, MSCR, FACP 28 ; W Karlson, MD 29 ; Jordan Smoller, MD, ScD 29 ; Shawn N Murphy, MD, PhD 29 ; Margaret Elizabeth Ross, MD, PhD 30 ; Rainu Kaushal, MD, MPH 30 ; Eboni Winford, PhD 31 ; Febe Wallace, MD 31 ; Parinda Khatri, PhD 31 ; Vik Kheterpal 32 ; Monica Kraft 33 ; Francisco A Moreno, MD 33 ; Irving Kron* 33 ; Rachele Peterson, MS* 33 ; Patricia Watkins Lattimore* 34 ; Cheryl Thomas 34 ; Mitchell Lunn, MD, MAS, FASN 35 ; Juno Obedin-Maliver 35 ; Oscar Marroquin, MD 36 ; Shyam Visweswaran, MD, PhD 36 ; Steven Reis, MD 36 ; Patrick McGovern 37 ; Fatima Munoz, MD, MPH 38 ; Gregory Talavera, MD, MPH 38 ; George T O'Connor, MD, MS 39 ; Christopher O'Donnell, MD, MPH* 40 ; Lucila Ohno-Machado, MD, PhD 41 ; Greg Orr* 42 ; Fornessa Randal, MCRP 43 ; Andreas A Theodorou, MD 44 ; Eric Reiman, MD 44 ; Mercedita Roxas-Murray 45 ; Louisa Stark 46 ; Ronnie Tepp, MPP 47 ; Alicia Zhou, PhD 48 ; Scott Topper, PhD, FACMG 48 ; Rhonda Trousdale, MD 49 ; Phil Tsao, PhD 50 ; Scott T Weiss, MD, MS 51 ; David Wellis, PhD 52 ; Jeffrey Whittle, MD, MPH 53 ; Amanda Wilson, MS 54 ; Stephan Zuchner, MD, PhD 55 ; Olveen Carrasquillo, MD, PhD 55 ; Margaret Pericak-Vance 55 ; Michael E Zwick, PhD 56 ; Megan Lewis 57 ; Jen Uhrig 57 ; May Okihiro 58 Note: This is the list of individuals who were Principal Investigators or equivalent with the All of Us Research Program during the period that this paper was in development. + Principal Investigator/Lead Author for the All of Us Research Program protocol ( [email protected] ) Affiliations : 1. Henry Ford Health System 2. University of Chicago Medical Center 3. Jackson-Hinds Comprehensive Health Center 4. University of California, Irvine 5. Scripps Research Translational Institute 6. Vibrent Health 7. University of Alabama at Birmingham 8. Columbia University 9. University of Texas Health Science Center at Houston 10. Marshfield Clinic Research Institute 11. University of Wisconsin at Madison 12. Community Health Center, Inc. 13. Sun River Health 14. Mayo Clinic and Foundation, Rochester 15. Cooperative Health 16. University of Illinois at Chicago 17. Vanderbilt University Medical Center 18. Johns Hopkins University School of Medicine 19. University of Washington 20. FiftyForward 21. Broad Institute 22. Baylor University 23. National Alliance for Hispanic Health 24. Verily Life Sciences 25. MITRE Corporation 26. Northwestern University 27. University of Florida 28. Morehouse School of Medicine, Atlanta 29. Partners Health Care 30. Cornell University, Weill Medical College 31. Cherokee Health Systems 32. CareEvolution, Inc. 33. University of Arizona, Tucson 34. Delta Research and Educational Foundation 35. Stanford University 36. University of Pittsburgh 37. Wondros 38. San Ysidro Health Center 39. Boston Medical Center 40. VA All of Us Coordinating Center, Boston 41. University of California, San Diego 42. Walgreen Co. 43. Asian Health Coalition 44. Banner Health 45. Montage Marketing Group 46. University of Utah 47. HCM Strategists 48. Color Genomics, Inc. 49. NYC Health + Hospitals 50. VA All of Us Coordinating Center - Palo Alto 51. Brigham and Women's Hospital 52. San Diego Blood Bank 53. Medical College of Wisconsin 54. National Library of Medicine (NLM) 55. University of Miami School of Medicine 56. Emory University 57. Research Triangle Institute 58. Waianae Coast CHC References EPA. 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Ambient air pollution and primary liver cancer incidence in four European cohorts within the ESCAPE project. Environ Res. 2017;154:226–33. Raaschou-Nielsen O, Pedersen M, Stafoggia M, Weinmayr G, Andersen ZJ, Galassi C, et al. Outdoor air pollution and risk for kidney parenchyma cancer in 14 European cohorts. Int J Cancer. 2017;140(7):1528–37. Nagel G, Stafoggia M, Pedersen M, Andersen ZJ, Galassi C, Munkenast J, et al. Air pollution and incidence of cancers of the stomach and the upper aerodigestive tract in the European Study of Cohorts for Air Pollution Effects (ESCAPE). Int J Cancer. 2018;143(7):1632–43. IARC. Outdoor Air Pollution. Humans IWGotEoCRt, editor. Lyon, France: International Agency for Research on Cancer, World Health Organization; 2013. Pritchett N, Spangler EC, Gray GM, Livinski AA, Sampson JN, Dawsey SM, et al. Exposure to Outdoor Particulate Matter Air Pollution and Risk of Gastrointestinal Cancers in Adults: A Systematic Review and Meta-Analysis of Epidemiologic Evidence. Environ Health Perspect. 2022;130(3):36001. Turner MC, Andersen ZJ, Baccarelli A, Diver WR, Gapstur SM, Pope III CA, et al. Outdoor air pollution and cancer: An overview of the current evidence and public health recommendations. CA: A Cancer Journal for Clinicians. 2020;70(6):460–79. Raaschou-Nielsen O, Beelen R, Wang M, Hoek G, Andersen ZJ, Hoffmann B, et al. Particulate matter air pollution components and risk for lung cancer. Environ Int. 2016;87:66–73. IARC. Diesel and Gasoline Engine Exhausts and Some Nitroarenes. Humans IWGotEoCRt, editor. Lyon, France: International Agency for Research on Cancer, World Health Organization; 2014. IARC. Arsenic, Metals, Fibres and Dusts. Humans IWGotEoCRt, editor. Lyon, France: International Agency for Research on Cancer, World Health Organization; 2012. Denny JC, Devaney SA, Gebo KA. The "All of Us" Research Program. Reply. N Engl J Med. 2019;381(19):1884–5. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372(9):793–5. Aldrighetti CM, Niemierko A, Van Allen E, Willers H, Kamran SC. Racial and Ethnic Disparities Among Participants in Precision Oncology Clinical Studies. JAMA Network Open. 2021;4(11):e2133205-e. Li J, Li X, Zhang S, Snyder M. Gene-Environment Interaction in the Era of Precision Medicine. Cell. 2019;177(1):38–44. McCarthy M, Birney E. Personalized profiles for disease risk must capture all facets of health. Nature. 2021;597(7875):175–7. Whitsel LP, Wilbanks J, Huffman MD, Hall JL. The Role of Government in Precision Medicine, Precision Public Health and the Intersection With Healthy Living. Prog Cardiovasc Dis. 2019;62(1):50–4. Aschebrook-Kilfoy B, Zakin P, Craver A, Shah S, Kibriya MG, Stepniak E, et al. An Overview of Cancer in the First 315,000 All of Us Participants. PLoS One. 2022;17(9):e0272522. Denny JC, Rutter JL, Goldstein DB, Philippakis A, Smoller JW, Jenkins G, et al. The "All of Us" Research Program. N Engl J Med. 2019;381(7):668–76. All of Us Research Hub: National Institutes of Health 2022 [cited 2022 October 25]. Available from: https://www.researchallofus.org/ . Di Q, Amini H, Shi L, Kloog I, Silvern R, Kelly J, et al. An ensemble-based model of PM 2.5 concentration across the contiguous United States with high spatiotemporal resolution. Environ Int. 2019;130:104909. Di Q, Kloog I, Koutrakis P, Lyapustin A, Wang Y, Schwartz J. Assessing PM 2.5 Exposures with High Spatiotemporal Resolution across the Continental United States. Environ Sci Technol. 2016;50(9):4712–21. Di Q, Koutrakis P, Schwartz J. A hybrid prediction model for PM 2.5 mass and components using a chemical transport model and land use regression. Atmospheric Environment. 2016;131:390–9. Stanaway JD, Afshin A, Gakidou E, Lim SS, Abate D, Abate KH, et al. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet. 2018;392(10159):1923–94. State of Global Air 2019. Boston, MA; 2019. Zare Sakhvidi MJ, Lequy E, Goldberg M, Jacquemin B. Air pollution exposure and bladder, kidney and urinary tract cancer risk: A systematic review. Environ Pollut. 2020;267:115328. Brook RD, Urch B, Dvonch JT, Bard RL, Speck M, Keeler G, et al. Insights into the mechanisms and mediators of the effects of air pollution exposure on blood pressure and vascular function in healthy humans. Hypertension. 2009;54(3):659–67. Guo L, Byun HM, Zhong J, Motta V, Barupal J, Zheng Y, et al. Effects of short-term exposure to inhalable particulate matter on DNA methylation of tandem repeats. Environ Mol Mutagen. 2014;55(4):322–35. Panni T, Mehta AJ, Schwartz JD, Baccarelli AA, Just AC, Wolf K, et al. Genome-Wide Analysis of DNA Methylation and Fine Particulate Matter Air Pollution in Three Study Populations: KORA F3, KORA F4, and the Normative Aging Study. Environ Health Perspect. 2016;124(7):983–90. Brook RD, Franklin B, Cascio W, Hong Y, Howard G, Lipsett M, et al. Air pollution and cardiovascular disease: a statement for healthcare professionals from the Expert Panel on Population and Prevention Science of the American Heart Association. Circulation. 2004;109(21):2655–71. Hu H, Dailey AB, Kan H, Xu X. The effect of atmospheric particulate matter on survival of breast cancer among US females. Breast Cancer Res Treat. 2013;139(1):217–26. Coussens LM, Werb Z. Inflammation and cancer. Nature. 2002;420(6917):860–7. DeNardo DG, Coussens LM. Inflammation and breast cancer. Balancing immune response: crosstalk between adaptive and innate immune cells during breast cancer progression. Breast Cancer Res. 2007;9(4):212. Baumgarten SC, Frasor J. Minireview: Inflammation: an instigator of more aggressive estrogen receptor (ER) positive breast cancers. Mol Endocrinol. 2012;26(3):360–71. Gabet S, Lemarchand C, Guénel P, Slama R. Breast Cancer Risk in Association with Atmospheric Pollution Exposure: A Meta-Analysis of Effect Estimates Followed by a Health Impact Assessment. Environ Health Perspect. 2021;129(5):57012. DuPré NC, Hart JE, Holmes MD, Poole EM, James P, Kraft P, et al. Particulate Matter and Traffic-Related Exposures in Relation to Breast Cancer Survival. Cancer Epidemiology, Biomarkers & Prevention. 2019;28(4):751–9. Goldberg MS, Villeneuve PJ, Crouse D, To T, Weichenthal SA, Wall C, et al. Associations between incident breast cancer and ambient concentrations of nitrogen dioxide from a national land use regression model in the Canadian National Breast Screening Study. Environ Int. 2019;133(Pt B):105182. Villeneuve PJ, Goldberg MS, Crouse DL, To T, Weichenthal SA, Wall C, et al. Residential exposure to fine particulate matter air pollution and incident breast cancer in a cohort of Canadian women. Environmental Epidemiology. 2018;2(3):e021. Bai L, Shin S, Burnett RT, Kwong JC, Hystad P, van Donkelaar A, et al. Exposure to ambient air pollution and the incidence of lung cancer and breast cancer in the Ontario Population Health and Environment Cohort. Int J Cancer. 2020;146(9):2450–9. Hart JE, Bertrand KA, DuPre N, James P, Vieira VM, VoPham T, et al. Exposure to hazardous air pollutants and risk of incident breast cancer in the Nurses’ Health Study II. Environmental Health. 2018;17(1):28. Taj T, Poulsen AH, Ketzel M, Geels C, Brandt J, Christensen JH, et al. Exposure to PM 2.5 constituents and risk of adult leukemia in Denmark: A population-based case–control study. Environmental Research. 2021;196:110418. Jerrett M, Burnett RT, Ma R, Pope CA, 3rd, Krewski D, Newbold KB, et al. Spatial analysis of air pollution and mortality in Los Angeles. Epidemiology. 2005;16(6):727–36. Miller KA, Siscovick DS, Sheppard L, Shepherd K, Sullivan JH, Anderson GL, et al. Long-term exposure to air pollution and incidence of cardiovascular events in women. N Engl J Med. 2007;356(5):447–58. