The COVID-19 hospitalization metric in the pre- and postvaccination eras as a measure of pandemic severity: A retrospective, nationwide cohort study

Estimating the Number of Primary vs Incidental COVID-19 Hospitalizations in Santa Clara County

Background

The goal of this study was to evaluate whether International Classification of Diseases, 10th Revision (ICD-10), discharge data can be used to accurately differentiate primary coronavirus disease 2019 (COVID-19) hospitalizations, which are specifically due to COVID-19, from incidental COVID-19 hospitalizations for monitoring COVID-19 trends in a large county health department. We sought to explore the use of machine learning algorithms for enhancing surveillance capabilities in a local public health setting.

Methods

Discharge data for 5122 Santa Clara County hospitalizations with a positive severe acute respiratory syndrome coronavirus 2 polymerase chain reaction or antigen test occurring between December 15, 2021, and August 15, 2022, were used to train a series of models for classifying primary COVID-19 hospitalizations using chart review as a gold standard. Area under the receiver operating characteristic curve (AUROC) was used as the evaluation metric.

Results

Each model performed well when trained on the full set of available predictors. AUROC values ranged from 0.808 (random forest) to 0.818 (SuperLearner). After evaluating each model, we implemented a reporting process based on Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, as the performance was comparable with SuperLearner and it had the advantage of being transparent and familiar to health department staff.

Conclusions

In Santa Clara County, ICD-10 discharge data were successfully used to develop a low-burden method for monitoring primary COVID-19 hospitalization, demonstrating one way that predictive algorithms can help local health jurisdictions meet surveillance needs while minimizing manual effort.

Leveraging near-real-time patient and population data to incorporate fluctuating risk of severe COVID-19: development and prospective validation of a personalised risk prediction tool

Background

Novel strategies that account for population-level changes in dominant variants, immunity, testing practices and changes in individual risk profiles are needed to identify patients who remain at high risk of severe COVID-19. The aim of this study was to develop and prospectively validate a tool to predict absolute risk of severe COVID-19 incorporating dynamic parameters at the patient and population levels that could be used to inform clinical care.

Methods

A retrospective cohort of vaccinated US Veterans with SARS-CoV-2 from July 1, 2021, through August 25, 2023 was created. Models were estimated using logistic-regression-based machine learning with backward selection and included a variable with fluctuating absolute risk of severe COVID-19 to account for temporal changes. Age, sex, vaccine type, fully boosted status, and prior infection before vaccination were included a priori. Variations in individual risk over time, e.g., due to receipt of immune suppressive medications, were also potentially included. The model was developed using data from July 1, 2021, through August 31, 2022 and prospectively validated on a subsequent second cohort (September 1, 2022, through August 25, 2023). Model performance was quantified by the area under the receiver operating characteristic curve (AUC) and calibration by Brier score. The final model was used to compare observed rates of severe disease to predicted rates among patients who received oral antivirals.

Findings

216,890 SARS-CoV-2 infections in Veterans not treated with oral antivirals were included (median age, 65; 88% male). The development cohort included 165,303 patients (66,121 in the training set, 49,591 in the tuning set, and 49,591 in the testing set) and the prospective validation cohort included 51,587 patients. The percentage of severe infections ranged from 5% to 25%. Model performance improved until 24 clinical predictor variables including age, co-morbidities, and immune-suppressive medications plus a 30-day rolling risk window were included (AUC in development cohort, 0.88 (95% CI, 0.87-0.88), AUC in prospective validation, 0.85 (95% CI, 0.84-0.85), Brier Score, 0.13). The most important variables for predicting severe disease included age, chronic kidney disease, chronic obstructive pulmonary disease, Alzheimer's disease, heart failure, and anaemia. Glucocorticoid use during the one-month prior to COVID-19 diagnosis was the next most important predictor. Models that included a near-real time fluctuating population risk variable performed better than models stratified by circulating variant and models with dominant variant included as a predictor. Patients with predicted severe disease risk >3% who received oral antivirals had approximately 4-fold lower rates of severe COVID-19 untreated patients at a similar risk level.

Interpretation

Our novel risk prediction tool uses a simple method to adjust for temporal changes and can be implemented to facilitate uptake of evidence-based therapies. The study provides proof-of-concept for leveraging real-time data to support risk prediction that incorporates changing population-level trends and variation patient-level risk.

Funding

This work was supported by the VA Boston Cooperative Studies Programme. WBE was supported by VA HSR&D IIR 20-076; VA HSR&D IIR 20-101; VA National Artificial Intelligence Institute.

