Factors associated with deaths due to COVID-19 versus other causes: population-based cohort analysis of UK primary care data and linked national death registrations within the OpenSAFELY platform

Real-world cost-effectiveness of nirmatrelvir-ritonavir as treatment for SARS-CoV-2 infection in the Belgian setting with omicron variant

Background

Nirmatrelvir-ritonavir is an oral treatment for SARS-CoV-2 infection in patients who are at high risk of developing severe COVID-19 disease. This antiviral has proven to significantly reduce the risk of hospitalization and death compared to no anti-SARS-CoV-2 treatment in this target population. This paper aims to assess the cost-effectiveness of nirmatrelvir-ritonavir in Belgium using real-world evidence.

Methods

A static decision tree model was developed to capture the health progression of patients infected with the SARS-CoV-2 virus. Outcomes were expressed in Quality Adjusted-Life Years (QALYs), hospitalizations, Intensive Care Unit (ICU) admissions, deaths and Long Covid cases, derived from epidemiological data over the first full year of the Omicron variant's circulation (2022). Costs were calculated for the year 2023 from the healthcare payer's perspective. Extensive sensitivity analyses were conducted to test the robustness of the cost-effectiveness results.

Results

In a cohort of 1,000 patients, treatment with nirmatrelvir-ritonavir is projected to save 95 QALYs and €82,658 compared to no anti-SARS-CoV-2 treatment over a lifetime horizon. These savings primarily stem from the reduction in hospitalizations among vulnerable patients who typically require a longer recovery time. The analysis also indicates 5 fewer ICU admissions and 8 fewer premature deaths per 1,000 infected patients.

Conclusion

In the context of Omicron SARS-CoV-2 infection, administering nirmatrelvir-ritonavir to patients at high risk of severe disease improves health outcomes and reduces costs. Nirmatrelvir-ritonavir is 100% likely to be cost-effective at a willingness to pay of €2,000 per QALY.

Are Pre-Hospitalization ECG Abnormalities Associated With Increased Mortality in COVID-19 Patients? A Quantitative Systematic Literature Review

BACKGROUND

While COVID-19 is predominantly a respiratory disease, cardiovascular complications occur and are associated with worse outcomes. Electrocardiogram (ECG) abnormalities are frequently observed in hospitalized COVID-19 patients, some of which are associated with increased mortality. It is unclear whether ECG abnormalities occurring before hospitalization are associated with increased mortality. This quantitative systematic literature review aims to determine which ECG changes occurring before hospitalization are associated with mortality and discuss whether these findings can aid the assessment of patients and decision-making in the pre-hospital environment.

METHODS

A systematic search of the following digital databases was conducted: CINAL, PUBMED, MEDLINE, and Coronavirus Research Database. Eight cohort studies (primary papers) including COVID-19 patients with ECGs taken in the Emergency Department before hospitalization were selected for quantitative synthesis and results were obtained for the prevalence of ECG changes among survivors compared with non-survivors. Odds and hazard ratios for ECG abnormalities associated with mortality were also collected and compared.

RESULTS

Identification of ECG abnormalities on pre-hospitalization ECG is associated with increased mortality in COVID-19 patients. These ECG abnormalities include non-sinus rhythm, QTc prolongation, left bundle branch block, axis deviation, atrial fibrillation, atrial flutter, right ventricular strain patterns, ST segment changes, T wave abnormalities, and evidence of left ventricular hypertrophy.

CONCLUSION

Electrocardiogram assessment in the pre-hospital environment may be beneficial when assessing COVID-19 patients and could help identify patients at increased risk of mortality.

Increased 30-day all-cause mortality associated with Gram-negative bloodstream infections in England during the COVID-19 pandemic

BACKGROUND

Our aim was to assess the impact of COVID-19 pandemic on mortality in patients hospitalised with Gram-negative bloodstream infections (GNBSIs).

METHODS

A retrospective cohort study including cases of Escherichia coli, Klebsiella species and Pseudomonas aeruginosa in England (January 2015-December 2021) reported to UKHSA's Second Generation Surveillance System. The outcome was 30-day all-cause mortality. Multivariable logistic regression models were built, and adjusted Odds Ratios (ORs) with 95% confidence intervals were reported.

RESULTS

Total E. coli, Klebsiella spp. and P. aeruginosa infections were 206,030, 53,819 and 21,129, respectively. Compared to the pre-pandemic period, odds of death during the pandemic (March 2020 onwards) in E. coli, Klebsiella spp. and P. aeruginosa infections with no COVID-19 infection within 28-days of onset were 1.13 (1.08-1.18), 1.15 (1.07-1.25) and 1.09 (0.97-1.22), while odds in GNBSIs with an associated COVID-19 infection were 2.45 (2.26-2.66), 2.96 (2.62-3.34) and 3.15 (2.61-3.80), respectively. Asian patients with an associated COVID-19 infection were more likely to die during the pandemic compared to White patients (E. coli: OR 1.28 (0.95-1.71); Klebsiella spp. OR 1.59 (1.20-2.11); P. aeruginosa: OR 2.02 (1.23-3.31)).

CONCLUSIONS

Patients suffering from a GNBSI had increased risk of death during the pandemic, with the risk higher in patients with an associated COVID-19 infection.

Does timing of tocilizumab administration affect mortality in COVID-19? A Scottish multicentre retrospective cohort study

BACKGROUND

The optimal timing of tocilizumab treatment during the disease course of COVID-19 has yet to be adequately defined in the context of randomised controlled trials and the effect of tocilizumab on real-world populations remains unclear. We examined the effect of different timing of tocilizumab, on mortality, in a cohort of adults with COVID-19.

METHODS

All adults (≥18 years old) with confirmed COVID-19 admitted to four hospitals in the West of Scotland between 8 January 2021 and 31 March 2021 and who received tocilizumab were included in a retrospective observational cohort study. Patients were assigned to either an early (day of admission or first day after admission) or late (days 2-7 of admission) cohort based on tocilizumab initiation. The primary outcome was 90-day all-cause mortality in early versus late cohorts. Secondary outcomes were 28 and 180-day all-cause mortality.

RESULTS

203 patients were included in the analysis (138 in the early cohort, 65 in the late cohort). Mortality in 90 days in the early cohort was 22% (n=30) compared with 45% (n=29) in the late cohort (p<0.001). The adjusted mortality was significantly higher in the late cohort compared with the early cohort (adjusted OR: 3.33; 95% CI: 1.29 to 8.54; p=0.012). The secondary outcomes demonstrated the same effect with higher rates of death in 28 days (late cohort adjusted OR: 3.28; 95% CI: 1.23 to 8.75; p=0.018) and 180 days (late cohort adjusted OR: 3.70; 95% CI: 1.45 to 9.45; p=0.006). The effect was seen whether the outcome was adjusted or unadjusted.

CONCLUSION

Early administration of tocilizumab within the first 2 days of hospitalisation was associated with a significant survival benefit compared with late exposure. Late administration was associated with particularly high mortality. The observed association may be a result of residual confounders and further research is needed.

Health insurance system fragmentation and COVID-19 mortality: Evidence from Peru

Peru has a fragmented health insurance system in which most insureds can only access the providers in their insurer's network. The two largest sub-systems covered about 53% and 30% of the population at the start of the pandemic; however, some individuals have dual insurance and can thereby access both sets of providers. We use data on 24.7 million individuals who belonged to one or both sub-systems to investigate the effect of dual insurance on COVID-19 mortality. We estimate recursive bivariate probit models using the difference in the distance to the nearest hospital in the two insurance sub-systems as Instrumental Variable. The effect of dual insurance was to reduce COVID-19 mortality risk by 0.23% compared with the sample mean risk of 0.54%. This implies that the 133,128 COVID-19 deaths in the sample would have been reduced by 56,418 (95%CI: 34,894, 78,069) if all individuals in the sample had dual insurance.

High Prevalence of Diabetes Among Hospitalized COVID-19 Minority Patients: Data from a Single Tertiary Hospital

BACKGROUND AND AIM

Type 2 diabetes mellitus (DM) is a common comorbidity in the minority population and is associated with poor outcomes in COVID-19 patients. We hypothesized that COVID-19 patients with pre-existing diabetes mellitus are prone to fatal outcomes compared to non-diabetic patients. We aimed to illustrate the characteristics and outcomes and identify the risk factors for in-hospital mortality of COVID-19 patients with DM.

METHODS

In this single-center retrospective study, electronic medical records of hospitalized patients with confirmed COVID-19 diagnosis at Howard University Hospital (HUH) from March 2020 to Dec 2021 were analyzed. Clinical, demographic, and serological information, as well as outcomes, were recorded and analyzed.

RESULTS

Among 463 COVID-19 patients, 66.3% (n = 307) were African Americans (AA) and 35.9% (n = 166) had diabetes, with a mean age of 64 years. The majority of the diabetic patients were AA (n = 123, 74.1%) and had a higher mortality rate (n = 26, 74.3%) compared to others. Length of stay in the hospital is significantly more for the diabetic than for the non-diabetic patients (11.3 vs. 8.3 days, p = 0.03). A higher proportion of ICU admission (32.3% vs. 17.9%, p =  < 0.001), intubation (17% vs. 11.7%, p = 0.04), and increased mortality (21.1% vs. 12.2%, p = 0.01) were identified in COVID-19 patients with DM than in those with no DM. Among DM patients, non-survivors were older (69.9 vs. 62.9 years). DM patients were more likely to have underlying hypertension (72.3% vs. 43.3%, p =  < 0.001), obesity (44.8% vs. 32.1%, p = 0.007), chronic kidney disease (23.6 vs. 11.8%, p = 0.001), and cardiovascular disease (29.5% vs. 14.3%, p = 0.001) than the non-DM patients. HbA1C above 9%, indicating poorly controlled hyperglycemia, was associated with poor outcome among the DM subjects. AST (23.5% vs. 31.3%) and creatinine (61.4% vs. 37.9%) were significantly more elevated in DM COVID-19 patients (all p-values < 0.05). The levels of serum troponin (42.5% vs. 30.9%, p = 0.03), interleukin-6 (67.2 vs. 50%, p = 0.04), ferritin (65.6% vs. 44.6%, p = 0.03), procalcitonin (58.1% vs. 46.1, p = 0.03), and D-dimers (92.8% vs. 86.5%, p = 0.04) were significantly higher in DM patients as compared to those in non-DM COVID-19 patients, indicating more susceptibility of diabetic COVID-19 patients to coagulation dysfunction and inflammatory storm.

