The Impact of Initial COVID-19 Episode Inflammation Among Adults on Mortality Within 12 Months Post-hospital Discharge

Authors: Arch G. Mainous III1,2*Benjamin J. Rooks1 and Frank A. Orlando1 May 12, 2022 Frontiers in Medicine

Background: Inflammation in the initial COVID-19 episode may be associated with post-recovery mortality. The goal of this study was to determine the relationship between systemic inflammation in COVID-19 hospitalized adults and mortality after recovery from COVID-19.

Methods: An analysis of electronic health records (EHR) for patients from 1 January, 2020 through 31 December, 2021 was performed for a cohort of COVID-19 positive hospitalized adult patients. 1,207 patients were followed for 12 months post COVID-19 episode at one health system. 12-month risk of mortality associated with inflammation, C-reactive protein (CRP), was assessed in Cox regressions adjusted for age, sex, race and comorbidities. Analyses evaluated whether steroids prescribed upon discharge were associated with later mortality.

Results: Elevated CRP was associated other indicators of severity of the COVID-19 hospitalization including, supplemental oxygen and intravenous dexamethasone. Elevated CRP was associated with an increased mortality risk after recovery from COVID-19. This effect was present for both unadjusted (HR = 1.60; 95% CI 1.18, 2.17) and adjusted analyses (HR = 1.61; 95% CI 1.19, 2.20) when CRP was split into high and low groups at the median. Oral steroid prescriptions at discharge were found to be associated with a lower risk of death post-discharge (adjusted HR = 0.49; 95% CI 0.33, 0.74).

Discussion: Hyperinflammation present with severe COVID-19 is associated with an increased mortality risk after hospital discharge. Although suggestive, treatment with anti-inflammatory medications like steroids upon hospital discharge is associated with a decreased post-acute COVID-19 mortality risk.

Introduction

The impact of coronavirus disease 2019 (COVID-19) has been immense. In terms of directly measured outcomes, as of February, 2022, worldwide more than 5.9 million people have died from directly linked COVID-19 episodes. More than 950,000 direct deaths from COVID-19 have been documented in the United States (1). Some evidence has suggested that some patients with COVID-19 may be at risk for developing health problems after the patient has recovered from the initial episode (24). Common sequelae that have been noted are fatigue, shortness-of-breath, and brain fog. Perhaps more concerningly, in addition to these symptoms, several studies have shown that following recovery from the initial COVID-19 episode, some patients are at risk for severe morbidity and mortality (58). Patients who have recovered from COVID-19 are at increased risk for hospitalization and death within 6–12 months after the initial episode. This morbidity and mortality is typically not listed or considered as a COVID-19 linked hospitalization or death in the medical records and thus are underreported as a post-acute COVID-19 sequelae.

The reason for this phenomenon of severe outcomes as post-acute sequelae of COVID-19 is not well understood. Early in COVID-19 episode, the disease is primarily driven by the replication of SARS-CoV-2. COVID-19 also exhibits a dysregulated immune/inflammatory response to SARS-CoV-2 that leads to tissue damage. The downstream impact of the initial COVID-19 episode is consistently higher in people with more severe acute infection (569). Cytokine storm, hyperinflammation, and multi-organ failure have also been indicated in patients with a severe COVID-19 episode (10). Cerebrospinal fluid samples indicate neuroinflammation during acute COVID-19 episodes (11). Moreover, even 40–60 days post-acute COVID-19 infection there is evidence of a significant remaining inflammatory response in patients (12). Thus, it could be hypothesized that the hyperinflammation that some COVID-19 patients have during the initial COVID-19 episode creates a systemic damage to multiple organ systems (1314). Consequently, that hyperinflammation and the corresponding systemic damage to multiple organ systems may lead to severe post-acute COVID-19 sequelae.

Following from this hyperinflammation, the use of steroids as anti-inflammatory treatments among patients with high inflammation during the initial COVID-19 episode may do more than just help in the initial episode but may act as a buffer to the downstream morbidity and mortality from the initial COVID-19 episode (1415).

The purpose of this study was to examine the relationship between substantial systemic inflammation, as measured by C-reactive protein (CRP), with post-acute COVID-19 sequelae among patients hospitalized with COVID-19. This 12-month mortality risk was examined in a longitudinal cohort of patients who tested positive for COVID-19 as determined by Polymerase Chain Reaction (PCR) testing within a large healthcare system.

Methods

The data for this project comes from a de-identified research databank containing electronic health records (EHR) of patients tested for or diagnosed with COVID-19 in any setting in the University of Florida (UF) Health system. Usage of the databank for research is not considered human subjects research, and IRB review was not required to conduct this study.

Definition of Cohort

The cohort for this study consisted of all adult patients aged 18 and older who were tested for COVID-19 between January 01, 2020 and December 31, 2021 within the UF Health system, in any encounter type (ambulatory, Emergency Department, inpatient, etc.). Although a patient in the cohort could have had a positive test administered in any of these settings, a patient was only included into the cohort if that patient experienced a hospitalization for COVID-19. Since this study included data from the early stages of the pandemic before consistent coding standards for documenting COVID-19 in the EHR had been established, a patient was considered to have been hospitalized for COVID-19 if they experienced any hospitalization within 30 days of a positive test for COVID-19. The databank contained EHR data for all patients in the cohort current through December 31, 2021. COVID-19 diagnosis was validated by PCR. Baseline dates for COVID-19 positive patients were established at the date of their earliest recorded PCR-confirmed positive COVID-19 test. Each patient was only included once in the analysis. For patients with multiple COVID-19 tests, if at least one test gave a positive result, the patient was classified as COVID-19 positive, and the date of their earliest positive COVID-19 test result was used as their baseline date. Patients who did not have a positive COVID-19 test were not included in the analysis. Patients were tested in the context of seeking care for COVID-19; the tests were not part of general screening and surveillance.

Only patients with at least 365 days of follow-up time after their baseline date were retained in the cohort. Patients with more than 365 days of follow-up were censored at 365 days. The cohort was also left censored at the 30-day mark post-hospital discharge to ensure that health care utilization was post-acute and not part of the initial COVID-19 episode of care (e.g., not a readmission).

Inflammation

C-reactive protein (CRP) was used as the measure of inflammation in this study. The UF Health laboratory measured CRP in serum using latex immunoturbidimetry assay. CRP measures were sourced from patient EHR data. The cohort was restricted to only include patients with at least one CRP measurement within their initial COVID-19 episode of care (between the date of their initial positive COVID-19 test and the left-hand censoring date). For patients with multiple measurements of CRP, the maximum value available was used.

Steroids

Intravenous dexamethasone during their initial COVID-19 hospitalization was assessed. Prescriptions for oral steroids (tablets of dexamethasone) that were prescribed either at or post-hospital discharge for their initial COVID-19 episode of care were included into the analysis. Prescriptions were identified using RxNorm codes available in each patient’s EHR.

Severity of Initial COVID-19 Hospitalization

We also measured the severity of the initial episode of COVID-19 hospitalization. This severity should track with the level of inflammation in the initial COVID-19 episode. We used the National Institutes of Health’s “Therapeutic Management of Hospitalized Adults With COVID-19” disease severity levels and definitions (16). The recommendations are based on four ascending levels: hospitalized but does not require supplemental oxygen, hospitalized and requires supplemental oxygen, hospitalized and requires supplemental oxygen through a high-flow device or noninvasive ventilation, hospitalized and requires mechanical ventilation or extracorporeal membrane oxygenation. For this study, because of the general conceptual model of severity moving from no supplemental oxygen to supplemental oxygen to mechanical ventilation, we collapsed the two supplemental non-mechanical ventilation oxygen into one intermediate category of severity.

Outcome Variables

The primary outcome investigated in this study was the 365-day all-cause mortality. Mortality data was sourced both from EHR data and the Social Security Death Index (SSDI), allowing for the assessment of deaths which occurred outside of UF’s healthcare system. When conflicting dates of death were observed between the EHR and SSDI, the date recorded in the patient’s medical record was used. Patients who died within their 365-day follow-up window were censored at the date of their recorded death. The cause of death was not available in the EHR based database and was not routinely and reliably reported in either the SSDI or EHR. We were unable to estimate the cause of death.

Comorbidities

Comorbidities and demographic variables which could potentially confound the association between inflammation represented by CRP and mortality post-acute COVID-19 were collected at baseline for each member of the cohort. Demographic variables included patient age, race, ethnicity, and sex. The Charlson Comorbidity Index was also calculated, accounting for the conditions present for each patient at their baseline. The Charlson Comorbidity Index was designed to be used to predict 1-year mortality and is a widely used measure to account for comorbidities (17).

Analysis

CRP was evaluated using descriptive statistics. We performed a median split of the CRP levels and defined elevated inflammation as a CRP level at or above the median and levels below the median as low inflammation. Additionally, as a way to examine greater separation between high and low inflammation, we segmented CRP levels into tertiles and categorized elevated inflammation as the top tertile and compared it to the first tertile by chi-square tests.

CRP level was also cross classified by severity of COVID-19 hospitalization and associations between the two variables were assessed using one-way ANOVA tests.

Kaplan-Meier curves comparing the survival probabilities of the high and low inflammation groups were created and compared using a log-rank test. Hazard ratios for the risk of death for post-acute COVID-19 complications by COVID-19 status were determined using Cox proportional hazard models. We obtained hazard ratios for mortality based on tertile and median splits of CRP. These analyses were then modified to control for age, sex, race, ethnicity, and the Charlson Comorbidity Index.

Additional analyses stratified by use of steroids were performed to compare the strength of the association between inflammation and death. The proportional hazards assumption was confirmed by inspection of the Schoenfeld residual plots for each variable included in the models and testing of the time-dependent beta coefficients. Analyses were conducted using the survival package in R v4.0.5.

Results

A total of 1,207 patients were included in the final cohort (Table 1). The characteristics of the patients are featured in Table 1. The mean CRP rises with the severity of illness in these COVID-19 inpatients. The mean CRP in the lowest severity (no supplemental oxygen) is 59.4 mg/L (SD = 61.8 mg/L), while the mean CRP in the intermediate severity group (supplemental oxygen) is 126.9 mg/L (SD = 98.6 mg/L), and the mean CRP in the highest severity group (ventilator or ECMO) is 201.2 mg/L (SD = 117.0 mg/L) (p < 0.001). Similarly, since dexamethasone is only recommended for the most severe patients with COVID-19, patients with dexamethasone had higher CRP (158.8 mg/L; SD = 114.9 mg/L) than those not on Dexamethasone (102.8 mg/L; SD = 90/8 mg/L) (p < 0.001).TABLE 1

Table 1. Characteristics of the patients in the cohort.

Figure 1 presents the Kaplan-Meier curves comparing the risk of mortality by inflammation over time. A log-rank test indicated there was a statistically significant difference in survival probabilities between the two groups (p = 0.002).FIGURE 1

Figure 1. All-cause mortality Kaplan-Meier curve comparing individuals with median or greater vs. below median C-reactive protein levels. Log rank test = p.002.

Table 2 shows the relationship between levels of inflammation and mortality post-recovery from COVID-19. In both unadjusted and adjusted analyses, elevated inflammation has a significantly increased risk compared to those with low inflammation in the initial COVID-19 episode. This finding of higher inflammation during the initial COVID-19 hospitalization and increased mortality risk after recovery was similar when CRP was split at the median and when the third tertile of CRP was compared to the first tertile of CRP. The proportional hazards assumption was met when the Schoenfeld plots.TABLE 2

Table 2. All-cause mortality hazard ratios by inflammation and steroid use.

We examined the hypothesized relationship that potentially decreasing inflammation in COVID-19 patients with an initial severe episode may have beneficial downstream effects on post-acute COVID-19 sequelae. Oral steroid prescriptions at discharge among these hospitalized COVID-19 patients were found to be associated with a lower risk of death post-discharge (Table 2).

Discussion

The results of this study reaffirm the importance of post-acute COVID-19 sequelae. This study is the first to show the impact of inflammation in the initial COVID-19 hospitalization episode on downstream mortality after the patient has recovered. This expands our understanding of post-acute COVID-19 sequelae by providing a better concept of why certain patients have post-acute COVID-19 mortality risk.

Previous studies have shown that patients who are hospitalized with COVID-19 have an increased risk of mortality 12 months after recovery (5). Those findings suggest that prevention of COVID-19 hospitalizations is of paramount importance. However, some patients will be hospitalized. The finding that elevated inflammation during the initial hospitalization episode is associated with mortality risk after recovery suggests that it may be worthwhile treating the viral episode but also consider treating the hyperinflammation. The NIH recommendations for care of COVID-19 hospitalized patients recommend steroids only for patients who need supplemental oxygen (16). The finding that the use of steroids prescribed upon discharge from the hospital and the corresponding reduced risk of mortality indicate that treating inflammation after the acute COVID-19 episode may act as a buffer to the downstream mortality risk from the initial COVID-19 episode (1415). Perhaps this requires a reconceptualization of COVID-19 as both an acute disease and potentially a chronic disease because of the lingering risks. Future research is needed to see if ongoing treatment for inflammation in a clinical trial has positive benefits.

There are several strengths and limitations to this study. The strengths of this study include the PCR validated COVID-19 tests at baseline for the cohort. Further, the linked electronic health record allows us to look not only at health care utilization like hospitalizations and both inpatient and outpatient medication but also laboratory tests like CRP levels. The cohort also allows us to have a substantial follow-up time.

