Role of noscomial infection informatics surveillance system in the prevention and control of multidrug-resistant organisms infections

OBJECTIVE: To analyze the role of nosocomial infection informatics surveillance system in the prevention and control of multidrug-resistant organisms(MDROs) infections. METHODS: The First Affiliated Hospital of Guangdong Pharmaceutical University was selected as the study subjects, which had adopted the nosocomial infection informatics surveillance system since Jan.2020. The period of Jan.to Dec.2020 were regarded as the study period, and Jan.to Dec.2019 were regarded as the control period. The situation of nosocomial infection and MDROs infections in the two periods were retrospectively analyzed. RESULTS: The incidence of nosocomial infections and underreporting of nosocomial infection cases in this hospital during the study period were 2.52%(1 325/52 624) and 1.74%(23/1 325), respectively, and the incidences of ventilator associated pneumonia(VAP), catheter related bloodstream infection(CRBSI), catheter related urinary tract infection(CAUTI)were 4.10(31/7 568), 2.11(14/6 634), and 2.50(25/9 993) respectively, which were lower than those during the control period(P< 0.05). The positive rate of pathogenic examination in the hospital during the study period was 77.95%(1 269/1 628), which was higher than that during the control period(P<0.05), the overall detection rate of MDROs was 15.77%(206/1 306), the detection rates of MDROs in Escherichia coli, Acinetobacter baumannii, Klebsiella pneumoniae, Staphylococcus epidermidis, Pseudomonas aeruginosa and Staphylococcus aureus were lower than those during the control period(P<0.05). CONCLUSION: The development and application of the informatics technology-based surveillance system of nosocomial infection could effectively reduce the incidence of nosocomial infections and device related infections, decrease the under-reporting of infection cases, and also reduce the detection rate of MDROs as well as the proportion of MDROs detected in common pathogenic species.

Within-group estimators for unbalanced-panel data regression model of the open unemployment rate data in east kalimantan province

The COVID-19 pandemic has hit hard the Indonesian economy. Many businesses had to close because they could not cover operational costs, and many workers were laid off creating an unemployment crisis. Unemployment causes people's productivity and income to decrease, leading to poverty and other social problems, making it a crucial problem and great concern for the nation. Economic conditions during this pandemic have also provided an unusual pattern in economic data, in which outliers may occur, leading to biased parameter estimation results. For that reason, it is necessary to deal with outliers in research data appropriately. This study aims to find within-group estimators for unbalanced panel data regression model of the Open Unemployment Rate (OUR) in East Kalimantan Province and the factors that influence it. The method used is the within transformation with mean centering and median centering processing methods. The results of this study may provide advice on factors that can increase and decrease the OUR of East Kalimantan Province. The results show that the best model for estimating OUR data in East Kalimantan Province is the within-transformation estimation method using median centering. According to the best model, the Human Development Index (HDI) and Gross Regional Domestic Product (GRDP) are two factors that influence the OUR of East Kalimantan Province (GRDP). © 2023, International Association of Engineers. All rights reserved.

From the Bretton Woods system to the global non-system: The trials and tribulations of slow learning

In this paper we analyse why an understanding of the global ‘non-system', in which we now live, took so long to arrive after the Bretton Woods system collapsed in 1971. We first describe how knowledge of how an inflation-targeting regime would operate—what we call ‘Taylor-rule macroeconomics'—was only gradually created during the 1970s, 1980s, and 1990s. We then describe how, subsequent to this, an awareness emerged, also gradually, of how the international non-system might work, depending, as it does, on Taylor-rule macroeconomics being already in place. We then discuss the Great Moderation, making clear that a well-functioning global non-system would require not just inflation targeting and floating exchange rates in each country, but also adequate fiscal discipline, and a satisfactory form of financial regulation. We describe how a well-functioning version of this global non-system would actually fit together. We then discuss how this non-system has responded to two enormous challenges of the last 15 years, namely the Global Financial Crisis and the Covid pandemic. This discussion of what has happened in the recent past provides the background to a discussion, in the companion paper by Subacchi and Vines in this issue of the Oxford Review of Economic Policy, of the challenges that the global non-system will face in the future. © The Author(s) 2023. Published by Oxford University Press.

