Exploring spatiotemporal pattern in the association between short-term exposure to fine particulate matter and COVID-19 incidence in the continental United States: a Leroux-conditional-autoregression-based strategy

Abstract

Background: Numerous studies have demonstrated that fine particulate matter (PM_2.5) is adversely associated with COVID-19 incidence. However, few studies have explored the spatiotemporal heterogeneity in this association, which is critical for developing cost-effective pollution-related policies for a specific location and epidemic stage, as well as, understanding the temporal change of association between PM_2.5 and an emerging infectious disease like COVID-19.

Methods: The outcome was state-level daily COVID-19 cases in 49 native United States between April 1, 2020 and December 31, 2021. The exposure variable was the moving average of PM_2.5 with a lag range of 0-14 days. A latest proposed strategy was used to investigate the spatial distribution of PM_2.5-COVID-19 association in state level. First, generalized additive models were independently constructed for each state to obtain the rough association estimations, which then were smoothed using a Leroux-prior-based conditional autoregression. Finally, a modified time-varying approach was used to analyze the temporal change of association and explore the potential causes spatiotemporal heterogeneity.

Results: In all states, a positive association between PM_2.5 and COVID-19 incidence was observed. Nearly one-third of these states, mainly located in the northeastern and middle-northern United States, exhibited statistically significant. On average, a 1 μg/m³ increase in PM_2.5 concentration led to an increase in COVID-19 incidence by 0.92% (95%CI: 0.63-1.23%). A U-shaped temporal change of association was examined, with the strongest association occurring in the end of 2021 and the weakest association occurring in September 1, 2020 and July 1, 2021. Vaccination rate was identified as a significant cause for the association heterogeneity, with a stronger association occurring at a higher vaccination rate.

Conclusion: Short-term exposure to PM_2.5 and COVID-19 incidence presented positive association in the United States, which exhibited a significant spatiotemporal heterogeneity with strong association in the eastern and middle regions and with a U-shaped temporal change.

Keywords: PM2.5 exposure; conditional autoregression; spatial dependence; spatiotemporal heterogeneity; vaccination modification.

Figure 1

Descriptive analysis for COVID-19 cases, PM_2.5 exposure and vaccination rates. (A,B) Exhibits the spatial distributions of the cumulative COVID-19 cases and incidence from April 1, 2020 to December 31, 2021, respectively. (C) Exhibits spatial distribution of average daily PM_2.5 concentrations across the studied period, where the asterisks mark the three states with the highest concentrations. (D) Exhibits the temporal trends of vaccination rates. (E,F) Exhibit the spatial distributions of the cumulative vaccination rates for one or full dose, respectively.

Figure 2

Spatial distribution of the association between PM_2.5 exposure and COVID-19 risk. The asterisk (*) indicates that the relative risk (RR) is statistically significant (p < 0.05).

Figure 3

Meta-analyses of the effect of short-term PM_2.5 exposure on the risk of COVID-19 incidence in the western states.

Figure 4

Temporal trend in the association between PM_2.5 exposure and COVID-19 risk.

Figure 5

The nonlinear modification effects of vaccination and temperature on the association between PM_2.5 exposure and COVID-19 risk. (A) Exhibits the modification effect of vaccination rates and (B) exhibits the modification effect of daily temperature.