Assessing the Distribution of Air Pollution Health Risks within Cities: A Neighborhood-Scale Analysis Leveraging High-Resolution Data Sets in the Bay Area, California
2021; National Institute of Environmental Health Sciences; Volume: 129; Issue: 3 Linguagem: Inglês
10.1289/ehp7679
ISSN1552-9924
AutoresVeronica Southerland, Susan C. Anenberg, Maria H. Harris, Joshua S. Apte, Perry Hystad, Aaron van Donkelaar, Randall V. Martin, Matt Beyers, Ananya Roy,
Tópico(s)Climate Change and Health Impacts
ResumoVol. 129, No. 3 ResearchOpen AccessAssessing the Distribution of Air Pollution Health Risks within Cities: A Neighborhood-Scale Analysis Leveraging High-Resolution Data Sets in the Bay Area, California Veronica A. Southerland, Susan C. Anenberg, Maria Harris, Joshua Apte, Perry Hystad, Aaron van Donkelaar, Randall V. Martin, Matt Beyers, and Ananya Roy Veronica A. Southerland Milken Institute School of Public Health, George Washington University, Washington, District of Columbia, USA , Susan C. Anenberg Address correspondence to Susan C. Anenberg, Milken Institute School of Public Health, George Washington University, Washington, DC 20052 USA. Telephone: (202) 994-2392. Email: E-mail Address: [email protected] Milken Institute School of Public Health, George Washington University, Washington, District of Columbia, USA , Maria Harris Environmental Defense Fund, San Francisco, California, USA , Joshua Apte Department of Civil & Environmental Engineering and School of Public Health, University of California, Berkeley, USA , Perry Hystad School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, Oregon, USA , Aaron van Donkelaar Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada Energy, Environmental & Chemical Engineering, McKelvey School of Engineering, Washington University in St. Louis, St. Louis, Missouri, USA , Randall V. Martin Energy, Environmental & Chemical Engineering, McKelvey School of Engineering, Washington University in St. Louis, St. Louis, Missouri, USA , Matt Beyers Alameda County Public Health Department, Oakland, California, USA , and Ananya Roy Environmental Defense Fund, San Francisco, California, USA Published:31 March 2021CID: 037006https://doi.org/10.1289/EHP7679AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack CitationsCopy LTI LinkHTMLAbstractPDF ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground:Air pollution-attributable disease burdens reported at global, country, state, or county levels mask potential smaller-scale geographic heterogeneity driven by variation in pollution levels and disease rates. Capturing within-city variation in air pollution health impacts is now possible with high-resolution pollutant concentrations.Objectives:We quantified neighborhood-level variation in air pollution health risks, comparing results from highly spatially resolved pollutant and disease rate data sets available for the Bay Area, California.Methods:We estimated mortality and morbidity attributable to nitrogen dioxide (NO2), black carbon (BC), and fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] using epidemiologically derived health impact functions. We compared geographic distributions of pollution-attributable risk estimates using concentrations from a) mobile monitoring of NO2 and BC; and b) models predicting annual NO2, BC and PM2.5 concentrations from land-use variables and satellite observations. We also compared results using county vs. census block group (CBG) disease rates.Results:Estimated pollution-attributable deaths per 100,000 people at the 100-m grid-cell level ranged across the Bay Area by a factor of 38, 4, and 5 for NO2 [mean=30 (95% CI: 9, 50)], BC [mean=2 (95% CI: 1, 2)], and PM2.5, [mean=49 (95% CI: 33, 64)]. Applying concentrations from mobile monitoring and land-use regression (LUR) models in Oakland neighborhoods yielded similar spatial patterns of estimated grid-cell–level NO2-attributable mortality rates. Mobile monitoring concentrations captured more heterogeneity [mobile monitoring mean=64 (95% CI: 19, 107) deaths per 100,000 people; LUR mean=101 (95% CI: 30, 167)]. Using CBG-level disease rates instead of county-level disease rates resulted in 15% larger attributable mortality rates for both NO2 and PM2.5, with more spatial heterogeneity at the grid-cell–level [NO2 CBG mean=41 