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1.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-335992

ABSTRACT

Importance Mental health disorders were among the leading global contributors to years lived with disability prior to the COVID-19 pandemic onset, and growing evidence suggests that population mental health outcomes have worsened since the pandemic started. The extent that these changes have altered common age-related trends in psychological distress, where distress typically rises until mid-life and then falls in both sexes, is unknown. Objective To analyse whether long-term pre-pandemic psychological distress trajectories have altered during the pandemic, and whether these changes have been different across generations and by sex. Design Cross-cohort study with prospective data collection over a 40-year period (earliest time point: 1981;latest time point: February/March 2021). Setting Population-based (adult general population), Great Britain. Participants Members of three nationally representative birth cohorts which comprised all people born in Great Britain in a single week of 1946, 1958, or 1970, and who participated in at least one of the data collection waves conducted after the start of the pandemic (40.6%, 42.8%, 39.4%, respectively). Exposure(s) Time, COVID-19 pandemic. Main Outcome(s) and Measure(s) Psychological distress factor scores, as measured by validated self-reported questionnaires. Results 16,389 participants (2,175 from the 1946 birth cohort, 52.8% women;7,446 from the 1958 birth cohort, 52.4% women;and 6,768 from the 1970 birth cohort, 56.2% women) participated in the study. By September/October 2020, psychological distress levels had reached or exceeded the levels of the peak in the pre-pandemic life-course trajectories, with larger increases in younger cohorts: Standardised Mean Differences (SMD) and 95% confidence intervals (CIs) of -0.02 [-0.07, 0.04], 0.05 [0.02, 0.07], and 0.09 [0.07, 0.12] for the 1946, 1958, and 1970 birth cohorts, respectively. Increases in distress were larger among women than men, widening the pre-existing inequalities observed in the pre-pandemic peak and in the most recent pre-pandemic assessment. Conclusions and Relevance Pre-existing long-term psychological distress trajectories of adults born between 1946 and 1970 were disrupted during the COVID-19 pandemic, particularly among women, who reached the highest levels ever recorded in up to 40 years of follow-up data. This may impact future trends of morbidity, disability, and mortality due to common mental health problems.

2.
J Child Psychol Psychiatry ; 2022 Feb 23.
Article in English | MEDLINE | ID: covidwho-1704827

ABSTRACT

BACKGROUND: Adolescence is a critical period for social and emotional development. We sought to examine the impacts of Covid-19 and related social restrictions and school closures on adolescent mental health, particularly among disadvantaged, marginalised, and vulnerable groups. METHODS: We analysed four waves of data - 3 pre-Covid-19 (2016-2019) and 1 mid-Covid-19 (May-Aug 2020; n, 1074; 12-18 years old, >80% minority ethnic groups, 25% free school meals) from REACH (Resilience, Ethnicity, and AdolesCent Mental Health), an adolescent cohort based in inner-London, United Kingdom. Mental health was assessed using validated measures at each time point. We estimated temporal trends in mental distress and examined variations in changes in distress, pre- to mid-Covid-19, by social group, and by pre- and mid-pandemic risks. RESULTS: We found no evidence of an overall increase in mental distress midpandemic (15.9%, 95% CI: 13.0, 19.4) compared with prepandemic (around 18%). However, there were variations in changes in mental distress by subgroups. There were modest variations by social group and by pre-Covid risks (e.g., a small increase in distress among girls (b [unstandardised beta coefficient] 0.42 [-0.19, 1.03]); a small decrease among boys (b - 0.59 [-1.37, 0.19]); p for interaction .007). The most notable variations were by midpandemic risks: that is, broadly, increases in distress among those reporting negative circumstances and impacts (e.g., in finances, housing, social support and relationships, and daily routines) and decreases in distress among those reporting positive impacts. CONCLUSIONS: We found strong evidence that mental distress increased among young people who were most negatively impacted by Covid-19 and by related social restrictions during the first lockdown in the United Kingdom.