CONCLUSION

The prevalence of DM is high among hospitalized COVID-19 patients in our cohort. While DM patients have a higher mortality rate and ICU admission than non-DM patients, other factors such as underlying comorbidities, old age, elevated creatinine, AST, serum inflammatory markers, and D-dimer are more significant predictors of fatal outcomes. DM patients had higher metabolic derangements, hypercoagulability, and severe inflammatory response. No significant difference of outcome was noted between DM patients of different races in our cohort. In the diabetic group, it appears that race may not significantly contribute to the observed mortality disparity. This could be attributed to the significant influence of diabetes, which acts as a major effector, potentially overshadowing the significance of race in this context.

Comparison of fatalities due to COVID-19 and other nonexternal causes during the first five pandemic waves : Results from multiple cause of death statistics in Bavaria

BACKGROUND

Older age is a risk factor for a fatal course of SARS-CoV‑2 infection, possibly due to comorbidities whose exact role in this context, however, is not yet well understood. In this paper, the characteristics and comorbidities of persons who had died of COVID-19 in Bavaria by July 2022 are shown and compared with the characteristics of other fatalities during the pandemic.

METHODS

Based on data from multiple cause of death statistics, odds ratios for dying from COVID-19 (compared to dying from other nonexternal causes of death) were calculated by using logistic regression models, stratified by age, sex, and pandemic waves.

RESULTS

In Bavaria, a total of 24,479 persons (6.5% of all deaths) officially died from COVID-19 between March 2020 and July 2022. In addition to increasing age and male sex, preexisting diseases and comorbidities such as obesity, degenerative diseases of the nervous system, dementia, renal insufficiency, chronic lower respiratory diseases, and diabetes mellitus were significantly associated with COVID-19-related deaths. Dementia was mainly associated with increased COVID-19 mortality during the first and second waves, while obesity was strongly associated during the fourth wave.

DISCUSSION

The frequency of specific comorbidities in COVID-19 deaths varied over the course of the pandemic. This suggests that wave-specific results also need to be interpreted against the background of circulating virus variants, changing immunisation levels, and nonpharmaceutical interventions in place at the time.

Socioeconomic disparities in risk perceptions and precautionary actions against COVID-19 among the working age population aged 18-59 in Japan: a cross-sectional study

Introduction

Risk perceptions and precautionary actions against COVID-19 have been reported to be generally insufficient globally, and differences by subpopulation group have been concerning, as a key driver to widening health gaps. Although a body of literature examined these key constructs, critical comparative examinations of various risk perceptions and precautionary actions by socioeconomic group are still limited in Japan and Asia.

Methods

This study examines subjective and objective risk perceptions and precautionary actions against COVID-19 infection among the general working age population aged 18-59 in Japan, focusing on the differences by socioeconomic group and health status. A cross-sectional survey was conducted in March 2021, using an online self-reporting questionnaire, in selected prefectures in Japan where COVID-19 infection cases ranked the highest. Participants were randomly recruited, and quota sampling methods were employed with the weighting of the sample distribution by geographic location (n=2764).

Results

Subjective and objective risk perceptions and precautionary actions were significantly related to several of the socioeconomic variables, including gender, income, employment and household composition, as well as self-reported health status. These disparities were substantial even with the key basic preventive behaviour including mask wearing, avoidance of large gatherings and hand washing. Further, these risk perceptions and precautionary actions showed unexpected relationships with socioeconomic position and health status, contrary to existing evidence or theory, particularly among younger generations and worse health populations.

Conclusions

This evidence suggests that risk perceptions and precautionary actions do not always seem to align, and their disparities by socioeconomic group and health status have been underscored in Japan, which may suggest complex and distinct pathways by subpopulation group. Further evidence and strategies for COVID-19 and other infectious disease prevention would be critical in transitions of the infectious disease prevention and control strategy, targeting both the high-risk population group and higher risk-taking group.

The predictive power of data: machine learning analysis for Covid-19 mortality based on personal, clinical, preclinical, and laboratory variables in a case-control study

BACKGROUND AND PURPOSE

The COVID-19 pandemic has presented unprecedented public health challenges worldwide. Understanding the factors contributing to COVID-19 mortality is critical for effective management and intervention strategies. This study aims to unlock the predictive power of data collected from personal, clinical, preclinical, and laboratory variables through machine learning (ML) analyses.

METHODS

A retrospective study was conducted in 2022 in a large hospital in Abadan, Iran. Data were collected and categorized into demographic, clinical, comorbid, treatment, initial vital signs, symptoms, and laboratory test groups. The collected data were subjected to ML analysis to identify predictive factors associated with COVID-19 mortality. Five algorithms were used to analyze the data set and derive the latent predictive power of the variables by the shapely additive explanation values.

RESULTS

Results highlight key factors associated with COVID-19 mortality, including age, comorbidities (hypertension, diabetes), specific treatments (antibiotics, remdesivir, favipiravir, vitamin zinc), and clinical indicators (heart rate, respiratory rate, temperature). Notably, specific symptoms (productive cough, dyspnea, delirium) and laboratory values (D-dimer, ESR) also play a critical role in predicting outcomes. This study highlights the importance of feature selection and the impact of data quantity and quality on model performance.

CONCLUSION

This study highlights the potential of ML analysis to improve the accuracy of COVID-19 mortality prediction and emphasizes the need for a comprehensive approach that considers multiple feature categories. It highlights the critical role of data quality and quantity in improving model performance and contributes to our understanding of the multifaceted factors that influence COVID-19 outcomes.

Risk of COVID-19 death for people with a pre-existing cancer diagnosis prior to COVID-19-vaccination: A systematic review and meta-analysis

While previous reviews found a positive association between pre-existing cancer diagnosis and COVID-19-related death, most early studies did not distinguish long-term cancer survivors from those recently diagnosed/treated, nor adjust for important confounders including age. We aimed to consolidate higher-quality evidence on risk of COVID-19-related death for people with recent/active cancer (compared to people without) in the pre-COVID-19-vaccination period. We searched the WHO COVID-19 Global Research Database (20 December 2021), and Medline and Embase (10 May 2023). We included studies adjusting for age and sex, and providing details of cancer status. Risk-of-bias assessment was based on the Newcastle-Ottawa Scale. Pooled adjusted odds or risk ratios (aORs, aRRs) or hazard ratios (aHRs) and 95% confidence intervals (95% CIs) were calculated using generic inverse-variance random-effects models. Random-effects meta-regressions were used to assess associations between effect estimates and time since cancer diagnosis/treatment. Of 23 773 unique title/abstract records, 39 studies were eligible for inclusion (2 low, 17 moderate, 20 high risk of bias). Risk of COVID-19-related death was higher for people with active or recently diagnosed/treated cancer (general population: aOR = 1.48, 95% CI: 1.36-1.61, I2  = 0; people with COVID-19: aOR = 1.58, 95% CI: 1.41-1.77, I2  = 0.58; inpatients with COVID-19: aOR = 1.66, 95% CI: 1.34-2.06, I2  = 0.98). Risks were more elevated for lung (general population: aOR = 3.4, 95% CI: 2.4-4.7) and hematological cancers (general population: aOR = 2.13, 95% CI: 1.68-2.68, I2  = 0.43), and for metastatic cancers. Meta-regression suggested risk of COVID-19-related death decreased with time since diagnosis/treatment, for example, for any/solid cancers, fitted aOR = 1.55 (95% CI: 1.37-1.75) at 1 year and aOR = 0.98 (95% CI: 0.80-1.20) at 5 years post-cancer diagnosis/treatment. In conclusion, before COVID-19-vaccination, risk of COVID-19-related death was higher for people with recent cancer, with risk depending on cancer type and time since diagnosis/treatment.

COVID-19 inequalities in England: a mathematical modelling study of transmission risk and clinical vulnerability by socioeconomic status

BACKGROUND

The COVID-19 pandemic resulted in major inequalities in infection and disease burden between areas of varying socioeconomic deprivation in many countries, including England. Areas of higher deprivation tend to have a different population structure-generally younger-which can increase viral transmission due to higher contact rates in school-going children and working-age adults. Higher deprivation is also associated with a higher presence of chronic comorbidities, which were convincingly demonstrated to be risk factors for severe COVID-19 disease. These two major factors need to be combined to better understand and quantify their relative importance in the observed COVID-19 inequalities.

METHODS

We used UK Census data on health status and demography stratified by decile of the Index of Multiple Deprivation (IMD), which is a measure of socioeconomic deprivation. We calculated epidemiological impact using an age-stratified COVID-19 transmission model, which incorporated different contact patterns and clinical health profiles by decile. To separate the contribution of each factor, we considered a scenario where the clinical health profile of all deciles was at the level of the least deprived. We also considered the effectiveness of school closures and vaccination of over 65-year-olds in each decile.

RESULTS

In the modelled epidemics in urban areas, the most deprived decile experienced 9% more infections, 13% more clinical cases, and a 97% larger peak clinical size than the least deprived; we found similar inequalities in rural areas. Twenty-one per cent of clinical cases and 16% of deaths in England observed under the model assumptions would not occur if all deciles experienced the clinical health profile of the least deprived decile. We found that more deaths were prevented in more affluent areas during school closures and vaccination rollouts.

CONCLUSIONS

This study demonstrates that both clinical and demographic factors synergise to generate health inequalities in COVID-19, that improving the clinical health profile of populations would increase health equity, and that some interventions can increase health inequalities.