In terms of limitations, the first that needs to be considered is that the analysis was based on hospitalized patients seen in one health system with a regional catchment area. Although more than 1200 hospitalized patients with PCR validated COVID-19 diagnoses were included in the analysis, and the cohort was followed for 12 months, the primary independent variable was systemic inflammation which should not be substantially affected by region of the country. Second, the data are observational. Thus, the analyses related to steroids and downstream mortality require a clinical trial to confirm these suggestive findings. Third, we did not have death certificates available to us to compute cause of death. The Social Security Death Index in partnership with the EHR allows us to be confident that the patient died and so we have a strong measure of all-cause mortality but we were unable to determine specific causes of death within this database. Fourth, although there are a variety of other markers of inflammation (e.g., D dimer, IL 6), CRP is one of the most robust measures of systemic inflammation. Moreover, it is much more widely used and was the most prevalent marker among the patients in the study.

In conclusion, hyperinflammation present with severe COVID-19 is associated with an increased mortality risk after hospital discharge. Although suggestive, treatment with anti-inflammatory medications like steroids upon hospital discharge is associated with a decreased post-acute COVID-19 mortality risk. This suggests that treating inflammation may also benefit other post-acute sequelae like long COVID. A reconceptualization of COVID-19 as both an acute and chronic condition may be useful.

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Trends and associated factors for Covid-19 hospitalisation and fatality risk in 2.3 million adults in England

Authors: T. BeaneyA. L. NevesA. AlboksmatyH. AshrafianK. FlottA. FowlerJ. R. Benger

P. AylinS. ElkinA. Darzi & J. Clarke  Nature Communications volume 13, Article number: 2356 (2022) 

Abstract

The Covid-19 mortality rate varies between countries and over time but the extent to which this is explained by the underlying risk in those infected is unclear. Using data on all adults in England with a positive Covid-19 test between 1st October 2020 and 30th April 2021 linked to clinical records, we examined trends and risk factors for hospital admission and mortality. Of 2,311,282 people included in the study, 164,046 (7.1%) were admitted and 53,156 (2.3%) died within 28 days of a positive Covid-19 test. We found significant variation in the case hospitalisation and mortality risk over time, which remained after accounting for the underlying risk of those infected. Older age groups, males, those resident in areas of greater socioeconomic deprivation, and those with obesity had higher odds of admission and death. People with severe mental illness and learning disability had the highest odds of admission and death. Our findings highlight both the role of external factors in Covid-19 admission and mortality risk and the need for more proactive care in the most vulnerable groups.

Introduction

The Covid-19 case fatality ratio (CFR) varies widely between countries1 and definitions of mortality differ across the world, making comparisons challenging2. In England, the most widely reported measure is mortality within 28 days of a positive test3. Up to 21 September 2021, 539,921 hospital admissions and 118,846 deaths have occurred in England, out of a total of 6,398,633 cases, giving a crude case hospitalisation ratio (CHR) of 8.4% and a CFR of 1.9%4. Previous epidemiological studies have shown variation in the CFR over time1,5, but without individual level data, it is unclear the extent to which this variation is accounted for by differences in the risk of those infected.

Many risk factors for death from Covid-19 have been characterised, such as increased age, male gender, and obesity6. Several long-term conditions are strongly linked to a higher mortality risk; in England, this led to the early adoption of a ‘clinically extremely vulnerable’ (CEV) status for those deemed to be at highest risk, subsequently advised to isolate to reduce transmission7. Previous studies have focussed on the first wave of the pandemic in the first half of 2020, which may not be representative of subsequent pandemic waves, particularly given advances in the management of Covid-19 patients and the emergence of new variants8. Furthermore, to our knowledge, no study to date has used data with national coverage, including all laboratory-confirmed Covid-19 test results linked to electronic health record (EHR) data.

The main aim of this paper is to describe the changing trends in the Covid-19 case hospitalisation risk (CHR) and case fatality risk (CFR) in England, during the ‘second wave’ of the pandemic (i.e., from 1st October 2020 to 30th April 2021). The secondary aims are to identify patient characteristics associated with hospitalisation and mortality risk; and to evaluate whether residual unexplained variation in the CHR and CFR remains after accounting for differences in the underlying risk factors of those infected.

Results

From 1st October 2020 to 30th April 2021, data were available for 2,433,768 individuals with a positive Covid-19 test result in England. Data for 34,317 (1.4%) participants with a positive test result could not be linked to either primary or secondary care records and were excluded. Care home residents accounted for 3.7% of the total (n = 88,169) and were excluded from further analyses, resulting in a total population of 2,311,282.

Characteristics of the study population are provided in Table 1. The mean (SD) age of participants was 44.3 (17.1) years, with 43.6% under 40 years. The majority were female (54.3%) and of White ethnicity (72.8%). There were relatively higher proportions from more deprived deciles of IMD, with 56.7% in the bottom five deciles. Similar proportions of subjects with a healthy weight (28.4%), overweight (28.1%) or obese (26.1%) were observed, and only 3.4% were underweight. 16.3% were current smokers and 8.3% were designated as CEV. Chronic respiratory disease (21.2%), hypertension (15.0%) and diabetes (8.6%) were the three most prevalent chronic conditions in the population.Table 1 Characteristics of the study population with hospital admissions and deaths within 28 days (N = 2,311,282).Full size table

Case hospitalisation and fatality risk over time

Of the study population, 164,046 people were admitted to hospital at least once within 28 days of a positive test, giving a crude CHR of 7.1% over the seven-month period. 53,156 deaths occurred within 28 days of a positive test, giving a crude CFR of 2.3%. Of these, 49,172 (92.5%) had Covid-19 as a cause of death on the death certificate. There were significant differences over time in both the CHR and CFR (Supplementary Fig. 1). The age distribution of people with a positive test varied over time, with the highest proportions of all infection in people aged 60 years and above infected in November 2020 and January 2021 (Supplementary Table 1). Within all age groups, a similar pattern of change in the CHR and CFR over time was seen, with risk peaking in December 2020–February 2021 (Supplementary Tables 2 and 3, respectively, and Supplementary Fig. 2).

Factors associated with 28-day mortality and hospitalisation risk

Multiple imputation was used to impute missing data for 381,283 people. Multivariable logistic regression models were constructed for each outcome adjusting for all patient level covariates (model 2). Calibration plots indicated adequate calibration (Supplementary Figs. 3 and 4). Results for hospital admissions and mortality are presented in Figs. 1 and 2 (also Supplementary Tables 4 and 5). Males had 41% higher adjusted odds of admission (95% CI: 1.39–1.42) and 62% higher adjusted odds of mortality (95% CI: 1.58–1.65) compared to females. People of all four non-White ethnicities had higher odds of admission, and those of Asian and Black ethnicities also had higher odds of mortality compared to those of White ethnicity. People living in less deprived areas had lower odds of both admission and mortality compared to those in the most deprived areas. Compared to people of a healthy weight, those underweight had 10% higher odds of admission (95% CI: 1.05–1.14) and 99% higher odds of death (95% CI: 1.87–2.11). People who were overweight had a 24% increase in odds of admission (95% CI: 1.22–1.26) but 20% lower odds of death (95% CI: 0.77-0.82); those who were obese had 93% higher odds of admission (1.90–1.97) and 4% increased odds of death (95% CI: 1.01–1.07). Current smokers had lower odds of admission compared to non-smokers but an increase in the odds of death after adjustment.

figure 1
Fig. 1: Adjusted odds ratios for emergency hospital admission within 28 days of positive Covid-19 test.
figure 2
Fig. 2: Adjusted odds ratios for death within 28 days of positive Covid-19 test.

All chronic conditions included were strongly associated with an increase in odds of admission and death, except for dementia, which was associated with 6% lower odds of admission. People identified as CEV had 85% higher odds of being admitted to hospital (95% CI: 1.83–1.88) but 12% lower odds of death (95% CI: 0.86–0.90) after full adjustment. In a sub-analysis adjusting CEV status for age, time (and their interaction), sex, ethnicity, and deprivation only, odds of admission were significantly higher (aOR 2.62, 95% CI: 2.58–2.65) as were odds of death (aOR 1.52, 95% CI: 1.49–1.55).

A sensitivity analysis of the 1,929,999 complete cases showed similar estimates to the fully adjusted model (Supplementary Tables 6 and 7).

CHR and CFR over time

A significant association remained with time for both CHR and CFR models after adjusting for all patient covariates (p < 0.0001 in each model from likelihood ratio tests). The predicted CHR and CFR from the fully adjusted models are plotted for the whole population (Supplementary Fig. 5) and by age category in Fig. 3, showing that a significant time-varying relationship remained after adjustment. The relative change in predicted CHR and CFR from the baseline predicted risk in the first full week of October is shown in Fig. 4 (and Supplementary Figs. 6 and 7). The CFR increased across all age groups, peaking between late December 2020 to early February 2021in different age groups before declining towards April. A smaller relative increase in hospitalisation risk was seen across age groups. In most age groups, CHR peaked in January, except in the 18–39 age group, which continued to increase throughout the study period. After adjustment, the trends in absolute mortality and hospitalisation risk in each age group were similar to those in the unadjusted analyses (Fig. 4 and Supplementary Fig. 2) indicating that the distributions of risk factors of those infected within age groups did not change significantly over time.

figure 3
Fig. 3: 28-day case hospitalisation risk and fatality risk over time in people with Covid-19.
figure 4
Fig. 4: Relative change in 28-day case hospitalisation risk and fatality risk over time in people with Covid-19.

Discussion

In this retrospective cohort study including all adults in England with a positive Covid-19 test result, there was significant variation in the 28-day CHR and CFR by age group and over time, which remained after accounting for individual risk. Demographics and chronic conditions were strongly associated with hospitalisation and death.

Variation in CHR and CFR over time

Across the whole study population, CHR and CFR varied over time from 1st October 2020 to 30th April 2021. This was partially explained by the changing age distributions of those infected, but significant variation remained after adjustment. Within age groups, absolute differences in the CHR and CFR over time were greatest in older age groups, reflecting higher baseline risk, but the relative risk varied significantly across all groups. Historically, there is a strong seasonal component to mortality in England, with figures indicating 16.8% higher mortality in winter months compared to summer months9. An increased incidence of respiratory diseases, including influenza, are one of the main drivers of increased winter mortality, and the 28-day mortality metric used in this study includes deaths from non-Covid-19 causes. However, with influenza rates at lower levels than previous years, it is unlikely the variation in CFR over time can be explained by the incidence of other infectious diseases alone10.

Strain on the health system may also contribute to the patterns seen, with Covid-19 bed occupancy and critical care occupancy in England peaking in January 2021, associated with a lower proportion of patients seen in Accident & Emergency departments within 4 hours than in November 2020 and February 20214,11. Larger relative increases were seen in the CFR compared to the CHR, which may indicate a health system reaching full capacity and struggling to meet demand. A previous UK study of patients admitted to hospital with Covid-19 found a fall in mortality from March to July 2020, a time over which bed occupancy fell and evidence for new treatments, such as dexamethasone, became available, with similar findings from a US cohort between March and September 202012,13. Changes to care delivery at an organisational level may also have an impact, with triage models for Covid-19 patients on the national 111 urgent care service varying between services and over time14. The Alpha variant became the dominant Covid-19 strain in England in December 2020, and has been associated with a 64% increase in 28-day mortality compared to prior variants, which may explain part of the rise in the CHR and CFR15.

Declines in the CHR and CFR from January 2021 onwards are likely to be explained at least partially by the development of immunity, both through natural infection and by the vaccination programme, which was implemented from 8th December 2020 in England for the highest risk cohorts16. By February 2021, over 80% of over 80s had been vaccinated in most regions of the UK, with similar vaccine coverage in the 70–79 year age group by mid-February and in the 60–69 year age group by mid-March (Supplementary Figs. 810)17. However, our study population includes people with a positive Covid-19 test, who are more likely to be unvaccinated than the general population; population vaccine coverage is, therefore, unlikely to be representative of our study population and estimates could not be incorporated robustly into our modelling. Declines in CFR and CHR are most marked in older age groups, who were the first groups eligible for vaccination. However, declines in mortality are seen across all age groups, including the 18–39 year group, many of whom would not have been eligible for vaccination, suggesting vaccination does not fully account for the declines observed. Availability of new treatments may also explain the falls in mortality, with the RECOVERY trial demonstrating the benefit of tocilizumab published in February 2021, but is unlikely to explain the fall in admissions8,18.

Factors associated with hospitalisation and mortality

The findings of a higher risk of mortality in males, people of Asian and Black ethnic backgrounds, and those living in more deprived areas are consistent with a previous UK cohort and confirmed in our study, including an increased risk of admission6. People who were underweight were more likely to be admitted and had significantly higher risk of death, which might be partly accounted for by unmeasured associated conditions, such as frailty. People who were overweight and obese had higher risk of admission than those of a healthy weight, but mortality risk was lower in those overweight, which may indicate higher perceived risk amongst clinicians and a lower threshold for admission.