Pulmonary aspergillosis in US Veterans with COVID-19: a nationwide, retrospective cohort study

Background

COVID-associated pulmonary aspergillosis (CAPA) was described early in the pandemic as a complication of SARS-CoV-2. Data about incidence of aspergillosis and characteristics of affected patients after mid-2021 are limited.

Methods

A retrospective, nationwide cohort of US Veterans with SARS-CoV-2 from 1/1/2020 to 2/7/2024 was created. Potential cases of aspergillosis ≤12 weeks of a SARS-CoV-2 test were flagged electronically (based on testing results indicative of invasive fungal infection, antifungal therapy, and/or ICD-10 codes), followed by manual review to establish the clinical diagnosis of pulmonary aspergillosis. Incidence rates were calculated per 10,000 SARS-CoV-2 cases. Selected clinical characteristics included age >70, receipt of immune-compromising drugs, hematologic malignancy, chronic respiratory disease, vaccination status, and vaccine era. Multivariate logistic regression was used to estimate the independent effects of these variables via adjusted odds ratios (aOR).

Results

Among 674,343 Veterans with SARS-CoV-2, 165 were electronically flagged for review. Of these, 66 were judged to be cases of aspergillosis. Incidence proportions ranged from 0.30/10,000 among patients with zero risk factors to 34/10,000 among those with ≥3 risk factors; rates were similar in the pre- and post-vaccination eras. The 90-day mortality among aspergillosis cases was 50%. In the multivariate analysis, immune suppression (aOR 6.47, CI 3.84-10.92), chronic respiratory disease (aOR 3.57, CI 2.10-6.14), and age >70 (aOR 2.78, CI 1.64-4.80) were associated with aspergillosis.

Conclusions

Patients with underlying risk factors for invasive aspergillosis continue to be at some risk despite SARS-CoV-2 immunization. Risk in patients without immune suppression or preexisting lung disease is very low.

Meta-analysis of Cognitive Function Following Non-severe SARS-CoV-2 Infection

To effectively diagnose and treat subjective cognitive symptoms in post-acute sequalae of COVID-19 (PASC), it is important to understand objective cognitive impairment across the range of acute COVID-19 severity. Despite the importance of this area of research, to our knowledge, there are no current meta-analyses of objective cognitive functioning following non-severe initial SARS-CoV-2 infection. The aim of this meta-analysis is to describe objective cognitive impairment in individuals with non-severe (mild or moderate) SARS-CoV-2 cases in the post-acute stage of infection. This meta-analysis was pre-registered with Prospero (CRD42021293124) and utilized the PRISMA checklist for reporting guidelines, with screening conducted by at least two independent reviewers for all aspects of the screening and data extraction process. Fifty-nine articles (total participants = 22,060) with three types of study designs met our full criteria. Individuals with non-severe (mild/moderate) initial SARS-CoV-2 infection demonstrated worse objective cognitive performance compared to healthy comparison participants. However, those with mild (nonhospitalized) initial SARS-CoV-2 infections had better objective cognitive performance than those with moderate (hospitalized but not requiring ICU care) or severe (hospitalized with ICU care) initial SARS-CoV-2 infections. For studies that used normative data comparisons instead of healthy comparison participants, there was a small and nearly significant effect when compared to normative data. There were high levels of heterogeneity (88.6 to 97.3%), likely reflecting small sample sizes and variations in primary study methodology. Individuals who have recovered from non-severe cases of SARS-CoV-2 infections may be at risk for cognitive decline or impairment and may benefit from cognitive health interventions.

Differentiating patients admitted primarily due to coronavirus disease 2019 (COVID-19) from those admitted with incidentally detected severe acute respiratory syndrome corona-virus type 2 (SARS-CoV-2) at hospital admission: A cohort analysis of German hospital records

OBJECTIVE

The number of hospitalized patients with severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) does not differentiate between patients admitted due to coronavirus disease 2019 (COVID-19) (ie, primary cases) and incidental SARS-CoV-2 infection (ie, incidental cases). We developed an adaptable method to distinguish primary cases from incidental cases upon hospital admission.

DESIGN

Retrospective cohort study.

SETTING

Data were obtained from 3 German tertiary-care hospitals.

PATIENTS

The study included patients of all ages who tested positive for SARS-CoV-2 by a standard quantitative reverse-transcription polymerase chain reaction (RT-PCR) assay upon admission between January and June 2022.

