The evident shedding of the SARS-CoV-2 RNA particles from infected individuals into the wastewater opened up a tantalizing array of possibilities for prediction of COVID-19 prevalence prior to symptomatic case identification through community testing. Many countries have therefore explored the use of wastewater metrics as a surveillance tool, replacing traditional direct measurement of prevalence with cost-effective approaches based on SARS-CoV-2 RNA concentrations in wastewater samples. Two important aspects in building prediction models are: time over which the prediction occurs and space for which the predicted case numbers is shown. In this review, our main focus was on finding mathematical models which take into the account both the time-varying and spatial nature of wastewater-based metrics into account. We used six main characteristics as our assessment criteria: i) modelling approach; ii) temporal coverage; iii) spatial coverage; iv) sample size; v) wastewater sampling method; and vi) covariates included in the modelling. The majority of studies in the early phases of the pandemic recognized the temporal association of SARS-CoV-2 RNA concentration level in wastewater with the number of COVID-19 cases, ignoring their spatial context. We examined 15 studies up to April 2023, focusing on models considering both temporal and spatial aspects of wastewater metrics. Most early studies correlated temporal SARS-CoV-2 RNA levels with COVID-19 cases but overlooked spatial factors. Linear regression and SEIR models were commonly used , along with machine learning  and Bayesian approaches  in some cases. Three studies employed spatio-temporal modelling approach . We conclude that the development, validation and calibration of further spatio-temporally explicit models should be done in parallel with the advancement of wastewater metrics before the potential of wastewater as a surveillance tool can be fully realised. demonstrating the role and stage of wastewater-based modelling in the whole cycle of epidemic surveillance. •Many countries explored relationship of SARS-CoV-2 RNA concentrations in wastewater samples and COVID-19 cases.•Wastewater metrics can potentially be used as an early warning tool for the pandemic.•Most of the published work does not take account of the spatial context.•There is a high spatial heterogeneity in COVID-19 prevalence.•Development of models capturing spatio-temporal relationship of WW metrics are needed. Traditional surveillance often focuses on certain geographical areas resulting in limited coverage and lack of ability to capture mobility; additionally, it can be very expensive and resource-intensive and often can lack real-time data as operating on the basis of data gathered through set protocols with a time lag. In the case of COVID-19, in addition to the above limitations, the traditional surveillance methods are also limited to symptomatic cases who get tested and receive a positive COVID-19 test, which is a biased estimate of the infected population at each point in time. For a disease with low mortality rate, case-reporting tends to be incomplete, but this can be addressed by analysing case-reporting data jointly with data from randomised, and therefore unbiased, prevalence surveys . During The global capacity for real-time disease surveillance varies among countries, as has been partially demonstrated by analysis of self-reported data on detection, control and prevention of outbreaks across 182 countries . DetectiThe great advantage of a randomised study-design is the guarantee of unbiasedness, albeit with the proviso that non-compliance must be assumed to be ignorable, by which we mean that the fact of a person's non-compliance is independent of their COVID-19 infection status conditional on all of their measured characteristics . The mai1.2Wastewater has been used in epidemic surveillance and to create tools for monitoring infectious disease  and drug1.3Globally, there are more than 3000 active wastewater testing sites located across 58 countries . The majDespite all the progress in this area, using RNA viral load, the main source of the SARS-CoV-2 signal in wastewater, as a proxy for disease prevalence raises some complex issues: i) the RNA shedding level varies both between infected individuals and during the course of the disease for an infected individual ; ii) theA comprehensive analysis of wastewater data is further complicated by the fact that various contributory factors exhibit spatio-temporal variation. These include, but are not limited, to: i) catchment population - shedding profile, population mobility, vaccination status; ii) network properties - rainfall dilution affecting the quality of wastewater, pumping system, chemical status of the water; iii) sampling considerations - collection methods, processing methods, sampling frequency; iv) biochemical analysis - sample processing protocol . Therefo2To identify articles for this review, we followed PRISMA guidelines focusing on open access publication in this area  for searArticles were imported and managed in Mendeley Version 1.19.8. To assess quality of articles, including study design, outcome measurement and bias, we used the guidelines of the Centers for Disease Control . Althoug●Modelling approach: which methods were used to analyse the data and create predictive models?●Temporal Coverage: when did the data collection begin and end, and what was the time-interval used in the analysis, such as daily, weekly or a space-only analysis using aggregated data over time?●Spatial Coverage: the spatial resolution at which wastewater viral concentrations are measured, e.g. the Waste Water Treatment Plant (WWTP) catchment level, and the resolution of the modelling output.●Sample Size: Sample size and the type of sample used in each study directly impacts the statistical power and precision that can be achieved at any given spatial resolution. Also, the sample size required to achieve a given level of precision increases as the required spatial resolution is refined. We assessed each study based on number of samples that were included and how diverse the sampling locations were: from urban residential areas only, or including both urban and rural areas?●Wastewater sample origin: WWTP or other sources such as sewer networks●Covariates: which covariates were included in the model, and are they recorded as spatial-only, temporal-only or spatio-temporal measurements?We further described and assessed these 15 articles in detail based on the following 6 characteristics.3We observed an evolving theme whereby the initial focus up to July 2020 was on the development and optimization of water sampling protocols, curation of collected wastewater sample and lab-based analysis of viral load for generation of daily measures  see Tab. Another3.1I)linear regression and time series analysis: this category includes studies that use the wastewater measures collected over time to derive the relationships between COVID-19 case numbers and the SARS-CoV-2 Viral load in wastewater ,60,62,64II)Compartmental SEIR models and agent-based models: this category includes studies that used compartmental Susceptible-exposed-infectious-recovered (SEIR) approaches as traditionally used or in a more sophisticated form as in Proverbio et al. (2022) or approaches where each element is estimated through simulation of stochastic processes such as estimation of disease prevalence through Monte Carlo simulation ,41,63,66III)Machine learning models: This category includes a single study by Vaughan et al., 2023 which used a machine learning approachIV)Bayesian models: this category includes a single study by Srinivas et al., 2021 which developed a Bayesian model approach.V)spatio-temporal models: this category includes the models which specifically incorporated both spatial and temporal data into their prediction models and were able to provide estimates of viral load or COVID-19 prevalence at refined special resolutions ,40,43 (sWe categorised the modelling approaches into five themes.2) of approximately 0.8.Krivoňáková et al. (2021) conducted their analysis on a regional level from two major WWTPs, using a time series modelling approach originally on daily data, then moving to weekly time series. They treated samples for each city separately in their models. One of the characteristics of the Krivoňáková et al. approach was their ability to minimize the size of wastewater sample required for detecting a SARS-CoV-2 case through a positive RT-qPCR signal in wastewater. They reported achieved detection limits of 1 positive RT-PCR case per 4808 and 1 per 8099 inhabitants for the two cities considered. They used a linear regression model with weekly numbers of deaths in the city of Bratislava and Petrzalka, Slovakia, as the response and the log-transformed weekly averaged viral particle counts from two WWTPs lagged by four weeks as the explanatory variable, obtaining a coefficient determination value  did not create an explicit calibration between wastewater measures and clinical cases. Rather, they developed a Bayesian network model aiming to locate wastewater sites that had a high probability of indicating a local outbreak. They used 11 factors including demographic variables, migration rates, healthcare and educational facilities as well as general population measures such as age and comorbidities including: respiratory, kidney, cardiovascular, obesity, liver, diabetes and hypertension conditions. The structure of their model was informed by applying fuzzy logic to the judgements of a group of experts. While assessment and characterisation of this specific study did not completely fit the five defined categories of our study, we considered the approach to be relevant both from the design angle and through its incorporation of comorbidity factors.Omori et al. (2021) investigated the lag between time series of case numbers within different age groups and wastewater viral load. They showed that a rise in incidence can be detected up to 10 days earlier using wastewater viral load than by reported case numbers.McMahan et al. (2021) modelled the time series of wastewater data from three WWTPs that covered the city of Clemson, South Carolina, USA by linking a classical SEIR model of case-prevalence with a non-linear deterministic model for the time-varying SARS-CoV-2 RNA load in wastewater. This modelling embeds an approximation of the shedding profile of an infected individual within the SEIR model to predict COVID case numbers, which were then validated against the observed clinical case numbers. For surveillance purpose, they also proposed a simple calibration criterion for the relationship between viral load per litre in wastewater and the number of incident cases per day.Fitzgerald et al. (2021) investigated associations of viral load and daily case numbers at the catchment area level in Scotland, using Spearman correlation followed by a series of univariate linear regression models and then the development of a linear mixed model with random intercepts and slopes specified at the catchment area level.Galani et al. (2022) proposed a Bayesian non-linear Poisson distributed-lag model that uses wastewater-based measurements to predict the number of confirmed positive COVID-19 cases as well as COVID hospitalisation and ICU admissions. Their modelling suggested that viral load in wastewater leads the above three clinical indicators by between 2 and 8 days.On March 10, 2022, following the work of Fitzgerald et al. (2021), the Scottish government published initial figures comparing trends of average viral load in wastewater against case positivity rates from the CIS survey covering the period May 14, 2020 to March 4, 2022 ,67. ViraKuhn et al. (2022) analysed wastewater data from 13 locations in Oklahoma City, USA. They used Poisson log-linear models fitted separately to wastewater from each location and to all 13 locations combined, but did not attempt to fit an explicitly spatio-temporal model. They considered a range of socio-demographic factors at the sewershed catchment area level, including proportions of the population in different ethnic groups, proportion of the population aged 65 or more, mean income and numbers of notified COVID-19 cases. Their modelling showed a lead time of between 4 and 10 days in detecting COVID-19 outbreaks.Proverbio et al. (2022) developed a more generalized pipeline, the “COVID-19 Wastewater