An Empirical Analysis of Efficiency in Accommodation Industry in Australian Tourism Regions

A body of empirical literature exists which sets out how the accommodation industry performs across a range of locations. However, research on tourism regions in terms of its accommodation industry remains underdeveloped, especially in the Covid-19 pandemic when tourism faced unprecedented adversity and need to find a way to move forward. In an attempt to address this and take the Australian accommodation industry as a case study, this paper sought to investigate the efficiency of Australian tourism regions in the accommodation industry for the period of 2014/15–2017/18. The findings clearly showed that Australian tourism regions had seen significant growth in terms of their efficiency in the accommodation industry over the surveyed period. The Australian commercial large cities, namely Sydney, Melbourne, Brisbane, and the Goal Coast, represent perhaps the best example, having obtained a higher efficiency than all other tourism regions. Exogenous factors, such as the occupancy rate, the average daily rate, the number of international visitors and the number of domestic visitors overnight were identified as influencing the technical efficiency score of tourism regions, with policy formulation and implementation identified as being key to improving the efficiency of the accommodation industry at the regional level for a post-Covid-19 period. [ FROM AUTHOR] Copyright of International Journal of Hospitality & Tourism Administration is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Determinants of Foreign exchange exposure during the COVID-19 crisis: A Multi-country evidence

The research question of which firm-level factors make firms more vulnerable to exchange rate fluctuations during periods of crisis has rarely been explored by prior literature. Using a large sample of 1577 firms from 9 developed and 11 emerging countries, this study presents a comprehensive analysis of how firm-level factors affect firms' foreign exchange exposure before and during the COVID-19 crisis. The results provide evidence of a substantial increase in firms' linear exposure during the COVID-19 period. The cross-sectional analysis reveals that the effects of firm-level variables on exposure are more pronounced during crisis periods and are different from non-crisis periods. Firms that have effective asset utilization or large operating profit margins remain less exposed during times of stress. Contrary to hedging theory, firms that have high incentives to hedge such as firms with high financial leverage become highly exposed to currency fluctuations during crisis periods. The interaction analysis provides further evidence that firms with high leverage can limit their foreign exchange exposure during periods of crisis if they have high asset turnover or high operating profits. The results offer important practical implications to firms for risk management during periods of crisis.

Conditional dependence structure and risk spillovers between Bitcoin and fiat currencies

This paper investigates the extreme dependence and risk spillovers between Bitcoin and the currencies of the BRICS and G7 economies. We find time-varying dependence between Bitcoin and all currencies. Moreover, when analysing risk spillovers from Bitcoin to currencies, we find that Bitcoin exercises significant power over most currencies, with the South African rand and Brazilian real holding both the highest downside and upside risk before and during the COVID-19 pandemic period, respectively. When considering risk spillovers from currencies towards Bitcoin, the Japanese yen exhibits the highest downside spillovers. Importantly, we find asymmetric spillovers between extreme upward and downward movements. • We study dependencies between Bitcoin and the currencies of the BRICS and G7 economies. • We find time-varying dependence between Bitcoin and all of the fiat currencies. • Bitcoin exercises significant power over most of the considered currencies. • We find asymmetric spillovers between extreme upward and downward movements. [ FROM AUTHOR] Copyright of Emerging Markets Review is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Effect of Remittance Inflows on External Debt in Developing Countries

This paper analyzes the effect of remittance inflows on external debt in developing countries, by identifying international reserves as a potential transmission channel. Using panel data over the period 1970–2017 and covering 50 low-and middle-income countries worldwide, we find a positive and significant effect of remittance inflows on the external debt-to-GDP ratio. We also find a negative and significant effect of international reserves on external debt. After controlling for international reserves, the effect of remittance inflows on external debt increases;it remains positive and significant. The results suggest that the role of international reserves as a self-insurance mechanism, and the Dutch disease effect related to remittance inflows are at play. In addition, we find negative and significant effects of economic growth and savings-investment gap on external debt. We also find positive and significant effects of the nominal exchange rate and the United States lending interest rate on external debt. We discuss the policy implications of these findings, while highlighting factors that policymakers should focus on for containing external debt in developing countries in the post-COVID-19.

Deep CNN and LSTM Architecture-Based Approach for COVID-19 Detection

The severe acute respiratory syndrome(SARS-CoV2) led to a pandemic of respiratory disease, namely COVID19. The disease has scaled worldwide and has become a global health concern. Unfortunately, the pandemic not just cost several individuals their lives but also, resulted in many people losing their jobs and life savings. In times like these, ordinary people become fearful of their resources in a world that gives its best resources to the wealthiest beings. Following the pandemic, the world suffered greatly and survival was rather difficult. As a result, numerous analytical techniques were developed to address this issue, with the key one being the discovery that the efficacy of clinically tested vaccines is actually quite poor. When researchers and medical professionals were unable to find a cure, radiologists and engineers created techniques to detect infected chests with the help of X-rays. Our proposed solution involves a CNN + LSTM model which has secured an accuracy of 98% compared to 95% of the trusted VGG-16 architecture. Our model's area under the curve (AUC) scores reached 99.458% while using RMSprop. A crucial feature of image processing till depth is accessible through scanning features from the layers of images using CNN. Our model uses 5 convolution blocks to detect the features. The coordination of activator functions, learning rates, and flattening has enabled accurate in-point predictions. With merely X-rays, models like ours ensure that anyone can easily detect covid-19. The best results obtained were at a learning rate =0.01 with RMSprop and Adam functions. The model has good fortune in detecting any other lung disease which occurs in the near future, as our data collectively rounds up to 4.5 gigabytes of data providing higher precision. © 2023 IEEE.

