Systematic review of the association between long-term exposure to fine particulate matter and mortality

Figures & data

Table 1. Quality criteria for epidemiology studies of PM_2.5 and mortality.

Category	Aspect	Criteria for Higher Quality
Study Design	Study Objectives	Objectives/hypotheses are clearly described
	Study Design	Cohort studies and nested case-control studies are considered better than cross-sectional, other case-control, or ecologic studies
	Study Location	Multiple cities rather than a single city
	Sample Size	Power calculation is presented to indicate sufficient sample size
	Study Duration	Conducted over multiple years
Study Population	Participant Characteristics	Characteristics (e.g. age, race, sex, eligibility criteria) are reported
	Inclusion/Exclusion Criteria	Inclusion/exclusion criteria clearly reported and consistent with study objectives
	Recruitment -Representativeness of Source	Representative of the source population
	Participation Rate	High participation rate, not dependent on exposure or outcome
	Underlying Health Conditions	Underlying conditions ascertained by independent clinical assessment or self-report of physician diagnosis
	Follow-up	Minimal or non-differential loss to follow-up
Pollutant	Pollutant Description	Pollutant clearly described (e.g. size of PM fraction)
	Pollutant Source	Pollutant source-related indicators evaluated
Exposure Assessment	Measurement Methods	Utilized and compared more than one exposure assessment method
	Measurement Validity	Used well-established, sensitive methods: Direct measurements of exposure or indirect measurements that have been validated
	Spatial Variability	Sufficiently captured the spatial variability of the exposure: When only using monitoring data, exposure was estimated from the closest central site monitor (within ≤5km) or from averaging concentrations from multiple monitors; when models were used, exposure was estimated with sufficient spatial resolution (e.g. at 10km^2 grid)
	Temporal Variability	Used time-varying or multiple lags of exposure estimates
	Temporality	Exposure occurred before the outcome
	Assignment to Participants’ Locations	Measured or estimated ambient air pollutant data were assigned to participants’ locations (e.g. ZIP codes, census tracts, counties) recorded in the same period
	Residential Mobility	Residential mobility was accounted for
	Personal Activities	Personal activities (e.g. time spent indoors) were accounted for
Outcome Assessment	Blinding	Assessors of outcome were blinded to exposure levels
	Time Points	Time points of outcome evaluation are consistent with study objectives
Confounding	Key Confounders	Identified and adjusted for potential confounders and primary covariates (e.g. socioeconomic status, age, sex, race, body mass index, diet, physical activity, temperature, relative humidity, medication use, smoking status, and other chemical exposures)
	Proper Adjustment	Did not adjust for inappropriate covariates (e.g. mediators).
	Temporal Conceptualization	Appropriately conceptualized temporality and accounted for temporal variation (e.g. when examining time-varying PM, also appropriately adjusted for time-varying covariates. Note: this does not apply to characteristics that in general do not change over time, such as sex, race, education, etc.)
	Copollutant Adjustment	Copollutant (e.g. two-pollutant) modeling was conducted
	Copollutant Correlations	Correlations between the pollutant of interest and copollutants were considered
	Copollutant Measurement	Appropriately accounted for temporal variation, spatial variation, residential mobility, and personal activities
Statistical Methods	Descriptive Statistics	Summary statistics for the study population are presented
	Univariate Analyses	Univariate analyses with pollutant of interest, covariates, and copollutants were conducted and results are presented
	Multivariate– Model Specification	Employed appropriate statistical models for multivariate analyses (e.g. appropriately accounted for correlated data, complex survey design, time-varying data)
	Multivariate– Model Assumptions	Model assumptions were tested and satisfied
	Multivariate– Nonlinearity	Nonlinearity was assessed statistically
	Multivariate– Multiple Comparison	Multiple comparison-corrected, if applicable
	Sensitivity Analyses	Sensitivity analyses were conducted

Notes:

Bold font indicates the aspects and criteria that assess important dimensions of study conduct.

PM = Particulate Matter.

Table 2. Final study selection rationales.

