Exploring the Spatial Relative Risk of COVID-19 in Berlin-Neukölln.

Identifying areas with high and low infection rates can provide important etiological clues. Usually, areas with high and low infection rates are identified by aggregating epidemiological data into geographical units, such as administrative areas. This assumes that the distribution of population numbers, infection rates, and resulting risks is constant across space. This assumption is, however, often false and is commonly known as the modifiable area unit problem. This article develops a spatial relative risk surface by using kernel density estimation to identify statistically significant areas of high risk by comparing the spatial distribution of address-level COVID-19 cases and the underlying population at risk in Berlin-Neukölln. Our findings show that there are varying areas of statistically significant high and low risk that straddle administrative boundaries. The findings of this exploratory analysis further highlight topics such as, e.g., Why were mostly affluent areas affected during the first wave? What lessons can be learned from areas with low infection rates? How important are built structures as drivers of COVID-19? How large is the effect of the socio-economic situation on COVID-19 infections? We conclude that it is of great importance to provide access to and analyse fine-resolution data to be able to understand the spread of the disease and address tailored health measures in urban settings.

Short-term exposure to air pollution and COVID-19 in India: spatio-temporal analysis of relative risk from 20 metropolitan cities

The present study investigates the possible association between major air pollutants and COVID-19. We hypothesized that the post-lockdown surge in air pollution is the major cause of the increment in COVID-19 cases and deaths. The statistical results showed that pollutant concentrations of PM2.5 (20%), PM10(24%), SO2 (12%), and O3 (19%) were raised. So, we attempted to quantify the relative risk due to all major air pollutants by fitting generalized additive models. The results suggest that the pollution concentration escalated the COVID-19 cases and deaths. The pooled study suggests that for every 10 μg/m3 increment in pollutant concentration, an increment of COVID-19 cases is observed for PM2.5 (3%), PM10 (1%), SO2 (7.7%), and O3 (10%). Similarly, there is an increment in COVID-19 deaths for PM2.5 (2.8%), PM10 (1%), SO2 (4.5%), and O3 (7.2%). The spatial maps of relative risk revealed the most vulnerable regions due to each pollutant, thus steering the policymakers to implement region-specific mitigation strategies. © 2023, The Author(s), under exclusive licence to Korea Spatial Information Society.

Estimating the Reciprocal of a Binomial Proportion

The binomial proportion is a classic parameter with many applications and has also been extensively studied in the literature. By contrast, the reciprocal of the binomial proportion, or the inverse proportion, is often overlooked, even though it also plays an important role in various fields. To estimate the inverse proportion, the maximum likelihood method fails to yield a valid estimate when there is no successful event in the Bernoulli trials. To overcome this zero-event problem, several methods have been introduced in the previous literature. Yet to the best of our knowledge, there is little work on a theoretical comparison of the existing estimators. In this paper, we first review some commonly used estimators for the inverse proportion, study their asymptotic properties, and then develop a new estimator that aims to eliminate the estimation bias. We further conduct Monte Carlo simulations to compare the finite sample performance of the existing and new estimators, and also apply them to handle the zero-event problem in a meta-analysis of COVID-19 data for assessing the relative risks of physical distancing on the infection of coronavirus. © 2023 The Authors. International Statistical Review published by John Wiley & Sons Ltd on behalf of International Statistical Institute.

SARS-CoV-2 infection and smoking: What is the association? A brief review.

Susceptibility to severe illness from COVID-19 is anticipated to be associated with cigarette smoking as it aggravates the risk of cardiovascular and respiratory illness, including infections. This is particularly important with the advent of a new strain of coronaviruses, the severe acute respiratory syndrome coronavirus (SARS-CoV-2) that has led to the present pandemic, coronavirus disease 2019 (COVID-19). Although, the effects of smoking on COVID-19 are less described and controversial, we presume a link between smoking and COVID-19. Smoking has been shown to enhance the expression of the angiotensin-converting enzyme-2 (ACE-2) and transmembrane serine protease 2 (TMPRSS2) key entry genes utilized by SARS-CoV-2 to infect cells and induce a 'cytokine storm', which further increases the severity of COVID-19 clinical course. Nevertheless, the impact of smoking on ACE-2 and TMPRSS2 receptors expression remains paradoxical. Thus, further research is necessary to unravel the association between smoking and COVID-19 and to pursue the development of potential novel therapies that are able to constrain the morbidity and mortality provoked by this infectious disease. Herein we present a brief overview of the current knowledge on the correlation between smoking and the expression of SARS-CoV-2 key entry genes, clinical manifestations, and disease progression.

