COVID-19 caused by SARS-CoV-2 ranges from asymptomatic to severe disease and can cause fatal and devastating outcome in many cases. In this study, we have compared the clinical, biochemical and immunological parameters across the different disease spectrum of COVID-19 in Bangladeshi patients.This longitudinal study was conducted in two COVID-19 hospitals and also around the community in Dhaka city in Bangladesh between November 2020 to March 2021. A total of 100 patients with COVID-19 infection were enrolled and classified into asymptomatic, mild, moderate and severe cases (n = 25/group). In addition, thirty age and sex matched healthy participants were enrolled and 21 were analyzed as controls based on exclusion criteria. After enrollment (study day1), follow-up visits were conducted on day 7, 14 and 28 for the cases.Older age, male gender and co-morbid conditions were the risk factors for severe COVID-19 disease. Those with moderate and severe cases of infection had low lymphocyte counts, high neutrophil counts along with a higher neutrophil-lymphocyte ratio (NLR) at enrollment; this decreased to normal range within 42 days after the onset of symptom. At enrollment, D-dimer, CRP and ferritin levels were elevated among moderate and severe cases. The mild, moderate, and severe cases were seropositive for IgG antibody by day 14 after enrollment. Moderate and severe cases showed significantly higher IgM and IgG levels of antibodies to SARS-CoV-2 compared to mild and asymptomatic cases.We report on the clinical, biochemical, and hematological parameters associated with the different severity of COVID-19 infection. We also show different profile of antibody response against SARS-CoV-2 in relation to disease severity, especially in those with moderate and severe disease manifestations compared to the mild and asymptomatic infection. In Bangladesh, very little is known about disease characteristics and serological responses in different spectrum of COVID-19 disease in patients. The risk factors responsible for causing severe COVID-19 illness, as well as data on clinical, biochemical and immunological parameters is still lacking or insufficient across different geographical regions for the prediction of the disease outcomes. This is the first longitudinal study in Bangladesh to evaluate the data on clinical, hematological parameters, viral load and antibody responses in COVID-19 infected asymptomatic, mild, moderate, and severe patients. Our study showed that older age, male gender, multiple co-morbid conditions and elevated NLR, D-dimer, ferritin and CRP levels can help clinicians to predict progression to more severe outcomes. Patients with moderate and severe disease developed higher levels of IgM and IgG antibodies to the receptor binding domain (RBD) of the spike protein of SARS-CoV-2 compared to patients suffering from mild disease or those with asymptomatic infections. However, all infected individuals developed antibody responses that were higher than that seen in healthy controls. The COVID-19 pandemic caused by Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) emerged in late 2019 in China and has rapidly spread globally . At presThe severity of COVID-19 infection is related to co-morbid conditions, alteration of immune response and lack of immunity to SARS-CoV-2 . The virThis study was approved by the Institutional Review Board of International Centre for Diarrhoeal Disease Research  and the Directorate General of Health Services (DGHS) of Bangladesh. Informed written consent was obtained from all participants according to the ‘Declaration of Helsinki’ regulation and guidelines.2) according to the WHO guidelines [2<90% was considered as severe whereas respiratory distress with SpO2 ≥90% was moderate. The mild cases did not have respiratory distress but could have other symptoms. Asymptomatic cases were defined as RT-PCR positive (cycle threshold less than 40) having no sign or symptom during enrollment [We report on the longitudinal cohort study, conducted in Dhaka, Bangladesh between November, 2020 to March, 2021. We enrolled 100 patients who were SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) test positive (≥18 years of age) who were categorized as asymptomatic, mild, moderate, and severe cases  and compared them with 30 age and sex matched healthy controls and 21 were analyzed as controls. Nine participants were excluded from the analysis due to SARS-CoV-2 seropositivity at baseline. These controls were judged healthy by medical personnel, had no history of COVID-19, were RT-PCR negative for SARS-CoV-2 during enrollment and had no clinical signs and symptoms in the two weeks prior to enrollment. We classified the disease severity of the patients based on clinical symptoms and oxygen saturation  in Dhaka city. Follow-up of participants were continued in these two hospitals and in the Mirpur field site.Socio-demographic data, clinical features, vital signs, co-morbidities, exposure or contact history with COVID-19 patients and visit to a health care facility 14 days prior to diagnosis were recorded. Patients were enrolled on day 1 and prospectively followed up at day 7, 14 and 28. The date of first symptom for symptomatic cases was considered as the date of onset and date of exposure with any COVID-19 patient or RT-PCR positive report was considered as the date of onset for asymptomatic cases.We collected NPS and blood from all patients on all study days. Clinical laboratory investigation consisting of complete blood count (CBC), random blood sugar (RBS), alanine aminotransferase, aspartate aminotransferase, creatinine, C-reactive protein (CRP), lactate dehydrogenase (LDH), ferritin, creatine kinase (CK) and D-dimer were measured on study day 1 and day 28, while procalcitonin was only measured on day 1 as these tests are usually performed for COVID-19 patients according to national guideline of management of COVID-19 patients in Bangladesh . The NLRViral RNA was extracted using the magnetic bead based Nexor 32 Fully Automated Nucleic Acid Extractor . RT-PCR and estimated viral load were quantified using the Chinese CDC 2019-nCoV_N2 primers and probe set . BrieflyWe determined the IgM and IgG antibody response specific to RBD in sera collected from patients and healthy controls ,14. The To determine the antibody concentration in serum, CR3022 was used to generate a standard curve (starting concentration 25 ng/ml) for both IgG and IgM. We have previously validated this assay using serum specimens n = 100) collected from RT-PCR positive COVID-19 patients, pre-pandemic healthy controls (frozen sera specimens that had been collected between 2015–2018 and stored in the freezer archive), influenza infected patients and serum collected from surveillance of Japanese encephalitis 00 collec.The demographic and clinical characteristics were stratified by severity and measured as mean with 95% confidence interval (CI) for continuous data, median with inter-quartile range (IQR) for ordinal data and percentage for categorical data. For the comparison of clinical laboratory values, we calculated geometric mean (GM) with 95% CI by disease severity. If two CI did not overlap, it was assumed that the point estimates and GMs were significantly different from each other. Scatter plots with predicted smoothing lines were created to see the changes of clinical laboratory values over time after disease onset. Spline regression method was used to smooth the predicted lines with three polynomial segments. Comparison of antibody responses (IgG and IgM) between symptomatic cases , asymptomatic cases and healthy controls were analyzed by the Mann-Whitney U test . All ana2) compared to the others. About 56% of the mild cases had an exposure history to COVID-19  recorded during hospitalization was 97.7%, 94.0% and 82.6% among the mild, moderate, and severe cases, respectively , while mild cases more commonly had fever (92%), loss of taste (60%) and cough (52%). Gastrointestinal  and other systemic symptoms were least reported symptoms (0–20%). The mean oxygen saturation  followed by hypertension (36%), and asthma (32%) .The viral load was higher in mild cases compared to the others . The medThe clinical, hematological, and biochemical findings for the cases and healthy controls on day 1 and 28 are summarized in Elevated RBS (>7.8 mmol/L) were observed in patients with moderate (GM 11.7) and severe (GM 10.8) disease . Of the The inflammatory markers, LDH, CRP, and ferritin were significantly elevated on study day 1 among moderate and severe cases compared to other groups. Severe cases had elevated levels of D-dimer (625.4 ng/mL) on day 1 compared to other groups , Fig 3B.The RBD-specific IgG and IgM antibody responses in the different groups of COVID-19 patients were higher on all study days when compared to controls (P<0.0001). Patients with asymptomatic or mild disease had less prominent IgG and IgM responses to RBD compared to those with moderate or severe disease. More than 96% of patients with all disease severities became seropositive for IgG by day 14, and these remained elevated for over a month .We carried out correlation analyses of baseline laboratory parameters  with SARS-CoV-2 specific antibodies (IgG and IgM) at day 28 . ModeratHowever, no significant correlation was seen in the level of inflammatory markers on day 1 in patients with moderate and severe illness with the subsequent level of immune responses at day 28, except for weak correlation for NLR and IgM for moderately severe disease . In patients with mild illness, the levels of NLR and CRP on day 1 correlated strongly with subsequent immune responses on day 28, suggesting that this group with mild illness may represent a continuum of disease severity, with those with higher initial inflammatory markers developing immune responses more similar to those with moderate or severe illness. The levels of D-dimer on day 1 did not correlate with immune responses on day 28 in any group.This is the first longitudinal study in Bangladesh to evaluate the clinical, laboratory and antibody responses in patients with differing levels of severity of illness after SARS-CoV-2 infection and also explored immune response according to disease severity. As SARS-CoV-2 is a new disease and there are many aspects regarding clinical and the immune responses that are not yet explored and remains unclear and unpredictable. Our result will facilitate to determine effective vaccination against COVID-19 and treatment of the disease. This study brought important public health information on COVID-19 in this geographical area and which may help guide for prediction of poor outcome from COVID-19. The geographical distribution of this virus’s rapid progression around the globe has unfolded a variety of clinical manifestations, outcomes and antibody responses of the disease. The clinical and immunological outcomes of COVID-19 disease and its association with various pathophysiological factors in different geographical scenario have not been studied with necessary rigor. Investigating the epidemiological characteristics with antibody responses with COVID-19 in Bangladesh will help provide a proper insight into the clinical characterization and patterns in progression of the disease. It is vital to examine these aspects and factors related to the outcomes of COVID-19 to endorse suitable means of prevention and treatment.Bangladesh is similar to that seen globally, with age, gender and co-morbid conditions mostly associated with infections due to COVID-19 ,16. HoweSARS-CoV-2 infection can lead to asymptomatic or symptomatic infection, ranging from mild to severe disease and even death . In our Mild cases of COVID-19 had higher viral loads compared to moderate and severe cases on enrollment. Moderate and severe cases were hospitalized and enrolled at a later time point from the disease onset than mild cases. Viral load in upper respiratory specimens usually peak early in illness ,23, and NLR, D-dimer, CRP, and ferritin were significantly higher at enrollment among patients with moderate and severe disease as reported earlier –28, suggThe antibody responses and kinetics following COVID-19 of differing severity are now beginning to be elucidated ,30. In oWe found correlations between an increased level of NLR, D-dimer and CRP on study enrollment