Differential effects of neighborhood ambient PM2.5 exposure and social vulnerability on cancer-related systemic inflammation by race in a large health care system population from 2000--2020

Differential effects of neighborhood ambient PM2.5 exposure and social vulnerability on cancer-related systemic inflammation by race in a large health care system population from 2000--2020 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Differential effects of neighborhood ambient PM2.5 exposure and social vulnerability on cancer-related systemic inflammation by race in a large health care system population from 2000--2020 Benjamin A. Rybicki, Zihan Lin, Amber L. Pearson This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7754269/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Mar, 2026 Read the published version in International Journal of Health Geographics → Version 1 posted 9 You are reading this latest preprint version Abstract Background The environment can impact cancer risk both directly, such as through the air we breathe, and indirectly, through the neighborhoods we live in. These risk factors often work in concert but can have disparate effects. Methods To understand how air pollution, as measured by ambient particulate matter 2.5 (PM 2.5 ) levels and neighborhood disadvantage based on a geographically assigned social vulnerability index (SVI), may act together to impact cancer risk, we used 2000–2020 statewide cohort data from a large health system based in metropolitan Detroit, MI, USA (n = 245,438 members). Systemic inflammation was used as a surrogate indicator of cancer risk and was measured via the white blood cell (WBC) count ratios of the neutrophil-to-lymphocyte ratio (NLR) and the neutrophil-to-monocyte ratio (NMR). Results After adjusting for these and other confounding variables, PM 2.5 concentration had a greater positive association with the NMR than with the NLR (Z score = 37.7 vs. 21.8). According to the race-stratified multivariable models, PM 2.5 had a greater association with both inflammatory indices in White members. PM 2.5 levels had the strongest positive linear relationship with both the Charlson comorbidity index and the SVI among Black members. A PM 2.5 × SVI interaction term was found to be statistically significant only for White members, suggesting that these two variables act synergistically to increase systemic inflammation in White members, whereas in Black members, there was evidence that the SVI may mediate the effects of PM 2.5 exposure on both inflammatory indices. Conclusion At the population level, neighborhood environmental factors linked with both air pollution and neighborhood disadvantage appear to have an impact on systemic inflammation; however, these factors may act in a disparate fashion according to race. inequity prevention community chronic disease inflammation race Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Many studies have demonstrated a link between neighborhood disadvantage (often measured as household income or poverty) and cancer outcomes ( 1 – 3 ), but investigations of the biological factors that underpin tumor development, progression, and survival are lacking. Individuals living in deprived neighborhoods have increased levels of proinflammatory biomarkers ( 4 , 5 ) and chronic inflammation, resulting in altered tumor biology ( 1 , 6 – 8 ). One dimension of neighborhood disadvantage, air pollution, has been an area of substantial health research ( 9 – 12 ). Generally measured as exposure to fine particulate matter, PM 2.5 , clear inequalities exist in air pollution exposure ( 13 ), whereby minority and lower income populations tend to have greater exposure ( 14 ). Low-income communities and communities of color have older and more polluting industrial sites, more truck routes, and higher ambient air pollution levels ( 14 – 17 ). Even with air pollution levels steadily declining nationwide over the last 10–20 years, there remain disadvantaged communities that have not reaped the same benefit from overall cleaner air ( 13 , 18 ). Air pollutants, particularly PM 2.5 and PM 10 (coarse air particulate) have been associated with systemic inflammation, as measured by counts of blood neutrophils, monocytes and lymphocytes ( 19 – 22 ). In fact, a recent review concluded that the neutrophil-mediated immune response to particulate matter exposure (in blood and lung tissue) is related to cellular activation and proinflammatory effects, leading to infiltration in the lungs ( 22 ), which increases the risk of cancer as well as accelerated disease progression ( 23 , 24 ). White blood cell markers of inflammation, such as the neutrophil-to-lymphocyte ratio (NLR), have been extensively studied, and elevated levels have been linked with cancer progression ( 25 – 27 ). More recent large population-based cohort studies have also revealed that increased NLRs may increase cancer risk ( 28 – 30 ). Complete blood count (CBC) panels are routinely administered and include numbers of neutrophils, monocytes and lymphocytes, which can be obtained from medical records and used as a measure of systemic inflammation for research studies ( 31 – 34 ). Until now, the major obstacle to large-scale air pollution research has been a lack of fine spatiotemporal PM 2.5 data over time. Recent work combining chemical transport modeling, satellite remote sensing, and ground-based measurements has produced reliable monthly estimates for a 1 km x 1 km gridded surface across North America from 1989 to 2020 ( 13 , 35 ). These PM 2.5 exposure data have been used to study diverse disease outcomes, such as gestational diabetes ( 36 ), Parkinson’s disease ( 37 ) and lung cancer ( 38 ). In the present study, these fine-resolution spatiotemporal air pollution data were leveraged and linked with residential address history data of racially and socioeconomically diverse members of a large midwestern health system to determine whether ambient PM 2.5 levels were associated with blood-based markers of systemic inflammation. Moreover, we addressed the question of whether these associations are modified by race and neighborhood disadvantage with the intent of identifying new biologic mechanisms driven by an inflammatory response to environmental agents underlying observed cancer disparities that could be amenable to early public health interventions. Methods Study sample and data on systemic inflammation The Henry Ford IRB administration office reviewed the study and determined that it did not meet the definition of human subjects research as defined by the US Department of Health and Human Services. A cohort of more than 6 million records of patients aged 30–70 years receiving medical care at a Henry Ford medical facility from 2000–2020 was identified (Fig. 1 ). Systemic inflammation was measured via neutrophil, monocyte and lymphocyte counts from a complete blood count (CBC) panel retrieved from health system laboratory data downloaded from electronic medical records. To focus on blood tests likely conducted as part of a routine exam or preventive health visit, the CBC data were filtered to remove sequences of temporally clustered tests that suggested an acute illness, hospitalization and/or that the study participant was under surveillance for an infection on an outpatient basis. To screen the data for clusters of CBC tests, we identified all tests that occurred within 30 days of another blood test, which typically identified sequences of daily blood tests occurring over several days. For all blood tests occurring within 30 days of another blood test, we retained the last test identified in the 30-day window in the dataset. In retaining the last test result, we assumed that this test was indicative of the CBC test at the end of an acute illness or period of surveillance. Two white blood cell measures of systemic inflammation were created: the ratio of neutrophils to lymphocytes (NLR) and the ratio of neutrophils to monocytes (NMR). As these ratios were right skewed, the ratio data were natural log transformed for analyses. We generated an ordinal NLR variable ranging from 1 to 5, using predefined cutoff points ( https://emcrit.org/pulmcrit/nlr/ ). Air pollutant and Neighborhood social vulnerability data The concentrations of PM 2.5 from 2000 through 2020 were obtained from a fine-resolution geoscience-derived model that combined chemical transport modeling, satellite remote sensing, and ground-based measurements via geographically weighted regression ( 35 ). This model provided validated, publicly available monthly PM 2.5 level data at a 1-km resolution over North America, which was linked to cohort members by geocoded address history data corresponding to the month and year of CBC test results. On the basis of the patients’ geocoded address, we also assigned each member a 2016 social vulnerability index (SVI) score ( https://www.atsdr.cdc.gov/place-health/php/svi/index.html ) for the census tract within which they resided. While neighborhood disadvantage can be measured via many different constructs, we chose to use the SVI because a recent systematic review highlighted its relevance to examining geographic disparities in cancer-related exposures and mortality disparities and its utility in informing targeted interventions to prevent cancer at the neighborhood level ( 39 ). Additional data retrieved from the electronic health records included the patients’ date of birth, sex, and race. From the racial data, we generated the following variables for analysis: White, nonwhite, and Black members, as other racial groups had small numbers. To account for comorbidities that might influence systemic inflammation, a Charlson comorbidity index for each calendar year was calculated based on ICD9 and ICD10 codes assigned to outpatient visits ( 40 ). The values range from 0 to 3, where higher values indicate more comorbidities. Statistical analysis Associations between PM 2.5 levels, inflammatory indices and other potential confounding variables were first explored a univariate level with t-tests and analysis of variance. For these comparisons, race was stratified in White, Black, Asian and Other races. This was followed by analyses that involved multilevel regression models. For the NLR ordinal outcome measure, these models were ordinal logit models. For NMR, these models were linear. We included the following independent variables: PM 2.5 , sex, race (White versus nonWhite), age, comorbidity index and neighborhood social vulnerability index. We fitted multilevel models due to the nested nature of our data over time within cohort members. In stratified analyses, we fitted models for White and Black members separately. An interaction term for SVI* PM 2.5 was also tested using binary variables of each. To create the binary variables, we used median values for SVI (median = 0.495) and the EPA threshold of 9 micrograms per cubic meter (µg/m 3 ) for PM 2.5 . All analyses were conducted via Stata v16 (Statacorps, College Station, TX). Causal mediation analysis was employed to assess whether SVI mediated the effect of PM 2.5 exposure on the NLR and NMR levels via Proc CAUSALMED in SAS. This procedure supports a limited set of generalized linear models that describe the relationships among an outcome variable, a treatment (or effect) variable and a mediator variable. Potential confounders (sex, race, age, comorbidity index) were added as covariates in the model. The model computes the following main causal mediation effects: 1) total effect; 2) controlled direct effect; 3) natural direct effect; and 4) natural indirect effect. Evidence for mediation was based on whether the natural indirect effect was a significant portion of the total effect. Results Study population description Race differences were observed in the distribution of the demographic and clinical study variables (supplementary Table 1). The White cohort members were older on average than the other race groups whereas the Black cohort members had the lowest percentage of males. Compared with the other race groups, Black members had, on average, approximately one more complete blood count (CBC) test (7.37 ± 7.84) and more than one year between their initial and last CBC tests (7.00 ± 6.50). Over half of the Black sample members (58%) lived at an address in a location that mapped to the highest quartile of the social vulnerability index, whereas only 10–12% of White and Asian members lived at addresses that mapped to this highest quartile. The Charlson comorbidity index did not show any striking differences across race groups, with over three quarters of the sample not having any comorbidities. The overall cancer prevalence was highest in Black members (8.1%), and 7.3% of White members had a history of cancer. PM 2.5 levels Average PM 2.5 levels in the Detroit metropolitan area are shown in Fig. 2 , as are the distributions of the population by race. The highest average PM 2.5 levels were in the down and midtown areas of Detroit, which also has a predominantly Black population. Overall, Black members in our cohort had a significantly greater average PM 2.5 exposure level than White members, at 9.90 ± 2.94 vs. 9.06 ± 2.80 µg/m 3 , respectively (Table 1 ). Younger individuals (aged 30–40) had a notably greater PM 2.5 exposure level (11.21 ± 3.45 µg/m 3 ) than did the other age groups. PM 2.5 levels significantly varied according to the two main inflammatory indices we studied, the NLR and the NMR. However, only for NMR did we find that PM 2.5 levels vary in a positive linear fashion. Similarly, the PM 2.5 levels increased as the average neighborhood SVI increased, with those in the highest SVI quartile exposed to the highest PM 2.5 levels (9.54 ± 2.52 µg/m 3 ). No clear pattern of PM 2.5 levels with increasing comorbidity index emerged; however, a history of cancer was associated with a significantly lower average PM 2.5 exposure than was no history of cancer (8.93 ± 2.54 vs. 9.39 ± 2.90 µg/m 3 ). Over the 20-year period, data were collected from this cohort, and the average PM 2.5 levels in the Detroit tri- Table 1 Mean PM2.5 levels by study variables Variable N PM2.5 mean P value Sex Male 143,024 9.34 ± 2.90 0.11 Female 102,564 9.36 ± 2.85 Race White 155,018 9.06 ± 2.80 < .0001 Black 63,698 9.90 ± 2.94 Asian 6,584 9.70 ± 2.96 Other 7,473 9.53 ± 2.77 Age 30–40 53,568 11.21 ± 3.45 < .0001 41–50 75,573 9.64 ± 3.02 51–60 69,246 9.21 ± 2.75 60–70 47,204 8.73 ± 2.40 Neutrophil Lymphocyte Ratio < .0001 18 3,093 8.99 ± 2.56 Neutrophil Monocyte Ratio < .0001 30 4,145 10.57 ± 3.38 Social Vulnerability index < .0001 0-0.25 48,442 9.05 ± 2.52 0.26–0.5 45,041 8.77 ± 2.46 0.51–0.75 40,918 8.95 ± 2.55 0.76-1 46,167 9.54 ± 2.52 Comorbidity index < .0001 0 150,448 9.49 ± 2.96 1 44,532 9.10 ± 2.72 2 20,456 8.84 ± 2.57 3 16,428 8.63 ± 2.42 Diagnosed with Cancer < .0001 No 227,571 9.39 ± 2.90 Yes 18,020 8.93 ± 2.54 county area steadily decreased (Fig. 3). The PM 2.5 level data linked to the addresses of the members of our cohort declined in a mirrored fashion to the larger area, with PM 2.5 levels consistently 0.5–1 µ/m 3 higher among cohort members than the tri-county average PM 2.5 levels. When we examined PM 2.5 levels by major confounding factors stratified by race, we observed a clear positive linear relationship with increasing PM 2.5 levels and comorbidities, primarily in Black members (Fig. 4 A). For example, PM 2.5 levels in Black members ranged from 12.14 to 12.27 to 12.42 to 12.75 µ/m 3 in Black members with 0, 1, 2 and 3 + comorbidities, respectively. Similarly, with each increasing quartile of the SVI, the PM 2.5 concentration in Black individuals increased from 11.48 to 11.54 to 11.90 to 12.10 µ/m 3 (Fig. 4 B). This type of stepwise increase in PM 2.5 levels was not observed in the other race groups for these two confounding variables. Table 2. Mean PM 2.5 levels by study variables Variable N PM2.5 mean P value Sex Male 143,024 9.34 ± 2.90 0.11 Female 102,564 9.36 ± 2.85 Race White 155,018 9.06 ± 2.80 < .0001 Black 63,698 9.90 ± 2.94 Asian 6,584 9.70 ± 2.96 Other 7,473 9.53 ± 2.77 Age 30–40 53,568 11.21 ± 3.45 < .0001 41–50 75,573 9.64 ± 3.02 51–60 69,246 9.21 ± 2.75 60–70 47,204 8.73 ± 2.40 Neutrophil Lymphocyte Ratio < .0001 18 3,093 8.99 ± 2.56 Neutrophil Monocyte Ratio < .0001 30 4,145 10.57 ± 3.38 Social Vulnerability index < .0001 0-0.25 48,442 9.05 ± 2.52 0.26–0.5 45,041 8.77 ± 2.46 0.51–0.75 40,918 8.95 ± 2.55 0.76-1 46,167 9.54 ± 2.52 Comorbidity index < .0001 0 150,448 9.49 ± 2.96 1 44,532 9.10 ± 2.72 2 20,456 8.84 ± 2.57 3 16,428 8.63 ± 2.42 Diagnosed with Cancer < .0001 No 227,571 9.39 ± 2.90 Yes 18,020 8.93 ± 2.54 Inflammation indices Two neutrophil-based combinations of white blood cell tests, the neutrophil-to-lymphocyte ratio (NLR) and the neutrophil-to-monocyte ratio (NMR), were used as measures of systemic inflammation (Table 2 ). Males had slightly higher NLRs, whereas females had higher NMRs. Among the race groups, White members had the highest mean levels of both inflammatory indices, and the youngest age group also had higher NLRs and NMRs than did their older cohort members. Both the NLR and the NMR appeared to increase in a stepwise fashion with increasing SVI and number of comorbidities. Those with a history of cancer also had slightly higher NLRs and NMRs. When all other confounding factors were adjusted, PM 2.5 levels increased in a stepwise fashion across all race groups only for the NMR (Fig. 5 B). Multivariable Models According to the multivariable models of the full sample, based on Z scores race had the strongest association with the NLR, and age had a strong association with the NMR (Table 3 ). PM 2.5 exposure and SVI had similar associations with the NLR, but PM 2.5 exposure had a Z score twofold greater than the SVI for the NMR model. In the race-stratified models, the SVI and PM 2.5 associations were similar for both the NLR and NMR models, as was observed in the full sample. However, for Black members, we observed that the SVI was a much stronger predictor of the NLR than was PM 2.5 exposure, and for the NMR model, the strength of the associations for PM 2.5 exposure and SVI were comparable. We next fit interaction models of PM 2.5 exposure and SVI, where the dependent outcome variables were the NLR or NMR and age, sex, race and the Charlson comorbidity index were also included as covariates in the model (Table 4 ). The coefficients for both the PM 2.5 levels and the SVI variables were statistically significant for both the full and race-stratified samples. For the race-stratified samples, the beta coefficient for PM 2.5 was greater in White members, whereas the beta coefficient for SVI was greater in Black Table 3 Mean inflammatory indices by Study variables Variable Neutrophil to Lymphocyte Ratio (NLR) Neutrophil to Monocyte Ratio (NMR) n Mean ± Standard Deviation P value n Mean ± Standard Deviation P value Sex Male 102,395 3.46 ± 4.16 < 0.0001 102,554 8.96 ± 7.25 < 0.0001 Female 142,805 3.11 ± 3.66 142,574 9.91 ± 7.82 Race Group Whites 154,700 3.39 ± 3.87 < 0.0001 154,490 9.55 ± 7.36 < 0.0001 Blacks 63,659 3.01 ± 3.98 63,553 9.28 ± 8.03 Asians 6,578 2.81 ± 3.14 6,562 9.72 ± 7.05 Other 7,469 3.63 ± 3.63 7,450 9.89 ± 7.15 Age groups 20–40 23,117 3.23 ± 3.42 < 0.0001 23,060 10.81 ± 8.68 40–50 65,779 3.08 ± 3.54 65,676 9.84 ± 7.75 50–60 73,633 3.12 ± 3.69 73,540 9.33 ± 7.13 60+ 82,674 3.55 ± 4.39 82,555 9.04 ± 7.51 Social Vulnerability index quartiles (%) 0-0.25 48,365 3.19 ± 3.71 0.0004 48,307 9.20 ± 6.70 < 0.0001 0.26–0.5 44,932 3.29 ± 3.88 44,879 9.28 ± 6.72 0.51–0.75 40,821 3.25 ± 3.85 40,755 9.50 ± 7.37 0.76-1 46,112 3.28 ± 4.11 46,050 9.70 ± 8.17 Comorbidity index 0 150,242 3.12 ± 3.59 < 0.0001 150,027 10.37 ± 8.92 0.026 1 44,469 3.27 ± 3.85 44,404 9.48 ± 7.47 2 20,408 3.54 ± 4.44 20,354 9.49 ± 7.43 3 16,402 4.00 ± 5.02 16,380 9.35 ± 8.01 Diagnosed with Cancer Yes 18,006 3.59 ± 4.49 < 0.0001 17,956 9.28 ± 7.71 < 0.0001 No 227,197 3.25 ± 3.68 226,875 9.53 ± 7.59 members. Given the strong associations of the SVI and PM 2.5 exposure with the two inflammatory indices, we also tested race-stratified models that included an interaction term for Table 4 Factors associated with inflammatory indices stratified by race Variable Neutrophil to Lymphocyte Ratio (NLR) Neutrophil to Monocyte Ratio (NMR) β Z score P value β Z score P value Full Sample Age -0.0007 -1.59 0.11 -0.060 -66.32 < 0.0001 Female sex -0.289 -25.89 < 0.0001 1.035 46.34 < 0.0001 White race 0. 643 51.05 < 0.0001 0. 609 24.33 < 0.0001 Comorbidity 0.123 48.41 < 0.0001 0.049 7.73 < 0.0001 SVI 0.394 22.99 < 0.0001 0.679 18.77 < 0.0001 PM 2.5 0.021 21.83 < 0.0001 0.088 37.68 < 0.0001 Whites Age 0.005 8.98 < 0.0001 -0.066 -58.87 < 0.0001 Female sex -0.306 -22.17 < 0.0001 1.021 38.17 < 0.0001 Comorbidity 0. 136 41.75 < 0.0001 0. 084 10.47 < 0.0001 SVI 0.374 16.52 < 0.0001 0.711 15.23 < 0.0001 PM 2.5 0.021 17.38 < 0.0001 0.099 34.35 < 0.0001 Blacks Age -0.010 -13.31 0.11 -0.052 -29.73 < 0.0001 Female sex -0.309 -14.94 < 0.0001 1.061 23.56 < 0.0001 Comorbidity 0. 103 24.25 < 0.0001 0. 609 0.02 0.986 SVI 0.454 15.17 < 0.0001 0.865 12.65 < 0.0001 PM 2.5 0.016 9.64 < 0.0001 0.071 16.29 < 0.0001 these two variables. In White members, a positive beta coefficient significantly different from zero was observed for both the NLR and the NMR outcome variable. However, in Black members, the interaction beta coefficient was much smaller and not significantly different from zero. We further explored the relationship between PM 2.5 exposure and the SVI in causal mediation models where the SVI was modeled as a mediating factor between PM 2.5 exposure and the systemic inflammation causal pathway (supplementary Table 2). In the full sample, there was nominal evidence of an indirect effect from SVI on the PM 2.5 exposure association with both the NLR and the NMR, with each model resulting in approximately two percent mediation. In race-stratified models, the only evidence for mediation was found in Black members. In this subpopulation, the SVI had a 19% mediation effect on the association between PM 2.5 exposure and the NLR and a 6% mediation effect on the association between PM 2.5 exposure and the NMR; both effects were statistically significant. Discussion Despite decreasing levels of air pollution as measured by PM 2.5 levels over the past twenty years, ambient PM 2.5 levels were found to be associated with the neutrophil-to-lymphocyte ratio (NLR) and the neutrophil-to-monocyte ratio (NMR), two blood-based measures of systemic inflammation, in a large health system-based cohort. Moreover, these associations remained significant even after adjusting for confounders such as age, sex, race, the comorbidity index, and neighborhood deprivation. Interestingly, PM 2.5 levels appeared to have a greater association with systemic inflammation in White members, whereas neighborhood deprivation was more strongly associated with systemic inflammation in Black members. A significant interaction was also found between these two factors in modeling their association with systemic inflammation but was only present in White members, whereas neighborhood deprivation was a mediating factor of the association of PM 2.5 with systemic inflammation in Black members. The NLR is associated with changes induced by disease and environmental challenges to the immune system and varies with both age and sex ( 41 ). Compared with non-Hispanic Whites, Blacks tend to have lower mean NLR values ( 42 – 44 ), which is consistent with the racial differences observed in our cohort. Increasing NLR values are associated with increased age and a greater risk of mortality ( 45 ). Moreover, a recent study of the NHANEs cohort revealed that a higher NLR was directly correlated with an increased risk of developing cancer in adults ( 30 ). In addition, in this same NHANEs cohort, both the NLR and the NMR were found to be associated with high-risk prostate cancer ( 46 ). In a UK Biobank study of 443,540 cancer-free adults, the NLR was associated with an increased risk of overall cancer incidence ( 28 ). Only a few reports exist that have linked air pollution as measured by PM 2.5 levels with systemic inflammation based on NLR and NMR levels in blood ( 47 , 48 ). In an urban cohort in China, on the basis of a Bayesian kernel machine regression model, the NLR was positively associated with PM 2.5 levels ( 48 ). Another cohort study in Sweden reported associations between ambient PM 2.5 levels and the NLR in three different models that were adjusted for an increasing number of confounding factors ( 47 ). In our cohort, a robust association between ambient PM 2.5 levels and both the NLR and the NMR existed even after adjusting for all confounding variables, including the neighborhood deprivation index and comorbidity status. These associations appeared to differ by race, with PM 2.5 having a stronger association with both the NLR and the NMR in Whites than in Blacks on the basis of the magnitude of the beta coefficients. Racial disparities exist in PM 2.5 exposure across the United States, which can be attributed to industrial emissions ( 49 , 50 ) and redlining ( 51 ). In 2019, the least White communities experienced 16% higher PM 2.5 exposure levels than the most White communities did ( 52 ), and the Black American population was reported to have the highest proportion of deaths attributable to PM 2.5 exposure in all years from 1990–2016 ( 53 ). In a study across the state of North Carolina from 2002–2016, despite declines in PM 2.5 levels over time, disparities in exposure increased for racially and educationally isolated communities ( 18 ). In our health system-based cohort, Black members, on average, had PM 2.5 exposure levels almost 1 µ/m 3 higher than those of White members, and the highest PM 2.5 exposure levels mapped to the neighborhoods with the highest percentage of Black residents. Higher levels of ambient PM 2.5 exposure are known to have detrimental health effects and increase cancer risk ( 54 ). In a large US cohort of women spanning six states and two metropolitan areas, PM 2.5 was associated with an increased risk of estrogen receptor-positive breast cancer ( 55 ). In the Surveillance Epidemiology and End Results national cancer database from 2002–2012, PM 2.5 exposure was associated with incident head and neck cancer incidence ( 56 ). A population-based case‒control study of upper aerodigestive cancer cases conducted in Los Angeles County from 1999–2004 revealed that PM 2.5 exposure increased risk irrespective of tobacco smoking status ( 57 ). While we did not directly estimate the cancer risk associated with PM 2.5 exposure levels in our cohort, over 18,000 health system members who developed cancer during the 20-year observation period were identified. Interestingly, PM 2.5 exposure levels were lower in cancer patients than in those who did not develop cancer (8.93 ± 2.54 vs. 9.39 ± 2.90 µ/m 3 ). The dates of these PM 2.5 levels were based on when the lab blood tests were taken and likely do not reflect the PM 2.5 exposure levels that preceded the cancer diagnosis. This finding can also be interpreted as reflecting a lagged effect of PM2.5 on cancer development or a delay in cancer diagnosis ( 58 ). Using the address data in our health system-based cohort, we assigned a social vulnerability (SVI) index score ( https://www.atsdr.cdc.gov/place-health/php/svi/index.html ) ranging from 0 (lowest) to 1 (highest) based on 16 U.S. census variables from the 5-year American Community Survey. Numerous studies have linked social vulnerability to cancer risk ( 59 – 62 ), but only a few have explored how this factor influences inflammation indices. In the All of Us cohort, area deprivation was associated with C-reactive protein in cancer survivors ( 63 ). In a large cohort of health professionals, higher neighborhood SES was associated with a lower inflammation score in both men and women ( 5 ). Considerable research has been conducted on the disparate effects of neighborhood deprivation by race on disease outcomes. Higher neighborhood deprivation scores were associated with increased stress, sleep disturbance, and inflammatory biomarkers in Black adults with heart failure ( 64 ). A prostate cancer case‒control study based in Maryland reported that residing in the most deprived vs. least deprived neighborhoods corresponded to 88% higher disease odds among all men and an approximate 3-fold increase among African American men, but no association was noted among European American men ( 65 ). Another large cohort study of stroke risk factors that included over 30,000 Black and White participants aged 45 years and over at the time of enrollment reported similar effects of higher neighborhood deprivation scores on inflammation indices ( 66 ). In our cohort, while the SVI was associated with the NLR and NMR in both Black and White members, the magnitude of the SVI association was clearly greater in Black participants. Neighborhood disadvantage or social deprivation has been found to be associated with increased PM 2.5 exposure levels as well as systemic inflammation. Clear racial disparities exist in both social deprivation and PM 2.5 exposure; however, the strong correlation between the two factors makes it difficult to disentangle each factor’s individual effects on disease markers and outcomes. In a study of noncancerous breast tissue collected from women who underwent reduction mammoplasty or breast cancer surgery, air pollution was more strongly associated with CD68-positive macrophages, a measure of the inflammatory response, in breast adipose tissue in White women than in Black women ( 67 ). Furthermore, neither group exhibited an association between neighborhood deprivation and the number of CD68-positive macrophages in the breast. In another study of Pennsylvania residents, chronic rhinosinusitis incidence was not associated with the area deprivation index, but the latter was positively correlated with PM 2.5 exposure levels ( 68 ). No studies have examined whether social deprivation and PM 2.5 exposure act independently and/or synergistically to increase the risk for disease outcomes. In our health system-based cohort, there was evidence that PM 2.5 exposure and SVI act synergistically to increase systemic inflammation in White members. However, in Black members it appears that SVI may mediate the putative effect of PM 2.5 on systemic inflammation. The health system data in our study spanned 20 years from 2000–2020 and included over 200,000 individuals, with an average of six white blood cell measurements over 5–7 years. Linking these data with reliable estimates of monthly ambient fine spatiotemporal PM 2.5 data during this time overcame a major past obstacle in producing reliable exposure data that has plagued previous air pollution research. These PM 2.5 data were generated from chemical transport modeling, satellite remote sensing, and ground-based measurements ( 35 ). The geographic mapping of PM 2.5 data was based on patient addresses and therefore reflects only potential exposures during the time a person was physically at his or her address. In addition to the laboratory data, we were able to analyze patient visit data and construct a Charlson comorbidity index to adjust for disease-related factors that might influence systemic inflammation. Neutrophil-based measures such as the NLR and NMR have been widely used as measures of systemic inflammation and are associated with numerous disease processes ( 25 ). The laboratory data used in the current study were not collected in a systematic manner and were subject to bias based on what subset of patients had these data and if white blood cell tests were performed in response to an acute disease process. To control for the latter, when a series of white blood cell tests were performed in a short period, we restricted the data we used to the last test in the series of tests. Even so, the average race-specific values of the NLR and NMR in our cohort were much higher than those reported in population-based cohorts such as NHANEs ( 42 ) or the UK biobank ( 28 ), which suggests that the study sample had a higher level of morbidity than did the general population. In summary, using a large health system-based cohort, we demonstrated that even at historically low levels, ambient PM 2.5 exposure may influence systemic inflammation. This effect appears to differ by race and social factors that can influence a person’s health. While PM 2.5 exposure and neighborhood deprivation may act synergistically in White populations, in Black populations, these two factors are more strongly correlated, and the latter may be a mediating factor of the association of PM 2.5 exposure with systemic inflammation. Given that inflammation is a leading risk factor for cancer and other chronic diseases, these results support the notion that intervening on modifiable environmental factors may play a key role in disease prevention. In devising intervention strategies, it will be important to consider both the environmental factor(s) and the target population to maximize the benefit of the intervention. Declarations Human Ethics and Consent to Participate declarations not applicable Funding: Funding for the conduct of the study was provided by Henry Ford Health and Michigan State University Health Sciences Author Contribution B.R. and A.P. conceived the study and designed study protocol. B.R. and A.P. wrote the main manuscript text and prepared the tables. A.P prepared figures 1-3 and B.R. prepared figures 4-5. A.P. conducted the main study analysis with the assistance of Z.L. All authors reviewed the manuscript. Acknowledgement We thank Drs. Randall Martin, Jun Meng, Melanie Hammer, and Aaron van Donkelaar for providing the satellite and model-derived PM2.5 data, which were essential for estimating ambient PM2.5 exposures of the patient cohort in this study. We also thank DevakiPurushothaman who performed the programming that built the Henry Ford patient datasets. Data Availability All data and analysis code used for this study is available from the authors upon request. References Barber LE, Zirpoli GR, Cozier YC, Rosenberg L, Petrick JL, Bertrand KA, Palmer JR. Neighborhood disadvantage and individual-level life stressors in relation to breast cancer incidence in US Black women. Breast cancer research: BCR. 2021;23(1):108. Wheeler DC, Boyle J, Carli M, Ward MH, Metayer C. Neighborhood Deprivation, Indoor Chemical Concentrations, and Spatial Risk for Childhood Leukemia. Int J Environ Res Public Health. 