Impact of Electronic Health Record Use on Cognitive Load and Burnout Among Clinicians: Narrative Review

The cognitive load theory suggests that completing a task relies on the interplay between sensory input, working memory, and long-term memory. Cognitive overload occurs when the working memory's limited capacity is exceeded due to excessive information processing. In health care, clinicians face increasing cognitive load as the complexity of patient care has risen, leading to potential burnout. Electronic health records (EHRs) have become a common feature in modern health care, offering improved access to data and the ability to provide better patient care. They have been added to the electronic ecosystem alongside emails and other resources, such as guidelines and literature searches. Concerns have arisen in recent years that despite many benefits, the use of EHRs may lead to cognitive overload, which can impact the performance and well-being of clinicians. We aimed to review the impact of EHR use on cognitive load and how it correlates with physician burnout. Additionally, we wanted to identify potential strategies recommended in the literature that could be implemented to decrease the cognitive burden associated with the use of EHRs, with the goal of reducing clinician burnout. Using a comprehensive literature review on the topic, we have explored the link between EHR use, cognitive load, and burnout among health care professionals. We have also noted key factors that can help reduce EHR-related cognitive load, which may help reduce clinician burnout. The research findings suggest that inadequate efforts to present large amounts of clinical data to users in a manner that allows the user to control the cognitive burden in the EHR and the complexity of the user interfaces, thus adding more "work" to tasks, can lead to cognitive overload and burnout; this calls for strategies to mitigate these effects. Several factors, such as the presentation of information in the EHR, the specialty, the health care setting, and the time spent completing documentation and navigating systems, can contribute to this excess cognitive load and result in burnout. Potential strategies to mitigate this might include improving user interfaces, streamlining information, and reducing documentation burden requirements for clinicians. New technologies may facilitate these strategies. The review highlights the importance of addressing cognitive overload as one of the unintended consequences of EHR adoption and potential strategies for mitigation, identifying gaps in the current literature that require further exploration.

Excess natural-cause mortality in US counties and its association with reported COVID-19 deaths

In the United States, estimates of excess deaths attributable to the COVID-19 pandemic have consistently surpassed reported COVID-19 death counts. Excess deaths reported to non-COVID-19 natural causes may represent unrecognized COVID-19 deaths, deaths caused by pandemic health care interruptions, and/or deaths from the pandemic's socioeconomic impacts. The geographic and temporal distribution of these deaths may help to evaluate which explanation is most plausible. We developed a Bayesian hierarchical model to produce monthly estimates of excess natural-cause mortality for US counties over the first 30 mo of the pandemic. From March 2020 through August 2022, 1,194,610 excess natural-cause deaths occurred nationally [90% PI (Posterior Interval): 1,046,000 to 1,340,204]. A total of 162,886 of these excess natural-cause deaths (90% PI: 14,276 to 308,480) were not reported to COVID-19. Overall, 15.8 excess deaths were reported to non-COVID-19 natural causes for every 100 reported COVID-19 deaths. This number was greater in nonmetropolitan counties (36.0 deaths), the West (Rocky Mountain states: 31.6 deaths; Pacific states: 25.5 deaths), and the South (East South Central states: 26.0 deaths; South Atlantic states: 25.0 deaths; West South Central states: 24.2 deaths). In contrast, reported COVID-19 death counts surpassed estimates of excess natural-cause deaths in metropolitan counties in the New England and Middle Atlantic states. Increases in reported COVID-19 deaths correlated temporally with increases in excess deaths reported to non-COVID-19 natural causes in the same and/or prior month. This suggests that many excess deaths reported to non-COVID-19 natural causes during the first 30 mo of the pandemic in the United States were unrecognized COVID-19 deaths.

CCL18, CHI3L1, ANG2, IL-6 systemic levels are associated with the extent of lung damage and radiomic features in SARS-CoV-2 infection

OBJECTIVE AND DESIGN

We aimed to identify cytokines whose concentrations are related to lung damage, radiomic features, and clinical outcomes in COVID-19 patients.

MATERIAL OR SUBJECTS

Two hundred twenty-six patients with SARS-CoV-2 infection and chest computed tomography (CT) images were enrolled.

METHODS

CCL18, CHI3L1/YKL-40, GAL3, ANG2, IP-10, IL-10, TNFα, IL-6, soluble gp130, soluble IL-6R were quantified in plasma samples using Luminex assays. The Mann-Whitney U test, the Kruskal-Wallis test, correlation and regression analyses were performed. Mediation analyses were used to investigate the possible causal relationships between cytokines, lung damage, and outcomes. AVIEW lung cancer screening software, pyradiomics, and XGBoost classifier were used for radiomic feature analyses.

RESULTS

CCL18, CHI3L1, and ANG2 systemic levels mainly reflected the extent of lung injury. Increased levels of every cytokine, but particularly of IL-6, were associated with the three outcomes: hospitalization, mechanical ventilation, and death. Soluble IL-6R showed a slight protective effect on death. The effect of age on COVID-19 outcomes was partially mediated by cytokine levels, while CT scores considerably mediated the effect of cytokine levels on outcomes. Radiomic-feature-based models confirmed the association between lung imaging characteristics and CCL18 and CHI3L1.

CONCLUSION

Data suggest a causal link between cytokines (risk factor), lung damage (mediator), and COVID-19 outcomes.

Risk of death due to COVID-19 among current and former smokers in the Netherlands: a population-based quasi-cohort study

BACKGROUND

Research on smoking as a risk factor for death due to COVID-19 remains inconclusive, with different studies demonstrating either an increased or decreased risk of COVID-19 death among smokers. To investigate this controversy, this study uses data from the Netherlands to assess the relationship between smoking and death due to COVID-19.

METHODS

In this population-based quasi-cohort study, we linked pseudonymized individual data on smoking status from the 2016 and 2020 'Health Monitor Adults and Elderly' in the Netherlands (n = 914 494) to data from the cause-of-death registry (n = 2962). Death due to COVID-19 in 2020 or 2021 was taken as the main outcome. Poisson regression modelling was used to calculate relative risks (RRs) and 95% CIs of death due to COVID-19 for current and former smokers compared with never smokers while adjusting for relevant confounders (age, sex, educational level, body mass index and perceived health).

RESULTS

Former smokers had a higher risk of death due to COVID-19 compared with never smokers across unadjusted (RR, 2.22; 95% CI, 2.04-2.42), age-sex-adjusted (RR, 1.38; 95% CI, 1.22-1.55) and fully adjusted (RR, 1.30; 95% CI, 1.16-1.45) models. Current smokers had a slightly higher risk of death due to COVID-19 compared with never smokers after adjusting for age and sex (RR, 1.21; 95% CI, 1.00-1.48) and after full adjustment (RR, 1.08; 95% CI, 0.90-1.29), although the results were statistically non-significant.

CONCLUSIONS

People with a history of smoking appear to have a higher risk of death due to COVID-19. Further research is needed to investigate which underlying mechanisms may explain this.

Perceptions of COVID-19 Risk: How Did People Adapt to the Novel Risk?

BACKGROUND

There is limited understanding of how risk perceptions changed as the US population gained experience with COVID-19. The objectives were to examine risk perceptions and determine the factors associated with risk perceptions and how these changed over the first 18 mo of the pandemic.

METHODS

Seven cross-sectional online surveys were fielded between May 2020 and October 2021. The study included a population-weighted sample of 138,303 US adults drawn from a market research platform, with an average 68% cooperation rate. Respondents' risk perception of developing COVID in the next 30 days was assessed at each time point. We examined relationships between 30-day risk perceptions and various factors (including sociodemographic features, health, COVID-19 experience, political affiliation, and psychological variables).

RESULTS

COVID risk perceptions were stable across the 2020 surveys and showed a significant decrease in the 2021 surveys. Several factors, including older age, worse health, high COVID worry, in-person employment type, higher income, Democratic political party affiliation (the relatively more liberal party in the United States), low tolerance of uncertainty, and high anxiety were strongly associated with higher 30-d risk perceptions in 2020. One notable change occurred in 2021, in that younger adults (aged 18-29 y) had significantly higher 30-d risk perceptions than older adults did (aged 65 y and older) after vaccination. Initial differences in perception by political party attenuated over time. Higher 30-d risk perceptions were significantly associated with engaging in preventive behaviors.

LIMITATIONS

Cross-sectional samples, risk perception item focused on incidence not severity.

CONCLUSIONS

COVID risk perceptions decreased over time. Understanding the longitudinal pattern of risk perceptions and the factors associated with 30-d risk perceptions over time provides valuable insights to guide public health communication campaigns.

HIGHLIGHTS

The study assessed COVID-19 risk perceptions at 7 time points over 18 mo of the pandemic in large samples of US adults.Risk perceptions were fairly stable until the introduction of vaccines in early 2021, at which point they showed a marked reduction.Higher COVID-19 30-d risk perceptions were significantly associated with the preventive behaviors of masking, limiting social contact, avoiding restaurants, and not entertaining visitors at home.

Evidence on COVID-19 Mortality and Disparities Using a Novel Measure, COVID excess mortality percentage: Evidence from Indiana, Wisconsin, and Illinois

COVID-19 mortality rates increase rapidly with age, are higher among men than women, and vary across racial/ethnic groups, but this is also true for other natural causes of death. Prior research on COVID-19 mortality rates and racial/ethnic disparities in those rates has not considered to what extent disparities reflect COVID-19-specific factors, versus preexisting health differences. This study examines both questions. We study the COVID-19-related increase in mortality risk and racial/ethnic disparities in COVID-19 mortality, and how both vary with age, gender, and time period. We use a novel measure validated in prior work, the COVID Excess Mortality Percentage (CEMP), defined as the COVID-19 mortality rate (Covid-MR), divided by the non-COVID natural mortality rate during the same time period (non-Covid NMR), converted to a percentage. The CEMP denominator uses Non-COVID NMR to adjust COVID-19 mortality risk for underlying population health. The CEMP measure generates insights which differ from those using two common measures-the COVID-MR and the all-cause excess mortality rate. By studying both CEMP and COVID-MRMR, we can separate the effects of background health from Covid-specific factors affecting COVID-19 mortality. We study how CEMP and COVID-MR vary by age, gender, race/ethnicity, and time period, using data on all adult decedents from natural causes in Indiana and Wisconsin over April 2020-June 2022 and Illinois over April 2020-December 2021. CEMP levels for racial and ethnic minority groups can be very high relative to White levels, especially for Hispanics in 2020 and the first-half of 2021. For example, during 2020, CEMP for Hispanics aged 18-59 was 68.9% versus 7.2% for non-Hispanic Whites; a ratio of 9.57:1. CEMP disparities are substantial but less extreme for other demographic groups. Disparities were generally lower after age 60 and declined over our sample period. Differences in socio-economic status and education explain only a small part of these disparities.

