Implications of Frailty in COVID-19.

As the global coronavirus disease-19 (COVID-19) pandemic caused by severe acute respiratory distress syndrome coronavirus 2 continues to cause higher mortality and hospitalization rates among older adults, strategies such as frailty screening have been suggested for resource allocation and clinical management. Frailty is a physiologic condition characterized by a decreased reserve to stressors and is associated with disability, hospitalization, and death. Measuring frailty can be a useful tool to determine the risk and prognosis of COVID-19 patients in the acute setting, and to provide higher quality of care for vulnerable individuals in the outpatient setting. A literature review was conducted to examine current research regarding frailty and COVID-19. Frailty can inform holistic care of COVID-19 patients, and further investigation is needed to elucidate how measuring frailty should guide treatment and prevention of COVID-19.

Estimates of Bivalent mRNA Vaccine Durability in Preventing COVID-19-Associated Hospitalization and Critical Illness Among Adults with and Without Immunocompromising Conditions - VISION Network, September 2022-April 2023.

On September 1, 2022, CDC's Advisory Committee on Immunization Practices (ACIP) recommended a single bivalent mRNA COVID-19 booster dose for persons aged ≥12 years who had completed at least a monovalent primary series. Early vaccine effectiveness (VE) estimates among adults aged ≥18 years showed receipt of a bivalent booster dose provided additional protection against COVID-19-associated emergency department and urgent care visits and hospitalizations compared with that in persons who had received only monovalent vaccine doses (1); however, insufficient time had elapsed since bivalent vaccine authorization to assess the durability of this protection. The VISION Network* assessed VE against COVID-19-associated hospitalizations by time since bivalent vaccine receipt during September 13, 2022-April 21, 2023, among adults aged ≥18 years with and without immunocompromising conditions. During the first 7-59 days after vaccination, compared with no vaccination, VE for receipt of a bivalent vaccine dose among adults aged ≥18 years was 62% (95% CI = 57%-67%) among adults without immunocompromising conditions and 28% (95% CI = 10%-42%) among adults with immunocompromising conditions. Among adults without immunocompromising conditions, VE declined to 24% (95% CI = 12%-33%) among those aged ≥18 years by 120-179 days after vaccination. VE was generally lower for adults with immunocompromising conditions. A bivalent booster dose provided the highest protection, and protection was sustained through at least 179 days against critical outcomes, including intensive care unit (ICU) admission or in-hospital death. These data support updated recommendations allowing additional optional bivalent COVID-19 vaccine doses for certain high-risk populations. All eligible persons should stay up to date with recommended COVID-19 vaccines.

Comparative mRNA booster effectiveness against death or hospitalization with COVID-19 pneumonia across at-risk US Veteran populations.

Studies of comparative mRNA booster effectiveness among high-risk populations can inform mRNA booster-specific guidelines. The study emulated a target trial of COVID-19 vaccinated U.S. Veterans who received three doses of either mRNA-1273 or BNT162b2 vaccines. Participants were followed for up to 32 weeks between July 1, 2021 to May 30, 2022. Non-overlapping populations were average and high risk; high-risk sub-groups were age ≥65 years, high-risk co-morbid conditions, and immunocompromising conditions. Of 1,703,189 participants, 10.9 per 10,000 persons died or were hospitalized with COVID-19 pneumonia over 32 weeks (95% CI: 10.2, 11.8). Although relative risks of death or hospitalization with COVID-19 pneumonia were similar across at-risk groups, absolute risk varied when comparing three doses of BNT162b2 with mRNA-1273 (BNT162b2 minus mRNA-1273) between average-risk and high-risk populations, confirmed by the presence of additive interaction. The risk difference of death or hospitalization with COVID-19 pneumonia for high-risk populations was 2.2 (0.9, 3.6). Effects were not modified by predominant viral variant. In this work, the risk of death or hospitalization with COVID-19 pneumonia over 32 weeks was lower among high-risk populations who received three doses of mRNA-1273 vaccine instead of BNT162b2 vaccine; no difference was found among the average-risk population and age >65 sub-group.