The change in the relationship between temperature and respiratory diseases among children in Guangzhou, China.

Temperature is closely associated with respiratory disease (RD) in children, but few studies have examined whether the relationship between ambient temperature and RD in children changed after the COVID-19 epidemic. The aim of this study was to assess the relationship between temperature and RD in children after the COVID-19 epidemic in Guangzhou, China. We used a distributed lag nonlinear model to compare the relationship between temperature and RD among children in Guangzhou from 2018 to 2022. The results showed an S-shaped relationship between temperature and RD in the post-COVID-19 period with a reference minimum risk at a temperature of 21 °C and an increasing relative risk (RR) at extremely low temperature (ELT) and extremely high temperature (EHT). The highest RR associated with EHT was 1.935 (95% confidence interval [CI]: 1.314-2.850) at a lag of 0-14 days. The on-the-day lag effects were found to be strongest at the lag 0 day of EHT with a RR of 1.167 (95% CI: 1.021-1.334). Furthermore, each 1 °C increase in post-COVID-19 temperature increased the risk of RD by 8.2% (95% CI: 1.044-1.121). Our study provides evidence that the relationship between temperature and RD in children in Guangzhou changed after the COVID-19 epidemic, and high temperature is more likely to cause RD in children. Relevant government departments and parents should understand the relationship between temperature and RD in children and develop new preventive measures.

Association of Neutralizing Antispike Monoclonal Antibody Treatment With Coronavirus Disease 2019 Hospitalization and Assessment of the Monoclonal Antibody Screening Score.

Objective: To test the hypothesis that the Monoclonal Antibody Screening Score performs consistently better in identifying the need for monoclonal antibody infusion throughout each "wave" of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant predominance during the coronavirus disease 2019 (COVID-19) pandemic and that the infusion of contemporary monoclonal antibody treatments is associated with a lower risk of hospitalization. Patients and Methods: In this retrospective cohort study, we evaluated the efficacy of monoclonal antibody treatment compared with that of no monoclonal antibody treatment in symptomatic adults who tested positive for SARS-CoV-2 regardless of their risk factors for disease progression or vaccination status during different periods of SARS-CoV-2 variant predominance. The primary outcome was hospitalization within 28 days after COVID-19 diagnosis. The study was conducted on patients with a diagnosis of COVID-19 from November 19, 2020, through May 12, 2022. Results: Of the included 118,936 eligible patients, hospitalization within 28 days of COVID-19 diagnosis occurred in 2.52% (456/18,090) of patients who received monoclonal antibody treatment and 6.98% (7,037/100,846) of patients who did not. Treatment with monoclonal antibody therapies was associated with a lower risk of hospitalization when using stratified data analytics, propensity scoring, and regression and machine learning models with and without adjustments for putative confounding variables, such as advanced age and coexisting medical conditions (eg, relative risk, 0.15; 95% CI, 0.14-0.17). Conclusion: Among patients with mild to moderate COVID-19, including those who have been vaccinated, monoclonal antibody treatment was associated with a lower risk of hospital admission during each wave of the COVID-19 pandemic.

COVID-19 incidence and vaccination rates among Canadian dental hygienists.

Background: Oral health care settings potentially carry a high risk of cross-infection due to close contact and aerosol-generating procedures. There is limited evidence of the impact of COVID-19 among dental hygienists. This longitudinal study aimed to 1) estimate COVID-19 incidence rates among Canadian dental hygienists over a 1-year period; and 2) estimate vaccination rates among Canadian dental hygienists. Methods: A prospective cohort study design was used to collect self-reported COVID-19 status from 876 registered dental hygienists across Canada via an online baseline survey and then 6 follow-up questionnaires delivered between December 2020 and January 2022. Bayesian Poisson and binomial models were used to estimate the incidence rate and cumulative incidence of self-reported COVID-19. Results: The estimated cumulative incidence of COVID-19 in dental hygienists in Canada from December 2020 to January 2022 was 2.39% (95% CrI, 1.49%-3.50%), while the estimated cumulative incidence of COVID-19 in corresponding Canadian provinces was 5.12% (95% CrI, 5.12%-5.13%) during the same period. At last follow-up, 89.4% of participants self-reported that they had received at least 1 dose of a COVID-19 vaccine. Conclusion: The low infection rate observed among Canadian dental hygienists between December 2020 and January 2022 is reassuring to the dental hygiene and general community.