2023;20(4). Erhunmwunsee L, Wing SE, Zou X, Coogan P, Palmer JR, Lennie Wong F. Neighborhood disadvantage and lung cancer risk in a national cohort of never smoking Black women. Lung Cancer. 2022;173:21–7. Schootman M, Andresen EM, Wolinsky FD, Malmstrom TK, Morley JE, Miller DK. Adverse housing and neighborhood conditions and inflammatory markers among middle-aged African Americans. J urban health: Bull New York Acad Med. 2010;87(2):199–210. Iyer HS, Hart JE, James P, Elliott EG, DeVille NV, Holmes MD, et al. Impact of neighborhood socioeconomic status, income segregation, and greenness on blood biomarkers of inflammation. Environ Int. 2022;162:107164. Ashing KT, Jones V, Bedell F, Phillips T, Erhunmwunsee L. Calling Attention to the Role of Race-Driven Societal Determinants of Health on Aggressive Tumor Biology: A Focus on Black Americans. JCO Oncol Pract. 2022;18(1):15–22. Ma SJ, Gill J, Waldman O, Yendamuri K, Dunne-Jaffe C, Chatterjee U, et al. Association of Neighborhood-Level Household Income With 21-Gene Recurrence Score and Survival Among Patients With Estrogen Receptor-Positive Breast Cancer. JAMA Netw Open. 2023;6(2):e230179. Ahmed F, Moseley I, Lin E, Lim R, Baranwal N, Hoang M, et al. The Association of Neighborhood-Level Socioeconomic Status and Tumor Diameter in Squamous Cell Carcinoma. J Invest Dermatol. 2023;143(1):170–2. Stewart JA, Mitchell MA, Edgerton VS, VanCott R. Environmental Justice and Health Effects of Urban Air Pollution. J Natl Med Assoc. 2015;107(1):50–8. Rubio R, Grineski S, Collins T. Carcinogenic air pollution along the United States' southern border: Neighborhood inequities in risk. Environ Res. 2022;212:113251. Pt B). Esie P, Daepp MIG, Roseway A, Counts S. Neighborhood Composition and Air Pollution in Chicago: Monitoring Inequities With a Dense, Low-Cost Sensing Network, 2021. Am J Public Health. 2022;112(12):1765–73. Deguen S, Padilla M, Padilla C, Kihal-Talantikite W. Do Individual and Neighborhood Characteristics Influence Perceived Air Quality? Int J Environ Res Public Health. 2017;14(12). Jbaily A, Zhou X, Liu J, Lee TH, Kamareddine L, Verguet S, Dominici F. Air pollution exposure disparities across US population and income groups. Nature. 2022;601(7892):228–33. Schulz AJ, Mentz GB, Sampson N, Ward M, Dvonch JT, de Majo R et al. Independent and Joint Contributions of Fine Particulate Matter Exposure and Population Vulnerability to Mortality in the Detroit Metropolitan Area. Int J Environ Res Public Health. 2018;15(6). Schulz AJ, Williams DR, Israel BA, Lempert LB. Racial and Spatial Relations as Fundamental Determinants of Health in Detroit. Milbank Q. 2002;80(4):677–707. Downey L. Environmental Racial Inequality in Detroit. Social forces; a scientific medium of social study and interpretation. 2006;85(2):771 – 96. Downey L. The Unintended Significance of Race: Environmental Racial Inequality in Detroit. Social forces; a scientific medium of social study and interpretation. 2005;83(3):971–1007. Bravo MA, Warren JL, Leong MC, Deziel NC, Kimbro RT, Bell ML, Miranda ML. Where Is Air Quality Improving, and Who Benefits? A Study of PM2.5 and Ozone Over 15 Years. Am J Epidemiol. 2022;191(7):1258–69. Riaz H, Syed BM, Laghari Z, Pirzada S. Analysis of inflammatory markers in apparently healthy automobile vehicle drivers in response to exposure to traffic pollution fumes. Pak J Med Sci. 2020;36(4):657–62. Leonardi GS, Houthuijs D, Steerenberg PA, Fletcher T, Armstrong B, Antova T, et al. Immune biomarkers in relation to exposure to particulate matter: a cross-sectional survey in 17 cities of Central Europe. Inhal Toxicol. 2000;12(Suppl 4):1–14. Verheyen VJ, Remy S, Bijnens EM, Colles A, Govarts E, Martin LR, et al. Long-term residential exposure to air pollution is associated with hair cortisol concentration and differential leucocyte count in Flemish adolescent boys. Environ Res. 2021;201:111595. Valderrama A, Zapata MI, Hernandez JC, Cardona-Arias JA. Systematic review of preclinical studies on the neutrophil-mediated immune response to air pollutants, 1980–2020. Heliyon. 2022;8(1):e08778. Santibanez-Andrade M, Quezada-Maldonado EM, Rivera-Pineda A, Chirino YI, Garcia-Cuellar CM, Sanchez-Perez Y. The Road to Malignant Cell Transformation after Particulate Matter Exposure: From Oxidative Stress to Genotoxicity. Int J Mol Sci. 2023;24(2). Lagunas-Rangel FA, Liu W, Schioth HB. Can Exposure to Environmental Pollutants Be Associated with Less Effective Chemotherapy in Cancer Patients? Int J Environ Res Public Health. 2022;19(4). Guthrie GJ, Charles KA, Roxburgh CS, Horgan PG, McMillan DC, Clarke SJ. The systemic inflammation-based neutrophil-lymphocyte ratio: experience in patients with cancer. Crit Rev Oncol Hematol. 2013;88(1):218–30. Shi L, Qin X, Wang H, Xia Y, Li Y, Chen X, et al. Elevated neutrophil-to-lymphocyte ratio and monocyte-to-lymphocyte ratio and decreased platelet-to-lymphocyte ratio are associated with poor prognosis in multiple myeloma. Oncotarget. 2017;8(12):18792–801. Xiang J, Zhou L, Li X, Bao W, Chen T, Xi X, et al. Preoperative Monocyte-to-Lymphocyte Ratio in Peripheral Blood Predicts Stages, Metastasis, and Histological Grades in Patients with Ovarian Cancer. Translational Oncol. 2017;10(1):33–9. Song M, Graubard BI, Loftfield E, Rabkin CS, Engels EA. White Blood Cell Count, Neutrophil-to-Lymphocyte Ratio, and Incident Cancer in the UK Biobank. Cancer Epidemiol Biomarkers Prev. 2024;33(6):821–9. Wang L, Li X, Liu M, Zhou H, Shao J. Association between monocyte-to-lymphocyte ratio and prostate cancer in the U.S. population: a population-based study. Front Cell Dev Biology. 2024;12. Li GP, Zhang D, Li MH, Yuan FF, Hou XJ, He DJ, et al. Association between the neutrophil-to-lymphocyte ratio and cancer in adults from NHANES 2005–2018: a cross-sectional study. Sci Rep. 2024;14(1):23678. Jiang C, Wu Y, Huang J, Wang Y, Cong H. Clinical value of complete blood count ratio in benign and malignant thyroid diseases. Cancer Epidemiol. 2024;92:102636. Popęda M, Żok J, Tomasik B, Duchnowska R, Bieńkowski M. Complete blood counts as potential risk factors of early dissemination to liver and lungs in resected colorectal cancer: a retrospective cohort study. Int J Colorectal Dis. 2025;40(1):21. Rundle AG, Sadasivan SM, Chitale DA, Gupta NS, Williamson SR, Kryvenko ON, et al. Racial differences in the systemic inflammatory response to prostate cancer. PLoS ONE. 2021;16(7):e0252951. Peng H, Luo X. Prognostic significance of elevated pretreatment systemic inflammatory markers for patients with prostate cancer: a meta-analysis. Cancer Cell Int. 2019;19:70. Meng J, Li C, Martin RV, van Donkelaar A, Hystad P, Brauer M, Estimated Long-Term. (1981–2016) Concentrations of Ambient Fine Particulate Matter across North America from Chemical Transport Modeling, Satellite Remote Sensing, and Ground-Based Measurements. Environmental science & technology. 2019;53(9):5071-9. Sun Y, Li X, Benmarhnia T, Chen JC, Avila C, Sacks DA, et al. Exposure to air pollutant mixture and gestational diabetes mellitus in Southern California: Results from electronic health record data of a large pregnancy cohort. Environ Int. 2022;158:106888. Kwon D, Paul KC, Yu Y, Zhang K, Folle AD, Wu J, et al. Traffic-related air pollution and Parkinson's disease in central California. Environ Res. 2024;240:117434. Cheng I, Yang J, Tseng C, Wu J, Shariff-Marco S, Park SL, et al. Traffic-related Air Pollution and Lung Cancer Incidence: The California Multiethnic Cohort Study. Am J Respir Crit Care Med. 2022;206(8):1008–18. Tran T, Rousseau MA, Farris DP, Bauer C, Nelson KC, Doan HQ. The social vulnerability index as a risk stratification tool for health disparity research in cancer patients: a scoping review. Cancer Causes Control. 2023;34(5):407–20. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–83. Pellegrino R, Paganelli R, Di Iorio A, Bandinelli S, Moretti A, Iolascon G, et al. Temporal trends, sex differences, and age-related disease influence in Neutrophil, Lymphocyte count and Neutrophil to Lymphocyte-ratio: results from InCHIANTI follow-up study. Immun Ageing. 2023;20(1):46. Calixte R, Ye Z, Haq R, Aladhamy S, Camacho-Rivera M. Demographic and Social Patterns of the Mean Values of Inflammatory Markers in U.S. Adults: A 2009–2016 NHANES Analysis. Diseases. 2023;11(1). Howard R, Scheiner A, Kanetsky PA, Egan KM. Sociodemographic and lifestyle factors associated with the neutrophil-to-lymphocyte ratio. Ann Epidemiol. 2019. Azab B, Camacho-Rivera M, Taioli E. Average values and racial differences of neutrophil lymphocyte ratio among a nationally representative sample of United States subjects. PLoS ONE. 2014;9(11):e112361. Pellegrino R, Paganelli R, Di Iorio A, Bandinelli S, Moretti A, Iolascon G et al. Neutrophil, lymphocyte count, and neutrophil to lymphocyte ratio predict multimorbidity and mortality-results from the Baltimore Longitudinal Study on Aging follow-up study. Geroscience. 2024. Gao S, Jiang P, Tian R. Neutrophil-and-monocyte-to-lymphocyte ratio is positively associated with elevated prostate-specific antigen levels and high-risk prostate cancer: evidence from the NHANES (2003–2008). Front Cell Dev Biol. 2025;13:1573932. Azzouz M, Xu Y, Barregard L, Fagerberg B, Zöller B, Molnár P, et al. Air pollution and biomarkers of cardiovascular disease and inflammation in the Malmö Diet and Cancer cohort. Environ Health. 2022;21(1):39. Qiu W, He H, Wang B, Wang D, Mu G, Xu T, et al. Short-term impacts of air pollution on the platelet-lymphocyte ratio and neutrophil-lymphocyte ratio among urban adults in China. J Environ Sci (China). 2023;125:101–11. Mikati I, Benson AF, Luben TJ, Sacks JD, Richmond-Bryant J. Disparities in Distribution of Particulate Matter Emission Sources by Race and Poverty Status. Am J Public Health. 2018;108(4):480–5. Tessum CW, Paolella DA, Chambliss SE, Apte JS, Hill JD, Marshall JD. PM(2.5) polluters disproportionately and systemically affect people of color in the United States. Sci Adv. 2021;7(18). Lane HM, Morello-Frosch R, Marshall JD, Apte JS. Historical Redlining Is Associated with Present-Day Air Pollution Disparities in U.S. Cities. Environ Sci Technol Lett. 2022;9(4):345–50. Kerr GH, van Donkelaar A, Martin RV, Brauer M, Bukart K, Wozniak S, et al. Increasing Racial and Ethnic Disparities in Ambient Air Pollution-Attributable Morbidity and Mortality in the United States. Environ Health Perspect. 2024;132(3):37002. Geldsetzer P, Fridljand D, Kiang MV, Bendavid E, Heft-Neal S, Burke M, et al. Disparities in air pollution attributable mortality in the US population by race/ethnicity and sociodemographic factors. Nat Med. 2024;30(10):2821–9. Feng Y, Li J, Wang Y, Yin T, Wang Q, Cheng L. Fine particulate matter exposure and cancer risk: a systematic review and meta-analysis of prospective cohort studies. Rev Environ Health. 2025. White AJ, Fisher JA, Sweeney MR, Freedman ND, Kaufman JD, Silverman DT, Jones RR. Ambient fine particulate matter and breast cancer incidence in a large prospective US cohort. J Natl Cancer Inst. 2024;116(1):53–60. Peleman J, Ruan M, Dey T, Chiang S, Dilger A, Mitchell MB, et al. Air pollution exposure and head and neck cancer incidence. Sci Rep. 2024;14(1):26998. Fang F, Ritz B, Rao J, Zhu Y, Tashkin DP, Morgenstern H, Zhang ZF. Association between ambient exposure to PM(2.5) and upper aerodigestive tract cancer in Los Angeles. Int J Cancer. 2024;154(9):1579–86. Wei Y, Danesh Yazdi M, Ma T, Castro E, Liu CS, Qiu X, et al. Additive effects of 10-year exposures to PM2.5 and NO2 and primary cancer incidence in American older adults. Environ Epidemiol. 2023;7(4):e265. Georgantopoulos P, Eberth JM, Cai B, Emrich C, Rao G, Bennett CL, et al. Patient- and area-level predictors of prostate cancer among South Carolina veterans: a spatial analysis. Cancer Causes Control. 2020;31(3):209–20. Patel PG, Chowdhury S, Wiener HW, George JT, Abdalla E, Ye Y et al. Community-level Social Vulnerability and Cervical Cancer Mortality Among Young and Old Adults in the State of Alabama. J Community Health. 2025. Councell KA, Polcari AM, Nordgren R, Skolarus TA, Benjamin AJ, Shubeck SP. Social vulnerability is associated with advanced breast cancer presentation and all-cause mortality: a retrospective cohort study. Breast cancer research: BCR. 2024;26(1):176. Mehta A, Jeon WJ, Nagaraj G. Association of US county-level social vulnerability index with breast, colorectal, and lung cancer screening, incidence, and mortality rates across US counties. Front Oncol. 2024;14:1422475. Byrne CA, Oddo VM, Karayeva E, Kopetsky G, Kim S. Area Deprivation and Clinical Biomarkers of Inflammation in Cancer Survivors of the National Institutes of Health All of Us Research Program. Cancer Med. 2025;14(7):e70784. Butts B, Herring C, Madariaga A, Graven LJ, Higgins MK, Dunbar SB. Inflammation markers associated with symptoms and neighborhood deprivation in black adults with heart failure. Heart Lung. 2025;74:121–8. Pichardo MS, Minas TZ, Pichardo CM, Bailey-Whyte M, Tang W, Dorsey TH, et al. Association of Neighborhood Deprivation With Prostate Cancer and Immune Markers in African American and European American Men. JAMA Netw Open. 2023;6(1):e2251745. Keita AD, Judd SE, Howard VJ, Carson AP, Ard JD, Fernandez JR. Associations of neighborhood area level deprivation with the metabolic syndrome and inflammation among middle- and older- age adults. BMC Public Health. 2014;14:1319. Harris AR, Hughes JD, Lawrence WR, Lenz P, Franklin J, Bhawsar PMS, et al. Neighborhood Environment, DNA Methylation, and Presence of Crown-Like Structures of the Breast. JAMA Netw Open. 2025;8(2):e2461334. Velasquez N, Gardiner L, Cheng TZ, Moore JA, Boudreau RM, Presto AA, Lee SE. Relationship between socioeconomic status, exposure to airborne pollutants, and chronic rhinosinusitis disease severity. Int Forum Allergy Rhinol. 2022;12(2):172–80. Additional Declarations No competing interests reported. 