Validity of prognostic models of critical COVID-19 is variable. A systematic review with external validation.

OBJECTIVES: To identify prognostic models which estimate the risk of critical COVID-19 in hospitalized patients and to assess their validation properties. STUDY DESIGN AND SETTING: We conducted a systematic review in Medline (up to January 2021) of studies developing or updating a model that estimated the risk of critical COVID-19, defined as death, admission to intensive care unit, and/or use of mechanical ventilation during admission. Models were validated in two datasets with different backgrounds (HM [private Spanish hospital network], n = 1,753, and ICS [public Catalan health system], n = 1,104), by assessing discrimination (area under the curve [AUC]) and calibration (plots). RESULTS: We validated 18 prognostic models. Discrimination was good in nine of them (AUCs ≥ 80%) and higher in those predicting mortality (AUCs 65%-87%) than those predicting intensive care unit admission or a composite outcome (AUCs 53%-78%). Calibration was poor in all models providing outcome's probabilities and good in four models providing a point-based score. These four models used mortality as outcome and included age, oxygen saturation, and C-reactive protein among their predictors. CONCLUSION: The validity of models predicting critical COVID-19 by using only routinely collected predictors is variable. Four models showed good discrimination and calibration when externally validated and are recommended for their use.

Night shift work and sleep deprivation in relation to vaccine induced SARS-CoV-2 antibody responses in a general population cohort (COVICAT study)

IntroductionNight shift work and sleep deprivation have been associated with lower antibody responses induced by vaccination against seasonal influenza, meningitis-C and hepatitis A. We examined the association of exposure to night shift work and sleep deprivation with antibody levels induced by COVID-19 vaccines.Materials and MethodsThis study was nested in an ongoing population-based cohort in Catalonia, Spain. Blood samples were collected in 2021 from a random subsample of 1,090 participants. We measured 3 immunoglobulins (Ig)M, IgG, and IgA antibodies against 5 SARS-CoV-2 antigens, including RBD (receptor-binding domain), S (spike-protein), and S2 (subunit 2 from spike-protein). We collected data on night shift work (current night work, frequency, duration) and sleep metrics (sleep duration, sleep problems, changes in sleep duration since the beginning of the pandemic). We adjusted linear regression estimates (% change) for individual- and area-level covariates, time since vaccination, vaccine doses and type. Analyses were restricted to participants without previous COVID-19 infection (N=639). Infection status was defined using questionnaires, SARS-CoV-2 test registry and serology information (seropositivity to N-antigen).ResultsParticipants' mean age was 57.6 years, 57% were female, 73% received 2 vaccine doses (42% Pfizer, 44% AstraZeneca),5.8% were current night workers and 36.5% of the sample reported sleep problems. No overall association pattern was observed between current? night work and vaccine-induced antibody responses. IgG levels tended to be lower (differences in the range of 3.6–53.7%) among night workers, compared to day workers but differences were not statistically significant. Participants with short sleep (<=6 hours) had significantly lower IgM antibody levels compared to those that reported 7 hours of sleep. No clear pattern was observed with sleep quality.ConclusionsFurther research in larger studies is needed to evaluate the influence of night shift work and impaired sleep on vaccine induced immune responses and risk of breakthrough infections.

Differences and similarities of occupational risk factors for COVID-19 across waves of the pandemic;extended follow-up of the COVICAT cohort

IntroductionDuring the first pandemic lockdown in Spain certain workers have been at increased risk of COVID-19. Results from published studies are heterogeneous, possibly due to differences in public health interventions, availability of personal protective equipment (PPE), virulence of variants of concern, population-wide immunity or methodological issues.MethodsThe COVICAT study (IEC approved) pooled ongoing population-based cohort studies from Catalonia. Occupational analyses of COVICAT were restricted to working age and included 8,422 participants, of which 3,563 were tested for SARS-CoV-2 antibodies during the first wave;study participants were re-contacted in mid-2021. Participants responded to a web-based or telephone survey including questions on socio-demographics, pre-pandemic health, behavioural and environmental risk factors. Occupational questions covered mode of work, job title, PPE, and mode of commuting. COVID-19 cases were defined by self-reported symptoms or hospitalisation and SARS CoV-2 seropositivity. Association of type of work, job titles and job-exposure matrix (JEM) with COVID-19 was assed using log-binomial models adjusted for potential confounders, such as age, sex, education, deprivation index, population density and survey type. Analyses for the extended follow-up were stratified by pandemic waves.ResultsThe relative risk (RR) for COVID-19 for working at the usual workplace compared to telework was 1.83 (95% CI: 1.41, 2.38), and 1.63 (95% CI: 1.05, 2.52) among the serology study participants. The RR by job title was increased for all health care workers and highest for personal health care workers in health services (6.19;3.71, 10.33);PPE was associated with a stronger protective effect by increasing protection level. Using public transport for commuting was associated with a 50% increase in COVID risk. Results for the extended follow-up will be presented.ConclusionsThe extended follow-up of the COVICAT cohort provides data to illuminate occupational risk factors for COVID-19 infection over time, which may contribute to explain heterogeneities across countries.