Contexte : Les milieux de soins buccodentaires présentent potentiellement un risque élevé d'infections croisées en raison des contacts étroits et des procédures qui produisent des aérosols. Il y a peu de preuves de l'effet de la COVID-19 chez les hygiénistes dentaires. La présente étude longitudinale visait à 1) estimer les taux d'incidence de la COVID-19 chez les hygiénistes dentaires canadiens sur une période d'un an; et 2) estimer les taux de vaccination chez les hygiénistes dentaires canadiens. Méthodologie : Une méthodologie prospective des cohortes a été utilisée pour recueillir le statut de COVID-19 autodéclaré de 876 hygiénistes dentaires autorisés au Canada par l'intermédiaire d'une enquête initiale en ligne, puis de 6 questionnaires de suivi, distribués entre décembre 2020 et janvier 2022. Des modèles bayésiens de Poisson et binomiaux ont été utilisés pour estimer le taux d'incidence et l'incidence cumulative de la COVID-19 autodéclarée. Résultats : L'incidence cumulative estimée de la COVID-19 chez les hygiénistes dentaires au Canada entre décembre 2020 et janvier 2022 était de 2,39 % (intervalle de crédibilité à 95 %, 1,49 % ­ 3,50 %), alors que l'incidence cumulative estimée de la COVID-19 dans les provinces canadiennes correspondantes était de 5,12 % (intervalle de crédibilité à 95 %, 5,12 % ­ 5,13 %) au cours de la même période. Lors du dernier suivi, 89,4 % des participants ont déclaré avoir reçu au moins une dose du vaccin contre la COVID-19. Conclusion : Le faible taux d'infection constaté chez les hygiénistes dentaires canadiens entre décembre 2020 et janvier 2022 est rassurant pour la communauté d'hygiène dentaire et la communauté générale.

COVID-19: Relative Risk of Non-Vaccinated to Vaccinated Individuals.

Following the outbreak of the COVID-19 pandemic, Italy has implemented an extensive vaccination campaign involving individuals above the age of 12, both sexes. The public opinion and the medical community alike questioned the usefulness and efficacy of the vaccines against SARS-CoV-2. The widespread opinion was that the vaccines protected individuals especially against serious conditions which could require intensive care and may lead to the death of the patient rather than against the possibility of infection. In order to quantify the effect of the vaccination campaign, we calculated the relative risks of non-vaccinated and vaccinated individuals for all possible outcomes of the disease: infection, hospitalization, admission to intensive care and death. Relative risk was assessed by means of likelihood ratios, the ratios of the probability of an outcome in non-vaccinated individuals to the probability of the same outcome in vaccinated individuals. Results support the hypothesis that vaccination has an extensive protective effect against both critical conditions and death. Nonetheless, the relative magnitude of the protection in vaccinated individuals compared to those non-vaccinated appears to be higher against the former outcome than the latter, for reasons which need to be investigated further.

Predicting COVID-19 community infection relative risk with a Dynamic Bayesian Network.

As COVID-19 continues to impact the United States and the world at large it is becoming increasingly necessary to develop methods which predict local scale spread of the disease. This is especially important as newer variants of the virus are likely to emerge and threaten community spread. We develop a Dynamic Bayesian Network (DBN) to predict community-level relative risk of COVID-19 infection at the census tract scale in the U.S. state of Indiana. The model incorporates measures of social and environmental vulnerability-including environmental determinants of COVID-19 infection-into a spatial temporal prediction of infection relative risk 1-month into the future. The DBN significantly outperforms five other modeling techniques used for comparison and which are typically applied in spatial epidemiological applications. The logic behind the DBN also makes it very well-suited for spatial-temporal prediction and for "what-if" analysis. The research results also highlight the need for further research using DBN-type approaches that incorporate methods of artificial intelligence into modeling dynamic processes, especially prominent within spatial epidemiologic applications.