Cohort	Included Studies (PM2.5 Measurement Approach)^a	Excluded Studies of the Same Cohort	Exclusion Rationale
AHS	Weichenthal et al. (2014) (Modeled)	NA	NA
CanCHEC 1991	Crouse et al. (2015) (Modeled); Weichenthal et al. (2016) (Monitored)	Crouse et al. (2012)	Updated by Weichenthal et al. (2016) and Crouse et al. (2015) with additional follow-up, greater population coverage, improved PM measurement, and further copollutant adjustments.
		Crouse et al. (2016)	Focused on combined effects of PM concentration and particulate composition. Only one reported estimate is relevant to this review, and it did not adjust for copollutants.
CanCHEC 2001	Weichenthal et al. (2017) (Modeled)	Pinault et al. (2017)	PM estimation was very similar to the other two studies, but did not exclude outliers. The study also did not adjust for copollutants.
		Crouse et al. (2019)	PM estimation was very similar to the other two studies, but did not examine non-linearity.
CCHS	Pinault et al. (2016) (Modeled)	NA	NA
CCR-HCC	Deng et al. (2017 (Modeled)	NA	NA
Cleveland Clinic GeneBank Study	Hartiala et al. (2016) (Modeled)	NA	NA
CNBSS	Villeneuve et al. (2015) (Modeled)	NA	NA
CPSII	Jerrett et al. (2009) (Monitored); Turner et al. (2016) (Modeled)	Turner et al. (2017)	The study only compared high (75th percentile) vs. low (25th percentile) levels of modeled PM, which is less comparable to the exposure contrasts in other studies in this review. The study also did not adjust for copollutants.
CTS	Ostro et al. (2010) (Monitored); Ostro et al. (2015) (Modeled)	Lipsett et al. (2011)	PM estimation had lower temporal and spatial resolution compared to that by Ostro et al. (2015).
EFFECT	Chen et al. (2016) (Modeled)	NA	NA
Harvard Six Cities Study	Lepeule et al. (2012) (Monitored)	NA	NA
HPFS	Puett et al. (2011) (Modeled)	NA	NA
Medicare	Di et al. (2017) (Monitored & Modeled)	Greven et al. (2011)	Ecological study, using data at each monitor location. The study used a new statistical model to investigate confounding.
		Kioumourtzoglou et al. (2015)	The study population is a subset of that in the study by Kioumourtzoglou et al. (2016), which used the same source of monitored PM data and statistical analysis approach. The study also used a causal modeling approach.
		Kioumourtzoglou et al. (2016)	The study population is a subset of that in the study by Di et al. (2017), which used the same source of monitored PM data but with improved spatial resolution. The study also used a causal modeling approach.
		Makar et al. (2017)	The study used a causal modeling approach.
		Pun et al. (2017)	Ecological study, using data at each monitor location. Part of the analysis followed the statistical methods described by Greven et al. (2011).
		Shi et al. (2016)	Ecological study, using data at ZIP code or county level.
		Wang et al. (2017a)	The majority of the study population (i.e. Medicare 2000–2013) is a subset of that in the study by Di et al. (2017) (e.g. Medicare 2000–2012), which used improved PM estimate and statistical analyses (i.e. non-linearity, copollutant and confounder adjustment).
		Wang et al. (2017b)	The study applied a new causal modeling approach to the analysis of data from Wang et al. (2017a).
		Wu et al. (2019)	Ecological study, using data at ZIP code/area level. The study also used a new causal modeling approach.
		Yitshak-Sade et al. (2019)	The study was built on the causal modeling approach used by Wang et al. (2016) with extended confounding adjustment.
NHIS	Lefler et al. (2019) (Modeled)	Pope et al. (2017)	The study population for the fully adjusted analysis is a subset of that in the study by Lefler et al. (2019), which had additional follow-up, more comprehensive PM estimates for copollutant adjustments, and additional confounding adjustments.
NHS	Hart et al. (2015) (Monitored & Modeled)	Puett et al. (2009)	Updated by Hart et al. (2015), with additional follow-up, greater population coverage, and improved PM estimates and statistical analysis.
		Liao et al. (2018)	The study applied a new method to the analysis of Hart et al. (2015) for PM measurement error adjustment.
NHS & NHSII	DuPre et al. (2019) (Modeled)	NA	NA
NIH-AARP	Thurston et al. (2016) (Modeled)	NA	NA
NJ Residents	NA	Wang et al. (2016)	Ecological study, using data at census tract level. The study also used a causal modeling approach.
Older CA Residents	NA	Garcia et al. (2016)	Ecological study, using data at ZIP code level.
TrIPS	Hart et al. (2011) (Monitored)	NA	NA
TRIUMPH & PREMIER	Malik et al. (2019) (Modeled)	NA	NA
VCMS	Lipfert and Wygza (2019) (Modeled)	NA	NA

Notes:

(a) We considered direct site measurements of PM_2.5 without any statistical modeling as ‘monitored.’