Post-COVID-19 Cardiopulmonary Symptoms: Predictors and Imaging Features in Patients after Hospital Discharge. / Sintomas Cardiopulmonares Pós-COVID-19: Preditores e Características de Imagem de Pacientes após a Alta Hospitalar.

BACKGROUND: Most of the evidence about the impact of the post-acute COVID-19 Syndrome (PACS) reports individual symptoms without correlations with related imaging. OBJECTIVES: To evaluate cardiopulmonary symptoms, their predictors and related images in COVID-19 patients discharged from hospital. METHODS: Consecutive patients who survived COVID-19 were contacted 90 days after discharge. The Clinic Outcome Team structured a questionnaire evaluating symptoms and clinical status (blinded for hospitalization data). A multivariate analysis was performed to address the course of COVID-19, comorbidities, anxiety, depression, and post-traumatic stress during hospitalization, and cardiac rehabilitation after discharge. The significance level was set at 5%. RESULTS: A total of 480 discharged patients with COVID-19 (age: 59±14 years, 67.5% males) were included; 22.3% required mechanical ventilation. The prevalence of patients with PACS-related cardiopulmonary symptoms (dyspnea, tiredness/fatigue, cough, and chest discomfort) was 16.3%. Several parameters of chest computed tomography and echocardiogram were similar in patients with and without cardiopulmonary symptoms. The multivariate analysis showed that PACS-related cardiopulmonary-symptoms were independently related to female sex (OR 3.023; 95% CI 1.319-6.929), in-hospital deep venous thrombosis (OR 13.689; 95% CI 1.069-175.304), elevated troponin I (OR 1.355; 95% CI 1.048-1.751) and C-reactive protein during hospitalization (OR 1.060; 95% CI 1.023-1.097) and depression (OR 6.110; 95% CI 2.254-16.558). CONCLUSION: PACS-related cardiopulmonary symptoms 90 days post-discharge are common and multifactorial. Beyond thrombotic and markers of inflammation/myocardial injury during hospitalization, female sex and depression were independently associated with cardiopulmonary-related PACS. These results highlighted the need for a multifaceted approach targeting susceptible patients.

FUNDAMENTO: A maioria da evidência sobre o impacto da síndrome COVID pós-aguda (PACS, do inglês, post-acute COVID-19 syndrome) descreve sintomas individuais sem correlacioná-los com exames de imagens. OBJETIVOS: Avaliar sintomas cardiopulmonares, seus preditores e imagens relacionadas em pacientes com COVID-19 após alta hospitalar. MÉTODOS: Pacientes consecutivos, que sobreviveram à COVID-19, foram contatados 90 dias após a alta hospitalar. A equipe de desfechos clínicos (cega quanto aos dados durante a internação) elaborou um questionário estruturado avaliando sintomas e estado clínico. Uma análise multivariada foi realizada abordando a evolução da COVID-19, comorbidades, ansiedade, depressão, e estresse pós-traumático durante a internação, e reabilitação cardíaca após a alta. O nível de significância usado nas análises foi de 5%. RESULTADOS: Foram incluídos 480 pacientes (idade 59±14 anos, 67,5% do sexo masculino) que receberam alta hospitalar por COVID-19; 22,3% necessitaram de ventilação mecânica. A prevalência de pacientes com sintomas cardiopulmonares relacionados à PACS (dispneia, cansaço/fadiga, tosse e desconforto no peito) foi de 16,3%. Vários parâmetros de tomografia computadorizada do tórax e de ecocardiograma foram similares entre os pacientes com e sem sintomas cardiopulmonares. A análise multivariada mostrou que sintomas cardiopulmonares foram relacionados de maneira independente com sexo feminino (OR 3,023; IC95% 1,319-6,929), trombose venosa profunda durante a internação (OR 13,689; IC95% 1,069-175,304), nível elevado de troponina (OR 1,355; IC95% 1,048-1,751) e de proteína C reativa durante a internação (OR 1,060; IC95% 1,023-1,097) e depressão (OR 6,110; IC95% 2,254-16,558). CONCLUSÃO: Os sintomas cardiopulmonares relacionados à PACS 90 dias após a alta hospitalar são comuns e multifatoriais. Além dos marcadores trombóticos, inflamatórios e de lesão miocárdica durante a internação, sexo feminino e depressão foram associados independentemente com sintomas cardiopulmonares relacionados à PACS. Esses resultados destacaram a necessidade de uma abordagem multifacetada direcionada a pacientes susceptíveis.

