Simple decision rules to predict local surges in COVID-19 hospitalizations during the winter and spring of 2022

Healthcare resource utilisation and costs of hospitalisation and primary care among adults with COVID-19 in England: a population-based cohort study

OBJECTIVES

To quantify direct costs and healthcare resource utilisation (HCRU) associated with acute COVID-19 in adults in England.

DESIGN

Population-based retrospective cohort study using Clinical Practice Research Datalink Aurum primary care electronic medical records linked to Hospital Episode Statistics secondary care administrative data.

SETTING

Patients registered to primary care practices in England.

POPULATION

1 706 368 adults with a positive SARS-CoV-2 PCR or antigen test from August 2020 to January 2022 were included; 13 105 within the hospitalised cohort indexed between August 2020 and March 2021, and 1 693 263 within the primary care cohort indexed between August 2020 and January 2022. Patients with a COVID-19-related hospitalisation within 84 days of a positive test were included in the hospitalised cohort.

MAIN OUTCOME MEASURES

Primary and secondary care HCRU and associated costs ≤4 weeks following positive COVID-19 test, stratified by age group, risk of severe COVID-19 and immunocompromised status.

RESULTS

Among the hospitalised cohort, average length of stay, including critical care stays, was longer in older adults. Median healthcare cost per hospitalisation was higher in those aged 75-84 (£8942) and ≥85 years (£8835) than in those aged <50 years (£7703). While few (6.0%) patients in critical care required mechanical ventilation, its use was higher in older adults (50-74 years: 8.3%; <50 years: 4.3%). HCRU and associated costs were often greater in those at higher risk of severe COVID-19 than in the overall cohort, although minimal differences in HCRU were found across the three different high-risk definitions. Among the primary care cohort, general practitioner or nurse consultations were more frequent among older adults and the immunocompromised.

CONCLUSIONS

COVID-19-related hospitalisations in older adults, particularly critical care stays, were the primary drivers of high COVID-19 resource use in England. These findings may inform health policy decisions and resource allocation in the prevention and management of COVID-19.

Adaptive metrics for an evolving pandemic A dynamic approach to area-level COVID-19 risk designations

Throughout the COVID-19 pandemic, policymakers have proposed risk metrics, such as the CDC Community Levels, to guide local and state decision-making. However, risk metrics have not reliably predicted key outcomes and often lack transparency in terms of prioritization of false positive versus false negative signals. They have also struggled to maintain relevance over time due to slow and infrequent updates addressing new variants and shifts in vaccine- and infection-induced immunity. We make two contributions to address these weaknesses of risk metrics. We first present a framework to evaluate predictive accuracy based on policy targets related to severe disease and mortality, allowing for explicit preferences toward false negative versus false positive signals. This approach allows policymakers to optimize metrics for specific preferences and interventions. Second, we propose a novel method to update risk thresholds in real-time. We show that this adaptive approach to designating areas as "high risk" improves performance over static metrics in predicting 3-week-ahead mortality and intensive care usage at both state and county levels. We also demonstrate that with our approach, using only new hospital admissions to predict 3-week-ahead mortality and intensive care usage has performed consistently as well as metrics that also include cases and inpatient bed usage. Our results highlight that a key challenge for COVID-19 risk prediction is the changing relationship between indicators and outcomes of policy interest. Adaptive metrics therefore have a unique advantage in a rapidly evolving pandemic context.

Significance Statement

In the rapidly-evolving COVID-19 pandemic, public health risk metrics often become less relevant over time. Risk metrics are designed to predict future severe disease and mortality based on currently-available surveillance data, such as cases and hospitalizations. However, the relationship between cases, hospitalizations, and mortality has varied considerably over the course of the pandemic, in the context of new variants and shifts in vaccine- and infection-induced immunity. We propose an adaptive approach that regularly updates metrics based on the relationship between surveillance inputs and future outcomes of policy interest. Our method captures changing pandemic dynamics, requires only hospitalization input data, and outperforms static risk metrics in predicting high-risk states and counties.

Understanding motivations and deterrents for COVID-19 vaccination among US working adults: A mixed method approach

COVID-19 vaccines have been developed and administered in the United States. Despite evidence from clinical trials for the effectiveness of the COVID-19 vaccines, many individuals are still hesitant or even unwilling to receive one. The purposes of this study are (1) to examine characteristics associated with those willing and unwilling to receive a COVID-19 vaccine and (2) to illuminate the reasons behind their willingness and unwillingness to receive the vaccine using both quantitative and qualitative data. Data collected from 505 US working adults showed that several demographic variables (i.e. education, the size of their organization, the number of dependents, political orientation, and religion) and influence sources (i.e. family members, workplace leaders, political leaders, social media influencers, and healthcare workers) significantly correlated with people's willingness/unwillingness to receive a COVID-19 vaccine. Furthermore, protecting oneself was the most common reason cited by participants for willingness to get the vaccine, while being concerned about vaccine side effects was the most frequently given reason for being unwilling to receive a COVID-19 vaccine. This study expands our current understanding of the COVID-19 vaccine motivators and intention factors. Practically, the findings can help develop health campaign messages effectively target working adults who are unwilling to receive the COVID-19 vaccines and ultimately increase the vaccination rate in the United States.