AHS = Agricultural Health Study; CA = California; CanCHEC = Canadian Census Health and Environmental Cohort; CCHS = Canadian Community Health Survey; CCR-HCC = California Cancer Registry patients with hepatocellular carcinoma; CNBSS = Canada National Breast Screening Study; CPSII = Cancer Prevention Study II; CTS = California Teachers Study; EFFECT = Enhanced Feedback for Effective Cardiac Treatment; HPFS = Health Professionals Follow-up Study; NA = Not Applicable; NHIS = National Health Interview Survey; NHS = Nurses’ Health Study; NIH-AARP = National Institutes of Health-AARP; NJ = New Jersey; PM = Particulate Matter; PREMIER = Prospective Registry Evaluating Myocardial Infarction: Events and Recovery; TrIPS = Trucking Industry Particle Study; TRIUMPH = Translational Research Investigating Underlying Disparities in Acute Myocardial Infarction Patients’ Health Status; VCMS = Veterans Cohort Mortality Study.

Table 3. Characteristics of epidemiology studies of long-term PM_2.5 exposure and all-cause or non-accidental mortality.

Study	Cohort Name	Location	Source Population	Age (years)	Sex	Sample Size	Study Period	Mortality Type	Exposure Measurement	Air Model	PM2.5 Concentration (μg/m^3)
											Mean (SD)	Median (IQR)
Weichenthal et al. (2014)	AHS	US, Iowa and North Carolina	Commercial pesticide applicators, farmers, and their families	46–47 on average	Both	83,378	1993–2009	Non-accidental	Modeled	GEOS-Chem CTM	9.52 (1.66)	NR
Crouse et al. (2015)	CanCHEC 1991	Canada, multiple cities	General population	25–89	Both	2,521,525	1991–2006	Non-accidental	Modeled	LUR	8.9 (3.4)	8.6 (5.8)
Weichenthal et al. (2016)	CanCHEC 1991	Canada, multiple cities	General population	25–89	Both	193,300	1991–2009	Non-accidental	Monitored	NA	9.81(1.59)	10.09 (2.17)
Weichenthal et al. (2017)	CanCHEC 2001	Canada, multiple cities	General population	25–89	Both	2,448,500	2001–2011	Non-accidental	Modeled	GEOS-Chem CTM + geographically weighted regression	7.37 (NA)	7.12 (3.7)
Pinault et al. (2016)	CCHS	Canada, multiple cities	General population	25–90	Both	299,500	2000–2011	Non-accidental	Modeled	GEOS-Chem CTM + geographical weighted regression	6.32 (2.54)	5.9 (4.1)
Deng et al. (2017)	CCR-HCC	US, California	Patients with hepatocellular cancer	63.7 ± 12.4	Both	20,221	2000–2011	All-cause	Modeled	Inverse distance-squared weighing model	13.3 (5.0)	NR
Hartiala et al. (2016)	Cleveland Clinic GeneBank study	US, Ohio	Patients undergoing elective diagnostic coronary angiography	64 ± 11	Both	6,575	2001–2010	All-cause	Modeled	Inverse distance-squared weighing model	14.6 (1.1)	NR
Villeneuve et al. (2015)	CNBSS	Canada, multiple cities	General population	40–59	Females	89,248	1980–2005	All-cause; Non-accidental	Modeled	GEOS-Chem CTM	NR	9.1 (6)
Jerrett et al. (2009)	CPS II	US, multiple cities	Residents living in MSAs	≥30	Both	448,850	1982–2000	All-cause	Monitored	NA	13.76 (2.71)^a	NR
Turner et al. (2016)	CPS II	US, multiple cities	General population	≥30	Both	669,046	1982–2004	All-cause	Modeled	CMAQ + LUR + BME HBM	LURBME: 12.6 (2.9) HBM 12.1 (2.6)	LURBME 12.5 (3.9) HBM 12.1 (3.6)
Ostro et al. (2010)	CTS	US, California	Female teachers in California	30+	Females	8 km buffer: 7,888 30 km buffer: 44,847	2002–2007	Non-accidental	Monitored	NA	8-km buffer: 17.0 (NR) 30-km buffer: 17.5 (NR)	NR (6.1)
Ostro et al. (2015)	CTS	US, California	Female teachers in California	30+	Females	101,884	2001–2007	Non-accidental	Modeled	UCD/CIT SOCTM	18.2 (9.6)^a	18.2 (9.6)
Chen et al. (2016)	EFFECT	Canada, Ontario	Newly admitted AMI patients	35+	Both	8,873	1999–2011	Non-accidental	Modeled	GEOS-Chem CTM	10.7 (2.2–16.5) (4)	NR
Lepeule et al. (2012)	Harvard Six Cities Study	US, multiple cities	White adults	25–74	Both	8,096	1974–2009	All-cause	Monitored	NA	15.9 (NA)	NR
Puett et al. (2011)	HPFS	US, multiple cities	Male health professionals	43–78^a	Males	17,545	1989–2003	Non-accidental	Modeled	GIS-based spatial smoothing model	17.8 (3.4)	NR (4.3)
Di et al. (2017)	Medicare	US, multiple cities	Medicare beneficiaries	65+	Both	60,925,443	2000–2012	All-cause	Modeled and monitored	GEOS-Chem CTM + geographical linear regression	Modelled: 11 (NR)	NR
Lefler et al. (2019)	NHIS	US, multiple cities	General population	18–84	Both	635,539	1987–2015	All-cause	Modeled	GEOS-Chem CTM + LUR + v1 Empirical model by the center for Air, Climate, and Energy Solutions	10.67 (2.37)	NR (3.12)
Hart et al. (2015)	NHS	US, multiple cities	Female nurses	54–79^a	Females	108,767	2000–2006	Non-accidental	Modeled and monitored	GIS-based spatial smoothing model	Modeled: 12.0 (2.8) Monitored: 12.7 (3.1)	NR
DuPre et al. (2019)	NHS & NHSII	US, multiple cities	Female nurses with breast cancer	64.4 ± 8.3^a NHS; 47.1 ± 5.7^a NHS II	Females	8,936	1988–2014	All-cause	Modeled	GIS-based spatial smoothing model	Pooled 13.19 (3.40)^a	NR
Thurston et al. (2016)	NIH-AARP	US, multiple cities	AARP members	50–71	Both	517,041	2000–2009	Non-accidental	Modeled	GEOS-Chem CTM + LUR + BME	12.2 (3.4)	NR
Hart et al. (2011)	TrIPS	US, multiple cities	Male trucking industry workers	15–84	Males	53,814	1985–2000	All-cause	Monitored	NA	14.1 (4.0)	NR (4)
Malik et al. (2019)	TRIUMPH & PREMIER	US, multiple cities	Patients with MI	60.6 ± 12.2	Both	5,650	2003–2013	All-cause	Modeled	Bayesian space-time downscaling fusion model	11.96 (2.11)	12.05 (2.6)
Lipfert and Wyzga (2019)	VCMS	US, multiple cities	Male ostensibly hypertensive veterans	~51 on average	Males	70,000	1976–2001	All-cause	Modeled	GIS-based smoothing models	White Vets: 13.9 Black Vets: 15.7	NR