Analyser,” which incorporates a range of pre-validated parameters such as infectivity rate and population size, as well as transition rates from Exposed status to Infected status (E to I) and from Infectious to Removed status (I to R)  within aPájaro et al. (2022) focused on predicting the evolution of the pandemic with a stochastic Susceptible-Infected-Recovered (SIR) model that was validated using data of small and medium size municipalities with total populations of 2000 to 23,000 individuals from Galicia, Spain. The SIR model was developed using a Chemical Master Equation (CME) as the base for incorporating the stochasticity of chemical status. The group used a Stochastic Simulation Algorithm and considered the number of clinically positive cases as one realisation of the simulation.Petros et al. (2022) developed a linear regression model using the Mesa University-USA campus data. Their model incorporates uniquely a set of widely accessible measures such as demography, contact tracing, wifi-based location data, viral load and COVID testing data.Zhao et al. (2022) developed four statistical modelling approach: liner regression, autoregressive integrated moving average (ARIMA), seasonal ARIMA, vector autoregressive model using wastewater samples from 407 WWTPs over a 12 month period. They have shown that seasonal ARIMA and vector autoregressive models had an optimized prediction for COVID-19 cases.Li et al. (2023) developed a Bayesian geostatistical model that uses weekly measurements of wastewater viral concentration at the 303 sewage treatment works across England to predict wastewater viral concentration for all the 32,844 lower super output areas in the country. To help prediction, this model incorporated a range of covariates including index of multiple deprivation, ethnicity proportion based on census 2011, land cover, age structure and wastewater genomic data.Vaughan et al. (2023) investigated the challenges associated with machine learning approaches for obtaining early detection signals from wastewater samples. They used data from 108 cities from 5 countries.3.2https://sphere.waterpathogens.org/map) with variable coverage range. The other 10 studies used less than one year's data and we found no indication of regular data updates , , . Krivoňá3.5Across all the studies, a range of epidemiological COVID outcomes have been considered, including number of reported cases  or number of new cases (incidence), hospitalisation and death. None of the studies directly reports outputs at the exact level of the WW measurements, but some studies have addressed the problem of spatial misalignment between WW input and health outcome data. For example, McMahan et al. (2021) and Amereh et al. (2021) matched the WW data to the sewershed catchment area level data to estimate under-reporting rates; this provides a measure of the accuracy of disease burden; specifically when using WW as an early warning system, knowledge about accuracy of estimates contributes directly to effective allocation of lockdown or other measures and resources.Four studies took the approach of predicting the population prevalence using the viral load measurement in wastewater as a covariate ,36,60,62The SEIR models developed by McMahan et al. (2021) and Proverbio et al. (2022) also used pandemic-related measures such as median incubation period.4In this review, we have summarised a variety of wastewater-based surveillance models developed for studying the evolution of COVID-19 across diverse geographical regions. Our review has revealed that while the potential utility of wastewater resources is widely recognized, and global efforts to harness this potential are underway, only a limited number of studies have explicitly explored the spatio-temporal relationship between wastewater viral contents and prevalence of COVID-19 infections. Wade et al. (2022) have documented the associated challenges, while Nicholson et al. (2021) have explicitly investigated the complexities of achieving an unbiased estimate of prevalence. The well-established advantages of wastewater sampling over direct sampling of at-risk populations are noteworthy. Firstly, wastewater sampling is more cost-effective than community testing. Secondly, it represents a non-intrusive approach. Thirdly, it is virtually immune to the selection biases that can arise with direct epidemiological measurements. Lastly, it can offer early detection signals to identify regional outbreaks. Carducci et al. (2020) and Kirby et al. (2021) have suggested a lead time of 5–10 days in the wastewater viral load before the onset of a regional outbreak that would be identified by clinical testing .To fully realise the potential of wastewater data for COVID monitoring, several challenges persist. These encompass the need to account for variability in the characteristics of wastewater treatment plant sites, the selection of metric and the choice of sampling methodologies. For instance, Peccia et al. (2020) demonstrated the presence of N1 and N2 gene targets of SARS-CoV-2 in the solid component of wastewater, known as sludge. However, none of the model-based approaches that we identified have utilized a sludge samples. Shedding profiles, rainwater dilution and treatment plant variability have been identified as some of the relevant factors, as discussed by Petala et al. (2022), Wang et al. (2020), Zhu et al. (2021a) and Morvan et al. (2021). These factors need to be appropriately incorporated in the modelling of wastewater data. The utilization of RNA viral load as a proxy for disease prevalence in wastewater-based surveillance presents intricate challenges. Variability in RNA shedding levels among infected individuals and over the course of infection , as wellFurthermore, the harmonisation of sampling and measurement protocols holds promise for enhancing the comparability and reproducibility of findings from wastewater studies . Our review has revealed considerations across all studies for various contributing factors in the multi-dimensional nature of the wastewater sampling and testing process, as discussed in Olesen et al. (2021) and Zhao et al. (2022). Nevertheless, adopting a comprehensive framework that accounts for sampling, testing and reporting variables in a spatio-temporal manner would bring us closer to a harmonised approach.(https://www.gov.scot/publications/coronavirus-covid-19-modelling-epidemic-issue-no-97/) of progress with the wastewater monitoring program [https://gov.wales/sites/default/files/publications/2022-06/wastewater-monitoring-23-june-2022.pdf) of the wastewater monitoring program was released on June 23, 2022 [USA, 43 health departments were funded to provide data on SARS-CoV-2. Here, we summarise the identified dashboards across states. The COVID Data Tracker dashboard (https://covid.cdc.gov/covid-data-tracker/#wastewater-surveillance) of SARS-CoV-2 RNA levels, developed by the Centers for Disease Control and Prevention (CDC), illustrates for 407 treatment plant sites across the United States the 15-day detection proportions across the country . The dashboard of the SARS-CoV-2 sewage monitoring project run by Utah Department of Environmental Quality illustrates insights from 42 treatment plants (80 % of state's population) and shows: for wastewater, in each plant the trends of overall viral load as gene copies in sewage per person per day; for community, the total numbers of new daily cases per 100,000 population per day [dashboard(https://covid19.tempe.gov/#Explore) illustrates: for wastewater, trends of viral load as weekly average of gene copies per litre of wastewater; for community, total number of COVID-19 positive test results per 100,000 per zip code; for demographics, area statistics by age group, sex, ethnicity and household ownership status [dashboard (https://wastewater.covid19.mathematica.org/) provides: for wastewater, trends of viral load as flow normalised viral load (MGC/person/day); for community, summaries of cases and deaths with a 7-day rolling average of new cases [https://covid19.ncdhhs.gov/dashboard/data-behind-dashboards.Since the start of the pandemic, many groups have worked on wastewater-based surveillance methods, using various ways of communicating their findings to diverse groups of stakeholders, describing logistical challenges for WBE in Africa, the Netherlands, Turkey and England . Takeda  program . In Wale23, 2022 . In the  per day . Anotherp status . The Norew cases . Researcew cases  also useIt is essential to acknowledge a notable limitation within the scope of this study. Despite our comprehensive analysis of 15 selected articles, we did not identify a substantial body of research that explicitly stratified wastewater data based on variants of concern or vaccination status. The absence of such stratification is a limitation as it prevents us from fully understanding the impact of emerging variants and vaccination efforts on viral load dynamics in wastewater. Given the evolving nature of the COVID-19 pandemic, these factors play a crucial role in shaping disease dynamics and warrant further investigation. Future studies that incorporate stratification based on variants of concern and vaccination status are needed to provide a more comprehensive and nuanced understanding of how these factors influence the interpretation of wastewater-based surveillance data. Such research could offer valuable insights into the effectiveness of vaccination campaigns and the prevalence of specific viral variants within communities, ultimately enhancing the utility of wastewater surveillance as a public health tool.Many countries have initiated surveillance approaches based on wastewater data. However, our review, up to April 2023, suggests that three important statistical issues have not been addressed adequately. Firstly, while several studies demonstrated spatial and temporal variability in the RNA viral load, most have focused on spatially aggregated temporal changes. Secondly, only one study has developed a spatio-temporal model using wastewater data collected at sewage treatment plants to predict viral loads at fine spatio-temporal resolution across an entire country. Thirdly, most existing work either considers wastewater data in isolation, or relates it to COVID-19 infection rates as an explanatory variable within a regression modelling framework, with little consideration of the fact that wastewater viral load and clinical outcomes such as infection rates are observed with error. Instead, we believe that the spatio-temporal variation in viral concentration and risk of infection should be modelled jointly as a latent process whose evolving state is informed by observations of wastewater RNA measurements and clinical data such as PCR or LFD test data; see In conclusion, our review has shown that the global effort in harnessing wastewater data for its known potential in providing an efficient and timely surveillance tool is evolving rapidly. However, there remains a need for more research to develop an explicitly spatio-temporal wastewater-based surveillance system that can be validated for use in a range of public health settings.10.13039/100012338Alan Turing Institute (EP/W037211/1) and in-kind support from 10.13039/100012100The Royal Statistical Society.This work was funded by The Department for Health and Social Care (Grant ref: 2020/045) with additional support from The The data associated with this study has not been deposited into a publicly available repository. For the reproducibility of our search approach, the data about our search is included in the supplementary material of this article and all research pieces are referenced. No other data has been used as part of this review.Fatemeh Torabi: Writing – review & editing, Writing – original draft, Visualization, Formal analysis, Data curation, Conceptualization. Guangquan Li: Writing – review & editing, Writing – original draft, Investigation. Callum Mole: Writing – review & editing. George Nicholson: Writing – review & editing, Methodology. Barry Rowlingson: Writing – review & editing. Camila Rangel Smith: Writing – review & editing. Radka Jersakova: Writing – review & editing. Peter J. Diggle: Writing – review & editing, Writing – original draft, Supervision, Conceptualization. Marta Blangiardo: Writing – review & editing, Writing – original draft, Supervision, Conceptualization.Authors have declared no competing interest with this work.