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.docx Cite Share Download PDF Status: Published Journal Publication published 25 Dec, 2023 Read the published version in Cancer Causes & Control → Version 1 posted Editorial decision: Major revision 30 Jul, 2023 Reviews received at journal 27 Jul, 2023 Reviewers agreed at journal 20 Jun, 2023 Reviewers agreed at journal 09 Feb, 2023 Reviewers invited by journal 08 Feb, 2023 Editor assigned by journal 19 Jan, 2023 Submission checks completed at journal 19 Jan, 2023 First submitted to journal 17 Jan, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2489321","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":169119648,"identity":"67185b6b-4680-441f-9b75-c1332029ffa7","order_by":0,"name":"Andrew Craver","email":"","orcid":"","institution":"University of Chicago","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Craver","suffix":""},{"id":169119658,"identity":"e0f5862a-11b8-4fbf-b164-c33a3548e48d","order_by":1,"name":"Jiajun Luo","email":"","orcid":"","institution":"University of Chicago","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiajun","middleName":"","lastName":"Luo","suffix":""},{"id":169119659,"identity":"9ef834d1-a542-445b-ac1c-3ef10c01cbbd","order_by":2,"name":"Muhammad G. Kibriya","email":"","orcid":"","institution":"University of Chicago","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"G.","lastName":"Kibriya","suffix":""},{"id":169119667,"identity":"f0be8fab-1754-4119-9b5e-f70df7cd326f","order_by":3,"name":"Nina Randorf","email":"","orcid":"","institution":"University of Chicago","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nina","middleName":"","lastName":"Randorf","suffix":""},{"id":169119672,"identity":"ec60ff92-3b52-4d9b-b302-00968bf9e174","order_by":4,"name":"Kendall Bahl","email":"","orcid":"","institution":"University of Chicago","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kendall","middleName":"","lastName":"Bahl","suffix":""},{"id":169119678,"identity":"923b4a75-1b26-4632-bf5e-61afbd230241","order_by":5,"name":"Elizabeth Connellan","email":"","orcid":"","institution":"University of Chicago","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Connellan","suffix":""},{"id":169119679,"identity":"9d0f309c-b22c-4d03-949d-b395186fd2d8","order_by":6,"name":"Johnny Powell","email":"","orcid":"","institution":"University of Chicago","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Johnny","middleName":"","lastName":"Powell","suffix":""},{"id":169119682,"identity":"64c0ad38-e2c2-4a4c-bcdf-6819e9aa8c95","order_by":7,"name":"Paul Zakin","email":"","orcid":"","institution":"University of Chicago","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Zakin","suffix":""},{"id":169119684,"identity":"15aae151-eddd-4be1-a247-660f2fcdb0b2","order_by":8,"name":"Rena R. Jones","email":"","orcid":"","institution":"National Cancer Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rena","middleName":"R.","lastName":"Jones","suffix":""},{"id":169119687,"identity":"ff0f1440-694e-45b0-9d3d-f31445d0e352","order_by":9,"name":"Maria Argos","email":"","orcid":"","institution":"University of Illinois at Chicago","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Argos","suffix":""},{"id":169119688,"identity":"8e33e195-08e1-4663-8465-935a5df3a1e6","order_by":10,"name":"Joyce Ho","email":"","orcid":"","institution":"Northwestern University Feinberg School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Joyce","middleName":"","lastName":"Ho","suffix":""},{"id":169119689,"identity":"bde27eed-d269-423a-820c-c1abe7980fab","order_by":11,"name":"Karen Kim","email":"","orcid":"","institution":"University of Chicago","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Karen","middleName":"","lastName":"Kim","suffix":""},{"id":169119691,"identity":"b63471ca-4009-4b1b-a24a-033f75818e61","order_by":12,"name":"Martha L. Daviglus","email":"","orcid":"","institution":"University of Illinois at Chicago","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Martha","middleName":"L.","lastName":"Daviglus","suffix":""},{"id":169119692,"identity":"e5648d7c-0deb-4a33-a6fb-8f8ba5580afe","order_by":13,"name":"Philip Greenland","email":"","orcid":"","institution":"Northwestern University Feinberg School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Philip","middleName":"","lastName":"Greenland","suffix":""},{"id":169119695,"identity":"b139d749-9d52-4465-8847-d337082600a1","order_by":14,"name":"Habibul Ahsan","email":"","orcid":"","institution":"University of Chicago","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Habibul","middleName":"","lastName":"Ahsan","suffix":""},{"id":169119697,"identity":"6a40c3a9-b09b-4ca0-8059-701946956c3c","order_by":15,"name":"Briseis Aschebrook-Kilfoy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACAwbGBgaGCiCDGSZ0gCgtZ0jTAgSMbVAGUVrMpQ83f/g577C8OTvzw49fKhjk+G4k4Ndi2ZfYJtm77bDhzmY2Y2mZMwzGkoS0GJwBuop32+EEg8M8DNKSbQyJG4jQ0vzx7xywFubfQC31xGhpkOZtAGthk/zYxpBgQNAvPYxt0jLH0g03HGYzs2Y4I2E488wD/FrMedgff3xTYy1vcP7w45s/Kmzk+Y4TsAUFMPMwSJCgHAQYf5CoYRSMglEwCkYGAAB5vET1hRb2uwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Chicago","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Briseis","middleName":"","lastName":"Aschebrook-Kilfoy","suffix":""}],"badges":[],"createdAt":"2023-01-17 21:44:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2489321/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2489321/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10552-023-01823-7","type":"published","date":"2023-12-25T15:01:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":31951754,"identity":"d178e30e-9d7b-4e5d-ab0d-520cac072072","added_by":"auto","created_at":"2023-01-23 15:19:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":758623,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAll of Us \u003c/em\u003eParticipant Population Distribution by 3-Digit Zip Code Prefix\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2489321/v1/daae93bf666eeaf1c8c90864.png"},{"id":31951755,"identity":"9245586b-e493-494e-9969-2298fba1c631","added_by":"auto","created_at":"2023-01-23 15:19:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1111183,"visible":true,"origin":"","legend":"\u003cp\u003eAmbient mean PM2.5 estimates in \u003cem\u003eAll of Us\u003c/em\u003e participant locations\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2489321/v1/ea4845995690b07dd79100ba.png"},{"id":31949226,"identity":"6a456f42-d7ad-4493-bef2-de5bc7eb5afd","added_by":"auto","created_at":"2023-01-23 15:11:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":55519,"visible":true,"origin":"","legend":"\u003cp\u003eNon-linear relationship between PM\u003csub\u003e2.5\u003c/sub\u003e and cancers\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2489321/v1/e139b20e09429dc67c4e9f98.png"},{"id":49028188,"identity":"ed22907d-0460-4a13-bac4-58861ace9279","added_by":"auto","created_at":"2024-01-01 15:04:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1792344,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2489321/v1/89df2653-6931-492f-922c-2a431705c3d4.pdf"},{"id":31949223,"identity":"0b4e6ed5-a5b0-428f-b243-b4b862dc72df","added_by":"auto","created_at":"2023-01-23 15:11:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19370,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-2489321/v1/a64c199455c5dfb223983487.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Air quality and cancer risk in the All of Us Research Program","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDespite decades of improvements in ambient air quality in the United States (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), air pollution remains an environmental exposure of significant interest given disproportionate exposure (\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) and the impact of even relatively low exposures on health (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and health outcomes (\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). There is ample evidence of its adverse impact on cardiovascular health (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) and excess mortality (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The impact of poor air quality has also been extensively studied for lung cancer (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), and associations with cancer have been observed at other organ sites; however, the epidemiological evidence is limited (\u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Outdoor air pollution and airborne particulate matter are classified as carcinogenic to humans for lung cancer (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), and evidence points to the need for further investigation of air quality\u0026rsquo;s impact on cancers including those of the bladder, breast, brain, liver, and kidney (\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eAll of Us\u003c/em\u003e Research Program is enrolling a cohort of over one million participants, offering researchers an unprecedented opportunity to investigate diseases including cancers (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Notably, \u003cem\u003eAll of Us\u003c/em\u003e includes participants from racial and ethnic minority groups that have been underrepresented in previous cancer research cohorts (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). \u003cem\u003eAll of Us\u003c/em\u003e may therefore confer sufficient statistical power to understand the burden of cancer in these populations and identify opportunities for intervention. In the era of precision prevention and precision medicine, investigating the role of the environment in cancer risk is critical (\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Realizing the potential of precision health will call for holistic measures of individual risk that take the physical environment into account.\u003c/p\u003e \u003cp\u003eWe recently conducted a preliminary investigation of cancer in the \u003cem\u003eAll of Us\u003c/em\u003e Research Program (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) as part of a demonstration project to show the quality, usefulness, validity, and diversity of the \u003cem\u003eAll of Us\u003c/em\u003e data (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). We generated descriptive statistics for the most common cancers and considered differences in cancer case ascertainment compared to what would be expected in the broader US population by data source type (self-reported cancer in survey data and/or from the electronic health record). We found that over 13,000 cancer cases were self-reported in the study population of 315,000 people and nearly 24,000 cancer cases were detected in the electronic health records collected for \u003cem\u003eAll of Us\u003c/em\u003e research participants.