People identified as CEV were significantly more likely to be admitted but were found to have significantly lower mortality, after adjusting for other risk factors including co-morbidities. However, in partially adjusted models not including BMI, smoking, or clinical co-morbidities, those identified as CEV had significantly higher odds of death. Taken together, these findings indicate a lower threshold for clinical assessment and/or admission and escalation in CEV patients with a protective effect on mortality. All twelve included clinical co-morbidities were associated with significant increases in the odds of mortality and admission. Severe mental illness and learning disability had the strongest associations with mortality and admission, highlighting a need for more proactive care in these groups and more research into the reasons for mortality differences19. Those with dementia had significantly increased odds of mortality but were less likely to be admitted, suggesting they are more likely to receive care at home, although the cohort did not include those living in care homes and so will not represent the full population of those with dementia.

The emergence of the Delta and Omicron variants have shown the potential of Covid-19 to vary in both transmissibility and pathogenicity over time. In England, December 2021–January 2022 saw the highest case numbers but without the resulting number of hospitalisations and deaths associated with earlier variants and before widespread vaccination4. Despite the emergence of new variants, the findings of our study are relevant in highlighting that the risk of mortality was independent of an extensive panel of clinical and demographic factors in the winter of 2020/21, pointing to the role of wider strain on the health system as an important feature in outcomes in people with Covid-19. While the Omicron variant has contributed to an increase in hospitalisations and emergency department presentations in England and elsewhere, its impact on staff absence has been particularly marked. At the peak of the Omicron wave in early January 2022, almost 50,000 NHS staff were absent due to Covid-19, almost a five-fold increase from the end of November 202120,21,22. Ensuring health systems possess the resilience to weather the dual shocks of an increased demand for care and decreased capacity to provide it, without adversely affecting the quality and safety of healthcare, is an ongoing area of concern.

Strengths and limitations

A strength of this study is the inclusion of routine national laboratory data for positive Covid-19 test results in adults in England with only 1.5% unable to be linked to EHR data, and as a result, has lower risk of sampling bias23. To our knowledge, this is the largest such study including individual level data at a national level. Previous studies in England on predictors of mortality are reported on a smaller cohort of patients with 40% national coverage6. The use of multiple imputation assumes that data are missing at random, and we cannot rule out non-random missing patterns, particularly for data on ethnicity and deprivation, where more marginalised groups are less likely to be registered in the primary care record. However, sensitivity analyses showed inferences were similar between the complete case analysis and imputed results, suggesting limited impact of the missing data on model estimates. Associations with risk factors may also be confounded by differential uptake of vaccinations among risk groups; for example, if those with co-morbidities or defined as CEV were more likely to be vaccinated, the odds ratios for hospitalisation and death may be under-estimated.

Data represented here include only those who died within 28 days of a positive test result, in line with estimates reported by PHE. Deaths mentioning Covid-19 on a death certificate are an alternative metric used widely in many countries as recommended by the World Health Organisation24 and have tended to give a larger estimate of deaths in England, due to those attributable to Covid-19 after 28 days4. Over 90% of deaths within 28 days in our study also had Covid-19 as a cause of death on the death certificate, but we did not have corresponding data for those cases recorded on a death certificate without a positive Covid-19 test. The associations found in our study might be different if using deaths recorded on death certificates, rather than deaths within 28 days of a positive Covid-19 test, particularly if there were changes to death certification practices over time.

Through use of linked EHR data, we were able to incorporate detailed medical factors for the study cohort. However, we were unable to explore the relationship with external factors such as Covid-19 variants. Geographical and time-varying system factors, such as proximity to a hospital and hospital capacity are likely to impact on a person’s health-seeking behaviour. Our study included people living in the community and given patients in England may attend any hospital, and the size of hospital markets vary considerably across the country, we could not reliably model the impact of nearby hospital bed availability at an individual level. However, our modelling showed only minimal residual variation accounted for by CCG level clustering (intraclass correlation coefficient <1%), suggesting these additional factors would have minimal impact on the findings. Access to testing may also impact the probability of having a positive test. Positivity rates in England peaked on 31st December 2020 at 18.3% and fell to 1.7% by 1st April 20214, but the extent to which this reflects increased incidence or a lack of test availability is uncertain. It is possible that if testing were limited during the peak in cases in December 2020–January 2021, those with more symptomatic disease may have been more likely to receive a test, compared to those who were asymptomatic or with mild symptoms. This may in turn lead to an apparent increase in risk of mortality due to changes in the severity of illness of those testing positive, rather than the severity of disease within the community as a whole. Furthermore, access to testing may be driven by sociodemographic factors, and the finding of lower hospitalisation and mortality risk in less deprived areas could reflect better availability of testing. Exploring mortality risk in patients admitted to hospital or to intensive care units and whether this changed over time was outside the scope of the current study but is an area for further research.

The risk of hospitalisation and death from Covid-19 varied significantly over time from October 2020 to April 2021 in all age groups, independent of the underlying risk in those infected. Time-varying risks should be considered by researchers and policymakers in assessing the risks of hospitalisation and mortality from Covid-19. People with severe mental illness and learning disability were amongst those with the highest odds of both admission and mortality, indicating the need for proactive care in these groups.

Methods

The work was conducted as part of a wider service evaluation, approved by Imperial College Healthcare Trust on December 3rd 2020. Data access was approved by the Independent Group Advising on the Release of Data (IGARD; DARS-NIC-421524-R0Y3P) on April 15th 2021.

Study design and population

We conducted a retrospective cohort study including all adults (≥18 years) resident in England with a positive Covid-19 test result (polymerase chain reaction or lateral flow tests) from 1st October 2020 to 30th April 2021, excluding people resident in care homes. Study participants were followed-up for 28 days from the date of a first positive test. The two primary outcomes were (i) one or more emergency hospital admissions and (ii) death from any cause, each within 28 days from the date of the positive test.

Data sources and data processing

Several datasets were linked for this study and provided by NHS Digital as part of an evaluation of the NHS England Covid Oximetry @home programme25. Covid-19 testing data was sourced from the Public Health England (PHE) Second Generation Surveillance System26, the national laboratory reporting system for positive Covid-19 tests, covering the period from 1st October 2020 to 30th April 2021. Primary care data came from the General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR)27. CEV status was linked to GDPPR from the Shielded Patient List (see Supplementary Methods)28. Data on hospital admissions came from Hospital Episode Statistics (HES) data set up to 31st May 2021, linked to Office for National Statistics (ONS) data on death registrations up to 5th July 2021. Datasets were linked using a de-identified NHS patient ID. Participants who could not be linked from testing data to at least one of GDPPR or HES were excluded.

Patient demographics were derived from GDPPR, or  where missing, from HES. Lower layer super output area (LSOA) of residence was linked to indices of relative deprivation using deciles of Index of Multiple Deprivation (IMD) 201929. Residence in a care home, CEV status, body mass index (BMI), and smoking status were derived from GDPPR only. BMI was categorised as underweight (<18.5 kg/m2), healthy weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2) and obese (≥30.0 kg/m2). Chronic conditions were extracted from GDPPR based on Systematised Nomenclature of Medicine Clinical Terms (SNOMED-CT) codes pertaining to relevant diagnosis code clusters. Only codes recorded prior to the date of a positive Covid-19 test were included, to exclude any diagnoses following Covid-19 infection. Where the latest code indicated resolution of a condition, the diagnosis was excluded for that individual. Further details on data curation are given in the Supplementary Methods.

Statistical analysis

Patients were followed from date of first positive Covid-19 test to emergency hospital admission or death within 28 days. Mixed effects logistic regression was conducted for each outcome, with a two-level hierarchical model incorporating Clinical Commissioning Group (CCG, of which there are 106 in England) of residence as a random intercept. Time, represented by the week of Covid-19 test, was modelled as a restricted cubic spline with five knots placed at equally spaced percentiles30. Two models were run for each outcome:

  1. 1.Model 1: incorporating age category and time splines along with their interaction.
  2. 2.Model 2: incorporating age category and time splines along with their interaction and including all additional patient level covariates: sex, ethnicity, IMD decile, BMI category, CEV status, smoking status, and presence of chronic conditions.

For model 2, multiple imputation using chained equations was used to impute missing values of covariates, under the assumption that values were missing at random. All variables included in the analysis model were included in the imputation model31. Fifteen imputations were created, with a burn-in of 10 iterations which gave adequate precision and convergence, respectively (Supplementary Methods). A sensitivity analysis was performed using complete cases only. Calibration was assessed using plots of predicted against observed probabilities for each decile of predicted probability.

For each outcome, the predicted probability of the outcome was computed within each age group and study week stratum to calculate age- and time-specific case hospitalisation risk (CHR) and case fatality risk (CFR). These were calculated using the fixed portion of the model (assuming zero random effects). The relative changes in the CHR and CFR over time were calculated as the predicted probability in each week relative to the week of 5th–11th October 2020 in each age group. In adjusted models (model 2), other model covariates were set to the population mean (or proportion for categorical variables) within each age group. For CEV status, an additional sub-analysis was conducted adjusting only for the age category and time splines (and their interaction), sex, ethnicity, and IMD decile. Further details of the statistical methods are given in Supplementary Methods.

Analyses were conducted in the Big Data and Analytics Unit Secure Environment, Imperial College, using Python version 3.9.5, Pandas version 1.2.3, and Stata version 17.0 (StataCorp).

Data availability

The patient level data used in this study are not publicly available but are available to applicants meeting certain criteria through application of a Data Access Request Service (DARS) and approval from the Independent Group Advising on the Release of Data. Further information is given below: https://digital.nhs.uk/about-nhs-digital/corporate-information-and-documents/independent-group-advising-on-the-release-of-data.

Code availability

The SNOMED terms used in defining chronic conditions are available in our GitHub repository: https://github.com/tbeaney/Imperial-COh-evaluation. Further analysis codes are available on request to the corresponding author.

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COVID-19 Cases and Hospitalizations by COVID-19 Vaccination Status and Previous COVID-19 Diagnosis — California and New York, May–November 2021

Weekly / January 28, 2022 / 71(4);125–131 January 19, 2022, this report was posted online as an MMWR Early Release.

Authors: Tomás M. León, PhD1; Vajeera Dorabawila, PhD2; Lauren Nelson, MPH1; Emily Lutterloh, MD2,3; Ursula E. Bauer, PhD2; Bryon Backenson, MPH2,3; Mary T. Bassett, MD2; Hannah Henry, MPH1; Brooke Bregman, MPH1; Claire M. Midgley, PhD4; Jennifer F. Myers, MPH1; Ian D. Plumb, MBBS4; Heather E. Reese, PhD4; Rui Zhao, MPH1; Melissa Briggs-Hagen, MD4; Dina Hoefer, PhD2; James P. Watt, MD1; Benjamin J. Silk, PhD4; Seema Jain, MD1; Eli S. Rosenberg, PhD2,3

Summary

What is already known about this topic?

Data are limited regarding the risks for SARS-CoV-2 infection and hospitalization after COVID-19 vaccination and previous infection.

What is added by this report?

During May–November 2021, case and hospitalization rates were highest among persons who were unvaccinated without a previous diagnosis. Before Delta became the predominant variant in June, case rates were higher among persons who survived a previous infection than persons who were vaccinated alone. By early October, persons who survived a previous infection had lower case rates than persons who were vaccinated alone.

What are the implications for public health practice?

Although the epidemiology of COVID-19 might change as new variants emerge, vaccination remains the safest strategy for averting future SARS-CoV-2 infections, hospitalizations, long-term sequelae, and death. Primary vaccination, additional doses, and booster doses are recommended for all eligible persons. Additional future recommendations for vaccine doses might be warranted as the virus and immunity levels change.

By November 30, 2021, approximately 130,781 COVID-19–associated deaths, one in six of all U.S. deaths from COVID-19, had occurred in California and New York.* COVID-19 vaccination protects against infection with SARS-CoV-2 (the virus that causes COVID-19), associated severe illness, and death (1,2); among those who survive, previous SARS-CoV-2 infection also confers protection against severe outcomes in the event of reinfection (3,4). The relative magnitude and duration of infection- and vaccine-derived protection, alone and together, can guide public health planning and epidemic forecasting. To examine the impact of primary COVID-19 vaccination and previous SARS-CoV-2 infection on COVID-19 incidence and hospitalization rates, statewide testing, surveillance, and COVID-19 immunization data from California and New York (which account for 18% of the U.S. population) were analyzed. Four cohorts of adults aged ≥18 years were considered: persons who were 1) unvaccinated with no previous laboratory-confirmed COVID-19 diagnosis, 2) vaccinated (14 days after completion of a primary COVID-19 vaccination series) with no previous COVID-19 diagnosis, 3) unvaccinated with a previous COVID-19 diagnosis, and 4) vaccinated with a previous COVID-19 diagnosis. Age-adjusted hazard rates of incident laboratory-confirmed COVID-19 cases in both states were compared among cohorts, and in California, hospitalizations during May 30–November 20, 2021, were also compared. During the study period, COVID-19 incidence in both states was highest among unvaccinated persons without a previous COVID-19 diagnosis compared with that among the other three groups. During the week beginning May 30, 2021, compared with COVID-19 case rates among unvaccinated persons without a previous COVID-19 diagnosis, COVID-19 case rates were 19.9-fold (California) and 18.4-fold (New York) lower among vaccinated persons without a previous diagnosis; 7.2-fold (California) and 9.9-fold lower (New York) among unvaccinated persons with a previous COVID-19 diagnosis; and 9.6-fold (California) and 8.5-fold lower (New York) among vaccinated persons with a previous COVID-19 diagnosis. During the same period, compared with hospitalization rates among unvaccinated persons without a previous COVID-19 diagnosis, hospitalization rates in California followed a similar pattern. These relationships changed after the SARS-CoV-2 Delta variant became predominant (i.e., accounted for >50% of sequenced isolates) in late June and July. By the week beginning October 3, compared with COVID-19 cases rates among unvaccinated persons without a previous COVID-19 diagnosis, case rates among vaccinated persons without a previous COVID-19 diagnosis were 6.2-fold (California) and 4.5-fold (New York) lower; rates were substantially lower among both groups with previous COVID-19 diagnoses, including 29.0-fold (California) and 14.7-fold lower (New York) among unvaccinated persons with a previous diagnosis, and 32.5-fold (California) and 19.8-fold lower (New York) among vaccinated persons with a previous diagnosis of COVID-19. During the same period, compared with hospitalization rates among unvaccinated persons without a previous COVID-19 diagnosis, hospitalization rates in California followed a similar pattern. These results demonstrate that vaccination protects against COVID-19 and related hospitalization, and that surviving a previous infection protects against a reinfection and related hospitalization. Importantly, infection-derived protection was higher after the Delta variant became predominant, a time when vaccine-induced immunity for many persons declined because of immune evasion and immunologic waning (2,5,6). Similar cohort data accounting for booster doses needs to be assessed, as new variants, including Omicron, circulate. Although the epidemiology of COVID-19 might change with the emergence of new variants, vaccination remains the safest strategy to prevent SARS-CoV-2 infections and associated complications; all eligible persons should be up to date with COVID-19 vaccination. Additional recommendations for vaccine doses might be warranted in the future as the virus and immunity levels change.