Long-Term Exposure to Air Pollution and COVID-19 Vaccine Antibody Response in a General Population Cohort (COVICAT Study, Catalonia).

BACKGROUND: Ambient air pollution has been associated with COVID-19 disease severity and antibody response induced by infection. OBJECTIVES: We examined the association between long-term exposure to air pollution and vaccine-induced antibody response. METHODS: This study was nested in an ongoing population-based cohort, COVICAT, the GCAT-Genomes for Life cohort, in Catalonia, Spain, with multiple follow-ups. We drew blood samples in 2021 from 1,090 participants of 2,404 who provided samples in 2020, and we included 927 participants in this analysis. We measured immunoglobulin M (IgM), IgG, and IgA antibodies against five viral-target antigens, including receptor-binding domain (RBD), spike-protein (S), and segment spike-protein (S2) triggered by vaccines available in Spain. We estimated prepandemic (2018-2019) exposure to fine particulate matter [PM ≤2.5µm in aerodynamic diameter (PM2.5)], nitrogen dioxide (NO2), black carbon (BC), and ozone (O3) using Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE) models. We adjusted estimates for individual- and area-level covariates, time since vaccination, and vaccine doses and type and stratified by infection status. We used generalized additive models to explore the relationship between air pollution and antibodies according to days since vaccination. RESULTS: Among vaccinated persons not infected by SARS-CoV-2 (n=632), higher prepandemic air pollution levels were associated with a lower vaccine antibody response for IgM (1 month post vaccination) and IgG. Percentage change in geometric mean IgG levels per interquartile range of PM2.5 (1.7 µg/m3) were -8.1 (95% CI: -15.9, 0.4) for RBD, -9.9 (-16.2, -3.1) for S, and -8.4 (-13.5, -3.0) for S2. We observed a similar pattern for NO2 and BC and an inverse pattern for O3. Differences in IgG levels by air pollution levels persisted with time since vaccination. We did not observe an association of air pollution with vaccine antibody response among participants with prior infection (n=295). DISCUSSION: Exposure to air pollution was associated with lower COVID-19 vaccine antibody response. The implications of this association on the risk of breakthrough infections require further investigation. https://doi.org/10.1289/EHP11989.

Changes in Population Health-Related Behaviors During a COVID-19 Surge: A Natural Experiment.

BACKGROUND: The study of impact of lockdowns on individual health-related behaviors has produced divergent results. PURPOSE: To identify patterns of change in multiple health-related behaviors analyzed as a whole, and their individual determinants. METHODS: Between March and August 2020, we collected data on smoking, alcohol, physical activity, weight, and sleep in a population-based cohort from Catalonia who had available pre-pandemic data. We performed multiple correspondence and cluster analyses to identify patterns of change in health-related behaviors and built multivariable multinomial logistic regressions to identify determinants of behavioral change. RESULTS: In 10,032 participants (59% female, mean (SD) age 55 (8) years), 8,606 individuals (86%) modified their behavior during the lockdown. We identified five patterns of behavioral change that were heterogeneous and directed both towards worsening and improvement in diverse combinations. Patterns ranged from "global worsening" (2,063 participants, 21%) characterized by increases in smoking, alcohol consumption, and weight, and decreases in physical activity levels and sleep time, to "improvement" (2,548 participants, 25%) characterized by increases in physical activity levels, decreases in weight and alcohol consumption, and both increases and decreases in sleep time. Being female, of older age, teleworking, having a higher education level, assuming caregiving responsibilities, and being more exposed to pandemic news were associated with changing behavior (all p < .05), but did not discriminate between favorable or unfavorable changes. CONCLUSIONS: Most of the population experienced changes in health-related behavior during lockdowns. Determinants of behavior modification were not explicitly associated with the direction of changes but allowed the identification of older, teleworking, and highly educated women who assumed caregiving responsibilities at home as susceptible population groups more vulnerable to lockdowns.