METHODS

We present 2 distinct models: (1) a point-of-care model that can be used shortly after admission based on a limited range of parameters and (2) a more extended point-of-care model based on parameters that are available within the first 24-48 hours after admission. We used regression and tree-based classification models with internal and external validation.

RESULTS

In total, 1,150 patients were included (mean age, 49.5±28.5 years; 46% female; 40% primary cases). Both point-of-care models showed good discrimination with area under the curve (AUC) values of 0.80 and 0.87, respectively. As main predictors, we used admission diagnosis codes (ICD-10-GM), ward of admission, and for the extended model, we included viral load, need for oxygen, leucocyte count, and C-reactive protein.

CONCLUSIONS

We propose 2 predictive algorithms based on routine clinical data that differentiate primary COVID-19 from incidental SARS-CoV-2 infection. These algorithms can provide a precise surveillance tool that can contribute to pandemic preparedness. They can easily be modified to be used in future pandemic, epidemic, and endemic situations all over the world.

Identification of predictive patient characteristics for assessing the probability of COVID-19 in-hospital mortality

As the world emerges from the COVID-19 pandemic, there is an urgent need to understand patient factors that may be used to predict the occurrence of severe cases and patient mortality. Approximately 20% of SARS-CoV-2 infections lead to acute respiratory distress syndrome caused by the harmful actions of inflammatory mediators. Patients with severe COVID-19 are often afflicted with neurologic symptoms, and individuals with pre-existing neurodegenerative disease have an increased risk of severe COVID-19. Although collectively, these observations point to a bidirectional relationship between severe COVID-19 and neurologic disorders, little is known about the underlying mechanisms. Here, we analyzed the electronic health records of 471 patients with severe COVID-19 to identify clinical characteristics most predictive of mortality. Feature discovery was conducted by training a regularized logistic regression classifier that serves as a machine-learning model with an embedded feature selection capability. SHAP analysis using the trained classifier revealed that a small ensemble of readily observable clinical features, including characteristics associated with cognitive impairment, could predict in-hospital mortality with an accuracy greater than 0.85 (expressed as the area under the ROC curve of the classifier). These findings have important implications for the prioritization of clinical measures used to identify patients with COVID-19 (and, potentially, other forms of acute respiratory distress syndrome) having an elevated risk of death.

Risk of severe coronavirus disease 2019 despite vaccination in patients requiring treatment with immune-suppressive drugs: A nationwide cohort study of US Veterans

BACKGROUND

Patients taking immune-suppressive drugs are at increased risk of severe coronavirus disease 2019 (COVID-19), not fully ameliorated by vaccination. We assessed the contributions of clinical and demographic factors to the risk of severe disease despite vaccination in patients taking immune-suppressive medications for solid organ transplantation (SOT), rheumatoid arthritis (RA), inflammatory bowel disease (IBD), or psoriasis.

METHODS

Veterans Health Administration electronic health records were used to identify patients diagnosed with RA, IBD, psoriasis, or SOT who had been vaccinated against severe acute respiratory syndrome coronavirus 2, were subsequently infected, and had received immune-suppressive drugs within 3 months before infection. The association of severe (defined as hypoxemia, mechanical ventilation, dexamethasone use, or death) versus non-severe COVID-19 with the use of immune-suppressive and antiviral drugs and clinical covariates was assessed by multivariable logistic regression.

RESULTS

Severe COVID-19 was more common in patients with SOT (230/1011, 22.7%) than RA (173/1355, 12.8%), IBD (51/742, 6.9%), or psoriasis (82/1125, 7.3%). Age was strongly associated with severe COVID-19, adjusted odds ratio (aOR) of 1.04 (CI 1.03-1.05) per year. Comorbidities indicating chronic brain, heart, lung, or kidney damage were also associated with severity, aOR 1.35-2.38. The use of glucocorticoids was associated with increased risk (aOR 1.66, CI 1.39-2.18). Treatment with antivirals was associated with reduced severity, for example, aOR 0.28 (CI 0.13-0.62) for nirmatrelvir/ritonavir.

CONCLUSION

The risk of severe COVID-19 despite vaccination is substantial in patients taking immune-suppressive drugs, more so in patients with SOT than in patients with inflammatory diseases. Age and severe comorbidities contribute to risk, as in the general population. Oral antivirals were very beneficial but not widely used.

Maintaining the Utility of Coronavirus Disease 2019 Pandemic Severity Surveillance: Evaluation of Trends in Attributable Deaths and Development and Validation of a Measurement Tool

BACKGROUND

Death within a specified time window following a positive SARS-CoV-2 test is used by some agencies for attributing death to COVID-19. With Omicron variants, widespread immunity, and asymptomatic screening, there is cause to re-evaluate COVID-19 death attribution methods and develop tools to improve case ascertainment.