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20:20:16","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":46637,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7754269/v1/b58f8a56ab692193e8eeb091.png"},{"id":94142118,"identity":"63d4a93c-a266-4c2f-ad7e-c6df5d213994","added_by":"auto","created_at":"2025-10-22 20:20:16","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":191202,"visible":true,"origin":"","legend":"","description":"","filename":"9148f1492e6f4ae8aa8c4f61aced703d1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7754269/v1/e5aba593830c93b8559c6ed4.xml"},{"id":94142120,"identity":"112c4d95-da26-4baf-b66e-0b8ea8406867","added_by":"auto","created_at":"2025-10-22 20:20:16","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":199370,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7754269/v1/0a45f3f2f18ba345f5f17596.html"},{"id":94142096,"identity":"db4b401d-d474-4027-9672-2e95baf00055","added_by":"auto","created_at":"2025-10-22 20:20:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":24445,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDevelopment of study cohort analytic sample from Henry Ford medical record data\u003c/strong\u003e. Cohort was limited to patients having at least one residential address (middle box) and presence of one or more Complete Blood Count (CBC) test (bottom box).\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7754269/v1/034b184230358f2efe3db040.jpg"},{"id":94142097,"identity":"c18668cd-02ab-42b7-9330-e9b25c070278","added_by":"auto","created_at":"2025-10-22 20:20:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33223,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePopulation Distribution and PM\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2.5 \u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003elevel heat maps.\u003c/strong\u003e Heat maps of A) non-Hispanic Black population distribution; B) non-Hispanic White population distribution; and C) average PM\u003csub\u003e2.5 \u003c/sub\u003elevels in Detroit metropolitan tri-county area during 2020.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7754269/v1/3c27cf01ff097f61ee51c80d.jpg"},{"id":94142107,"identity":"9f4d7e2d-ebb0-4ba6-bcb2-f9c04c0230d6","added_by":"auto","created_at":"2025-10-22 20:20:15","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":45529,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRace stratified mean PM2.5 levels by selected confounding variables.\u003c/strong\u003e Mean PM2.5 levels by A) race and comorbidity index (p\u0026lt;0.0001) and by B) race and social vulnerability index (p\u0026lt;0.0001).\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7754269/v1/d97bbe55c4b6f44c82489f3d.jpg"},{"id":94142104,"identity":"c2dfcf80-6162-4a4e-8958-c91aef0554a8","added_by":"auto","created_at":"2025-10-22 20:20:15","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":54486,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRace stratified mean PM2.5 levels by inflammatory indices.\u003c/strong\u003e Mean PM2.5 levels by A) race and neutrophil lymphocyte ration (p=0.22) and by B) race and neutrophil monocyte ratio (p\u0026lt;0.0001).\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7754269/v1/7a6293872d85b4fad5bcb7d2.jpg"},{"id":105756056,"identity":"d13c0e16-ab38-4999-b70d-89d160dfe6fc","added_by":"auto","created_at":"2026-03-30 16:34:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1414463,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7754269/v1/87dc7efa-9801-4e45-95db-c16e1059876e.pdf"},{"id":94142098,"identity":"c46c132a-060c-486d-8c5f-08dd2b5e9015","added_by":"auto","created_at":"2025-10-22 20:20:14","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":23284,"visible":true,"origin":"","legend":"","description":"","filename":"PM25papersupplementrytables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7754269/v1/41b74c01a4269b9e9c31231a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Differential effects of neighborhood ambient PM2.5 exposure and social vulnerability on cancer-related systemic inflammation by race in a large health care system population from 2000--2020","fulltext":[{"header":"Background","content":"\u003cp\u003eMany studies have demonstrated a link between neighborhood disadvantage (often measured as household income or poverty) and cancer outcomes (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), but investigations of the biological factors that underpin tumor development, progression, and survival are lacking. Individuals living in deprived neighborhoods have increased levels of proinflammatory biomarkers (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and chronic inflammation, resulting in altered tumor biology (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). One dimension of neighborhood disadvantage, air pollution, has been an area of substantial health research (\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Generally measured as exposure to fine particulate matter, PM\u003csub\u003e2.5\u003c/sub\u003e, clear inequalities exist in air pollution exposure (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), whereby minority and lower income populations tend to have greater exposure (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Low-income communities and communities of color have older and more polluting industrial sites, more truck routes, and higher ambient air pollution levels (\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Even with air pollution levels steadily declining nationwide over the last 10\u0026ndash;20 years, there remain disadvantaged communities that have not reaped the same benefit from overall cleaner air (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAir pollutants, particularly PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e (coarse air particulate) have been associated with systemic inflammation, as measured by counts of blood neutrophils, monocytes and lymphocytes (\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). In fact, a recent review concluded that the neutrophil-mediated immune response to particulate matter exposure (in blood and lung tissue) is related to cellular activation and proinflammatory effects, leading to infiltration in the lungs (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), which increases the risk of cancer as well as accelerated disease progression (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). White blood cell markers of inflammation, such as the neutrophil-to-lymphocyte ratio (NLR), have been extensively studied, and elevated levels have been linked with cancer progression (\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). More recent large population-based cohort studies have also revealed that increased NLRs may increase cancer risk (\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Complete blood count (CBC) panels are routinely administered and include numbers of neutrophils, monocytes and lymphocytes, which can be obtained from medical records and used as a measure of systemic inflammation for research studies (\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUntil now, the major obstacle to large-scale air pollution research has been a lack of fine spatiotemporal PM\u003csub\u003e2.5\u003c/sub\u003e data over time. Recent work combining chemical transport modeling, satellite remote sensing, and ground-based measurements has produced reliable monthly estimates for a 1 km x 1 km gridded surface across North America from 1989 to 2020 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). These PM\u003csub\u003e2.5\u003c/sub\u003e exposure data have been used to study diverse disease outcomes, such as gestational diabetes (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), Parkinson\u0026rsquo;s disease (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) and lung cancer (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). In the present study, these fine-resolution spatiotemporal air pollution data were leveraged and linked with residential address history data of racially and socioeconomically diverse members of a large midwestern health system to determine whether ambient PM\u003csub\u003e2.5\u003c/sub\u003e levels were associated with blood-based markers of systemic inflammation. Moreover, we addressed the question of whether these associations are modified by race and neighborhood disadvantage with the intent of identifying new biologic mechanisms driven by an inflammatory response to environmental agents underlying observed cancer disparities that could be amenable to early public health interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy sample and data on systemic inflammation\u003c/h2\u003e\u003cp\u003eThe Henry Ford IRB administration office reviewed the study and determined that it did not meet the definition of human subjects research as defined by the US Department of Health and Human Services. A cohort of more than 6\u0026nbsp;million records of patients aged 30\u0026ndash;70 years receiving medical care at a Henry Ford medical facility from 2000\u0026ndash;2020 was identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Systemic inflammation was measured via neutrophil, monocyte and lymphocyte counts from a complete blood count (CBC) panel retrieved from health system laboratory data downloaded from electronic medical records. To focus on blood tests likely conducted as part of a routine exam or preventive health visit, the CBC data were filtered to remove sequences of temporally clustered tests that suggested an acute illness, hospitalization and/or that the study participant was under surveillance for an infection on an outpatient basis. To screen the data for clusters of CBC tests, we identified all tests that occurred within 30 days of another blood test, which typically identified sequences of daily blood tests occurring over several days. For all blood tests occurring within 30 days of another blood test, we retained the last test identified in the 30-day window in the dataset. In retaining the last test result, we assumed that this test was indicative of the CBC test at the end of an acute illness or period of surveillance. Two white blood cell measures of systemic inflammation were created: the ratio of neutrophils to lymphocytes (NLR) and the ratio of neutrophils to monocytes (NMR). As these ratios were right skewed, the ratio data were natural log transformed for analyses. We generated an ordinal NLR variable ranging from 1 to 5, using predefined cutoff points (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://emcrit.org/pulmcrit/nlr/\u003c/span\u003e\u003cspan address=\"https://emcrit.org/pulmcrit/nlr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAir pollutant and Neighborhood social vulnerability data\u003c/h3\u003e\n\u003cp\u003eThe concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e from 2000 through 2020 were obtained from a fine-resolution geoscience-derived model that combined chemical transport modeling, satellite remote sensing, and ground-based measurements via geographically weighted regression (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). This model provided validated, publicly available monthly PM\u003csub\u003e2.5\u003c/sub\u003e level data at a 1-km resolution over North America, which was linked to cohort members by geocoded address history data corresponding to the month and year of CBC test results.\u003c/p\u003e\u003cp\u003eOn the basis of the patients\u0026rsquo; geocoded address, we also assigned each member a 2016 social vulnerability index (SVI) score (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.atsdr.cdc.gov/place-health/php/svi/index.html\u003c/span\u003e\u003cspan address=\"https://www.atsdr.cdc.gov/place-health/php/svi/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for the census tract within which they resided. While neighborhood disadvantage can be measured via many different constructs, we chose to use the SVI because a recent systematic review highlighted its relevance to examining geographic disparities in cancer-related exposures and mortality disparities and its utility in informing targeted interventions to prevent cancer at the neighborhood level (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Additional data retrieved from the electronic health records included the patients\u0026rsquo; date of birth, sex, and race. From the racial data, we generated the following variables for analysis: White, nonwhite, and Black members, as other racial groups had small numbers. To account for comorbidities that might influence systemic inflammation, a Charlson comorbidity index for each calendar year was calculated based on ICD9 and ICD10 codes assigned to outpatient visits (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). The values range from 0 to 3, where higher values indicate more comorbidities.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAssociations between PM\u003csub\u003e2.5\u003c/sub\u003e levels, inflammatory indices and other potential confounding variables were first explored a univariate level with t-tests and analysis of variance. For these comparisons, race was stratified in White, Black, Asian and Other races. This was followed by analyses that involved multilevel regression models. For the NLR ordinal outcome measure, these models were ordinal logit models. For NMR, these models were linear. We included the following independent variables: PM\u003csub\u003e2.5\u003c/sub\u003e, sex, race (White versus nonWhite), age, comorbidity index and neighborhood social vulnerability index. We fitted multilevel models due to the nested nature of our data over time within cohort members. In stratified analyses, we fitted models for White and Black members separately. An interaction term for SVI* PM\u003csub\u003e2.5\u003c/sub\u003e was also tested using binary variables of each. To create the binary variables, we used median values for SVI (median\u0026thinsp;=\u0026thinsp;0.495) and the EPA threshold of 9 micrograms per cubic meter (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) for PM\u003csub\u003e2.5\u003c/sub\u003e. All analyses were conducted via Stata v16 (Statacorps, College Station, TX). Causal mediation\u003c/p\u003e\u003cp\u003eanalysis was employed to assess whether SVI mediated the effect of PM\u003csub\u003e2.5\u003c/sub\u003e exposure on the NLR and NMR levels via Proc CAUSALMED in SAS. This procedure supports a limited set of generalized linear models that describe the relationships among an outcome variable, a treatment (or effect) variable and a mediator variable. Potential confounders (sex, race, age, comorbidity index) were added as covariates in the model. The model computes the following main causal mediation effects: 1) total effect; 2) controlled direct effect; 3) natural direct effect; and 4) natural indirect effect. Evidence for mediation was based on whether the natural indirect effect was a significant portion of the total effect.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStudy population description\u003c/h2\u003e\u003cp\u003eRace differences were observed in the distribution of the demographic and clinical study variables (supplementary Table\u0026nbsp;1). The White cohort members were older on average than the other race groups whereas the Black cohort members had the lowest percentage of males.