Spatial associations of long-term exposure to diesel particulate matter with seasonal and annual mortality due to COVID-19 in the contiguous United States

In 2020, more than 20 million cases of coronavirus disease 2019 (COVID-19) were identified in the United States (U.S.), and more than 350,000 people died [5, 9, 19]. In addition to age, socioeconomic status, access to healthcare, physical environment, and education have been identified as social determinants of COVID-19 hospitalization and mortality [30, 36]. Several studies have observed a disproportionate share of COVID-19 incidence and mortality among predominantly Black U.S. communities, which may be partly attributable to social and economic inequalities and preexisting comorbidities [7, 8, 33 36, 40, 55], as well as to disproportionately high exposures to air pollution [29].

The impact of particulate matter exposures on COVID-19 outcomes have also been evaluated, with some studies centered on diesel particulate matter (DPM). In an investigation of the role of long-term exposure (2000-2016) to air pollution during the first months of the pandemic, Wu et al. [54] found that an increase of 1 μg/m³ in particulate matter with a nominal diameter of 2.5 μm (PM_2.5) was associated with an 11% increase in the COVID-19 death rate for January 1-June 18, 2020. Bozack et al. [3] performed a similar analysis to test associations of COVID-19 intensive care unit (ICU) admission and mortality with long-term concentrations of PM_2.5, nitrogen dioxide, and black carbon for the period March 8-August 30, 2020 in New York City. They noted an association of ICU admission and mortality with long-term PM_2.5 concentrations (collected December, 2018-December, 2019). Petroni et al. [35] investigated the association of COVID-19 mortality with respiratory hazard index calculated across 3223 U.S. counties using emissions data for 2014 and COVID-19 data through May 13, 2020. They observed a 9% increase in COVID-19 mortality per unit increase in respiratory hazard index, which includes DPM. Their analyses with only DPM demonstrated an increased effect of 182% in the mortality rate ratio with a 0.5 μg/m³ increase in DPM concentration. Hendryx and Luo [18] studied the association of long-term exposure to ozone (obtained from 2016), PM_2.5 (obtained from 2016), and DPM (obtained from 2014) with COVID-19 prevalence and mortality through May 31, 2020. They showed an increase of 14.3 deaths per 100,000 U.S. residents for each DPM concentration increase of 1 μg/m³ in a single-pollutant model adjusted for demographic, health, smoking, and COVID-19 testing covariates. These findings collectively suggest that long-term PM exposure may predispose an individual to COVID-19 mortality. However, association of COVID-19 mortality with long-term DPM may change over time with the evolution of the coronavirus and changes in policies and personal behaviors. Our understanding of the effect of long-term DPM exposure on COVID-19 mortality during different waves of the disease and over the locations impacted by those waves remain unknown, hampering anticipation of disease hotspots.

DPM is composed of a complex mixture of black carbon and organic carbon. Studies have shown that 80-90% of particles emitted by diesel engines are smaller than 2.0 μm [12, 24], small enough to penetrate the alveoli [41]. Long-term DPM exposure has been associated with adverse respiratory and cardiovascular effects [12, 37, 41]. Diesel engines power school buses, heavy-duty trucks, a variety of off-road heavy equipment, shipping, and commercial boating [12, 24]. DPM emissions are higher in urban areas, where most of the global population lives [12, 41]. Likewise, greater DPM concentrations have been observed in socioeconomically disadvantaged communities [8, 12].

Our study explores spatial associations between long term average concentrations of DPM, as a metric for past air pollution exposure, and COVID-19 mortality across each pandemic wave and throughout 2020 in the U.S. The objectives of the study are 1) to assess if living near DPM sources increased the risk of death from COVID-19, 2) to estimate how associations between mortality and long-term exposure to DPM (using the U.S. Environmental Protection Agency [50] broad definition of long-term exposure measured over “months to years”) may have changed over time with changes in the Coronavirus and in the population’s behavior, and 3) to test if models accounting for spatial autocorrelation improve model estimates. Data for air pollution, health, demographic, and social determinants of health were merged for this analysis, and global and local models were both applied to examine these relationships.

County-level annual average DPM concentration varied from 0.000202 to 1.72 μg/m³ with a nationwide median of 0.204 μg/m³. Elevated DPM concentration could be observed at specific points corresponding to cities (Fig. 1). Across New York metropolitan area counties, which was greatly impacted during the first wave of the pandemic, the average DPM concentration was 0.425 μg/m³.