Notes:

(a) Estimates based on data shown in the paper.

AHS = Agricultural Health Study; AMI = Acute Myocardial Infarction; BME = Bayesian Maximum Entropy; CA = California; CanCHEC = Canadian Census Health and Environmental Cohort; CCHS = Canadian Community Health Survey; CCR-HCC = California Cancer Registry patients with hepatocellular carcinoma; CMAQ = Community Multiscale Air Quality; CNBSS = Canada National Breast Screening Study; CPSII = Cancer Prevention Study II; CTM = Chemical Transport Model; CTS = California Teachers Study; EFFECT = Enhanced Feedback for Effective Cardiac Treatment; GIS = Geographic Information System; HBM = Hierarchical Bayesian Model; HPFS = Health Professionals Follow-up Study; LUR = Land Use Regression; MI = Myocardial Infarction; MSA = Metropolitan Statistical Area; NA = Not Applicable; NHIS = National Health Interview Survey; NHS = Nurses’ Health Study; NIH-AARP = National Institutes of Health-AARP; NJ = New Jersey; PM = Particulate Matter; PREMIER = Prospective Registry Evaluating Myocardial Infarction: Events and Recovery; SOCTM = Source Oriented Chemical Transport Model; TrIPS = Trucking Industry Particle Study; TRIUMPH = Translational Research Investigating Underlying Disparities in Acute Myocardial Infarction Patients’ Health Status; UCD/CIT = University of California Davis/California Institute of Technology; VCMS = Veterans Cohort Mortality Study.

Table 4. Study quality evaluation of epidemiology studies of PM_2.5 and mortality.