Atopic dermatitis (AD) is a chronic inflammatory skin disease with severe itch. The eosinophil-to-lymphocyte ratio (ELR), neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) are reported to reflect itch or the severity of AD. We examined if these parameters may act as indicators for therapeutic effects of the Janus kinase 1 inhibitor upadacitinib for patients with AD in real-world clinical practice. Between August 2021 and September 2023, 65 Japanese patients (aged ≥ 12 years) with moderate to severe AD were treated with 15 mg/day of oral upadacitinib, plus twice daily topical corticosteroids. Before treatment, the baseline ELR, NLR, MLR, and PLR levels positively correlated with the eczema area and severity index (EASI), while the baseline NLR and PLR levels positively correlated with the peak pruritus-numerical rating scale (PP-NRS). After upadacitinib treatment, ELR and NLR remarkably decreased at week 4 and the reduced levels were maintained until week 24, in parallel with EASI and PP-NRS, while MLR and PLR transiently reduced at week 4, but returned to baseline levels after week 12. The percent reduction of ELR significantly correlated with the percent reductions of EASI and PP-NRS at weeks 4, 12, and 24 of upadacitinib treatment. ELR may act as an indicator for the improvement of clinical signs and itch by upadacitinib treatment in AD. Atopic dermatitis (AD) is a chronic inflammatory skin disease with type 2-skewed abnormal immunity, skin barrier impairment, and severe itch ,2. UpadaThe neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) have been identified as indicators of systemic inflammation ,5,6,7. IIn this study, we examined how the values of ELR, NLR, MLR, and PLR might be altered by treatment with upadacitinib in patients with moderate-to-severe AD in real-world practice. We examined if changes in these parameters can reflect the improvement of clinical signs or itch by upadacitinib treatment, as assessed by the eczema area and severity index (EASI) or the peak pruritus-numerical rating scale (PP-NRS), respectively. We also attempted to detect background factors that predict the responsiveness to treatment with upadacitinib for AD.Between August 2021 and December 2022, 65 Japanese patients (aged ≥ 12 years) with moderate-to-severe AD (EASI > 7) living in Chiba Prefecture were treated with oral 15 mg/day of upadacitinib, plus twice daily topical corticosteroids of moderate-to-strongest classes. This study did not involve the patients treated with upadacitinib alone or those with topical corticosteroids alone. The diagnosis of AD was made clinically based on the Japanese Atopic Dermatitis Guidelines 2021 . The patt-test for variables with a normal distribution, and Mann–Whitney’s U test for variables with a non-parametric distribution. Statistical significance was set at p < 0.05. A linear multivariate regression analysis was performed to determine the predictive factors for the high-percentage reduction of EASI or PP-NRS at weeks 4, 12, or 24 of upadacitinib treatment. The analysis included only the variables with a p value < 0.05 in univariate analyses and was adjusted for age and sex. Variables with a variance inflation factor >10 were excluded to avoid multicollinearity.All statistical analyses were performed using EZR . The Shapiro–Wilk test was used to assess normality. Results are expressed as mean ± standard deviation for variables with a normal distribution, and as median and interquartile range for variables with a nonparametric distribution. Differences in measurements at weeks 0, 4, 12, and 24 of treatment were assessed using repeated measures of analysis of variance for normally distributed variables and using Friedman’s test for non-parametrically distributed variables. Post hoc analysis was performed using Bonferroni correction. Correlations between variables were tested using Spearman’s correlation coefficient. Differences between the two groups were assessed using Student’s A total of 65 Japanese patients with AD  were enrolled in the study . BaselinBaseline (week 0) values of ELR, NLR, MLR, and PLR positively correlated with baseline EASI . BaselinEASI and PP-NRS significantly reduced at week 4, and the reduced levels were maintained at weeks 12 and 24 . The ELRWe then analyzed if percent reductions of ELR, NLR, MLR, and PLR may correlate with those of EASI or PP-NRS . The perWe then examined if patients’ background factors, sex, age, BMI, disease duration, baseline clinical indexes, or laboratory parameters might influence the improvement of EASI by upadacitinib treatment at weeks 4, 12, or 24 . The perLinear multivariate regression analysis showed that female sex was associated with a high percent reduction of EASI at week 4 ; howeverWe then examined if patients’ background factors might influence the improvement of PP-NRS by upadacitinib treatment at weeks 4, 12, or 24 . Age posLinear multivariate regression analysis showed that older age was associated with a high percent reduction of PP-NRS at weeks 4 and 12 . High baBefore upadacitinib treatment, baseline ELR, NLR, MLR, and PLR significantly correlated with baseline EASI . The resBaseline PP-NRS was significantly correlated with baseline NLR and PLR, but not with baseline ELR. This may be related to the contribution of other cells, such as basophils or mast cells, to the baseline pruritus in AD patients, in addition to eosinophils; these cells are a close proximity of sensory nerves in AD lesions and produce substance P and interleukin (IL)-4/13/31, generating itch .The percent reduction of ELR correlated with those of EASI and PP-NRS at weeks 4, 12, and 24. This suggests that the reduction of ELR may reflect the improvement of clinical signs and itch by upadacitinib. The results also indicate that eosinophils may play key roles in the rash and itch of AD and may be the target of upadacitinib treatment. Eosinophils in AD skin lesions secrete granules, such as major basic protein (MBP), eosinophil peroxidase (EPO), and eosinophil cationic protein (ECP) . These gIn the upper dermis of AD lesions, eosinophils are in close proximity to substance P+ nerve fibers and are abundantly producing brain-derived neurotrophic factor (BDNF). The eosinophil-derived BDNF promotes the growth of sensory nerves and induces chemotaxis, and prolongs the survival of eosinophils in an autocrine manner . ConversThe change of NLR, MLR, and PLR did not correlate with that of EASI or PP-NRS in upadacitinib treatment, except for the transient correlation . GranuloOlder age was associated with a high percent reduction of PP-NRS at weeks 4 and 12, indicating that older age may predict better improvement of itch by upadacitinib. This may possibly be because older patients might show reduced type 2 activities compared to younger patients ; in adulHigh baseline PP-NRS was associated with a high percent reduction of PP-NRS at weeks 4 and 24, indicating that high baseline PP-NRS may predict better improvement of itch by upadacitinib. This is possibly because patients with higher PP-NRS values before treatment may have more room to be improved by upadacitinib compared to the patients with lower PP-NRS. A similar trend can be seen in patients with psoriasis; patients with higher pretreatment PASI result in a higher percent reduction of PASI by treatment with biologics .Female sex was associated with a high percent reduction of EASI at week 4, indicating that female patients may predict better improvement of rash by upadacitinib than male patients. However, the bias caused by the male-preponderance (71% of the patients) and small sample size cannot be denied in this study. The sex difference in the response to upadacitinib treatment should be further examined strictly in larger cohorts with a uniform assignment of sexes.This study has several limitations. First, this study evaluated the efficacy of only 15 mg/day of upadacitinib. The results for 30 mg/day upadacitinib should be further examined. Second, this study is a retrospective one with a small sample size. Third, this study included patients treated with oral upadacitinib plus topical corticosteroids, but not those with oral upadacitinib alone or with topical corticosteroids alone. Therefore, it is unclear whether upadacitinib plus topical corticosteroids additively or synergistically improved clinical signs and itch. Further prospective study with a larger cohort should compare the therapeutic effects of upadacitinib alone, topical corticosteroids alone, and both treatments.Before treatment, ELR, NLR, MLR, and PLR positively correlated with EASI, while NLR and PLR positively correlated with PP-NRS. After upadacitinib treatment, ELR and NLR remarkably decreased throughout the treatment period from weeks 4 to 24 in parallel with EASI and PP-NRS, while the decrease of MLR and PLR at week 4 was transient. The percent reduction of ELR significantly correlated with those of both EASI and PP-NRS at weeks 4, 12, and 24 of upadacitinib treatment. Linear multivariate regression analyses revealed that female sex was associated with a high percent reduction of EASI at week 4; older age was associated with a high percent reduction of PP-NRS at weeks 4 and 12; and high baseline PP-NRS was associated with a high percent reduction of PP-NRS at weeks 4 and 24. ELR may act as an indicator for the improvement of clinical signs and itch by upadacitinib treatment for AD.