During the January-to-May wave, the highest cumulative numbers of COVID-19 deaths were found in roughly the same regions as elevated DPM (Fig. 2a). As 2020 progressed, most counties experienced a higher mortality rate. The New York region exhibited lower cumulative mortality rate during the October-to-December wave of our study (Fig. 2c), with a mean of 98 deaths per 100,000 compared with the January-to-May wave, which had a mean of 280 deaths per 100,000 (Fig. 2a). As shown in Fig. 2a and b, cumulative mortality rate increased substantially from the first wave to the second wave in the Southeast region. In the West region, New Mexico, Arizona and California displayed the same pattern as the Southeast, with a large increase during the second wave. For the September-to-December wave, COVID-19 mortality rate increased across almost all of the US, exhibiting nearly the same pattern as for the all-year distribution (Fig. 2c and d).

At a global level, all models demonstrated a statistically significant association between long-term average DPM concentration and COVID-19 mortality rate for the first 9 months of 2020, as represented by the January-to-May and June-to-September waves (Table 3). SLM and SEM produced slightly higher associations for the June-to-September wave. For the October-to-December wave, none of the global models were found to produce positive associations or to be statistically significant. For the entire year, both the OLS and SLM produced positive associations, while the SEM produced a negative association.

OLS did not seem to be the most appropriate model to study spatial associations between COVID-19 mortality rate and DPM. Smaller associations for the spatial autoregression models compared with OLS suggested that the OLS covariates were positively biased due to spatial autocorrelation. Moran’s I and visual inspection of the residuals maps (Supplemental Fig. S1) indicated spatial clusters of high values and of low values. The Breusch-Pagan test provided support for heteroscedasticity of the residuals (p < < 10^− 6) in the OLS models, which may have been partially attributed to spatial autocorrelation [28]. The SLM and SEM models provided modest improvements in model fit, as indicated by slightly higher values of coefficient of determination (R²). Model fit testing indicates that the SLM and SEM provided comparable fits, based on the Lagrange multiplier test.

The local spatial differences estimated using the GWR model are presented as a range of values (Table 3). The mean COVID-19 mortality rate – DPM association for the GWR is identical to that of the OLS, but overall R² for the GWR indicates improved performance over all global models. Spatial distribution of the DPM coefficients indicates changing conditions across the country during the three parts of the year (Fig. 3). During the January-to-May wave, associations were mostly positive across the U.S. (Fig. 3a), up to an increase of 76.94 deaths per 100,000 for every interquartile range (IQR) increase in DPM concentration. During the June-to-September wave, about half of the contiguous US presented a positive association (Fig. 3b), while associations were more negative for the October-to-December wave (Fig. 3c). Year-round COVID-19 mortality rate associations with DPM were similar to those for the October-to-December wave, likely due to the large number of cases during that timeframe. Local variations in R² across the waves showed high (> 70%) values in the Northeast and Southwest during the January-to-May and June-to-September waves and in the year-long model. High R² persisted into the October-to-December wave for the Southwest, albeit with a smaller area (Fig. 4). Low values of R² (< 40%) were observed in the areas with greatest decrease in mortality with increasing DPM concentration, suggesting much greater uncertainty in those associations than in the positive ones seen in the New York area during the first wave. Moreover, COVID-19 mortality rate was statistically significantly associated with DPM concentration during the January-to-May and June-to-September waves but not during the October-to-December wave.