METHODS

All patients who died following microbiologically confirmed SARS-CoV-2 in the Veterans Health Administration (VA) and at Tufts Medical Center (TMC) were identified. Records of selected vaccinated VA patients with positive tests in 2022, and of all TMC patients with positive tests in 2021-2022, were manually reviewed to classify deaths as COVID-19-related (either directly caused by or contributed to), focused on deaths within 30 days. Logistic regression was used to develop and validate a surveillance model for identifying deaths in which COVID-19 was causal or contributory.

RESULTS

Among vaccinated VA patients who died ≤30 days after a positive test in January-February 2022, death was COVID-19-related in 103/150 cases (69%) (55% causal, 14% contributory). In June-August 2022, death was COVID-19-related in 70/150 cases (47%) (22% causal, 25% contributory). Similar results were seen among the 71 patients who died at TMC. A model including hypoxemia, remdesivir, and anti-inflammatory drugs had positive and negative predictive values of 0.82-0.95 and 0.64-0.83, respectively.

CONCLUSIONS

By mid-2022, "death within 30 days" did not provide an accurate estimate of COVID-19-related death in 2 US healthcare systems with routine admission screening. Hypoxemia and use of antiviral and anti-inflammatory drugs-variables feasible for reporting to public health agencies-would improve classification of death as COVID-19-related.

Impact of changing case definitions for coronavirus disease 2019 (COVID-19) hospitalization on pandemic metrics

OBJECTIVE

To examine the impact of commonly used case definitions for coronavirus disease 2019 (COVID-19) hospitalizations on case counts and outcomes.

DESIGN, PATIENTS, AND SETTING

Retrospective analysis of all adults hospitalized between March 1, 2020, and March 1, 2022, at 5 Massachusetts acute-care hospitals.

INTERVENTIONS

We applied 6 commonly used definitions of COVID-19 hospitalization: positive severe acute respiratory coronavirus virus 2 (SARS-CoV-2) polymerase chain reaction (PCR) assay within 14 days of admission, PCR plus dexamethasone administration, PCR plus remdesivir, PCR plus hypoxemia, institutional COVID-19 flag, or COVID-19 International Classification of Disease, Tenth Revision (ICD-10) codes. Outcomes included case counts and in-hospital mortality. Overall, 100 PCR-positive cases were reviewed to determine each definition's accuracy for distinguishing primary or contributing versus incidental COVID-19 hospitalizations.

RESULTS

Of 306,387 hospital encounters, 15,436 (5.0%) met the PCR-based definition. COVID-19 hospitalization counts varied substantially between definitions: 4,628 (1.5% of all encounters) for PCR plus dexamethasone, 5,757 (1.9%) for PCR plus remdesivir, 11,801 (3.9%) for PCR plus hypoxemia, 15,673 (5.1%) for institutional flags, and 15,868 (5.2%) for ICD-10 codes. Definitions requiring dexamethasone, hypoxemia, or remdesivir selected sicker patients compared to PCR alone (mortality rates 12.2%, 10.7%, and 8.8% vs 8.3%, respectively). Definitions requiring PCR plus remdesivir or dexamethasone did not detect a reduction in in-hospital mortality associated with the SARS-CoV-2 Omicron variant. ICD-10 codes had the highest sensitivity (98.4%) but low specificity (39.5%) for distinguishing primary or contributing versus incidental COVID-19 hospitalizations. PCR plus dexamethasone had the highest specificity (92.1%) but low sensitivity (35.5%).

CONCLUSIONS

Commonly used definitions for COVID-19 hospitalizations generate variable case counts and outcomes and differentiate poorly between primary or contributing versus incidental COVID-19 hospitalizations. Surveillance definitions that better capture and delineate COVID-19-associated hospitalizations are needed.

Comparing Natural Language Processing and Structured Medical Data to Develop a Computable Phenotype for Patients Hospitalized Due to COVID-19: Retrospective Analysis

Background

Throughout the COVID-19 pandemic, many hospitals conducted routine testing of hospitalized patients for SARS-CoV-2 infection upon admission. Some of these patients are admitted for reasons unrelated to COVID-19 and incidentally test positive for the virus. Because COVID-19-related hospitalizations have become a critical public health indicator, it is important to identify patients who are hospitalized because of COVID-19 as opposed to those who are admitted for other indications.