\u003c/p\u003e\u003cp\u003eCompared with the other race groups, Black members had, on average, approximately one more complete blood count (CBC) test (7.37\u0026thinsp;\u0026plusmn;\u0026thinsp;7.84) and more than one year between their initial and last CBC tests (7.00\u0026thinsp;\u0026plusmn;\u0026thinsp;6.50). Over half of the Black sample members (58%) lived at an address in a location that mapped to the highest quartile of the social vulnerability index, whereas only 10\u0026ndash;12% of White and Asian members lived at addresses that mapped to this highest quartile. The Charlson comorbidity index did not show any striking differences across race groups, with over three quarters of the sample not having any comorbidities. The overall cancer prevalence was highest in Black members (8.1%), and 7.3% of White members had a history of cancer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePM\u003csub\u003e2.5\u003c/sub\u003e levels\u003c/h2\u003e\u003cp\u003eAverage PM\u003csub\u003e2.5\u003c/sub\u003e levels in the Detroit metropolitan area are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, as are the distributions of the population by race. The highest average PM\u003csub\u003e2.5\u003c/sub\u003e levels were in the down and\u003c/p\u003e\u003cp\u003emidtown areas of Detroit, which also has a predominantly Black population. Overall, Black members in our cohort had a significantly greater average PM\u003csub\u003e2.5\u003c/sub\u003e exposure level than White members, at 9.90\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94 vs. 9.06\u0026thinsp;\u0026plusmn;\u0026thinsp;2.80 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Younger individuals\u003c/p\u003e\u003cp\u003e(aged 30\u0026ndash;40) had a notably greater PM\u003csub\u003e2.5\u003c/sub\u003e exposure level (11.21\u0026thinsp;\u0026plusmn;\u0026thinsp;3.45 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) than did the other age groups. PM\u003csub\u003e2.5\u003c/sub\u003e levels significantly varied according to the two main inflammatory\u003c/p\u003e\u003cp\u003eindices we studied, the NLR and the NMR. However, only for NMR did we find that PM\u003csub\u003e2.5\u003c/sub\u003e levels vary in a positive linear fashion. Similarly, the PM\u003csub\u003e2.5\u003c/sub\u003e levels increased as the average neighborhood SVI increased, with those in the highest SVI quartile exposed to the highest PM\u003csub\u003e2.5\u003c/sub\u003e levels (9.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e). No clear pattern of PM\u003csub\u003e2.5\u003c/sub\u003e levels with increasing comorbidity index emerged; however, a history of cancer was associated with a significantly lower average PM\u003csub\u003e2.5\u003c/sub\u003e exposure than was no history of cancer (8.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.54 vs. 9.39\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e). Over the 20-year period, data were collected from this cohort, and the average PM\u003csub\u003e2.5\u003c/sub\u003e levels in the Detroit tri-\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean PM2.5 levels by study variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePM2.5 mean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e143,024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.34\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e102,564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.36\u0026thinsp;\u0026plusmn;\u0026thinsp;2.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e155,018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.06\u0026thinsp;\u0026plusmn;\u0026thinsp;2.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e63,698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.90\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6,584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.70\u0026thinsp;\u0026plusmn;\u0026thinsp;2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.53\u0026thinsp;\u0026plusmn;\u0026thinsp;2.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e53,568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e11.21\u0026thinsp;\u0026plusmn;\u0026thinsp;3.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e41\u0026ndash;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75,573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e51\u0026ndash;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69,246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.21\u0026thinsp;\u0026plusmn;\u0026thinsp;2.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60\u0026ndash;70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47,204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e8.73\u0026thinsp;\u0026plusmn;\u0026thinsp;2.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil Lymphocyte Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e161,447\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.31\u0026thinsp;\u0026plusmn;\u0026thinsp;2.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u0026ndash;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60,252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u0026ndash;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12,213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u0026ndash;18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8,586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.37\u0026thinsp;\u0026plusmn;\u0026thinsp;2.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e8.99\u0026thinsp;\u0026plusmn;\u0026thinsp;2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil Monocyte Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e174,258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.19\u0026thinsp;\u0026plusmn;\u0026thinsp;2.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u0026ndash;15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47,323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.64\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12,189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e10.15\u0026thinsp;\u0026plusmn;\u0026thinsp;3.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6,927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e10.49\u0026thinsp;\u0026plusmn;\u0026thinsp;3.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e10.57\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial Vulnerability index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0-0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48,442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.05\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.26\u0026ndash;0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45,041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e8.77\u0026thinsp;\u0026plusmn;\u0026thinsp;2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.51\u0026ndash;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e40,918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e8.95\u0026thinsp;\u0026plusmn;\u0026thinsp;2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.76-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46,167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComorbidity index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e150,448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.49\u0026thinsp;\u0026plusmn;\u0026thinsp;2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44,532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.10\u0026thinsp;\u0026plusmn;\u0026thinsp;2.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20,456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e8.84\u0026thinsp;\u0026plusmn;\u0026thinsp;2.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16,428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e8.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosed with Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e227,571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.39\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18,020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e8.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ecounty area steadily decreased (Fig.\u0026nbsp;3). The PM\u003csub\u003e2.5\u003c/sub\u003e level data linked to the addresses of the members of our cohort declined in a mirrored fashion to the larger area, with PM\u003csub\u003e2.5\u003c/sub\u003e levels consistently 0.5\u0026ndash;1 \u0026micro;/m\u003csup\u003e3\u003c/sup\u003e higher among cohort members than the tri-county average PM\u003csub\u003e2.5\u003c/sub\u003e levels. When we examined PM\u003csub\u003e2.5\u003c/sub\u003e levels by major confounding factors stratified by race, we observed a clear positive linear relationship with increasing PM\u003csub\u003e2.5\u003c/sub\u003e levels and comorbidities, primarily in Black members (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). For example, PM\u003csub\u003e2.5\u003c/sub\u003e levels in Black members ranged from 12.14 to 12.27 to 12.42 to 12.75 \u0026micro;/m\u003csup\u003e3\u003c/sup\u003e in Black members with 0, 1, 2 and 3\u0026thinsp;+\u0026thinsp;comorbidities, respectively. Similarly, with each increasing quartile of the SVI, the PM\u003csub\u003e2.5\u003c/sub\u003e concentration in Black individuals increased from 11.48 to 11.54 to 11.90 to 12.10 \u0026micro;/m\u003csup\u003e3\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). This type of stepwise increase in PM\u003csub\u003e2.5\u003c/sub\u003e levels was not observed in the other race groups for these two confounding variables.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003cp\u003e\u003cstrong\u003eTable 2. Mean PM\u003csub\u003e2.5\u003c/sub\u003e levels by study variables\u003c/strong\u003e\u003c/p\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePM2.5 mean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e143,024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.34\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e102,564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.36\u0026thinsp;\u0026plusmn;\u0026thinsp;2.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e155,018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.06\u0026thinsp;\u0026plusmn;\u0026thinsp;2.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e63,698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.90\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6,584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.70\u0026thinsp;\u0026plusmn;\u0026thinsp;2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.53\u0026thinsp;\u0026plusmn;\u0026thinsp;2.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e53,568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e11.21\u0026thinsp;\u0026plusmn;\u0026thinsp;3.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e41\u0026ndash;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75,573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e51\u0026ndash;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69,246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.21\u0026thinsp;\u0026plusmn;\u0026thinsp;2.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60\u0026ndash;70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47,204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e8.73\u0026thinsp;\u0026plusmn;\u0026thinsp;2.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil Lymphocyte Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e161,447\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.31\u0026thinsp;\u0026plusmn;\u0026thinsp;2.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u0026ndash;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60,252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u0026ndash;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12,213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u0026ndash;18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8,586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.37\u0026thinsp;\u0026plusmn;\u0026thinsp;2.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e8.99\u0026thinsp;\u0026plusmn;\u0026thinsp;2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil Monocyte Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e174,258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.19\u0026thinsp;\u0026plusmn;\u0026thinsp;2.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u0026ndash;15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47,323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.64\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12,189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e10.15\u0026thinsp;\u0026plusmn;\u0026thinsp;3.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6,927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e10.49\u0026thinsp;\u0026plusmn;\u0026thinsp;3.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e10.57\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial Vulnerability index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0-0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48,442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.05\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.26\u0026ndash;0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45,041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e8.77\u0026thinsp;\u0026plusmn;\u0026thinsp;2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.51\u0026ndash;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e40,918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e8.95\u0026thinsp;\u0026plusmn;\u0026thinsp;2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.76-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46,167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComorbidity index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e150,448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.49\u0026thinsp;\u0026plusmn;\u0026thinsp;2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44,532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.10\u0026thinsp;\u0026plusmn;\u0026thinsp;2.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20,456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e8.84\u0026thinsp;\u0026plusmn;\u0026thinsp;2.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16,428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e8.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosed with Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e227,571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9.39\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18,020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e8.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eInflammation indices\u003c/h3\u003e\n\u003cp\u003eTwo neutrophil-based combinations of white blood cell tests, the neutrophil-to-lymphocyte ratio (NLR) and the neutrophil-to-monocyte ratio (NMR), were used as measures of systemic inflammation (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Males had slightly higher NLRs, whereas females had higher\u003c/p\u003e\u003cp\u003eNMRs. Among the race groups, White members had the highest mean levels of both inflammatory indices, and the youngest age group also had higher NLRs and NMRs than did their older cohort members. Both the NLR and the NMR appeared to increase in a stepwise fashion with increasing SVI and number of comorbidities. Those with a history of cancer also had slightly higher NLRs and NMRs. When all other confounding factors were adjusted, PM\u003csub\u003e2.5\u003c/sub\u003e levels increased in a stepwise fashion across all race groups only for the NMR (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e\n\u003ch3\u003eMultivariable Models\u003c/h3\u003e\n\u003cp\u003eAccording to the multivariable models of the full sample, based on Z scores race had the strongest association with the NLR, and age had a strong association with the NMR (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). PM\u003csub\u003e2.5\u003c/sub\u003e exposure and SVI had similar associations with the NLR, but PM\u003csub\u003e2.5\u003c/sub\u003e exposure had a Z score twofold greater than the SVI for the NMR model. In the race-stratified models, the SVI and PM\u003csub\u003e2.5\u003c/sub\u003e associations were similar for both the NLR and NMR models, as was observed in the full sample. However, for Black members, we observed that the SVI was a much stronger predictor of the NLR than was PM\u003csub\u003e2.5\u003c/sub\u003e exposure, and for the NMR model, the strength of the associations for PM\u003csub\u003e2.5\u003c/sub\u003e exposure and SVI were comparable. We next fit interaction models of PM\u003csub\u003e2.5\u003c/sub\u003e exposure and SVI, where the dependent outcome variables were the NLR or NMR and age, sex, race and the Charlson comorbidity index were also included as covariates in the model (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The coefficients for both the PM\u003csub\u003e2.5\u003c/sub\u003e levels and the SVI variables were statistically significant for both the full and race-stratified samples. For the race-stratified samples, the beta coefficient for PM\u003csub\u003e2.5\u003c/sub\u003e was greater in White members, whereas the beta coefficient for SVI was greater in Black\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean inflammatory indices by Study variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eNeutrophil to Lymphocyte Ratio (NLR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eNeutrophil to Monocyte Ratio (NMR)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean \u0026plusmn; Standard Deviation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMean \u0026plusmn; Standard Deviation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102,395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.46\u0026thinsp;\u0026plusmn;\u0026thinsp;4.