Health-related quality of life in mild-to-moderate COVID-19 in the UK: a cross-sectional study from pre- to post-infection

BACKGROUND

The aim of this study was to estimate the impact of mild-to-moderate COVID-19 on health-related quality of life (HRQoL) over time among individuals in the United Kingdom, adding to the evidence base that had focussed on severe COVID-19.

METHODS

A bespoke online survey was administered to individuals who self-reported a positive COVID-19 test. An amended version of a validated generic HRQoL instrument (EQ-5D-5L) was used to measure HRQoL retrospectively at different timepoints over the course of an infection: pre-COVID-19, acute COVID-19, and long COVID. In addition, HRQoL post-COVID-19 was captured by the original EQ-5D-5L questionnaire. A mixed-effects model was used to estimate changes in HRQoL over time, adjusted for a range of variables correlated with HRQoL.

RESULTS

The study recruited 406 participants: (i) 300 adults and 53 adolescents with mild-to-moderate COVID-19 who had not been hospitalised for COVID-19 during acute COVID-19, and (ii) 53 adults who had been hospitalised for COVID-19 in the acute phase and who had been recruited for validation purposes. Data were collected between January and April 2022. Among participants included in the base-case analysis, EQ-5D-5L utility scores were lower during both acute COVID-19 (β=-0.080, p = 0.001) and long COVID (β=-0.072, p < 0.001) compared to pre COVID-19. In addition, EQ-5D-5L utility scores post-COVID-19 were found to be similar to the EQ-5D-5L utility scores before COVID-19, including for patients who had been hospitalised for COVID-19 during the acute phase or for those who had experienced long COVID. Moreover, being hospitalised in the acute phase was associated with additional utility decrements during both acute COVID-19 (β=-0.147, p = 0.026) and long (β=-0.186, p < 0.001) COVID.

CONCLUSION

Patients perceived their HRQoL to have varied significantly over the course of a mild-to-moderate COVID-19 infection. However, HRQoL was found to return to pre-COVID-19 levels, even for patients who had been hospitalised for COVID-19 during the acute phase or for those who had experienced long COVID.

Evolution of in-hospital patient characteristics and predictors of death in the COVID-19 pandemic across four waves: are they moving targets with implications for patient care?

Objectives

The aim of this work was to study characteristics, outcomes and predictors of all-cause death in inpatients with SARS-CoV-2 infection across the pandemic waves in one large teaching hospital in Italy to optimize disease management.

Methods

All patients with SARS-CoV-2 infection admitted to our center from March 2020 to June 2022 were included in this retrospective observational cohort study. Both descriptive and regression tree analyses were applied to identify factors influencing all-cause mortality.

Results

527 patients were included in the study (65.3% with moderate and 34.7% with severe COVID-19). Significant evolutions of patient characteristics were found, and mortality increased in the last wave with respect to the third wave notwithstanding vaccination. Regression tree analysis showed that in-patients with severe COVID-19 had the greatest mortality across all waves, especially the older adults, while prognosis depended on the pandemic waves in patients with moderate COVID-19: during the first wave, dyspnea was the main predictor, while chronic kidney disease emerged as determinant factor afterwards.

Conclusion

Patients with severe COVID-19, especially the older adults during all waves, as well as those with moderate COVID-19 and concomitant chronic kidney disease during the most recent waves require more attention for monitoring and care. Therefore, our study drives attention towards the importance of co-morbidities and their clinical impact in patients with COVID-19 admitted to hospital, indicating that the healthcare system should adapt to the evolving features of the epidemic.

Three Years of the Coronavirus Disease 2019 Pandemic in a European Region: A Population-Based Longitudinal Assessment in Madrid Between 2020 and 2022

Background

Our objective was to assess the health impact of coronavirus disease 2019 (COVID-19) during 2020-2022 in the Madrid region.

Methods

We included all individuals registered in the Madrid Health System Registry as of 31 December 2019, and followed them until 31 December 2022. Using a unique personal identifier, we linked the databases of primary care, hospitals, pharmacies, certified laboratories performing diagnostic tests, vaccines, and mortality.

Results

Of 6 833 423 individuals, 21.4% had a confirmed COVID-19 diagnosis, and 1.5% had a COVID-19 hospitalization (primary diagnosis). Thirty-day mortality was 1.6% for confirmed COVID-19 (from 11.4% in first semester 2020 to 0.4% in first semester 2022). Thirty-day mortality was 10.8% for COVID-19 hospitalizations (from 14.0% in first semester 2020 to 6.0% in second semester 2022). There were 24 073 deaths within 30 days of a confirmed COVID-19 diagnosis. Advanced age, male sex, higher socioeconomic deprivation, and comorbidities were associated with higher mortality.

Conclusions

By linking administrative and clinical databases, we characterized the burden of the COVID-19 pandemic in Madrid over 3 years. Our analysis proposes a high-level framework for comparisons of the burden of COVID-19 across areas worldwide.

The Role of COVID-19 Vaccination for Patients With Atherosclerotic Cardiovascular Disease in the Upcoming Endemic Era

COVID-19 vaccination has played a pivotal role in coping with the COVID-19 pandemic by providing a powerful tool to curb the spread of the virus, reduce severe illness and hospitalizations, and ultimately save lives and facilitate a return to normal daily routines. As COVID-19 vaccination has become more widespread and more individuals have recovered from the infection, COVID-19 has entered an endemic disease phase. This phase is characterized by a less severe and more stable pattern of infection within certain regions, similar to the predictability of seasonal influenza. In this endemic era, COVID-19 vaccines may appear to be less important, and many people are reluctant to receive COVID-19 vaccination for various reasons, including the fear of adverse events. However, COVID-19 remains a major public health problem, in that the incidence rate of new COVID-19 infections is still high and the morbidity and mortality in high-risk populations are substantial. Therefore, the role of COVID-19 vaccines in protecting high-risk individuals is crucial, and ongoing research and surveillance are imperative to refine vaccination recommendations in the ever-changing landscape of the COVID-19 endemic era. This review explores the role of COVID-19 vaccination in the upcoming COVID-19 endemic era.

Adverse drug reactions and hospital admissions: Large case-control study of patients aged 65-100 years using linked English primary care and hospital data

BACKGROUND

Adverse drug reactions (ADRs) are common and a leading cause of injury. However, information on ADR risks of individual medicines is often limited. The aim of this hypothesis-generating study was to assess the relative importance of ADR-related and emergency hospital admission for large group of medication classes.

METHODS

This study was a propensity-matched case-control study in English primary care. Data sources were Clinical Practice Research Databank and Aurum with longitudinal, anonymized, patient level electronic health records (EHRs) from English general practices linked to hospital records. Cases aged 65-100 with ADR-related or emergency hospital admission were matched to up to six controls by age, sex, morbidity and propensity scores for hospital admission risk. Medication groups with systemic administration as listed in the British National Formulary (used by prescribers for medication advice). Prescribing in the 84 days before the index date was assessed. Only medication groups with 50+ cases exposed were analysed. The outcomes of interest were ADR-related and emergency hospital admissions. Conditional logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CI).

RESULTS

The overall population included 121 546 cases with an ADR-related and 849 769 cases with emergency hospital admission. The percentage of hospitalizations with an ADR-related code for admission diagnosis was 1.83% and 6.58% with an ADR-related code at any time during hospitalization. A total of 137 medication groups was included in the main ADR analyses. Of these, 13 (9.5%) had statistically non-significant adjusted ORs, 58 (42.3%) statistically significant ORs between 1.0 and 1.5, 37 (27.0%) between 1.5-2.0, 18 (13.1%) between 2.0-3.0 and 11 (8.0%) 3.0 or higher. Several classes of antibiotics (including penicillins) were among medicines with largest ORs. Evaluating the 14 medications most often associated with ADRs, a strong association was found between the number of these medicines and the risk of ADR-related hospital admission (adjusted OR of 7.53 (95% CI 7.15-7.93) for those exposed to 6+ of these medicines).

CONCLUSIONS AND RELEVANCE

There is a need for a regular systematic assessment of the harm-benefit ratio of medicines, harvesting the information in large healthcare databases and combining it with causality assessment of individual case histories.

Effects of COVID-19 on the Liver and Mortality in Patients with SARS-CoV-2 Pneumonia Caused by Delta and Non-Delta Variants: An Analysis in a Single Centre

The aim of this study was to ascertain patient characteristics, outcomes, and liver injuries in patients infected with different SARS-CoV-2 variants. Data from consecutive adult patients with severe/critical COVID-19 admitted to our hospital during the peak month of the Delta wave were compared to the ancestral, Alpha, and Omicron waves. The dataset of 551 hospitalized patients was similar in the Delta/non-Delta waves. At admission and discharge, the median aminotransferase levels were normal or slightly increased. During the Delta wave (172 vs. 379 non-Delta patients), more patients died (OR 1.69, 95%CI 1.09-2.56) or had liver injury at discharge (alanine aminotransferase, ALT ≥ 2 ULN) (OR 1.97, 95%CI 1.08-3.54). In-hospital mortality was associated with age, lung injury, intensive care unit admission, number of and cardiovascular comorbidities, diabetes, chronic kidney disease, and all inflammatory biomarkers. Serious liver injury at admission (ALT ≥ 5 × ULN) was significantly associated with in-hospital mortality (OR = 7.9, 95%CI 2-28.9). At discharge, drug-induced liver injury (DILI) was found in patients treated with remdesivir, ALT ≥ 2 ULN (OR = 2.62, 95%CI 1.22-5.75). Treatment with dexamethasone, remdesivir, and immunomodulators showed improved survival, OR = 0.50 (95%CI 0.33-0.77). Regardless of the variant and treatment options, less than 2% of patients displayed serious liver injury, which was not found to be a death predictor in multivariable analysis.

Associations of diabetes, hypertension and obesity with COVID-19 mortality: a systematic review and meta-analysis

INTRODUCTION

Despite a growing body of scholarly research on the risks of severe COVID-19 associated with diabetes, hypertension and obesity, there is a need for estimating pooled risk estimates with adjustment for confounding effects. We conducted a systematic review and meta-analysis to estimate the pooled adjusted risk ratios of diabetes, hypertension and obesity on COVID-19 mortality.

METHODS

We searched 16 literature databases for original studies published between 1 December 2019 and 31 December 2020. We used the adapted Newcastle-Ottawa Scale to assess the risk of bias. Pooled risk ratios were estimated based on the adjusted effect sizes. We applied random-effects meta-analysis to account for the uncertainty in residual heterogeneity. We used contour-funnel plots and Egger's test to assess possible publication bias.