3.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-327517

ABSTRACT

Background Research suggests that there have been inequalities in the impact of the COVID-19 pandemic and related non-pharmaceutical interventions on population mental health. We explored these inequalities during the first year of the pandemic using nationally representative cohorts from the UK. Methods We analysed data from 26,772 participants from five longitudinal cohorts representing generations born between 1946 and 2000, collected in May 2020, September-October 2020, and February-March 2021 across all five cohorts. We used a multilevel growth curve modelling approach to explore sociodemographic and socioeconomic differences in levels of anxiety and depressive symptomatology, loneliness, and life satisfaction over time. Results Younger generations had worse levels of mental and social wellbeing throughout the first year of the pandemic. Whereas these generational inequalities narrowed between the first and last observation periods for life satisfaction (−0.33 [95% CI: −0.51, −0.15]), they became larger for anxiety (0.22 [0.10, 0.33]). Pre-existing generational inequalities in depression and loneliness did not change, but initial depression levels of the youngest cohort were worse than expected if the generational inequalities had not accelerated. Women and those experiencing financial difficulties had worse initial mental and social wellbeing levels than men and those financially living comfortably, respectively, and these gaps did not substantially differ between the first and last observation periods. Inequalities by additional factors are reported. Conclusions By March 2021, mental and social wellbeing inequalities persisted in the UK adult population. Pre-existing generational inequalities may have been exacerbated with the pandemic onset. Policies aimed at protecting vulnerable groups are needed.

4.
Preprint in English | medRxiv | ID: ppmedrxiv-22269978

ABSTRACT

Data analysis is widely used to generate new insights into human disease mechanisms and provide better treatment methods. In this work, we used the mechanistic models of viral infection to generate synthetic data of influenza and COVID-19 patients. We then developed and validated a supervised machine learning model that can distinguish between the two infections. Influenza and COVID-19 are contagious respiratory illnesses that are caused by different pathogenic viruses but appeared with similar initial presentations. While having the same primary signs COVID-19 can produce more severe symptoms, illnesses, and higher mortality. The predictive model performance was externally evaluated by the ROC AUC metric (area under the receiver operating characteristic curve) on 100 virtual patients from each cohort and was able to achieve at least AUC=91% using our multiclass classifier. The current investigation highlighted the ability of machine learning models to accurately identify two different diseases based on major components of viral infection and immune response. The model predicted a dominant role for viral load and productively infected cells through the feature selection process.

5.
Preprint in English | medRxiv | ID: ppmedrxiv-21264957

ABSTRACT

SummaryO_ST_ABSBackgroundC_ST_ABSThe lipid nanoparticle (LNP)-formulated mRNA vaccines are a widely adopted two-dose vaccination public health strategy to manage the COVID-19 pandemic. Clinical trial data has described the immunogeneicity of the vaccine, albeit within a limited study time frame. Our aims were to use a within-host mathematical model for LNP-formulated mRNA vaccines, informed by available clinical trial data, to project a longer term understanding of humoral immunity as a function of vaccine type, dosage amount, age, and sex. MethodsWe developed a mathematical model describing the immunization process of LNP-formulated mRNA vaccines, and fit our model to twenty-two clinical humoral and cytokine BNT162b2 or mRNA-1273 human two-dose vaccination data sets. We incorporated multi-dose effects in our model to specify whether the dosage is standard or low-dose. We further specify the age groups 18-55, 56-70, and 70+ in our fits for two-standard doses of mRNA-1273, and sex in our fits for two-standard doses of BNT162b2. We used non-linear mixed effect models to fit to all similar data types (e.g. standard two-dose BNT162b2 or mRNA-1273, or two low-dose mRNA-1273). Therefore, in our fits all estimated parameters are statistically correlated, which allowed us to determine the underlying population-dynamics structure common to a data type. We therefore made accurate long-term predictions informed by all clinical data used in this study. FindingsWe estimate that two standard doses of either mRNA-1273 or BNT162b2, with dosage times separated by the company-mandated intervals, results in individuals loosing more than 99% humoral immunity relative to peak immunity by eight months following the second dose. We predict that within an eight month period following dose two (corresponding to the CDC time-frame for administration of a third dose), there exists a period of time longer than one month where an individual has less then 99% humoral immunity relative to peak immunity, regardless of which vaccine was administered. We further find that age has a strong influence in maintaining humoral immunity; by eight months following dose two we predict that individuals aged 18-55 have a four-fold humoral advantage compared to aged 56-70 and 70+ individuals. We find that sex has little effect on the vaccine uptake and long-term IgG counts. Finally, we find that humoral immunity generated from two low doses of mRNA-1273 decays substantially slower relative to peak immunity gained than compared to two standard doses of either mRNA-1273 or BNT162b2. InterpretationFor the two dose mRNA vaccines, our predictions highlight the importance of the recommended third booster dose in order to maintain elevated levels of antibodies. We further show that age plays a critical role in determining the antibody levels. Hence, a third booster dose may confer an immuno-protective advantage in older individuals. FundingThis research is supported by NSERC Discovery Grant (RGPIN-2018-04546), NSERC COVID-19 Alliance Grant ALLRP 554923-20, CIHR-Fields COVID Immunity Task Force, NRC Pandemic Response Challenge Program Grant No. PR016-1.