Category	Criteria	Weichenthal et al. 2014	Crouse et al. 2015	Weichenthal et al. 2016	Weichenthal et al. 2017	Pinault et al. 2016	Deng et al. 2017	Hartiala et al. 2016	Villeneuve et al. 2015	Jerrett et al. 2009	Turner et al. 2016	Ostro et al. 2010	Ostro et al. 2015	Chen et al. 2016	Lepeule et al. 2012	Puett et al. 2011	Di et al. 2017	Lefler et al. 2019	Hart et al. 2015	DuPre et al. 2019	Thurston et al. 2016	Hart et al. 2011	Malik et al. 2019	Lipfert and Wyzga 2019
Study Design	Study Objectives	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+
	Study Design	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+
	Study Location	+	+	+	+	+			+	+	+				+	+	+	+	+	+	+	+	+	+
	Sample Size		+		+												+
	Study Duration	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+
Study Population	Participant Characteristics	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+
	Inclusion/Exclusion Criteria	+	+	+	+	+		+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+
	Representativeness of Source	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
	Participation Rate	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
	Underlying Health Conditions	NA	NA	NA	NA	NA	+	+	NA	NA	NA	NA	NA	+	NA	NA	NA	NA	NA	+	NA	NA	+	+
	Follow-up	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Pollutant	Pollutant Description	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+
	Pollutant Source										+	+	+					+
Exposure Assessment	Multiple Methods										+						+		+
	Validity	+	+	+	+	+	+		+	+	+	+	+	+	+	+	+		+	+	+	+	+	+
	Spatial Variability	+	+	+	+	+			+	+	+		+	+		NR	+	+	+	+	+	NR
	Temporal Variability		+		+	+							+	+	+	+	+	+	+	+	+
	Temporality	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+
	Assignment to Participants’ Locations				+		+	+				+	+	+		+	+	+	+				+
	Residential Mobility	+	+		+							+	+	+		+	+		+	+
	Personal Activities	+
Outcome Assessment	Blinding	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+
	Time Points	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+
Confounding	Key Confounders
	Proper adjustment	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+		+
	Temporal conceptualization	+	+	+	+		+	+	+	+	+	+				+	+	+	+	+		+	+	+
	Copollutant adjustment		+	+	+					+	+					+	+	+			+		+
	Copollutant correlations	NA	+	+	+	NA	NA	NA	NA	+	+	NA	NA	NA	NA		+	+	NA	NA		NA	+	NA
	Copollutant Measurement	NA				NA	NA	NA	NA			NA	NA	NA	NA				NA	NA		NA		NA
Statistical Methods	Descriptive Statistics	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+
	Univariate Analyses
	Model Specification	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+
	Model Assumptions													+	+					+			+
	Nonlinearity		+		+	+	+		+					+	+		+		+		+		+
	Multiple Comparison				NA			NA						NA					NA				NA
	Sensitivity Analyses	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+		+

Criteria correspond to the aspects and criteria listed in Table 1. Bold font indicates the criteria that assess important dimensions of study conduct.

Columns for studies that meet a specific criterion are indicated with a ‘+’ in the row for that criterion, while columns for studies that do not meet a specific criterion are blank in the row for that criterion. NA = Not Applicable.

Table 5. Results from linear association analyses.