Heart failure after dengue infection- a population-based self-controlled case-series study.

BACKGROUND: Major cardiovascular events (MACEs) have been described with dengue infection. Among these MACEs, heart failure (HF) is the most common but has not been thoroughly assessed. This study aimed to evaluate the association between dengue and HF. METHODS: Under the self-controlled case-series study design, we used the Notifiable Infectious Disease dataset linkage with the National Health Insurance claims data to obtain the study subjects. All laboratory-confirmed dengue cases who were hospitalized for HF after dengue infection within one year between 2009 and 2015 in Taiwan were included. We identified the first 7 and 14 days after dengue infection as the risk intervals. The incidence rate ratio (IRR) and 95% confidence interval (CI) for HF were estimated by conditional Poisson regression. RESULTS: Among the 65,906 dengue patients, 230 had admission for HF after dengue infection within one year. The IRR of HF admission within the first week after dengue infection was 56.50 (95% C.I. 43.88-72.75). This risk was highest in >60 years (IRR = 59.32, 95% C.I. 45.43-77.43) and lower in 0-40 years (IRR = 25.82, 95% C.I. 2.89-231.02). The risk was nearly nine times higher among admission (for dengue infection) than among nonadmission cases (IRR 75.35 vs. 8.61, p < 0.0001). The risks increased slightly in the second week 8.55 and became less obvious after the third and fourth week. CONCLUSIONS: Patients with dengue infection have a risk of developing acute heart failure within one week, especially in >60 years, men, and dengue admission subjects. The findings emphasize the awareness of diagnosis and further appropriate treatment of HF.

The effect of higher protein dosing in critically ill patients with high nutritional risk (EFFORT Protein): an international, multicentre, pragmatic, registry-based randomised trial.

BACKGROUND: On the basis of low-quality evidence, international critical care nutrition guidelines recommend a wide range of protein doses. The effect of delivering high-dose protein during critical illness is unknown. We aimed to test the hypothesis that a higher dose of protein provided to critically ill patients would improve their clinical outcomes. METHODS: This international, investigator-initiated, pragmatic, registry-based, single-blinded, randomised trial was undertaken in 85 intensive care units (ICUs) across 16 countries. We enrolled nutritionally high-risk adults (≥18 years) undergoing mechanical ventilation to compare prescribing high-dose protein (≥2·2 g/kg per day) with usual dose protein (≤1·2 g/kg per day) started within 96 h of ICU admission and continued for up to 28 days or death or transition to oral feeding. Participants were randomly allocated (1:1) to high-dose protein or usual dose protein, stratified by site. As site personnel were involved in both prescribing and delivering protein dose, it was not possible to blind clinicians, but patients were not made aware of the treatment assignment. The primary efficacy outcome was time-to-discharge-alive from hospital up to 60 days after ICU admission and the secondary outcome was 60-day morality. Patients were analysed in the group to which they were randomly assigned regardless of study compliance, although patients who dropped out of the study before receiving the study intervention were excluded. This study is registered with ClinicalTrials.gov, NCT03160547. FINDINGS: Between Jan 17, 2018, and Dec 3, 2021, 1329 patients were randomised and 1301 (97·9%) were included in the analysis (645 in the high-dose protein group and 656 in usual dose group). By 60 days after randomisation, the cumulative incidence of alive hospital discharge was 46·1% (95 CI 42·0%-50·1%) in the high-dose compared with 50·2% (46·0%-54·3%) in the usual dose protein group (hazard ratio 0·91, 95% CI 0·77-1·07; p=0·27). The 60-day mortality rate was 34·6% (222 of 642) in the high dose protein group compared with 32·1% (208 of 648) in the usual dose protein group (relative risk 1·08, 95% CI 0·92-1·26). There appeared to be a subgroup effect with higher protein provision being particularly harmful in patients with acute kidney injury and higher organ failure scores at baseline. INTERPRETATION: Delivery of higher doses of protein to mechanically ventilated critically ill patients did not improve the time-to-discharge-alive from hospital and might have worsened outcomes for patients with acute kidney injury and high organ failure scores. FUNDING: None.