6.
Lancet Reg Health Eur ; 11: 100228, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1458579

ABSTRACT

BACKGROUND: People with mental disorders and intellectual disabilities experience excess mortality compared with the general population. The impact of COVID-19 on exacerbating this, and in widening ethnic inequalities, is unclear. METHODS: Prospective data (N=167,122) from a large mental healthcare provider in London, UK, with deaths from 2019 to 2020, used to assess age- and gender-standardised mortality ratios (SMRs) across nine psychiatric conditions (schizophrenia-spectrum disorders, affective disorders, somatoform/ neurotic disorders, personality disorders, learning disabilities, eating disorders, substance use disorders, pervasive developmental disorders, dementia) and by ethnicity. FINDINGS: Prior to the World Health Organization (WHO) declaring COVID-19 a public health emergency on 30th January 2020, all-cause SMRs across all psychiatric cohorts were more than double the general population. By the second quarter of 2020, when the UK experienced substantial peaks in COVID-19 deaths, all-cause SMRs increased further, with COVID-19 SMRs elevated across all conditions (notably: learning disabilities: SMR: 9.24 (95% CI: 5.98-13.64), pervasive developmental disorders: 5.01 (95% CI: 2.40-9.20), eating disorders: 4.81 (95% CI: 1.56-11.22), schizophrenia-spectrum disorders: 3.26 (95% CI: 2.55-4.10), dementia: 3.82 (95% CI: 3.42, 4.25) personality disorders 4.58 (95% CI: 3.09-6.53)). Deaths from other causes remained at least double the population average over the whole year. Increased SMRs were similar across ethnic groups. INTERPRETATION: People with mental disorders and intellectual disabilities were at a greater risk of deaths relative to the general population before, during and after the first peak of COVID-19 deaths, with similar risks by ethnicity. Mortality from non-COVID-19/ other causes was elevated before/ during the pandemic, with higher COVID-19 mortality during the pandemic. FUNDING: ESRC (JD, CM), NIHR (JD, RS, MH), Health Foundation (JD), GSK, Janssen, Takeda (RS).

7.
Preprint in English | bioRxiv | ID: ppbiorxiv-462270

ABSTRACT

The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), has been sequenced at an unprecedented scale, leading to a tremendous amount of viral genome sequencing data. To understand the evolution of this virus in humans, and to assist in tracing infection pathways and designing preventive strategies, we present a set of computational tools that span phylogenomics, population genetics and machine learning approaches. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic, using 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets, enabling real-time analyses. Furthermore, time series change of Tajimas D provides a powerful metric of population expansion. Unsupervised learning techniques further highlight key steps in variant detection and facilitate the study of the role of this genomic variation in the context of SARS-CoV-2 infection, with Multiscale PHATE methodology identifying fine-scale structure in the SARS-CoV-2 genetic data that underlies the emergence of key lineages. The computational framework presented here is useful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of worldwide populations of humans and other organisms.