Objective

We compared the performance of different computable phenotype definitions for COVID-19 hospitalizations that use different types of data from electronic health records (EHRs), including structured EHR data elements, clinical notes, or a combination of both data types.

Methods

We conducted a retrospective data analysis, using clinician chart review-based validation at a large academic medical center. We reviewed and analyzed the charts of 586 hospitalized individuals who tested positive for SARS-CoV-2 in January 2022. We used LASSO (least absolute shrinkage and selection operator) regression and random forests to fit classification algorithms that incorporated structured EHR data elements, clinical notes, or a combination of structured data and clinical notes. We used natural language processing to incorporate data from clinical notes. The performance of each model was evaluated based on the area under the receiver operator characteristic curve (AUROC) and an associated decision rule based on sensitivity and positive predictive value. We also identified top words and clinical indicators of COVID-19-specific hospitalization and assessed the impact of different phenotyping strategies on estimated hospital outcome metrics.

Results

Based on a chart review, 38.2% (224/586) of patients were determined to have been hospitalized for reasons other than COVID-19, despite having tested positive for SARS-CoV-2. A computable phenotype that used clinical notes had significantly better discrimination than one that used structured EHR data elements (AUROC: 0.894 vs 0.841; P<.001) and performed similarly to a model that combined clinical notes with structured data elements (AUROC: 0.894 vs 0.893; P=.91). Assessments of hospital outcome metrics significantly differed based on whether the population included all hospitalized patients who tested positive for SARS-CoV-2 or those who were determined to have been hospitalized due to COVID-19.

Conclusions

These findings highlight the importance of cause-specific phenotyping for COVID-19 hospitalizations. More generally, this work demonstrates the utility of natural language processing approaches for deriving information related to patient hospitalizations in cases where there may be multiple conditions that could serve as the primary indication for hospitalization.

Improving COVID-19 Disease Severity Surveillance Measures: Statewide Implementation Experience

Measurement of the burden of COVID-19 on U.S. hospitals has been an important element of the public health response to the pandemic. However, because of variation in testing density and policies, the metric is not standardized across facilities. Two types of burdens exist, one related to the infection control measures that patients who test positive for SARS-CoV-2 require and one from the care of severely ill patients receiving treatment of COVID-19. With rising population immunity from vaccination and infection, as well as the availability of therapeutics, severity of illness has declined. Prior research showed that dexamethasone administration was highly correlated with other disease severity metrics and sensitive to the changing epidemiology associated with the emergence of immune-evasive variants.

On 10 January 2022, the Massachusetts Department of Public Health began requiring hospitals to expand surveillance to include reports of both the total number of "COVID-19 hospitalizations" daily and the number of inpatients who received dexamethasone at any point during their hospital stay. All 68 acute care hospitals in Massachusetts submitted COVID-19 hospitalization and dexamethasone data daily to the Massachusetts Department of Public Health over a 1-year period. A total of 44 196 COVID-19 hospitalizations were recorded during 10 January 2022 to 9 January 2023, of which 34% were associated with dexamethasone administration. The proportion of patients hospitalized with COVID-19 who had received dexamethasone was 49.6% during the first month of surveillance and decreased to a monthly average of approximately 33% by April 2022, where it has remained since (range, 28.7% to 33%).

Adding a single data element to mandated reporting to estimate the frequency of severe COVID-19 in hospitalized patients was feasible and provided actionable information for health authorities and policy makers. Updates to surveillance methods are necessary to match data collection with public health response needs.

The burden of incidental SARS-CoV-2 infections in hospitalized patients across pandemic waves in Canada

Many health authorities differentiate hospitalizations in patients infected with SARS-CoV-2 as being "for COVID-19" (due to direct manifestations of SARS-CoV-2 infection) versus being an "incidental" finding in someone admitted for an unrelated condition. We conducted a retrospective cohort study of all SARS-CoV-2 infected patients hospitalized via 47 Canadian emergency departments, March 2020-July 2022 to determine whether hospitalizations with "incidental" SARS-CoV-2 infection are less of a burden to patients and the healthcare system. Using a priori standardized definitions applied to hospital discharge diagnoses in 14,290 patients, we characterized COVID-19 as (i) the "Direct" cause for the hospitalization (70%), (ii) a potential "Contributing" factor for the hospitalization (4%), or (iii) an "Incidental" finding that did not influence the need for admission (26%). The proportion of incidental SARS-CoV-2 infections rose from 10% in Wave 1 to 41% during the Omicron wave. Patients with COVID-19 as the direct cause of hospitalization exhibited significantly longer LOS (mean 13.8 versus 12.1 days), were more likely to require critical care (22% versus 11%), receive COVID-19-specific therapies (55% versus 19%), and die (17% versus 9%) compared to patients with Incidental SARS-CoV-2 infections. However, patients hospitalized with incidental SARS-CoV-2 infection still exhibited substantial morbidity/mortality and hospital resource use.