\u003c/p\u003e \u003cp\u003eResearchers currently have access to data from 372,380 \u003cem\u003eAll of Us\u003c/em\u003e participants through the Researcher Workbench, including residential data for linkage to air pollution exposure. Although the program does not target enrollment by health status, the sample includes sufficient participants with a history of cancer, prevalent cancers, and incident cancers to enable initial investigation of the role of the environment on cancer in the \u003cem\u003eAll of Us\u003c/em\u003e Research Program. Here we investigate the association between ambient air pollution and any health outcome in \u003cem\u003eAll of Us\u003c/em\u003e for the first time, and we present preliminary findings on the association of air quality and cancer in this key precision medicine cohort. We focus on fine particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e), but our analysis suggests that this is only a first step toward understanding the full impact of diverse environmental factors on cancer and the extensive health outcomes collected by the \u003cem\u003eAll of Us\u003c/em\u003e Research Program.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. \u003cb\u003eThe\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eAll of Us\u003c/span\u003e \u003cb\u003eResearch Program\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eData collected from 2017 to 2022 were accessed from the \u003cem\u003eAll of Us\u003c/em\u003e Research Program, a cohort of over 544,000 adults aged 18 and over living in the United States and its territories. The goals, recruitment methods and sites, and scientific rationale for \u003cem\u003eAll of Us\u003c/em\u003e have been described previously (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). \u003cem\u003eAll of Us\u003c/em\u003e data include participants\u0026rsquo; responses to a series of questionnaires, physical measurements collected by study staff at time of enrollment, and information from participants\u0026rsquo; Electronic Health Records (EHR). These data are collected either at an \u003cem\u003eAll of Us\u003c/em\u003e affiliated health care provider organization (HPO) or through a \u0026ldquo;direct-volunteer\u0026rdquo; mechanism and are made available to re-searchers via the Researcher Workbench in registered, controlled, and restricted access tiers. Because zip code was required for this analysis, the data for this project were accessed at the controlled tier.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eAll of Us\u003c/span\u003e \u003cb\u003eQuestionnaire Data and Physical Measurements\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eParticipant-provided information for our analysis including self-reported cancer diagnoses was derived from the \u003cem\u003eBasics\u003c/em\u003e, \u003cem\u003eLifestyle\u003c/em\u003e, and \u003cem\u003ePersonal Medical History\u003c/em\u003e questionnaires. The full text of these questionnaires is available in the Survey Explorer found on the \u003cem\u003eAll of Us\u003c/em\u003e Research Hub, a publicly available website designed to support both researchers and the public (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). The \u003cem\u003eBasics\u003c/em\u003e questionnaire elicits demographic information including age, race/ethnicity, education, marital status, household income, and geography. The \u003cem\u003eLifestyle\u003c/em\u003e questionnaire collects data on the use of tobacco, alcohol, and other drugs. The \u003cem\u003ePersonal Medical History\u003c/em\u003e questionnaire collects self-reported cancer history. The \u003cem\u003eBasics\u003c/em\u003e and \u003cem\u003eLifestyle\u003c/em\u003e questionnaires are collected at baseline. Until recently, \u003cem\u003ePersonal Medical History\u003c/em\u003e was collected during retention efforts 3 months after enrollment; participants now have the option to complete this questionnaire at the time of enrollment. Body Mass Index (BMI) was calculated using participant height and weight collected by \u003cem\u003eAll of Us\u003c/em\u003e study staff at time of enrollment; height and weight data are housed in the \u003cem\u003ePhysical Measurements\u003c/em\u003e section of the Re-searcher Workbench.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. \u003cb\u003eEHR-Derived Cancer Diagnoses\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eCancer diagnosis data were also derived from participant electronic health records linked to their \u003cem\u003eAll of Us\u003c/em\u003e data. EHR-derived diagnoses were determined using Systematized Nomenclature of Medicine -- Clinical Terms (SNOMED CT) codes and mapped to Observational Health and Medicines Outcomes Partnership (OMOP) concept ID by the \u003cem\u003eAll of Us\u003c/em\u003e Data and Research Center. EHR data include procedures, medications, laboratory tests, and health care provider visits. Our analysis used the following OMOP parent concept IDs for cancers/cancer sites: bladder: 93689003, 4095756, 4095755, 197508, 73712, 4312802; blood: 93143009, 109989006, 118601006; bone: 93725000, 78097; brain: 93727008, 4246451; breast: 372137005, 4157332, 4112853; cervix: 372024009, 198984; colon and rectum: 93761005, 36683531, 93984006, 435754, 4180790, 443382, 4180791, 4180792, 443390, 443381, 4181344, 443384; endocrine system: 4241776, 4156115, 371983001; endometrium: 4247238, 4095749; esophagus: 371984007, 4095316, 4094856, 4094854, 4181343, 4089656, 4092060, 4092059, 4094855; eye: 371986009; head, neck, and mouth: 372123001, 372001002, 4090224, 4177101, 4114222, 4089530, 25189, 4178964, 4181350, 4118989, 4090226; kidney: 93849006, 196653, 4091485; lung: 93880001, 443388, 4110587, 254591; ovary: 4116073, 4112864, 93934004, 4181351, 199752; pancreas 372003004, 4092072, 4112734, 4111024, 4178967, 4180793, 4095436; prostate: 93974005, 4163261; stomach: 372014001, 4095320, 4095319, 4149838, 4149837, 4092061, 4095317, 443387l; and thyroid: 94098005, 4178976, 36676291.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. \u003cb\u003eAir Pollution Exposure Data\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eDaily PM\u003csub\u003e2.5\u003c/sub\u003e concentrations were estimated at a resolution of 1km\u0026times;1km across the contiguous US using a well-validated ensemble-based prediction model that integrates random forest regression, gradient boosting machine, and artificial neural networking (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Over 100 variables were used for prediction in this approach including satellite data, land-use information, weather variables, and modelled chemical transport characteristics. Output from this approach has been validated with daily PM\u003csub\u003e2.5\u003c/sub\u003e concentrations measured at 2,156 US EPA monitoring sites. The validation results yielded an average cross-validated R-squared value of 0.86 for daily PM\u003csub\u003e2.5\u003c/sub\u003e predictions, indicating outperformance compared to prior approaches (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile residential addresses are not available in the \u003cem\u003eAll of Us\u003c/em\u003e Researcher Workbench, the dataset does contain 3-digit residential zip code prefix for each participant. We therefore used zonal statistics to calculate the daily average PM\u003csub\u003e2.5\u003c/sub\u003e concentration based on all 1km\u0026times;1km grids within the zip code. Specifically, we identified the 1km\u0026times;1km grids with centroid in one 3-digit zip code area and then averaged daily PM\u003csub\u003e2.5\u003c/sub\u003e concentrations across all these grids. The average concentration was thus the PM\u003csub\u003e2.5\u003c/sub\u003e exposure level for participants in that 3-digit zip code area. Analysis was restricted to 3-digit zip code areas with 50 or more \u003cem\u003eAll of Us\u003c/em\u003e participants (N\u0026thinsp;=\u0026thinsp;379). At the time of analysis zip codes beginning in \u0026ldquo;0\u0026rdquo; were unavailable in the Researcher Workbench, and therefore participants from Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, Rhode Island, and Vermont could not be included. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the distribution of \u003cem\u003eAll of Us\u003c/em\u003e participants represented in this analysis as well as the location of \u003cem\u003eAll of Us\u003c/em\u003e HPO sites.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. \u003cb\u003eCovariates\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eFollowing a review of known risk factors for cancer, we selected appropriate variables from the \u003cem\u003eAll of Us\u003c/em\u003e Researcher Workbench data for inclusion in all analyses. Baseline measurements of socioeconomic and demographic covariates including age (19\u0026ndash;35, 36\u0026ndash;50, 51\u0026ndash;65, \u0026gt;\u0026thinsp;65), gender (female, male, other), race/ethnicity (White, Black, Hispanic/Latino, Asian, other, multiracial, none of the above), current smoking status (yes, no), education (less than high school, high school graduate, some college, college graduate), and BMI (underweight, normal weight, overweight, obese) were included as covariates in the model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. \u003cb\u003eData Analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eData were analyzed in the \u003cem\u003eAll of Us\u003c/em\u003e Researcher Workbench. The Researcher Workbench offers a secure environment and tools to enable users to select cohorts, create datasets for analysis, and conduct analysis using R and Python programming languages in a Jupyter Notebook. We generated descriptive statistics and prevalence for 19 cancers and conducted Chi-square tests to determine the difference in the categorical distribution of data source types (survey data, EHR, and both) across key categories. Descriptive analysis was undertaken on the prevalence of cancer as well as air pollution, and how these were distributed between the different groups of the covariates. To investigate the association between PM\u003csub\u003e2.5\u003c/sub\u003e and cancer, univariable and multi-variable logistic regression was performed given the rare disease assumption and ability to approximate odds ratio from relative risk for interpretation convenience. The first model introduced the unadjusted association between PM\u003csub\u003e2.5\u003c/sub\u003e exposure and the outcome of interest (cancer overall and by type). The second model was adjusted for age, gender, race/ethnicity, smoking status, education, and BMI. PM\u003csub\u003e2.5\u003c/sub\u003e concentration was analyzed as a continuous variable as well as categorical variable (quartiles) in the regression models. To evaluate the non-linear relationship between PM\u003csub\u003e2.5\u003c/sub\u003e exposure and cancer odds, we fitted a generalized additive model (GAM) including a spline term for the accessibility score with 3 degrees of freedom and visualized the exposure\u0026ndash;outcome response with adjustment for other covariates. Participants with missing cancer data were excluded and missing values in covariates were treated as an independent category in the analysis. All analyses were conducted using the statistical software R version 4.2.1.