RESULTS

We reviewed 34 830 records identified in literature search, of which 145 original studies were included in the meta-analysis. Pooled adjusted risk ratios were 1.43 (95% CI 1.32 to 1.54), 1.19 (95% CI 1.09 to 1.30) and 1.39 (95% CI 1.27 to 1.52) for diabetes, hypertension and obesity (body mass index ≥30 kg/m2) on COVID-19 mortality, respectively. The pooled adjusted risk ratios appeared to be stronger in studies conducted before April 2020, Western Pacific Region, low- and middle-income countries, and countries with low Global Health Security Index scores, when compared with their counterparts.

CONCLUSIONS

Diabetes, hypertension and obesity were associated with an increased risk of COVID-19 mortality independent of other known risk factors, particularly in low-resource settings. Addressing these chronic diseases could be important for global pandemic preparedness and mortality prevention.

PROSPERO REGISTRATION NUMBER

CRD42021204371.

Adjusting expected deaths for mortality displacement during the COVID-19 pandemic: a model based counterfactual approach at the level of individuals

BACKGROUND

Near-real time surveillance of excess mortality has been an essential tool during the COVID-19 pandemic. It remains critical for monitoring mortality as the pandemic wanes, to detect fluctuations in the death rate associated both with the longer-term impact of the pandemic (e.g. infection, containment measures and reduced service provision by the health and other systems) and the responses that followed (e.g. curtailment of containment measures, vaccination and the response of health and other systems to backlogs). Following the relaxing of social distancing regimes and reduction in the availability of testing, across many countries, it becomes critical to measure the impact of COVID-19 infection. However, prolonged periods of mortality in excess of the expected across entire populations has raised doubts over the validity of using unadjusted historic estimates of mortality to calculate the expected numbers of deaths that form the baseline for computing numbers of excess deaths because many individuals died earlier than they would otherwise have done: i.e. their mortality was displaced earlier in time to occur during the pandemic rather than when historic rates predicted. This is also often termed "harvesting" in the literature.

METHODS

We present a novel Cox-regression-based methodology using time-dependent covariates to estimate the profile of the increased risk of death across time in individuals who contracted COVID-19 among a population of hip fracture patients in England (N = 98,365). We use these hazards to simulate a distribution of survival times, in the presence of a COVID-19 positive test, and then calculate survival times based on hazard rates without a positive test and use the difference between the medians of these distributions to estimate the number of days a death has been displaced. This methodology is applied at the individual level, rather than the population level to provide a better understanding of the impact of a positive COVID-19 test on the mortality of groups with different vulnerabilities conferred by sociodemographic and health characteristics. Finally, we apply the mortality displacement estimates to adjust estimates of excess mortality using a "ball and urn" model.

RESULTS

Among the exemplar population we present an end-to-end application of our methodology to estimate the extent of mortality displacement. A greater proportion of older, male and frailer individuals were subject to significant displacement while the magnitude of displacement was higher in younger females and in individuals with lower frailty: groups who, in the absence of COVID-19, should have had a substantial life expectancy.

CONCLUSION

Our results indicate that calculating the expected number of deaths following the first wave of the pandemic in England based solely on historical trends results in an overestimate, and excess mortality will therefore be underestimated. Our findings, using this exemplar dataset are conditional on having experienced a hip fracture, which is not generalisable to the general population. Fractures that impede mobility in the weeks that follow the accident/surgery considerably shorten life expectancy and are in themselves markers of significant frailty. It is therefore important to apply these novel methods to the general population, among whom we anticipate strong patterns in mortality displacement - both in its length and prevalence - by age, sex, frailty and types of comorbidities. This counterfactual method may also be used to investigate a wider range of disruptive population health events. This has important implications for public health monitoring and the interpretation of public health data in England and globally.

Taking stock of the mutations in human SARS-CoV-2 spike proteins: From early days to nearly the end of COVID-19 pandemic

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), causative agent of the coronavirus disease-2019 (COVID-19) has resulted in several deaths and severe economic losses throughout the world. The spike protein in the virus binds to the human ACE-2 receptor in order to mediate virus-host interactions required for the viral transmission. Since first report of the SARS-CoV-2 sequence during December 2019 from patient infected with the virus in Wuhan, China, the virus has undergone rapid changes leading to mutations comprising substitutions, deletions and insertions in the sequence resulting in several variants of the virus that were more virulent and transmissible or less virulent but highly transmissible. The timely intervention with COVID-19 vaccines proved to be effective in controlling the number of infections. However, rapid mutations in the virus led to the lowering of vaccine efficacies being administered to people. In May 2023, the World Health Organization declared COVID-19 was not a public health emergency of international concern anymore. In order to take stock of mutations in the virus from early days to nearly end of COVID-19 pandemic, sequence analyses of the SARS-CoV-2 spike proteins available in the NCBI Virus database was carried out. The mutations and invariant residues in the SARS-CoV-2 spike protein sequences relative to the reference sequence were analysed. The location of the invariant residues and residues at interface of the protein chains in the spike protein trimer complex structure were examined. A total of 111,298 non-redundant SARS-CoV-2 spike protein sequences representing 2,345,585 spike proteins in the NCBI Virus database showed mutations at 1252 of the 1273 positions in the amino acid sequence. The mutations represented 6129 different mutation types in the sequences analysed. Besides, some sequences also contained insertion mutations. The SARS-CoV-2 spike protein sequences represented 1435 lineages. In addition, several spike protein sequences with mutations whose lineages were either 'not classified' or were 'unclassifiable' indicated the virus could still be evolving.

The effect of obesity on the mortality of hospitalized adults with COVID-19 considering the human development index: A systematic review and meta-analysis

The objective of this study is to verify the association between obesity and mortality in hospitalized patients with SARS-CoV-2 taking into account the Human Development Index (HDI). A search was performed in the PubMed, Virtual Health Library (Lilacs/Bireme/VHL Brazil), Embase, Web of Science, and Scopus databases from inception to May 2022. To be eligible, studies had to have cohort or case-control designs, be conducted with hospitalized adults (≥18 years old), and evaluate mortality rates between with obesity and without obesity individuals with SARS-CoV-2 confirmed by laboratory tests. The analyses were performed in Stata 12.0 using relative risk (RR) as a summary measure. Heterogeneity was explored by meta-regression and subgroup analyses considering the HDI, age, sex, and follow-up period. Out of 912 studies screened, 49 studies were eligible for qualitative synthesis, and 33 studies were eligible for quantitative analysis, representing 42,905 patients. The mortality risk from SARS-CoV-2 was higher in individuals with obesity compared with without obesity individuals only in the subgroups of patients <60 years old (RR = 1.31; 95% CI 1.18-1.45, I2  = 0.0%) and living in countries with a low HDI (RR = 1.28; 95% CI 1.10-1.48, I2  = 45.4%).

Association Between Family Support and Coping Strategies of People With Covid-19: A Cross-Sectional Study

Purpose

The study aimed to determine the association between family support and coping strategies of people diagnosed with COVID-19.

Methods

The study was analytical and cross-sectional. The sample consisted of 500 participants who were selected by non-probabilistic and snowball sampling and included residents of both sexes who belonged to the city of Lima, with a diagnosis of COVID-19, who lived with relatives, and who accepted to participate in the research. For data collection, the scales "family support" and "Coping and Adaptation Process-Coping Adaptation Processing Scale (CAPS)" were used. The technique used was the survey through the home visit and the questionnaire instrument. To measure the relationship of the study variables, binary logistic regression was chosen, considering coping strategies as the dependent variable and socio-demographic data and family support as independent variables.

Results

Of the 500 participants, 50.4% were women, and 49.6% were men. The results revealed that most participants presented a high capacity for coping strategies and high perceived family support (97.2% and 81%, respectively). In the bivariate analysis, socio-demographic aspects and family support and their dimensions were related to high or low capacity for coping strategies. Significant differences were verified between marital status (p=0.026), having children (p=0.037), family support (p=0.000), and its dimensions with coping strategies. Finally, the multivariate analysis found that people with COVID-19 who perceived high family support were 33.74 times (95% CI: 7266-156,739) more likely to have a high capacity for coping strategies.

Conclusion

Therefore, it is necessary to promote the development of parental and family support skills in the face of the health emergency caused by COVID-19.

Covid-19 pandemic and asthma: What did we learn?

This review addresses some of the major lessons we have learnt regarding asthma and the covid-19 pandemic, including susceptibility to SARS-CoV-2 infection and severe covid-19, potentially protective factors, comparison to other respiratory infections, changes in healthcare behaviour from the perspective of patients and clinicians, medications to treat or prevent covid-19, and post-covid syndrome.

The Relationship Between Patient-Specific Factors and Discharge Destination After COVID-19 Hospitalization

OBJECTIVE

The aim of this study was to determine the discharge destinations and associated patient-specific factors among patients hospitalized with COVID-19.

DESIGN

A retrospective cohort study was carried out at a single-site tertiary acute care hospital.

RESULTS

Among 2872 patients, discharge destination included home without services ( n = 2044, 71.2%), home with services ( n = 379, 13.2%), skilled nursing facility (117, 4.1%), long-term acute care hospital ( n = 39, 1.3%), inpatient rehabilitation facility ( n = 97, 3.4%), acute care facility ( n = 23, 0.8%), hospice services ( n = 20, 0.7%), or deceased during hospitalization ( n = 153, 5.3%). Adjusting by covariates, patients had higher odds of discharge to a rehabilitation facility (skilled nursing facility, long-term acute care hospital, or inpatient rehabilitation facility) than home (with or without services) when they were older (odds ratio [OR], 2.37; 95% confidence interval [CI], 1.80-3.11; P < 0.001), had a higher Charlson Comorbidity Index score (3-6: OR, 2.36; 95% CI, 1.34-4.15; P = 0.003; ≥7: OR, 2.76; 95% CI, 1.56-4.86; P < 0.001), were intubated or required critical care (OR, 2.15; 95% CI, 1.48-3.13; P < 0.001), or had a longer hospitalization (3-7 days: OR, 12.48; 95% CI, 3.77-41.32; P < 0.001; 7-14 days: OR, 28.14; 95% CI, 8.57-92.43; P < 0.001). Patients were less likely to be discharged to a rehabilitation facility if they received remdesivir (OR, 0.44; 95% CI, 0.31-0.64; P < 0.001).