Home oxygen therapy from the emergency department for COVID-19 an observational study

STUDY OBJECTIVE

During the COVID-19 pandemic, prescribing supplemental oxygen was a common reason for hospitalization of patients. We evaluated outcomes of COVID-19 patients discharged from the Emergency Department (ED) with home oxygen as part of a program to decrease hospital admissions.

METHODS

We retrospectively observed COVID-19 patients with an ED visit resulting in direct discharge or observation from April 2020 to January 2022 at 14 hospitals in a single healthcare system. The cohort included those discharged with new oxygen supplementation, a pulse oximeter, and return instructions. Our primary outcome was subsequent hospitalization or death outside the hospital within 30 days of ED or observation discharge.

RESULTS

Among 28,960 patients visiting the ED for COVID-19, providers admitted 11,508 (39.7%) to the hospital, placed 907 (3.1%) in observation status, and discharged 16,545 (57.1%) to home. A total of 614 COVID-19 patients (535 discharge to home and 97 observation unit) went home on new oxygen therapy. We observed the primary outcome in 151 (24.6%, CI 21.3-28.1%) patients. There were 148 (24.1%) patients subsequently hospitalized and 3 (0.5%) patients who died outside the hospital. The subsequent hospitalized mortality rate was 29.7% with 44 of the 148 patients admitted to the hospital dying. Mortality all cause at 30 days in the entire cohort was 7.7%.

CONCLUSIONS

Most patients discharged to home with new oxygen for COVID-19 safely avoid later hospitalization and few patients die within 30 days. This suggests the feasibility of the approach and offers support for ongoing research and implementation efforts.

Factors associated with the speed and scope of diffusion of COVID-19 therapeutics in a nationwide healthcare setting: a mixed-methods investigation

BACKGROUND

The global COVID-19 pandemic is an opportunity to evaluate factors associated with high levels of adoption of different therapeutics in a real-world setting. The aim of this nationwide, retrospective cohort study was to evaluate the diffusion and adoption of novel therapeutics with an emerging evidence basis and to identify factors that influenced physicians' treatment decisions.

METHODS

Cohort creation: A cohort of Veteran patients with a microbiologically confirmed diagnosis of SARS-CoV2 were identified, and cases were classified by disease severity (outpatient, inpatient with mild and severe disease, intensive care unit ICU]). After classification of disease severity, the proportion of cases (outpatients) and admissions (inpatients) in each category receiving each type of medication were plotted as a function of time. Identification of milestones and guidance changes: Key medications used for the management of COVID-19 milestones in the release of primary research results in various forms (e.g. via press release, preprint or publication in a traditional medical journal), policy events and dates of key guidelines were identified and plotted as a timeline. After a timeline was created, time points were compared to changes in medication use, and factors potentially impacting the magnitude (i.e. proportion of patients who received the treatment) and the speed (i.e. the slope of the change in use) of practice changes were evaluated.

RESULTS

Dexamethasone and remdesivir, the first two medications with clinical trial data to support their use, underwent the most rapid, complete and sustained diffusion and adoption; the majority of practice changes occurred after press releases and preprints were available and prior to guideline changes, although some additional uptake occurred following guideline updates. Medications that were not "first in class", that were identified later in the pandemic, and that had higher perceived risk had slower and less complete uptake regardless of the strength and quality of the evidence supporting the intervention.

CONCLUSIONS

Our findings suggest that traditional and social media platforms and preprint releases were major catalysts of practice change, particularly prior to the identification of effective treatments. The "first available treatment in class" impact appeared to be the single most important factor determining the speed and scope of diffusion.

Factors Associated With Severe COVID-19 Among Vaccinated Adults Treated in US Veterans Affairs Hospitals

IMPORTANCE

With a large proportion of the US adult population vaccinated against SARS-CoV-2, it is important to identify who remains at risk of severe infection despite vaccination.

OBJECTIVE

To characterize risk factors for severe COVID-19 disease in a vaccinated population.

DESIGN, SETTING, AND PARTICIPANTS

This nationwide, retrospective cohort study included US veterans who received a SARS-CoV-2 vaccination series and later developed laboratory-confirmed SARS-CoV-2 infection and were treated at US Department of Veterans Affairs (VA) hospitals. Data were collected from December 15, 2020, through February 28, 2022.