and IgM and IgG antibody titers on day 28 in those patients with mild infection, suggesting that this group may represent a continuum of severity, with those with initially higher inflammatory markers having more activation of the innate immune system more similar to those with moderate or severe infection, perhaps coupled to subsequent activation of the adaptive immune response and development of higher levels of RBD-specific antibodies. A previous study has suggested that increased neutrophil levels in early infection might lead to release of inflammatory mediators which results increased antibody responses and neutralizing antibodies to SARS-CoV-2 . InteresPrevious reports raised doubts as to the efficacy of the protection conferred by neutralizing antibodies (nAbs) in severe COVID-19 patients and suggest that enhanced nAbs might correlate with a negative clinical outcome ,44. We, There are some limitations to the study carried. First, we enrolled the participants who were able to provide consent for the study. No critical cases were enrolled who were unconscious or on life support. Secondly, the study sample size was calculated to determine the proportion of immune responses in SARS-CoV-2 confirmed patients not with the clinical parameters of COVID-19 patients. We could not measure the risk of association in different disease severity groups. In summary, we found that clinical and laboratory findings were predictive of disease severity of COVID-19. Older age, male gender, co-morbid conditions and elevated NLR, D-dimer, ferritin and CRP levels can help clinicians predict severe outcomes. Patients with moderate and severe disease developed higher levels of IgM and IgG antibodies of SARS-CoV-2 than mild or asymptomatic cases, although all infected individuals developed antibody responses higher than healthy controls.S1 Data(ZIP)Click here for additional data file.S1 STROBE Checklist(DOC)Click here for additional data file.


Countries in the World Health Organization (WHO) European Region differ in terms of the COVID-19 vaccine supply conditions. We evaluated the health and economic impact of different age-based vaccine prioritisation strategies across this demographically and socio-economically diverse region.We fitted age-specific compartmental models to the reported daily COVID-19 mortality in 2020 to inform the immunity level before vaccine roll-out. Models capture country-specific differences in population structures, contact patterns, epidemic history, life expectancy, and GDP per capita.We examined four strategies that prioritise: all adults (V+), younger (20-59 year-olds) followed by older adults (60+) (V20), older followed by younger adults (V60), and the oldest adults (75+) (V75) followed by incrementally younger age groups. We explored four roll-out scenarios (R1-4) — the slowest scenario (R1) reached 30% coverage by December 2022 and the fastest (R4) 80% by December 2021. Five decision-making metrics were summarised over 2021-22: mortality, morbidity, and losses in comorbidity-adjusted life expectancy, comorbidity- and quality-adjusted life years, and human capital. Six vaccine profiles were tested — the highest performing vaccine has 95% efficacy against both infection and disease, and the lowest 50% against diseases and 0% against infection.Of the 20 decision-making metrics and roll-out scenario combinations, the same optimal strategy applied to all countries in only one combination; V60 was more or similarly desirable than V75 in 19 combinations. Of the 38 countries with fitted models, 11-37 countries had variable optimal strategies by decision-making metrics or roll-out scenarios. There are greater benefits in prioritising older adults when roll-out is slow and when vaccine profiles are less favourable.The optimal age-based vaccine prioritisation strategies were sensitive to country characteristics, decision-making metrics, and roll-out speeds. A prioritisation strategy involving more age-based stages (V75) does not necessarily lead to better health and economic outcomes than targeting broad age groups (V60). Countries expecting a slow vaccine roll-out may particularly benefit from prioritising older adults.World Health Organization, Bill and Melinda Gates Foundation, the Medical Research Council (United Kingdom), the National Institute of Health Research (United Kingdom), the European Commission, the Foreign, Commonwealth and Development Office (United Kingdom), Wellcome Trust We searched PubMed and medRxiv for articles published in English from inception to 9 June 2021, with the search terms: (“COVID-19” OR “SARS-CoV-2”) AND (“priorit*) AND (“model*”) AND (“vaccin*”) and identified 66 studies on vaccine prioritization strategies. Of the 25 studies that compared two or more age-based prioritisation strategies, 12 found that targeting younger adults minimised infections while targeting older adults minimised mortality; an additional handful of studies found similar outcomes between different age-based prioritisation strategies where large outbreaks had already occurred. However, only two studies have explored age-based vaccine prioritisation using models calibrated to observed outbreaks in more than one country, and no study has explored the effectiveness of vaccine prioritisation strategies across settings with different population structures, contact patterns, and outbreak history.We evaluated various age-based vaccine prioritisation strategies for 38 countries in the WHO European Region using various health and economic outcomes for decision-making, by parameterising models using observed outbreak history, known epidemiologic and vaccine characteristics, and a range of realistic vaccine roll-out scenarios. We showed that while targeting older adults was generally advantageous, broadly targeting everyone above 60 years might perform better than or comparably to a more detailed strategy that targeted the oldest age group above 75 years followed by those in the next younger five-year age band. Rapid vaccine roll-out has only been observed in a small number of countries. If vaccine coverage can reach 80% by the end of 2021, prioritising older adults may not be optimal in terms of health and economic impact. Lower vaccine efficacy was associated with greater relative benefits in prioritising older adults.COVID-19 vaccine prioritization strategies that require more precise targeting of individuals of a specific and narrow age range may not necessarily lead to better outcomes compared to strategies that prioritise populations across broader age ranges. In the WHO European Region, prioritising all adults equally or younger adults first will only optimise health and economic impact when roll-out is rapid, which may raise between-country equity issues given the global demand for COVID-19 vaccines.Alt-text: Unlabelled boxThe COVID-19 pandemic poses unprecedented challenges to public health, health systems and economies globally. While non-pharmaceutical interventions  have effectively mitigated COVID-19 transmission,Some countries have signed bilateral advanced purchasing agreements with vaccine manufacturers to independently procure enough vaccine doses to cover significant proportions of their populations. Many low- and middle-income countries (LMICs) do not have the resources for such an option.,The global initiative “COVID-19 Vaccines Global Access” (COVAX) has been set up to ensure equitable vaccine access across countries ,Several additional challenges remain while deciding on the optimal vaccine prioritisation strategies, besides the diverse supply conditions. First, although there is some evidence supporting prioritising older adults in COVID-19 vaccine roll-out,To address these gaps in evidence, this work was commissioned by WHO/Europe to inform the European Technical Advisory Group of Experts on Immunization (ETAGE) group for the regionalisation of the WHO Strategic Advisory Group of Experts on immunization (SAGE) Roadmap for prioritising population groups for vaccines against COVID-19, based on the regional specific context and published preliminary recommendations of selected national immunization technical advisory groups (NITAG) in the Region.More specifically, this study evaluates different age-based vaccine prioritisation strategies given different vaccine supply conditions between 2021 and 2022 in the WHO European Region. We aim to identify strategies that maximise the health and economic impacts of COVID-19 vaccines for each country, measured by five decision-making metrics ((1) mortality, (2) cases, (3) comorbidity-adjusted life expectancy (cLE) loss, (4) comorbidity- and quality-adjusted life-years loss (cQALY), and (5) human capital (HC) loss). These metrics allowed us to explore the trade-offs between minimising COVID-19 mortality and morbidity. We consider the known epidemiology of SARS-CoV-2 and explore how demographic factors, government COVID-19 response policy stringency, community mobility in each country, and a wide range of vaccine characteristics may affect the optimal vaccine allocation strategies in the WHO European Region. Stakeholders to whom our research is of great value and interest include vaccination and immunisation experts advising government implementations, country-level policymakers focusing on public health planning and health economics, and vaccine programme managers.Our model framework consisted of two stages . The objFor both stages, we used a previously described COVID-19 compartmental dynamic transmission model, CovidM.nity (R) . The modModels for each country were parameterised with country-specific age structures 2020, , B,13 lo0s) were obtained by deterministically fitting CovidM to daily reported COVID-19 mortality using maximum likelihood estimation (MLE) and differential evolution global optimisation algorithms assuming a Poisson likelihood function 0s were constrained to the 1.5 to 5 range.Country-specific infection introduction dates and basic reproduction numbers  in 2020 from work, school, home, and others.work and other settings during the pandemic.,school setting were based on school-related NPI stringency from the OxCGRT database.school setting contacts at level 0, no school-based contacts at level 3, and 50% school-based contacts at level 1 or 2. Contacts at home were assumed to stay unchanged compared to pre-pandemic periods. More information on mobility and NPI stringency can be found in Supplemental Tables 3-5, Supplemental Figures 2-6.Age-specific contact patterns (contacts per day by age-group and country) were captured by country-specific synthetic contact matrices, which include information from different contact settings: Evidence suggests COVID-19 mortality may have been higher than reported in many countries work and other) gradually recover over a year after March 2021 but never fully return to pre-pandemic levels due to long-term policies and behavioural changes . More specifically, we assumed that NPI intensity would gradually decrease as vaccination is rolled out. We did not consider scenarios where NPIs may be reinstated if further outbreaks occur because the focus of our study was to evaluate vaccine strategies rather than NPIs. Contact patterns in the school setting followed school terms.We assume that contact patterns  and immunisation (i.e. becoming protected by the vaccine).,,,Infection-blocking and disease-reducing mechanisms of vaccines were both modelled. As baseline vaccine efficacy (VE), we assume that vaccination prevents 95% of infections and diseases based on the highest performing vaccines in clinical trials and post-introduction observational data.We used an ordinal logistic regression model to explore whether the optimal vaccine prioritisation strategies identified are associated with country-specific characteristics such as population size, age structure, contact patterns, the proportions of non-susceptible individuals by 01 January 2021, and roll-out scenario. To reduce collinearity, when a pair of variables are strongly associated with each other (Pearson's correlation > 0.7), we only keep one variable of the pair that has smaller correlations with other variables considered (outside the pair).We assessed the impact of different vaccine prioritisation strategies in each country using the projected cumulative measure of five health and economic