Four cohorts of persons aged ≥18 years were assembled via linkages of records from electronic laboratory reporting databases and state-specific immunization information systems. Persons were classified based on whether they had had a laboratory-confirmed SARS-CoV-2 infection by March 1, 2021 (i.e., previous COVID-19 diagnosis)§; had received at least the primary COVID-19 vaccination series by May 16, 2021; had a previous COVID-19 diagnosis and were fully vaccinated**; or had neither received a previous COVID-19 diagnosis by March 1 nor received a first COVID-19 vaccine dose by the end of the analysis period. The size of the unvaccinated group without a previous diagnosis was derived by subtracting the observed groups from U.S. Census estimates.†† To maintain each defined cohort, persons who received a COVID-19 diagnosis during March 1–May 30, 2021, or who died before May 30, 2021, were excluded (to maintain eligibility for incident cases for all cohorts on May 30, 2021),§§ as were persons who received a first vaccine dose during May 30–November 20, 2021. During May 30–November 20, 2021, incident cases were defined using a positive nucleic acid amplification test (NAAT) result from the California COVID-19 Reporting System (CCRS) or a positive NAAT or antigen test result from the New York Electronic Clinical Laboratory Reporting System. In California, person-level hospitalization data from CCRS and supplementary hospitalization reports were used to identify COVID-19–associated hospitalizations. A lifetable method was used to calculate hazard rates (average daily cases during a 7-day interval or hospitalizations over a 14-day interval), hazard ratios, and 95% CIs for each cohort. Rates were age-adjusted to 2000 U.S. Census data using direct standardization.¶¶ Supplementary analyses stratified case rates by timing of previous diagnoses and primary series vaccine product. SAS (version 9.4; SAS Institute) and R (version 4.0.4; The R Foundation) were used to conduct all analyses. Institutional review boards (IRBs) in both states determined this surveillance activity to be necessary for public health work, and therefore, it did not require IRB review.

Approximately three quarters of adults from California (71.2%) and New York (72.2%) included in this analysis were vaccinated and did not have a previous COVID-19 diagnosis; however, 18.0% of California residents and 18.4% of New York residents were unvaccinated with no previous COVID-19 diagnosis (Table 1). In both states, 4.5% of persons were vaccinated and had a previous COVID-19 diagnosis; 6.3% in California and 4.9% in New York were unvaccinated with a previous diagnosis. Among 1,108,600 incident COVID-19 cases in these cohorts (752,781 in California and 355,819 in New York), the median intervals from vaccination or previous COVID-19 diagnosis to incident diagnosis were slightly shorter in California (138–150 days) than in New York (162–171 days).

Before the Delta variant became predominant in each state’s U.S. Department of Health and Human Services region (June 26 in Region 9 [California] and July 3 in Region 2 [New York]),*** the highest incidence was among unvaccinated persons without a previous COVID-19 diagnosis; during this time, case rates were relatively low among the three groups with either previous infection or vaccination and were lowest among vaccinated persons without a previous COVID-19 diagnosis (Supplementary Figure 1, https://stacks.cdc.gov/view/cdc/113253) (Supplementary Figure 2, https://stacks.cdc.gov/view/cdc/113253). During the week beginning May 30, 2021, compared with COVID-19 case rates among unvaccinated persons without a previous COVID-19 diagnosis, COVID-19 case rates were 19.9-fold (California) and 18.4-fold (New York) lower among vaccinated persons without a previous diagnosis; rates were 7.2-fold (California) and 9.9-fold (New York) lower among unvaccinated persons with a previous COVID-19 diagnosis and 9.6-fold (California) and 8.5-fold (New York) lower among vaccinated persons with a previous COVID-19 diagnosis (Table 2).

As the Delta variant prevalence increased to >95% (97% in Region 9 and 98% in Region 2 on August 1), rates increased more rapidly among the vaccinated group with no previous COVID-19 diagnosis than among both the vaccinated and unvaccinated groups with a previous COVID-19 diagnosis (Supplementary Figure 1, https://stacks.cdc.gov/view/cdc/113253) (Supplementary Figure 2, https://stacks.cdc.gov/view/cdc/113253). For example, during the week of October 3, compared with rates among unvaccinated persons without a previous COVID-19 diagnosis, rates among vaccinated persons without a previous diagnosis were 6.2-fold lower (95% CI = 6.0–6.4) in California and 4.5-fold lower (95% CI = 4.3–4.7) in New York (Table 2). Further, rates among unvaccinated persons with a previous COVID-19 diagnosis were 29-fold lower (95% CI = 25.0–33.1) than rates among unvaccinated persons without a previous COVID-19 diagnosis in California and 14.7-fold lower (95% CI = 12.6–16.9) in New York. Rates among vaccinated persons who had had COVID-19 were 32.5-fold lower (95% CI = 27.5–37.6) than rates among unvaccinated persons without a previous COVID-19 diagnosis in California and 19.8-fold lower (95% CI = 16.2–23.5) in New York. Rates among vaccinated persons without a previous COVID-19 diagnosis were consistently higher than rates among unvaccinated persons with a history of COVID-19 (3.1-fold higher [95% CI = 2.6–3.7] in California and 1.9-fold higher [95% CI = 1.5–2.3] in New York) and rates among vaccinated persons with a history of COVID-19 (3.6-fold higher [95% CI = 2.9–4.3] in California and 2.8-fold higher [95% CI = 2.1–3.4] in New York).

COVID-19 hospitalization rates in California were always highest among unvaccinated persons without a previous COVID-19 diagnosis (Table 2) (Figure). In the pre-Delta period during June 13–June 26, for example, compared with hospitalization rates among unvaccinated persons without a previous COVID-19 diagnosis, hospitalization rates were 27.7-fold lower (95% CI = 22.4–33.0) among vaccinated persons without a previous COVID-19 diagnosis, 6.0-fold lower (95% CI = 3.3–8.7) among unvaccinated persons with a previous COVID-19 diagnosis, and 7.1-fold lower (95% CI = 4.0–10.3) among vaccinated persons with a previous COVID-19 diagnosis. However, this pattern also shifted as the Delta variant became predominant. During October 3–16, compared with hospitalization rates among unvaccinated persons without a previous COVID-19 diagnosis, hospitalization rates were 19.8-fold lower (95% CI = 18.2–21.4) among vaccinated persons without a previous COVID-19 diagnosis, 55.3-fold lower (95% CI = 27.3–83.3) among unvaccinated persons with a previous COVID-19 diagnosis, and 57.5-fold lower (95% CI = 29.2–85.8) among vaccinated persons with a previous COVID-19 diagnosis.

Among the two cohorts with a previous COVID-19 diagnosis, no consistent incidence gradient by time since the previous diagnosis was observed (Supplementary Figure 3, https://stacks.cdc.gov/view/cdc/113253). When the vaccinated cohorts were stratified by the vaccine product received, among vaccinated persons without a previous COVID-19 diagnosis, the highest incidences were observed among persons receiving the Janssen (Johnson & Johnson), followed by Pfizer-BioNTech, then Moderna vaccines (Supplementary Figure 4, https://stacks.cdc.gov/view/cdc/113253). No pattern by product was observed among vaccinated persons with a previous COVID-19 diagnosis.

Discussion

This analysis integrated laboratory testing, hospitalization surveillance, and immunization registry data in two large states during May–November 2021, before widespread circulation of the SARS-CoV-2 Omicron variant and before most persons had received additional or booster COVID-19 vaccine doses to protect against waning immunity. Rate estimates from the analysis describe different experiences stratified by COVID-19 vaccination status and previous COVID-19 diagnosis and during times when different SARS-CoV-2 variants predominated. Case rates were initially lowest among vaccinated persons without a previous COVID-19 diagnosis; however, after emergence of the Delta variant and over the course of time, incidence increased sharply in this group, but only slightly among both vaccinated and unvaccinated persons with previously diagnosed COVID-19 (6). Across the entire study period, persons with vaccine- and infection-derived immunity had much lower rates of hospitalization compared with those in unvaccinated persons. These results suggest that vaccination protects against COVID-19 and related hospitalization and that surviving a previous infection protects against a reinfection. Importantly, infection-derived protection was greater after the highly transmissible Delta variant became predominant, coinciding with early declining of vaccine-induced immunity in many persons (5). Similar data accounting for booster doses and as new variants, including Omicron, circulate will need to be assessed.

The understanding and epidemiology of COVID-19 has shifted substantially over time with the emergence and circulation of new SARS-CoV-2 variants, introduction of vaccines, and changing immunity as a result. Similar to the early period of this study, two previous U.S. studies found more protection from vaccination than from previous infection during periods before Delta predominance (3,7). As was observed in the present study after July, recent international studies have also demonstrated increased protection in persons with previous infection, with or without vaccination, relative to vaccination alone†††, §§§ (4). This might be due to differential stimulation of the immune response by either exposure type.¶¶¶ Whereas French and Israeli population-based studies noted waning protection from previous infection, this was not apparent in the results from this or other large U.K. and U.S. studies**** (4,8). Further studies are needed to establish duration of protection from previous infection by variant type, severity, and symptomatology, including for the Omicron variant.

The findings in this report are subject to at least seven limitations. First, analyses were not stratified by time since vaccine receipt, but only by time since previous diagnosis, although earlier studies have examined waning of vaccine-induced immunity (Supplementary Figure 3, https://stacks.cdc.gov/view/cdc/113253) (2). Second, persons with undiagnosed infection are misclassified as having no previous COVID-19 diagnosis; however, this misclassification likely results in a conservative bias (i.e., the magnitude of difference in rates would be even larger if misclassified persons were not included among unvaccinated persons without a previous COVID-19 diagnosis). California seroprevalence data during this period indicate that the ratio of actual (presumptive) infections to diagnosed cases among adults was 2.6 (95% CI = 2.2–2.9).†††† Further, California only included NAAT results, whereas New York included both NAAT and antigen test results. However, antigen testing made up a smaller percentage of overall testing volume reported in California (7% of cases) compared with New York (25% of cases) during the study period. Neither state included self-tests, which are not easily reportable to public health. State-specific hazard ratios were generally comparable, although differences in rates among unvaccinated persons with a previous COVID-19 diagnosis were noteworthy. Third, potential exists for bias related to unmeasured confounding (e.g., behavioral or geographic differences in exposure risk) and uncertainty in the population size of the unvaccinated group without a previous COVID-19 diagnosis. Persons might be more or less likely to receive testing based on previous diagnosis or vaccination status; however, different trajectories between vaccinated persons with and without a previous COVID-19 diagnosis, and similar findings for cases and hospitalizations, suggest that these biases were minimal. Fourth, this analysis did not include information on the severity of initial infection and does not account for the full range of morbidity and mortality represented by the groups with previous infections. Fifth, this analysis did not ascertain receipt of additional or booster COVID-19 vaccine doses and was conducted before many persons were eligible or had received additional or booster vaccine doses, which have been shown to confer additional protection.§§§§ Sixth, some estimates lacked precision because of sample size limitations. Finally, this analysis was conducted before the emergence of the Omicron variant, for which vaccine or infection-derived immunity might be diminished.¶¶¶¶ This study offers a surveillance data framework to help evaluate both infections in vaccinated persons and reinfections as new variants continue to emerge.

Vaccination protected against COVID-19 and related hospitalization, and surviving a previous infection protected against a reinfection and related hospitalization during periods of predominantly Alpha and Delta variant transmission, before the emergence of Omicron; evidence suggests decreased protection from both vaccine- and infection-induced immunity against Omicron infections, although additional protection with widespread receipt of booster COVID-19 vaccine doses is expected. Initial infection among unvaccinated persons increases risk for serious illness, hospitalization, long-term sequelae, and death; by November 30, 2021, approximately 130,781 residents of California and New York had died from COVID-19. Thus, vaccination remains the safest and primary strategy to prevent SARS-CoV-2 infections, associated complications, and onward transmission. Primary COVID-19 vaccination, additional doses, and booster doses are recommended by CDC’s Advisory Committee on Immunization Practices to ensure that all eligible persons are up to date with COVID-19 vaccination, which provides the most robust protection against initial infection, severe illness, hospitalization, long-term sequelae, and death.***** Additional recommendations for vaccine doses might be warranted in the future as the virus and immunity levels change.