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e102,554\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.96\u0026thinsp;\u0026plusmn;\u0026thinsp;7.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e142,805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e142,574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.91\u0026thinsp;\u0026plusmn;\u0026thinsp;7.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhites\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e154,700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.39\u0026thinsp;\u0026plusmn;\u0026thinsp;3.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e154,490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.55\u0026thinsp;\u0026plusmn;\u0026thinsp;7.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlacks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63,659\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.01\u0026thinsp;\u0026plusmn;\u0026thinsp;3.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63,553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.28\u0026thinsp;\u0026plusmn;\u0026thinsp;8.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsians\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.81\u0026thinsp;\u0026plusmn;\u0026thinsp;3.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6,562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.72\u0026thinsp;\u0026plusmn;\u0026thinsp;7.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.63\u0026thinsp;\u0026plusmn;\u0026thinsp;3.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7,450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.89\u0026thinsp;\u0026plusmn;\u0026thinsp;7.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge groups\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23,117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.23\u0026thinsp;\u0026plusmn;\u0026thinsp;3.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23,060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.81\u0026thinsp;\u0026plusmn;\u0026thinsp;8.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65,779\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.08\u0026thinsp;\u0026plusmn;\u0026thinsp;3.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65,676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.84\u0026thinsp;\u0026plusmn;\u0026thinsp;7.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50\u0026ndash;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73,633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.12\u0026thinsp;\u0026plusmn;\u0026thinsp;3.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73,540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.33\u0026thinsp;\u0026plusmn;\u0026thinsp;7.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82,674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.55\u0026thinsp;\u0026plusmn;\u0026thinsp;4.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e82,555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.04\u0026thinsp;\u0026plusmn;\u0026thinsp;7.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eSocial Vulnerability index quartiles (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0-0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48,365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48,307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.20\u0026thinsp;\u0026plusmn;\u0026thinsp;6.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.26\u0026ndash;0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44,932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.29\u0026thinsp;\u0026plusmn;\u0026thinsp;3.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44,879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.28\u0026thinsp;\u0026plusmn;\u0026thinsp;6.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.51\u0026ndash;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40,821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40,755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.50\u0026thinsp;\u0026plusmn;\u0026thinsp;7.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.76-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46,112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.28\u0026thinsp;\u0026plusmn;\u0026thinsp;4.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46,050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.70\u0026thinsp;\u0026plusmn;\u0026thinsp;8.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComorbidity index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e150,242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.12\u0026thinsp;\u0026plusmn;\u0026thinsp;3.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e150,027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.37\u0026thinsp;\u0026plusmn;\u0026thinsp;8.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44,469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44,404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.48\u0026thinsp;\u0026plusmn;\u0026thinsp;7.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20,408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;4.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20,354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.49\u0026thinsp;\u0026plusmn;\u0026thinsp;7.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16,402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.00\u0026thinsp;\u0026plusmn;\u0026thinsp;5.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16,380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.35\u0026thinsp;\u0026plusmn;\u0026thinsp;8.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDiagnosed with Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18,006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.59\u0026thinsp;\u0026plusmn;\u0026thinsp;4.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17,956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.28\u0026thinsp;\u0026plusmn;\u0026thinsp;7.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e227,197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e226,875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.53\u0026thinsp;\u0026plusmn;\u0026thinsp;7.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003emembers. Given the strong associations of the SVI and PM\u003csub\u003e2.5\u003c/sub\u003e exposure with the two inflammatory indices, we also tested race-stratified models that included an interaction term for\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFactors associated with inflammatory indices stratified by race\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eNeutrophil to Lymphocyte Ratio (NLR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eNeutrophil to Monocyte Ratio (NMR)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZ score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eZ score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFull Sample\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.0007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-66.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-25.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e46.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0. 643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0. 609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComorbidity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSVI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.394\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhites\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-58.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-22.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComorbidity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0. 136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0. 084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSVI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e34.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlacks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-13.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-29.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-14.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComorbidity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0. 103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0. 609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.986\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSVI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.454\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ethese two variables. In White members, a positive beta coefficient significantly different from zero was observed for both the NLR and the NMR outcome variable. However, in Black members, the interaction beta coefficient was much smaller and not significantly different from zero. We further explored the relationship between PM\u003csub\u003e2.5\u003c/sub\u003e exposure and the SVI in causal mediation models where the SVI was modeled as a mediating factor between PM\u003csub\u003e2.5\u003c/sub\u003e exposure and the systemic inflammation causal pathway (supplementary Table\u0026nbsp;2). In the full sample, there was nominal evidence of an indirect effect from SVI on the PM\u003csub\u003e2.5\u003c/sub\u003e exposure association with both the NLR and the NMR, with each model resulting in approximately two percent mediation. In race-stratified models, the only evidence for mediation was found in Black members. In this subpopulation, the SVI had a 19% mediation effect on the association between PM\u003csub\u003e2.5\u003c/sub\u003e exposure and the NLR and a 6% mediation effect on the association between PM\u003csub\u003e2.5\u003c/sub\u003e exposure and the NMR; both effects were statistically significant.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite decreasing levels of air pollution as measured by PM\u003csub\u003e2.5\u003c/sub\u003e levels over the past twenty years, ambient PM\u003csub\u003e2.5\u003c/sub\u003e levels were found to be associated with the neutrophil-to-lymphocyte ratio (NLR) and the neutrophil-to-monocyte ratio (NMR), two blood-based measures of systemic inflammation, in a large health system-based cohort. Moreover, these associations remained significant even after adjusting for confounders such as age, sex, race, the comorbidity index, and neighborhood deprivation. Interestingly, PM\u003csub\u003e2.5\u003c/sub\u003e levels appeared to have a greater association with systemic inflammation in White members, whereas neighborhood deprivation was more strongly associated with systemic inflammation in Black members. A significant interaction was also found between these two factors in modeling their association with systemic inflammation but was only present in White members, whereas neighborhood deprivation was a mediating factor of the association of PM\u003csub\u003e2.5\u003c/sub\u003e with systemic inflammation in Black members.\u003c/p\u003e\u003cp\u003eThe NLR is associated with changes induced by disease and environmental challenges to the immune system and varies with both age and sex (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Compared with non-Hispanic Whites, Blacks tend to have lower mean NLR values (\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), which is consistent with the racial differences observed in our cohort. Increasing NLR values are associated with increased age and a greater risk of mortality (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Moreover, a recent study of the NHANEs cohort revealed that a higher NLR was directly correlated with an increased risk of developing cancer in adults (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In addition, in this same NHANEs cohort, both the NLR and the NMR were found to be associated with high-risk prostate cancer (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). In a UK Biobank study of 443,540 cancer-free adults, the NLR was associated with an increased risk of overall cancer incidence (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOnly a few reports exist that have linked air pollution as measured by PM\u003csub\u003e2.5\u003c/sub\u003e levels with systemic inflammation based on NLR and NMR levels in blood (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). In an urban cohort in China, on the basis of a Bayesian kernel machine regression model, the NLR was positively associated with PM\u003csub\u003e2.5\u003c/sub\u003e levels (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Another cohort study in Sweden reported associations between ambient PM\u003csub\u003e2.5\u003c/sub\u003e levels and the NLR in three different models that were adjusted for an increasing number of confounding factors (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). In our cohort, a robust association between ambient PM\u003csub\u003e2.5\u003c/sub\u003e levels and both the NLR and the NMR existed even after adjusting for all confounding variables, including the neighborhood deprivation index and comorbidity status. These associations appeared to differ by race, with PM\u003csub\u003e2.5\u003c/sub\u003e having a stronger association with both the NLR and the NMR in Whites than in Blacks on the basis of the magnitude of the beta coefficients.\u003c/p\u003e\u003cp\u003eRacial disparities exist in PM\u003csub\u003e2.5\u003c/sub\u003e exposure across the United States, which can be attributed to industrial emissions (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e) and redlining (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). In 2019, the least White communities experienced 16% higher PM\u003csub\u003e2.5\u003c/sub\u003e exposure levels than the most White communities did (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), and the Black American population was reported to have the highest proportion of deaths attributable to PM\u003csub\u003e2.5\u003c/sub\u003e exposure in all years from 1990\u0026ndash;2016 (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). In a study across the state of North Carolina from 2002\u0026ndash;2016, despite declines in PM\u003csub\u003e2.5\u003c/sub\u003e levels over time, disparities in exposure increased for racially and educationally isolated communities (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In our health system-based cohort, Black members, on average, had PM\u003csub\u003e2.5\u003c/sub\u003e exposure levels almost 1 \u0026micro;/m\u003csup\u003e3\u003c/sup\u003e higher than those of White members, and the highest PM\u003csub\u003e2.5\u003c/sub\u003e exposure levels mapped to the neighborhoods with the highest percentage of Black residents.\u003c/p\u003e\u003cp\u003eHigher levels of ambient PM\u003csub\u003e2.5\u003c/sub\u003e exposure are known to have detrimental health effects and increase cancer risk (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). In a large US cohort of women spanning six states and two metropolitan areas, PM\u003csub\u003e2.5\u003c/sub\u003e was associated with an increased risk of estrogen receptor-positive breast cancer (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). In the Surveillance Epidemiology and End Results national cancer database from 2002\u0026ndash;2012, PM\u003csub\u003e2.5\u003c/sub\u003e exposure was associated with incident head and neck cancer incidence (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). A population-based case‒control study of upper aerodigestive cancer cases conducted in Los Angeles County from 1999\u0026ndash;2004 revealed that PM\u003csub\u003e2.5\u003c/sub\u003e exposure increased risk irrespective of tobacco smoking status (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). While we did not directly estimate the cancer risk associated with PM\u003csub\u003e2.5\u003c/sub\u003e exposure levels in our cohort, over 18,000 health system members who developed cancer during the 20-year observation period were identified. Interestingly, PM\u003csub\u003e2.5\u003c/sub\u003e exposure levels were lower in cancer patients than in those who did not develop cancer (8.