decision-making metrics summarised over 01 January 2021 — 31 December 2022: (1) mortality counts, (2) case counts, (3) comorbidity-adjusted life expectancy (cLE) loss, (4) comorbidity- and quality-adjusted life years (cQALY) loss, and (5) value of human capital (HC) loss. The optimal strategy minimises these outcomes. Since the top-ranking strategies may have similar effects, we additionally calculated the regional totals for each health and economic metric when one strategy is applied to the entire region. For direct health outcomes, we focused only on (1) mortality and (2) cases. The method used to calculate COVID-19 mortality is described above. Projected COVID-19 cases are calculated by summing all infected individuals progressing through Life expectancy is the number of years an individual would be expected to live had they not died prematurely.,The cQALY is similar to the cLE but weigh the years someone is alive based on their health-related quality of life.The HC method weighs the number of life-years someone loses by the country-specific GDP per capita as a proxy for the value of that individual's lost lifetime productivity. These values are not adjusted for age or sex since there is growing consensus that doing so undervalues unpaid labour.https://cmmid-lshtm.shinyapps.io/demo/ to enable users to explore additional values for parameters such as roll-out speed, vaccination program starting date, and final up takes not explored in this study.The analyses were conducted using R (4.0.4).The funders of the study had no role in study design; data collection, analysis, and interpretation; preparation of the manuscript; or the decision to publish.The results of the fitting process for Georgia, Hungary and the United Kingdom are shown in 0 estimate was 1⋅67  . These estimates are relatively low compared to existing literature,We found that the inferred infection introduction in most countries occurred in January 2020  . The median RWe observed substantial differences in the optimal vaccine prioritisation strategy. Of 38 countries with fitted models, 7, 16, 27, and 22 countries have consistent optimal strategies across decision-making metrics within R1, R2, R3, and R4, respectively. Only one of the 38 countries with fitted models have the same optimal strategy across decision-making metrics and across roll-out scenarios. Of the 20 decision-making metric and roll-out scenario combinations (n = 20 = 5 metrics × 4 scenarios), only one identified the same optimal strategy across the entire region - while minimising morbidity under R1, V20 is the optimal strategy for all countries with fitted models.Under R1 and R2, in most countries, the optimal strategy is V75 to minimise COVID-19 deaths and V20 to minimise COVID-19 cases  with the optimal vaccine prioritisation strategies were roll-out scenarios . Faster roll-out scenarios were associated with having optimal strategies that target younger or all adults. Additionally, there was weak evidence (with type I error rate < 0.1) supporting the association between the intensity of adult to older adult contact patterns with having optimal strategies that prioritise older adults.When the duration of vaccine-induced immunity is longer than expected (i.e. three years), the advantage of prioritising older adults tends to diminish . A more transmissible VOC would make the advantages of V60 and V75 under R1-R3 and of V20 under R4 even more universal within the WHO European Region . After incorporating underreporting of COVID-19 mortality, we estimated higher proportions of non-susceptible individuals than baseline scenarios. However, the optimal vaccine allocation strategies for the entire region remained broadly unchanged .The optimal prioritisation strategies could be sensitive to the decision time frame . The time frame in the baseline analysis (i.e. with a time horizon of December 2022) was decided following discussions with stakeholders. Such a long time horizon was agreed on because a shorter horizon would bias decisions against those that may prevent outbreaks that occur later in 2022. However, a time horizon beyond 2022 was decided against because of the large uncertainty around viral evolution and changes in human behaviour.In our sensitivity analyses using lower vaccine uptake thresholds, we showed that the optimal prioritisation strategies stayed broadly unchanged . Lastly, our model shows that vaccinating adolescents with younger adults may bring additional health and economic benefits in 5-18 of 38 countries while using V60 and 25-28 of 38 countries while using V75 .The optimal vaccine prioritisation strategy is sensitive to the vaccine profile . Under RUsing a transmission model of SARS-CoV-2 fitted to reported COVID-19 mortality data in the WHO European Region, this study evaluates different vaccine prioritisation strategies under a range of possible vaccine roll-out scenarios in a wide range of social and demographic contexts. We found that country characteristics affect optimal vaccine prioritisation strategies. We also found that age-based vaccine prioritisation strategies with more stages do not necessarily perform better. A two-stage approach that prioritises all older adults (V60) consistently performs better than or comparably to a five-stage approach that initially prioritises the oldest age group and only extends to the next five-year age group younger when vaccination goals of the last group have been met (V75). The choice between V60 and V75 may not depend on the health and economic impact but on context-dependent factors such as delivery feasibility.When vaccine supply and delivery capacity are in place to reach 80% coverage by the end of 2021, the benefits of precisely targeting older adults diminish. Prioritising older adults may be more valuable to countries that expect a longer timeline for vaccine uptake to increase . These conclusions continue to hold when in the scenario with a 50% more transmissible VOC. These findings raise issues around within-country and between-country health equity. Countries with sufficient supplies of vaccines may find that they maximise benefits domestically by offering vaccination without any age restrictions, as this could expedite roll-out. However, there are insufficient supplies of COVID-19 vaccines globally to allow all countries to pursue such a strategy. Increased supplies for one country come at the expense of another.Evidence on infection-blocking and disease-reducing VE of different vaccine products is emerging. Here, we examined six different vaccine profiles, ranging from a highly effective vaccine that prevents both infection and disease at 95% to a vaccine that only prevents 50% of disease and does not provide any protection against infection. This range is designed around the vaccine products currently licensed in the WHO European Region. If vaccines become disease-reducing but not infection-blocking, more countries expecting slower roll-out may prioritise older adults (V60 or V75), given that the severity of disease burden increases by age.First, while existing literature focuses on evaluating COVID-19 vaccine prioritisation in any city or country, our study is the first to investigate the impact of such strategies under different circumstances across a large region. Through working with country-specific characteristics , we have highlighted the value of local evidence. Different age structures, contact patterns, and sizes of existing epidemics may affect the health and economic impacts of vaccination prioritisation strategies.Second, we built our analyses around realistic vaccine roll-out scenarios based on projecting the broad patterns in vaccine roll-out rates across the region. Third, we evaluated a wide range of age-prioritisation strategies, not only under different roll-out scenarios but also at different supply levels . The results inform policy recommendations, providing the necessary details for continuous implementation. The models and analyses presented here have benefited from continuous input since late 2020 from the WHO Regional Office for Europe and technical advisors from many countries in the region. Fourth, we included additional decision-making metrics beyond mortality and morbidity, providing a more comprehensive assessment of the various trade-offs that need to be considered between outcomes.We did not examine exposure risk-  or occupation-based  vaccine prioritisation strategies. The evaluation of these strategies requires parameters governing their transmission risk , most of which are not currently available. We did not project healthcare system outcomes such as hospital bed and ICU occupancy as there are substantial variations across countries and subnational regions and overtime in patient pathways, healthcare capacity and hospital organisation.The fitting stage is based on mortality data before 2021, i.e. before the start of vaccine roll-out and the large-scale emergence of VOCs that are more transmissible, more clinically severe, and potentially immune escaping.The projection stage uses constant vaccine efficacy values before December 2022, which may not remain valid as VOCs spread, although we used a range of vaccine profiles that likely cover performance against the most common variants in the WHO European Region as of mid-2021.In this study, we provide an important piece of evidence to inform the decision-making processes for designing age-based vaccine prioritisation strategies. In practice, stakeholders involved in such processes may need to consider additional pieces of evidence, such as vaccine supply chain capacity and healthcare system structure and capacity. From early 2021, Israel and Portugal have adopted V60-type approaches, drawing lines at 60 and 50 years respectively while prioritising older adults. The United Kingdom and Germany have adopted the V75-type approach, starting from individuals above 80 years old.In conclusion, we identified optimal age-based vaccine prioritisation strategies under different vaccine roll-out scenarios and vaccine effectiveness profiles at the country level in the WHO European Region. We showed that the benefits of prioritising older adults were more evident for relatively slow vaccine roll-out scenarios. When prioritising older adults, broadly targeting everyone above 60 years consistently performed comparably or better than targeting the oldest adults first followed by the next younger five-year age group - the additional stages does not lead to health or economic gains. Prioritising younger adults or not prioritising by age is only beneficial when vaccines can be rolled out quickly (e.g. reaching 80% of the population vaccinated in 1 year).YL, FS, MJ, and RPebody conceptualised research ideas; YL and MJ conducted literature search and synthesised existing evidence; YL and FS curated data; YL, FS, RCB, CABP were involved in data analyses and software/ application development; YL wrote the original draft; all named co-authors are involved in results visualisation, interpretation, and writing - review & editing; working group members conducted pre-submission peer-review.https://cmmid-lshtm.shinyapps.io/demo/ to enable users to explore additional sets of parameters not covered in this study. A TREND statement checklist is presented in Supplemental Table 9.We used publicly available data in this study, cited in the reference list or in the Supplemental Material. The CovidM modelling framework used has been published previously and is available on the CMMID COVID-19 GitHub page. All code used and country-specific intermediate results have been archived via Zenodo. An R Shiny application is available at Editor note: The Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations.YL and MJ report grants from the National Institute of Health Research outside the submitted work (16/137/109). RCB and MJ are participants of the Scientific Pandemic Influenza Group on Modelling. The views expressed in this publication are those of the author(s) and not necessarily those of the European Commission, National Institute of Health Research (NIHR) (UK), Public Health England (PHE) or the Department of Health and Social Care (UK). The authors alone are responsible for the views expressed in this publication, and they do not necessarily represent the decisions or policies of the World Health Organization.