EXPOSURES

Demographic characteristics, comorbidities, immunocompromised status, and vaccination-related variables.

MAIN OUTCOMES AND MEASURES

Development of severe vs nonsevere SARS-CoV-2 infection. Severe disease was defined as hospitalization within 14 days of a positive SARS-CoV-2 diagnostic test and either blood oxygen level of less than 94%, receipt of supplemental oxygen or dexamethasone, mechanical ventilation, or death within 28 days. Association between severe disease and exposures was estimated using logistic regression models.

RESULTS

Among 110 760 patients with infections following vaccination (97 614 [88.1%] men, mean [SD] age at vaccination, 60.8 [15.3] years; 26 953 [24.3%] Black, 11 259 [10.2%] Hispanic, and 71 665 [64.7%] White), 10 612 (9.6%) had severe COVID-19. The strongest association with risk of severe disease after vaccination was age, which increased among patients aged 50 years or older with an adjusted odds ratio (aOR) of 1.42 (CI, 1.40-1.44) per 5-year increase in age, such that patients aged 80 years or older had an aOR of 16.58 (CI, 13.49-20.37) relative to patients aged 45 to 50 years. Immunocompromising conditions, including receipt of different classes of immunosuppressive medications (eg, leukocyte inhibitor: aOR, 2.80; 95% CI, 2.39-3.28) or cytotoxic chemotherapy (aOR, 2.71; CI, 2.27-3.24) prior to breakthrough infection, or leukemias or lymphomas (aOR, 1.87; CI, 1.61-2.17) and chronic conditions associated with end-organ disease, such as heart failure (aOR, 1.74; CI, 1.61-1.88), dementia (aOR, 2.01; CI, 1.83-2.20), and chronic kidney disease (aOR, 1.59; CI, 1.49-1.69), were also associated with increased risk. Receipt of an additional (ie, booster) dose of vaccine was associated with reduced odds of severe disease (aOR, 0.50; CI, 0.44-0.57).

CONCLUSIONS AND RELEVANCE

In this nationwide, retrospective cohort of predominantly male US Veterans, we identified risk factors associated with severe disease despite vaccination. Findings could be used to inform outreach efforts for booster vaccinations and to inform clinical decision-making about patients most likely to benefit from preexposure prophylaxis and antiviral therapy.

Effectiveness of COVID-19 vaccines against Omicron and Delta hospitalisation, a test negative case-control study

The Omicron variant has been associated with reduced vaccine effectiveness (VE) against mild disease with rapid waning. Meanwhile Omicron has also been associated with milder disease. Protection against severe disease has been substantially higher than protection against infection with previous variants. We used a test-negative case-control design to estimate VE against hospitalisation with the Omicron and Delta variants using PCR testing linked to hospital records. We investigated the impact of increasing the specificity and severity of hospitalisation definitions on VE. Among 18-64-year-olds using cases admitted via emergency care, VE after a 3rd dose peaked at 82.4% and dropped to 53.6% by 15+ weeks after the 3rd dose; using all admissions for > = 2 days stay with a respiratory code in the primary diagnostic field VE ranged from 90.9% to 67.4%; further restricting to those on oxygen/ventilated/intensive care VE ranged from 97.1% to 75.9%. Among 65+ year olds the equivalent VE estimates were 92.4% to 76.9%; 91.3% to 85.3% and 95.8% to 86.8%. Here we show that with milder Omicron disease contamination of hospitalisations with incidental cases is likely to reduce VE estimates. VE estimates increase, and waning is reduced, when specific hospitalisation definitions are used.

Effectiveness of COVID-19 vaccines against Omicron and Delta hospitalisation, a test negative case-control study

The Omicron variant has been associated with reduced vaccine effectiveness (VE) against mild disease with rapid waning. Meanwhile Omicron has also been associated with milder disease. Protection against severe disease has been substantially higher than protection against infection with previous variants. We used a test-negative case-control design to estimate VE against hospitalisation with the Omicron and Delta variants using PCR testing linked to hospital records. We investigated the impact of increasing the specificity and severity of hospitalisation definitions on VE. Among 18-64-year-olds using cases admitted via emergency care, VE after a 3rd dose peaked at 82.4% and dropped to 53.6% by 15+ weeks after the 3rd dose; using all admissions for > = 2 days stay with a respiratory code in the primary diagnostic field VE ranged from 90.9% to 67.4%; further restricting to those on oxygen/ventilated/intensive care VE ranged from 97.1% to 75.9%. Among 65+ year olds the equivalent VE estimates were 92.4% to 76.9%; 91.3% to 85.3% and 95.8% to 86.8%. Here we show that with milder Omicron disease contamination of hospitalisations with incidental cases is likely to reduce VE estimates. VE estimates increase, and waning is reduced, when specific hospitalisation definitions are used.