Among all potentially confounding covariates incorporated in the global models, fraction Black race and fraction American Indian ethnicity were statistically significantly positive in all global models. In addition to these two covariates, inactivity was significant in the June-to-September and October-to-December waves and in the year-long model, and the confounders Hispanic, Mining or Agriculture, Public Transportation, Time to Work, Income Inequality, and Population Density were significant at different time periods of the model. A negative relationship was found for smoking for the January-to-May wave, while a strong positive association was obtained for the October-to-December wave. In evaluations of the effects of smoking on COVID-19 incidence or mortality, inconsistent results have been found in the literature [1, 27, 31]. Benowitz et al. [1] highlighted the need for further investigations to better understand the mechanisms and effect of smoking on COVID-19 related outcomes. Correlation analysis suggests that DPM was moderately correlated with COVID-19 mortality rate in the January-to-May wave (Fig. 5a) and for the year-long (Fig. 5d) model, but that correlation was reduced in the June-to-September (Fig. 5b) and October-to-December (Fig. 5c) waves. Population density was correlated with COVID-19 mortality rate (ρ = 0.74) and DPM (ρ = 0.59) in the January-to-May wave. Fraction using public transportation was moderately correlated with COVID-19 mortality rate (ρ = 0.48) and DPM (ρ = 0.62) in the January-to-May wave. Therefore, population density and public transportation usage had the potential to act as confounders in a model testing the association between COVID-19 mortality and DPM concentration for the January-to-May wave. A model including just DPM in the SLM and SEM produced effect estimates of 7.728 and 9.713 deaths due to COVID-19 per 100,000 people for an IQR change in DPM (with R² = 0.2 for both models), respectively. Inclusion of the covariates in the model produced effect estimates of 1.710 and 1.845 deaths due to COVID-19 per 100,000 people for an IQR change in DPM (with R² = 0.42 and 0.45), respectively. These differences suggest that the final models controlled for those confounders. In the model that only included the covariates, the effect estimates for population density and use of public transportation were slightly lower than in the full model, while R² for the SLM and SEM were the same as for the models including DPM.

Detailed results from the models examining associations between CFR and DPM are included in the Supplemental Material (see Supplemental text, Supplemental Table S1, and Supplemental Figs. S2-S5). The relationships between CFR and DPM were similar to associations between mortality rate and DPM at each wave and throughout 2020. During the January-to-May wave, associations were positive and strongest in the Northeast. Associations were visible through parts of the Midwest and the Pacific Northwest. Associations persisted in the Northeast for the June-to-September and October-to-December waves, but the magnitude of the associations was lower than for January-to-May. Negative associations were observed across the Southern and Mountain states for the June-to-September and October-to-December waves. Model fit (R²) was consistently lower for the CFR models across model type and wave compared with the models testing associations between mortality rate and DPM.

Our study analyzed the spatial correlation of COVID-19 mortality rate and case fatality rate with long-term DPM concentration as a surrogate for exposure across the continental United States during three waves of the COVID-19 pandemic during 2020. Our results suggested that long-term exposure to DPM may have been an important factor in COVID-19 mortality during the first two waves of the disease and that long-term DPM exposure may have been more highly influential during the January-May wave. Sidell et al. [44] examined associations between air pollution exposure and COVID-19 incidence for monthly and annual averages of PM_2.5, nitrogen dioxide (NO₂), and ozone (O₃) over four waves corresponding to those in our study plus January-February, 2021 for a Southern California cohort. They similarly observed that PM_2.5 had a larger effect during the first wave and that the effect diminished over time. A spatial autocorrelation term was controlled for in these models, but Sidell et al. [44] did not incorporate local methods. Differences in the outcome variable and the specific exposure also necessitates a further examination of spatial and temporal patterns.

Our results indicate that the OLS model does not account for the spatial associations of COVID-19 mortality rate or CFR with DPM concentrations. These results are similar to those of Sidell et al. [44] and Mollalo et al. [30], although their studies considered COVID-19 incidence rate rather than mortality. Mollalo et al. [30] used OLS, SLM, SEM, and two versions of the GWR to model COVID-19 incidence and mortality for the time period of January 22-April 9, 2020 and found notable spatial associations of both COVID-19 incidence and mortality with several predictors. The study of Hendryx and Luo [18], covering the January-to-May wave, revealed strong associations of COVID-19 prevalence and mortality with long-term DPM and PM_2.5 concentrations. Their study estimated a coefficient of 14.3-18.7 deaths per 100,000 U.S. residents for each increase of 1 μg/m³ in DPM concentration. Inflation of the DPM effect shown in their results is possibly due to the correlation between covariates and their mixed linear multiple regression model that does not account for spatial correlation. Stakhovych and Bijmolt [46] emphasized that correlated spatial errors lead to bias and uncertainty in the OLS results. Moreover, LeSage and Fischer [26] noted that spatial correlation in the OLS error terms is a sufficient motivation to employ spatial autoregression models for discerning spatial relationships between dependent and independent variables.

The spatial global models outperformed the OLS model fit for all models for both mortality metrics. This improved performance may be related to spatial autocorrelation. A difference in coefficients and R² among the OLS, SLM, and SEM models was not observed for mortality rate during the June-to-September wave. Kim [21] reported an inflated effect of spatial autocorrelation on OLS predictor coefficients, suggesting less spatial autocorrelation during the June-to-September wave consistent with Bini et al. [2] and Smith and Lee [45].