deaths per 100,000 people (95% CI: 12, 68); NO2county mean=38 (95% CI: 11, 64); PM2.5CBG mean=59 (95% CI: 40, 77); and PM2.5county mean=55 (95% CI: 37, 71)].Discussion:Air pollutant-attributable health burdens varied substantially between neighborhoods, driven by spatial variation in pollutant concentrations and disease rates. https://doi.org/10.1289/EHP7679IntroductionAir pollution is associated with a large burden of death and disability worldwide, with fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] estimated to be responsible for 4.9 million deaths globally in 2015 (GBD 2017 Risk Factor Collaborators 2018). Nitrogen dioxide (NO2), a traffic-related air pollutant, is also linked with adverse health outcomes, although it is often not quantified in pollution-attributable disease burden studies, potentially because coarsely resolved concentration estimates are often unable to capture highly spatially variable patterns in NO2 (Anenberg et al. 2017). Recent advances in the understanding of the health effects of NO2, meta-analyses (Atkinson and Butland 2018; U.S. EPA 2016), and published recommendations from a committee of scientists (Atkinson and Butland 2018) provide guidance on evaluating and interpreting NO2, as a marker of the mixture of traffic air pollution, in health impact assessments.Much of the air pollution disease burden is concentrated in cities (Anenberg et al. 2019). Cities are home to about half the world’s population (United Nations 2019) and 80% of the U.S. population (U.S. Census Bureau 2018). Many cities also experience both high air pollution levels (Krzyzanowski et al. 2014; Marlier et al. 2016) and health inequity challenges (Grant et al. 2017; Kioumourtzoglou et al. 2015; Stephens 2018). However, estimated health impacts from air pollution have typically been reported at the country, state, or county level, masking potential heterogeneity in impacts at fine spatial scales.Understanding how air pollution-related health risks vary within cities could help inform policies aimed at improving public health and reducing population disparities in exposure and risk in urban areas. Recent efforts have estimated air pollution health impacts at the city level, finding dramatic variation in health risks across cities globally (Achakulwisut et al. 2019; Anenberg et al. 2019). However, only a limited number of studies have assessed air pollution mortality risks at the neighborhood level, and these have focused on individual cities and have generally not compared the advantages and disadvantages of different concentration data sources (Brønnum-Hansen et al. 2018; Kheirbek et al. 2013; Kihal-Talantikite et al. 2018; Martenies et al. 2018; Mueller et al. 2017, 2018, 2020; Pierangeli et al. 2020). In addition, these previous city-scale studies may not have captured the spatial distribution of air pollution-related health risks given that the grid sizes used in those studies can dilute hotspots of high concentrations co-located with large populations (Fenech et al. 2018; Korhonen et al. 2019; Li et al. 2016; Punger and West 2013). Beyond horizontal grid size, the resolution of emissions inputs to estimate concentrations can also influence the resulting estimated air pollution-related health impacts. Two studies examining the impacts of both varying horizontal grid and emissions resolution on health burden estimates report mixed results. Paolella et al. (2018) reported a reduced ability of coarse resolution concentration estimates to identify disparities in health impacts, whereas a study by Thompson et al. (2014) found limited difference for PM2.5 attributable health impacts with varying emissions and grid resolution (Thompson et al. 2014).Despite these differences, finer-resolution exposure estimates may decrease the potential for exposure misclassification. Estimating air pollution health impacts at the “hyperlocal” scale (resolving neighborhoods within cities) is now possible with high-resolution pollutant concentrations derived from mobile monitoring and modeling, complemented by satellite remote sensing. Here, we exploit a novel and extremely high-spatial–resolution pollution concentration data set from mobile monitoring of NO2 and black carbon (BC) using Google Street View (hereafter referred to as Street View) cars throughout the