RESUMO
Wastewater surveillance for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA can be integrated with COVID-19 case data to inform timely pandemic response. However, more research is needed to apply and develop systematic methods to interpret the true SARS-CoV-2 signal from noise introduced in wastewater samples (e.g., from sewer conditions, sampling and extraction methods, etc.). In this study, raw wastewater was collected weekly from five sewersheds and one residential facility. The concentrations of SARS-CoV-2 in wastewater samples were compared to geocoded COVID-19 clinical testing data. SARS-CoV-2 was reliably detected (95% positivity) in frozen wastewater samples when reported daily new COVID-19 cases were 2.4 or more per 100,000 people. To adjust for variation in sample fecal content, four normalization biomarkers were evaluated: crAssphage, pepper mild mottle virus, Bacteroides ribosomal RNA (rRNA), and human 18S rRNA. Of these, crAssphage displayed the least spatial and temporal variability. Both unnormalized SARS-CoV-2 RNA signal and signal normalized to crAssphage had positive and significant correlation with clinical testing data (Kendall's Tau-b (τ)=0.43 and 0.38, respectively), but no normalization biomarker strengthened the correlation with clinical testing data. Locational dependencies and the date associated with testing data impacted the lead time of wastewater for clinical trends, and no lead time was observed when the sample collection date (versus the result date) was used for both wastewater and clinical testing data. This study supports that trends in wastewater surveillance data reflect trends in COVID-19 disease occurrence and presents tools that could be applied to make wastewater signal more interpretable and comparable across studies.
RESUMO
BACKGROUND: Air pollution-attributable disease burdens reported at global, country, state, or county levels mask potential smaller-scale geographic heterogeneity driven by variation in pollution levels and disease rates. Capturing within-city variation in air pollution health impacts is now possible with high-resolution pollutant concentrations. OBJECTIVES: We quantified neighborhood-level variation in air pollution health risks, comparing results from highly spatially resolved pollutant and disease rate data sets available for the Bay Area, California. METHODS: We estimated mortality and morbidity attributable to nitrogen dioxide (NO2), black carbon (BC), and fine particulate matter [PM ≤2.5µm in aerodynamic diameter (PM2.5)] using epidemiologically derived health impact functions. We compared geographic distributions of pollution-attributable risk estimates using concentrations from a) mobile monitoring of NO2 and BC; and b) models predicting annual NO2, BC and PM2.5 concentrations from land-use variables and satellite observations. We also compared results using county vs. census block group (CBG) disease rates. RESULTS: Estimated pollution-attributable deaths per 100,000 people at the 100-m grid-cell level ranged across the Bay Area by a factor of 38, 4, and 5 for NO2 [mean=30 (95% CI: 9, 50)], BC [mean=2 (95% CI: 1, 2)], and PM2.5, [mean=49 (95% CI: 33, 64)]. Applying concentrations from mobile monitoring and land-use regression (LUR) models in Oakland neighborhoods yielded similar spatial patterns of estimated grid-cell-level NO2-attributable mortality rates. Mobile monitoring concentrations captured more heterogeneity [mobile monitoring mean=64 (95% CI: 19, 107) deaths per 100,000 people; LUR mean=101 (95% CI: 30, 167)]. Using CBG-level disease rates instead of county-level disease rates resulted in 15% larger attributable mortality rates for both NO2 and PM2.5, with more spatial heterogeneity at the grid-cell-level [NO2 CBG mean=41 deaths per 100,000 people (95% CI: 12, 68); NO2 county mean=38 (95% CI: 11, 64); PM2.5 CBG mean=59 (95% CI: 40, 77); and PM2.5 county mean=55 (95% CI: 37, 71)]. DISCUSSION: Air pollutant-attributable health burdens varied substantially between neighborhoods, driven by spatial variation in pollutant concentrations and disease rates. https://doi.org/10.1289/EHP7679.