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.54 vs. 9.39\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90 \u0026micro;/m\u003csup\u003e3\u003c/sup\u003e). The dates of these PM\u003csub\u003e2.5\u003c/sub\u003e levels were based on when the lab blood tests were taken and likely do not reflect the PM\u003csub\u003e2.5\u003c/sub\u003e exposure levels that preceded the cancer diagnosis. This finding can also be interpreted as reflecting a lagged effect of PM2.5 on cancer development or a delay in cancer diagnosis (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUsing the address data in our health system-based cohort, we assigned a social vulnerability (SVI) index score (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.atsdr.cdc.gov/place-health/php/svi/index.html\u003c/span\u003e\u003cspan address=\"https://www.atsdr.cdc.gov/place-health/php/svi/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) ranging from 0 (lowest) to 1 (highest) based on 16 U.S. census variables from the 5-year American Community Survey. Numerous studies have linked social vulnerability to cancer risk (\u003cspan additionalcitationids=\"CR60 CR61\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e), but only a few have explored how this factor influences inflammation indices. In the All of Us cohort, area deprivation was associated with C-reactive protein in cancer survivors (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). In a large cohort of health professionals, higher neighborhood SES was associated with a lower inflammation score in both men and women (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Considerable research has been conducted on the disparate effects of neighborhood deprivation by race on disease outcomes. Higher neighborhood deprivation scores were associated with increased stress, sleep disturbance, and inflammatory biomarkers in Black adults with heart failure (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). A prostate cancer case‒control study based in Maryland reported that residing in the most deprived vs. least deprived neighborhoods corresponded to 88% higher disease odds among all men and an approximate 3-fold increase among African American men, but no association was noted among European American men (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). Another large cohort study of stroke risk factors that included over 30,000 Black and White participants aged 45 years and over at the time of enrollment reported similar effects of higher neighborhood deprivation scores on inflammation indices (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). In our cohort, while the SVI was associated with the NLR and NMR in both Black and White members, the magnitude of the SVI association was clearly greater in Black participants.\u003c/p\u003e\u003cp\u003eNeighborhood disadvantage or social deprivation has been found to be associated with increased PM\u003csub\u003e2.5\u003c/sub\u003e exposure levels as well as systemic inflammation. Clear racial disparities exist in both social deprivation and PM\u003csub\u003e2.5\u003c/sub\u003e exposure; however, the strong correlation between the two factors makes it difficult to disentangle each factor\u0026rsquo;s individual effects on disease markers and outcomes. In a study of noncancerous breast tissue collected from women who underwent reduction mammoplasty or breast cancer surgery, air pollution was more strongly associated with CD68-positive macrophages, a measure of the inflammatory response, in breast adipose tissue in White women than in Black women (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). Furthermore, neither group exhibited an association between neighborhood deprivation and the number of CD68-positive macrophages in the breast. In another study of Pennsylvania residents, chronic rhinosinusitis incidence was not associated with the area deprivation index, but the latter was positively correlated with PM\u003csub\u003e2.5\u003c/sub\u003e exposure levels (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). No studies have examined whether social deprivation and PM\u003csub\u003e2.5\u003c/sub\u003e exposure act independently and/or synergistically to increase the risk for disease outcomes. In our health system-based cohort, there was evidence that PM\u003csub\u003e2.5\u003c/sub\u003e exposure and SVI act synergistically to increase systemic inflammation in White members. However, in Black members it appears that SVI may mediate the putative effect of PM\u003csub\u003e2.5\u003c/sub\u003e on systemic inflammation.\u003c/p\u003e\u003cp\u003eThe health system data in our study spanned 20 years from 2000\u0026ndash;2020 and included over 200,000 individuals, with an average of six white blood cell measurements over 5\u0026ndash;7 years. Linking these data with reliable estimates of monthly ambient fine spatiotemporal PM\u003csub\u003e2.5\u003c/sub\u003e data during this time overcame a major past obstacle in producing reliable exposure data that has plagued previous air pollution research. These PM\u003csub\u003e2.5\u003c/sub\u003e data were generated from chemical transport modeling, satellite remote sensing, and ground-based measurements (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). The geographic mapping of PM\u003csub\u003e2.5\u003c/sub\u003e data was based on patient addresses and therefore reflects only potential exposures during the time a person was physically at his or her address. In addition to the laboratory data, we were able to analyze patient visit data and construct a Charlson comorbidity index to adjust for disease-related factors that might influence systemic inflammation. Neutrophil-based measures such as the NLR and NMR have been widely used as measures of systemic inflammation and are associated with numerous disease processes (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The laboratory data used in the current study were not collected in a systematic manner and were subject to bias based on what subset of patients had these data and if white blood cell tests were performed in response to an acute disease process. To control for the latter, when a series of white blood cell tests were performed in a short period, we restricted the data we used to the last test in the series of tests. Even so, the average race-specific values of the NLR and NMR in our cohort were much higher than those reported in population-based cohorts such as NHANEs (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) or the UK biobank (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), which suggests that the study sample had a higher level of morbidity than did the general population.\u003c/p\u003e\u003cp\u003eIn summary, using a large health system-based cohort, we demonstrated that even at historically low levels, ambient PM\u003csub\u003e2.5\u003c/sub\u003e exposure may influence systemic inflammation. This effect appears to differ by race and social factors that can influence a person\u0026rsquo;s health. While PM\u003csub\u003e2.5\u003c/sub\u003e exposure and neighborhood deprivation may act synergistically in White populations, in Black populations, these two factors are more strongly correlated, and the latter may be a mediating factor of the association of PM\u003csub\u003e2.5\u003c/sub\u003e exposure with systemic inflammation. Given that inflammation is a leading risk factor for cancer and other chronic diseases, these results support the notion that intervening on modifiable environmental factors may play a key role in disease prevention. In devising intervention strategies, it will be important to consider both the environmental factor(s) and the target population to maximize the benefit of the intervention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eHuman Ethics and Consent to Participate declarations\u003c/h2\u003e\u003cp\u003enot applicable\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eFunding for the conduct of the study was provided by Henry Ford Health and Michigan State University Health Sciences\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eB.R. and A.P. conceived the study and designed study protocol. B.R. and A.P. wrote the main manuscript text and prepared the tables. A.P prepared figures 1-3 and B.R. prepared figures 4-5. A.P. conducted the main study analysis with the assistance of Z.L. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank Drs. Randall Martin, Jun Meng, Melanie Hammer, and Aaron van Donkelaar for providing the satellite and model-derived PM2.5 data, which were essential for estimating ambient PM2.5 exposures of the patient cohort in this study. We also thank DevakiPurushothaman who performed the programming that built the Henry Ford patient datasets.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data and analysis code used for this study is available from the authors upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBarber LE, Zirpoli GR, Cozier YC, Rosenberg L, Petrick JL, Bertrand KA, Palmer JR. Neighborhood disadvantage and individual-level life stressors in relation to breast cancer incidence in US Black women. Breast cancer research: BCR. 2021;23(1):108.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWheeler DC, Boyle J, Carli M, Ward MH, Metayer C. Neighborhood Deprivation, Indoor Chemical Concentrations, and Spatial Risk for Childhood Leukemia. Int J Environ Res Public Health. 2023;20(4).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eErhunmwunsee L, Wing SE, Zou X, Coogan P, Palmer JR, Lennie Wong F. Neighborhood disadvantage and lung cancer risk in a national cohort of never smoking Black women. Lung Cancer. 2022;173:21\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchootman M, Andresen EM, Wolinsky FD, Malmstrom TK, Morley JE, Miller DK. Adverse housing and neighborhood conditions and inflammatory markers among middle-aged African Americans. J urban health: Bull New York Acad Med. 2010;87(2):199\u0026ndash;210.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIyer HS, Hart JE, James P, Elliott EG, DeVille NV, Holmes MD, et al. Impact of neighborhood socioeconomic status, income segregation, and greenness on blood biomarkers of inflammation. Environ Int. 2022;162:107164.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAshing KT, Jones V, Bedell F, Phillips T, Erhunmwunsee L. Calling Attention to the Role of Race-Driven Societal Determinants of Health on Aggressive Tumor Biology: A Focus on Black Americans. JCO Oncol Pract. 2022;18(1):15\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa SJ, Gill J, Waldman O, Yendamuri K, Dunne-Jaffe C, Chatterjee U, et al. Association of Neighborhood-Level Household Income With 21-Gene Recurrence Score and Survival Among Patients With Estrogen Receptor-Positive Breast Cancer. JAMA Netw Open. 2023;6(2):e230179.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhmed F, Moseley I, Lin E, Lim R, Baranwal N, Hoang M, et al. The Association of Neighborhood-Level Socioeconomic Status and Tumor Diameter in Squamous Cell Carcinoma. J Invest Dermatol. 2023;143(1):170\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStewart JA, Mitchell MA, Edgerton VS, VanCott R. Environmental Justice and Health Effects of Urban Air Pollution. J Natl Med Assoc. 2015;107(1):50\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRubio R, Grineski S, Collins T. Carcinogenic air pollution along the United States' southern border: Neighborhood inequities in risk. Environ Res. 2022;212:113251. Pt B).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEsie P, Daepp MIG, Roseway A, Counts S. Neighborhood Composition and Air Pollution in Chicago: Monitoring Inequities With a Dense, Low-Cost Sensing Network, 2021. Am J Public Health. 2022;112(12):1765\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeguen S, Padilla M, Padilla C, Kihal-Talantikite W. Do Individual and Neighborhood Characteristics Influence Perceived Air Quality? Int J Environ Res Public Health. 2017;14(12).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJbaily A, Zhou X, Liu J, Lee TH, Kamareddine L, Verguet S, Dominici F. Air pollution exposure disparities across US population and income groups. Nature. 2022;601(7892):228\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchulz AJ, Mentz GB, Sampson N, Ward M, Dvonch JT, de Majo R et al. Independent and Joint Contributions of Fine Particulate Matter Exposure and Population Vulnerability to Mortality in the Detroit Metropolitan Area. Int J Environ Res Public Health. 2018;15(6).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchulz AJ, Williams DR, Israel BA, Lempert LB. Racial and Spatial Relations as Fundamental Determinants of Health in Detroit. Milbank Q. 2002;80(4):677\u0026ndash;707.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDowney L. Environmental Racial Inequality in Detroit. Social forces; a scientific medium of social study and interpretation. 2006;85(2):771\u0026thinsp;\u0026ndash;\u0026thinsp;96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDowney L. The Unintended Significance of Race: Environmental Racial Inequality in Detroit. Social forces; a scientific medium of social study and interpretation. 2005;83(3):971\u0026ndash;1007.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBravo MA, Warren JL, Leong MC, Deziel NC, Kimbro RT, Bell ML, Miranda ML. Where Is Air Quality Improving, and Who Benefits? A Study of PM2.5 and Ozone Over 15 Years. Am J Epidemiol. 2022;191(7):1258\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRiaz H, Syed BM, Laghari Z, Pirzada S. Analysis of inflammatory markers in apparently healthy automobile vehicle drivers in response to exposure to traffic pollution fumes. Pak J Med Sci. 2020;36(4):657\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeonardi GS, Houthuijs D, Steerenberg PA, Fletcher T, Armstrong B, Antova T, et al. Immune biomarkers in relation to exposure to particulate matter: a cross-sectional survey in 17 cities of Central Europe. Inhal Toxicol. 2000;12(Suppl 4):1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVerheyen VJ, Remy S, Bijnens EM, Colles A, Govarts E, Martin LR, et al. Long-term residential exposure to air pollution is associated with hair cortisol concentration and differential leucocyte count in Flemish adolescent boys. Environ Res. 2021;201:111595.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eValderrama A, Zapata MI, Hernandez JC, Cardona-Arias JA. Systematic review of preclinical studies on the neutrophil-mediated immune response to air pollutants, 1980\u0026ndash;2020. Heliyon. 2022;8(1):e08778.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSantibanez-Andrade M, Quezada-Maldonado EM, Rivera-Pineda A, Chirino YI, Garcia-Cuellar CM, Sanchez-Perez Y. The Road to Malignant Cell Transformation after Particulate Matter Exposure: From Oxidative Stress to Genotoxicity. Int J Mol Sci. 2023;24(2).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLagunas-Rangel FA, Liu W, Schioth HB. Can Exposure to Environmental Pollutants Be Associated with Less Effective Chemotherapy in Cancer Patients? Int J Environ Res Public Health. 2022;19(4).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuthrie GJ, Charles KA, Roxburgh CS, Horgan PG, McMillan DC, Clarke SJ. The systemic inflammation-based neutrophil-lymphocyte ratio: experience in patients with cancer. Crit Rev Oncol Hematol. 2013;88(1):218\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShi L, Qin X, Wang H, Xia Y, Li Y, Chen X, et al. Elevated neutrophil-to-lymphocyte ratio and monocyte-to-lymphocyte ratio and decreased platelet-to-lymphocyte ratio are associated with poor prognosis in multiple myeloma. Oncotarget. 2017;8(12):18792\u0026ndash;801.