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the distribution of the mean annual PM\u003csub\u003e2.5\u003c/sub\u003e exposure and the base-line characteristics of all participants (N\u0026thinsp;=\u0026thinsp;325,264), among whom 31,143 participants had at least one self-reported or EHR-derived cancer diagnosis. Differences in age, race, smoking status, education, and BMI were observed between the participants overall, with older, female, White, non-smoking, more educated, and obese participants more likely to have data on cancer history. We also note differences in cancer outcomes as captured from the EHR (N\u0026thinsp;=\u0026thinsp;23,745), via self-report in the survey database (N\u0026thinsp;=\u0026thinsp;14,950 completed the \u003cem\u003ePersonal Medical History\u003c/em\u003e questionnaire), and from participants with both survey and EHR data (N\u0026thinsp;=\u0026thinsp;7,552). However, mean PM\u003csub\u003e2.5\u003c/sub\u003e did not vary across these different populations. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows PM\u003csub\u003e2.5\u003c/sub\u003e levels across the 379 3-digit zip code areas included in this analysis.\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\u003eDistribution of cancer types by ascertainment source\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAll participants w/ zip code*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eParticipants with cancer any source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eParticipants with cancer in EHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eParticipants with cancer in survey\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eParticipants with cancer in survey and EHR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\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\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e325,264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31,143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23,745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14,950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7,552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean PM2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.42 \u0026micro;g/m3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.41 \u0026micro;g/m3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.52 \u0026micro;g/m3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.30 \u0026micro;g/m3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.30 \u0026micro;g/m3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61,777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72,891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96,652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6,655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3,990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1,963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e26.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93,888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18,705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14,316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e60.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9,232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e61.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4,843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e64.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190,033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18,448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13,792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9,240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e61.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4,584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e60.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119,760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11,584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e39.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4,985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e33.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2,658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e35.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e~\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eRace/Ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169,313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20,962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15,250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e64.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11,730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e78.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6,018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e79.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack/AA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68,611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHispanic/Latino\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60,491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1 population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone of these\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64,794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e10.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e252,178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27,541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20,866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e87.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13,751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e91.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7,076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e93.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54,534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.27%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.62%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18,552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.68%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt; High School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31,089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63,695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSome College\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82,776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6,091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3,612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1,807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e23.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinished College\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134,546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15,912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11,499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9,039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e60.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4,626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e61.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.27%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.87%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal Weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22.29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e25.73%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80,505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32.03%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e32.85%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111,030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11,407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e33.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e37.45%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60,053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2,369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e*\u0026ge; 50 participants (excl. AK, HI CT, MA, ME, NH, NJ, RI, VT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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 \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that \u003cem\u003eAll of Us\u003c/em\u003e participants\u0026rsquo; EHR data indicate a history of breast cancer most frequently (N\u0026thinsp;=\u0026thinsp;6,785; 21.7% of cases) followed by prostate cancer (N\u0026thinsp;=\u0026thinsp;5,422; 13.2%), and blood cancers (N\u0026thinsp;=\u0026thinsp;4,605; 11.2%). More cancers were detected in the EHR passively as opposed to self-reported in the surveys, and the total case numbers are much lower (N\u0026thinsp;=\u0026thinsp;7,692) for cancers cross-referenced in both the EHR and survey data. For the analysis of PM\u003csub\u003e2.5\u003c/sub\u003e and cancer risk, the case population includes cases detected in either the EHR or in survey data (N\u0026thinsp;=\u0026thinsp;41,069). The number of cancer cases per participant is summarized in the supplemental table.\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\u003eCancer type distribution by data source\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEHR or Survey (Total Cases)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eEHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eSurvey Data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eEHR\u0026thinsp;+\u0026thinsp;Survey\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e% dist\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% dist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e% dist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e% dist\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Cancers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41,069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32,073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16,688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7,692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBladder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCervix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColon \u0026amp; Rectum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndocrine System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEsophagus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEye\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead \u0026amp; Neck\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePancreas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProstate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStomach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.7%\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents cancer type distribution across the quartile distribution of PM\u003csub\u003e2.5\u003c/sub\u003e exposure. More than 25% of blood, breast, endometrium, and stomach cancers are observed in the highest exposure quartile (11.22\u0026ndash;15.08 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e).