Resilience characterized and quantified from physical activity data: A tutorial in R

Consistent physical activity is key for health and well-being, but it is vulnerable to stressors. The process of recovering from such stressors and bouncing back to the previous state of physical activity can be referred to as resilience. Quantifying resilience is fundamental to assess and manage the impact of stressors on consistent physical activity. In this tutorial, we present a method to quantify the resilience process from physical activity data. We leverage the prior operationalization of resilience, as used in various psychological domains, as area under the curve and expand it to suit the characteristics of physical activity time series. As use case to illustrate the methodology, we quantified resilience in step count time series (length = 366 observations) for eight participants following the first COVID-19 lockdown as a stressor. Steps were assessed daily using wrist-worn devices. The methodology is implemented in R and all coding details are included. For each person's time series, we fitted multiple growth models and identified the best one using the Root Mean Squared Error (RMSE). Then, we used the predicted values from the selected model to identify the point in time when the participant recovered from the stressor and quantified the resulting area under the curve as a measure of resilience for step count. Further resilience features were extracted to capture the different aspects of the process. By developing a methodological guide with a step-by-step implementation, we aimed at fostering increased awareness about the concept of resilience for physical activity and facilitate the implementation of related research. • R tutorial to quantify resilience from physical activity time series. • Physical activity resilience is measured using an idiographic approach. • Physical activity resilience is operationalized as the AUC. • Growth models are fitted to step count time series to define the limits of the AUC. • Further indicators of resilience are provided to describe the phenomenon. [ FROM AUTHOR]

Resilience characterized and quantified from physical activity data: A tutorial in R

Consistent physical activity is key for health and well-being, but it is vulnerable to stressors. The process of recovering from such stressors and bouncing back to the previous state of physical activity can be referred to as resilience. Quantifying resilience is fundamental to assess and manage the impact of stressors on consistent physical activity. In this tutorial, we present a method to quantify the resilience process from physical activity data. We leverage the prior operationalization of resilience, as used in various psychological domains, as area under the curve and expand it to suit the characteristics of physical activity time series. As use case to illustrate the methodology, we quantified resilience in step count time series (length = 366 observations) for eight participants following the first COVID-19 lockdown as a stressor. Steps were assessed daily using wrist-worn devices. The methodology is implemented in R and all coding details are included. For each person’s time series, we fitted multiple growth models and identified the best one using the Root Mean Squared Error (RMSE). Then, we used the predicted values from the selected model to identify the point in time when the participant recovered from the stressor and quantified the resulting area under the curve as a measure of resilience for step count. Further resilience features were extracted to capture the different aspects of the process. By developing a methodological guide with a step-by-step implementation, we aimed at fostering increased awareness about the concept of resilience for physical activity and facilitate the implementation of related research.

SARS-CoV-2 infection, vaccination, and antibody response trajectories in adults: a cohort study in Catalonia.

BACKGROUND: Heterogeneity of the population in relation to infection, COVID-19 vaccination, and host characteristics is likely reflected in the underlying SARS-CoV-2 antibody responses. METHODS: We measured IgM, IgA, and IgG levels against SARS-CoV-2 spike and nucleocapsid antigens in 1076 adults of a cohort study in Catalonia between June and November 2020 and a second time between May and July 2021. Questionnaire data and electronic health records on vaccination and COVID-19 testing were available in both periods. Data on several lifestyle, health-related, and sociodemographic characteristics were also available. RESULTS: Antibody seroreversion occurred in 35.8% of the 64 participants non-vaccinated and infected almost a year ago and was related to asymptomatic infection, age above 60 years, and smoking. Moreover, the analysis on kinetics revealed that among all responses, IgG RBD, IgA RBD, and IgG S2 decreased less within 1 year after infection. Among vaccinated, 2.1% did not present antibodies at the time of testing and approximately 1% had breakthrough infections post-vaccination. In the post-vaccination era, IgM responses and those against nucleoprotein were much less prevalent. In previously infected individuals, vaccination boosted the immune response and there was a slight but statistically significant increase in responses after a 2nd compared to the 1st dose. Infected vaccinated participants had superior antibody levels across time compared to naïve-vaccinated people. mRNA vaccines and, particularly the Spikevax, induced higher antibodies after 1st and 2nd doses compared to Vaxzevria or Janssen COVID-19 vaccines. In multivariable regression analyses, antibody responses after vaccination were predicted by the type of vaccine, infection age, sex, smoking, and mental and cardiovascular diseases. CONCLUSIONS: Our data support that infected people would benefit from vaccination. Results also indicate that hybrid immunity results in superior antibody responses and infection-naïve people would need a booster dose earlier than previously infected people. Mental diseases are associated with less efficient responses to vaccination.