Bay Area, California, from 2015 to 2017. Previously, these measurements have been used to create street-level annual average concentrations of NO2 and BC, a land-use regression (LUR) model (Messier et al. 2018), and an epidemiological analysis of relationships between long-term exposure to NO2 and cardiovascular disease (CVD) outcomes (Alexeeff et al. 2018), all for Oakland, California. Jointly, these efforts demonstrated the application of highly resolved concentration data to analyze intra-urban variation in pollutant exposure and the associated health risks. Building upon these efforts, here we use Street View concentrations to assess air pollution health impacts at the neighborhood scale. To our knowledge, our analysis is the first to use air pollution levels from sensor-aided mobile monitoring in a health impact assessment. Given that most cities globally do not have the same availability of highly spatially resolved concentration data as the Bay Area, we compare pollutant-attributable health risks estimated using the Street View concentrations vs. less data- and resource-intensive predictive models. These predictive models use land-use variables and satellite observations of aerosol optical depth (AOD) that can be applied in any city globally to create street-level annual average concentrations of NO2 and PM2.5 (Larkin et al. 2017; van Donkelaar et al. 2016).Neighborhood-level health risks from air pollution are driven not just by exposure levels but also by baseline disease rates, which themselves vary within cities (e.g., Fann et al. 2012), influencing attributable mortality estimates (Chowdhury and Dey 2016; Hubbell et al. 2009). Prior air pollution morbidity and mortality assessments have typically used baseline disease rates at the state, county, or national level owing to the limited availability of more highly resolved health data (Alotaibi et al. 2019; Caiazzo et al. 2013; Cohen et al. 2017; Fann et al. 2012, 2017; Zhang et al. 2018). Here, in addition to comparing across concentration data sets, we also assess the influence of baseline disease rates with varying spatial resolutions (i.e., county-level vs. census block group (CBG)-level baseline disease rates) on estimated pollution-attributable health risks.The San Francisco Bay Area of California has a population of >7 million people. This case study for the Bay Area, where high-resolution concentration and disease rate data are available, allows us to explore intra-urban disparities in air pollutant exposure, pollution-attributable health risks, and pollution-attributable disease burdens—three related but distinct metrics that are used in policy contexts. The objectives of our study were to a) identify the degree of spatial heterogeneity in air pollution-related health impacts at the neighborhood scale within a city; b) compare the spatial patterns of air pollution disease burdens estimated using different concentration and baseline disease rate data sets; and c) draw lessons learned for conducting neighborhood-scale air pollution health impacts in cities where highly resolved concentration and baseline disease rate data sets are not available. We anticipate that our results can be used to inform best practices (currently under development) for assessing air pollution-related health risks within cities globally, as well as efforts by policymakers to address disparities in the health impacts of air pollution.MethodsWe used epidemiologically derived health impact functions to estimate mortality and morbidity that may be attributable to NO2, BC, and PM2.5, on a 100-m grid resolution for the Bay Area, using different concentration inputs and varying spatial resolutions for baseline disease rates. We used the Bay Area Air Quality Management District’s (BAAQMD) nine-county definition of the Bay Area, which included Alameda, Contra Costa, Marin, Napa, San Francisco, Santa Clara, San Mateo, Solano, and Sonoma counties (Figure 1). Within the Bay Area, we focused on Alameda County, for which we were able to obtain CBG-level disease rates, and within Alameda County, the areas of West, Downtown, and East Oakland, where the Street View cars measured pollution levels (Table 1). Oakland is home to a major container port and has four large interstates (I-880 to the south and west; I-80 and I-580 