Acknowledgments

Dana Jaffe, California Department of Public Health; Rebecca Hoen, Meng Wu, New York State Department of Health; Citywide Immunization Registry Program, New York City Department of Health and Mental Hygiene.

Corresponding author: Tomás M. León, tomas.leon@cdph.ca.gov.


1California Department of Public Health; 2New York State Department of Health; 3University at Albany School of Public Health, SUNY, Rensselaer, New York; 4CDC.

All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

 https://covid.cdc.gov/covid-data-tracker/#cases_deathsper100klast7days

 Statewide immunization databases in California are the California Immunization Registry, Regional Immunization Data Exchange, and San Diego Immunization Registry; the laboratory system is the California COVID Reporting System (CCRS). In New York, immunization information systems include Citywide Immunization Registry and the New York State Immunization Information System; the laboratory system is the Electronic Clinical Laboratory Reporting System (ECLRS). California data were matched between the immunization and case registries using a probabilistic algorithm with exact match for zip code and date of birth and fuzzy match on first name and last name. New York data were matched to the ECLRS with the use of a deterministic algorithm based on first name, last name, and date of birth. In California, person-level hospitalization data from CCRS and supplementary hospitalization reports were used to identify COVID-19–associated hospitalizations.

§ For both classification into cohorts of persons with previous COVID-19 diagnoses and for measuring incident cases, laboratory-confirmed infection was defined as the receipt of a new positive SARS-CoV-2 nucleic acid amplification test (NAAT) or antigen test (both for New York and NAAT only for California) result, but not within 90 days of a previous positive result.

 Fully vaccinated with the primary vaccination series is defined as receipt of a second dose of an mRNA COVID-19 vaccine (Pfizer-BioNTech or Moderna) or 1 dose of the Janssen (Johnson & Johnson) vaccine ≥14 days before May 30, 2021.

** Because of the timing of full vaccination, the cohort definitions, and analysis timeframe, this cohort consisted nearly exclusively of persons who had previously received a laboratory-confirmed diagnosis of COVID-19 and later were fully vaccinated (California: 99.9%, New York: 99.7%), as opposed to the reverse order.

†† Whereas vaccinated cohorts were directly observed in the immunization information system databases, unvaccinated persons without a previous COVID-19 diagnosis were defined using U.S. Census population estimates minus the number of persons partially or fully vaccinated by December 11, 2021, and unvaccinated persons with a previous laboratory-confirmed infection before May 30, 2021. In California, the California Department of Finance population estimates were used for 2020, and the 2018 CDC National Center for Health Statistics Bridged Race file for U.S. Census population estimates were used in New York, consistent with other COVID-19 surveillance reporting.

§§ In California, a person-level match was performed to exclude deaths in each cohort before May 30, 2021. In New York, COVID-19 deaths were removed in aggregate from the starting number of unvaccinated persons with a previous COVID-19 diagnosis on May 30, 2021.

¶¶ https://www.cdc.gov/nchs/data/statnt/statnt20.pdfpdf icon

*** https://covid.cdc.gov/covid-data-tracker/#variant-proportions

††† https://www.medrxiv.org/content/10.1101/2021.09.12.21263461v1external icon

§§§ https://www.medrxiv.org/content/10.1101/2021.11.29.21267006v1external icon

¶¶¶ https://www.cdc.gov/coronavirus/2019-ncov/science/science-briefs/vaccine-induced-immunity.html#anchor_1635540449320

**** https://www.medrxiv.org/content/10.1101/2021.12.04.21267114v1external icon

†††† https://www.medrxiv.org/content/10.1101/2021.12.09.21267565v1external icon

§§§§ https://covid.cdc.gov/covid-data-tracker/#rates-by-vaccine-status

¶¶¶¶ https://www.medrxiv.org/content/10.1101/2021.12.30.21268565v1external iconhttps://www.medrxiv.org/content/10.1101/2022.01.07.22268919v1external icon

***** https://www.cdc.gov/vaccines/covid-19/clinical-considerations/covid-19-vaccines-us.html

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References

  1. Rosenberg ES, Holtgrave DR, Dorabawila V, et al. New COVID-19 cases and hospitalizations among adults, by vaccination status—New York, May 3–July 25, 2021. MMWR Morb Mortal Wkly Rep 2021;70:1306–11. https://doi.org/10.15585/mmwr.mm7037a7external icon PMID:34529645external icon
  2. Rosenberg ES, Dorabawila V, Easton D, et al. Covid-19 vaccine effectiveness in New York State. N Engl J Med 2021. Epub December 1, 2021. https://doi.org/10.1056/NEJMoa2116063external icon PMID:34942067external icon
  3. Cavanaugh AM, Spicer KB, Thoroughman D, Glick C, Winter K. Reduced risk of reinfection with SARS-CoV-2 after COVID-19 vaccination—Kentucky, May–June 2021. MMWR Morb Mortal Wkly Rep 2021;70:1081–3. https://doi.org/10.15585/mmwr.mm7032e1external icon PMID:34383732external icon
  4. Grant R, Charmet T, Schaeffer L, et al. Impact of SARS-CoV-2 Delta variant on incubation, transmission settings and vaccine effectiveness: Results from a nationwide case-control study in France. Lancet Reg Health Eur 2021. Epub November 26, 2021.  https://doi.org/10.1016/j.lanepe.2021.100278external icon
  5. Self WH, Tenforde MW, Rhoads JP, et al.; IVY Network. Comparative effectiveness of Moderna, Pfizer-BioNTech, and Janssen (Johnson & Johnson) vaccines in preventing COVID-19 hospitalizations among adults without immunocompromising conditions—United States. MMWR Morb Mortal Wkly Rep 2021;70:1337–43. https://doi.org/10.15585/mmwr.mm7038e1external icon PMID:34555004external icon
  6. Lin D-Y, Gu Y, Wheeler B, et al. Effectiveness of Covid-19 vaccines in the United States over 9 months: surveillance data from the state of North Carolina. [Preprint posted online October 26, 2021.] https://www.medrxiv.org/content/10.1101/2021.10.25.21265304v1external icon
  7. Bozio CH, Grannis SJ, Naleway AL, et al. Laboratory-confirmed COVID-19 among adults hospitalized with COVID-19–like illness with infection-induced or mRNA vaccine-induced SARS-CoV-2 immunity—nine states, January–September 2021. MMWR Morb Mortal Wkly Rep 2021;70:1539–44. https://doi.org/10.15585/mmwr.mm7044e1external icon PMID:34735425external icon
  8. Kim P, Gordon SM, Sheehan MM, Rothberg MB. Duration of SARS-CoV-2 natural immunity and protection against the Delta variant: a retrospective cohort study. Clin Infect Dis 2021. Epub December 3, 2021. https://doi.org/10.1093/cid/ciab999external icon PMID:34864907external icon

Pandemic health consequences: Grasping the long COVID tail

Emerging evidence suggests that approximately 10% of people who survive Coronavirus Disease 2019 (COVID-19) will have lingering symptoms that negatively affect their quality of life, ability to work, and function [1,2]. This important group of people with the post-COVID-19 condition may seem small in comparison to the overall number of people with COVID-19 infection [3]. However, many patients who survive COVID-19 are likely to have considerable symptom burden, high resource utilization and health service needs, reduced economic productivity, and possibly a shortened life expectancy. The study by Bhaskaran and colleagues published in PLOS Medicine addresses an evolving, poorly studied, and important area of health policy and planning related to the care of patients who survive hospitalization for COVID-19 [4].

At face value, the scope of the COVID-19 pandemic is enormous. Within 2 years, nearly 300 million people have been infected with the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus, and more than 5 million people have died from it [5]. But, there is also a long tail to this statistical distribution of hardship. Studies report that numerous patients will continue to experience fatigue, shortness of breath, pain, sleep disturbances, anxiety, and depression [6]. More serious organ dysfunction such as pulmonary fibrosis, cognitive impairment, myocarditis, and renal failure may also develop [6]. Whether these translate into clinical diagnoses of chronic diseases like interstitial lung disease, dementia, heart failure, and chronic kidney disease remains to be seen. Collectively, the prospect for immense suffering among these individuals will undoubtedly have huge and enduring impacts on healthcare systems globally. As the world continues its largest vaccination effort in history and looks to eliminate the impacts of acute COVID-19, we must not forget that a meaningful minority who survive will transition from an acute to chronic disease state. In turn, management strategies and health resource planning must also appropriately transition. As a multisystem disease, the post-COVID-19 condition will require the involvement of multidisciplinary care teams [7]: Who will help to look after these patients?

Bhaskaran and colleagues studied over 164,000 hospitalized adults with COVID-19 matched to an “active control” group of adults hospitalized with influenza and to general population controls. They compared the medium- and long-term risks of hospital admission and death across the 3 study groups. The main findings were that people discharged following hospitalization for COVID-19 had a 2-fold higher associated risk for rehospitalization and death than the general population and similar risks compared to those hospitalized for influenza. These outcomes were most pronounced in the first 30 days following discharge yet remained substantially elevated over time. Further, those hospitalized with COVID-19 were more likely to be rehospitalized or die from mental health or cognitive-related causes, especially if they had preexisting dementia, compared to those hospitalized with influenza.

Initial hospitalization with COVID-19 represents a crucial touch point within the healthcare system. The study by Bhaskaran and colleagues sheds important light on the health service needs of patients who survive hospitalization for COVID-19. It further helps disentangle the effects of hospitalization from respiratory viral infection on important outcomes. The current work builds on similar findings from a recent study of 47,780 hospitalized adults with COVID-19 who survived to discharge with a mean follow-up of 140 days [8]. In that study, rates of hospital readmission and mortality were 3.5 and 7.7 times greater in the previously hospitalized group of COVID-19 patients, compared to general population controls, respectively. Other studies from the United States and China followed patients hospitalized for COVID-19 and reported lower 60-day and 1-year rehospitalization rates ranging from 13% to 19.9%. However, these studies did not account for the competing risk of death as was done in the current study [911].

There were also noteworthy limitations of Bhaskaran and colleagues’ study. First, cause-specific outcomes among adults with COVID-19 may be artificially higher than those with influenza due to availability bias. Put simply, patients and providers may be much more aware of COVID-19 and its complications, including those related to return to hospital, than might be the case for those with pneumonia or even confirmed influenza. Second, the study used administrative data from primary care. While 98% of the population in England are registered with a general practice (thereby minimizing selection biases due to health-seeking behaviors), there are some geographical differences in the use of the OpenSAFELY platform, which may introduce the potential for selection bias. Third, this study was conducted in a high-income nation with substantial resources to support patients following infection with COVID-19. The generalizability of these findings to middle- and low-income nations, or those with limited resources, is unknown.

The study by Bhaskaran and colleagues has clear applications to healthcare resource planning and policy in the care of individuals who survive COVID-19. This suggests a substantial added burden on global healthcare systems. It further builds on our evolving knowledge of the post-COVID-19 condition and its lingering impacts, including on many previously healthy adults in their prime years of productivity. Still, a wealth of research is required to develop prediction tools to proactively identify and support the healthcare needs of survivors, including end-of-life care, develop new strategies to prevent and treat the post-COVID-19 condition, and encourage interprofessional teams to provide longitudinal care through innovative health policy interventions.

Early pandemic public messaging strategies focused on flattening the peak of the acute COVID-19 infection curve to preserve healthcare system capacity and its ability to deliver high-quality care. These efforts were generally successful. To preserve ongoing system capacity and provide high-quality patient care, the long COVID tail does not require further flattening, but rather demands new clinical and health policy strategies to address its potential for long-term suffering. Here, we must recognize that the head of the pandemic often demands our immediate attention, but we must not ignore its long and deadly tail.