Background: Evidence supporting the use of steroid pulse therapy in severely ill patients with coronavirus disease 2019 (COVID-19) pneumonia is lacking. Few studies have evaluated the efficacy of high-dose (1000 mg/day) methylprednisolone (mPSL), which is commonly used in Japan.Aim: This study aimed to compare the clinical outcomes with and without steroid pulse therapy (mPSL 1000 or 500 mg/day for three days) in patients with COVID-19 pneumonia, admitted to an intensive care unit (ICU).Methods: Study design was retrospective observational study. The inclusion criterion was severe to critically ill adult patients with COVID-19 pneumonia requiring ICU admission. The exclusion criteria were as follows: patients (1) with a “Do not attempt to resuscitate” order; (2) with a “Do not intubate” order; or (3) admitted to the ICU owing to other infectious diseases were excluded. Treatment strategy was as follows: Patients were divided into two groups: steroid pulse therapy (Group P) and steroids without pulse therapy (Group NP). Group P received mPSL 1000 or 500 mg/day on ICU days 1-3, and Group NP received dexamethasone 6.6 mg or mPSL 1 or 2 mg/kg/day. The primary outcome was 28-day mortality.Results: We enrolled 82 patients. Out of 70 who met the inclusion criteria, 48 and 22 were included in Groups P and NP, respectively. No difference in 28-day survival was observed between the Groups P and NP (log-rank P=0.11). After adjusting for covariates  using a multivariate Cox proportional hazard model, treatment with steroid pulse therapy significantly improved 28-day mortality .Conclusion: Steroid pulse therapy may improve the 28-day mortality in patients with COVID-19 pneumonia in the ICU. Guidelines for the treatment of coronavirus disease 2019 (COVID-19) pneumonia have been significantly revised over the past few years . HoweverThe Japanese COVID-19 guidelines  do not pRespiratory failure requiring intensive care unit (ICU) admission includes not only patients requiring intubation but also patients receiving noninvasive respiratory support, such as nasal high-flow oxygen and patients receiving high-concentration oxygen. Therefore, this study aimed to compare the clinical outcomes of patients with COVID-19 pneumonia admitted to an ICU between those treated with steroid pulse therapy and those treated without pulse therapy (non-pulse therapy).Study design and settingThis is a single-center, retrospective, observational study. This study was approved by the Ethics Committees of the National Hospital Organization Yokohama Medical Center (No. 2021-14), where this study was conducted, and International University School of Health and Welfare Graduate School of Public Health (No. 21-Ig-216).PatientsAll adult patients with COVID-19 admitted to the ICU at the National Hospital Organization Yokohama Medical Center between April 1, 2020, and September 15, 2021, were enrolled.Inclusion criteriaThe inclusion criteria were as follows: 1) age ≥18 years with a (2) microbiologically confirmed COVID-19 diagnosis and respiratory failure  < 300 or oxygen saturation < 90%) on admission to the ICU. We included patients with "Severe" and "Critical" illness according to the WHO severity classification [ age ≥18 Diagnosis of COVID-19Detection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in nasopharyngeal or oropharyngeal swabs using polymerase chain reaction testing  was considered as microbiologically confirmed COVID-19.Diagnosis of COVID-19 pneumoniaCOVID-19 pneumonia was defined as a confirmed COVID-19 infection with pneumonia on computed tomography (CT).Exclusion criteriaIndividuals were excluded if they were patients: (1) with a “Do not attempt to resuscitate” order, (2) with a “Do not intubate” order, or (3) admitted to the ICU owing to other infectious diseases.Treatment strategyAfter assessment by several intensivists, patients with type H or L CT images  for quantitative data, and numbers and percentages for categorical data. Two-sided Chi-square and Fisher’s exact tests were used to assess the associations between the intervention groups and categorical variables. The Mann-Whitney U test was used to compare continuous variables between the groups. The Kaplan-Meier survival curve was used to estimate the time to death in the intervention groups. The treatment effects in both groups were evaluated using the log-rank test and multivariate Cox proportional hazard model and expressed as hazard ratios (HRs) with 95% confidence intervals (CIs), as appropriate. We performed multiple imputation analyses for missing values . All staPatient characteristicsWe enrolled 82 patients with COVID-19 pneumonia, who required ICU admission. Of these, 70 patients met our inclusion criteria. The patients were treated with steroid pulse therapy  or steroids without pulse therapy  . Figure .Secondary outcomeNo significant increase in steroid-associated complications was observed during the 28-day follow-up period Table .Our findings suggest that steroid pulse therapy may be effective in patients admitted to the ICU with COVID-19 pneumonia. Our results also suggest that higher IL-6 levels may be associated with increased mortality.An increasingly critical issue in COVID-19 treatment is how to manage cytokines. Cytokine release syndrome, characterized by increased levels of cytokines (such as IL-6), can cause or worsen acute respiratory distress syndrome (ARDS) and multiple organ failure . ExpensiSteroids are less expensive and are more widely used than monoclonal antibodies. Furthermore, the high mortality rate in patients with severe COVID-19 can be explained by the rapid development of organized pneumonia secondary to SARS-CoV-2 infection . This coThere is ample experience in the treatment of interstitial pneumonia with steroid pulse therapy such as mPSL 1000 mg/day. The Japanese guidelines do not recommend steroid pulse therapy for patients with ARDS ; howeverTwo studies ,8 reportTo improve survival with steroid pulse therapy, it is important to avoid the complications caused by steroid administration. An increased risk of secondary infections has been reported in studies on steroid pulse therapy for patients with early ARDS . HoweverHence, steroid pulse therapy may be effective in the treatment of severe-to-critical COVID-19 pneumonia in selected patients. We hope that future studies will further clarify the patient groups for whom steroid pulse therapy is effective.LimitationsFirst, the single-center design and short data collection period resulted in a relatively small sample size, which might have influenced the accuracy of our findings. Moreover, the sample size may have been insufficient for the multiple regression analysis. We assumed that the APACHE II score was not a significant predictor of mortality because of small sample size. Further studies with larger sample sizes are required to improve our understanding. Second, there may be many confounding factors between Groups P and NP that have not been examined here, and randomized controlled trials adjusted for patient background are warranted. Third, tracheal intubation was performed based on the judgment of the individual physician. Observational studies using strict intubation criteria are required. Fourth, infectious complications are probably an important factor affecting mortality but were not considered in this study owing to difficulties in defining them. Therefore, improved study designs focusing on infectious complications and mortality associated with steroid pulse therapy are warranted. Fifth, our results may not reflect the effect of steroids alone because the drugs used, in addition to steroids, were changed during the data collection period. Not only the steroid dose but also the combination of drugs, such as antiviral drugs, may have affected the prognosis of the patients. Sixth, the SARS-CoV-2 strain during the data collection period was mainly B.1.617.2 . HoweverSteroid pulse therapy may improve the 28-day mortality in patients admitted to the ICU with COVID-19 pneumonia. However, the safety and efficacy of steroid pulse therapy need to be demonstrated using RCTs or big data analysis.