CONCLUSIONS

Patient-specific factors associated with COVID-19 hospitalization should be considered by physicians when prognosticating patient rehabilitation.

COVID-19 and metabolic syndrome

Convergence of the two pandemics: metabolic syndrome and COVID-19 over last two years has posed unprecedented challenges to individuals as well as healthcare systems. Epidemiological data suggest a close association between metabolic syndrome and COVID-19 while variety of possible pathogenic connections have been proposed while some have been proven. Despite the evidence of high risk for adverse COVID-19 outcomes in people with metabolic syndrome, little is known about the differences in efficacy and safety among people with metabolic syndrome and without. It is important to recognize that among people with metabolic syndrome This review summarizes the current knowledge and epidemiological evidence on the association between metabolic syndrome and adverse COVID-19 outcomes, pathogenic interrelationships, management considerations for acute COVID-19 and post-COVID sequalae and sustaining care of people living with metabolic syndrome with appraisal of evidence and gaps in knowledge.

Leveraging real-world data to improve cochlear implant outcomes: Is the data available?

OBJECTIVES

A small but persistent proportion of individuals do not gain the expected benefit from cochlear implants(CI). A step-change in the understanding of factors affecting outcomes could come through data science. This study evaluates clinical data capture to assess the quality and utility of CI user's health records for data science, by assessing the recording of otitis media. Otitis media was selected as it is associated with the development of sensorineural hearing loss and may affect cochlear implant outcomes.

METHODS

A retrospective service improvement project evaluating the medical records of 594 people with a CI under the care of the University of Southampton Auditory Implant Service between 2014 and 2020.

RESULTS

The clinical records are suitable for data science research. Of the cohort studied 20% of Adults and more than 40% of the paediatric cases have a history of middle ear inflammation.

DISCUSSION

Data science has potential to improve cochlear implant outcomes and improve understanding of the mechanisms underlying poor performance, through retrospective secondary analysis of real-world data.

CONCLUSION

Implant centres and the British Cochlear Implant Group National Hearing Implant Registry are urged to consider the importance of consistently and accurate recording of patient data over time for each CI user. Data where links to hearing loss have been identified, such as middle ear inflammation, may be particularly valuable in future analyses and to inform clinical trials.

Risk of COVID-19 breakthrough infection and hospitalization in individuals with comorbidities

BACKGROUND

The successful development of multiple COVID-19 vaccines has led to a global vaccination effort to reduce severe COVID-19 infection and mortality. However, the effectiveness of the COVID-19 vaccines wane over time leading to breakthrough infections where vaccinated individuals experience a COVID-19 infection. Here we estimate the risks of breakthrough infection and subsequent hospitalization in individuals with common comorbidities who had completed an initial vaccination series.

METHODS

Our study population included vaccinated patients between January 1, 2021 to March 31, 2022 who are present in the Truveta patient population. Models were developed to describe 1) time from completing primary vaccination series till breakthrough infection; and 2) if a patient was hospitalized within 14 days of breakthrough infection. We adjusted for age, race, ethnicity, sex, and year-month of vaccination.

RESULTS

Of 1,218,630 patients in the Truveta Platform who had completed an initial vaccination sequence between January 1, 2021 and March 31, 2022, 2.85, 3.42, 2.75, and 2.88 percent of patients with CKD, chronic lung disease, diabetes, or are in an immunocompromised state experienced breakthrough infection, respectively, compared to 1.46 percent of the population without any of these four comorbidities. We found an increased risk of breakthrough infection and subsequent hospitalization in individuals with any of the four comorbidities when compared to individuals without these four comorbidities.

CONCLUSIONS

Vaccinated individuals with any of the studied comorbidities experienced an increased risk of breakthrough COVID-19 infection and subsequent hospitalizations compared to the people without any of the studied comorbidities. Individuals with immunocompromising conditions and chronic lung disease were most at risk of breakthrough infection, while people with CKD were most at risk of hospitalization following breakthrough infection. Patients with multiple comorbidities have an even greater risk of breakthrough infection or hospitalization compared to patients with none of the studied comorbidities. Individuals with common comorbidities should remain vigilant against infection even if vaccinated.

Analysis of fatality impact and seroprevalence surveys in a community sustaining a SARS-CoV-2 superspreading event

There is an ongoing debate on the COVID-19 infection fatality rate (IFR) and the impact of COVID-19 on overall population mortality. Here, we addressed these issues in a community in Germany with a major superspreader event analyzing deaths over time and auditing death certificates in the community.18 deaths that occurred within the first six months of the pandemic had a positive test for SARS-CoV-2. Six out of 18 deaths had non-COVID-19 related causes of death (COD). Individuals with COVID-19 COD typically died of respiratory failure (75%) and tended to have fewer reported comorbidities (p = 0.029). Duration between first confirmed infection and death was negatively associated with COVID-19 being COD (p = 0.04). Repeated seroprevalence essays in a cross-sectional epidemiological study showed modest increases in seroprevalence over time, and substantial seroreversion (30%). IFR estimates accordingly varied depending on COVID-19 death attribution. Careful ascertainment of COVID-19 deaths is important in understanding the impact of the pandemic.

Risk factors for deaths associated with COVID-19 according to the cause of death classification in Republic of Korea

OBJECTIVES

This study aimed to classify coronavirus disease 2019 (COVID-19)-related deaths according to whether COVID-19 was listed as the cause of death, and to investigate the differences in demographic characteristics and risk factors for COVID-19 death classifications.

METHODS

A total of 5,625 deaths in South Korea among patients with confirmed COVID-19 from January 20, 2020 to December 31, 2021 were selected. Excluding false reports and unnatural deaths, 5,597 deaths were analyzed. Based on death report data, deaths were classified according to whether the cause of death was listed as COVID-19 (CD) or not (NCD). The epidemiological characteristics and causes of deaths were investigated using descriptive, univariate, and multivariate statistical analyses. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to analyze the risk factors.

RESULTS

The case fatality ratio was 0.89% and increased with age. Additionally, 96.4% of the subjects had an underlying disease, and 53.4% died in winter. The proportion of NCDs was 9.3%, of whom 19.1% died at home and 39.0% were confirmed to have COVID-19 after death. Malignant neoplasms (102/416 vs. 637/4,442; OR, 1.71; 95% CI, 1.36-2.16; p<0.001) were significantly associated with NCD.

CONCLUSION

This is the first study to analyze risk factors by cause of death using COVID-19 death report data in South Korea. These results are expected to be used as evidence for establishing a death monitoring system that can collect timely information in a new infectious disease pandemic.

Temporal variation of excess deaths from diabetes during the COVID-19 pandemic in the United States

BACKGROUND

Although the COVID-19 pandemic has persisted for more than two years with the evident excess mortality from diabetes, few studies have investigated its temporal patterns. This study aims to estimate the excess deaths from diabetes in the United States (US) during the COVID-19 pandemic and evaluate the excess deaths by spatiotemporal pattern, age groups, sex, and race/ethnicity.

METHODS

Diabetes as one of multiple causes of death or an underlying cause of death were both considered into analyses. The Poisson log-linear regression model was used to estimate weekly expected counts of deaths during the pandemic with adjustments for long-term trend and seasonality. Excess deaths were measured by the difference between observed and expected death counts, including weekly average excess deaths, excess death rate, and excess risk. We calculated the excess estimates by pandemic wave, US state, and demographic characteristic.

RESULTS

From March 2020 to March 2022, deaths that diabetes as one of multiple causes of death and an underlying cause of death were about 47.6 % and 18.4 % higher than the expected. The excess deaths of diabetes had evident temporal patterns with two large percentage increases observed during March 2020, to June 2020, and June 2021 to November 2021. The regional heterogeneity and underlying age and racial/ethnic disparities of the excess deaths were also clearly observed.

CONCLUSIONS

This study highlighted the increased risks of diabetes mortality, heterogeneous spatiotemporal patterns, and associated demographic disparities during the pandemic. Practical actions are warranted to monitor disease progression, and lessen health disparities in patients with diabetes during the COVID-19 pandemic.

Spread and seasonality of COVID-19 pandemic confirmed cases in sub-Saharan Africa: experience from Democratic Republic of Congo, Nigeria, Senegal, and Uganda

BACKGROUND

The COVID-19 pandemic has impacted the world negatively with huge health and socioeconomic consequences. This study estimated the seasonality, trajectory, and projection of COVID-19 cases to understand the dynamics of the disease spread and inform response interventions.

METHOD

Descriptive analysis of daily confirmed COVID-19 cases from January 2020 to 12^th March 2022 was conducted in four purposefully selected sub-Saharan African countries (Nigeria, Democratic Republic of Congo (DRC), Senegal, and Uganda). We extrapolated the COVID-19 data from (2020 to 2022) to 2023 using a trigonometric time series model. A decomposition time series method was used to examine the seasonality in the data.

RESULTS

Nigeria had the highest rate of spread (β) of COVID-19 (β = 381.2) while DRC had the least rate (β = 119.4). DRC, Uganda, and Senegal had a similar pattern of COVID-19 spread from the onset through December 2020. The average doubling time in COVID-19 case count was highest in Uganda (148 days) and least in Nigeria (83 days). A seasonal variation was found in the COVID-19 data for all four countries but the timing of the cases showed some variations across countries. More cases are expected in the 1^st (January-March) and 3^rd (July-September) quarters of the year in Nigeria and Senegal, and in the 2^nd (April-June) and 3^rd (October-December) quarters in DRC and Uganda.

CONCLUSION

Our findings show a seasonality that may warrant consideration for COVID-19 periodic interventions in the peak seasons in the preparedness and response strategies.

Associations between mortality from COVID-19 and other causes: A state-level analysis

BACKGROUND

During the COVID-19 pandemic, the high death toll from COVID-19 was accompanied by a rise in mortality from other causes of death. The objective of this study was to identify the relationship between mortality from COVID-19 and changes in mortality from specific causes of death by exploiting spatial variation in these relationships across US states.