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiang J, Zhou L, Li X, Bao W, Chen T, Xi X, et al. Preoperative Monocyte-to-Lymphocyte Ratio in Peripheral Blood Predicts Stages, Metastasis, and Histological Grades in Patients with Ovarian Cancer. Translational Oncol. 2017;10(1):33\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong M, Graubard BI, Loftfield E, Rabkin CS, Engels EA. White Blood Cell Count, Neutrophil-to-Lymphocyte Ratio, and Incident Cancer in the UK Biobank. Cancer Epidemiol Biomarkers Prev. 2024;33(6):821\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang L, Li X, Liu M, Zhou H, Shao J. Association between monocyte-to-lymphocyte ratio and prostate cancer in the U.S. population: a population-based study. Front Cell Dev Biology. 2024;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi GP, Zhang D, Li MH, Yuan FF, Hou XJ, He DJ, et al. Association between the neutrophil-to-lymphocyte ratio and cancer in adults from NHANES 2005\u0026ndash;2018: a cross-sectional study. Sci Rep. 2024;14(1):23678.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang C, Wu Y, Huang J, Wang Y, Cong H. Clinical value of complete blood count ratio in benign and malignant thyroid diseases. Cancer Epidemiol. 2024;92:102636.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePopęda M, Żok J, Tomasik B, Duchnowska R, Bieńkowski M. Complete blood counts as potential risk factors of early dissemination to liver and lungs in resected colorectal cancer: a retrospective cohort study. Int J Colorectal Dis. 2025;40(1):21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRundle AG, Sadasivan SM, Chitale DA, Gupta NS, Williamson SR, Kryvenko ON, et al. Racial differences in the systemic inflammatory response to prostate cancer. PLoS ONE. 2021;16(7):e0252951.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeng H, Luo X. Prognostic significance of elevated pretreatment systemic inflammatory markers for patients with prostate cancer: a meta-analysis. Cancer Cell Int. 2019;19:70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeng J, Li C, Martin RV, van Donkelaar A, Hystad P, Brauer M, Estimated Long-Term. (1981\u0026ndash;2016) Concentrations of Ambient Fine Particulate Matter across North America from Chemical Transport Modeling, Satellite Remote Sensing, and Ground-Based Measurements. Environmental science \u0026amp; technology. 2019;53(9):5071-9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun Y, Li X, Benmarhnia T, Chen JC, Avila C, Sacks DA, et al. Exposure to air pollutant mixture and gestational diabetes mellitus in Southern California: Results from electronic health record data of a large pregnancy cohort. Environ Int. 2022;158:106888.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKwon D, Paul KC, Yu Y, Zhang K, Folle AD, Wu J, et al. Traffic-related air pollution and Parkinson's disease in central California. Environ Res. 2024;240:117434.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheng I, Yang J, Tseng C, Wu J, Shariff-Marco S, Park SL, et al. Traffic-related Air Pollution and Lung Cancer Incidence: The California Multiethnic Cohort Study. Am J Respir Crit Care Med. 2022;206(8):1008\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTran T, Rousseau MA, Farris DP, Bauer C, Nelson KC, Doan HQ. The social vulnerability index as a risk stratification tool for health disparity research in cancer patients: a scoping review. Cancer Causes Control. 2023;34(5):407\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCharlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePellegrino R, Paganelli R, Di Iorio A, Bandinelli S, Moretti A, Iolascon G, et al. Temporal trends, sex differences, and age-related disease influence in Neutrophil, Lymphocyte count and Neutrophil to Lymphocyte-ratio: results from InCHIANTI follow-up study. Immun Ageing. 2023;20(1):46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCalixte R, Ye Z, Haq R, Aladhamy S, Camacho-Rivera M. Demographic and Social Patterns of the Mean Values of Inflammatory Markers in U.S. Adults: A 2009\u0026ndash;2016 NHANES Analysis. Diseases. 2023;11(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoward R, Scheiner A, Kanetsky PA, Egan KM. Sociodemographic and lifestyle factors associated with the neutrophil-to-lymphocyte ratio. Ann Epidemiol. 2019.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAzab B, Camacho-Rivera M, Taioli E. Average values and racial differences of neutrophil lymphocyte ratio among a nationally representative sample of United States subjects. PLoS ONE. 2014;9(11):e112361.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePellegrino R, Paganelli R, Di Iorio A, Bandinelli S, Moretti A, Iolascon G et al. Neutrophil, lymphocyte count, and neutrophil to lymphocyte ratio predict multimorbidity and mortality-results from the Baltimore Longitudinal Study on Aging follow-up study. Geroscience. 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGao S, Jiang P, Tian R. Neutrophil-and-monocyte-to-lymphocyte ratio is positively associated with elevated prostate-specific antigen levels and high-risk prostate cancer: evidence from the NHANES (2003\u0026ndash;2008). Front Cell Dev Biol. 2025;13:1573932.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAzzouz M, Xu Y, Barregard L, Fagerberg B, Z\u0026ouml;ller B, Moln\u0026aacute;r P, et al. Air pollution and biomarkers of cardiovascular disease and inflammation in the Malm\u0026ouml; Diet and Cancer cohort. Environ Health. 2022;21(1):39.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQiu W, He H, Wang B, Wang D, Mu G, Xu T, et al. Short-term impacts of air pollution on the platelet-lymphocyte ratio and neutrophil-lymphocyte ratio among urban adults in China. J Environ Sci (China). 2023;125:101\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMikati I, Benson AF, Luben TJ, Sacks JD, Richmond-Bryant J. Disparities in Distribution of Particulate Matter Emission Sources by Race and Poverty Status. Am J Public Health. 2018;108(4):480\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTessum CW, Paolella DA, Chambliss SE, Apte JS, Hill JD, Marshall JD. PM(2.5) polluters disproportionately and systemically affect people of color in the United States. Sci Adv. 2021;7(18).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLane HM, Morello-Frosch R, Marshall JD, Apte JS. Historical Redlining Is Associated with Present-Day Air Pollution Disparities in U.S. Cities. Environ Sci Technol Lett. 2022;9(4):345\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKerr GH, van Donkelaar A, Martin RV, Brauer M, Bukart K, Wozniak S, et al. Increasing Racial and Ethnic Disparities in Ambient Air Pollution-Attributable Morbidity and Mortality in the United States. Environ Health Perspect. 2024;132(3):37002.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeldsetzer P, Fridljand D, Kiang MV, Bendavid E, Heft-Neal S, Burke M, et al. Disparities in air pollution attributable mortality in the US population by race/ethnicity and sociodemographic factors. Nat Med. 2024;30(10):2821\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFeng Y, Li J, Wang Y, Yin T, Wang Q, Cheng L. Fine particulate matter exposure and cancer risk: a systematic review and meta-analysis of prospective cohort studies. Rev Environ Health. 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWhite AJ, Fisher JA, Sweeney MR, Freedman ND, Kaufman JD, Silverman DT, Jones RR. Ambient fine particulate matter and breast cancer incidence in a large prospective US cohort. J Natl Cancer Inst. 2024;116(1):53\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeleman J, Ruan M, Dey T, Chiang S, Dilger A, Mitchell MB, et al. Air pollution exposure and head and neck cancer incidence. Sci Rep. 2024;14(1):26998.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFang F, Ritz B, Rao J, Zhu Y, Tashkin DP, Morgenstern H, Zhang ZF. Association between ambient exposure to PM(2.5) and upper aerodigestive tract cancer in Los Angeles. Int J Cancer. 2024;154(9):1579\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWei Y, Danesh Yazdi M, Ma T, Castro E, Liu CS, Qiu X, et al. Additive effects of 10-year exposures to PM2.5 and NO2 and primary cancer incidence in American older adults. Environ Epidemiol. 2023;7(4):e265.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeorgantopoulos P, Eberth JM, Cai B, Emrich C, Rao G, Bennett CL, et al. Patient- and area-level predictors of prostate cancer among South Carolina veterans: a spatial analysis. Cancer Causes Control. 2020;31(3):209\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePatel PG, Chowdhury S, Wiener HW, George JT, Abdalla E, Ye Y et al. Community-level Social Vulnerability and Cervical Cancer Mortality Among Young and Old Adults in the State of Alabama. J Community Health. 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCouncell KA, Polcari AM, Nordgren R, Skolarus TA, Benjamin AJ, Shubeck SP. Social vulnerability is associated with advanced breast cancer presentation and all-cause mortality: a retrospective cohort study. Breast cancer research: BCR. 2024;26(1):176.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMehta A, Jeon WJ, Nagaraj G. Association of US county-level social vulnerability index with breast, colorectal, and lung cancer screening, incidence, and mortality rates across US counties. Front Oncol. 2024;14:1422475.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eByrne CA, Oddo VM, Karayeva E, Kopetsky G, Kim S. Area Deprivation and Clinical Biomarkers of Inflammation in Cancer Survivors of the National Institutes of Health All of Us Research Program. Cancer Med. 2025;14(7):e70784.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eButts B, Herring C, Madariaga A, Graven LJ, Higgins MK, Dunbar SB. Inflammation markers associated with symptoms and neighborhood deprivation in black adults with heart failure. Heart Lung. 2025;74:121\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePichardo MS, Minas TZ, Pichardo CM, Bailey-Whyte M, Tang W, Dorsey TH, et al. Association of Neighborhood Deprivation With Prostate Cancer and Immune Markers in African American and European American Men. JAMA Netw Open. 2023;6(1):e2251745.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKeita AD, Judd SE, Howard VJ, Carson AP, Ard JD, Fernandez JR. Associations of neighborhood area level deprivation with the metabolic syndrome and inflammation among middle- and older- age adults. BMC Public Health. 2014;14:1319.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarris AR, Hughes JD, Lawrence WR, Lenz P, Franklin J, Bhawsar PMS, et al. Neighborhood Environment, DNA Methylation, and Presence of Crown-Like Structures of the Breast. JAMA Netw Open. 2025;8(2):e2461334.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVelasquez N, Gardiner L, Cheng TZ, Moore JA, Boudreau RM, Presto AA, Lee SE. Relationship between socioeconomic status, exposure to airborne pollutants, and chronic rhinosinusitis disease severity. Int Forum Allergy Rhinol. 2022;12(2):172\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-health-geographics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijhg","sideBox":"Learn more about [International Journal of Health Geographics](http://ij-healthgeographics.biomedcentral.com/)","snPcode":"12942","submissionUrl":"https://submission.nature.com/new-submission/12942/3","title":"International Journal of Health Geographics","twitterHandle":"@IJHGeo","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"inequity, prevention, community, chronic disease, inflammation, race","lastPublishedDoi":"10.21203/rs.3.rs-7754269/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7754269/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe environment can impact cancer risk both directly, such as through the air we breathe, and indirectly, through the neighborhoods we live in. These risk factors often work in concert but can have disparate effects.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eTo understand how air pollution, as measured by ambient particulate matter 2.5 (PM\u003csub\u003e2.5\u003c/sub\u003e) levels and neighborhood disadvantage based on a geographically assigned social vulnerability index (SVI), may act together to impact cancer risk, we used 2000\u0026ndash;2020 statewide cohort data from a large health system based in metropolitan Detroit, MI, USA (n\u0026thinsp;=\u0026thinsp;245,438 members). Systemic inflammation was used as a surrogate indicator of cancer risk and was measured via the white blood cell (WBC) count ratios of the neutrophil-to-lymphocyte ratio (NLR) and the neutrophil-to-monocyte ratio (NMR).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAfter adjusting for these and other confounding variables, PM\u003csub\u003e2.5\u003c/sub\u003e concentration had a greater positive association with the NMR than with the NLR (Z score\u0026thinsp;=\u0026thinsp;37.7 vs. 21.8). According to the race-stratified multivariable models, PM\u003csub\u003e2.5\u003c/sub\u003e had a greater association with both inflammatory indices in White members. PM\u003csub\u003e2.5\u003c/sub\u003e levels had the strongest positive linear relationship with both the Charlson comorbidity index and the SVI among Black members. A PM\u003csub\u003e2.5\u003c/sub\u003e \u0026times; SVI interaction term was found to be statistically significant only for White members, suggesting that these two variables act synergistically to increase systemic inflammation in White members, whereas in Black members, there was evidence that the SVI may mediate the effects of PM\u003csub\u003e2.5\u003c/sub\u003e exposure on both inflammatory indices.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eAt the population level, neighborhood environmental factors linked with both air pollution and neighborhood disadvantage appear to have an impact on systemic inflammation; however, these factors may act in a disparate fashion according to race.\u003c/p\u003e","manuscriptTitle":"Differential effects of neighborhood ambient PM2.5 exposure and social vulnerability on cancer-related systemic inflammation by race in a large health care system population from 2000--2020","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-22 20:20:10","doi":"10.21203/rs.3.rs-7754269/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-19T14:36:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-02T17:55:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-30T23:12:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66744123253688952935710964446774598283","date":"2025-11-11T19:32:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62096881113398786421160811565423126864","date":"2025-11-03T17:35:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-08T17:34:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-03T13:48:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-03T13:48:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Health Geographics","date":"2025-09-30T18:35:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-health-geographics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijhg","sideBox":"Learn more about [International Journal of Health Geographics](http://ij-healthgeographics.biomedcentral.com/)","snPcode":"12942","submissionUrl":"https://submission.nature.com/new-submission/12942/3","title":"International Journal of Health Geographics","twitterHandle":"@IJHGeo","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"94781df3-fbef-4df8-9653-90393e84d247","owner":[],"postedDate":"October 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:34:23+00:00","versionOfRecord":{"articleIdentity":"rs-7754269","link":"https://doi.org/10.1186/s12942-026-00464-8","journal":{"identity":"international-journal-of-health-geographics","isVorOnly":false,"title":"International Journal of Health Geographics"},"publishedOn":"2026-03-25 16:12:10","publishedOnDateReadable":"March 25th, 2026"},"versionCreatedAt":"2025-10-22 20:20:10","video":"","vorDoi":"10.1186/s12942-026-00464-8","vorDoiUrl":"https://doi.org/10.1186/s12942-026-00464-8","workflowStages":[]},"version":"v1","identity":"rs-7754269","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7754269","identity":"rs-7754269","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}