to the north; and I-980 transecting West and Downtown Oakland), as well as numerous rail yards and rail lines. East and West Oakland have been designated by the California Environmental Protection Agency (EPA) Environmental Justice Task Force as priority communities bearing disproportionate pollution burdens (Environmental Justice Task Force 2017).Figure 1. Geographic area of analysis for (A) the Bay Area, California, highlighting (B) Alameda County and (C) West, (D) Downtown, and (E) East Oakland within Alameda County. Base map data from ArcMap (version 10.4; Esri), HERE, Garmin, FAO, NOAA, USGS, © OpenStreetMap contributors, and the GIS User Community.Table 1 Relative risks (RRs) used for estimating the health impacts (95% CIs in parentheses) and inputs used for calculating each pollutant–health outcome pair.Table 1 has seven columns, namely, Pollutant, Concentrations, Health Outcome, Baseline disease rates, Age group, Study, and Relative Risk.PollutantConcentrationsHealth outcomeBaseline disease ratesAge groupStudyRR (95% CI)BCStreet view for Oakland and van Donkelaar et al. 2019 for Bay AreaAll-cause mortalityAlameda County: CBG levelAdultsJanssen et al. 20111.007 (1.004, 1.009)Bay Area: county levelCVD mortalityAlameda County: CBG levelAdultsJanssen et al. 20111.018 (1.011, 1.031)Bay Area: county levelCVD hospitalizationsBay Area: county levelElderlyPeng et al. 20091.020 (1.008, 1.032)NO2Street View for Oakland, Larkin et al. 2017 (main results) and Bechle et al. 2015 (sensitivity) for Bay AreaAll-cause mortalityAlameda County: CBG levelAdultsAtkinson and Butland 20181.040 (1.011, 1.069)Bay Area: county levelAlameda County: CBG levelAdultsCrouse et al. 20151.1025 (1.082, 1.145)Bay Area: county levelAlameda County: CBG levelElderlyEum et al. 20191.023 (1.021, 1.026)Bay Area: county levelCVD mortalityAlameda County: CBG levelAdultsAtkinson et al. 20181.006 (1.004, 1.009)Bay Area: county levelAlameda County: CBG levelElderlyEum et al. 20191.103 (1.099, 1.108)Bay Area: county levelAsthma incidenceState level for CaliforniaPediatricKhreis et al. 20171.258 (1.098, 1.374)Asthma ER visitsBay Area: ZIP-code levelAll agesOrellano et al. 20171.024 (1.005, 1.043)Bay Area: ZIP-code levelAll agesZheng et al. 20151.002 (1.001, 1.003)Bay Area: ZIP-code levelPediatricOrellano et al. 20171.040 (1.001, 1.081)Bay Area: ZIP-code levelPediatricZheng et al. 20151.003 (1.002, 1.004)PM2.5van Donkelaar et al. 2016 (main results) and Di et al. 2016 (sensitivity) for Bay AreaAll-cause mortalityAlameda County: CBG levelAdultsKrewski et al. 20091.06 (1.04, 1.08)Bay Area: county levelAlameda County: CBG levelElderlyDi et al. 20171.084 (1.081, 1086)Bay Area: county levelCVD mortalityAlameda County: CBG levelAdultsTurner et al. 20161.12 (1.09, 1.15)Bay Area: county levelAlameda County: CBG levelElderlyThurston et al. 20161.100 (1.050, 1.150)Bay Area: county levelCVD hospitalizationsBay Area: county levelElderlyBravo et al. 20171.008 (1.006, 1.010)Asthma incidenceState level for CaliforniaPediatricKhreis et al. 20171.344 (1.105, 1.629)Asthma ER visitsBay Area: ZIP-code levelPediatricLim et al. 20161.048 (1.028, 1.067)Note: RRs are reported per 10 μg/m3 for PM2.5, per 10 ppb for NO2, and per 1 μg/m3 for BC. RRs for NO2 reported per 10 μg/m3 were converted to RR per 10 ppb assuming ambient air pressure of 1 atmosphere and temperature of 25° C. Adults, 25–99 years of age; BC, black carbon; CBG, census block group; CI, confidence interval; CVD, cardiovascular disease; elderly, 65–99 years of age; ER, emergency room; NO2, nitrogen dioxide; PM2.5, fine particulate matter; pediatric, 0–17 years of age.Health Impact FunctionFor each pollutant–outcome pair, we derived concentration–response factors (CRFs) from relative risk (RR) estimates (Table 1) identified through a literature review using PubMed and Google Scholar (see the Supplemental Material “Literature Review” and Tables S1–S15 and Figures S1–S11). We used epidemiological studies with large geographic areas as opposed to those conducted in single cities, assuming large epidemiological studies more fully account for population variation and confounding factors and have more statistical power. Where available, we used pooled risk estimates from meta-analyses. We applied a log-linear function to all analyses, based on current