Effect of COVID-19 on Alzheimer's and dementia measured through ocular indications

Screening for early detection of Alzheimer's disease (AD) through a comprehensive eye exam appears to be promising and could potentially provide a more sensitive, inexpensive way to visualize early signs of AD for early detection in large populations. Optical coherence tomography (OCT), as well as retinal imaging techniques such as Doppler and fluorescence lifetime imaging ophthalmoscopy (FLIO), can detect signs of early AD such as vascular changes or accumulations of Tau proteins and beta-amyloid proteins. In the age of COVID-19, this screening opportunity is threatened by increased no-show rates leading to decreased early detection of AD. Through the combination of COVID-19 neuroinflammation potentially augmenting AD neurodegeneration, as well as missed opportunity in the use of early ophthalmic detection, the pandemic may have significantly worsened the trajectory of AD. © 2023 Elsevier Inc. All rights reserved.

Assessing the Impact of Disruptive Events on Urban Mobility: A Case Study of Chicago Taxis during COVID-19

The rise of Transportation Network Companies (TNCs) over the last decade has significantly disrupted the taxi industry. Studies have shown that taxi ridership has plummeted, and their capacity utilization rates are lower than 50% in five major U.S. cities. Additionally, the COVID-19 pandemic has dealt a severe blow to the already struggling taxi industry. To monitor the evolution of the taxi industry and its impacts on society, our study evaluates changes in the utilization rates, fuel consumption, and emissions among Chicago taxis, using taxi data with rich information on trip profiles from pre-pandemic and pandemic times. Our findings indicate that the taxi utilization rate decreased during the pandemic. While fuel consumption and emissions per kilometer decreased thanks to the reduced traffic during the pandemic, the overall fuel consumption and emissions increased due to increased deadhead travel. The methods developed in this study can be applied to monitor and evaluate the impact of future disruptive events on urban mobility and transportation systems more effectively. By utilizing mobility data to better understand transportation systems, we can develop more efficient, sustainable, and resilient mobility solutions for smart cities. © 2023 ACM.

Identifying the Influencing Factors of Depressive Symptoms among Nurses in China by Machine Learning: A Multicentre Cross-Sectional Study

Background. Nurses' high workload can result in depressive symptoms. However, the research has underexplored the internal and external variables, such as organisational support, career identity, and burnout, which may predict depressive symptoms among Chinese nurses via machine learning (ML). Aim. To predict nurses' depressive symptoms and identify the relevant factors by machine learning (ML) algorithms. Methods. A self-administered smartphone questionnaire was delivered to nurses to evaluate their depressive symptoms;1,431 questionnaires and 28 internal and external features were collected. In the training set, the use of maximum relevance minimum redundancy ranked the features' importance. Five ML algorithms were used to establish models to identify nurses' depressive symptoms using different feature subsets, and the area under the curve (AUC) determined the optimal feature subset. Demographic characteristics were added to the optimal feature subset to establish the combined models. Each model's performance was evaluated using the test set. Results. The prevalence rate of depressive symptoms among Chinese nurses was 31.86%. The optimal feature subset comprised of sleep disturbance, chronic fatigue, physical fatigue, exhaustion, and perceived organisation support. The five models based on the optimal feature subset had good prediction performance on the test set (AUC: 0.871–0.895 and accuracy: 0.798–0.815). After adding the significant demographic characteristics, the performance of the five combined models slightly improved;the AUC and accuracy increased to 0.904 and 0.826 on the test set, respectively. The logistic regression analysis results showed the best and most stable performance while the univariate analysis results showed that external and internal personal features (AUC: 0.739–0.841) were more effective than demographic characteristics (AUC: 0.572–0.588) for predicting nurses' depressive symptoms. Conclusions. ML could effectively predict nurses' depressive symptoms. Interventions to manage physical fatigue, sleep disorders, burnout, and organisational support may prevent depressive symptoms.