Despite the close linkage between extreme floods and snowmelt, particularly through rain-on-snow (ROS), hydrologic infrastructure is mostly designed based on standard precipitation Intensity-Duration-Frequency curves (PREC-IDF) that neglect snow processes in runoff generation. For snow-dominated regions, such simplification could result in substantial errors in estimating extreme events and infrastructure design risk. To address this long-standing problem, we applied the Next Generation IDF (NG-IDF) technique to estimate design basis extreme events for different durations and return periods in the conterminous United States (CONUS) to distinctly represent the contribution of rain, snowmelt, and ROS events to the amount of water reaching the land surface. A suite of datasets were developed to characterize the magnitude, trend, seasonality, and dominant mechanism of extreme events for over 200,000 locations. Infrastructure design risk associated with the use of PREC-IDF was estimated. Accuracy of the model simulations used in the analyses was confirmed by long-term snow data at over 200 Snowpack Telemetry stations. The presented spatially continuous datasets are readily usable and instrumental for supporting site-specific infrastructure design.  PREC-IDF such as the National Oceanic and Atmospheric Administration (NOAA) Atlas 147 assumes that precipitation is in the form of rainfall that is immediately available for rainfall-runoff processes. This assumption has obvious shortcomings, especially in snow-dominated regions where winter precipitation is primarily snowfall. At locations where runoff is released slowly from accumulated snowpack, PREC-IDF can lead to infrastructure overdesign and incur unnecessary costs. Conversely, underdesign will occur where the snow-driven runoff rate is higher than the rate of precipitation. This was confirmed by previous research9, which demonstrated that PREC-IDF underestimates the 100-year, 24-hour extreme events in 45% of the Snow Telemetry (SNOTEL) stations examined, and the resulting peak design flood could be underestimated by up to 324%.Although it is well understood in the scientific community that extreme hydrometeorological events in cold climates are often related to snow processes, i.e., snowmelt and rain-on-snow (ROS)10. The exclusion of snow processes in the PREC-IDF technique is likely to cause greater errors in estimating extremes in a warming climate and present higher infrastructure design risk. For example, increased frequency and intensity of atmospheric rivers in the future are anticipated to cause more extreme orographic precipitation and subsequently increase flood risk along the U.S. West Coast14.The Oroville Dam failure in February 2017 that required $1.5 billion to repair is a notable example of costly infrastructure damages resulting from floods driven by ROS events8 was developed to estimate extreme events based on the amount of water reaching the land surface (W) during rain, snowmelt, and ROS events. By including snow processes, NG-IDF provides a systematic and consistent technique for all environments from rain-dominated, transitional, to snow-dominated locations. Despite the marked advantages of NG-IDF over PREC-IDF, its wide adoption by engineers and planners is hindered by rather limited snow observations for estimating W, especially relative to widely available precipitation products17. Using physics-based models to produce reasonable simulations of W, while feasible, is rather challenging, given the significant requirements of expert knowledge in model calibration and considerable cost of computational resources.Given the limitations in PREC-IDF and implications for design risk, the Next-Generation IDF (NG-IDF) techniqueW events (AMW) over 1951‒2013; and infrastructure design risk associated with the PREC-IDF technique. These datasets were developed based on sub-daily simulations of W by a well-validated physics-based hydrological model . To examine the accuracy of the simulations, the simulated snow water equivalent (SWE) was evaluated against long-term observations of SWE from 246 SNOTEL stations distributed across the Western U.S. These datasets intend to offer spatially distributed, quantitative measures of extreme events that are readily usable by the science and engineering communities to understand the climatology and driving mechanism(s) of extreme events, identify potential infrastructure design risk associated with the standard PREC-IDF method, and improve estimation of extremes.To support the broad adoption of the NG-IDF approach, we developed a suite of datasets that characterize extreme events relevant to hydrologic design over 1951‒2013 at a 1/16th-degree (~6 km) resolution across the conterminous United States (CONUS). For over 200,000 locations in the CONUS, the datasets provide: the magnitude and dominant driving mechanism  of extreme events for different durations (24‒72 hours) and return periods (2‒500 years) derived from the NG-IDF curves; the magnitude of design extreme events associated with different hydrometeorological drivers; trend and seasonality of annual maximum W) for the bare ground condition with no canopy cover, which can be represented mathematically by:P is precipitation, ΔSWE is the change in ground snowpack water content, and S indicates condensation (positive) or evaporation/sublimation (negative) of snowpack. In this development, as described in more detail in the following sections, P was taken from the gridded, gauge-based meteorological dataset, ΔSWE and S were simulated by the DHSVM snow model. The resulting W data is a 3-hourly time series for 1950–2013 at 207,173 grid cells covering the land surface of the CONUS at a 1/16th-degree resolution.The method to develop the NG-IDF datasets is illustrated in Fig. 19, NG-IDF curves were developed for W events with 24-, 48- and 72-hour durations, respectively. We first developed series of annual maximum W (AMW) for years 1951–2013 based on subdaily estimations of W aggregated over different durations. For example, AMW with a 72-hour duration is the maximum of the 72-hour moving sum of W over a given year. Given variations in precipitation seasonality across the CONUS, we defined AMW events for both water year (October 1st through September 30th) and calendar year (January 1st through December 31st). AMW based on calendar year is more appropriate for locations with extreme precipitation occurring during the summer season such as the Southwest U.S., and AMW based on water year is better for locations with extreme winter precipitation such as the Western U.S. Given the focus on snow-driven extreme events, we used AMW defined by water years for IDF curves.As observations suggested that snowfall events largely last up to 72 hours21 on the AMW series. Where trends were significant at the 5% confidence level, we detrended the time series using Sen’s slope22 while maintaining the long-term average of the time series. As suggested by NOAA Atlas 14, the generalized extreme value (GEV) distribution was fitted to AMW across all locations using the L-moments statistics8. Here we used the same GEV distribution for analysis of extreme events with different durations across the CONUS so that direct comparisons can be made in frequency estimates across durations or between locations. To quantify the implications of using PREC-IDF for infrastructure design risk, we also developed PREC-IDF curves following the same approach based on annual maximum precipitation (P). The design risk was quantified by comparing the NG-IDF and PREC-IDF values of extreme events  over the CONUS.We applied the nonparametric Mann-Kendall test23 and NOAA Atlas 147 was used to consider sample data uncertainty in frequency analysis. For each location, we generated 1,000 MC synthetic annual maximum series. We then fitted the GEV distribution to each MC series using the L-moments statistics and estimated the associated NG-IDF values. The uncertainties in NG-IDF curves for each location were quantified using the 5% and 95% quantiles  of ensemble members.A Monte Carlo (MC) simulation procedureP, air temperature, relative humidity, downward shortwave and longwave radiation, and wind speed. The meteorological input used here was derived from disaggregating the gridded (~6 km), daily land surface meteorological dataset24 over the CONUS using the Mountain Microclimate Simulation Model (MTCLIM) algorithm25. Distinct from reanalysis products, this dataset is one of the few gauge-based gridded datasets with long-term continuous records. It includes daily records of P, maximum and minimum air temperature, and wind speed spanning the period 1950–2013. The disaggregation algorithms25 estimate subdaily air temperatures with a 3rd-order polynomial fit based on daily temperatures range. Radiation and relative humidity are estimated based on daily temperature range, P, and solar geometry25. P is assumed to occur at a uniform rate throughout the day.For simulations of snow processes, the model requires subdaily input of 26 were applied to remove stations with missing data, outliers, and problematic SWE values . The BCQC procedures and the resulting BCQC datasets27 are available to the public at https://www.pnnl.gov/data-products.Daily SWE measurements were retrieved from SNOTEL stations for snow model parameterization and evaluation. Among the 785 SNOTEL stations, we selected 246 stations that shared the longest common period (2007‒2013) of bias-corrected and quality-controlled (BCQC) daily SWE records. Briefly, standard quality control procedures18 was implemented at the point scale to simulate snowpack dynamics under a bare ground and flat terrain condition. The model was run at the 3-hourly time step from 1950–2013 for 207,173 point locations at a 1/16th-degree grid spacing that coincides with the center of the meteorological grids. Model output includes 3-hourly times series of SWE and S, which are used together with observed P for calculating W in Eq. The physics-based, snow submodel of