COVID-19 policy responses have included mobility restrictions, and many people have chosen to stay at home to avoid exposure. These actions have ambiguous impacts on food prices, lowering demand for food away from home and perishables, while increasing supply costs for items where workers are most affected by the pandemic. We use evidence from 160 countries to identify the net direction and magnitude of association between countries' real cost of all food and mobility restriction stringency. We investigate the deviation of each month's price level in 2020 from that month's average price level during the previous three years and find that an increase in mobility restriction stringency from no restrictions to most restrictive is associated with an increase in the real cost of all food of more than one percentage point across all models. We then examine the relationship between retail food price levels by food group and stay-at-home behaviour around markets in 36 countries and find positive associations for non-perishables, dairy and eggs. •Worldwide retail prices rose more for food than all consumer goods during COVID.•We find larger food price rises at times and places with more stringent restrictions.•For non-perishables we find prices were higher when more people stayed home.•For fruits and vegetables we find prices were lower when more people stayed home.•New retail price data can help track response to shocks in global food systems. Global monitoring and response has focused on the supply of agricultural commodities traded on international markets , while iFocusing on consumer prices of retail food items, instead of the traditional focus on prices of farm commodities, is particularly important now due to disease impacts on consumers and food system workers in processing and distribution. Policies and behavioral changes designed to restrict transmission could raise costs along the supply chain in tasks that involve face-to-face contact among workers, while lowering demand for retail items and services that involve face-to-face contact with consumers.To quantify the nature and magnitude of policy restrictions we use the Oxford COVID-19 Government Response Tracker (OxCGRT) , which iGovernments' mobility restrictions and individuals’ stay-at-home behaviour, as well as the disease itself, could affect supply chains and retail prices through impact on workers or through consumer demand for different types of food . Some diWhile the available evidence suggests that food supplies were largely maintained during the period covered in our analysis , the altA small number of studies focusing on specific countries or groups of countries has begun to examine the relationship between COVID-19 mobility restriction policies and retail food prices, though none provide a global perspective . This stThis study provides a first global assessment of how retail consumer prices differ during periods of more stringent mobility restriction policies, and at places and times with more stay-at-home behavior, when controlling for disease case counts and other factors. Previous work on global retail food prices has shown higher prices associated with higher COVID-19 case counts . Adding 22.1Our national-level analysis combines: 1) national-level monthly CPI data, for all items and for all food and beverage items, 2) national-level monthly mobility restriction policy stringency data, and 3) national-level monthly data on COVID-19 cases and deaths per million. These data are described in detail below.2.1.1Our analysis uses monthly national overall consumer price indices (CPI) and its sub-index for food and non-alcoholic beverages (FCPI) for 160 countries from January 2017 to December 2020, as reported by each country's national statistical organization to the United Nations Statistics Division and the International Monetary Fund , and com2.1.2Data on the stringency of government policies to restrict mobility were obtained on April 28, 2021 from the Oxford COVID-19 Government Response Tracker Stringency Index. That composite metric ranges from 0 (no restriction) to 100 (most restrictive), constructed by researchers in the Blavatnik School of Government from nine sub-indices reflecting 1) School closures; 2) Workplace closures; 3) Cancelled public events; 4) Restrictions on gatherings; 5) Public transport closures; 6) Public information campaigns; 7) Stay-at-home measures; 8) Restrictions on internal movement; and 9) International travel controls  monthly retail item price data collected from markets monitored by early warning systems, 2) admin 1-level monthly stay-at-home behavior data, and 3) national-level monthly data on COVID-19 cases and deaths per million. These data are described in detail below.2.2.12.2.2 below). We then calculate month-to-month percentage point change in price level for each food item in each market. Food items were grouped into one of eight categories  based on an adapted version of the COICOP classification system, detailed in Monthly food item price data were obtained from EWS databases published by three different international agencies, including the FAO Global Information and Early Warning System (GIEWS), the Famine Early Warning System Network (FEWS NET) funded by the United States Agency for International Development (USAID), and the Vulnerability Analysis and Mapping (VAM) system from the World Food Programme (WFP). We use monthly retail item price data from markets in 36 countries from January 2020 to December 2020. Each price for each food item in each market and month was normalized to the baseline period of January 2020 to transform the data from local currencies to unitless price levels that can be compared across countries. We use a baseline period of January 2020 in order to align as closely as possible with the pre-COVID-19 baseline period used by Google's COVID-19 Community Mobility Trends data  at Johns Hopkins University and provides better coverage of the countries included in our market-level analysis. COVID-19 case counts and mortality data were used to calculate COVID-19 cases per million per month and COVID-19 mortality per million per month for each country and were matched to market locations with EWS retail food item price data.2.3In our national-level analysis, we compiled all available consumer price index data from the years 2017–2020, COVID-19 mobility restriction stringency data from the year 2020, and COVID-19 case and mortality data from the year 2020. An initial set of 168 countries had consumer price index data, stringency data, and COVID-19 case and mortality data, from which we excluded 8 countries due to anomalous outlier values of consumer price indices. Our exclusion criterion was having one or more observations beyond 15 times the interquartile range. Excluded countries were Argentina, Iran, Lebanon, South Sudan, Sudan, Syria, Venezuela, and Zimbabwe, for which descriptive statistics are provided in Our market-level analysis includes all EWS monthly retail item price data from the year 2020, in markets that can be matched to admin-1 level stay-at-home behavior data and national-level COVID-19 case and mortality data. We used the winsor2 Stata package to trim the top and bottom 0.5 percent of price observations within each food group category to remove extreme values likely due to data entry errors. This resulted in a final sample of 68,167 price observations from 395 markets, shown in panel B of 2.4Stata SE/17.0 was used for statistical analysis and visualizations; Datawrapper was used to create maps.2.4.1P represents the outcome of interest, which is the price level in each country, i, and month, t, of calendar year 2020 relative to the same month in the three previous years (2017–2019). Data are percentage point changes, to show for example that price levels in June 2020 were 11 percentage points above the average of June 2017, June 2018 and June 2019. We test for price differences using three different price levels, first for all food and beverages in each country (FCPI), then for all goods and services of any type (CPI), and then the real food price level relative to all other things (FPI). Our model includes terms for COVID-19 cases per million and mortality per million as proxies to control for pandemic severity in each country and month. COVID-19 case and mortality data were log(x+1)-transformed to reduce skewedness in their distributions. Our model includes country fixed effects and country time trends, and uses robust standard errors clustered at the country level to account for heteroskedasticity. In alternative specifications, we test the robustness of our results to the inclusion of one period lag terms for mobility restriction stringency, COVID-19 cases, and COVID-19 mortality. All data sources and variables included in our models are summarized in To measure the relationship between stringency in COVID-19 mobility restrictions and the deviation of each month's food price level from the average 2017–2019 price level in the same country and month, we use OLS regression with the following baseline specification:2.4.2j) at each market location (i) and month (t):Pjit– Pji(t-1) represents the percentage point change in retail price level between month t and the previous month, t-1, for each food item (j) at every market (i) for which data are available. The term StayAtHomeBehaviorit - StayAtHomeBehaviori(t-1) represents the percentage point change in stay-at-home behaviour between month t and the previous month, t-1. FGj represents a categorical variable that assigns each food item to one of eight different food groups, using breads and cereals as the reference category. The coefficient, 3, on the interaction term is the effect of interest and represents the effect of month-to-month change in stay-at-home behaviour on month-to-month change in retail prices for a given food group, compared to the reference change in price level for breads and cereals. X'it represents a vector of time-varying factors including confirmed new COVID-19 cases and mortality per month per million, which were log(x + 1)-transformed to minimize skewedness in their distributions. We also include fixed effects for each market location, To measure the correlation between retail food prices and stay-at-home behavior, we use OLS regression with the following specification for each food item  in 2020 from that month's average price level during the previous three years (2017–2019).  omitted . Interes omitted . All spe3.23.2.1Next, we look in detail at stay-at-home behavior and retail food prices by food group. Panel A of Panel B of In our market-level analysis, we investigated the relationship between month-to-month change in retail food price levels by food group and month-to-month change in stay-at-home behavior using OLS regression with market fixed effects, month fixed effects, and country time trends. Predictive margins from our regressions, controlling for COVID-19 cases and mortality, are shown in panel A of We additionally test the relationship between month-to-month change in retail food price levels by food group and month-to-month change in mobility restriction stringency in the same sample of 36 countries .This work was supported by the The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:William A. Masters reports financial support and article publishing charges were provided by Bill & Melinda Gates Foundation through the Food Prices for Nutrition Project (INV-016158), with additional financial support from the Foreign Commonwealth & Development Office of the United Kingdom.