Higher mortality and intubation rate in COVID-19 patients treated with noninvasive ventilation compared with high-flow oxygen or CPAP.

The effectiveness of noninvasive respiratory support in severe COVID-19 patients is still controversial. We aimed to compare the outcome of patients with COVID-19 pneumonia and hypoxemic respiratory failure treated with high-flow oxygen administered via nasal cannula (HFNC), continuous positive airway pressure (CPAP) or noninvasive ventilation (NIV), initiated outside the intensive care unit (ICU) in 10 university hospitals in Catalonia, Spain. We recruited 367 consecutive patients aged ≥ 18 years who were treated with HFNC (155, 42.2%), CPAP (133, 36.2%) or NIV (79, 21.5%). The main outcome was intubation or death at 28 days after respiratory support initiation. After adjusting for relevant covariates and taking patients treated with HFNC as reference, treatment with NIV showed a higher risk of intubation or death (hazard ratio 2.01; 95% confidence interval 1.32-3.08), while treatment with CPAP did not show differences (0.97; 0.63-1.50). In the context of the pandemic and outside the intensive care unit setting, noninvasive ventilation for the treatment of moderate to severe hypoxemic acute respiratory failure secondary to COVID-19 resulted in higher mortality or intubation rate at 28 days than high-flow oxygen or CPAP. This finding may help physicians to choose the best noninvasive respiratory support treatment in these patients.Clinicaltrials.gov identifier: NCT04668196.

Ambient Air Pollution in Relation to SARS-CoV-2 Infection, Antibody Response, and COVID-19 Disease: A Cohort Study in Catalonia, Spain (COVICAT Study).

BACKGROUND: Emerging evidence links ambient air pollution with coronavirus 2019 (COVID-19) disease, an association that is methodologically challenging to investigate. OBJECTIVES: We examined the association between long-term exposure to air pollution with SARS-CoV-2 infection measured through antibody response, level of antibody response among those infected, and COVID-19 disease. METHODS: We contacted 9,605 adult participants from a population-based cohort study in Catalonia between June and November 2020; most participants were between 40 and 65 years of age. We drew blood samples from 4,103 participants and measured immunoglobulin M (IgM), IgA, and IgG antibodies against five viral target antigens to establish infection to the virus and levels of antibody response among those infected. We defined COVID-19 disease using self-reported hospital admission, prior positive diagnostic test, or more than three self-reported COVID-19 symptoms after contact with a COVID-19 case. We estimated prepandemic (2018-2019) exposure to fine particulate matter [PM with an aerodynamic diameter of ≤2.5µm (PM2.5)], nitrogen dioxide (NO2), black carbon (BC), and ozone (O3) at the residential address using hybrid land-use regression models. We calculated log-binomial risk ratios (RRs), adjusting for individual- and area-level covariates. RESULTS: Among those tested for SARS-CoV-2 antibodies, 743 (18.1%) were seropositive. Air pollution levels were not statistically significantly associated with SARS-CoV-2 infection: Adjusted RRs per interquartile range were 1.07 (95% CI: 0.97, 1.18) for NO2, 1.04 (95% CI: 0.94, 1.14) for PM2.5, 1.00 (95% CI: 0.92, 1.09) for BC, and 0.97 (95% CI: 0.89, 1.06) for O3. Among infected participants, exposure to NO2 and PM2.5 were positively associated with IgG levels for all viral target antigens. Among all participants, 481 (5.0%) had COVID-19 disease. Air pollution levels were associated with COVID-19 disease: adjusted RRs=1.14 (95% CI: 1.00, 1.29) for NO2 and 1.17 (95% CI: 1.03, 1.32) for PM2.5. Exposure to O3 was associated with a slightly decreased risk (RR=0.92; 95% CI: 0.83, 1.03). Associations of air pollution with COVID-19 disease were more pronounced for severe COVID-19, with RRs=1.26 (95% CI: 0.89, 1.79) for NO2 and 1.51 (95% CI: 1.06, 2.16) for PM2.5. DISCUSSION: Exposure to air pollution was associated with a higher risk of COVID-19 disease and level of antibody response among infected but not with SARS-CoV-2 infection. https://doi.org/10.1289/EHP9726.

Infection induced SARS-CoV-2 seroprevalence and heterogeneity of antibody responses in a general population cohort study in Catalonia Spain.