Trends in the Quality of Primary Care and Acute Care in Korea From 2008 to 2020: A Cross-sectional Study.

OBJECTIVES: Measuring the quality of care is paramount to inform policies for healthcare services. Nevertheless, little is known about the quality of primary care and acute care provided in Korea. This study investigated trends in the quality of primary care and acute care. METHODS: Case-fatality rates and avoidable hospitalization rates were used as performance indicators to assess the quality of primary care and acute care. Admission data for the period 2008 to 2020 were extracted from the National Health Insurance Claims Database. Case-fatality rates and avoidable hospitalization rates were standardized by age and sex to adjust for patients' characteristics over time, and significant changes in the rates were identified by joinpoint regression. RESULTS: The average annual percent change in age-/sex-standardized case-fatality rates for acute myocardial infarction was -2.3% (95% confidence interval, -4.6 to 0.0). For hemorrhagic and ischemic stroke, the age-/sex-standardized case-fatality rates were 21.8% and 5.9%, respectively in 2020; these rates decreased since 2008 (27.1 and 8.7%, respectively). The average annual percent change in age-/sex-standardized avoidable hospitalization rates ranged from -9.4% to -3.0%, with statistically significant changes between 2008 and 2020. In 2020, the avoidable hospitalization rates decreased considerably compared with the 2019 rate because of the coronavirus disease 2019 pandemic. CONCLUSIONS: The avoidable hospitalization rates and case-fatality rates decreased overall during the past decade, but they were relatively high compared with other countries. Strengthening primary care is an essential requirement to improve patient health outcomes in the rapidly aging Korean population.

Clinical characteristics and risk factors associated with COVID-19 mortality in a non-Intensive Care Unit.

Introduction: The Coronavirus disease 2019 caused by a new Coronavirus (SARS-CoV-2) throughout the pandemic period has been characterised by a wide spectrum of clinical manifestations, courses, and outcomes. In particular, most patients with severe or critical symptoms re-quired hospitalization. The demographic and clinical characteristics of patients upon admission to the hospital, as well as pre-existing medical conditions, seem to have affected the clinical out-come. Predictive factors of inauspicious outcome in non-Intensive Care Unit hospitalized patients were investigated. Methods: A retrospective, single-centre, observational study of 239 patients with confirmed COVID-19 disease admitted during the first waves of the pandemic to the Infectious Disease Operative Unit of a hospital in Southern Italy was conducted. Demographic characteristics, under-lying diseases, and clinical, laboratory, and radiological findings were collected from the patient's medical records. Information about in-hospital medications, days of admission, and out-come were also considered. Inferential statistical analysis was performed to evaluate the association between patients' characteristics upon hospital admission and during in-hospital length of stay and death. Results: Mean age was 67.8 ± 15.8 years; 137/239 (57.3%) patients were males, and 176 (73.6%) had at least one comorbidity. More than half of patients (55.3%) suffered from hypertension. The length of stay in hospital was 16.5 ± 9.9 days and mortality rate of 12.55%. In multivariable logistic regression analysis, predictors of mortality of COVID-19 patients included age (OR, 1.09; CI, 1.04-1.15), Chronic Kidney Disease (OR, 4.04; CI, 1.38-11.85), and need of High Flow Oxygen therapy (OR, 18.23; CI, 5.06-65.64). Conclusions: Patients who died in the hospital had shorted length of stay than that of the surviving patients. Older age, pre-existent chronic renal disease and need of supplemental oxygen represented independent predictors of mortality in patients hospitalized in non-Intensive Care Unit with COVID-19. The determination of these factors allows retrospectively a greater understanding of the disease also in comparison with the successive epidemic waves.