What is the period prevalence and incidence of SARS-CoV-2 infection among people experiencing homelessness in Toronto, Canada?In this prospective cohort study of 736 people experiencing homelessness in Toronto, 30% of individuals had a history of infection by summer 2021 and a further 30% experienced incident infection within 6 months. Incident infection was significantly associated with reporting after the SARS-CoV-2 Omicron variant became dominant, recent immigration to Canada, and recent alcohol consumption.In this study, people experiencing homelessness in Toronto had high SARS-CoV-2 incident infection rates. This cohort study examines the SARS-CoV-2 incident infection rate among people experiencing homelessness in Toronto, Canada, in 2021 and 2022 and assesses factors associated with incident infection. People experiencing homelessness are at high risk of SARS-CoV-2 infection. Incident infection rates have yet to be established in these communities and are needed to inform infection prevention guidance and related interventions.To quantify the SARS-CoV-2 incident infection rate among people experiencing homelessness in Toronto, Canada, in 2021 and 2022 and to assess factors associated with incident infection.This prospective cohort study was conducted among individuals aged 16 years and older who were randomly selected between June and September 2021 from 61 homeless shelters, temporary distancing hotels, and encampments in Toronto, Canada.Self-reported housing characteristics, such as number sharing living space.Prevalence of prior SARS-CoV-2 infection in summer 2021, defined as self-reported or polymerase chain reaction (PCR)– or serology-confirmed evidence of infection at or before the baseline interview, and SARS-CoV-2 incident infection, defined as self-reported or PCR- or serology-confirmed infection among participants without history of infection at baseline. Factors associated with infection were assessed using modified Poisson regression with generalized estimating equations.The 736 participants  had a mean (SD) age of 46.1 (14.6) years; 486 (66.0%) self-identified as male. Of these, 224  had a history of SARS-CoV-2 infection by summer 2021. Of the remaining 415 participants with follow-up, 124 experienced infection within 6 months, representing an incident infection rate of 29.9% , or 5.8%  per person-month. Report after onset of the SARS-CoV-2 Omicron variant was associated with incident infection, with an adjusted rate ratio (aRR) of 6.28 . Other factors associated with incident infection included recent immigration to Canada  and alcohol consumption over the past interval . Self-reported housing characteristics were not significantly associated with incident infection.In this longitudinal study of people experiencing homelessness in Toronto, SARS-CoV-2 incident infection rates were high in 2021 and 2022, particularly once the Omicron variant became dominant in the region. Increased focus on homelessness prevention is needed to more effectively and equitably protect these communities. These studies similarly show wide variability in individual-, network-, and system-level factors associated with infection, suggesting these too may be context-specific.To date, seroprevalence estimates from studies describing SARS-CoV-2 infection rates among people experiencing homelessnessCurrently, there has been no estimate of the rate of prior SARS-CoV-2 infection among people experiencing homelessness since the emergence of the Omicron variants and no estimates of incident infection (incidence among people without prior history of infection). Thus, in the present study we report, among a random sample of people experiencing homelessness in Toronto, Canada, the period prevalence of SARS-CoV-2 at baseline and incident SARS-CoV-2 infection over 6 months. We also examine characteristics associated with incident infection by 6 months.19This longitudinal analysis uses data collected between June 2021 and April 2022 from participants of the Ku-gaa-gii pimitizi-win study, a prospective cohort study of people experiencing homelessness in Toronto, a city on Treaty 13 territory in Canada. Ku-gaa-gii pimitizi-win, which translates in English to “life is always/forever moving,” is a spirit name given in ceremony by Elder Dylan Courchene from Anishnawbe Health Toronto to reflect and honor the movement of homeless individuals across the land, the spirit and growth of the land we are on, and the force that connects us all to the future. The Ku-gaa-gii pimitizi-win protocol is available elsewhere.STROBE) guidelines and received ethics approval from the Research Ethics Board at Unity Health Toronto. All participants provided written informed consent.This study follows the Strengthening the Reporting of Observational Studies in Epidemiology  or on the streets.21 At the pandemic’s onset, a series of infection prevention strategies were implemented to protect individuals experiencing homelessness in Toronto, including enhanced infection prevention and control funding, routine screening and testing at shelters, opening infection recovery sites with medical supports, and moving thousands of individuals to temporary shelters  to support physical distancing.22Approximately 18 000 individuals experienced homelessness in Toronto in 2021.23 Viral activity was very low over the summer, with slow increases in the fall. However, by December 2021 the Omicron variant BA.1 replaced Delta, increasing from less than 1% to greater than 95% of infections over the month.24 Omicron variants BA.1, then BA.2, predominated in a large wave of activity from January to March of 2022.23At the time of recruitment (June to September 2021) the SARS-CoV-2 Delta variant (B.1.617.2) was rapidly replacing the Alpha variant (B.1.1.7) in infections in Toronto.19 After ascertaining history of SARS-CoV-2 at baseline, participants with a history of infection at baseline were excluded from the remainder of this analysis.Individuals were recruited by random number schedule assigned to beds or rooms of 61 participating shelters and physical distancing hotels from June 16 to September 9, 2021; participants were also recruited from 1 urban encampment. To be eligible, individuals had to be experiencing homelessness; not yet be recruited into the study; be aged at least 16 years old; be willing to conduct follow-up interviews; and provide informed consent. Recruited participants were recontacted, through contact information or personal contacts provided at baseline, for follow-up at 3 months (±45 days) between September 15, 2021, and January 10, 2022, and at 6 months (±45 days) between November 17, 2021, and April 13, 2022. Additional information regarding recruitment procedure, follow-up procedure, and sample size calculation details are provided in our published protocol.26 Participants also provided a recent housing history, which was used to create housing-related exposure variables believed to increase risk for infection, such as number of moves during an interval, average number of people who shared participant living space, and proportion of time spent in various housing types. The survey was reviewed with community partners and piloted with individuals having lived experience of homelessness to confirm face validity and ensure questions did not cause discomfort or harm.Participants completed a detailed survey at baseline and follow-up intervals, detailing sociodemographic information , history of known SARS-CoV-2 infection, and activities and behaviors related to COVID-19  or that have been shown in literature to increase risk for infection .27 for evidence of current SARS-CoV-2 infection; and (2) a blood sample (plasma tube [BD 365985] or as a dried blood spot [Whatman 903]), to determine by enzyme-linked immunosorbent assay28 the presence of past SARS-CoV-2 infection or vaccination-related antibodies . In an initial validation, the combined assays had a sensitivity of 91% and specificity close to 100% for plasma or serum.28 A full description of variables used in this study is available in eAppendix 1 in Participants further provided at each interval (1) a saliva sample (swish and gargle method), tested using standard quantitative reverse transcription–polymerase chain reaction (RT-qPCR)7 and anti–SARS-CoV-2 antibodies may decay over time.29 As further detailed in eAppendix 2 in 28Our 2 outcomes of interest were (1) period prevalence of prior SARS-CoV-2 infection at baseline, defined as the number of participants with evidence of current or prior SARS-CoV-2 infection at baseline over the number of participants overall, and (2) incident SARS-CoV-2 infection by 6 months, defined as SARS-CoV-2 infection any time up to and including active infection during the 6-month interview among participants without history of infection at baseline. We ascertained infection through a combination of self-report and biological samples, as people routinely experience infection without their knowledge30 Because our outcome is adjudicated using combined biological and self-report data, we did not adjust for test characteristics; as the tests have high sensitivity and specificity, this would only result in a slight underestimate of rates.31 Instead, 95% CIs for rates were calculated using the Wilson Score method for proportions.We provided period prevalence of prior SARS-CoV-2 infection at baseline, incident infection rate, and incident infection rate per person-month  overall and by reporting period (before and after Omicron variants became dominant). When participants seroconverted without a positive PCR test, observation time was stopped at a randomly assigned day between the first and last days of the interval.We also compared participants uninfected at baseline by incident infection status at 6 months. Because housing and behavioral characteristics were assessed at each interval and could thus vary, unadjusted and adjusted rate ratios  with 95% CIs were assessed on interval-level data using univariable and multivariable modified Poisson regression with generalized estimating equations to account for the correlated nature of repeated measures, offset by the log of person-months of observation time.12; gender14; alcohol use25) were considered for inclusion in the multivariable model. We further considered housing-related exposure variables as well as interviews occurring after Omicron variants became dominant to account for the large wave of infections and outbreaks that occurred in that period.23 Correlation coefficients were estimated using variance inflation factor and polychoric correlation, with coefficients greater than 2.5 and 0.4, respectively, flagged for further review prior to modeling. As missingness was nonexistent in the outcome and very low (<1%) across all other factors, we provide complete-case analysis results, as multiple imputation had inconsequential influence on results.Factors with significant differences in univariable models or associated with infection in existing literature  was considered significant. We did not adjust for multiple comparisons, following recommended guidelines.32We conducted all analyses using SAS version 7.1 (SAS Institute). Throughout, Of 2643 randomly selected beds, rooms, or tents, individuals were unavailable (n = 1098) or not present (n = 443) in 1553 instances . A furthOf the remaining 512 participants eligible for this analysis, 96 were lost to follow-up and 1 participant had no useable outcome data. eAppendix 3B in Characteristics of participants are presented in eAppendix 4 in Incident infection over the period and by interval are presented in 18 but these reports are not comparable with ours, as they reflect earlier periods of the pandemic as well as differing local epidemiology and social and policy contexts.Nearly one-third of our cohort had prior history of SARS-CoV-2 infection by summer 2021 and an additional 29.9% became infected for the first time during 3- or 6-month follow-up. In total, at least 348 participants out of 736 (or >47% of the cohort) had a history of SARS-CoV-2 infection by spring 2022. In other seroprevalence studies conducted within populations experiencing homelessness, rates range widely, from 4.7% in Denmark to 69.8% in France,33 and another report from Canadian Blood Services which estimated seroprevalence in Ontario at 4.0% in November 2021, increasing to 34.9% by April 2022.34 Additionally, unpublished data from 2 ongoing Toronto-based studies involving education35 and health care36 workers  found that 17.8% and 16.2% of uninfected participants became infected between September 2021 and April 2022 . If we assume the populations assessed in these reports are not substantially different than Toronto residents generally, we can infer that while incidence rates between people experiencing homelessness and the general population may have become more similar after Omicron variants became dominant,33 the overall rate of SARS-CoV-2 incident infection is higher among people experiencing homelessness than among people with stable housing in the region.36The 2 most applicable, published comparison estimates (both measured using serologic assay data in the broader population) include 1 Canadian report covering the first Omicron wave, which estimated 6-month incident infection at 30% ,37 to protect people experiencing homelessness from COVID-19. These interventions included improving infection prevention and control; implementing screening, testing, and vaccination at shelters; opening recovery sites with medical supports; and moving individuals to physical distancing hotels to reduce crowding and support distancing at shelters.22 While commendable, these measures focused on mitigation strategies rather than the second series of recommendations targeting upstream factors: namely, decreasing the prevalence of homelessness through appropriately scaled housing strategies. Our results, gleaned from a population benefiting from substantial mitigation efforts, support the conclusion that homelessness may be an independent risk factor for COVID-19 (and future respiratory pandemics), separate from housing-related conditions common to homelessness that are believed to correlate with COVID-19 infection risk.The City of Toronto, with prompting by advocates, made significant efforts to implement recommendations38 and elsewhere.39 These studies attribute this finding to the disproportionate representation of immigrants in high-exposure work settings and overcrowded living environments.39 However, in our study, our closest proxy for overcrowding (average number sharing living space) was not significantly associated with incident infection, and although recent immigrants were more likely to be working in the past interval, work status was not associated with incident infection either. Our findings suggest the reason for the association between recent immigration and risk for infection requires more evaluation: it is possible there is a need to further incorporate modified approaches to mitigation efforts in the shelter and hotel system; it is also possible recent immigration status is correlated with other, unmeasured factors.Our findings also show that incident infection was higher among individuals with a recent history of immigration to Canada. Similar associations were found in population studies, with recent immigrants having higher SARS-CoV-2 infection rates in Canada26 It has been proposed that because alcohol induces cognitive changes, such changes may lead to exposures that increase risk for SARS-CoV-2 transmission.26 At the same time, alcohol alters biological susceptibility by impairing