DHSVMP, S, changes in SWE, and melt from the snowpack. The partition of P into rain and snow is based on air temperature thresholds:Ta is air temperature, TS and TR is the temperature threshold for P to be completely snowfall and rain, respectively. If Ta ≥ TS, 100% of the P is rain (R); if Ta < TR, 100% of the P is snow; if Ta falls between the two thresholds, rain and snow are proportionally allocated to represent mixed rain and snow events. Energy balance at the snow surface is driven by net radiation, sensible and latent heat, and advected heat by rain. Energy and mass exchange between the thin surface layer and deep snowpack layer occurs via the exchange of meltwater. When liquid water in the deep snowpack exceeds its holding capacity, excess water is released to the underlying soil column. Detailed descriptions of the DHSVM snow model physics and governing algorithms can be found in a large body of literature30.DHSVM simulates ground snowpack accumulation and melt using a two-layer mass and energy balance ground snowpack module. The mass balance components consist of 29 that documented the robustness of regionally coherent snow parameters in modeling snowpack dynamics, we developed snow parameters for five spatial clusters covering the CONUS  TS (defined in Eq. amax), (3) albedo decay coefficient during snow accumulation (λA) and (4) snowmelt (λM). The last three parameters are applied in the snow albedo decay curve of Laramie & Schaake35 for estimating snow surface albedo evolution, given by:αA and αM are the snow surface albedo during the accumulation and melting seasons, respectively, and d is the number of days since the last snowfall. For other model parameters, we used the default values. The cluster-based snow parameters , 58 stations with no qualified posterior parameter values were removed from subsequent analyses. Most of these stations are located in high-latitude areas of Montana and Wyoming, where model skill is challenged by the lack of representation of wind effects on snow redistribution, or the maritime Pacific Northwest where snowmelt tends to be sensitive to errors in modeled energy balance and precipitation partitioning when temperature is near freezing29; (4) for each cluster, final parameter values were calculated as the ensemble mean over the posterior parameter space of all stations within the cluster. For the Southern cluster (C3), no SNOTEL observations are available for parameter development. Because snowfall is very limited for most of the Southern cluster where snow parameters have negligible effects on extreme events, we used the parameter values of the maritime cluster for the Southern cluster given their commonality of warm winter. The cluster parameter values are presented in Table Parameter development focused on four snow parameters that were identified by previous workamics29:  TS (defi(λA) and  snowmeltamics29:  TS : precipitation on snow-free ground;Rainfall only (M): decreasing SWE with no concurrent precipitation;Snowmelt only (ROS): decreasing SWE with concurrent precipitation. Given the interest in flood potential, a ROS event is further refined as one with at least 10 mm rainfall per day falling on a snowpack with at least 10 mm SWE over the selected duration, and the sum of rain and snowmelt contains at least 20% of snowmelt38.Rain-on-snow  and the mean date (MD) relative to October 1st due to the use of water year. They were calculated using the circular statistics40, given by:The seasonality of AMW at the 24-, 48- and 72-hour durations was represented by the seasonality index ; n is the total number of water years used in the analysis. SI, ranging from 0 to 1, measures the temporal variability of the occurrence of events. A smaller SI suggests weaker seasonality, and the associated MD is therefore less reflective of the actual timing of the extreme events.For a given water year denoted by 41 are available to the public through an unrestricted repository at 10.5281/zenodo.5827028 in comma-separated value (.csv) format. Table The NG-IDF datasetsW and all related datasets depends primarily on the accuracy of daily SWE simulations given that P was observational   of interest the magnitude of extreme nts Fig.  and theints Fig. . Throughnts Fig. . Informaur) Fig. . The preDiurnal variability is not represented in the precipitation data used here to force the snow model and construct IDF curves. As a result, our estimates of extreme events are limited to daily or longer durations. While the daily temporal resolution is mostly sufficient to capture snow-related extreme events, short-duration IDF curves based on high-resolution rainfall data are more appropriate for capturing short-duration extremes  or floods in small catchments with fast response times (<24 hours).8, the choice of probability distribution can contribute to uncertainties in estimates of extremes42. Depending on the usage, one can apply the distribution of choice or ensemble distributions for analysis of extreme W events using provided AMW datasets.Although generally smaller compared to sample data uncertaintyGreater uncertainties in NG-IDF estimates are expected for locations with lower snow model skill, such as the maritime Pacific Northwest and locations exposed to high winds during winter.44, here we demonstrate the application of the Non-stationary Extreme Value Analysis (NEVA) Software45 to construct the NG-IDF curve based on the 24-hour AMW at a location in the Oregon State, where AMW has the highest positive trend of 1.12 mm/decade over the CONUS  impacts on NG-IDF curves.Consideration of land cover. The NG-IDF datasets provided in this study are developed for open conditions (as opposed to forest conditions). Given strong forest-snow interactionsLastly, it should be noted that the datasets are subject to a few limitations:Supplemental Information


ObjectiveThe COVID-19 pandemic is of special concern for pregnant women. A growing body of evidence suggests the virus can have a deleterious impact upon outcomes related to birth and newborn health. There is a paucity of published research demonstrating the factors that influence disease severity among those who are pregnant, while a growing body of evidence demonstrates that vertical transmission occurs. Our study investigated the impact of maternal characteristics upon COVID-19 outcomes, as well as whether disease severity impacted pregnancy outcomes.MethodsWe conducted a retrospective cohort study of pregnant women with COVID-19 who were admitted to two public hospitals in our state between April-August, 2020. Pregnancy outcomes and clinical, laboratory, and placental data were collected.ResultsP=0.004). Screening of biochemical records demonstrated that symptomatic women had lower potassium levels compared with those who were asymptomatic . The lowest potassium level (3.0 mEq/L) and one of the highest BMIs (42.4 kg/m2) was observed in the only case of postpartum mortality among the symptomatic women. We did not observe any influence of maternal COVID-19 severity on placental histopathology/infant health or evidence of vertical transmission.Thirty-four pregnant women tested positive for SARS-CoV-2. Among them, 55% (19/34) were symptomatic. Of those who were symptomatic, 68% (13/19) presented with fever and cough. Those with symptoms had a statistically significant higher pregestational mean body mass index (BMI) compared with asymptomatic women (35.7±7.9 vs 26.7±6.9, ConclusionHigh pregestational BMI and lower potassium levels were associated with the presence of COVID-19 symptoms among pregnant women. As the global impact of coronavirus disease 2019 (COVID-19) has grown, so has our understanding of the plethora of presenting symptoms. Known symptoms now include anosmia, myalgia, and gastrointestinal symptoms, including diarrhea, vomiting, nausea, and abdominal pain, in addition to respiratory symptoms of fever, dry cough, and dyspnea . It is aA number of studies, including a large systematic review, suggest that the overall risk of vertical transmission and clinical symptoms in newborns of women with COVID-19 is small , 8, althOur study sought to investigate the impact of maternal demographic, clinical and biochemical characteristics on disease severity in pregnant women infected with SARS-Cov-2. The ability to identify factors that place pregnant women at particular risk of developing COVID-19 symptoms may help healthcare providers determine which women need to be closely monitored for a potential rapid progression of the illness. We further investigated the influence of disease severity on pregnancy outcomes, vertical transmission of infection, histopathological evaluation of the placenta, and outcomes of the newborn.This article appeared as a preprint on Research Square on October 1, 2021. It was also presented as an electronic poster at the 2021 virtual meeting of the Pediatric Academic Societies (PAS 2021).Study design and study populationDuring the peak of the first wave of COVID-19 infections in the US, testing for COVID-19 became universal for all pregnancy admissions from April 2020 in Illinois, USA. We conducted a retrospective cohort study between April 1 and August 15, 2020, that included all pregnant women who were admitted at any gestational age, at any stage of labor, and had laboratory-confirmed COVID-19. Admissions were from two Chicago maternity hospitals - John H. Stroger, Jr. Hospital of Cook County and AMITA Health Saints Mary and Elizabeth Medical Center. Our study was possible because of the April 2020 implementation of universal COVID-19 testing for all pregnant women admitted to hospitals in Illinois. We followed the World Health Organization (WHO) guidelines for diagnosis, which define positive real-time reverse transcriptase-polymerase chain reaction (RT-PCR) assay of nasal or pharyngeal swabs as laboratory-confirmed SARS-CoV-2 . We exclEthical approval and data collectionThe study's protocol was expeditiously approved by the institutional research ethics committees associated with John