METHODS

We use cause-specific mortality data from CDC Wonder and population estimates from the US Census Bureau to examine relationships at the state level between mortality from COVID-19 and changes in mortality from other causes of death. We calculate age-standardized death rates (ASDR) for three age groups, nine underlying causes of death, and all 50 states and the District of Columbia between the first full year of the pandemic (March 2020-February 2021) and the year prior (March 2019-February 2020). We then estimate the relationship between changes in cause-specific ASDR and COVID-19 ASDR using linear regression analysis weighted by the size of the state's population.

RESULTS

We estimate that causes of death other than COVID-19 represent 19.6% of the total mortality burden associated with COVID-19 during the first year of the COVID-19 pandemic. At ages 25+, circulatory disease accounted for 51.3% of this burden while dementia (16.4%), other respiratory diseases (12.4%), influenza/pneumonia (8.7%) and diabetes (8.6%) also contribute. In contrast, there was an inverse association across states between COVID-19 death rates and changes in death rates from cancer. We found no state-level association between COVID-19 mortality and rising mortality from external causes.

CONCLUSIONS

States with unusually high death rates from COVID-19 experienced an even larger mortality burden than implied by those rates alone. Circulatory disease served as the most important route through which COVID-19 mortality affected death rates from other causes of death. Dementia and other respiratory diseases made the second and third largest contributions. In contrast, mortality from neoplasms tended to decline in states with the highest death rates from COVID-19. Such information may help to inform state-level responses aimed at easing the full mortality burden of the COVID-19 pandemic.

Risk Factors for Mortality of Hospitalized Adult Patients with COVID-19 Pneumonia: A Two-Year Cohort Study in a Private Tertiary Care Center in Mexico

During the COVID-19 pandemic, the high prevalence of comorbidities and the disparities between the public and private health subsystems in Mexico substantially contributed to the severe impact of the disease. The objective of this study was to evaluate and compare the risk factors at admission for in-hospital mortality of patients with COVID-19. A 2-year retrospective cohort study of hospitalized adult patients with COVID-19 pneumonia was conducted at a private tertiary care center. The study population consisted of 1258 patients with a median age of 56 ± 16.5 years, of whom 1093 recovered (86.8%) and 165 died (13.1%). In the univariate analysis, older age (p < 0.001), comorbidities such as hypertension (p < 0.001) and diabetes (p < 0.001), signs and symptoms of respiratory distress, and markers of acute inflammatory response were significantly more frequent in non-survivors. The multivariate analysis showed that older age (p < 0.001), the presence of cyanosis (p = 0.005), and previous myocardial infarction (p = 0.032) were independent predictors of mortality. In the studied cohort, the risk factors present at admission associated with increased mortality were older age, cyanosis, and a previous myocardial infarction, which can be used as valuable predictors for patients' outcomes. To our knowledge, this is the first study analyzing predictors of mortality in COVID-19 patients attended in a private tertiary hospital in Mexico.

Postmortem lung and heart examination of COVID-19 patients in a case series from Jordan

BACKGROUND

Coronavirus disease 2019 (COVID-19) has emerged as a pandemic for more than 2 years. Autopsy examination is an invaluable tool to understand the pathogenesis of emerging infections and their consequent mortalities. The aim of the current study was to present the lung and heart pathological findings of COVID-19-positive autopsies performed in Jordan.

METHODS

The study involved medicolegal cases, where the cause of death was unclear and autopsy examination was mandated by law. We included the clinical and pathologic findings of routine gross and microscopic examination of cases that were positive for COVID-19 at time of death. Testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was confirmed through molecular detection by real-time polymerase chain reaction, serologic testing for IgM and electron microscope examination of lung samples.

RESULTS

Seventeen autopsies were included, with male predominance (76.5%), Jordanians (70.6%), and 50 years as the mean age at time of death. Nine out of 16 cases (56.3%) had co-morbidities, with one case lacking such data. Histologic examination of lung tissue revealed diffuse alveolar damage in 13/17 cases (76.5%), and pulmonary microthrombi in 8/17 cases (47.1%). Microscopic cardiac findings were scarcely detected. Two patients died as a direct result of acute cardiac disease with limited pulmonary findings.

CONCLUSIONS

The detection of SARS-CoV-2 in postmortem examination can be an incidental or contributory finding which highlights the value of autopsy examination to determine the exact cause of death in controversial cases.

Inborn errors of OAS-RNase L in SARS-CoV-2-related multisystem inflammatory syndrome in children

Multisystem inflammatory syndrome in children (MIS-C) is a rare and severe condition that follows benign COVID-19. We report autosomal recessive deficiencies of OAS1, OAS2, or RNASEL in five unrelated children with MIS-C. The cytosolic double-stranded RNA (dsRNA)-sensing OAS1 and OAS2 generate 2'-5'-linked oligoadenylates (2-5A) that activate the single-stranded RNA-degrading ribonuclease L (RNase L). Monocytic cell lines and primary myeloid cells with OAS1, OAS2, or RNase L deficiencies produce excessive amounts of inflammatory cytokines upon dsRNA or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) stimulation. Exogenous 2-5A suppresses cytokine production in OAS1-deficient but not RNase L-deficient cells. Cytokine production in RNase L-deficient cells is impaired by MDA5 or RIG-I deficiency and abolished by mitochondrial antiviral-signaling protein (MAVS) deficiency. Recessive OAS-RNase L deficiencies in these patients unleash the production of SARS-CoV-2-triggered, MAVS-mediated inflammatory cytokines by mononuclear phagocytes, thereby underlying MIS-C.

Presence of trans-Fatty Acids Containing Ingredients in Pre-Packaged Foods and the Availability of Reported trans-Fat Levels in Kenya and Nigeria

In most African countries, the prevalence of industrially produced trans-fatty acids (iTFA) in the food supply is unknown. We estimated the number and proportion of products containing specific (any hydrogenated edible oils) and non-specific (vegetable fat, margarine, and vegetable cream) ingredients potentially indicative of iTFAs among pre-packaged foods collected in Kenya and Nigeria. We also summarized the number and proportion of products that reported trans-fatty acids levels and the range of reported trans-fatty acids levels. In total, 99 out of 5668 (1.7%) products in Kenya and 310 out of 6316 (4.9%) products in Nigeria contained specific ingredients indicative of iTFAs. Bread and bakery products and confectioneries in both countries had the most foods that contained iTFAs-indicative ingredients. A total of 656 products (12%) in Kenya and 624 products (10%) in Nigeria contained non-specific ingredients that may indicate the presence of iTFAs. The reporting of levels of trans-fatty acids was low in both Kenya and Nigeria (11% versus 26%, respectively, p < 0.001). With the increasing burden of ischemic heart disease in Kenya and Nigeria, the rapid adoption of WHO best-practice policies and the mandatory declaration of trans-fatty acids are important for eliminating iTFAs.

Clinical Relevance of Drug-Drug Interactions With Antibiotics as Listed in a National Medication Formulary: Results From Two Large Population-Based Case-Control Studies in Patients Aged 65-100 Years Using Linked English Primary Care and Hospital Data

This study evaluated drug-drug interactions (DDIs) between antibiotic and nonantibiotic drugs listed with warnings of severe outcomes in the British National Formulary based on adverse drug reaction (ADR) detectable with routine International Classification of Diseases, Tenth Revision coding. Data sources were Clinical Practice Research Databank GOLD and Aurum anonymized electronic health records from English general practices linked to hospital admission records. In propensity-matched case-control study, outcomes were ADR or emergency admissions. Analyzed were 121,546 ADR-related admission cases matched to 638,238 controls. For most antibiotics, adjusted odds ratios (aORs) for ADR-related hospital admission were large (aOR for trimethoprim 4.13; 95% confidence interval (CI), 3.97-4.30). Of the 51 DDIs evaluated for ADR-related admissions, 38 DDIs (74.5%) had statistically increased aORs of concomitant exposure compared with nonexposure (mean aOR 3.96; range 1.59-11.42); for the 89 DDIs for emergency hospital admission, the results were 75 (84.3%) and mean aOR 2.40; range 1.43-4.17. Changing reference group to single antibiotic exposure reduced aORs for concomitant exposure by 76.5% and 83.0%, respectively. Medicines listed to cause nephrotoxicity substantially increased risks that were related to number of medicines (aOR was 2.55 (95% CI, 2.46-2.64) for current use of 1 and 10.44 (95% CI, 7.36-14.81) for 3 or more medicines). In conclusion, no evidence of substantial risk was found for multiple DDIs with antibiotics despite warnings of severe outcomes in a national formulary and flagging in electronic health record software. It is proposed that the evidence base for inclusion of DDIs in national formularies be strengthened and made publicly accessible and indiscriminate flagging, which compounds alert fatigue, be reduced.

Differences Between Reported COVID-19 Deaths and Estimated Excess Deaths in Counties Across the United States, March 2020 to February 2022

Accurate and timely tracking of COVID-19 deaths is essential to a well-functioning public health surveillance system. The extent to which official COVID-19 death tallies have captured the true toll of the pandemic in the United States is unknown. In the current study, we develop a Bayesian hierarchical model to estimate monthly excess mortality in each county over the first two years of the pandemic and compare these estimates to the number of deaths officially attributed to Covid-19 on death certificates. Overall, we estimated that 268,176 excess deaths were not reported as Covid-19 deaths during the first two years of the Covid-19 pandemic, which represented 23.7% of all excess deaths that occurred. Differences between excess deaths and reported COVID-19 deaths were substantial in both the first and second year of the pandemic. Excess deaths were less likely to be reported as COVID-19 deaths in the Mountain division, in the South, and in nonmetro counties. The number of excess deaths exceeded COVID-19 deaths in all Census divisions except for the New England and Middle Atlantic divisions where there were more COVID-19 deaths than excess deaths in large metro areas and medium or small metro areas. Increases in excess deaths not assigned to COVID-19 followed similar patterns over time to increases in reported COVID-19 deaths and typically preceded or occurred concurrently with increases in reported COVID-19 deaths. Estimates from this study can be used to inform targeting of resources to areas in which the true toll of the COVID-19 pandemic has been underestimated.

