Expansion of wastewater-based disease surveillance to improve health equity in California’s Central Valley: sequential shifts in case-to-wastewater and hospitalization-to-wastewater ratios

Abstract

IntroductionOver a third of the communities (39%) in the Central Valley of California, a richly diverse and important agricultural region, are classified as disadvantaged—with inadequate access to healthcare, lower socio-economic status, and higher exposure to air and water pollution. The majority of racial and ethnic minorities are also at higher risk of COVID-19 infection, hospitalization, and death according to the Centers for Disease Control and Prevention. Healthy Central Valley Together established a wastewater-based disease surveillance (WDS) program that aims to achieve greater health equity in the region through partnership with Central Valley communities and the Sewer Coronavirus Alert Network. WDS offers a cost-effective strategy to monitor trends in SARS-CoV-2 community infection rates.MethodsIn this study, we evaluated correlations between public health and wastewater data (represented as SARS-CoV-2 target gene copies normalized by pepper mild mottle virus target gene copies) collected for three Central Valley communities over two periods of COVID-19 infection waves between October 2021 and September 2022. Public health data included clinical case counts at county and sewershed scales as well as COVID-19 hospitalization and intensive care unit admissions. Lag-adjusted hospitalization:wastewater ratios were also evaluated as a retrospective metric of disease severity and corollary to hospitalization:case ratios.ResultsConsistent with other studies, strong correlations were found between wastewater and public health data. However, a significant reduction in case:wastewater ratios was observed for all three communities from the first to the second wave of infections, decreasing from an average of 4.7 ± 1.4 over the first infection wave to 0.8 ± 0.4 over the second.DiscussionThe decline in case:wastewater ratios was likely due to reduced clinical testing availability and test seeking behavior, highlighting how WDS can fill data gaps associated with under-reporting of cases. Overall, the hospitalization:wastewater ratios remained more stable through the two waves of infections, averaging 0.5 ± 0.3 and 0.3 ± 0.4 over the first and second waves, respectively.

A Mixed-Effects Model to Predict COVID-19 Hospitalizations Using Wastewater Surveillance

“…The Data Descriptor describes data from San Jose, Southeast, SAC, and Davis collected through 12/31/22 and Merced and Modesto through 4/27/22 and 12/2/22-12/31/22. In addition, data from Merced and Modesto from 5/4/21 through 09/29/22 were included in a paper by (Kadonsky et al, 2023), and data from San Jose, Southeast, SAC, and Davis after 12/31/22 were included in a paper by Boehm et al (2023c). The other data have not been published previously.…”

“…We accounted for these factors by incorporating a fixed effect into the model for discrete time periods associated with three waves of infections that were apparent over this study period (Figure 3). Kadonsky et al (2023) observed that the wastewater-to-hospitalization ratios remained relatively stable during two COVID-19 waves in two geographically adjacent counties in California’s Central Valley between October 2021 and September 2022. This suggests that the wastewater-to-hospitalization ratios may remain constant in some cases, but the intercept may differ.…”

“…A two-week lag was incorporated to accurately capture the relationship between predictor variables and hospitalization outcomes. This lag duration was chosen based on previous research showing a nearly two-week delay between hospitalizations and measurements of wastewater, confirmed cases, and test positivity rate (Kadonsky et al, 2023; Montesinos-López et al, 2021). Specifically, we used the hospitalization data two weeks ahead (denoted as Y i ) as the response variable for concentration, cases, and TPR at time i .…”

Policy impact evaluation: A potential use case for longitudinal monitoring of viruses in wastewater at small geographic scales

“…It is not possible to deduce the main driver for the change in correlation between wastewater SARS-CoV-2 RNA concentrations and incident COVID-19 cases over time, but we suspect that the change is due to several factors including changes in routine COVID-19 surveillance testing requirements, changes in test reporting, and overall decreases in test-seeking behaviors as the pandemic continues. 19,73,74 Wastewater monitoring data are independent of test-seeking behaviors or test reporting patterns so may be a less biased tool for monitoring public health, particularly in periods characterized by low test-seeking and reporting rates.…”

Optimizing Spatial Distribution of Wastewater-Based Disease Surveillance to Advance Health Equity

“…DWT uses sub-band coding, which is a technique of dividing the signal into frequency bands or sub-bands, and then sampling each sub-band at a lower rate. The DWT 21 achieves this by passing the signal through a series of high-pass and low-pass filters, which please refer to [33]. We used the normalized wastewater concentration of SARS-CoV-2 RNA 56 detected in each wastewater sample for the two laboratories.…”