evidence for PM2.5 and, for NO2, a combination of limited evidence for linear vs. log-linear functions and only small differences between the two at the concentrations in our study. Equation 1 describes the log-linear health impact function used for all pollutant–health end point pairs: yh,i,a=mh,i,a×Pi,a×(1−e−βh,aΔxi) [1] where yh,i,a represents the number of cases of the health outcome (h) for age group (a) attributable to the pollutant for each grid cell (i); mh,i,a represents the baseline disease rate for each health end point (h), age group (a), and grid cell (i); Pi,a represents the population count for each grid cell (i) and age group (a); and 1−e−βh,aΔxi represents the attributable fraction, with βh,a the natural log of the RR per x concentration above the baseline (Δx) in each grid cell (i), for each health end point (h) and age group (a). We accounted for uncertainty by calculating the attributable cases at the 2.5th and 97.5th percentiles of the RR estimates. All health impact calculations were conducted in R (version 3.5.3; R Development Core Team).For all pollutants, we assumed no threshold for low concentrations because a recent study identified health impacts at PM2.5 concentrations as low as 2 μg/m3 (Crouse et al. 2012) and a recent NO2 epidemiological study included concentrations as low as 2 ppb (Khreis et al. 2017). Given that we applied a log-linear function to both PM2.5 and BC, we likewise assumed no threshold for BC. For NO2, the U.S. EPA (2016) determined that there are causal and likely causal relationships for short-term and long-term exposure and respiratory effects, respectively. Because we were able to obtain baseline disease rates for pediatric asthma emergency room (ER) visits and pediatric asthma incidence, two of which included respiratory outcomes included in the U.S. EPA’s “Integrated Science Assessment for Nitrogen Oxides” (U.S. EPA 2016), we included these health end points for short- and long-term exposure to NO2. Recent meta-analyses have also determined that there is a likely causal relationship between long-term exposure to NO2 and increased risk of mortality (COMEAP 2018) and potentially for CVD mortality, the most commonly included cause-specific mortality end point among included studies in the meta-analysis (Atkinson et al. 2018). We estimated impacts of NO2 on all-cause and CVD mortality. Although we examined NO2, there remains active debate on the independent causal relationship between long-term NO2 on mortality and other health outcomes. NO2 is, however, a well-established marker of localized traffic-related air pollution, such as ultrafine particles and polycyclic aromatic hydrocarbons and is used as a proxy to estimate the mortality burden due to highly variable local traffic-related air pollution (Atkinson and Butland 2018) important for urban air pollution policy decision making.For PM2.5, we included health end points determined to be causal or likely to be causal by the U.S. EPA, including all-cause mortality, CVD mortality, CVD hospitalizations among the elderly, and pediatric asthma incidence and ER visits (U.S. EPA 2019). For BC, the U.S. EPA concluded that there is currently insufficient evidence to ascribe any one component of PM2.5 as more strongly associated than total PM2.5 mass, although some studies found associations between long-term exposure to BC and all-cause and CVD mortality, and between short-term BC exposure and CVD hospitalizations (U.S. EPA 2019). We therefore included all-cause and CVD mortality, as well as CVD hospitalizations for BC. Because applying the log-linear model to individual PM2.5 components can distort the risk estimates given nonlinearity at the low end of the curve (Anenberg et al. 2012), we performed a sensitivity analysis in which we assumed the BC contribution to PM2.5 mortality was the same as its contribution to PM2.5 concentrations.NO2, BC, and PM2.5 ConcentrationsWe used multiple pollutant concentration data sets, including mobile monitoring (BC and NO2) and predictive models for the United States and globally using an LUR model (NO2), and for the United States (BC and PM2.5) and globally (PM2.5) using satellite-based models. Maps of concentrations for each pollutant, data set source, and geographical extent are provided in Figures S12–S28.For the mobile