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

doi:10.21203/rs.3.rs-2489321/v1

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Research Article

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

https://doi.org/10.21203/rs.3.rs-2489321/v1

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Journal Publication

published 25 Dec, 2023

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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

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.

2.1. 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.

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/m³).

Table 3

Cancer type distribution by mean annual outdoor PM_2.5 µg/m³ quartiles
		Q1 (3–7.91 µg/m³)		Q2 (7.91–10.07 µg/m³)		Q3 (10.07–11.22 µg/m³)		Q4 (11.22–15.08 µg/m³)
	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.

In this study, the median PM_2.5 concentration was 10 µg/m³, in line with the WHO health-based world air-quality guideline (49, 50). The highest concentration of 15 µg/m³ was observed in California, while prior review reported an annual average PM_2.5 concentration of 7 µg/m³ 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/m³ between 1999 and 2008, with the highest concentration of 28 µg/m³ (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/m³ increase in PM_2.5 concentration as well as an 8% (95% CI, 0%-17%) increase in risk per 10 µg/m³ for PM₁₀ (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/m³, 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, NO₂, PM_2.5, and PM₁₀ 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/m³, 1.26; 95% CI, 0.99–1.61) and NO₂ (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 SO₂, NO₂, NO_x, and O₃ 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 NO₂ and NO_x, but not PM_2.5 (60). Another meta-analysis on leukemia concluded that higher exposure to NO₂, 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.

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, MSW¹; Christine D Cole Johnson, PhD, MPH¹; Habib Ahsan, MD, MMedSc²; Donna Antoine-LaVigne, PhD, MPH, MSEd*³; Glendora Singleton*³; Pamelia Watson-McGee³; Arnita Ford Norwood, PhD, MPH, RDN³; Hoda Anton-Culver, PhD⁴; Eric Topol, MD⁵; Katie Baca-Motes, MBA⁵; Julia Moore-Vogel, PhD, MBA⁵; Steven Steinhubl, MD*⁵; Praduman Jain, MSEE⁶; Mark Begale⁶; Neeta Jain⁶; David Klein, MBA⁶; Scott Sutherland⁶; James Wade, MD*⁶; Bruce Korf, MD, PhD⁷; Mona Fouad, MD, PhD⁷; Beth Lewis⁷; David B Goldstein, PhD⁸; Louise Bier, MS⁸; Ali G Gharavi, MD⁸; George Hripcsak, MD, MS⁸; Eric Boerwinkle, PhD, MS, MA⁹; Murray H Brilliant, PhD*¹⁰; Narayana Murali¹⁰; Scott Joseph Hebbring¹⁰; Elizabeth Burnside¹¹; Dorothy Farrar-Edwards, PhD¹¹; Yashoda Sharma, PhD¹²; Amy Taylor¹²; Carmen Chinea, MD, MPH*¹³; Liliana Lombardi Desa¹³; Nancy Jenks, MS, CFNP, FAANP¹³; Steve Thibodeau¹⁴; Mine Cicek, PhD¹⁴; Eric Schlueter, MD¹⁵; Beverly Wilson Holmes, MSW¹⁵; Martha Daviglus, MD, PhD¹⁶; Robert Winn, MD*¹⁶; Paul Harris, PhD+¹⁷; Consuelo Wilkins, MD, MSCI¹⁷; Dan Roden, MD, CM¹⁷; Joshua Denny, MD, MS*¹⁷; Kim Doheny¹⁸; Debbie Nickerson, PhD¹⁹; Evan Eichler¹⁹; Gail Jarvik, MD, PhD¹⁹; Gretchen Funk²⁰; Sallie Hussey²⁰; Anthony Philippakis, MD, PhD²¹; Heidi Rehm, PhD, MMSc, FACMG²¹; Stacey Gabriel, PhD²¹; Richard Gibbs²²; Edgar M Gil Rico, MBA, MSc²³; David Glazer²⁴; Jessica Burke, MBA²⁵; Philip Greenland, MD²⁶; Elizabeth Shenkman, PhD²⁷; William R Hogan, MD, MS²⁷; Priscilla Igho-Pemu, MD, MSCR, FACP²⁸; W Karlson, MD²⁹; Jordan Smoller, MD, ScD²⁹; Shawn N Murphy, MD, PhD²⁹; Margaret Elizabeth Ross, MD, PhD³⁰; Rainu Kaushal, MD, MPH³⁰; Eboni Winford, PhD³¹; Febe Wallace, MD³¹; Parinda Khatri, PhD³¹; Vik Kheterpal³²; Monica Kraft³³; Francisco A Moreno, MD³³; Irving Kron*³³; Rachele Peterson, MS*³³; Patricia Watkins Lattimore*³⁴; Cheryl Thomas³⁴; Mitchell Lunn, MD, MAS, FASN³⁵; Juno Obedin-Maliver³⁵; Oscar Marroquin, MD³⁶; Shyam Visweswaran, MD, PhD³⁶; Steven Reis, MD³⁶; Patrick McGovern³⁷; Fatima Munoz, MD, MPH³⁸; Gregory Talavera, MD, MPH³⁸; George T O'Connor, MD, MS³⁹; Christopher O'Donnell, MD, MPH*⁴⁰; Lucila Ohno-Machado, MD, PhD⁴¹; Greg Orr*⁴²; Fornessa Randal, MCRP⁴³; Andreas A Theodorou, MD⁴⁴; Eric Reiman, MD⁴⁴; Mercedita Roxas-Murray⁴⁵; Louisa Stark⁴⁶; Ronnie Tepp, MPP⁴⁷; Alicia Zhou, PhD⁴⁸; Scott Topper, PhD, FACMG⁴⁸; Rhonda Trousdale, MD⁴⁹; Phil Tsao, PhD⁵⁰; Scott T Weiss, MD, MS⁵¹; David Wellis, PhD⁵²; Jeffrey Whittle, MD, MPH⁵³; Amanda Wilson, MS⁵⁴; Stephan Zuchner, MD, PhD⁵⁵; Olveen Carrasquillo, MD, PhD⁵⁵; Margaret Pericak-Vance⁵⁵; Michael E Zwick, PhD⁵⁶; Megan Lewis⁵⁷; Jen Uhrig⁵⁷; May Okihiro⁵⁸

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

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

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Abstract

Introduction

Materials and Methods

Results

Conclusions

Figures

1. Introduction

2. Materials and Methods

2.1. The All of Us Research Program

2.2. All of Us Questionnaire Data and Physical Measurements

2.3. EHR-Derived Cancer Diagnoses

2.4. Air Pollution Exposure Data

2.5. Covariates

2.6. Data Analysis

3. Results

4. Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1