Effect size quantification for interrupted time series analysis: implementation in R and analysis for Covid-19 research.

BACKGROUND: Interrupted time series (ITS) analysis is a time series regression model that aims to evaluate the effect of an intervention on an outcome of interest. ITS analysis is a quasi-experimental study design instrumental in situations where natural experiments occur, gaining popularity, particularly due to the Covid-19 pandemic. However, challenges, including the lack of a control group, have impeded the quantification of the effect size in ITS. The current paper proposes a method and develops a user-friendly R package to quantify the effect size of an ITS regression model for continuous and count outcomes, with or without seasonal adjustment. RESULTS: The effect size presented in this work, together with its corresponding 95% confidence interval (CI) and P-value, is based on the ITS model-based fitted values and the predicted counterfactual (the exposed period had the intervention not occurred) values. A user-friendly R package to fit an ITS and estimate the effect size was developed and accompanies this paper. To illustrate, we implemented a nation population-based ITS study from January 2001 to May 2021 covering the all-cause mortality of Israel (n = 9,350 thousand) to quantify the effect size of Covid-19 exposure on mortality rates. In the period unexposed to the Covid-19 pandemic, the mortality rate decreased over time and was expected to continue decreasing had Covid-19 not occurred. In contrast, the period exposed to the Covid-19 pandemic was associated with an increased all-cause mortality rate (relative risk = 1.11, 95% CI = 1.04, 1.18, P < 0.001). CONCLUSION: For the first time, the effect size in ITS: was quantified, can be estimated by end-users with an R package we developed, and was demonstrated with data showing an increase in mortality following the Covid-19 pandemic. ITS effect size reporting can assist public health policy makers in assessing the magnitude of the entire intervention effect using a single, readily understood measure.

Relative risk reduction: Misinformative measure in clinical trials and COVID-19 vaccine efficacy.

Treatment and vaccine efficacy in clinical trials are often reported in the media and medical journals as the relative risk reduction. The present article explains why the relative risk reduction is a misinformative measure that promotes disinformation when reporting efficacy in clinical research studies such as randomized controlled trials for COVID-19 vaccines. The relative risk reduction is based on the relative risk, a proportional measure or ratio used in epidemiologic studies to estimate the probability of a disease associated with an exposure. The present article demonstrates how the relative risk reduction and relative risk obscure the magnitude of disease risk reduction in clinical research. The absolute risk reduction is shown to be a more precise and reliable measure of treatment and vaccine efficacy in clinical research studies. The absolute risk reduction reciprocal also measures the number needed to treat or vaccinate, and is a more accurate measure than the relative risk reduction for comparing risk reductions of clinical studies. Additionally, the present article reviews consequences of COVID-19 vaccine efficacy misinformation disseminated through media reports. The article concludes that relative risk reduction should not be used to measure treatment and vaccine efficacy in clinical trials. What is new?: •Unreliability of relative measures in clinical trials is graphically illustrated, demonstrating constant relative measures as absolute measures change.•Misuse of relative measures in clinical research is historically linked to misinterpretation of Jerome Cornfield's advice on measuring causative and associative effects.•Consequences of disinformation and misinformation related to COVID-19 vaccine efficacy and modern clinical medicine are described.•The proper use of absolute measures in meta-analyses is explained.

Changes in lactate dehydrogenase on admission throughout the COVID-19 pandemic and possible impacts on prognostic capability.

Introduction: The enzyme lactate dehydrogenase (LDH) is a good marker of general hyperinflammation correlated with mortality for COVID-19, and is therefore used in prognosis tools. In a current COVID-19 clinical randomized trial (CRT), the blood level of LDH was selected as an inclusion criterion. However, LDH decreased during the pandemic; hence, the impact of this decrease on the prognostic value of LDH for mortality was evaluated. Methods: Data on LDH levels in 843 patients were obtained and analyzed. Relative risk, standard error and receiver operating characteristic curves were calculated for two cutoff values. Results: Relative risk lost validity and the area under the curve narrowed by trimester during the pandemic. Conclusion: The progressive decrease in LDH impacted the capacity to predict mortality in COVID-19. More studies are needed to validate this finding and its implications.