H. Stroger, Jr. Hospital of Cook County  and AMITA Health Saints Mary and Elizabeth Medical Center . The requirement of informed consent was waived due to the retrospective study design.The following demographic and baseline maternal data were collected: age, race and ethnicity, BMI, lifestyle habits , comorbidities including pre-gestational obesity, hypertension, gestational diabetes, and whether the patient received antepartum therapy including hydroxychloroquine treatment. All participants underwent clinical evaluation of presenting signs and symptoms, detailed laboratory assessment of blood and urine samples, and radiologic chest assessment if needed.Maternal blood sample assessment included hemoglobin, blood cell counts, inflammatory markers , serum concentration of electrolytes , liver function , and renal function (blood urea nitrogen [BUN] and creatinine). Data on pregnancy outcomes , and preeclampsia), and neonatal outcomes  were recorded. Preterm birth or premature birth was defined as one occurring at < 37 weeks. We used specific cut-off values to define blood dyscrasias for pregnant women according to their trimester: leukopenia as white cell count < 5.9 x 109/L, neutropenia as neutrophil count < 3.9 x 109/L, and lymphopenia as lymphocyte count < 1.0 x 109/L  for the We further performed laboratory assessment of blood samples taken from the newborns and followed up both mother and infant until six weeks after delivery. We defined the infant's specific blood dyscrasias according to their age of life: leukopenia as white cell count < 13.0 x 109/L for term infants at one to 12 hours of life and < 9.0 x 109/L for preterm infants at birth; neutropenia as neutrophil count < 6.0 x 109/L for term infants at one to 12 hours of life and < 6.0 x 109/L for preterm infants at birth; and lymphopenia as lymphocyte count < 2.0 x 109/L for both term infants at one to 12 hours of life and preterm infants at birth . EvidencAll the data collected was curated using a customized data collection form, and two study investigators (JM and BP) independently reviewed the data collection forms for any errors. The data was locked and secured appropriately according to rules and principles laid down in the Health Insurance Portability and Accountability Act of 1996 (HIPAA). The data collected from both sites were synchronized and any inaccuracies were verified with the concerned representative of the specific center.Study outcomeThe patients were further classified into two groups: symptomatic and asymptomatic, according to the existence of any of the following known signs and symptoms of COVID-19 infection: fever, cough, myalgia, anosmia, congestion, headache, chills, desaturation, dyspnea, nausea, vomiting, or malaise. The symptomatic group included women who displayed symptoms at any time before or after delivery.Statistical analysesStatistical analyses were conducted using STATA 16  and SAS 9.4 . Continuous variables were expressed as mean and standard deviation (SD) for normally distributed data, median and range for non-normally distributed data, and categorical variables as frequency and percentage. The Shapiro-Wilk, Kolmogorov-Smirnov, Cramer-von Mise, and Anderson-Darling tests were used to check the normality of the data. The continuous variables were compared between symptomatic and asymptomatic individuals using a t-test or Mann-Whitney-U test and categorical variables were compared using the Pearson chi-square test or Fisher's-exact test, as appropriate. We calculated the unadjusted odds ratio (OR) using the simple logistic regression model. A p-value of < 0.05 was considered statistically significant.A total of 37 pregnant women with COVID-19 were admitted to the study centers during the study period. One patient was excluded for lack of information on the existence of symptoms, while two were excluded due to lack of data related to maternal and perinatal outcomes. In total, 34 women were included in the study, and 19 were symptomatic (55%) for COVID-19 , gestational diabetes , and hypertension  were the most commonly occurring comorbid conditions. All three were overrepresented in symptomatic patients. We also observed that the symptomatic women had significantly higher pre-gestational BMI compared with asymptomatic women . We further observed that every third patient in our enrolled population was a Group B streptococcus (GBS) carrier; however, we failed to detect any influence of GBS status on the absence or presence of symptoms. Our cohort also had two women with a history of substance abuse, one symptomatic and one asymptomatic. We also detected syphilis in one of the asymptomatic patients.We failed to detect any significant difference in the method or mode of delivery in symptomatic vs asymptomatic women (P=0.493). Of the four cesarean deliveries performed in asymptomatic women, two were for twins in a breech presentation and two were indicated by non-reassuring fetal status.Laboratory characteristics of pregnant womenInitial evaluation of laboratory characteristics of the study population is shown in Table Placental histopathological featuresWe found no association of specific histopathological placental features with signs and symptoms of COVID-19 Table . HoweverLogistic regression analysis of significant maternal variablesSimple logistic regression analysis showed that the odds of severe COVID-19 increased as pre-gestational BMI increased and pre-delivery potassium levels decreased Table . For eacFetal and neonatal outcomesAmong 34 deliveries, there were two twin deliveries culminating in a total of 36 infants in our study population Table . Mean biLaboratory characteristics of newbornsWe did not detect any difference in laboratory characteristics in infants of symptomatic women compared with those of asymptomatic women Table . We alsoTo the best of our knowledge, this is the largest cohort of mother-baby dyads (SARS-Cov-2-positive women and their newborn infants) to undergo detailed clinical and biochemical investigation in the state of Illinois. In this study, high BMI and low potassium levels were associated with symptomatic COVID-19 in mothers.About half of pregnant women in our study developed symptoms of COVID-19, which falls in the middle range of similar studies. A recent study of 70 pregnant women with SARS-CoV-2 in New York City reported that only 21% presented with symptoms , while aWe found that women with a higher pre-gestational BMI were more likely to be symptomatic, consistent with several reports showing an association between pre-gestational BMI with severe maternal outcomes among women with COVID-19 ,14. An IOur finding of significantly lower potassium levels in symptomatic women is consistent with a report of lower potassium levels (< 3.5 mEq/L) in 119 patients (41%) of 290 with COVID-19 in the general population . In thatHispanic and non-Hispanic Black populations comprised 44% and 38% of the pregnant women in our study population, respectively. A recent study of 1,567 Hispanic pregnant and postpartum women identified obesity as a major risk factor for moderate and severe COVID-19 , consistOur results also showed significantly higher basophil cell count in symptomatic patients compared with asymptomatic patients. A recent study demonstrated an association of elevated basophil levels with the IgG antibodies against SARS-CoV-2 produced by B cells during the disease's recovery phase, suggesting the possibility of heightened humoral response in symptomatic pregnant women .We did not find a statistically significant association of specific placental pathologies with COVID-19 symptom status among the women in our study, and this result is consistent with a recent report by Hecht et al . HoweverLimitationsOur study is limited by the small sample size and the retrospective study design. The retrospective data collection contributed to missing values and an increased likelihood of information bias. The sample size was too small to perform a robust multivariable analysis, so we were unable to assess potential predictive variables or evaluate confounding. However, our posthoc power analysis showed that our sample size of 34 women achieved a power of 92%, which is higher than the 80% standard generally seen in the literature. Finally, because the participating hospitals draw from largely an urban population of Chicago, our findings may not be generalizable to communities in other areas of the United States.Even so, our findings have important implications for the clinical management of pregnant women with COVID-19. Our study suggests that pregnant women who test positive for SARS-CoV-2 should be closely monitored for a rapid progression of symptoms, especially women with a high pre-gestational BMI and/or low potassium levels. However, it remains to be seen whether low potassium levels are a cause or outcome of symptoms observed in pregnant women with COVID-19.The implications of SARS-CoV-2 infection in pregnant women are still not fully understood. However, our study observed that some pregnant women may be especially vulnerable to symptomatic infection. Our case series found that women with a high pregestational BMI had a higher risk of developing symptomatic COVID-19. The lower potassium levels we observed in symptomatic women could be a function of disease severity, or alternatively, could be a causal factor for the development of symptoms. We are unable to discern causality because of the retrospective nature of the study. Nevertheless, our study validates previous reports of an association of potassium levels with disease severity. We thereby strongly recommend closely following potassium levels to monitor disease severity in pregnant women with COVID-19 infection. Potassium levels may also be useful as a biomarker of symptom development. Finally, although it is encouraging that none of the newborns in our study tested positive for the virus, further research on the risks of vertical transmission for infants of women with COVID-19 during pregnancy is needed.