8.
BMJ Open ; 11(9): e052339, 2021 09 13.
Article in English | MEDLINE | ID: covidwho-1406662

ABSTRACT

INTRODUCTION: Improving the mental health of young people is a global public health priority. In Latin America, young people living in deprived urban areas face various risk factors for mental distress. However, most either do not develop mental distress in the form of depression and anxiety, or recover within a year without treatment from mental health services. This research programme seeks to identify the personal and social resources that help young people to prevent and recover from mental distress. METHODS AND ANALYSIS: A cross-sectional study will compare personal and social resources used by 1020 young people (aged 15-16 and 20-24 years) with symptoms of depression and/or anxiety and 1020 without. A longitudinal cohort study will follow-up young people with mental distress after 6 months and 1 year and compare resource use in those who do and do not recover. An experience sampling method study will intensively assess activities, experiences and mental distress in subgroups over short time periods. Finally, we will develop case studies highlighting existing initiatives that effectively support young people to prevent and recover from mental distress. The analysis will assess differences between young people with and without distress at baseline using t-tests and χ2 tests. Within the groups with mental distress, multivariate logistic regression analyses using a random effects model will assess the relationship between predictor variables and recovery. ETHICS AND DISSEMINATION: Ethics approvals are received from Ethics Committee in Biomedical Research, Faculty of Medicine, University of Buenos Aires; Faculty of Medicine-Research and Ethics Committee of the Pontificia Universidad Javeriana, Bogotá; Institutional Ethics Committee of Research of the Universidad Peruana Cayetano Heredia and Queen Mary Ethics of Research Committee. Dissemination will include arts-based methods and target different audiences such as national stakeholders, researchers from different disciplines and the general public. TRIAL REGISTRATION NUMBER: ISRCTN72241383.


Subject(s)
Longitudinal Studies , Adolescent , Cohort Studies , Cross-Sectional Studies , Humans , Latin America , Prospective Studies
9.
Preprint in English | medRxiv | ID: ppmedrxiv-21259460

ABSTRACT

During the SARS-CoV-2 global pandemic, several vaccines, including mRNA and ade-novirus vector approaches, have received emergency or full approval. However, supply chain logistics have hampered global vaccine delivery, which is impacting mass vaccination strategies. Recent studies have identified different strategies for vaccine dose administration so that supply constraints issues are diminished. These include increasing the time between consecutive doses in a two-dose vaccine regimen and reducing the dosage of the second dose. We consider both of these strategies in a mathematical modeling study of a non-replicating viral vector adenovirus vaccine in this work. We investigate the impact of different prime-boost strategies by quantifying their effects on immunological outcomes based on simple ordinary differential equations. The boost dose is administered either at a standard dose (SD) of 1000 or at a low dose (LD) of 500 or 250 vaccine particles. Simulated Second dose fractionation highlights previously shown dose-dependent features of the immune mechanism. In agreement with clinical characteristics of 175 COVID-19 recovered patients, the model predictions for either SD/SD or SD/LD regimens mainly show that by stretching the prime-boost interval until 18 or 20 weeks, the minimum promoted antibody (Nab) response is comparable with the neutralizing antibody level of COVID-19 recovered patients. The minimum stimulated antibody in SD/SD regimen is identical with the high level of clinical trial data. It is at the same range of the medium-high level of Nab in SD/LD, where the second dose is half or quarter of the standard dose.

10.
Preprint in English | bioRxiv | ID: ppbiorxiv-425420

ABSTRACT

To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mechanistic, within-host mathematical model and virtual patient cohort. Our results indicate that virtual patients with low production rates of infected cell derived IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these in silico patients, the maximum concentration of IL-6 was also a major predictor of CD8+ T cell depletion. Our analyses predicted that individuals with severe COVID-19 also have accelerated monocyte-to-macrophage differentiation that was mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings identify biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation. Author summaryUnderstanding of how the immune system responds to SARS-CoV-2 infections is critical for improving diagnostic and treatment approaches. Identifying which immune mechanisms lead to divergent outcomes can be clinically difficult, and experimental models and longitudinal data are only beginning to emerge. In response, we developed a mechanistic, mathematical and computational model of the immunopathology of COVID-19 calibrated to and validated against a broad set of experimental and clinical immunological data. To study the drivers of severe COVID-19, we used our model to expand a cohort of virtual patients, each with realistic disease dynamics. Our results identify key processes that regulate the immune response to SARS-CoV-2 infection in virtual patients and suggest viable therapeutic targets, underlining the importance of a rational approach to studying novel pathogens using intra-host models.

12.
Preprint in English | bioRxiv | ID: ppbiorxiv-019075

ABSTRACT

The 2019 novel coronavirus, SARS-CoV-2, is a pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable "choke points" for pharmacologic interventions, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce and iteratively refine a prototype of a multiscale model of SARS-CoV-2 dynamics in lung tissue. The first prototype model was built and shared internationally as open source code and an online interactive model in under 12 hours, and community domain expertise is driving regular refinements. In a sustained community effort, this consortium is integrating data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance computing, and other domains to accelerate our response to this critical threat to international health. More broadly, this effort is creating a reusable, modular framework for studying viral replication and immune response in tissues, which can also potentially be adapted to related problems in immunology and immunotherapy.

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