Comparative Analysis of the Implication of Periods Before and During Vaccination of COVID-19 Infection in Some Regional Leading African Countries

There has been a high expectation about the efficacy of coronavirus disease 2019 (COVID-19) vaccines. This research investigates and compares the efficiency of COVID-19 vaccines in five (5) African countries and evaluates the risk or preventive factors inherent in COVID-19 spread. Five different COVID-19 leading African countries in their respective regions (Nigeria, Ethiopia, South Africa, Morocco, and Cameroon) were considered in this study. Population sampling proportional to size concept was used to draw data for two periods (before and during COVID-19 vaccination). A sequential analysis approach was adopted, focusing on the estimates of some epidemiological metrics for the two distinct periods. Nigeria (a wet region) has the lowest risk of COVID-19 incidence during vaccination. The risk of being reported COVID-19 positive in South Africa (a high semi-arid region) is approximately 137 times the number in Nigeria. This study suggests that while vaccination has successfully reduced the case fatality rate in most countries considered except Ethiopia, infection and incidence rates increase during vaccination in all countries except Nigeria. Methods other than vaccination like wearing a face mask, washing hands, and avoiding large gatherings should be intensified to curtail incidence and infection rates. © 2022 Journal of the Nigerian Society of Physical Sciences. All rights reserved.

Using a Deep Neural Network in a Relative Risk Model to Estimate Vaccination Protection for COVID-19

The proportional hazard Cox model is traditionally used in survival analysis to estimate the effect of several variables on the hazard rate of an event. Recently, neural networks were proposed to improve the flexibility of the Cox model. In this work, we focus on an extension of the Cox model, namely on a non-proportional relative risk model, where the neural network approximates a non-linear time-dependent risk function. We address the issue of the lack of time-varying variables in this model, and to this end, we design a deep neural network model capable of time-varying regression. The target application of our model is the waning of post-vaccination and post-infection immunity in COVID-19. This task setting is challenging due to the presence of multiple time-varying variables and different epidemic intensities at infection times. The advantage of our model is that it enables a fine-grained analysis of risks depending on the time since vaccination and/or infection, all approximated using a single non-linear function. A case study on a data set containing all COVID-19 cases in the Czech Republic until the end of 2021 has been performed. The vaccine effectiveness for different age groups, vaccine types, and the number of doses received was estimated using our model as a function of time. The results are in accordance with previous findings while allowing greater flexibility in the analysis due to a continuous representation of the waning function. © 2022, Springer Nature Switzerland AG.

Vaccination policies for healthcare personnel: Current challenges and future perspectives.

Healthcare personnel (HCP) are at occupational risk for acquisition of several vaccine-preventable diseases and transmission to patients. Vaccinations of HCP are justified to confer them immunity but also to protect susceptible patients and healthcare services from outbreaks, HCP absenteeism and presenteeism. Mandatory vaccination policies for HCP are increasingly adopted and achieve high and sustainable vaccination rates in short term. In this article we review the scientific evidence for HCP vaccination. We also address issues pertaining to vaccination policies for HCP and present the challenges of implementation of mandatory versus voluntary vaccination policies. Finally, we discuss the issue of mandatory vaccination of HCP against COVID-19.

A compelling demonstration of why traditional statistical regression models cannot be used to identify risk factors from case data on infectious diseases: a simulation study.

BACKGROUND: Regression models are often used to explain the relative risk of infectious diseases among groups. For example, overrepresentation of immigrants among COVID-19 cases has been found in multiple countries. Several studies apply regression models to investigate whether different risk factors can explain this overrepresentation among immigrants without considering dependence between the cases. METHODS: We study the appropriateness of traditional statistical regression methods for identifying risk factors for infectious diseases, by a simulation study. We model infectious disease spread by a simple, population-structured version of an SIR (susceptible-infected-recovered)-model, which is one of the most famous and well-established models for infectious disease spread. The population is thus divided into different sub-groups. We vary the contact structure between the sub-groups of the population. We analyse the relation between individual-level risk of infection and group-level relative risk. We analyse whether Poisson regression estimators can capture the true, underlying parameters of transmission. We assess both the quantitative and qualitative accuracy of the estimated regression coefficients. RESULTS: We illustrate that there is no clear relationship between differences in individual characteristics and group-level overrepresentation -small differences on the individual level can result in arbitrarily high overrepresentation. We demonstrate that individual risk of infection cannot be properly defined without simultaneous specification of the infection level of the population. We argue that the estimated regression coefficients are not interpretable and show that it is not possible to adjust for other variables by standard regression methods. Finally, we illustrate that regression models can result in the significance of variables unrelated to infection risk in the constructed simulation example (e.g. ethnicity), particularly when a large proportion of contacts is within the same group. CONCLUSIONS: Traditional regression models which are valid for modelling risk between groups for non-communicable diseases are not valid for infectious diseases. By applying such methods to identify risk factors of infectious diseases, one risks ending up with wrong conclusions. Output from such analyses should therefore be treated with great caution.