Multidimensional analysis of immune cells from COVID-19 patients identified cell subsets associated with the severity at hospital admission.

BACKGROUND: SARS-CoV-2 emerged as a new coronavirus causing COVID-19, and it has been responsible for more than 760 million cases and 6.8 million deaths worldwide until March 2023. Although infected individuals could be asymptomatic, other patients presented heterogeneity and a wide range of symptoms. Therefore, identifying those infected individuals and being able to classify them according to their expected severity could help target health efforts more effectively. METHODOLOGY/PRINCIPAL FINDINGS: Therefore, we wanted to develop a machine learning model to predict those who will develop severe disease at the moment of hospital admission. We recruited 75 individuals and analysed innate and adaptive immune system subsets by flow cytometry. Also, we collected clinical and biochemical information. The objective of the study was to leverage machine learning techniques to identify clinical features associated with disease severity progression. Additionally, the study sought to elucidate the specific cellular subsets involved in the disease following the onset of symptoms. Among the several machine learning models tested, we found that the Elastic Net model was the better to predict the severity score according to a modified WHO classification. This model was able to predict the severity score of 72 out of 75 individuals. Besides, all the machine learning models revealed that CD38+ Treg and CD16+ CD56neg HLA-DR+ NK cells were highly correlated with the severity. CONCLUSIONS/SIGNIFICANCE: The Elastic Net model could stratify the uninfected individuals and the COVID-19 patients from asymptomatic to severe COVID-19 patients. On the other hand, these cellular subsets presented here could help to understand better the induction and progression of the symptoms in COVID-19 individuals.

Hospital admissions and deaths due to acute cardiovascular events during the COVID-19 pandemic in residents of long-term care facilities.

Hospital admissions due to acute cardiovascular events dropped during the COVID-19 pandemic in the general population; however, evidence for residents of long-term care facilities (LTCF) is sparse. We investigated rates of hospital admissions and deaths due to myocardial infarction (MI) and stroke in LTCF residents during the pandemic. Our nationwide cohort study used claims data. The sample comprised 1,140,139 AOK-ensured LTCF residents over 60 years of age (68.6% women; age 85.3 ± 8.5 years) from the largest statutory health insurance in Germany (AOK), which is not representative for all LTCF residents. We included MI and stroke admission and compared numbers of in-hospital deaths from January 2020 to end of April 2021 (i.e., during the first three waves of the pandemic) with the number of incidences in 2015-2019. To estimate incidence risk ratios (IRR), adjusted Poisson regression analyses were applied. During the observation period (2015-2021), there were 19,196 MI and 73,953 stroke admissions. MI admissions declined in the pandemic phase by 22.5% (IRR = 0.68 [CI 0.65-0.72]) compared to previous years. This decline was slightly more pronounced for NSTEMI than for STEMI. MI fatality risks remained comparable across years (IRR = 0.97 [CI95% 0.92-1.02]). Stroke admissions dropped by 15.1% (IRR = 0.75 [CI95% 0.72-0.78]) in the pandemic. There was an elevated case fatality risk for haemorrhagic stroke (IRR = 1.09 [CI95% 1.03-1.15]) but not for other stroke subtypes compared to previous years. This study provides first evidence of declines in MI and stroke admissions and in-hospital deaths among LTCF residents during the pandemic. The figures are alarming given the acute nature of the conditions and the vulnerability of the residents.

Impact of corticosteroids use on midterm sequelae in survivors of COVID-19 admitted to hospital: A prospective cohort study.