\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\u003eCancer type distribution by mean annual outdoor PM\u003csub\u003e2.5\u003c/sub\u003e \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e quartiles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eQ1 (3\u0026ndash;7.91 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eQ2 (7.91\u0026ndash;10.07 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eQ3 (10.07\u0026ndash;11.22 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eQ4 (11.22\u0026ndash;15.08 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003etotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41,069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10,070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10,299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9,675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBladder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2,287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCervix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColon \u0026amp; Rectum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndocrine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEsophagus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEye\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead \u0026amp; Neck\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.06%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25.04%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePancreas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProstate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStomach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22.1%\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports the odds ratio (OR) and 95% confidence interval (CI) for air pollution with all cancers. The ORs are reported per unit increase in PM\u003csub\u003e2.5\u003c/sub\u003e or using the first quartile as the reference group. We observed increased odds for blood cancer (per unit: OR\u0026thinsp;=\u0026thinsp;1.02, 95% CI: 1.01\u0026ndash;1.03), brain cancer (per unit: OR\u0026thinsp;=\u0026thinsp;1.06, 95% CI: 1.03\u0026ndash;1.09), breast cancer (per unit: OR\u0026thinsp;=\u0026thinsp;1.03, 95% CI: 1.02\u0026ndash;1.04), colon and rectum cancer (per unit: OR\u0026thinsp;=\u0026thinsp;1.02, 95% CI: 1.00-1.04), and endometrial cancer (per unit: OR\u0026thinsp;=\u0026thinsp;1.06, 95% CI: 1.03\u0026ndash;1.10). Comparing the highest quartile and lowest quartile of PM\u003csub\u003e2.5\u003c/sub\u003e, strong associations were observed for brain cancer (OR\u0026thinsp;=\u0026thinsp;1.24, 95% CI: 1.03\u0026ndash;1.49), breast cancer (OR\u0026thinsp;=\u0026thinsp;1.12, 95% CI: 1.04\u0026ndash;1.19), and endometrial cancer (OR\u0026thinsp;=\u0026thinsp;1.30, 95% CI: 1.08\u0026ndash;1.58). However, some inverse associations were also observed for bone cancer (2nd vs. 1st quartile: OR\u0026thinsp;=\u0026thinsp;0.80, 95% CI: 0.69\u0026ndash;0.92; 3rd vs. 1st quartile: OR\u0026thinsp;=\u0026thinsp;0.76, 95% CI: 0.66\u0026ndash;0.87; 4th vs. 1st quartile: OR\u0026thinsp;=\u0026thinsp;0.86, 95% CI: 0.75\u0026ndash;0.99), eye cancer (4th vs. 1st quartile: OR\u0026thinsp;=\u0026thinsp;0.64. 95% CI: 0.43\u0026ndash;0.97), head and neck cancer (4th vs. 1st quartile: OR\u0026thinsp;=\u0026thinsp;0.79. 95% CI: 0.70\u0026ndash;0.88), lung cancer (2nd vs. 1st quartile: OR\u0026thinsp;=\u0026thinsp;0.81, 95% CI: 0.70\u0026ndash;0.94), pancreatic cancer (3rd vs. 1st quartile: per unit: OR\u0026thinsp;=\u0026thinsp;0.74, 95% CI: 0.59\u0026ndash;0.94; 4th vs. 1st quartile: OR\u0026thinsp;=\u0026thinsp;0.69. 95% CI: 0.55\u0026ndash;0.94), and prostate cancer (4th vs. 1st quartile: OR\u0026thinsp;=\u0026thinsp;0.91. 95% CI: 0.84\u0026ndash;0.99). When we restrict to EHR as source of the cancer report, the ORs become significant for blood cancer (OR\u0026thinsp;=\u0026thinsp;1.03. 95% CI: 1.02\u0026ndash;1.05), brain cancer (OR\u0026thinsp;=\u0026thinsp;1.07. 95% CI: 1.04\u0026ndash;1.11), breast cancer (OR\u0026thinsp;=\u0026thinsp;1.06. 95% CI: 1.05\u0026ndash;1.07), colon \u0026amp; rectum cancer (OR\u0026thinsp;=\u0026thinsp;1.04, 95% CI: 1.02\u0026ndash;1.07), endocrine system cancer (OR\u0026thinsp;=\u0026thinsp;1.02, 95% CI: 1.00-1.04), endometrial cancer OR\u0026thinsp;=\u0026thinsp;1.14. 95% CI: 1.09\u0026ndash;1.19), ovarian cancer (OR\u0026thinsp;=\u0026thinsp;1.04, 95% CI: 1.01\u0026ndash;1.08), prostate cancer (OR\u0026thinsp;=\u0026thinsp;1.02, 95% CI: 1.01\u0026ndash;1.04), and thyroid cancer (OR\u0026thinsp;=\u0026thinsp;1.03, 95% CI: 1.00-1.05). There are significant differences in effect when comparing the effect of air quality on cancer from EHR versus from survey data. Gender and race stratified results are presented in \u003cb\u003eSupplementary Tables\u0026nbsp;2 and 3\u003c/b\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\u003eCancer odds by increasing mean annual PM\u003csub\u003e2.5\u003c/sub\u003e exposure overall and restricted to EHR and survey source\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=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eCombined Sources\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2 (7.91\u0026ndash;10.07 \u0026micro;g/m3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3 (10.07\u0026ndash;11.22 \u0026micro;g/m3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4 (11.22\u0026ndash;15.08 \u0026micro;g/m3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSurvey Data\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBladder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.98, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09 (0.93, 1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.88, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.85, 1.17)\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 (0.99, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99 (0.95, 1.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.02 (1.01, 1.03)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99 (0.91, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.93, 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08 (0.99, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.03 (1.02, 1.05)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98 (0.95, 1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.96, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.80 (0.69, 0.92)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.77 (0.67, 0.89)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.86 (0.75, 0.99)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98 (0.96, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98 (0.92, 1.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.06 (1.03, 1.09)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.41\u003c/b\u003e (\u003cb\u003e1.18, 1.68\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.33 (1.11, 1.59)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.24 (1.03, 1.49)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.07 (1.04, 1.11)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.06 (0.99, 1.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.03 (1.02, 1.04)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.92, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.05 (0.98, 1.12)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.12 (1.04, 1.19)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.06 (1.05, 1.07\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.02 (1.00, 1.03)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCervix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.96, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95 (0.82, 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.87, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87 (0.75, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.03 (0.99, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.97 (0.94, 0.99)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColon \u0026amp; Rectum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.02 (1.00, 1.04)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09 (0.96, 1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.92, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12 (0.99, 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.04 (1.02, 1.07\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99 (0.96, 1.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndocrine System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.99, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90 (0.79, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.91, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (0.88, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.02 (1.00, 1.04)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.96 (0.89, 1.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.06 (1.03, 1.10)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99 (0.82, 1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.23 (1.02, 1.48)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.30 (1.08, 1.58\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.14 (1.09, 1.19)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 (0.96, 1.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEsophagus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.92, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82 (0.59, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80 (0.57, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83 (0.59, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97 (0.92, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.97 (0.89, 1.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEye\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.92, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87 (0.61, 1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16 (0.83, 1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.64 (0.43, 0.97)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.07 (0.99, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.87 (0.79, 0.95)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead \u0026amp; Neck\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.99, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.88, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.90, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.79 (0.70, 0.88)\u003c/b\u003e\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 (0.99, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99 (0.94, 1.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.84, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90 (0.77, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97 (0.83, 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\u003e0.99 (0.97, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99 (0.95, 1.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.95, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91 (0.79, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.81 (0.70, 0.94)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88 (0.76, 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\u003e0.98 (0.95, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 (0.96, 1.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.99, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95 (0.79, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13 (0.95, 1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11 (0.93, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.04 (1.01, 1.08)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01 (0.96, 1.