Sparse data exist on the complex natural immunity to SARS-CoV-2 at the population level. We applied a well-validated multiplex serology test in 5000 participants of a general population study in Catalonia in blood samples collected from end June to mid November 2020. Based on responses to fifteen isotype-antigen combinations, we detected a seroprevalence of 18.1% in adults (n = 4740), and modeled extrapolation to the general population of Catalonia indicated a 15.3% seroprevalence. Antibodies persisted up to 9 months after infection. Immune profiling of infected individuals revealed that with increasing severity of infection (asymptomatic, 1-3 symptoms, ≥ 4 symptoms, admitted to hospital/ICU), seroresponses were more robust and rich with a shift towards IgG over IgA and anti-spike over anti-nucleocapsid responses. Among seropositive participants, lower antibody levels were observed for those ≥ 60 years vs < 60 years old and smokers vs non-smokers. Overweight/obese participants vs normal weight had higher antibody levels. Adolescents (13-15 years old) (n = 260) showed a seroprevalence of 11.5%, were less likely to be tested seropositive compared to their parents and had dominant anti-spike rather than anti-nucleocapsid IgG responses. Our study provides an unbiased estimate of SARS-CoV-2 seroprevalence in Catalonia and new evidence on the durability and heterogeneity of post-infection immunity.

Occupational risk factors for SARS-CoV-2 infection and COVID-19: Results from the COVICAT cohort study in Catalonia, Spain

IntroductionDuring the first lockdown in Spain (March-June, 2020) essential workers may have been at increased risk of coronavirus disease 2019 (COVID-19) via occupational exposure. Results from published studies are heterogeneous.MethodsOngoing population-based cohort studies from Catalonia were pooled to form the COVICAT study. A random sub-population donated a blood sample (May-July, 2020) for validated multiplex serology testing. Occupational analyses were restricted to working age (18–65 years). Participants responded to a web-based or telephone survey including questions on socio-demographics, pre-pandemic health, behavioural and environmental risk factors. Occupational questions covered mode of work (e.g. telework), job title, availability of personal protective equipment (PPE), and mode of commuting. Job titles were coded by an occupational hygienist to the Spanish CNO-11 and cross-walked to ISCO-08. COVID-19 cases were defined by symptoms or hospitalisation and SARS CoV-2 seropositivity based on immune responses to 15 isotype-antigen combinations (serology sub-cohort). Logistic regression models were built for type of work, job titles and job-exposure matrix (JEM), covering several dimensions and levels of SARS-CoV-2 transmission probabilities , and adjusted for age, sex, education, deprivation index, population density and survey type.ResultsThis analysis included 8,582 participants, of which 3,599 were tested for SARS-CoV-2 antibodies, median (SD) age 53.7 (6.3) years, 59.9% were women. The relative risk for COVID-19 for work in the usual workplace compared to telework was 1.87 (95% CI: 1.44, 2.42), and 1.44 (95% CI: 1.09, 1.90) among the serology study. The relative risk for nurses who worked in their usual workplace was 4.57 (95% CI: 3.12, 6.7). Detailed results by job title, JEM, availability of PPE and commuting mode will be presented.ConclusionsThis study has several strengths, including random serology testing and individual-level exposure data. Detailed results may support extended legal definitions of COVID-19 as a recognized occupational disease.

A Roadmap to Inform Development, Validation and Approval of Digital Mobility Outcomes: The Mobilise-D Approach.

Health care has had to adapt rapidly to COVID-19, and this in turn has highlighted a pressing need for tools to facilitate remote visits and monitoring. Digital health technology, including body-worn devices, offers a solution using digital outcomes to measure and monitor disease status and provide outcomes meaningful to both patients and health care professionals. Remote monitoring of physical mobility is a prime example, because mobility is among the most advanced modalities that can be assessed digitally and remotely. Loss of mobility is also an important feature of many health conditions, providing a read-out of health as well as a target for intervention. Real-world, continuous digital measures of mobility (digital mobility outcomes or DMOs) provide an opportunity for novel insights into health care conditions complementing existing mobility measures. Accepted and approved DMOs are not yet widely available. The need for large collaborative efforts to tackle the critical steps to adoption is widely recognised. Mobilise-D is an example. It is a multidisciplinary consortium of 34 institutions from academia and industry funded through the European Innovative Medicines Initiative 2 Joint Undertaking. Members of Mobilise-D are collaborating to address the critical steps for DMOs to be adopted in clinical trials and ultimately health care. To achieve this, the consortium has developed a roadmap to inform the development, validation and approval of DMOs in Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease and recovery from proximal femoral fracture. Here we aim to describe the proposed approach and provide a high-level view of the ongoing and planned work of the Mobilise-D consortium. Ultimately, Mobilise-D aims to stimulate widespread adoption of DMOs through the provision of device agnostic software, standards and robust validation in order to bring digital outcomes from concept to use in clinical trials and health care.