An understanding of the midterm sequelae in COVID-19 and their association with corticosteroids use are needed. Between March and July 2020, we evaluated 1227 survivors of COVID-19, 3 months posthospitalization, of whom 213 had received corticosteroids within 7 days of admission. Main outcome was any midterm sequelae (oxygen therapy, shortness of breath, one major clinical sign, two minor clinical signs or three minor symptoms). Association between corticosteroids use and midterm sequelae was assessed using inverse propensity-score weighting models. Our sample included 753 (61%) male patients, and 512 (42%) were older than 65 years. We found a higher rate of sequelae among users than nonusers of corticosteroids (42% vs. 35%, odds ratio [OR] 1.40 [1.16-1.69]). Midterm sequelae were more frequent in users of low-dose corticosteroids than nonusers (64% vs. 51%, OR 1.60 [1.10-2.32]), whereas no association between higher doses (≥20 mg/day equivalent of dexamethasone) and sequelae was evidenced (OR 0.95 [0.56-1.61]). Higher risk of sequelae with corticosteroids use was observed among subjects with propensity score below the 90th percentile. Our study suggest that corticosteroids use during hospitalization for COVID-19 is associated with higher risk of midterm sequelae.

Evidence-driven spatiotemporal COVID-19 hospitalization prediction with Ising dynamics.

In this work, we aim to accurately predict the number of hospitalizations during the COVID-19 pandemic by developing a spatiotemporal prediction model. We propose HOIST, an Ising dynamics-based deep learning model for spatiotemporal COVID-19 hospitalization prediction. By drawing the analogy between locations and lattice sites in statistical mechanics, we use the Ising dynamics to guide the model to extract and utilize spatial relationships across locations and model the complex influence of granular information from real-world clinical evidence. By leveraging rich linked databases, including insurance claims, census information, and hospital resource usage data across the U.S., we evaluate the HOIST model on the large-scale spatiotemporal COVID-19 hospitalization prediction task for 2299 counties in the U.S. In the 4-week hospitalization prediction task, HOIST achieves 368.7 mean absolute error, 0.6 [Formula: see text] and 0.89 concordance correlation coefficient score on average. Our detailed number needed to treat (NNT) and cost analysis suggest that future COVID-19 vaccination efforts may be most impactful in rural areas. This model may serve as a resource for future county and state-level vaccination efforts.

Disproportionate impacts of COVID-19 in a large US city.

COVID-19 has disproportionately impacted individuals depending on where they live and work, and based on their race, ethnicity, and socioeconomic status. Studies have documented catastrophic disparities at critical points throughout the pandemic, but have not yet systematically tracked their severity through time. Using anonymized hospitalization data from March 11, 2020 to June 1, 2021 and fine-grain infection hospitalization rates, we estimate the time-varying burden of COVID-19 by age group and ZIP code in Austin, Texas. During this 15-month period, we estimate an overall 23.7% (95% CrI: 22.5-24.8%) infection rate and 29.4% (95% CrI: 28.0-31.0%) case reporting rate. Individuals over 65 were less likely to be infected than younger age groups (11.2% [95% CrI: 10.3-12.0%] vs 25.1% [95% CrI: 23.7-26.4%]), but more likely to be hospitalized (1,965 per 100,000 vs 376 per 100,000) and have their infections reported (53% [95% CrI: 49-57%] vs 28% [95% CrI: 27-30%]). We used a mixed effect poisson regression model to estimate disparities in infection and reporting rates as a function of social vulnerability. We compared ZIP codes ranking in the 75th percentile of vulnerability to those in the 25th percentile, and found that the more vulnerable communities had 2.5 (95% CrI: 2.0-3.0) times the infection rate and only 70% (95% CrI: 60%-82%) the reporting rate compared to the less vulnerable communities. Inequality persisted but declined significantly over the 15-month study period. Our results suggest that further public health efforts are needed to mitigate local COVID-19 disparities and that the CDC's social vulnerability index may serve as a reliable predictor of risk on a local scale when surveillance data are limited.