immune response.26 It is unclear from our study whether either or both mechanisms explain this finding or whether alcohol consumption is simply associated with other, unmeasured risk factors.Alcohol consumption was also associated with incident infection in our study and has previously been linked with infectious disease susceptibility in the broader population, including for SARS-CoV-2.18 average number of people sharing living space,11 duration of stay in emergency shelters (both congregate and noncongregate),18 and stay in physical distancing hotels.40 Although associations all demonstrated the expected direction (with greater shared or exposed settings having higher rates of incident infection and vice versa), and the proportion of time residing in noncongregate shelters was significantly associated with reduced risk of infection in unadjusted analyses, these were not statistically significant in adjusted analyses. In part, the within-group nature of this analysis (as opposed to directly comparing housing-related factors among people experiencing homelessness as well as those not experiencing homelessness) may have contributed to this result. It is possible exposure summarized over the interval (vs isolating exposure immediately preceding infection events) lessened the effects of these exposures or that our sample was insufficiently powered to identify significant differences, having already excluded 30% of our cohort due to history of infection at baseline. Finally, it is possible that incident infection, as opposed to any occurrence of infection , is unrelated to housing-related exposure because individuals who would have become infected due to housing-related factors already had history of infection by the baseline interview.Finally, we assessed housing-related factors associated with SARS-CoV-2 infection in other studies involving people experiencing homelessness. These factors included average number of contacts per day,21; thus, our results approximate, but are not fully representative of, the experience of people experiencing homelessness in Toronto. Furthermore, survey and housing data were self-reported; while reports occurred every 3 months to help prevent issues of increasing unreliability over time, self-report data can suffer from social desirability bias, particularly among populations facing significant stigma.41 We also note that the reliance on a single antibody measurement (NP) for evidence of infection in vaccinated individuals may have led to underestimating infections, as sensitivity of NP as a single antigen at the selected cutoff was 79%.28This study has limitations. While we randomly recruited from numerous sites across Toronto as well as from one encampment, we were unable to sample from the street or other settings. Approximately 10% of people experiencing homelessness in Toronto are unsheltered42; they are also believed to occur more frequently among those experiencing homelessness.43 Future work should investigate full infection occurrence rates through combined incident infection and reinfection rates as well as assess the impact of incident infection and reinfection on adverse health outcomes, hospitalization, and mortality.Additionally, while we provide actionable data about SARS-CoV-2 infection among people experiencing homelessness in Toronto, we could not describe reinfections. Reinfection contributes nontrivial additional risks for adverse outcomes, regardless of vaccinationFurthermore, our recruitment included individuals with a baseline history of infection. While this allowed us to measure period prevalence at baseline, we were obliged to exclude these individuals from the incident infection analysis, which may have created a nonrepresentative incident cohort. A fully random incident cohort might have generated higher rates. A further 97 individuals lost to follow-up were younger, less adherent to public health measures, and spent less time in physical distancing hotels than average (eAppendix 3B in In this study, people experiencing homelessness in Toronto in 2021 and 2022 had elevated SARS-CoV-2 incident infection rates, potentially reflecting upstream structural risks that make unhoused individuals vulnerable to infection compared with housed counterparts. Among people experiencing homelessness, immigration status and alcohol consumption were associated with higher incident infection by 6 months, suggesting a possible need for modified approaches to infection mitigation efforts in shelters and hotels.


Below-average gross-motor development (BSID-III gross-motor score < 8) was associated with duration of NABE . The aOR for constipation was 0.81 (p = 0.04) per gestational week. Growth was inversely correlated with GA. Respiratory and atopic symptoms were not associated with NABE, nor GA. We observed that prolonged NABE after preterm birth was associated with below-average gross-motor development at 24 months’ CA, while a low GA was associated with lower weight and stature Z-scores and higher odds for constipation.Over 90% of preterm neonates are, often empirically, exposed to antibiotics as a potentially life-saving measure against sepsis. Long-term outcome in association with antibiotic exposure (NABE) has insufficiently been studied after preterm birth. We investigated the association of NABE-duration with early-childhood developmental and health outcomes in preterm-born children and additionally assessed the impact of GA on outcomes. Preterm children (GA < 30 weeks) participating in a multicenter cohort study were approached for follow-up. General expert-reviewed health questionnaires on respiratory, atopic and gastrointestinal symptoms were sent to parents of children > 24 months’ corrected age (CA). Growth and developmental assessments  III) were part of standard care assessment at 24 months’ CA. Uni- and multivariate regressions were performed with NABE (per 5 days) and GA (per week) as independent variables. Odds ratios (OR) for health outcomes were adjusted (aOR) for confounders, where appropriate. Of 1079 infants whose parents were approached, 347 (32%) responded at a mean age of 4.6 years (SD 0.9). In children with NABE (97%), NABE duration decreased by 1.6 days ( Approximately 90% of very-low-birth-weight infants are, often empirically, exposed to antibiotics within the first weeks of life ,2,3. SinThere is an additional increasing concern because of putative long-term adverse health effects correlated to antibiotics ,10. A loIn preterm infants, however, the low sensitivity of blood cultures and fear of the consequences of missed infection leads to prolongation of the antibiotics course ,15. ProlBifidobacteria and Bacteroides species .,30.29,30BSID-III Cognitive and Motor scores were classified as below-average below 90, as described in the Dutch BSID-III scoring manual and recently supported by Månsson et al. ,33. The Statistical analyses were conducted using the Statistical Package for Social Sciences (SPSS) version 26.0 . First, baseline perinatal and neonatal characteristics were compared between a representative sample of children included in the original neonatal cohort study and those participating in the current follow-up study. Secondly, baseline clinical and demographic characteristics were depicted as number and percentage for discrete variables and as mean/median and standard deviation (SD)/interquartile range (IQR) for continuous variables.p-value < 0.05). Variables that were tested as confounders are depicted in a direct acyclic graph (The association of the duration of NABE (continuous variable) with the main outcomes was assessed by uni- and multivariate linear and logistic regression. All regression analyses were first performed uncorrected and then corrected for relevant confounding. Confounders were defined as variables significantly associated with both the independent and dependent variables . Results from the logistic regression were reported as odds ratios (OR) and adjusted OR (aOR), along with the respective 95% CI. As a secondary objective, GA (per 7 days) was analyzed as a risk factor for poor childhood health outcome analogically to NABE. Of 1190 infants (GA at birth < 30 weeks), born between October 2014 and July 2019 in the eight participating centers, parents of 1079 children were approached. Completed questionnaires and informed consent forms were received from parents of 347 (32%) children; the children had a median CA of 53 months , were mop < 0.001).Baseline characteristics of the group of responders are depicted in Mean weight and stature at 24 months’ CA were 12 kg and 87 cm, respectively . Weight p = 0.04) (p = 0.03) was seen with decreasing gestational age at birth, but not after correction for confounding (p > 0.10), as depicted in Median BSID-III cognitive and motor scores were within normal range  . BSID co = 0.04) . An incrp = 0.04), independent from confounding (Forty-seven (14%) children were reported to meet at least two ROME IV criteria for functional constipation or being treated for constipation. There was no association between NABE and pediatric constipation. Odds for constipation decreased with increasing gestational age  reported to have ever sought medical attention  for their child due to acute lower respiratory tract symptoms. Sixty-five children (19%) were reported to have ever been hospitalized due to lower respiratory tract disease . Thirty-Fifty-five (17%) children were suspected of food allergies by their parents, of which nine (3%) were reported to be confirmed by a health care professional. Forty-three (14%) children were reported to have had symptoms of allergic rhinoconjunctivitis over the past year, and thirty-five (10%) were reported to have ever had atopic dermatitis. None of the respiratory and skin symptoms were correlated with NABE, nor GA .In this multicenter cohort study in infants born preterm (<30 weeks GA), we aimed at investigating the association of NABE with developmental and health outcomes after 24 months of corrected age. Almost all infants (97%) were exposed to antibiotics, with 38% being exposed for at least 14 days within the first 28 days of life. The duration of NABE was found to be associated with below-average BSID-III gross motor scores (<8) at 24 months’ CA. In line with our results, motor skills are shown to be impaired in adult mice after administration of antibiotics ,40. It iNABE has, furthermore, been suggested to affect adult cognitive development in term-born mice . In contPediatric constipation, respiratory and atopic symptoms were not associated with NABE. In contrast, in term-born children, chronic lower respiratory symptoms due to asthma and other atopic conditions have been correlated to early-life antibiotic exposure ,47. ThisAnthropometric parameters were only associated with GA and not NABE. Lower gestational age was associated with lower GA-normalized Z-scores for weight at 2 years’ CA, in line with a recent report comparing preterm- vs. term-born children ,50. InteFuture studies integrating clinical and early-life microbiota data are warranted to validate our results and test the hypothesized mechanisms of action of NABE and GA on health. Large observational studies might identify potential microbial signatures associated with disease. This could lead to new basic research opportunities and the design of preventative and therapeutic strategies. This study has several strengths. It contributes to mapping health problems after preterm birth in relation to early antibiotic exposure and GA, assessing relatively large numbers of preterm born infants. We followed preterm children longitudinally, and detailed information on daily base was collected on antibiotic use and indication of antibiotic administration in the first month of life. Growth and developmental scores were assessed by trained professionals. The survey was based on existing validated questionnaires, including the ISCAAC questionnaires . In addiThere are, however, several limitations to this study. First, inherent to the observational methodology, no causal role of NABE and GA on health could be tested. Secondly, the age at which the questionnaire was completed varied between two and seven years, which could be a confounding factor for airway problems and atopy. The influence of age at which the questionnaire was completed was, however, estimated to be minimal as it did not fulfil the criteria of a confounding factor described in the method section; i.e., it was not correlated with both NABE and health outcomes. A second factor potentially creating bias is the lack of information on prenatal antibiotic exposure and maternal health, which might influence the risk of motor impairment and atopic disease ,51. AddiAnother limitation is the relatively low response rate of 32%. This might be because at the time of inclusion at neonatal age, parents were not informed about the follow-up of this study. As it was decided upon follow-up of children after 5 years of inclusions, this part of the study could not be integrated in the standardized visits at the ambulatory and was performed outside the hospital setting . The lowOur results suggest that duration of NABE and GA might be associated with health complaints in early childhood after preterm birth (GA < 30 weeks). In the investigated cohort, NABE was associated with impaired gross motor development in toddlers, while a low GA was associated with poor weight gain and pediatric constipation. Further research is needed to evaluate the complex interplay between antibiotics, microbiota colonization and health outcome beyond the neonatal age.