Risk factors for impaired pulmonary diffusion function in convalescent COVID-19 patients: A systematic review and meta-analysis.

Background: The long-term prognosis of COVID-19 survivors remains poorly understood. It is evidenced that the lung is the main damaged organ in COVID-19 survivors, most notably in impairment of pulmonary diffusion function. Hence, we conducted a meta-analysis of the potential risk factors for impaired diffusing capacity for carbon monoxide (DLCO) in convalescent COVID-19 patients. Methods: We performed a systematic search of PubMed, Web of Science, Embase, and Ovid databases for relevant studies from inception until January 7, 2022, limited to papers involving human subjects. Studies were reviewed for methodological quality. Fix-effects and random-effects models were used to pool results. Heterogeneity was assessed using I2. The publication bias was assessed using the Egger's test. PROSPERO registration: CRD42021265377. Findings: A total of eighteen qualified articles were identified and included in the systematic review, and twelve studies were included in the meta-analysis. Our results showed that female (OR: 4.011; 95% CI: 2.928-5.495), altered chest computerized tomography (CT) (OR: 3.002; 95% CI: 1.319-6.835), age (OR: 1.018; 95% CI: 1.007-1.030), higher D-dimer levels (OR: 1.012; 95% CI: 1.001-1.023) and urea nitrogen (OR: 1.004;95% CI: 1.002-1.007) were identified as risk factors for impaired DLCO. Interpretation: Pulmonary diffusion capacity was the most common impaired lung function in recovered patients with COVID-19. Several risk factors, such as female, altered chest CT, older age, higher D-dimer levels and urea nitrogen are associated with impairment of DLCO. Raising awareness and implementing interventions for possible modifiable risk factors may be valuable for pulmonary rehabilitation. Funding: This work was financially supported by Emergency Key Program of Guangzhou Laboratory (EKPG21-29, EKPG21-31), Incubation Program of National Science Foundation for Distinguished Young Scholars by Guangzhou Medical University (GMU2020-207).

NVX-Cov2373 Novavax Covid-19 vaccine: A further analysis of its efficacy using multiple modes of expression.

A fifth vaccine against Covid-19, NVX-CoV2373 Nuvavoxid® (Novavax), a protein-based adjuvanted vaccine, was recently marketed in Europe. The main clinical trial before marketing concluded to a 'vaccine efficacy' of 89.7% without talking about other validated efficacy parameters. We further analysed the data of this clinical trial using the different validated methods of risk expression: absolute risks (AR), AR reduction (ARR) and number needed to treat (NNT). ARR and NNT values were 1.22% and 82, respectively, for an RR value of 0.10. Description of these parameters allowed defining some interesting characteristics of NVX-CoV2373 efficacy according to age, race, variant and coexisting illness. Finally, we ask that the results of clinical trials be systematically presented, using not only RR but also including AR, ARR and NNT.

Vaccines for Covid-19: An insight on their effectiveness and adverse effects.

An era of SARS-COVID-19 outbreak with a high contagious percentage around the globe has been the subject of multi-agency research aimed at generating vaccines for active immunization. Scientists across the world are joining hands for advanced tie-ups between medical start-ups and pharmaceutical industries for devices and vaccines development to hinder the progress of this outbreak. Moreover, the questions that need to be answered are how to improve the effectiveness and efficacy of vaccines with reduced side effects and the required doses of vaccines for enhanced surveillance. In this review article, we have discussed the effectiveness and efficacy of different Covid-19 vaccines.