Depression is a fairly common finding among end-stage renal disease (ESRD) patients on hemodialysis and is an independent risk factor for morbidity and mortality. The psychiatric manifestations of the disease may affect their compliance to medications and alter sleep quality that is often overlooked by nephrologists. This translates into poor quality of life and poorer disease prognosis. Thus, Our study aims to assess the prevalence of depression and its association with compliance to medical therapy and sleep quality among ESRD patients on hemodialysis.In this cross-sectional study, a total of 288 hemodialysis patients with a confirmed diagnosis of ESRD were evaluated for depression using Patient Health Questionnaire-9 (PHQ-9) scale. Only the patients with moderate to severe depressive symptoms on PHQ-9 were further evaluated for sleep quality and compliance to medications using the Pittsburgh Sleep Quality Index (PSQI) and Drug Attitude Inventory-10 (DAI-10) respectively. The characteristics of ESRD patients with depression were also assessed. Median PHQ-9, DAI-10, and PSQI scores were calculated and the correlation between study variables was assessed using spearman's correlation.Of the 288 included participants, 188 (65.27%) had depression as evaluated via PHQ-9. Of these 188 patients, 114 were males while 74 were females. A total of 113 (60.01%) of the depressed patients had poor compliance with medication while 137 (72.87%) patients had poor sleep quality. Higher PHQ-9 scores were positively correlated with disease duration, dialysis years, and time between diagnosis and therapy  and negatively with marital and employment status (r = −0.32 and −0.49 respectively). Spearman's correlation matrix showed that PHQ-9 scores were negatively correlated with DAI-10 but positively correlated with PSQI scores.The study indicates a high prevalence of depression among ESRD patients on hemodialysis. Poor sleep quality and non-adherence to medications are extremely common among ESRD patients with depression. These psychiatric components must be considered to optimize medical treatment and improve the quality of life in this subset of patients.


Fast-food consumption is an unhealthy dietary behaviour because it increases the risk of diet-related chronic diseases. We aimed to investigate factors associated with fast-food consumption, namely sociodemographic characteristics, body mass index-for-age, meal away from home habit, and intake of various food groups among adolescents in Malaysia.We analysed data from the National School-Based Nutrition Survey (NSNS). The NSNS was a nationwide, cross-sectional survey. Multiple-stage stratified cluster random sampling method was applied to obtain a representative sample of adolescents’ population. This study recruited adolescents aged 10–18 years who were attending schools. Pre-tested self-administered questionnaires in Malay language were used to obtain relevant information. Frequency of fast-food consumption per week was classified into three groups: “consumed fast-food four to seven days”, “consumed fast-food one to three days”, and “did not consume fast-food”. Intake of food groups was assessed by self-administered food frequency questionnaire. Descriptive and complex sample multinomial logistic regression analyses were performed in data analysis.A total of 26,383 from 40,012 selected adolescents completed all the relevant questions for this study. Of those surveyed, 13.5% of the respondents consumed fast-food 4 to 7 days, 69.3% of the respondents consumed fast food 1 to 3 days, and 17.2% of them did not consume fast-food in a typical week. Frequency of fast-food consumptions (4 to 7 days and 1 to 3 days per week) was significantly associated with age; sex; ethnicity; locality of schools; frequency of eating out; and not consuming recommended intake of cereals or grains, vegetables, and meat or poultry or eggs.In conclusion, age; sex; ethnicity; locality of schools; frequency of eating out per week; imbalanced intake of cereals or grains, meat, or poultry or eggs; and inadequate vegetable intake were significantly associated with fast-food consumption among adolescents in Malaysia. The findings of this study will be useful for policy makers in promoting healthy food choices among adolescents in Malaysia. Fast food is defined as “hot food such as hamburgers that is quick to cook or is already cooked and is therefore served very quickly in a restaurant” . The fasAdolescents who consumed unhealthy diet such as higher energy-dense food and inadequate intake of fruits and milk were more likely to consume fast-food frequently . Poor diDespite growing evidence of the increasing trend in fast-food consumption among adolescents, majority of the fast-food consumption studies in Malaysia were mainly conducted among children and adults , 13. MorData from the National School-Based Nutrition Survey (NSNS) was used for the present analysis. The NSNS was a sub survey of the 2012 National School-Based Health Survey (NSHS) in Malaysia . This naFrequency of fast-food consumption was assessed using the question, “Normally, how many days in a week do you eat fast food which was bought from fast-food restaurants, such as burgers, pizza, hotdogs, nuggets, fried chicken, French fries, and other similar things?” The answers provided by the respondents ranged from 0 to 7 days. The dependent variable was classified into three categories which were adolescents who consumed fast food 4 to 7 days, adolescents who consumed fast food 1 to 3 days, and adolescents who did not consume fast food in a week.The independent variables were age, sex, locality of schools, ethnicity, weight status, frequency of eating out per week, and intake of food groups per day. Age was grouped as 10–12 years, 13–15 years, and 16–18 years. Locality of schools was determined according to the geographical areas in both Peninsular Malaysia  and East Malaysia. The Northern Zone included the states of Perlis, Kedah, Pulau Pinang, and Perak. States in the Central Zone were Selangor, Putrajaya Federal Territory, and Kuala Lumpur Federal Territory. The Southern Zone consisted of Malacca, Negeri Sembilan, and Johor. The East Coast comprised of the states of Pahang, Terengganu, and Kelantan. Finally, East Malaysia was the states of Sabah and Sarawak. Ethnicities in Malaysia were grouped into Malay, Chinese, Indian, Indigenous from East Malaysia, and other ethnicities.Body weight and height of respondents were measured twice using TANITA HD-319 electronic weighing scales  and SECA 213 stadiometer  to ± 0.1kg and ± 0.1cm, respectively. The average values of body weight and body height were used for BMI-for-age calculation. BMI-for-age z-score was calculated using the AnthroPlus software. The weight status of students was classified into four categories: underweight (<−2SD z-scores), normal (≥−2SD to <+1SD z-scores), overweight (≥+1SD to <+2SD z-scores), and obese (≥+2SD z-scores) based on WHO’s growth reference for children aged 4 to 19 . FrequenA pretested, self-administered semi-quantitative FFQ for adolescents containing 135 items was developed by panel of experts to obtain information on food consumption based on seven food groups . The intake of various food groups per day obtained from the FFQ was translated into the Malaysian Dietary Guideline (MDG) standard serving size . Accordip-value < 0.05.Data analyses were conducted using SPSS version 22 . Weighing was applied to take into account the complex study design and non-response rate. Descriptive statistics was used to describe the sociodemographic characteristics and frequency of fast-food consumption among adolescents. Factors associated with frequency of fast-food consumption were analysed using complex sample multinomial logistic regression analysis. All statistical analyses were carried out at 95% confidence interval or As shown in Table Table Adolescents from central zone  and East Malaysia  were more likely to consume fast food 4 to 7 days per week compared with adolescents from Northern zone. Adolescents who were eating out one to two times, three to five times, and more than six times per week were more likely to consume fast food (aOR 2.57 to 9.16 for 4 to 7 days per week and aOR 2.33 to 2.92 for 1 to 3 days per week) compared with adolescents who did not eat out.Adolescents who consumed above recommended intake of cereals or grains were more likely to consume fast food 4 to 7 days per week  compared with those who achieved the recommendation. Adolescents who consumed less than recommended intake of vegetables  were more likely to consume fast food 4 to 7 days per week compared with their counterparts with adequate vegetable intake. Adolescents who consumed above recommended intake of meat or poultry or eggs were more likely to consume fast food  compared with adolescents who achieved the recommended intake of this food group.This study revealed that older age adolescents (16–18 years old) were less likely to consume fast food compared with the younger age adolescents (10–12 years old). This may be due to older age adolescents experienced physical changes in puberty. The physical changes caused them encountered body shape problems . A previIn this study, ethnicity was found to be significantly associated with fast-food consumption. Non-Chinese adolescents were more likely to consume fast food than Chinese adolescents. The findings from this study are also in line with a previous Malaysian study that found Chinese adolescents consumed healthier food than Malay adolescents . Two posAdolescents from the Central Zone and East Malaysia were significantly more likely to consume fast food compared with those from the Northern Zone. Availability of fast-food restaurants nearby school areas and neighbourhoods were crucial factors contributing to fast-food consumption habits among adolescents . In MalaThe findings indicate that fast-food consumption among adolescents was not associated with BMI for age. This is in line with previous studies , 28, 29,In this study, fast-food consumption was associated with frequency of eating out per week. Eating out of home is increasingly common among Malaysian households since 1990s , 33 alonThis study also revealed that consumption of cereals or grains, vegetables, and meat or poultry or eggs were factors significantly associated with fast-food consumption. Adolescents who consumed excessive cereals or grains (more than 11 servings per day) and meat or poultry or eggs (more than two servings per day) were significantly more likely to consume fast food. In addition, those who consumed inadequate vegetables (less than three servings per day) were also more likely to consume fast food . This fiThe major strength of this study is the use of a nationally representative sample to demonstrate findings applicable to the Malaysian adolescent population. In addition, the findings of this study provide new insights to the existing literature on dietary behaviour among adolescents in Malaysia. Fast-food outlets are expanding to rural areas in the recent years. This study also recruited adolescents from remote zones such as East Malaysia, adding new information on dietary behaviours of adolescents in the remote areas. There are a few limitations in this study. First, this is a cross-sectional study; therefore, it is not possible to establish cause and effect relationship in this study. Second, owing to self-recall of fast-food consumption, the data may contain self-recall bias. It is possible that frequency of fast-food consumption being over- or under-reported in this study.In conclusion, significant factors associated with fast-food consumption were age, sex, ethnicity, locality of schools, frequency of eating out, imbalanced intake of cereals or grains, meat or poultry or eggs, and inadequate vegetables intake. This study provides evidenced-based findings to help policy makers and professionals in designing more effective strategies and programmes to promote healthy food choices among adolescents in Malaysia. Despite the Ministry of Health Malaysia initiated a healthy eating strategy in fast-food restaurants to provide healthier food options to the public , all rel