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePancreas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.96 (0.92, 0.99)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07 (0.87, 1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.74 (0.59, 0.94)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.69\u003c/b\u003e (\u003cb\u003e0.55, 0.88\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97 \u003cb\u003e(\u003c/b\u003e0.93, 1.01\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.94 (0.86, 1.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProstate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.99, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07 (0.98, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.93, 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.91 (0.84, 0.99)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.02 (1.01, 1.04)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99 (0.98, 1.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStomach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.98, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16 (0.86, 1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 (0.80, 1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.15 (0.85, 1.56)\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, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98 (0.89, 1.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.99, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94 (0.82, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.89, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98 (0.86, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.03 (1.00, 1.05)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01 (0.98, 1.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e* adjusted for gender, race/ethnicity, age, smoking status, education, and BMI\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the non-linear relationship between PM\u003csub\u003e2.5\u003c/sub\u003e and cancers with a p-value for spline less than 0.10. A non-linear relationship was observed for blood cancer, bone cancer, brain cancer, breast cancer, colon \u0026amp; rectum cancer, endocrine system cancer, lung cancer, pancreatic cancer, prostate cancer, and thyroid cancer. Notably, although we observed inverse associations for bone cancer, lung cancer, and pancreatic cancer in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, results from GAM suggest that high PM\u003csub\u003e2.5\u003c/sub\u003e concentrations increase the odds for these cancers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":" \u003cp\u003eIn this study, the median PM\u003csub\u003e2.5\u003c/sub\u003e concentration was 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, in line with the WHO health-based world air-quality guideline (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). The highest concentration of 15 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e was observed in California, while prior review reported an annual average PM\u003csub\u003e2.5\u003c/sub\u003e concentration of 7 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e in the US (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The difference can be explained by the spatial distribution of our study population. At present, because urban residents have easier access to \u003cem\u003eAll of Us\u003c/em\u003e HPOs, most participants are concentrated in large cities such as New York City, Chicago, and Los Angeles where the level of air pollution is generally higher than rural areas. However, even the highest PM\u003csub\u003e2.5\u003c/sub\u003e concentration in this study indicates a recent reduction in average PM\u003csub\u003e2.5\u003c/sub\u003e exposure level across the US. For instance, a US-wide cohort study based on the American Cancer Society (ACS) Cancer Prevention Study II (CPS-II) reported a median PM\u003csub\u003e2.5\u003c/sub\u003e concentration of 12.5 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e between 1999 and 2008, with the highest concentration of 28 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003eOutdoor air pollution and PM in outdoor air pollution have been classified as Group 1 human carcinogens for lung cancer by the IARC since 2013 (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), a determination based largely on findings from outdoor air pollution exposure analysis in population cohort studies (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Similarly, a recent meta-analysis reported a 9% increase in risk for lung cancer incidence or mortality per each 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e increase in PM\u003csub\u003e2.5\u003c/sub\u003e concentration as well as an 8% (95% CI, 0%-17%) increase in risk per 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e for PM\u003csub\u003e10\u003c/sub\u003e (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Our study observed an inverse association between PM\u003csub\u003e2.5\u003c/sub\u003e and lung cancer. However, this inverse association was only manifest when the exposure level was low, which may reflect measurement error. In our analysis of the variables\u0026rsquo; non-linear relationship, the odds for lung cancer increased drastically when PM\u003csub\u003e2.5\u003c/sub\u003e level exceeded a certain threshold. Therefore, our observation is still consistent with prior conclusions.\u003c/p\u003e \u003cp\u003eWhile the IARC has reported adverse associations between outdoor air pollution and bladder cancer (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e), this association was not observed in our study.\u003c/p\u003e \u003cp\u003eSystemic inflammation, oxidative stress, and epigenetic changes induced by PM exposure (\u003cspan additionalcitationids=\"CR53 CR54\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) are thought to play a role in the progression of breast tumors (\u003cspan additionalcitationids=\"CR57 CR58 CR59\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e), and studies from a variety of settings demonstrate an association between PM\u003csub\u003e2.5\u003c/sub\u003e levels and breast cancer mortality rates as well as all-cause mortality (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). A recent analysis of 47,433 women in the US Sister Study found adverse associations between PM\u003csub\u003e2.5\u003c/sub\u003e (HR per 3.6 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, 1.05; 95% CI, 0.99\u0026ndash;1.11) and breast cancer incidence overall (n\u0026thinsp;=\u0026thinsp;2848) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). An analysis of 57,589 women in the Multiethnic Cohort observed adverse associations of NO\u003csub\u003ex\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, and PM\u003csub\u003e10\u003c/sub\u003e and breast cancer incidence among those living within 500 meters of major roads (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The Canadian National Breast Screening Study (n\u0026thinsp;=\u0026thinsp;89,247) found adverse associations of both PM\u003csub\u003e2.5\u003c/sub\u003e (HR per 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, 1.26; 95% CI, 0.99\u0026ndash;1.61) and NO\u003csub\u003e2\u003c/sub\u003e (HRs per 9.7 ppb, range 1.13\u0026ndash;1.17) and the risk of incident premenopausal disease (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). However, no other recent studies have reported clear associations with incident breast cancer risk (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). In our study, we did observe increased risk for breast cancer associated with PM\u003csub\u003e2.5\u003c/sub\u003e exposure. This association was more evident when the PM\u003csub\u003e2.5\u003c/sub\u003e level was high. The finding is generally consistent with previous studies that present suggestive associations for breast cancer. The larger number of breast cancer cases in this study yielded larger statistical power and may explain why we could observe associations in this study.\u003c/p\u003e \u003cp\u003eAdditionally, we also observed significant increased odds for blood and brain cancers. Previous studies have reported associations between air pollution and blood cancer (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e), while other cancers have rarely been studied. We report some of these associations for the first time and the findings warrant further investigation of these cancers in air pollution studies.\u003c/p\u003e \u003cp\u003eA limitation of this study is that we only examined the association of PM\u003csub\u003e2.5\u003c/sub\u003e with cancers while other pollutants such as SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003ex\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e were not included. PM\u003csub\u003e2.5\u003c/sub\u003e is the most investigated pollutant and is often used as an indicator of overall air quality. However, the sole investigation of PM\u003csub\u003e2.5\u003c/sub\u003e may lead to an underestimation of the association between air pollution and cancer risks. For instance, a recent review found that a higher risk of breast cancer was associated with NO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003ex\u003c/sub\u003e, but not PM\u003csub\u003e2.5\u003c/sub\u003e (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). Another meta-analysis on leukemia concluded that higher exposure to NO\u003csub\u003e2\u003c/sub\u003e, but not PM\u003csub\u003e2.5\u003c/sub\u003e, was associated with higher leukemia risk. Additionally, this study only includes ambient PM\u003csub\u003e2.5\u003c/sub\u003e exposure level and relies on historical data. Current indoor air pollution exposure may pose greater health threats, as people spend most of their time indoors and indoor air pollution is generally more complicated than outdoor pollution. Therefore, results from this study only reveal the partial impacts of air pollution. A multi-level approach accounts for multiple pollutants and sources is warranted in future studies.