References

  1. 1.Pizarro-Pennarolli C, Sánchez-Rojas C, Torres-Castro R, Vera-Uribe R, Sanchez-Ramirez DC, Vasconcello-Castillo L, et al. Assessment of activities of daily living in patients post COVID-19: a systematic review. PeerJ. 2021;9:e11026. pmid:33868804
  2. 2.Groff D, Sun A, Ssentongo AE, Ba DM, Parsons N, Poudel GR, et al. Short-term and Long-term Rates of Postacute Sequelae of SARS-CoV-2 Infection. JAMA Netw Open. 2021;4:e2128568. pmid:34643720
  3. 3.Rubin R. As Their Numbers Grow, COVID-19 “Long Haulers” Stump Experts. JAMA. 2020;324:1381–3. pmid:32965460
  4. 4.Bhaskaran K, Rentsch CT, Hickman G, Hulme WJ, Schultze A, Curtis HJ, et al. Overall and cause-specific hospitalisation and death after COVID-19 hospitalisation in England: A cohort study using linked primary care, secondary care and death registration data in the OpenSAFELY platform. PLoS Med. 2022.
  5. 5.World Health Organization. WHO Coronavirus (COVID-19) Dashboard. [cited 2021 Nov 10]. Available from: https://covid19.who.int.
  6. 6.Al-Aly Z, Xie Y, Bowe B. High-dimensional characterization of post-acute sequalae of COVID-19. Nature. 2021:1–8. pmid:33887749
  7. 7.Greenhalgh T, Knight M, A’Court C, Buxton M, Husain L. Management of post-acute COVID-19 in primary care. BMJ. 2020;370:m3026. pmid:32784198
  8. 8.Ayoubkhani D, Khunti K, Nafilyan V, Maddox T, Humberstone B, Diamond I, et al. Post-COVID syndrome in individuals admitted to hospital with COVID-19: retrospective cohort study. BMJ. 2021;372:n693. pmid:33789877
  9. 9.Chopra V, Flanders SA, O’Malley M, Malani AN, Prescott HC. Sixty-Day Outcomes Among Patients Hospitalized With COVID-19. Ann Intern Med. 2020. pmid:33175566
  10. 10.Donnelly JP, Wang XQ, Iwashyna TJ, Prescott HC. Readmission and Death After Initial Hospital Discharge Among Patients With COVID-19 in a Large Multihospital System. JAMA. 2021;325:304–6. pmid:33315057
  11. 11.Huang L, Yao Q, Gu X, Wang Q, Ren L, Wang Y, et al. 1-year outcomes in hospital survivors with COVID-19: a longitudinal cohort study. Lancet. 2021;398:747–58. pmid:34454673

COVID virus linked with headaches, altered mental status in hospitalized kids

Authors: UNIVERSITY OF PITTSBURGH Peer-Reviewed Publication

PITTSBURGH, Jan. 21, 2022 – Of hospitalized children who tested or were presumed positive for SARS-CoV-2, 44% developed neurological symptoms, and these kids were more likely to require intensive care than their peers who didn’t experience such symptoms, according to a new study led by a pediatrician-scientist at UPMC and the University of Pittsburgh School of Medicine

The most common neurologic symptoms were headache and altered mental status, known as acute encephalopathy. Published in Pediatric Neurology, these preliminary findings are the first insights from the pediatric arm of GCS-NeuroCOVID, an international, multi-center consortium aiming to understand how COVID-19 affects the brain and nervous system. 

“The SARS-CoV-2 virus can affect pediatric patients in different ways: It can cause acute disease, where symptomatic illness comes on soon after infection, or children may develop an inflammatory condition called MIS-C weeks after clearing the virus,” said lead author Ericka Fink, M.D., pediatric intensivist at UPMC Children’s Hospital of Pittsburgh, and associate professor of critical care medicine and pediatrics at Pitt. “One of the consortium’s big questions was whether neurological manifestations are similar or different in pediatric patients, depending on which of these two conditions they have.” 

To answer this question, the researchers recruited 30 pediatric critical care centers around the world. Of 1,493 hospitalized children, 1,278, or 86%, were diagnosed with acute SARS-CoV-2; 215 children, or 14%, were diagnosed with MIS-C, or multisystem inflammatory syndrome in children, which typically appears several weeks after clearing the virus and is characterized by fever, inflammation and organ dysfunction. 

The most common neurologic manifestations linked with acute COVID-19 were headache, acute encephalopathy and seizures, while youths with MIS-C most often had headache, acute encephalopathy and dizziness. Rarer symptoms of both conditions included loss of smell, vision impairment, stroke and psychosis.  

“Thankfully, mortality rates in children are low for both acute SARS-CoV-2 and MIS-C,” said Fink. “But this study shows that the frequency of neurological manifestations is high—and it may actually be higher than what we found because these symptoms are not always documented in the medical record or assessable. For example, we can’t know if a baby is having a headache.” 

The analysis showed that neurological manifestations were more common in kids with MIS-C compared to those with acute SARS-CoV-2, and children with MIS-C were more likely than those with acute illness to have two or more neurologic manifestations. 

According to Fink, the team recently launched a follow up study to determine whether acute SARS-CoV-2 and MIS-C—with or without neurologic manifestations—have lasting effects on children’s health and quality of life after discharge from hospital.  

“Another long-term goal of this study is to build a database that tracks neurological manifestations over time—not just for SARS-CoV-2, but for other types of infections as well,” she added. “Some countries have excellent databases that allow them to easily track and compare children who are hospitalized, but we don’t have such a resource in the U.S.” 

This study was partly funded by the Neurocritical Care Society Investing in Clinical Neurocritical Care Research (INCLINE) grant. 

Other researchers who contributed to the study include Courtney L. Robertson, M.D., Johns Hopkins Children’s Center; Mark S. Wainwright, M.D., Ph.D., University of Washington and Seattle Children’s Hospital; Juan D. Roa, M.D., Universidad Nacional de Colombia and Fundación Universitaria de Ciencias de la Salud; Michelle E. Schober, M.D., University of Utah, and other GCS-NeuroCOVID Pediatrics investigators who are listed in the paper. 

To read this release online or share it, visit http://www.upmc.com/media/news/012122-Fink-COVID-Children.  


JOURNAL

Pediatric Neurology

DOI

10.1016/j.pediatrneurol.2021.12.010 

METHOD OF RESEARCH

Observational study

SUBJECT OF RESEARCH

People

ARTICLE TITLE

Prevalence and Risk Factors of Neurologic Manifestations in Hospitalized Children Diagnosed with Acute SARS-CoV-2 or MIS-C

ARTICLE PUBLICATION DATE

21-Jan-2022

Gastrointestinal perforation secondary to COVID-19

Authors: Case reports and literature review Reem J. Al Argan, MBBS, SB-Med, SF-Endo, FACE, ECNU,Safi G. Alqatari, MBBS, MRCPI, MMedSc, CFP (Rheum), Abir H. Al Said, MBBS, SB-Med, CFP (Pulmo.), Raed M. Alsulaiman, MBBS, SB-Med, Abdulsalam Noor, MBBS, SB-Med, ArBIM, SF-Nephro, Lameyaa A. Al Sheekh, MD, SB-med, and Feda’a H. Al Beladi, MD

Introduction:

Corona virus disease-2019 (COVID-19) presents primarily with respiratory symptoms. However, extra respiratory manifestations are being frequently recognized including gastrointestinal involvement. The most common gastrointestinal symptoms are nausea, vomiting, diarrhea and abdominal pain. Gastrointestinal perforation in association with COVID-19 is rarely reported in the literature.

Patient concerns and diagnosis:

In this series, we are reporting 3 cases with different presentations of gastrointestinal perforation in the setting of COVID-19. Two patients were admitted with critical COVID-19 pneumonia, both required intensive care, intubation and mechanical ventilation. The first one was an elderly gentleman who had difficult weaning from mechanical ventilation and required tracheostomy. During his stay in intensive care unit, he developed Candidemia without clear source. After transfer to the ward, he developed lower gastrointestinal bleeding and found by imaging to have sealed perforated cecal mass with radiological signs of peritonitis. The second one was an obese young gentleman who was found incidentally to have air under diaphragm. Computed tomography showed severe pneumoperitoneum with cecal and gastric wall perforation. The third case was an elderly gentleman who presented with severe COVID-19 pneumonia along with symptoms and signs of acute abdomen who was confirmed by imaging to have sigmoid diverticulitis with perforation and abscess collection.

Interventions:

The first 2 cases were treated conservatively. The third one was treated surgically.

Outcome:

Our cases had a variable hospital course but fortunately all were discharged in a good clinical condition.

Conclusion:

Our aim from this series is to highlight this fatal complication to clinicians in order to enrich our understanding of this pandemic and as a result improve patients’ outcome.

Keywords: acute abdomen, acute diverticulitis, cecal mass, corona virus disease-2019, gastrointestinal perforation. 

Introduction

Corona virus disease-2019 (COVID-19) had been declared pandemic in March 2020.[1] It presents most commonly with fever in more than 80% of cases followed by respiratory symptoms which could progress to adult respiratory distress syndrome in critical cases.[2] However, extra respiratory manifestations are being frequently recognized in association with COVID-19.[3] The gastrointestinal (GI) manifestations have been reported in descriptive studies from China.[2] The most frequently reported GI symptoms are nausea, vomiting, diarrhoea, and abdominal pain.[2,4,5] However, it is rarely reported for COVID-19 to present with GI perforation. To the date of writing this report, there have been only 13 reported of GI perforation in association with COVID-19.

In this series, we are reporting 3 cases who developed GI perforation in association with COVID-19. The first 2 cases developed this fatal complication after presenting with critical COVID-19 pneumonia which required intensive care unit (ICU) admission and mechanical ventilation. The third case presented with severe COVID-19 pneumonia and was diagnosed to have GI perforation at the time of presentation. The first 2 cases were managed conservatively, and the third case was managed surgically. All of the 3 cases recovered and were discharged in good condition. We are reporting this series in order to highlight this rare but fatal complication of COVID-19. This will enhance awareness of clinicians to such complication where early diagnosis and management is crucial in order to improve the patients’ outcome.

2. Case reports

2.1. The patients provided informed consent for publication of their cases

2.1.1. First case

A 70-year old male patient known to have type 2 diabetes mellitus (T2DM), presented to our emergency department (ED) on 1st of June 2020 complaining of 3-day history of dry cough and fever. On examination: Vital signs were remarkable for tachypnea with respiratory rate (RR): 28/min and desaturation with oxygen saturation (O2 sat):81% on room air (RA) but maintained >94% on 15 litres of oxygen via a non-rebreather mask. Nasopharyngeal swab tested positive for SARS-CoV-2 polymerase chain reaction (PCR). Chest X-ray (CXR) showed bilateral lower lung fields air apace opacities (Fig. ​(Fig.1A)1A) consistent with COVID-19 pneumonia. Laboratory investigations were remarkable for high Lactate dehydrogenase (LDH), inflammatory markers, D-dimer and markedly elevated Ferritin (Table ​(Table1).1). He was started on Methylprednisolone 40 mg IV BID, Hydroxychloroquine, Ceftriaxone, Azithromycin, Oseltamivir, and Enoxaparin. After 5 days of hospital admission, he deteriorated and could not maintain saturation on non-rebreather mask, so he was shifted to ICU, intubated and mechanically ventilated. Ceftriaxone was upgraded to Meropenem in addition to same previous therapy. COVID-19 therapy was stopped after completing 10 days, but he was continued on steroids. Figure 1

The chest X-ray (CXR) of the 3 cases at the time of presentation. (A): CXR of the 1st case showing bilateral lower lung fields air apace opacities. (B): CXR of the 2nd case showing bilateral scattered air space consolidative patches throughout the lung fields predominantly over peripheral and basal lungs. (C): CXR of the 3rd case showing bilateral middle and lower zones peripheral ground glass opacities.

Table 1

The laboratory investigations of the 3 cases on presentation.

TestFirst caseSecond caseThird caseNormal range
Complete Blood Count
 White Blood cells6.44.25.7(4.0–11.0) K/uI
 Hemoglobin15.112.113.4(11.6–14.5) g/dL
 Platelets147232283(140–450) K/uI
Renal Profile
 Blood urea nitrogen101411(8.4–21) mg/dL
 Creatinine0.920.820.82(0.6–1.3) mg/dL
Liver Profile
 Total Bilirubin0.50.51.0(0.2–1.2) mg/dL
 Direct Bilirubin0.30.20.3(0.1–0.5) mg/dL
 Alanine Transferase (ALT)265241(7–55) U/L
 Aspartate transferase (AST)425052(5–34) U/L
 Alkaline phosphatase (ALP)745574(40–150) U/L
 Gamma-glutamyl transpeptidase (GGTP)532139(12–64) U/L
 Lactate dehydrogenase (LDH)434442617(81–234) U/L
Inflammatory Markers
 Erythrocyte Sedimentation rate (ESR)63101490–10 mm/h
 C-Reactive Protein (CRP)7.9218.3210.780–5 mg/dL
Others
 Ferritin1114.72565.86654.87(21.81–274.66) ng/mL
 D-Dimer0.60.411.66<=0.5 ug/mL

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Multiple trials of weaning from mechanical ventilation failed. So, tracheostomy was carried out on 20th day of ICU admission and then he was successfully extubated. During his stay in ICU, urine analysis was persistently positive for urinary tract infection secondary to Candida Abican. So, he was started on Caspofungin. At that time, blood culture was negative. After 4 days of Caspofungin, urine analysis and culture became negative. On 32nd day of hospital admission, he was stable clinically, requiring 40% FiO2 through tracheostomy mask, so he was transferred to COVID-19 isolation ward. Meropenem was stopped after 20 days of treatment. Steroid was tapered after transfer to the ward till it was discontinued after 28 days of therapy.

After 14 days of treatment with Caspofungin, follow up C-reactive protein was persistently high. Thus, full septic workup was requested and revealed Candida Albican bacteremia. At that time, urine analysis and culture were negative, Caspofungin was continued for additional 14 days. However, Candidemia persisted, so he was shifted to Anidulafungin for another 14 days. Patient at that time did not have any GI symptoms or signs. For work up of Candidemia, echocardiogram could not be done due to the hospital policy of isolation rooms. Bed side ophthalmology examination was unremarkable.