Background: Stroke is known to affect both men and women; however, incidence and outcomes differ between them. Therefore, the discovery of novel, sex-specific, blood-based biomarkers for acute ischemic stroke (AIS) patients has the potential to enhance the understanding of the etiology of this deadly disease in the content of sex. The objective of this study was to identify serum metabolites associated with male and female AIS patients. Methods: Metabolites were measured with the use of untargeted, reverse-phase ultra-performance liquid chromatography-tandem mass spectrometry quantification from blood specimens collected from AIS patients. Samples were collected from 36 patients comprising each of 18 men and women with matched controls. Metabolic pathway analysis and principal component analysis (PCA) was used to differentiate metabolite profiles for male and female AIS patients from the control, while logistic regression was used to determine differences in metabolites between male and female AIS patients. Results: In female AIS patients, 14 distinct altered metabolic pathways and 49 corresponding metabolites were identified, while 39 metabolites and 5 metabolic pathways were identified in male patients. Metabolites that are predictive of ischemic stroke in female patients were 1--2-arachidonoyl-GPC (P-16:0/20:4) , 1--2-palmitoyl-GPC (P-16:0/16:0) , and 5,6-dihydrouracil (P-16:0/20:2) . Significant metabolites that were predictive of stroke in male patients were 5alpha-androstan-3alpha,17beta-diol disulfate , alpha-hydroxyisocaproate , threonate , and bilirubin . Conclusions: In the current study, the untargeted serum metabolomics platform identified multiple pathways and metabolites associated with male and female AIS patients. Further research is necessary to characterize how these metabolites are associated with the pathophysiology in male and female AIS patients. Stroke presents a more significant health burden on women than in men as women present with more stroke events and are less likely to recover . While aThe human blood contains a wide variety of chemically diverse low molecular weight compounds, the metabolome, which can be measured in parallel through modern metabolomic technologies . AnalysiThe vulnerability to several diseases and the response to treatments differ between men and women . TherefoApproval for this study was obtained from the PRISMA Health Institutional review board (Pro00072801), and all methods were performed in accordance with the relevant guidelines and regulations. A total of 36 ischemic stroke patients comprising each of 18 female and male ischemic stroke subjects with matched controls were recruited for this study. Healthy controls were randomly selected from PRISMA Health system. Samples were collected from patients within 24 h of symptom onset based on relevant ischemic lesions on CT or brain MRI. The general criteria for inclusion were as follows: at least 18 years of age and meeting the diagnostic criteria for ischemic stroke. Patients with cardiac, kidney, or liver failure, acquired immunodeficiency syndrome, inflammatory bowel disease and systemic infection were excluded. Morning, fasting blood samples were collected from 36 ischemic stroke patients comprising each of 18 female and male ischemic stroke subjects with matched controls. The processing and preparation of samples have been described in a previous study . Brieflywww.metaboanalyst.ca, accessed on 15 September 2021). Skewed distributions for more symmetric distribution were determined by transforming the data to nonlinear conversions, while maximum variation between samples was determined using a PCA decomposition approach. This was used to determine whether the samples could be differentiated based on overarching components representing each sample’s global metabolite profile. Individual metabolites that did not have any variation between the samples were removed from the PCA analysis. The data were analyzed using a direct oblimin rotation to obtain a non-orthogonal solution. After plotting samples onto a graph based on their two components, outliers were removed from the PCA based on if they visually skewed the data. The PCA was performed for both female and male patients separately. The specific metabolites between classes were interpreted using variable importance in projection (VIP) and the correlation coefficient. A pairwise comparison was performed between the male and female stroke patients to discover distinct biomarkers associated with male and female patients among the thousands of variables using Student T-tests. The level of significance was set at p < 0.05. Data reduction was performed using phase and baseline correction. The corrected spectra, which correspond to the chemical shift, were imported into AMIX 3.9.5 ; all spectra were reduced into integral regions with equal lengths. Regions that contained the resonance from residual water were set to zero. To reduce the concentration differences between samples, data were normalized to the total spectral area, and datasets were analyzed by pattern recognition methods using the software packages , and MetaboAnalyst 3.0  were examined and selected for their discrimination power by multiple statistical criteria. VIP values were used as one method of identifying biomarkers with predictive value in separating men from women. Metabolites with VIP scores ≤1 at each time point were considered irrelevant to the prediction and excluded from analysis. A similar approach was used by other studies ,28. The All variables were adjusted for in our analysis, and the effect of confounding variables did not mask our results. Just as any typical clinical studies, AIS patients who were admitted for treatment vary widely, and some related factors may confound the results of our analysis. In this study, we considered the following potentially confounding variables: gender is potentially a confounder, since the stroke incidence differs between men and women, including hospitalization rates . Age at The metabolite profiles and pathways were analyzed for both men and women patients to reveal the metabolic network reprogramming of AIS with detailed impact using metabolomic pathways analysis of the MetaboAnalyst 3.0 software. This allowed us to determine both pathway enrichment and pathway topology, which identifies the most relevant metabolic pathways that are differentially affected in male and female ischemic stroke patients. A total of 1322 biochemicals, 1062 named compounds of known identity, and 260 unnamed compounds of unknown structural identity were identified. Of this, 55 metabolite levels were significantly different between the ischemic and control group in the female cohort . The PCANotably, 1--2-arachidonoyl-GPC (P-16:0/20:4) , 1--2-palmitoyl-GPC (P-16:0/16:0) , and 5,6-dihydrouracil (P-16:0/20:2)  were all found to be significant predictors in female ischemic stroke patients. The AUC of the optimized model was 0.945 (95% CI: 0.875–0.956) in the training set and 0.845 (95% CI: 0.721–0.923) in the validation set. None of the other metabolites were included in the model because of multicolinearity in the information provided by these compounds, and three metabolites contributed to the combined model. The constructed receiver operating characteristic (ROC) curve for the three individual metabolites is presented in The PCA did not reveal separation between male patients’ ischemic and control groups based on factor-reducing components . The conComputer tomography (CT) scans can resolve some aspects of stroke onset, and behavioral evaluations of diagnosis are entirely symptom-based, decreasing diagnostic reliability and hindering treatment . Blood bWhile gender differences in risk factors of stroke has been investigated ,48, diffGlycerophosphocholine (GPC) metabolites modulate atherosclerosis and are associated with many risk factors . GPC metDihydrouracil  is a metabolite of uracil that has been used as a marker to identify dihydropyrimidine dehydrogenase (DPD)-deficient . Deficie5 alpha-Androstane-3 alpha 17 beta-diol is a testosterone metabolite. The modulatory role of testosterone was initially thought to be via 5 alpha reduction to the potent androgen dihydrotestosterone (DHT) and its subsequent binding to the androgen receptor . HoweverAlpha-hydroxyisocaproate (HICA) is derived from leucine metabolism in the human connective tissue . HICA heBilirubin is an end product of heme metabolism, and higher levels of serum bilirubin have been proposed to offer therapeutic advantages in oxidative stress-mediated diseases . BlirubiMagnesium L-Threonate is a novel form of magnesium, and magnesium is a critical cofactor for many enzymes involved in glucose metabolism, protein production, and nucleic acid synthesis . TherefoSeveral studies have investigated different metabolites in stroke ,28,32,33The need to improve the diagnosis of stroke for both male and female patients has motivated the use of omic-based approaches to identify novel markers for stroke biology and biomarker development. Although several markers have shown promise, none has been translated into clinical practice. Ischemic stroke is a heterogeneous clinical condition and a single biomarker may not be able to capture the complicated pathophysiology changes associated with stroke in both male and female patients. In the current study, we identified -2-arachidonoyl-GPC, 1--2-palmitoyl-GPC and 5,6-dihydrouracil as metabolites that are predictive of ischemic stroke among women while 5alpha-androstan-3alpha,17beta-diol disulfate, alpha-hydroxyisocaproate, threonate and bilirubin predicted ischemic stroke in men patients. Our findings highlight the potential of metabolomics to reveal new pathways for IS and provide a new avenue to explore IS’s pathophysiological mechanisms among men and women ischemic stroke patients.