\u003c/p\u003e \u003cp\u003eTo preserve participant privacy, the \u003cem\u003eAll of Us\u003c/em\u003e Researcher Workbench only offers participant data at the 3-digit zip code prefix level, rather than at the full 6-digit level which would confer higher spatial resolution for exposure estimates. As the first three digits of a zip code designate a city or a larger rural area, exposure assessment in this study may underestimate geospatial variations in air pollution. Recent epidemiological research has demonstrated the importance of within-city variability in air pollution concentration (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). However, the current resolution in this study is not sufficient to account for this within-city variability and thus may overlook exposure inequalities faced by urban minorities and underestimate the true associations. Another notable limitation is that we relied on the self-report and electronic health record capture of both incident and prevalent cancers and did not distinguish between primary and secondary cancers. We report differences in the effect based on the source of cancer report. The degree of impact of multiple cancers is illustrated in \u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e. Likewise, self-report data are not sufficiently detailed to allow for finer-grained analysis including reproductive or menopausal factors for breast cancer. We also found significant disparity by race in the self-reported survey data. For example, while Black/African American participants comprised 21% of the overall sample population, they accounted for only 6.8% of self-reported cancers. Similarly, participants identifying as Hispanic/Latino comprised 18.6% of our sample, yet they accounted for only 6.2% of self-reported cancers. This disparity is consistent with our previous analysis of \u003cem\u003eAll of Us\u003c/em\u003e data and highlights the importance of continued engagement with populations historically underrepresented in biomedical research by both incentivizing and removing barriers to follow up data collection (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). The difference in association between cancer risk and PM\u003csub\u003e2.5\u003c/sub\u003e based on data source is clearly illustrated in our report. Furthermore, the representativeness of this work is limited given the sampling plan; as illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the health provider organizations that account for the greatest share of participant recruitment are generally located in metropolitan areas. Furthermore, at the current stage, the \u003cem\u003eAll of Us\u003c/em\u003e data used for this analysis are cross-sectional in nature as we relied on baseline data and limited longitudinal transfer of EHR. It is therefore difficult to establish temporality between air pollution and cancer outcomes and it is impossible to investigate cancer progression in relation to air pollution. However, reverse causation - the greatest concern in cross-sectional studies - is not likely in this study as higher cancer prevalence does not cause higher air pollution. The association between air pollution and cancer prevalence observed in this study still supports the adverse impact of air pollution on cancer outcomes. Likewise, the cross-sectional nature of the current data also presents the limitation of a lack of \u0026ldquo;latency\u0026rdquo; or \u0026ldquo;lag\u0026rdquo; of exposure. To address this limitation our analysis used the 10-year PM\u003csub\u003e2.5\u003c/sub\u003e average from 2006 to 2016, aiming to cover the cancer progression stages before the study enrollment period. However, we understand that these efforts cannot completely offset the limitation induced by the study design. Some inverse associations observed in this study may be the consequence of this limitation.\u003c/p\u003e \u003cp\u003eThe study has several notable strengths. First, \u003cem\u003eAll of Us\u003c/em\u003e is a nationwide cohort that can be representative of the general population in the US. While previous studies have been limited by small numbers of cancer cases, the sample size of this study, with more than 300,000 participants, entails the largest investigation of the association between air pollution and cancer to date. Second, research on the carcinogenicity of air pollution has long focused nearly exclusively on lung cancer, however outdoor air pollution might cause cancer at sites other than the lung through absorption, metabolism, and distribution of inhaled carcinogens. Other cancer types, including leukemia and breast cancer, have been also investigated in relation to air pollution. However, to our knowledge no study has simultaneously investigated as many cancer types as in this one. Third, the study design of \u003cem\u003eAll of Us\u003c/em\u003e enables researchers to analyze cancer risk longitudinally, thus providing evidence for the role of air pollution in cancer occurrence and development. Many prior studies have only been able to use cancer mortality as the outcome, thus may underestimate the true odds for some cancers.\u003c/p\u003e \u003cp\u003eIn summary, the \u003cem\u003eAll of Us\u003c/em\u003e Research Program presents significant opportunities to further evaluate the role of the environment and air pollution in cancer odds and outcomes. We have observed associations of PM\u003csub\u003e2.5\u003c/sub\u003e exposure with several types of cancer including blood cancer, bone cancer, brain cancer, breast cancer, colon and rectum cancer, endocrine system cancer, endometrial cancer, lung cancer, ovarian cancer, pancreatic cancer, prostate cancer, and thyroid cancer. This preliminary investigation suggests that some previous work on cancer and PM\u003csub\u003e2.5\u003c/sub\u003e is also observed in \u003cem\u003eAll of Us\u003c/em\u003e; for instance, our breast cancer results. Given the large and diverse \u003cem\u003eAll of Us\u003c/em\u003e study population, it may be possible to further consider the role of the environment on cancer disparities in addition to cancer risk in general. In the coming years, \u003cem\u003eAll of Us\u003c/em\u003e may confer sufficient study power to research the role of the environment in cancers that have historically been infeasible to investigate due to small sample size. This project should provide some preliminary insight and direction for future investigation.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Sharing Statement\u003c/strong\u003e: Data is owned by a third party, the \u003cem\u003eAll of Us\u003c/em\u003e Research Program. The data underlying this article were provided by the \u003cem\u003eAll of Us\u003c/em\u003e Research Program by permission. Data will be shared on request to the corresponding author with permission of \u003cem\u003eAll of Us\u003c/em\u003e. More information on data access can be found \u0026nbsp;in the \u003cem\u003eAll of Us\u003c/em\u003e Research Hub (https://www.researchallofus.org)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e: The authors declare no potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: The \u003cem\u003eAll of Us\u003c/em\u003e Research Program is supported by grants through the National Institutes of Health Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA #: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205; 3 OT2 OD023206; Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276; and the \u003cem\u003eAll of Us\u003c/em\u003e Pilot: 1 OT2 OD023132. \u0026nbsp;This work was also supported by the NIEHS funded Chicago Center for Health and the Environment (P30 ES027792-05A1).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e: All authors contributed to the study conception and design. Data management was performed by AC. Analysis was performed by AC and overseen by JL. Maps were generated by NR. The first draft of the manuscript was written by BAK, JL, and AC and all authors reviewed and edited subsequent versions of the manuscript. All authors read and approved the final manuscript.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e: Informed consent was obtained from all individual participants included in the study, and the \u003cem\u003eAll of Us\u003c/em\u003e Research Program protocol was approved by the NIH \u003cem\u003eAll of Us\u003c/em\u003e Institutional Review Board.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements: Past and Present \u003cem\u003eAll of Us\u003c/em\u003e Research Program Principal Investigators\u003c/strong\u003e: Brian Ahmedani, PhD, MSW\u003csup\u003e1\u003c/sup\u003e; Christine D Cole Johnson, PhD, MPH\u003csup\u003e1\u003c/sup\u003e; Habib Ahsan, MD, MMedSc\u003csup\u003e2\u003c/sup\u003e; Donna Antoine-LaVigne, PhD, MPH, MSEd*\u003csup\u003e3\u003c/sup\u003e; Glendora Singleton*\u003csup\u003e3\u003c/sup\u003e; Pamelia Watson-McGee\u003csup\u003e3\u003c/sup\u003e; Arnita Ford Norwood, PhD, MPH, RDN\u003csup\u003e3\u003c/sup\u003e; Hoda Anton-Culver, PhD\u003csup\u003e4\u003c/sup\u003e; Eric Topol, MD\u003csup\u003e5\u003c/sup\u003e; Katie Baca-Motes, MBA\u003csup\u003e5\u003c/sup\u003e; Julia Moore-Vogel, PhD, MBA\u003csup\u003e5\u003c/sup\u003e; Steven Steinhubl, MD*\u003csup\u003e5\u003c/sup\u003e; Praduman Jain, MSEE\u003csup\u003e6\u003c/sup\u003e; Mark Begale\u003csup\u003e6\u003c/sup\u003e; Neeta Jain\u003csup\u003e6\u003c/sup\u003e; David Klein, MBA\u003csup\u003e6\u003c/sup\u003e; Scott Sutherland\u003csup\u003e6\u003c/sup\u003e; James Wade, MD*\u003csup\u003e6\u003c/sup\u003e; Bruce Korf, MD, PhD\u003csup\u003e7\u003c/sup\u003e; Mona Fouad, MD, PhD\u003csup\u003e7\u003c/sup\u003e; Beth Lewis\u003csup\u003e7\u003c/sup\u003e; David B Goldstein, PhD\u003csup\u003e8\u003c/sup\u003e; Louise Bier, MS\u003csup\u003e8\u003c/sup\u003e; Ali G Gharavi, MD\u003csup\u003e8\u003c/sup\u003e; George Hripcsak, MD, MS\u003csup\u003e8\u003c/sup\u003e; Eric Boerwinkle, PhD, MS, MA\u003csup\u003e9\u003c/sup\u003e; Murray H Brilliant, PhD*\u003csup\u003e10\u003c/sup\u003e; Narayana Murali\u003csup\u003e10\u003c/sup\u003e; Scott Joseph Hebbring\u003csup\u003e10\u003c/sup\u003e; Elizabeth Burnside\u003csup\u003e11\u003c/sup\u003e; Dorothy Farrar-Edwards, PhD\u003csup\u003e11\u003c/sup\u003e; Yashoda Sharma, PhD\u003csup\u003e12\u003c/sup\u003e; Amy Taylor\u003csup\u003e12\u003c/sup\u003e; Carmen Chinea, MD, MPH*\u003csup\u003e13\u003c/sup\u003e; Liliana Lombardi Desa\u003csup\u003e13\u003c/sup\u003e; Nancy Jenks, MS, CFNP, FAANP\u003csup\u003e13\u003c/sup\u003e; Steve Thibodeau\u003csup\u003e14\u003c/sup\u003e; Mine Cicek, PhD\u003csup\u003e14\u003c/sup\u003e; Eric Schlueter, MD\u003csup\u003e15\u003c/sup\u003e; Beverly Wilson Holmes, MSW\u003csup\u003e15\u003c/sup\u003e; Martha Daviglus, MD, PhD\u003csup\u003e16\u003c/sup\u003e; Robert Winn, MD*\u003csup\u003e16\u003c/sup\u003e; Paul Harris, PhD+\u003csup\u003e17\u003c/sup\u003e; Consuelo Wilkins, MD, MSCI\u003csup\u003e17\u003c/sup\u003e; Dan Roden, MD, CM\u003csup\u003e17\u003c/sup\u003e; Joshua Denny, MD, MS*\u003csup\u003e17\u003c/sup\u003e; Kim Doheny\u003csup\u003e18\u003c/sup\u003e; Debbie Nickerson, PhD\u003csup\u003e19\u003c/sup\u003e; Evan Eichler\u003csup\u003e19\u003c/sup\u003e; Gail Jarvik, MD, PhD\u003csup\u003e19\u003c/sup\u003e; Gretchen Funk\u003csup\u003e20\u003c/sup\u003e; Sallie Hussey\u003csup\u003e20\u003c/sup\u003e; Anthony Philippakis, MD, PhD\u003csup\u003e21\u003c/sup\u003e; Heidi Rehm, PhD, MMSc, FACMG\u003csup\u003e21\u003c/sup\u003e; Stacey Gabriel, PhD\u003csup\u003e21\u003c/sup\u003e; Richard Gibbs\u003csup\u003e22\u003c/sup\u003e; Edgar M Gil Rico, MBA, MSc\u003csup\u003e23\u003c/sup\u003e; David Glazer\u003csup\u003e24\u003c/sup\u003e; Jessica Burke, MBA\u003csup\u003e25\u003c/sup\u003e; Philip Greenland, MD\u003csup\u003e26\u003c/sup\u003e; Elizabeth Shenkman, PhD\u003csup\u003e27\u003c/sup\u003e; William R Hogan, MD, MS\u003csup\u003e27\u003c/sup\u003e; Priscilla Igho-Pemu, MD, MSCR, FACP\u003csup\u003e28\u003c/sup\u003e; W Karlson, MD\u003csup\u003e29\u003c/sup\u003e; Jordan Smoller, MD, ScD\u003csup\u003e29\u003c/sup\u003e; Shawn N Murphy, MD, PhD\u003csup\u003e29\u003c/sup\u003e; Margaret Elizabeth Ross, MD, PhD\u003csup\u003e30\u003c/sup\u003e; Rainu