monitoring data set, two Street View cars equipped with fast-response instrumentation [NO2 via cavity attenuation phase shift spectroscopy (Model T500U, Teledyne Inc.), and BC via photoacoustic absorption spectroscopy (Droplet Measurement Technologies)] repeatedly drove every road in West, Downtown, and East Oakland during daytime hours (∼0900–1800 hours) on weekdays between 28 May 2015 and 21 December 2017, producing >3 million data points (Apte et al. 2017; Aclima et al. 2019). These measurements were aggregated to independent drive pass means, and then medians of the drive pass means were calculated for 30-m road segments, reflecting long-term spatial differences in concentrations (Messier et al. 2018). The resulting data set indicated substantial spatial variability at fine scales, with median concentrations for road segments within the same city blocks observed to vary by up to a factor of five. Here, we further aggregated the 30-m segment averages to a 100m×100m grid resolution using a mean of all the mobile measurement points in each grid cell. This resulted in a concentration data set with an annual average NO2 concentration range of 3.37 to 45 ppb [mean=12.7, standard deviation (SD)=6.6] and annual average BC concentration range of 0.2 (limit of detection) to 2.59 μg/m3 (mean=0.47, SD=0.35).For NO2, LUR models offer full spatial coverage in addition to the very high spatial resolution needed to capture near-roadway concentrations (Hystad et al. 2011). Here, we used a global LUR that estimated annual average NO2 at 100m×100m resolution for 2011 using satellite measurements, numerous land-use predictor variables, and annual measurement data from 5,220 air monitors in 58 countries (Larkin et al. 2017). The resulting NO2 concentrations for 2011 in the Bay Area ranged from 1 to 37 ppb (mean=8, SD=4), and the model explained 54% (adjusted R2 of 0.54) of the variance in global NO2 concentrations, with an absolute mean error of 3.7 ppb. This data set has been applied in recent health impact assessments quantifying the global burden of NO2 on pediatric asthma incidence (Achakulwisut et al. 2019). Because the global LUR model was not calibrated specifically for the United States, we also estimated results using a U.S.-specific LUR (Bechle et al. 2015). Results from the Street View concentrations were not included in the global LUR; therefore, we do not expect spatial distributions in concentrations to match. We reported estimates using the global LUR as the main results to inform best practices for neighborhood-scale health impact assessments in cities globally.Although PM2.5 was not measured by the Street View cars, PM2.5 is more spatially homogenous compared with NO2 and can therefore be estimated using more coarsely resolved predictive models. Therefore, we used surface concentrations derived from satellite observations of AOD from both global (van Donkelaar et al. 2016) and U.S.-specific models (van Donkelaar et al. 2019; Di et al. 2016). The global PM2.5 data set [0.01×0.01 (∼1 km2)-degree resolution] combined AOD from three satellite products, Goddard Earth Observing System (GEOS)-Chem chemical transport modeling, and geographically weighted regression to merge surface monitor in situ measurements of PM2.5. The model accounted for 81% of the variance in PM2.5 and resulted in annual average surface PM2.5 concentrations ranging from 3 to 18.5 μg/m3 (mean=9, SD=2.8) across the Bay Area for 2016. The global PM2.5 data set was inclusive of BC, although the authors recently developed a North American product, employing similar methods to estimate PM2.5 and speciated components of PM2.5 also at 0.01×0.01-degree resolution. Although U.S. estimates for BC explained 68% of the total variance in BC, estimates for BC in the U.S. Northwest are considerably lower (R2=0.29). For the North American data set in the Bay Area for 2016, BC concentrations for 2016 ranged from 0.1 to 0.7 μg/m3 (mean=0.3, SD=0.1) and PM2.5 was slightly lower than the global model with concentrations ranging from 2.9 to 11 μg/m3 (mean=5.9, SD=1.5). For PM2.5, we compared health burden estimates using global satellite-derived estimates to North American satellite-derived estimates, whereas for BC, our main analysis compared the satellite-derived model to Street View mobile monitoring