Dynamics of SARS-CoV-2 infection hospitalisation and infection fatality ratios over 23 months in England.

The relationship between prevalence of infection and severe outcomes such as hospitalisation and death changed over the course of the COVID-19 pandemic. Reliable estimates of the infection fatality ratio (IFR) and infection hospitalisation ratio (IHR) along with the time-delay between infection and hospitalisation/death can inform forecasts of the numbers/timing of severe outcomes and allow healthcare services to better prepare for periods of increased demand. The REal-time Assessment of Community Transmission-1 (REACT-1) study estimated swab positivity for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection in England approximately monthly from May 2020 to March 2022. Here, we analyse the changing relationship between prevalence of swab positivity and the IFR and IHR over this period in England, using publicly available data for the daily number of deaths and hospitalisations, REACT-1 swab positivity data, time-delay models, and Bayesian P-spline models. We analyse data for all age groups together, as well as in 2 subgroups: those aged 65 and over and those aged 64 and under. Additionally, we analysed the relationship between swab positivity and daily case numbers to estimate the case ascertainment rate of England's mass testing programme. During 2020, we estimated the IFR to be 0.67% and the IHR to be 2.6%. By late 2021/early 2022, the IFR and IHR had both decreased to 0.097% and 0.76%, respectively. The average case ascertainment rate over the entire duration of the study was estimated to be 36.1%, but there was some significant variation in continuous estimates of the case ascertainment rate. Continuous estimates of the IFR and IHR of the virus were observed to increase during the periods of Alpha and Delta's emergence. During periods of vaccination rollout, and the emergence of the Omicron variant, the IFR and IHR decreased. During 2020, we estimated a time-lag of 19 days between hospitalisation and swab positivity, and 26 days between deaths and swab positivity. By late 2021/early 2022, these time-lags had decreased to 7 days for hospitalisations and 18 days for deaths. Even though many populations have high levels of immunity to SARS-CoV-2 from vaccination and natural infection, waning of immunity and variant emergence will continue to be an upwards pressure on the IHR and IFR. As investments in community surveillance of SARS-CoV-2 infection are scaled back, alternative methods are required to accurately track the ever-changing relationship between infection, hospitalisation, and death and hence provide vital information for healthcare provision and utilisation.

Incidence, hospitalization, and mortality in children aged 5 years and younger with respiratory syncytial virus-related diseases: A systematic review and meta-analysis.

Objectives: Respiratory syncytial virus (RSV) is a leading cause of lower respiratory tract infection in young children. We aimed to analyze the factors affecting the estimation of RSV-related disease burden, and to provide evidence to help establish a surveillance system. Methods: We searched the English- and Chinese-language databases for articles published between January 1, 2010 and June 2, 2022. The quality of the included articles was assessed using the Agency for Healthcare Research and Quality scale. Random-effects models were used for data synthesis and subgroup analyses. This review was registered in the Prospective Register of Systematic Reviews (PROSPERO: CRD42022372972). Results: We included 44 studies (149,321,171 participants), all of which were of medium or high quality. The pooled RSV-related disease incidence, hospitalization rate, in-hospital mortality, and overall mortality rates in children aged 5 years and younger were 9.0 per 100 children per year (95% confidence interval [CI]: 7.0-11.0), 1.7 per 100 children per year (95% CI: 1.3-2.1), 0.5 per 100 children per year (95% CI: 0.4-0.5), and 0.05 per 100 children per year (95% CI: 0.04-0.06), respectively. Age, economics, surveillance types, case definition, and data source were all recognized as influencing factors. Conclusions: A standardized and unified RSV surveillance system is required. Case definition and surveillance types should be fully considered for surveillance of different age groups.

Balancing economic and epidemiological interventions in the early stages of pathogen emergence.