Kaushal, MD, MPH\u003csup\u003e30\u003c/sup\u003e; Eboni Winford, PhD\u003csup\u003e31\u003c/sup\u003e; Febe Wallace, MD\u003csup\u003e31\u003c/sup\u003e; Parinda Khatri, PhD\u003csup\u003e31\u003c/sup\u003e; Vik Kheterpal\u003csup\u003e32\u003c/sup\u003e; Monica Kraft\u003csup\u003e33\u003c/sup\u003e; Francisco A Moreno, MD\u003csup\u003e33\u003c/sup\u003e; Irving Kron*\u003csup\u003e33\u003c/sup\u003e; Rachele Peterson, MS*\u003csup\u003e33\u003c/sup\u003e; Patricia Watkins Lattimore*\u003csup\u003e34\u003c/sup\u003e; Cheryl Thomas\u003csup\u003e34\u003c/sup\u003e; Mitchell Lunn, MD, MAS, FASN\u003csup\u003e35\u003c/sup\u003e; Juno Obedin-Maliver\u003csup\u003e35\u003c/sup\u003e; Oscar Marroquin, MD\u003csup\u003e36\u003c/sup\u003e; Shyam Visweswaran, MD, PhD\u003csup\u003e36\u003c/sup\u003e; Steven Reis, MD\u003csup\u003e36\u003c/sup\u003e; Patrick McGovern\u003csup\u003e37\u003c/sup\u003e; Fatima Munoz, MD, MPH\u003csup\u003e38\u003c/sup\u003e; Gregory Talavera, MD, MPH\u003csup\u003e38\u003c/sup\u003e; George T O\u0026apos;Connor, MD, MS\u003csup\u003e39\u003c/sup\u003e; Christopher O\u0026apos;Donnell, MD, MPH*\u003csup\u003e40\u003c/sup\u003e; Lucila Ohno-Machado, MD, PhD\u003csup\u003e41\u003c/sup\u003e; Greg Orr*\u003csup\u003e42\u003c/sup\u003e; Fornessa Randal, MCRP\u003csup\u003e43\u003c/sup\u003e; Andreas A Theodorou, MD\u003csup\u003e44\u003c/sup\u003e; Eric Reiman, MD\u003csup\u003e44\u003c/sup\u003e; Mercedita Roxas-Murray\u003csup\u003e45\u003c/sup\u003e; Louisa Stark\u003csup\u003e46\u003c/sup\u003e; Ronnie Tepp, MPP\u003csup\u003e47\u003c/sup\u003e; Alicia Zhou, PhD\u003csup\u003e48\u003c/sup\u003e; Scott Topper, PhD, FACMG\u003csup\u003e48\u003c/sup\u003e; Rhonda Trousdale, MD\u003csup\u003e49\u003c/sup\u003e; Phil Tsao, PhD\u003csup\u003e50\u003c/sup\u003e; Scott T Weiss, MD, MS\u003csup\u003e51\u003c/sup\u003e; David Wellis, PhD\u003csup\u003e52\u003c/sup\u003e; Jeffrey Whittle, MD, MPH\u003csup\u003e53\u003c/sup\u003e; Amanda Wilson, MS\u003csup\u003e54\u003c/sup\u003e; Stephan Zuchner, MD, PhD\u003csup\u003e55\u003c/sup\u003e; Olveen Carrasquillo, MD, PhD\u003csup\u003e55\u003c/sup\u003e; Margaret Pericak-Vance\u003csup\u003e55\u003c/sup\u003e; Michael E Zwick, PhD\u003csup\u003e56\u003c/sup\u003e; Megan Lewis\u003csup\u003e57\u003c/sup\u003e; Jen Uhrig\u003csup\u003e57\u003c/sup\u003e; May Okihiro\u003csup\u003e58\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eNote: This is the list of individuals who were Principal Investigators or equivalent with the \u003cem\u003eAll of Us\u003c/em\u003e Research Program during the period that this paper was in development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e+ Principal Investigator/Lead Author for the \u003cem\u003eAll of Us\u003c/em\u003e Research Program protocol ([email protected])\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAffiliations\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e1. Henry Ford Health System\u003c/p\u003e\n\u003cp\u003e2. University of Chicago Medical Center\u003c/p\u003e\n\u003cp\u003e3. Jackson-Hinds Comprehensive Health Center\u003c/p\u003e\n\u003cp\u003e4. University of California, Irvine\u003c/p\u003e\n\u003cp\u003e5. Scripps Research Translational Institute\u003c/p\u003e\n\u003cp\u003e6. Vibrent Health\u003c/p\u003e\n\u003cp\u003e7. University of Alabama at Birmingham\u003c/p\u003e\n\u003cp\u003e8. Columbia University\u003c/p\u003e\n\u003cp\u003e9. University of Texas Health Science Center at Houston\u003c/p\u003e\n\u003cp\u003e10. Marshfield Clinic Research Institute\u003c/p\u003e\n\u003cp\u003e11. University of Wisconsin at Madison\u003c/p\u003e\n\u003cp\u003e12. Community Health Center, Inc.\u003c/p\u003e\n\u003cp\u003e13. Sun River Health\u003c/p\u003e\n\u003cp\u003e14. Mayo Clinic and Foundation, Rochester\u003c/p\u003e\n\u003cp\u003e15. Cooperative Health\u003c/p\u003e\n\u003cp\u003e16. University of Illinois at Chicago\u003c/p\u003e\n\u003cp\u003e17. Vanderbilt University Medical Center\u003c/p\u003e\n\u003cp\u003e18. Johns Hopkins University School of Medicine\u003c/p\u003e\n\u003cp\u003e19. University of Washington\u003c/p\u003e\n\u003cp\u003e20. FiftyForward\u003c/p\u003e\n\u003cp\u003e21. Broad Institute\u003c/p\u003e\n\u003cp\u003e22. Baylor University\u003c/p\u003e\n\u003cp\u003e23. National Alliance for Hispanic Health\u003c/p\u003e\n\u003cp\u003e24. Verily Life Sciences\u003c/p\u003e\n\u003cp\u003e25. MITRE Corporation\u003c/p\u003e\n\u003cp\u003e26. Northwestern University\u003c/p\u003e\n\u003cp\u003e27. University of Florida\u003c/p\u003e\n\u003cp\u003e28. Morehouse School of Medicine, Atlanta\u003c/p\u003e\n\u003cp\u003e29. Partners Health Care\u003c/p\u003e\n\u003cp\u003e30. Cornell University, Weill Medical College\u003c/p\u003e\n\u003cp\u003e31. Cherokee Health Systems\u003c/p\u003e\n\u003cp\u003e32. CareEvolution, Inc.\u003c/p\u003e\n\u003cp\u003e33. University of Arizona, Tucson\u003c/p\u003e\n\u003cp\u003e34. Delta Research and Educational Foundation\u003c/p\u003e\n\u003cp\u003e35. Stanford University\u003c/p\u003e\n\u003cp\u003e36. University of Pittsburgh\u003c/p\u003e\n\u003cp\u003e37. Wondros\u003c/p\u003e\n\u003cp\u003e38. San Ysidro Health Center\u003c/p\u003e\n\u003cp\u003e39. Boston Medical Center\u003c/p\u003e\n\u003cp\u003e40. VA All of Us Coordinating Center, Boston\u003c/p\u003e\n\u003cp\u003e41. University of California, San Diego\u003c/p\u003e\n\u003cp\u003e42. Walgreen Co.\u003c/p\u003e\n\u003cp\u003e43. Asian Health Coalition\u003c/p\u003e\n\u003cp\u003e44. Banner Health\u003c/p\u003e\n\u003cp\u003e45. Montage Marketing Group\u003c/p\u003e\n\u003cp\u003e46. University of Utah\u003c/p\u003e\n\u003cp\u003e47. HCM Strategists\u003c/p\u003e\n\u003cp\u003e48. Color Genomics, Inc.\u003c/p\u003e\n\u003cp\u003e49. NYC Health + Hospitals\u003c/p\u003e\n\u003cp\u003e50. VA All of Us Coordinating Center - Palo Alto\u003c/p\u003e\n\u003cp\u003e51. Brigham and Women\u0026apos;s Hospital\u003c/p\u003e\n\u003cp\u003e52. San Diego Blood Bank\u003c/p\u003e\n\u003cp\u003e53. Medical College of Wisconsin\u003c/p\u003e\n\u003cp\u003e54. National Library of Medicine (NLM)\u003c/p\u003e\n\u003cp\u003e55. University of Miami School of Medicine\u003c/p\u003e\n\u003cp\u003e56. Emory University\u003c/p\u003e\n\u003cp\u003e57. Research Triangle Institute\u003c/p\u003e\n\u003cp\u003e58. Waianae Coast CHC\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEPA. 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N Engl J Med. 2007;356(5):447\u0026ndash;58.\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":"[email protected]","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":"cancer risk, PM2.5, air pollution","lastPublishedDoi":"10.21203/rs.3.rs-2489321/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2489321/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp;The NIH \u003cem\u003eAll of Us\u003c/em\u003e Research Program has enrolled over 544,000 participants across the US with unprecedented racial/ethnic diversity, offering opportunities to investigate myriad exposures and diseases. This paper aims to investigate the association between PM\u003csub\u003e2.5\u003c/sub\u003e exposure and cancer risks.\u003c/p\u003e\n\u003ch2\u003eMaterials and Methods\u003c/h2\u003e\n\u003cp\u003eThis work was performed on data from 325,264 \u003cem\u003eAll of Us\u003c/em\u003e Research Program participants using the \u003cem\u003eAll of Us\u003c/em\u003e Researcher Workbench. Cancer case ascertainment was performed using data from electronic health records and the self-reported Personal Medical History questionnaire. PM\u003csub\u003e2.5\u003c/sub\u003e exposure was retrieved from NASA’s Earth Observing System Data and Information Center and assigned using participants’ 3-digit zip code prefixes. Multivariate logistic regression was used to estimate the odds ratio (OR) and 95% confidence interval (CI). Generalized additive models (GAMs) were used to investigate non-linear relationships.\u003c/p\u003e\n\u003ch2\u003eResults\u003c/h2\u003e\n\u003cp\u003eA total of 32,073 prevalent cancer cases were ascertained from participant EHR data, while 16,688 cases were ascertained from self-reported survey data; 7,692 cancer cases were captured in both the EHR and survey data. Average PM\u003csub\u003e2.5\u003c/sub\u003e level from 2006 to 2016 was 9.4 µg/m3 (min 3.0, max 15.1). In analysis of cancer cases from both sources combined (n = 41,069), each unit increase in PM\u003csub\u003e2.5\u003c/sub\u003e was associated with increased odds for blood cancer (OR = 1.02, 95% CI: 1.01–1.03), brain cancer (OR = 1.06, 95% CI: 1.03–1.09), breast cancer (OR = 1.03, 95% CI: 1.02–1.04), colon and rectum cancer (OR = 1.02, 95% CI: 1.00-1.04), and endometrial cancer (OR = 1.06, 95% CI: 1.03–1.10). In GAM, higher PM\u003csub\u003e2.5\u003c/sub\u003e concentration was associated with increased odds for blood cancer, bone cancer, brain cancer, breast cancer, colon and rectum cancer, endocrine system cancer, lung cancer, pancreatic cancer, prostate cancer, and thyroid cancer.\u003c/p\u003e\n\u003ch2\u003eConclusions\u003c/h2\u003e\n\u003cp\u003eWe found evidence of an association of PM\u003csub\u003e2.5\u003c/sub\u003e with brain, breast, blood, colon and rectum, and endometrial cancers. There is little to no prior evidence in the literature on the impact of PM\u003csub\u003e2.5\u003c/sub\u003e on risk of these cancers, warranting further investigation.\u003c/p\u003e","manuscriptTitle":"Air quality and cancer risk in the All of Us Research Program","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-01-23 15:11:27","doi":"10.21203/rs.3.rs-2489321/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-07-30T18:32:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-07-27T04:20:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7c0791e1-490e-4f17-bc09-cd8ea1102971","date":"2023-06-20T17:12:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"e45dcbea-38b3-4ba5-9901-e185bce8d01f","date":"2023-02-09T16:26:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-02-09T01:22:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-01-19T13:10:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-01-19T13:10:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Causes \u0026 Control","date":"2023-01-17T21:32:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"ad5315b7-2c7b-4b16-b4dc-b79b87cb135d","owner":[],"postedDate":"January 23rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-01-01T15:02:40+00:00","versionOfRecord":{"articleIdentity":"rs-2489321","link":"https://doi.org/10.1007/s10552-023-01823-7","journal":{"identity":"cancer-causes-and-control","isVorOnly":false,"title":"Cancer Causes \u0026 Control"},"publishedOn":"2023-12-25 15:01:00","publishedOnDateReadable":"December 25th, 2023"},"versionCreatedAt":"2023-01-23 15:11:27","video":"","vorDoi":"10.1007/s10552-023-01823-7","vorDoiUrl":"https://doi.org/10.1007/s10552-023-01823-7","workflowStages":[]},"version":"v1","identity":"rs-2489321","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2489321","identity":"rs-2489321","version":["v1"]},"buildId":"369fNeqWncA4NS6XSWjrt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}