On 44th day of hospital admission, he developed fresh bleeding per rectum. Hemodynamics were stable. The bleeding resulted in acute drop of 2 g/dL of hemoglobin over 24 hours. He denied abdominal pain, abdominal examination was negative for signs of peritonitis and per rectum examination was unremarkable. Therefore, computed tomography (CT) scan of the abdomen with contrast was carried out. It showed a well-defined mass within the posterior wall of the cecum measuring 3.1 × 3.2 cm associated with discontinuous enhancement and extra-luminal air foci suggestive of complicated perforated sealed cecal mass. This is in addition to radiological findings of peritonitis (Fig. ​(Fig.22A).Figure 2

The contrast enhanced computed tomography (CT) of the abdomen of the 3 cases. (A): CT scan abdomen of the 1st case (Coronal image) showing a well-defined rounded heterogeneous enhanced soft tissue mass lesion within the posterior wall of the cecum measuring (3.1 × 3.2 cm) in anteroposterior and transverse diameter associated with discontinuous enhancement of posterior cecum wall and extra-luminal air foci suggestive of complicated perforated sealed cecum mass. This is in addition to adjacent fat stranding with free fluid as well as enhancement of peritoneal reflection suggestive of peritonitis. (B &C): CT scan abdomen of the 2nd case (Axial & Coronal images). (2B): Axial image showing moderate to severe pneumoperitoneum with air seen more tracking along the ascending colon suggestive of a wall defect in the anterior aspect of the cecum. (2C): Coronal image showing a second defect in the stomach wall. (D): CT scan abdomen of the 3rd case (Coronal image) showing severe sigmoid diverticulosis with circumferential bowel wall thickening compatible with acute diverticulitis, small amount of free air compatible with bowel perforation likely arising from the sigmoid colon and a well-defined 3.3 × 1.5 cm abscess collection adjacent to the sigmoid colon.

In consideration of his stable clinical status, absent signs of peritonitis clinically and being a sealed perforation, he was managed conservatively. So, Meropenem was resumed and Clindamycin was started. 2 days later, bleeding stopped, and he stayed stable clinically without clinical signs of peritonitis. Feeding through nasogastric tube was introduced gradually as tolerated. Antibiotics were continued for a total of 8 days. Trial of weaning from oxygen was attempted gradually which he tolerated till he was maintained on RA. After closure of tracheostomy, on 70th day of hospital admission, the patient was discharged in a good condition with a plan of follow up of cecal mass progression. However, the patient did not follow up in outpatient clinics after discharge.

2.1.2. Second case

A 37-year old male patient, morbidly obese, negative past history, presented to our ED on 11th June 2020. He reported 3-day history of shortness of breath. Vital signs were remarkable for Temperature (Temp.): 38.5 C, pulse rate (PR): 111/min, RR: 30/min and O2 sat: 80% on RA. Laboratory investigations showed high LDH, inflammatory markers and Ferritin (Table ​(Table1).1). He had positive SARS-CoV-2 PCR and CXR showed bilateral air space consolidative patches scattered throughout the lung predominantly over peripheral and basal lungs (Fig. ​(Fig.1B).1B). He was admitted to COVID-19 isolation ward as a case of COVID-19 pneumonia and started on: Triple therapy in the form of: Interferon B1, Lopinavir/Ritonavir and Ribavirin, in addition to Hydroxychloroquine, Ceftriaxone, Azithromycin, Oseltamivir, Dexamethasone 6 mg IV OD and Enoxaparin.

On the 3rd day of admission, his condition deteriorated so, he was shifted to ICU and intubated because of respiratory failure. He was maintained on same treatment for COVID-19. On 2nd day postintubation, clinically he was vitally stable without active clinical GI signs but a routine follow-up CXR showed air under the diaphragm. Therefore, abdomen CT scan with contrast was carried out and showed moderate to severe pneumoperitoneum with air tracking along the ascending colon suggestive of wall defect at the cecum, in addition to another defect noted in the stomach wall (Fig. ​(Fig.2B2B & 2C). Ceftriaxone was upgraded to Piperacillin-Tazobactam and Caspofungin was added to cover for possibility of peritonitis. Again, the patient was managed conservatively, since he was asymptomatic. He remained vitally stable without signs of peritonitis. Enteral feeding was started gradually 3 days later and on the 10th day of hospital admission, he was extubated and shifted to COVID-19 isolation ward. COVID-19 therapy was continued for 12 days.

He tolerated feeding very well. Gradual weaning of oxygen supplementation was carried out till it was discontinued. After 14 days of antibiotics, a follow up CT scan of abdomen showed interval resolution of previously seen pneumoperitoneum. He was discharged on 30th day of hospitalization in a good condition.

2.1.3. Third case

A 74-year old male patient known case of T2DM presented to our ED on 17th July 2020. He gave 3-day history of dry cough, shortness of breath and generalized colicky abdominal pain. No other pulmonary or GI symptoms. He had negative past surgical history. Vital signs were remarkable for Temp: 38.4 C, PR: 105/min, RR: 22/min and O2 sat: 90% on RA, required 3 L/min O2 through nasal cannula. Chest examination was remarkable for reduced breath sound intensity bilaterally without added sounds. Abdomen was distended with generalized tenderness and guarding. Blood tests were remarkable for high LDH, inflammatory markers, Ferritin and D-dimer (Table ​(Table1).1). PCR for SARS-COV-2 was positive and CXR showed bilateral peripheral ground glass opacities at middle and lower lung lobes (Fig. ​(Fig.1C).1C). Due to the presence of abdominal pain along with signs of acute abdomen on examination, a CT scan of the abdomen was done. It showed severe sigmoid diverticulosis with radiological findings of acute diverticulitis, free air compatible with bowel perforation likely at the sigmoid colon with 3.3 cm adjacent abscess collection (Fig. ​(Fig.22D).

Therefore, the patient was started on Piperacillin-Tazobactam, Metronidazole in addition to COVID-19 therapy. He underwent emergency exploratory laparotomy. Intra-operatively, pus and fecal peritonitis along with perforation of 0.5 cm at the distal sigmoid colon were found. So, a Hartmann’s procedure was performed. Pathology result of resected sigmoid colon revealed diverticular disease with surrounding fibrosis, moderate mucosal inflammation with mixed acute and chronic inflammatory cells, negative for malignancy.

He had smooth postoperative course. Enteral feeding was started on 3rd day postoperation and he improved clinically. After a total of 10 days of hospitalization, supplemental oxygen and antibiotics were discontinued. He was discharged on 11th day of hospitalization in a good condition.

3. Discussion

The GI manifestations are the most frequently reported extra-pulmonary manifestations of COVID-19[2] with a prevalence of 10% to 50%.[4,5] The most commonly reported GI symptoms are nausea, vomiting, diarrhoea and abdominal pain.[2,4,5] However, there have been case reports of COVID-19 cases presenting with other GI manifestations. Those include acute surgical abdomen,[6] lower GI bleeding[7] and nonbiliary pancreatitis.[8] In fact, the GI manifestations could be the presenting symptoms of COVID-19 as was reported in a case report by Siegel et al where the patient presented with abdominal pain and upon abdominal imaging, the patient was found to have pulmonary manifestations of COVID-19 in the CT scan of the lung bases.[9]

GI perforation is a surgical emergency, carries a significant mortality rate that could reach up to 90% due to peritonitis especially if complicated by multiple organ failure.[10] It can be caused by many reasons. Those include foreign body perforation, extrinsic bowel obstruction like in cases of GI tumors, intrinsic bowel obstruction like in cases of diverticulitis/appendicitis, loss of GI wall integrity such as peptic ulcer and inflammatory bowel disease in addition to GI ischemia and infections.[11] Several infections have been reported to result in GI perforation like Clostridium difficile, Mycobacterium tuberculosis, Cytomegalovirus and others.[1214] COVID-19 have been rarely reported to result in GI perforation. Till the date of writing this report only 13 cases[1523] have been reported in the literature (Table ​(Table2).2). In addition, Meini et al reported a case of pneumatosis intestinalis in association with COVID-19 but without perforation.[25]

Table 2

Summary of the previously published cases of gastrointestinal perforation in association with COVID-19.

First Author [Reference]Age/ SexCo-morbid ConditionsPresenting symptomsSeverity of COVID-19 pneumoniaCOVID-19 TherapySymptoms prompted investigations for GI perforationSite of PerforationTiming of Perforation post admissionManagement of PerforationOutcome
1Gonzalvez Guardiola et al [15]66 Y/ MMetabolic syndromeNot mentionedCriticalMethylprednisoloneTocilizumab Hydroxychloroquine AzithromycinLopinavir/RitonavirAbdominal painIncreased WBC and CRP.CecumNot mentionedRight colectomyNot mentioned
2De Nardi et al [16]53 Y/MHypertension Supra-ventricular tachycardiaFeverCoughDyspneaCriticalAnakinra Lopinavir/Ritonavir Hydroxychloroquine + AntibioticsAbdominal pain Abdominal distentionSigns of PeritonitisCecum11th day of admissionRight colectomy & ileo-transverse anastomosisDischarged Home
3Kangas-Dick et al [17]74 Y/MNegativeFeverDyspneaDry coughCriticalHydroxychloroquine +AntibioticsIncreased Oxygen requirementMarkedly distended abdomenNot specified (CT scan: Not done)5th day of admissionConservativeDied
4Galvez et al [18]59 Y/MStatus post laparoscopic Roux-en-Y gastric bypass surgeryFeverDry coughMyalgiaHeadacheDyspneaModerateMethylprednisolone + COVID-19 protocol (Not specified)Acute abdominal painWorsening dyspneaGastro-jejunal anastomosis5th day of admissionLaparoscopy& Graham Patch RepairDischarged Home
5Poggiali et al [19]54 Y/ F§HypertensionFeverDry coughGERD symptomsSevereCOVID-19 therapy (Not specified) +AntibioticsAcute chest pain Painful abdomenDiaphragm StomachAt presentationSurgical RepairNot mentioned
6Corrêa Neto et al [20]80 Y/FHypertensionCoronary artery diseaseDry coughFeverDyspneaCriticalCOVID-19 therapy(Not specified) +AntibioticsDiffuse abdominal pain & stiffnessSigmoidAt PresentationLaparotomy with recto-sigmoidectomy & terminal colostomyDied
7Rojo et al [21]54 Y/FHypertensionObesityDyslipidemiaEpilepsyCough,MyalgiaCostal painCriticalHydroxychloroquine Lopinavir/Ritonavir MethylprednisoloneTocilizumabFeverHemodynamic instabilityAnemiaCecum15th day of admissionLaparotomy with right colectomy and ileostomyDied
8Kühn et al [22]59 Y/MNot mentionedFeverNauseaAbdominal pain Fatigue, HeadacheNot specifiedNot mentionedAbdominal painJejunal diverticulumAt presentationOpen small bowel segmental resection & anastomosisDischarged Home
9Seeliger et al [23]31Y/MNot mentionedDyspneaSevereNot mentionedNot mentionedLeft colonAt presentationLeft HemicolectomyDischarged Home
1082 Y/FDyspnea, DiarrhoeaCriticalSigmoidAt presentationOpen drainage of peritonitisDied
1171 Y/FFeverSevereGangrenous appendixAt presentationLaparoscopic appendectomyDischarged Home
1280Y/MNot mentionedSevereSigmoiditisAt presentationHartmann procedureDischarged Home
1377 Y/MDyspneaCriticalDuodenal ulcer23rd day of admissionOpen duodenal exclusion, omega gastro-enteric anastomosisDied
14This Report70Y/MT2DMFeverCoughCriticalMethylprednisolone HydroxychloroquineOseltamivir Enoxaparin+AntibioticsBleeding per rectumHemoglobin DropCecal mass44th day of admissionConservativeDischarged Home
1537Y/MMorbid obesityDyspneaCriticalInterferon B1Lopinavir/RitonavirRibavirinHydroxychloroquineOseltamivirDexamethasone+AntibioticsAir under diaphragm was found incidentally in a follow up CXRCecum4th day of admissionConservativeDischarged Home
1674Y/MT2DMCoughDyspnea Abdominal pain.SevereLopinavir/RitonavirRibavirinMethylprednisolone+AntibioticsAbdominal painSigns of peritonitisSigmoid diverticulosis/diverticulitisAt presentationExploratory laparotomy with Hartmann’s procedureDischarged Home

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Severity of COVID-19 pneumonia is based on classification of severity by Ministry of Health-Saudi Arabia.[24]†Y = Year.M = Male.§F = Female.