The global pandemic of COVID-19 has underlined the need for more coordinated responses to emergent pathogens. These responses need to balance epidemic control in ways that concomitantly minimize hospitalizations and economic damages. We develop a hybrid economic-epidemiological modeling framework that allows us to examine the interaction between economic and health impacts over the first period of pathogen emergence when lockdown, testing, and isolation are the only means of containing the epidemic. This operational mathematical setting allows us to determine the optimal policy interventions under a variety of scenarios that might prevail in the first period of a large-scale epidemic outbreak. Combining testing with isolation emerges as a more effective policy than lockdowns, substantially reducing deaths and the number of infected hosts, at lower economic cost. If a lockdown is put in place early in the course of the epidemic, it always dominates the "laissez-faire" policy of doing nothing.

A novel scale based on biomarkers associated with COVID-19 severity can predict the need for hospitalization and intensive care, as well as enhanced probabilities for mortality.

Prognostic scales may help to optimize the use of hospital resources, which may be of prime interest in the context of a fast spreading pandemics. Nonetheless, such tools are underdeveloped in the context of COVID-19. In the present article we asked whether accurate prognostic scales could be developed to optimize the use of hospital resources. We retrospectively studied 467 files of hospitalized patients after COVID-19. The odds ratios for 16 different biomarkers were calculated, those that were significantly associated were screened by a Pearson's correlation, and such index was used to establish the mathematical function for each marker. The scales to predict the need for hospitalization, intensive-care requirement and mortality had enhanced sensitivities (0.91 CI 0.87-0.94; 0.96 CI 0.94-0.98; 0.96 CI 0.94-0.98; all with p < 0.0001) and specificities (0.74 CI 0.62-0.83; 0.92 CI 0.87-0.96 and 0.91 CI 0.86-0.94; all with p < 0.0001). Interestingly, when a different population was assayed, these parameters did not change considerably. These results show a novel approach to establish the mathematical function of a marker in the development of highly sensitive prognostic tools, which in this case, may aid in the optimization of hospital resources. An online version of the three algorithms can be found at: http://benepachuca.no-ip.org/covid/index.php.

Retrospective Analysis of the Outcome of Hospitalized COVID-19 Patients with Coexisting Metabolic Syndrome and HIV Using Multinomial Logistic Regression.

Globally, the coexistence of metabolic syndrome (MetS) and HIV has become an important public health problem, putting coronavirus disease 19 (COVID-19) hospitalized patients at risk for severe manifestations and higher mortality. A retrospective cross-sectional analysis was conducted to identify factors and determine their relationships with hospitalization outcomes for COVID-19 patients using secondary data from the Department of Health in Limpopo Province, South Africa. The study included 15,151 patient clinical records of laboratory-confirmed COVID-19 cases. Data on MetS was extracted in the form of a cluster of metabolic factors. These included abdominal obesity, high blood pressure, and impaired fasting glucose captured on an information sheet. Spatial distribution of mortality among patients was observed; overall (21-33%), hypertension (32-43%), diabetes (34-47%), and HIV (31-45%). A multinomial logistic regression model was applied to identify factors and determine their relationships with hospitalization outcomes for COVID-19 patients. Mortality among COVID-19 patients was associated with being older (≥50+ years), male, and HIV positive. Having hypertension and diabetes reduced the duration from admission to death. Being transferred from a primary health facility (PHC) to a referral hospital among COVID-19 patients was associated with ventilation and less chance of being transferred to another health facility when having HIV plus MetS. Patients with MetS had a higher mortality rate within seven days of hospitalization, followed by those with obesity as an individual component. MetS and its components such as hypertension, diabetes, and obesity should be considered a composite predictor of COVID-19 fatal outcomes, mostly, increased risk of mortality. The study increases our understanding of the common contributing variables to severe manifestations and a greater mortality risk among COVID-19 hospitalized patients by investigating the influence of MetS, its components, and HIV coexistence. Prevention remains the mainstay for both communicable and non-communicable diseases. The findings underscore the need for improvement of critical care resources across South Africa.