The effectiveness of COVID-19 vaccines in reducing the incidence, hospitalization, and mortality from COVID-19: A systematic review and meta-analysis

Background

Vaccination, one of the most important and effective ways of preventing infectious diseases, has recently been used to control the COVID-19 pandemic. The present meta-analysis study aimed to evaluate the effectiveness of COVID-19 vaccines in reducing the incidence, hospitalization, and mortality from COVID-19.

Methods

A systematic search was performed independently in Scopus, PubMed via Medline, ProQuest, and Google Scholar electronic databases as well as preprint servers using the keywords under study. We used random-effect models and the heterogeneity of the studies was assessed using I ² and χ² statistics. In addition, the Pooled Vaccine Effectiveness (PVE) obtained from the studies was calculated by converting based on the type of outcome.

Results

A total of 54 studies were included in this meta-analysis. The PVE against SARS-COV 2 infection were 71% [odds ratio (OR) = 0.29, 95% confidence intervals (CI): 0.23-0.36] in the first dose and 87% (OR = 0.13, 95% CI: 0.08-0.21) in the second dose. The PVE for preventing hospitalization due to COVID-19 infection was 73% (OR = 0.27, 95% CI: 0.18-0.41) in the first dose and 89% (OR = 0.11, 95% CI: 0.07-0.17) in the second dose. With regard to the type of vaccine, mRNA-1273 and combined studies in the first dose and ChAdOx1 and mRNA-1273 in the second dose had the highest effectiveness in preventing infection. Regarding the COVID-19-related mortality, PVE was 68% (HR = 0.32, 95% CI: 0.23-0.45) in the first dose and 92% (HR = 0.08, 95% CI: 0.02-0.29) in the second dose.

Conclusion

The results of this meta-analysis indicated that vaccination against COVID-19 with BNT162b2 mRNA, mRNA-1273, and ChAdOx1, and also their combination, was associated with a favorable effectiveness against SARS-CoV2 incidence rate, hospitalization, and mortality rate in the first and second doses in different populations. We suggest that to prevent the severe form of the disease in the future, and, in particular, in the coming epidemic picks, vaccination could be the best strategy to prevent the severe form of the disease.

Systematic review registration

PROSPERO International Prospective Register of Systematic Reviews: http://www.crd.york.ac.uk/PROSPERO/, identifier [CRD42021289937].

COVID-19 patient reported symptoms using FLU-PRO Plus in a cohort study: associations with infecting genotype, vaccine history, and return-to-health

Background

Patient reported outcomes of SARS-CoV-2 infection are an important measure of the full burden of COVID. Here, we examine how 1) infecting genotype and COVID-19 vaccination correlate with FLU-PRO Plus score, including by symptom domains, and 2) FLU-PRO Plus scores predict return to usual activities and health.

Methods

The EPICC study was implemented to describe the short- and long-term consequences of SARS-CoV-2 infection in a longitudinal, observational cohort. Multivariable linear regression models were run with FLU-PRO Plus scores as the outcome variable and multivariable Cox proportional hazards models evaluated effects of FLU-PRO Plus scores on return to usual health or activities.

Results

Among the 764 participants included in this analysis, 63% were 18-44 years old, 40% were female, and 51% were white. Being fully vaccinated was associated with lower total scores (β=-0.39 (95% confidence interval (CI) -0.57, -0.21)). The Delta variant was associated with higher total scores (β=0.25 (95% CI 0.05, 0.45)). Participants with higher FLU-PRO Plus scores were less likely to report returning to usual health and activities (Health: hazard ratio (HR) 0.46 (95% CI 0.37, 0.57); Activities: HR 0.56 (95% CI 0.47, 0.67)). Fully vaccinated participants were more likely to report returning to usual activities (HR 1.24 (95% CI 1.04, 1.48)).

Conclusions

Full SARS-CoV-2 vaccination is associated with decreased severity of patient-reported symptoms across multiple domains, which in turn is likely to be associated with earlier return to usual activities. In addition, infection with the Delta variant was associated with higher FLU-PRO Plus scores than previous variants, even after controlling for vaccination status.