Study	Mortality Type	Grouping	Exposure Metric	Exposure Contrast (μg/m^3)	Statistical Model	Measure of Association	Risk Estimate (95% Confidence Interval)	Copollutant Adjustment	Other Confounding Adjustment
Weichenthal et al. (2014)	Non-accidental	Overall	6-year average	10	Cox	HR	0.95 (0.76, 1.20)	None	Age; sex; state of enrollment; birth year category; pack years of smoking; BMI; marital status; education level; alcoholic drinks per month; vegetable intake.
		Males				HR	1.05 (0.80, 1.39)
		Females				HR	0.78 (0.51, 1.19)
Crouse et al. (2015)	Non-accidental	Overall	7-year moving average	5	Cox	HR	1.011 (1.003–1.020)	O3, NO2	Sex; 5-year age group; aboriginal ancestry; visible minority status; immigrant status; marital status; highest level of education; employment status; occupational classification; quintiles of household income; census division and census tract-census division; percent adults without a high school diploma; percent individuals in the lowest income quintile; percent unemployed adults.
							1.035 (1.029, 1.041)	None
Weichenthal et al. (2016)	Non-accidental	Overall	Annual average	2.17	Cox	HR	1.025 (1.012,1.039)	OP^GSH	Age at entry; sex; aboriginal ancestry; visible minority status; immigrant status; marital status; highest level of education; employment status; occupational classification; household income.
							1.026 (1.012–1.039)	None
Weichenthal et al. (2017)	Non-accidental	Overall	3-year moving average	3.858	Cox	HR	1.041 (1.029–1.052)	Ox (combined oxidant capacity of O3 and NO2)	Sex; 5-year age group; airshed; population center size; visible minority status; aboriginal status; marital status; educational attainment; income quintile; labor force status; percent unemployed (aged 25 or older); percent not graduated from high school (aged 25 or older); percent low income status within census divisions; population density within dissemination areas.
							1.073 (1.062–1.083)	None
Pinault et al. (2016)	Non-accidental	Overall	3-year moving average	10	Cox	HR	1.26 (1.19, 1.34)	None	Age; sex; immigrant Status; visible minority status; aboriginal status; marital status; educational attainment; income adequacy quintile; employment status; smoking; BMI; fruit and vegetable consumption; alcohol consumption; proportion of recent immigrants; educational attainment; low income.
		Males				HR	1.344 (1.239, 1.457)		Age; immigrant status; visible minority status; aboriginal status; marital status; educational attainment; income adequacy quintile; employment status; smoking; BMI; fruit and vegetable consumption; alcohol consumption.
		Females				HR	1.181 (1.088, 1.282)		Age; immigrant status; visible minority status; aboriginal status; marital status; educational attainment; income adequacy quintile; employment status; smoking; BMI; fruit and vegetable consumption; alcohol consumption.
Deng et al. (2017)	All-cause	Overall	Survival-period average	5	Cox	HR	1.18 (1.16–1.20)	None	Age; sex; race/ethnicity; marital status; SES; rural-urban commuting area; distance to primary interstate highway; distance to primary US and state highways; month of diagnosis; year of diagnosis; initial treatments.
Hartiala et al. (2016)	All-cause	Overall	36-month average	2.2	Cox	HR	1.16 (0.96–1.41)	None	Age; sex; current smoking; education level; Framingham Adult Treatment Panel III risk score.
Villeneuve et al. (2015)	All-cause	Overall	Long-term (9-year) average	10	Cox	HR	1.10 (1.03–1.17)	None	Age at entry; marital status; occupation; attained education; cigarette smoking; BMI; mean income; proportion with high school education; percentage of low income households; unemployment rate.
	Non-accidental	Overall				HR	1.12 (1.04–1.19)
Jerrett et al. (2009)	All-cause	Overall	2-year average (as proxy of study period average)	10	Cox	HR	1.080 (1.048–1.113)	O3	Age; sex; race; education; marital status; BMI; smoking status; alcohol consumption; level of dietary-fat consumption; level of dietary-fiber consumption; level of occupational exposure to PM; self-reported exposure to dust or fumes; % nonwhite race; percent homes with air conditioning; percent high school education or greater; percent unemployment rate; Gini coefficient of income inequality; proportion of population with income <125% of poverty line; annual household income.
							1.048 (1.024–1.071)	None
Turner et al. (2016)	All-cause	Overall	6-year average (as proxy of study period average)	10	Cox	HR	1.06 (1.04–1.08) [HBM PM2.5]	O3	Age; sex; race; education; marital status; BMI; BMI squared; cigarette smoking status; cigarettes per day or cigarettes per day squared; years smoked and years smoked squared; started smoking at younger than 18 years of age; passive smoking; vegetable, fruit, fiber, and fat intake; alcohol consumption; occupational exposures; “occupational dirtiness index”; median household income; percent African American residents; percent Hispanic residents; percent adults with post-secondary education; percent unemployment; percent poverty.
							1.06 (1.04–1.08) [HBM PM2.5]	None
							1.07 (1.06–1.09) [PM2.5 LURBME]	None
Ostro et al. (2010)	Non-accidental	Females	Survival period average	6.1	Cox	HR	Within 8 km buffer: 1.49 (1.28, 1.74)	None	Age; race/ethnicity; smoking status; total pack-years; body mass index; marital status; alcohol consumption; secondhand smoke exposure at home; dietary fat, fiber, and caloric intake; physical activity; menopausal status; hormone therapy use; family history of myocardial infarction, stroke; hypertension medication/aspirin use; income; income inequality; population size; education; racial composition; percent unemployed.