concentrations. Given that satellite-derived PM2.5 concentrations are highly uncertain (Diao et al. 2019), we also estimated results using a more statistically based PM2.5 model for the United States (Di et al. 2016, 2017).Baseline Disease Rates and DemographicsMaps of baseline disease rates for all health end points and spatial resolutions are provided in Figures S29–S35. We obtained all-cause and CVD mortality rates at both the CBG and county levels (Table S16). For the CBG level, we obtained counts and rates for all-cause and CVD mortality [categorized according to the International Statistical Classification of Diseases, 10th Revision (ICD-10; WHO 2016) ICD-10 codes I10–I75] from the Alameda County Public Health Department for adults and the elderly. CBG rates were based on 7-y averages of death counts (2011–2017) over average population counts for 2012, 2014, and 2016 (Eayres and Williams 2004) and were age-adjusted using the standard 2000 U.S. Census population (Pickle and White 1995). In addition, CBGs with counts <10 were suppressed to protect confidentiality (Brillinger 1986). Combined, these methods avoid interannual variability for small-area (CBG-level) baseline disease rates and resulted in a conservative mean relative standard error of 15 (range=7–58, SD=5) for 1,046 CBGs. For all-cause mortality ages ≥25y, there were 6 (0.5%) missing block groups, and for all-cause mortality ages ≥65y, there were 9 (0.86%) missing block groups. For CVD mortality, there were 37 (3.53%) missing block groups for ages ≥25y and 71 (6.79%) missing block groups for ages ≥65y. To impute missing CBG baseline disease rates, we used an average of the five nearest neighbor rates. We obtained age-adjusted county-level mortality data for 2016 for both all-cause and CVD mortality most closely matching our CBG disease categories (ICD-10 codes I00–I78) from CDC Wonder (CDC 2018). CBG baseline mortality rates show more heterogeneity in the spatial distribution of disease. Annual all-cause mortality for adults ranged from 29 to 331 per 10,000, compared with 21 to 38 per 10,000 using the county rates.We were unable to obtain baseline disease rates at the CBG level for nonmortality end points. For CVD hospitalizations rates, we used county-level rates from the BenMap-CE 1.4.14 (BenMap) software produced by the U.S. EPA for conducting health impact assessment (Sacks et al. 2018). Rates were available in BenMap for the elderly in 5-y age groups: ages 65–69, 70–74, 75–79, 80–84, and 85–99 y. The BenMap program uses 2010 U.S. Census data as the denominator when pooling age groups into a single rate. We applied the 5-y age group rates to the 10-y age groups (65–74, 75–84, and ≥85y) available from the 2010 U.S. Census and used the U.S. Census data from BenMap as the denominator. We weighted the rates by age group count and created an aggregated rate per county (n=9) for CVD hospitalizations. CVD hospitalization (ICD-9 codes 390–429) rates in 2014 ranged from 296 to 604 per 10,000 for ages 65–99 y, across counties in the Bay Area. For asthma ER visits (ICD-9 code 493/ICD-10 code J45), we used county-level rates and ZIP-code–level rates from the California Department of Public Health for 2016 (CDPH 2017, 2019), and we used county rates to impute data for missing ZIP-code rates (17% of the pediatric population and 10% of the adult population). Across the ZIP codes in the Bay Area, 2016 baseline rates of asthma ER visits among children ranged from 1 to 154 per 10,000, and for adults, from 1 to 175 per 10,000. For pediatric asthma incidence, we applied a California statewide baseline rate for 2008 of 107 per 10,000 persons (n=96,550) (Milet et al. 2013) because more recent and finer resolution data were not available. Preprocessing of baseline disease rates was conducted in ArcMap (version 10.4; Esri).We used nighttime (i.e., estimates of permanent residents) population counts from the LandScan USA data set at 100m×100m resolution for 2017 given that it most closely aligned with the temporal availability of our pollutant and baseline disease rate data sets (Oak Ridge National Laboratories 2020; Bhaduri et al. 2007). Compared with the daytime population, we considered the nighttime population to be more consistent with the common approach of epidemiological studi
Referência(s)