Furthermore, no significant difference in antibody levels was observed between each UD subgroup and the HC group, except for neutralizing antibodies in the hypertension subgroup. PastoCovac Plus and PastoCovac boosters induced a higher fold rise in antibodies in UD individuals than BBIBP-CorV booster recipients. No serious AEs after the booster injection were recorded. The overall incidence of AEs after the booster injection was higher in the UD group than the HC group among whom the highest systemic rate of AEs was seen in the BBIBP-CorV booster recipients. In conclusion, administration of COVID-19 boosters could similarly induce robust and persistent humoral immune responses in individuals with or without UD primarily vaccinated with two doses of the BBIBP-CorV. Protein-based boosters with higher a higher fold rise in antibodies and lower AEs in individuals with comorbidities might be considered a better choice for these individuals.Data on immunogenicity, immune response persistency, and safety of COVID-19 boosters in patients with comorbidities are limited. Therefore, we aimed to evaluate three different boosters’ immunogenicity and safety in individuals with at least one underlying disease (UD)  with healthy ones (HC) who were primed with two doses of the BBIBP-CorV vaccine and received a booster shot of the same priming vaccine or protein subunit vaccines, PastoCovac Plus or PastoCovac. One hundred and forty subjects including sixty-three ones with a comorbidity and seventy-seven healthy ones were enrolled. The presence of SARS-CoV-2 antibodies was assessed before the booster injection and 28, 60, 90, and 180 days after it. Moreover, the adverse events (AEs) were recorded on days 7 and 21 postbooster shot for evaluating safety outcomes. Significantly increased titers of antispike, antiRBD, and neutralizing antibodies were observed in both UD and HC groups 28 days after the booster dose. Nevertheless, the titer of antispike IgG and anti-RBD IgG was lower in the UD group compared to the HC group. The long-term assessment regarding persistence of humoral immune responses showed that the induced antibodies were detectable up to 180 days postbooster shots though with a declined titer in both groups with no significant differences ( As of 20 January 2023, 663,640,386 confirmed cases of COVID-19 and 6,713,093 deaths were globally reported by the WHO ,6, whichFrom another point of view, some studies have shown that protein subunit vaccines could be administrated as a better alternative booster following two doses of inactivated vaccine ,20,21. TWe conducted a retrospective study to evaluate the safety, immunogenicity, and antibody persistency of different COVID-19 boosters in individuals with underlying diseases in comparison with healthy subjects after primary vaccination with the BBIBP-CorV.This study was conducted on 140 participants who were referred to Pasteur Institute of Iran between February 2022 and August 2022 to be vaccinated and subsequently agreed to be followed in a 180-day follow-up schedule. Data on the demographic and clinical features of all participants were collected through face-to-face or phone interviews. (1)BBIP-CorV primed/PastoCovac Plus boosted (BP);(2)BBIP-CorV primed/PastoCovac boosted (BPa);(3)BBIP-CorV primed/BBIP-CorV boosted (BB).The participants aged ≥18 years who were primed with BBIP-CorV  were investigated as below:The booster shot was administrated 3–6 months post the second dose in all groups.Participants were divided into two UD or HC groups according to their recorded medical history and profiles. UD group was defined as people with at least one of the three conditions including obesity, hypertension, and diabetes mellitus. Therefore, the UD group was divided into three subgroups including obesity, hypertension, and multi.2 have been considered obese people. Hypertension and diabetes mellitus were diagnosed by physicians’ screen based on a medical history and relevant medicine therapy. According to CDC guidelines, people with diabetes mellitus are diagnosed with high level of fasting blood sugar (≥126 mg/dL) ) .p = 0.04). In the HC group, the fold rise of neutralizing antibody on day 60 was significantly different among the booster groups, the BP group  showed higher neutralizing antibody fold rise compared to other boosters (p = 0.04). On day 28, there was a significant difference in antiRBD IgG fold rise among booster groups, while the BP  showed the highest fold rise compared to both BPa  and BB  groups (p = 0.02). Moreover, a lower rate of antiRBD IgG fold rise was observed in healthy people with a COVID-19 history on days 28 (p = 0.02), 60 (p = 0.01), and 90 (p = 0.02). In healthy subjects, other variables revealed insignificant effects on the fold rise of antibodies after booster administration  was significantly higher than in subjects aged ≤40  (stration .p < 0.99) reported local AEs within seven days after receiving a booster dose. Fifteen out of all participants reported at least one systemic AE. Furthermore, systematic AEs were significantly higher in the UD group (17.5%) in comparison with healthy people . The most frequently reported local AE in the UD and HC groups was pain at the injection site  (p = 0.01)  A. Weakne = 0.01) B. On dayp = 0.37). BBIBP-CorV injected people reported significantly more systemic AE (21.4%) than the BP (1.9%) and BPa (9.1%) groups (p = 0.005).Among booster subtypes, more local AEs (13.6%) were observed in the BPa group compared with the BP (5.6%) and BB (11.9%) groups, but it was not significant (p = 0.005)  than other booster groups (= 0.005) C,D.Based on the available scientific evidence, people with underlying diseases  are at risk of increased rate of severe complications and mortality induced by COVID-19 due to an impaired and dysregulated immune response ,6. ThereOur results revealed that antispike, antiRBD, and neutralizing antibody titers significantly increased on day 28 after the booster dose. This assessment indicates the impact of protein subunit vaccines on BBIBP-CorV primed individuals. Consistent with our study, no significant difference in antibody titers was observed between Chinese participants with and without underlying medical conditions after vaccination with Vero cell-derived inactivated COVID-19 vaccine . MoreoveIn our study, we evaluated the association between the fold rise of specific antibodies and demographic parameters such as age and sex, as well as types of UD and boosters. We found that the antibodies’ fold rise was not different between age subgroups. However, the antispike IgG fold rise on day 28 was different in the UD group aged >40 than younger people, which could be due to a higher baseline antibody level in this subgroup or/and a higher number of participants of this category compared to healthy people. In agreement with our results, a study revealed that the two doses of CoronaVac inactivated vaccines induced higher neutralizing antibody levels in senior people and cancer patients than in younger and healthy people . In contVarious types of studies established that patients with comorbidities showed different postvaccination humoral immune responses ,35,41. WSeveral studies have shown that men experience more severe COVID-19 outcomes ,43,44 anGood immunogenicity and safety of protein subunit COVID-19 vaccines have been reported in the recent data ,49. We dBesides immunogenicity, we surveyed the safety outcome of inactivated and protein-based COVID-19 boosters in people with UD. In our study, no serious AEs were found after the booster injection. We observed that the overall incidence of adverse events within 7 days was higher in the UD group than in the HC group. The most common local AE in healthy and UD people was pain at the injection site. Weakness is the most common systemic AE in people with UD. Among booster subgroups, the systemic AEs were significantly higher in UD individuals who received the BBIBP-CorV booster than in other groups. These results indicated that protein subunit vaccines could be proposed as a better alternative to booster doses with lower AEs in people with UD. Similarly, a study performed by Li et al. demonstrated that the incidence of injection-site pain, fatigue, and headache was higher in some disease groups than in healthy control after primary vaccination with CoronaVac vaccine . MoreoveThere are some limitations in the current study. According to the type of study, retrospective, the population study was small. Second, cellular immunity was not evaluated, which could possibly be performed in further studies to determine whether participants with a low rate of antibody response could develop adequate T-cell response to prevent COVID-19. Further long-term studies are required to evaluate the persistence of cellular immune responses in participants with chronic diseases after a booster vaccination. This may be important in determining the schedule of subsequent vaccinations.The humoral immune responses in people with obesity, hypertension, and diabetes mellitus were induced after three kinds of COVID-19 booster vaccines.There was no significant difference between the healthy individuals and those with underlying diseases regarding humoral immune responses.The protein subunit vaccine had a better impact on immune response induction in people with comorbidities.The induced antibodies were detectable 180 days after the booster shots in both healthy and comorbidity groups.Less adverse events were recorded after PastoCovac/Plus booster shots compared to BBIBP-CorV.What this study adds to the previous data:In conclusion, our data provide the first comprehensive picture of the safety, immunogenicity, and antibody persistency of COVID-19 three booster types in Iranian cases with comorbidities in comparison with healthy ones. Different types of booster vaccines could elicit strong and persistent humoral immune responses in BBIBP-CorV primed individuals in both healthy and comorbidity groups. Among UD people, protein-based booster vaccines had a higher fold rise in antibodies and lower AEs in comparison with inactivated booster vaccines. Owing to the fact that UD subjects are a high-risk group, PastoCovac/Plus protein subunit vaccines are recommended with an excellent immunogenicity and safety profile.