Risk Factors of Severe COVID-19: A Review of Host, Viral and Environmental Factors

The clinical course and outcome of COVID-19 are highly variable, ranging from asymptomatic infections to severe disease and death. Understanding the risk factors of severe COVID-19 is relevant both in the clinical setting and at the epidemiological level. Here, we provide an overview of host, viral and environmental factors that have been shown or (in some cases) hypothesized to be associated with severe clinical outcomes. The factors considered in detail include the age and frailty, genetic polymorphisms, biological sex (and pregnancy), co- and superinfections, non-communicable comorbidities, immunological history, microbiota, and lifestyle of the patient; viral genetic variation and infecting dose; socioeconomic factors; and air pollution. For each category, we compile (sometimes conflicting) evidence for the association of the factor with COVID-19 outcomes (including the strength of the effect) and outline possible action mechanisms. We also discuss the complex interactions between the various risk factors.

Influence of socioeconomic inequality on the distribution of COVID-19 hospitalizations and deaths in Brazilian municipalities, 2020: an ecological study

OBJECTIVE

to analyze the influence of socioeconomic inequality on COVID-19 distribution in larger Brazilian municipalities, controlling for effect of hospital infrastructure, comorbidities and other variables.

METHODS

this was an ecological study of COVID-19 hospitalizations and deaths in 2020; outcome data were obtained from the Ministry of Health; incidence ratios were estimated using a generalized linear model.

RESULTS

we identified 291,073 hospitalizations and 139,953 deaths; we found higher mortality rates in municipalities with a higher proportion of non-White people (95%CI 1.01;1.16) and with more households with more than two people per room (95%CI 1.01;1.13); presence of sewerage systems was protective for both outcomes (hospitalizations: 95%CI 0.87;0.99 - deaths: 95%CI 0.90;0.99), while a higher proportion of the population in subnormal housing clusters was a risk factor (hospitalizations: 95%CI 1.01;1.16 - deaths: 95%CI 1.09;1.21), with this variable interacting with the proportion of people receiving Emergency Aid (hospitalizations: 95%CI 0.88;1.00 - deaths: 95%CI 0.89;0.98).

CONCLUSION

socioeconomic conditions affected illness and death due to COVID-19 in Brazil.

The effect of diabetes on COVID-19 incidence and mortality: Differences between highly-developed-country and high-migratory-pressure-country populations

The objective of this study was to compare the effect of diabetes and pathologies potentially related to diabetes on the risk of infection and death from COVID-19 among people from Highly-Developed-Country (HDC), including Italians, and immigrants from the High-Migratory-Pressure-Countries (HMPC). Among the population with diabetes, whose prevalence is known to be higher among immigrants, we compared the effect of body mass index among HDC and HMPC populations. A population-based cohort study was conducted, using population registries and routinely collected surveillance data. The population was stratified into HDC and HMPC, according to the place of birth; moreover, a focus was set on the South Asiatic population. Analyses restricted to the population with type-2 diabetes were performed. We reported incidence (IRR) and mortality rate ratios (MRR) and hazard ratios (HR) with 95% confidence interval (CI) to estimate the effect of diabetes on SARS-CoV-2 infection and COVID-19 mortality. Overall, IRR of infection and MRR from COVID-19 comparing HMPC with HDC group were 0.84 (95% CI 0.82-0.87) and 0.67 (95% CI 0.46-0.99), respectively. The effect of diabetes on the risk of infection and death from COVID-19 was slightly higher in the HMPC population than in the HDC population (HRs for infection: 1.37 95% CI 1.22-1.53 vs. 1.20 95% CI 1.14-1.25; HRs for mortality: 3.96 95% CI 1.82-8.60 vs. 1.71 95% CI 1.50-1.95, respectively). No substantial difference in the strength of the association was observed between obesity or other comorbidities and SARS-CoV-2 infection. Similarly for COVID-19 mortality, HRs for obesity (HRs: 18.92 95% CI 4.48-79.87 vs. 3.91 95% CI 2.69-5.69) were larger in HMPC than in the HDC population, but differences could be due to chance. Among the population with diabetes, the HMPC group showed similar incidence (IRR: 0.99 95% CI: 0.88-1.12) and mortality (MRR: 0.89 95% CI: 0.49-1.61) to that of HDC individuals. The effect of obesity on incidence was similar in both HDC and HMPC populations (HRs: 1.73 95% CI 1.41-2.11 among HDC vs. 1.41 95% CI 0.63-3.17 among HMPC), although the estimates were very imprecise. Despite a higher prevalence of diabetes and a stronger effect of diabetes on COVID-19 mortality in HMPC than in the HDC population, our cohort did not show an overall excess risk of COVID-19 mortality in immigrants.

The impact of the COVID-19 pandemic on cardiovascular health behaviors and risk factors: A new troubling normal that may be here to stay

In March 2020, the Coronavirus disease 2019 (COVID-19) outbreak was officially declared a global pandemic, leading to closure of public facilities, enforced social distancing and stay-at-home mandates to limit exposures and reduce transmission rates. While the severity of this "lockdown" period varied by country, the disruptions of the pandemic on multiple facets of life (e.g., daily activities, education, the workplace) as well as the social, economic, and healthcare systems impacts were unprecedented. These disruptions and impacts are having a profound negative effect on multiple facets of behavioral health and psychosocial wellbeing that are inextricably linked to cardiometabolic health and associated with adverse outcomes of COVID-19. For example, adoption of various cardiometabolic risk behavior behaviors observed during the pandemic contributed to irretractable trends in weight gain and poor mental health, raising concerns on the possible long-term consequences of the pandemic on cardiometabolic disease risk, and vulnerabilities to future viral pandemics. The purpose of this review is to summarize the direct and indirect effects of the pandemic on cardiometabolic health risk behaviors, particularly related to poor diet quality, physical inactivity and sedentary behaviors, smoking, sleep patterns and mental health. Additional insights into how the pandemic has amplified cardiovascular risk behaviors, particularly in our most vulnerable populations, and the potential implications for the future if these modifiable risk behaviors do not become better controlled, are described.

Assessing the impact of coronavirus disease 2019 on mortality: a population-based, matched case-control study

OBJECTIVES

Estimating the isolated effect of coronavirus disease 2019 (COVID-19) on the risk of mortality is challenging. We aimed to determine whether COVID-19 was associated with high rates of mortality independently of age, sex and underlying disorders.

METHODS

A population-based, matched, case-control study of adults insured by Clalit Health Services was performed. Cases were defined as patients who died of all causes between July and December 2020. Each case was matched in a ratio of 1:1 with a living control based on age, sex and co-morbidities. An unconditional logistic regression analysis was performed to identify independent risk factors for mortality.

RESULTS

A total of 2874 patients who died were successfully matched with 2874 living controls. The prevalence of COVID-19 was higher among the patients who died than among the controls (13.5% [387/2874] vs. 4% [115/2874], respectively; OR, 3.73; 95% CI, 3.01-4.63; p < 0.001). A significantly increased odds of mortality was also observed in patients with COVID-19 without underlying diseases (OR, 3.67; 95% CI, 2.58-5.23) and in patients with COVID-19 and underlying diseases (OR, 3.77; 95% CI, 2.87-4.94). A multi-variate logistic analysis showed that COVID-19 (OR, 2.01; 95% CI, 1.07-3.77), low socio-economic status (OR, 1.36; 95% CI, 1.02-1.82), dementia (OR, 2.50; 95% CI, 2.10-3.01), smoking (OR, 1.35; 95% CI, 1.13-1.63) and an interaction variable of age >80 years and COVID-19 (OR, 2.27; 95% CI, 1.14-4.54) were independent risk factors for mortality, whereas influenza vaccination and high body mass index were associated with lower rates of mortality.

CONCLUSION

Testing positive for COVID-19 increased the risk of death three folds, regardless of underlying disorders. These results emphasize the effect of COVID-19 on mortality during the early period of the COVID-19 outbreak, when no vaccines or effective therapeutics were available.

Impact of the COVID-19 pandemic on liver disease-related mortality rates in the United States

BACKGROUND & AIMS

The pandemic has resulted in an increase of deaths not directly related to COVID-19 infection. We aimed to use a national death dataset to determine the impact of the pandemic on people with liver disease in the USA, focusing on alcohol-associated liver disease (ALD) and non-alcoholic fatty liver disease (NAFLD).

METHODS

Using data from the National Vital Statistic System from the Center for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) platform and ICD-10 codes, we identified deaths associated with liver disease. We evaluated observed vs. predicted mortality for 2020-2021 based on trends from 2010-2019 with joinpoint and prediction modelling analysis.

RESULTS

Among 626,090 chronic liver disease-related deaths between 2010 and 2021, Age-standardised mortality rates (ASMRs) for ALD dramatically increased between 2010-2019 and 2020-2021 (annual percentage change [APC] 3.5% to 17.6%, p <0.01), leading to a higher observed ASMR (per 100,000 persons) than predicted for 2020 (15.67 vs. 13.04) and 2021 (17.42 vs. 13.41). ASMR for NAFLD also increased during the pandemic (APC: 14.5%), whereas the rates for hepatitis B and C decreased. Notably, the ASMR rise for ALD was most pronounced in non-Hispanic Whites, Blacks, and Alaska Indians/Native Americans (APC: 11.7%, 10.8%, 18.0%, all p <0.05), with similar but less critical findings for NAFLD, whereas rates were steady for non-Hispanic Asians throughout 2010-2021 (APC: 4.9%). The ASMR rise for ALD was particularly severe for the 25-44 age group (APC: 34.6%, vs. 13.7% and 12.6% for 45-64 and ≥65, all p <0.01), which were also all higher than pre-COVID-19 rates (all p <0.01).

CONCLUSIONS

ASMRs for ALD and NAFLD increased at an alarming rate during the COVID-19 pandemic with the largest disparities among the young, non-Hispanic White, and Alaska Indian/Native American populations.

IMPACT AND IMPLICATIONS

The pandemic has led to an increase of deaths directly and indirectly related to SARS-CoV-2 infection. As shown in this study, age-standardised mortality rates for alcohol-associated liver disease and non-alcoholic fatty liver disease substantially increased during the COVID-19 pandemic in the USA and far exceeded expected levels predicted from past trends, especially among the young, non-Hispanic White, and Alaska Indian/Native American populations. However, much of this increase was not directly related to COVID-19. Therefore, for the ongoing pandemic as well as its recovery phase, adherence to regular monitoring and care for people with chronic liver disease should be prioritised and awareness should be raised among patients, care providers, healthcare systems, and public health policy makers.