Among the modeling techniques analyzed for our study, GWR provided the best model fit, based on estimated global R². Our results revealed where and when local long-term exposure to DPM may have been associated with COVID-19 mortality, consistent with results from both Karaye and Horney [20] and Mollalo et al. [30] regarding patterns of local prevalence and local mortality of the disease based on local R². Some areas in the Northeast and West regions presenting a high R² in our mortality rate model align with Mollalo et al. [30] estimates for incidence rate. As noted by Fotheringham et al. [15], our GWR results illustrate the need to account for local phenomena.

Socio-economic disparity could explain the non-stationary effect of DPM exposure on COVID-19 mortality rate, due to drastic differences between contiguous areas. Socially vulnerable communities, including minoritized racial groups, have seen spatially associated COVID-19 incidence [20]. This is consistent with the strong association we observed for the fraction Black confounder in the mortality rate model (Table 3). Moreover, Paolella et al. [32] pointed out spatial associations among fine particulate matter concentration, health effects, and minoritized groups and found out that finer spatial resolution revealed substantially higher fine particulate matter concentrations in Black and Hispanic communities.

The differences among associations of COVID-19 mortality rate and DPM concentrations found by the SLM and SEM for the year-long time period, when SLM was demonstrated to be more significant by a Lagrange test, helped to illustrate that neighboring effects were more relevant in modeling the spatial relationship with COVID-19 deaths than unobserved latent variables contained in the error term. Counties near other counties with high COVID-19 incidence are likely to have higher incidence. Nonetheless, since the weighting matrix chosen for our study was based on spatial adjacency, the county size differences between the Eastern and Western U.S. may have affected the parameter estimates creating more uncertainty in the larger counties [6]. Some variability in the association between COVID-19 and DPM exposure within counties might not have been captured, although DPM sources are more likely to be found in urban areas. However, since the SLM and SEM for the year-long time period were not statistically significant, other models should be considered when data are combined across multiple waves.

Several limitations of this study need to be acknowledged with respect to the input data. It is possible that, with more data and/or more time, the associations would disappear. Exposure measurement error could bias the results [52]. Our spatial modeling approach is intended to account for spatial exposure measurement errors. However, errors from applying cross-sectional analyses persist. Although we studied different waves of the disease, our models were not truly longitudinal. Long-term exposure to DPM was estimated using concentrations from 2018. The dataset likely includes higher DPM concentrations than for 2020 given reduced driving patterns during 2020 and, to a lesser extent, fleet turnover. This suggests that the magnitude of the effects of DPM calculated by our study and these other studies were underestimated. Widely reported undercounting of cases and deaths during the January-to-May wave would further contribute to this underestimation [13].

The set of potential confounders employed in our models was chosen to evaluate the influence of factors other than DPM potentially associated with COVID-19 outcomes [54]. However, it was impossible to represent all influential factors in the relationship between each wave of COVID-19 mortality and long-term DPM concentrations, so uncertainty in the potential for confounding existed [18, 54]. Furthermore, the study was designed at county level. Spatial variation within counties was not captured, and the difference in county size could have caused uncertainty since the weighting matrix defined for our SLM, SEM and GWR accounted for spatial adjacency. Therefore, associations at scales finer than county-level, including individual- and neighborhood-level associations, could not be inferred [54]. Despite these limitations, our study included a rigorous analysis of spatial relationships for different time periods and tested a variety of potential confounders to minimize these limitations.

Our study built on previous findings by exploring associations of COVID-19 mortality rate with long-term DPM concentrations across the first three waves of the pandemic. In doing so, our models provided a picture in which long-term DPM exposure may have influenced COVID-19 mortality during the early stages of the disease, as observed specifically for the periods of January-to-May and June-to-September, 2020. Waning influence of DPM during the October-to-December wave suggested that person-to-person disease transmission regardless of past DPM exposures may have become more influential in the spread of COVID-19 and in mortality rates once the Coronavirus became widespread throughout the U.S. Further investigation might focus on factors associated with COVID-19 mortality rate during the October-to-December wave. Although COVID-19 data were available beyond this period, the introduction of vaccines during 2021 were likely to have been so influential that combination of the 2 years of data may have produced misleading conclusions.