						HR	Within 30 km buffer: 1.45 (1.36–1.55)
Ostro et al. (2015)	Non-accidental	Females	Long-term average (up to each death)	9.6	Cox	HR	1.01 (0.98, 1.05)	None	Age; race/ethnicity; smoking status; smoking pack-years; secondhand smoke exposure; BMI; marital status; alcohol consumption; physical activity; menopausal status and hormone replacement therapy use combined; family history of heart disease; hypertension medication/aspirin use; dietary fat, fiber, and caloric intake.
Chen et al. (2016)	Non-accidental	Overall	Cumulative time-weighted exposure	10	Cox	HR	1.22 (1.03, 1.45)	None	Age; sex; region; marital status; employment status; smoking status; family history of coronary artery disease; DM; hyperlipidemia; hypertension; stroke; previous AMI; previous percutaneous coronary intervention; AMI type; acute pulmonary edema; preexisting angina, cancer; dementia; dialysis; chronic obstructive pulmonary disease; cardiovascular medications at hospital discharge (statins, aspirin, ACE inhibitors, and beta-blockers); ST elevation myocardial infarction (STEMI/non-STEMI); length of hospital stay in days; characteristics of attending physicians and hospitals; Global Registry of Acute Cardiac Events risk score (severity of AMI event); census division level unemployment rate, education and annual household income (plus the subtraction of these variables at the census-tract level from their census division mean).
Lepeule et al. (2012)	All-cause	Overall	Annual moving average	10	Cox	HR	1.14 (1.07–1.22)	None	Sex; age; time in the study; smoking history; education; BMI.
Puett et al. (2011)	Non-accidental	Overall	12-month moving average	4	Cox	HR	0.94 (0.87–1.00)	None	Age; state of residence; year; season; hypertension; DM; hypercholesterolemia; physical activity; alcohol consumption; BMI; smoking status; smoking pack-years; diet; family history of MI.
							0.94 (0.87–1.02)	PM10-2.5
Di et al. (2017)	All-cause	Overall	Annual average	10	Cox	HR	Modeled 1.073 (1.071–1.075) Monitored 1.061 (1.059–1.063)	O3	Age; race; sex; Medicaid eligibility; percent Hispanic; percent black; median household income; median value of owner occupied housing; percent above 65 years living below poverty; percent above 65 with less than a high school education; percent owner occupied housing units; population density; county level BMI; percent ever smokers; percent with blood lipid test; percent with HbA1c test; ≥1 ambulatory visit to a primary care clinician; temperature; humidity.
							1.084 (1.081–1.086) Modeled	None
		Males					1.087 (1.083, 1.090)	O3
		Females					1.060 (1.057, 1.063)	O3
		Whites					1.063 (1.060, 1.065)	O3
Lefler et al. (2019)	All-cause	Overall	Annual average	3.12	Cox	HR	1.045 (1.030–1.061) Complex model	PM2.5–10, SO2, NO2, O3, and CO	Age; sex; race-ethnicity; marital status; inflation-adjusted household income; education; smoking status; US Census region; urban vs. rural designation; survey year; BMI.
							1.045 (1.032–1.058) Basic model	PM2.5–10, SO2, NO2, O3, and CO
Hart et al. (2015)	Non-accidental	Overall	12-month moving average	10	Cox	HR	Modeled 1.13 (1.05–1.22)	None	Age; race; region; year; season; smoking status; smoking pack years; family history of MI; BMI; hypercholesterolemia; median family income in census tract of residence; median house value in census tract of residence; physical activity; AHEI (includes alcohol consumption); individual-level SES (nurses’ education level, occupation of both parents, marital status, and husband’s education).
							Monitored 1.12 (1.05–1.21)
DuPre et al. (2019)	All-cause	Overall	2-year moving average	10	Cox	HR	Pooled 1.12 (0.96–1.30)	None	Age; race/ethnicity; census tract median income; breast cancer diagnosis date, stage, ER status, treatments, hormones, grade; region of residence at diagnosis; pre- to post-diagnosis weight change categories; post-diagnostic physical activity, aspirin use, and smoking.
Thurston et al. (2016)	Non-accidental	Overall	Annual average	10	Cox	HR	1.03 (1.00, 1.05) [Time-independent]	None	Age; sex; region; race/ethnicity; education; marital status; BMI; alcohol consumption; smoking history; median census tract income; percent of census tract population with less than a high school education.
							1.03 (0.99, 1.05) [Time-dependent]	None
Hart et al. (2011)	All-cause	Males	Annual average	4	Cox	HR	1.04 (1.01, 1.07)	None	Age; race; census region of residence; calendar year and decade of hire; healthy worker survivor effect; years of work for each group (long-haul drivers, pick-up and delivery drivers, dockworkers, combination workers, mechanics, hostlers, clerks, and other workers).
Malik et al. (2019)	All-cause	Overall	12-month average	2.11	Cox	HR	1.13 (1.07–1.20)	O3	Age; sex; race; SES (education, insurance status, history of avoiding care because of cost, and end-of-the-month financial resources); date of enrollment; smoking status; comorbidities including hypertension, DM, CKD, and heart failure before MI; left ventricular function; Global Registry of Acute Coronary Events Mortality risk score.
Lipfert and Wyzga (2019)	All-cause	Black	Annual average	NR (annual average mean-min)	Cox	HR	0.817 (0.750, 0.891)	None	Age; race; smoking status; height; diastolic and systolic blood pressure; BMI; percent of ZIP code residents below 75% poverty level; zip-code average education; heating degree-days; percent blacks in ZIP code.
		White				HR	1.051 (1.005, 1.100)