Most of the previously reported cases presented initially with respiratory symptoms, 4 cases had also GI symptoms at presentation in the form of abdominal pain, stiffness, nausea and diarrhoea[19,20,22,23] [Table ​[Table2].2]. Eleven out of the 13 cases had severe-critical pneumonia that required either high flow oxygen, intubation or mechanical ventilation which is similar to our first 2 cases. This may indicate that GI perforation is more common in severe and critically ill COVID-19 cases. The most common symptoms which prompted investigations for bowel perforation were abdominal pain and distention [Table ​[Table2].2]. Other indications were signs of peritonitis,[16] worsening hemodynamics[17,18,21] and rising inflammatory markers.[15]

Only one of our cases had abdominal pain and tenderness at presentation. Another developed anemia due to active lower GI bleeding which is similar to the case published by Rojo et al[21] where the patient developed anemia and found to have hemoperitoneum with pericecal hematoma. This is probably explained by the site of perforation since both had cecal perforation. Our other case was diagnosed incidentally after demonstration of air under diaphragm in routine CXR. GI perforation was diagnosed from first day up to 23rd day of presentation with COVID-19 [Table ​[Table2].2]. Our patients had similar variable timing of GI perforation in relation to presentation with COVID-19. It ranged from the first day of diagnosis up to 40 days after presentation with COVID-19 pneumonia. This may tell us that GI perforation could happen at any time during the course of the infection. Our report demonstrates different presentation of GI perforation with COVID-19 since in 2 of the 3 cases, the infection predisposed to having perforation of an underlying GI lesions (cecal mass and diverticulosis). Only Kuhn et al reported similar presentation where the patient had perforation of jejunal diverticulum.[22] This may tell us that having COVID-19 predispose patients with underlying GI lesions to perforation. In addition, in our first case, we think that the source of Candidemia was most probably the bowel since it was persistent even after clearance of Candida Albican from the urine, but it was overlooked due to the absence of GI symptoms at the time of developing the Candidemia. In a study of 62 cases with peritonitis secondary to gastric perforation, Candida species was isolated in 23 cases in peritoneal fluid culture.[26] Therefore, in presence of Candidemia especially in absence of clear source, evaluation of the bowel as a potential source should always be kept in mind.

The effect of SARS-COV-2 virus on the GI system can be explained by different mechanisms. First, the virus uses the same access to enter respiratory and GI tract epithelium which are Angiotensin converting enzyme 2 receptors giving the virus the chance to replicate inside GI cells.[27] In addition, faecal-oral transmission has also been postulated, due to the presence of viral RNA in stool samples.[28] Perforation could result from altered colonic motility due to neuronal damage by the virus[29] in addition to local ischemia resulting from hypercoagulable state caused by the virus especially in critically ill patients.[30] Corrêa Neto et al reported finding ischemia of the entire GI tract during exploratory laparotomy for sigmoid perforation with COVID-19.[20] In addition, Rojo et al reported presence of microthrombi and wall necrosis in the pathology examination of his COVID-19 case with bowel perforation.[21] Other possible implicating factors are the use of Tocilizumab and high dose steroids.[21,31] Both are indicated in severe and critically ill COVID-19 cases. Steroids were used in all of our 3 cases since it is indicated in severe COVID-19 pneumonia according Saudi Arabian Ministry of health guidelines[24] but none of our patients received Tocilizumab. Some of these mechanisms could explain the higher risk of GI perforation in severe and critically ill COVID-19 patients.

The diagnosis of GI perforation is based mainly on radiological findings on CT scan. The most specific findings are segmental bowel wall thickening, focal bowel wall defect, or bubbles of extraluminal gas concentrated in close proximity to the bowel wall.[32] Treatment of GI perforation is mainly surgical in order to improve survival.[33] This is in line with the previously published cases where all were managed surgically except the one reported by Kangas-Dick et al due to the patient’s critical condition, so he was managed conservatively but unfortunately, he died.[17] However, in selected cases where there are no active signs of peritonitis, abdominal sepsis or having sealed perforation, conservative treatment is an acceptable management strategy.[34,35] This was the case in 2 of our cases who were managed conservatively. Fortunately, they did very well and had good outcome.

4. Conclusion

GI manifestations are common in patients with COVID-19. However, GI perforation is rarely reported in the literature. Severe and critically ill COVID-19 patients seem to be at a higher risk of this complication. It has a variable presentation in patients with COVID-19 ranging from incidental finding discovered only radiographically to acute abdomen. The presence of underlying GI lesion predisposes patients with COVID-19 to perforation. High index of suspicion is required in order to manage those patients further and thus, improve their outcome.

Author contributions

Conceptualization: Reem J. Al Argan, Safi G. Alqatari

Data curation: Reem J. Al Argan, Abdulsalam Noor, Lameyaa A. Al Sheekh

Writing – original draft: Reem J. Al Argan, Lameyaa A. Al Sheekh, Feda’a H. Al Beladi

Writing – review & editing: Reem J. Al Argan, Safi G. Alqatari, Abir H. Al Said, Raed M. AlsulaimanGo to:

Footnotes

Abbreviations: COVID-19 = corona virus disease-2019, CT = computed tomography, CXR = chest X-ray, ED = emergency department, GI = gastrointestinal, ICU = intensive care unit, LDH = lactate dehydrogenase, O2 sat = oxygen saturation, PCR = polymerase chain reaction, PR = Pulse rate, RA = room air, RR = respiratory rate, Temp = Temperature, T2DM = Type 2 diabetes mellitus.

How to cite this article: Al Argan RJ, Alqatari SG, Al Said AH, Alsulaiman RM, Noor A, Al Sheekh LA, Al Beladi FH. Gastrointestinal perforation secondary to COVID-19: Case reports and literature review. Medicine. 2021;100:19(e25771).

The authors have no funding and conflicts of interests to disclose.

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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“We Don’t Understand What’s Really Happening” – The CDC Is Under-Counting ‘Breakthrough’ COVID Cases

Authors: BY TYLER DURDENWEDNESDAY, AUG 25, 2021 – 01:04 PM

A growing number of public health officials working at the state level are worried that the federal government isn’t collecting enough accurate data about “breakthrough” infections, yet the Biden Administration has pushed ahead with plans to dole out booster shots, as well as other COVID policies.

According to Politico, 49 states are now regularly sending CDC information on hospitalized breakthrough patients. But more than a dozen have told Politico that they do not have the capacity to match hospital admission data with patients’ immunization records, forcing states to rely on hospital administrators to report breakthrough infections.

The result is data that is often aggregated, inaccurate and missing critical details like which vaccine the consumer received . Instead, those states rely on hospital administrators to report breakthrough infections. The resulting data is often aggregated, inaccurate and omits critical details for teasing out trends, such as which vaccine a person received and whether they have been fully vaccinated, a dozen state officials said.

The fact that the CDC and public health departments across the country are still struggling to collect data on breakthrough infections is almost embarrassing, considering we’re more than 18 months into the pandemic at this point, and scientists have repeatedly warned about the necessity of being prepared for the Omega Death Variant which is right around the corner, according to Dr. Fauci’s latest fearmongering blitz.

“I think it would be really challenging [for the CDC] to interpret the results or to interpret the data when you have only some jurisdictions reporting [breakthrough infections],” said Theresa Sokol, lead epidemiologist for Louisiana’s state public health department, which is working closely with the CDC on studies of breakthrough infections. “I know that there are some jurisdictions that don’t even have access to their vaccination data. They don’t have the authority or their permission.”

Perhaps the biggest obstacle to collecting data on breakthrough infections is the balkanized nature of state health-care systems. States can’t communicate with other states. For years, states have pleaded with the federal government to upgrade these systems to no avail.

Last year, the CDC allocated a small amount of money (described by Politico as “tens of millions of dollars”) to help states upgrade their systems. But the CDC admits it will take years for the necessary upgrades to be made.

“Nothing has changed since the pandemic began,” one senior Biden health official said. “We’re still dealing with this patchwork system — and it continues to fail us.”

Of particular concern for health officials now is how rapidly the Delta variant spreads, whether it is reducing the effectiveness of vaccines and whether it causes more severe disease. Tracking breakthrough infections is a critical step toward arriving at all of these assessments.

To complement data on hospitalized cases from the 50-state reporting network, the CDC is conducting a smaller study with a subset of states to examine all of their breakthrough infections, including mild cases that don’t send people to the hospital. The states participating in this smaller study have the ability to match lab reports with immunization records, but they don’t maintain their own databases of hospitalization data. ;

“We report what we have, but we know that it’s limited because it’s based on a direct report from a provider — as opposed to taking a data set of all hospitalizations and matching that against our vaccine registry,” said Sokol, the Louisiana epidemiologist. “We’re not able to do that for hospitalization. We rely on individual reports from hospitals. And some report well, others do not. So we know that it’s not complete.”

[…]

“We don’t have a clear understanding of what the data actually says about the Delta variant, transmission and boosters,” one of those officials said.

To be sure, deliberately under-counting breakthrough infections has its advantages: for example, the Biden Administration can mask the number of breakthrough infections reported, making the vaccines appear more effective than they actually are.

CDC Reports Show Unvaccinated Way More Likely To Be Hospitalized For COVID-19 Despite Drop In Vaccine Effectiveness

Authors: SEBASTIAN HUGHES CONTRIBUTOR August 24, 20216:52 PM ET

A Centers for Disease Control and Prevention report released Tuesday showed vaccinated individuals are far less likely to be hospitalized from COVID-19, even though immunity appears to decrease over time.

Approximately 3% of vaccinated people who caught COVID-19 between May 1 and July 25 in Los Angeles County had to be hospitalized, compared to 8% of the unvaccinated, according to the CDC report. Only 0.5% of those who were vaccinated had to be placed in the ICU, as opposed to 1.5% of the unvaccinated.

The vaccinated were five times less likely to test positive and 29 times less likely to be hospitalized than the unvaccinated in the county, the report stated. A report released earlier in August by the Los Angeles County Department of Public Health estimated vaccinated hospitalizations to be four and 14 times less likely, respectively.

Out of 43,127 COVID infections, 25% were among the vaccinated, 3% were among the partially vaccinated and 71% were among the unvaccinated, which indicates the effectiveness of the vaccines has moderately waned over time, according to the CDC.

A separate report released Tuesday showed the effectiveness of the vaccine in frontline healthcare workers had decreased to 66%, but didn’t specify how quickly effectiveness decreases. Another report released Aug. 18 showed vaccine effectiveness had decreased in New York from 92% in early May to 80% by late July.

Cases and deaths in the U.S. have surged because of the delta variant. About 93% of cases in the country can be attributed to it, the CDC has estimated. (RELATED: What Will It Take To Get Back To Normal? Here’s What The Experts Say)

The CDC recommended vaccinated individuals wear masks in areas of substantial or high transmission in July and announced on Wednesday that booster shots would become available to most people.

Director of the National Institute of Allergy and Infectious Diseases Dr. Anthony Fauci told CNN’s “Anderson Cooper 360” on Monday that the virus could be under control by spring 2022 if most people get vaccinated.

“You have either the overwhelming majority of the population vaccinated, those who have been infected and have cleared the virus will have a degree of protection, and we’re recommending that those people also get vaccinated because the degree of protection that you could induce in someone who has been infected, who has then recovered and then vaccinated, is an enormous increase in the degree of protection,” Fauci told CNN.

Asthmatics at no higher risk getting or dying from COVID-19, assessment of studies consisting of 587,000 people shows

Authors: February 19, 2021Source:Taylor & Francis Group

Summary:

A review of 57 studies shows people with asthma had a 14 percent lower risk of getting COVID-19 and were significantly less likely to be hospitalized with the virus.

A new study looking at how COVID-19 affects people with asthma provides reassurance that having the condition doesn’t increase the risk of severe illness or death from the virus.

George Institute for Global Health researchers in Australia analysed data from 57 studies with an overall sample size of 587,280. Almost 350,000 people in the pool had been infected with COVID-19 from Asia, Europe, and North and South America and found they had similar proportions of asthma to the general population.

The results, published in the peer-reviewed Journal of Asthma, show that just over seven in every 100 people who tested positive for COVID-19 also had asthma, compared to just over eight in 100 in the general population having the condition. They also showed that people with asthma had a 14 percent lower risk of acquiring COVID-19 and were significantly less likely to be hospitalized with the virus.

There was no apparent difference in the risk of death from COVID-19 in people with asthma compared to those without.

Head of The Institute’s Respiratory Program, co-author Professor Christine Jenkins said that while the reasons for these findings weren’t clear, there were some possible explanations — such as some inhalers perhaps limiting the virus’ ability to attach to the lungs.

“Chemical receptors in the lungs that the virus binds to are less active in people with a particular type of asthma and some studies suggest that inhaled corticosteroids — commonly used to treat asthma — can reduce their activity even further,” she said.

“Also, initial uncertainty about the impact of asthma on COVID-19 may have caused anxiety among patients and caregivers leading them to be more vigilant about preventing infection.”

Lead author Dr Anthony Sunjaya added that while this study provides some reassurance about the risks of exposure to COVID-19 in people with asthma, doctors and researchers were still learning about the effects of the virus.

“While we showed that people with asthma do not seem to have a higher risk of infection with COVID-19 compared to those without asthma and have similar outcomes, we need further research to better understand how the virus affects those with asthma,” he said.

For More Information: https://www.sciencedaily.com/releases/2021/02/210219091850.htm

Risk for COVID-19 Infection, Hospitalization, and Death By Age Group

Authors: CDC

Rate ratios compared to 18- to 29-year-olds1

 0-4 years old5-17 years old18-29 years old30-39 years old40-49 years old50-64 years old65-74 years old75-84 years old85+ years old
Cases2<1x1xReference group1x1x1x1x1x1x
Hospitalization3<1x<1xReference group2x2x4x6x9x15x
Death4<1x<1xReference group4x10x35x95x230x600x

All rates are relative to the 18- to 29-year-old age category. This group was selected as the reference group because it has accounted for the largest cumulative number of COVID-19 cases compared to other age groups. Sample interpretation: Compared with 18- to 29-year-olds, the rate of death is four times higher in 30- to 39-year-olds, and 600 times higher in those who are 85 years and older. (In the table, a rate of 1x indicates no difference compared to the 18- to 29-year-old age category.)

For More Information: https://www.cdc.gov/coronavirus/2019-ncov/covid-data/investigations-discovery/hospitalization-death-by-age.html