The Role of SARS-CoV-2 Testing on Hospitalizations in California

Abstract

The rapid spread of the new SARS-CoV-2 virus triggered a global health crisis, disproportionately impacting people with pre-existing health conditions and particular demographic and socioeconomic characteristics. One of the main concerns of governments has been to avoid health systems becoming overwhelmed. For this reason, they have implemented a series of non-pharmaceutical measures to control the spread of the virus, with mass tests being one of the most effective controls. To date, public health officials continue to promote some of these measures, mainly due to delays in mass vaccination and the emergence of new virus strains. In this research, we studied the association between COVID-19 positivity rate and hospitalization rates at the county level in California using a mixed linear model. The analysis was performed in the three waves of confirmed COVID-19 cases registered in the state to September 2021. Our findings suggest that test positivity rate is consistently associated with hospitalization rates at the county level for all study waves. Demographic factors that seem to be related to higher hospitalization rates changed over time, as the profile of the pandemic impacted different fractions of the population in counties across California.

Differential Effects of Race/Ethnicity and Social Vulnerability on COVID-19 Positivity, Hospitalization, and Death in the San Francisco Bay Area

“…Importantly, this elevated hospitalization rate was observed to be independent of any relationship with social vulnerability. The observation that US Asian groups have had poorer outcomes during the COVID-19 pandemic has been found in some studies [17][18][19], though how they have fared appears to differ depending on context [20], with at least one meta-analysis suggesting paradoxically that Asian individuals with higher socioeconomic status were more likely to have poorer outcomes. Clearly, further study is needed.…”

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

“…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 .…”

“…TPR has been used as a valuable alternative metric to track the spread of COVID-19 and assess the disease’s prevalence compared to the number of tests being conducted (Dallal et al, 2021; Dowdy and D’Souza, 2020). TPR has been demonstrated to reliably correlate with hospitalizations and intensive care units (Fenga and Gaspari, 2021; Montesinos-López et al, 2021). However, the interpretation and use of the TPR can be challenging in certain contexts, as it relies on accurate and comprehensive testing data, which may be hindered by factors such as limited testing capacity, variable testing practices, and variations in reporting standards.…”

Differential Effects of Race/Ethnicity and Social Vulnerability on COVID-19 Positivity, Hospitalization, and Death in the San Francisco Bay Area

“…Importantly, this elevated hospitalization rate was observed to be independent of any relationship with social vulnerability. The observation that US Asian groups have had poorer outcomes during the COVID-19 pandemic has been found in some studies [17, 18, 19], though how they have fared appears to differ depending on context [20], with at least one meta-analysis suggesting paradoxically that Asian individuals with higher socioeconomic status were likelier to have poorer outcomes. Clearly, further study is needed.…”