ACE = Angiotensin Converting Enzyme; AHEI = Alternate Healthy Eating Index; AMI = Acute Myocardial Infarction; BMI = Body Mass Index; CKD = Chronic Kidney Disease; CO = Carbon Monoxide; DM = Diabetes Mellitus; ER = Estrogen Receptor; HbA1c = Hemoglobin A1c; HR = Hazard Ratio; MI = Myocardial Infarction; NO₂ = Nitrogen Dioxide; NR = Not Reported; O₃ = Ozone; OP^GSH = Glutathione-related Oxidative Potential; PM = Particulate Matter; SES = Socioeconomic Status; SO₂ = Sulfur Dioxide; US = United States.

Table 6. Results of non-linear association analyses.

Study	Mortality Type	Splines (df)	Shape of E-R Curve	Threshold Identification	Threshold Estimate	Confounding Adjustment
Weichenthal et al. 2014	Non-accidental	Natural (2 df)	NR	NA	NA	Same as for linear analysis
Crouse et al. 2015	Non-accidental	Restricted cubic (2 df)	Supralinear	NT	NT	Same as for linear analysis
Weichenthal et al. 2017	Non-accidental	NA (Joint non-linear Ox – PM2.5 Relative Risk Model)	Supralinear (at Ox = 37.60 ppb or Ox = 20.26 ppb)	NT	NT	Same as for linear analysis
Pinault et al. 2016	Non-accidental	Splines (NR)	NR; appears supralinear	Hockey-stick threshold model (starting from 1–10 µg/m^3, at 1-µg/m^3 increment; minimizing −2LL)	0 μg/m^3, upper 95% CI = 4.5 μg/m^3	Same as for linear analysis of males and females combined
Deng et al. 2017	All-cause	Natural cubic (2 df)	J-shaped	NT	NT	Same as for linear analysis
Villenueve et al., 2015	Non-accidental	Natural cubic (3 df)	Nonlinear V-shaped	Hockey stick threshold model (starting from 2–14 μg/m^3, at 1 μg/m^3 increment; minimizing −2LL)	11 μg/m^3 (p = 0.004)	Same as for linear analysis
Chen et al. 2016	Non-accidental	Natural cubic (≤4 df)	Linear	NA	NA	Same as for linear analysis
Lepeule et al. 2012	All-cause	Penalized cubic (NA)	Linear (Wald test of non-linearity p-value NR)	NR^a	No threshold (down to 8 μg/m^3)	Same as for linear analysis
Di et al. 2017	All-cause	Penalized (NR)	Roughly linear	NR^a	No threshold (down to approximately 5 μg/m^3)	Same as for linear analysis
Hart et al. 2015	Non-accidental	Stepwise restricted cubic (NR)	Approximately linear (both modeled and monitored)	NT	NT	Same as for linear analysis^b
Thurston et al. 2016	Non-accidental	Natural (4 df)	Monotonically increasing	NA	NA	Same as for linear analysis
Malik et al. 2019	All-cause	Smoothing or restricted cubic (NA)	Linear (test of non-linearity p = 0.59)	NT	NT	Same as for linear analysis

(a) No threshold model was specified, but the authors concluded that there was no threshold based on the approximately linear curve.

(b) The paper reports that adjustment is different by hypertension and diabetes, but this appears to be a typographical error.

df = Degrees of Freedom; E-R = Exposure-response; LL = Log Likelihood; NA = Not Applicable; NR = Not Reported; NT = Not Tested.

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