What is the Future of Survey-Based Data Collection for Local Government Research? Trends, Strategies, and Recommendations

Surveys are an important vehicle for advancing research on urban policy and governance. The introduction of online tools eased survey-based data collection, making it cheaper and easier to obtain data from key informants like local elected officials or public administrators. However, the utility of web-based survey administration may be diminishing. To investigate this dynamic and search for strategies to support survey research in urban studies, we perform a systematic review of survey research in urban policy and administration scholarship and conduct an original survey follow-up experiment. Our findings identify a clear downward trend in survey response rates that was accentuated during the COVID-19 pandemic. Results from our survey experiment show distinctly different costs per solicitation and per completed survey, depending on administration mode. These findings stimulate discussion on how scholars may continue to use surveys to generate high-quality, empirically rigorous research on urban affairs in light of recent trends. [ FROM AUTHOR] Copyright of Urban Affairs Review is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The Course of the Russian Ruble Exchange Rate During the COVID-19 Pandemic: Trends and Influencing Factors

This article analyzes the impact of the following major factors influencing the Russian ruble exchange rate: oil prices, inflation, the balance of payments flows, and volatility, which are all considered against the background of the COVID-19 pandemic. The analysis reveals that all these factors continued to play a role in 2020, though the shock of the pandemic exhibited certain specific features.

A research on the using of bank loans in accommodation companies traded in Borsa Istanbul

The main purpose of this research is to analyze the using of bank loans provided by the banking sector in accommodation companies traded in Borsa Istanbul in terms of type, maturity and cost. The study also examined the impact of the Covid-19 outbreak on the accommodation companies' use of bank loans. In this context, the level of bank loan usage, the type of bank loans, interest rates, maturity and their distribution in currency between the years 2009 and 2021 were tried to be determined by ratio and document analysis. As a result of the analysis, it was determined that 10,84% of the assets in accommodation companies are financed by bank loans, the use of bank loans in total liabilities is 19.92% and short-term bank loans are preferred. It was also detected that accommodation companies mainly benefit from business loans, daily spot loans, revolving loans, current account loans, foreign exchange earning loans, vehicle loans and investment loans in Turkish Lira, Dollar, Euro and Sterling with interest rates varying every year. However, compared to the pre-Covid-19 outbreak period, it was observed that the level of bank loans used by accommodation companies first decreased, but then increased again.

Trade-based money laundering and informal remittance services: implications on the sustainability of the balance of payments of a small open economy

PurposeThis study aims to discuss the consequences of trade-based money laundering (TBML) and informal remittance services on the sustainability of the position of balance of payments and net foreign assets of a small open economy.Design/methodology/approachThis paper uses a case study design using facts related to TBML and informal remittance services on the balance of payment and net foreign assets of Sri Lanka.FindingsThe contextual analysis reveals that the growth of the informal economy promotes informal remittance services in Sri Lanka. The policy decision to peg local currency to US dollars as a result of a shortage of foreign exchange had forced people to use informal channels for different purposes. The unclear and vague customer due diligence process of the anti-money laundering and countering the financing of terrorism (AML/CFT) regime also has forced people to use informal remittance services. Criminals especially drug traffickers have grabbed the promoted informal remittance services to transfer proceeds from Sri Lanka to overseas drug suppliers. On the other hand, systematic deficiencies in monitoring and regulation of movement of fund transfers and merchandise across borders provide opportunities for criminals to use different TBML techniques to transfer funds. These limitations force policymakers and regulators to think of developing a comprehensive payment ecosystem to prevent money laundering and terrorist financing. Therefore, the global initiative is required to move towards a payment ecosystem from a recommendation-based AML/CFT regime to reduce global crimes.Research limitations/implicationsThis study was designed to discuss the implications of TBML and informal remittance services on the balance of payments and net foreign assets in a small open economy. The structure and size of the economy, the strength of the overall economy and the AML/CFT regime will play an important role in controlling criminal activities and combating money laundering of an economy;hence, the impact of TBML and informal remittance services will vary accordingly across the countriesOriginality/valueThis paper is an original work done by the authors, which discusses the implications of TBML and informal remittance services on the balance of payments and net foreign assets of an emerging market context.

BIST Tourism and BIST Electricity Index Relationship

In this study, which is based on daily data, the relationship between BIST electricity index and BIST tourism index was measured between 2012:M9 – 2022:M9 periods. The aim of the study is to measure the relationship between BIST electricity index and BIST tourism index. VAR Granger causality test was applied to determine whether there is any causal relationship between the variables. It has been determined as a result of the analysis that the BIST electricity index has no effect on the BIST tourism index. Two-way ineffectiveness was determined among the variables. In addition, it was obtained as a result of the analysis that the applied correlation relationship was weak between these variables. The results obtained from the study are important in terms of measuring the effects among BIST indices.

Spatial mapping of the incidence, case-fatality rate and recovery rate of COVID-19 in West Bengal: An epidemiology study

Information on the spatial and temporal distributions of COVID-19 cases is important for improved control, social distancing strategies and developing targeted prevention strategies. Towards this objective, we analyzed the spatial and temporal growth pattern of COVID-19 incidence and death counts in districts of West Bengal. This paper also analyzes the current trend or pattern of COVID-19 transmission in West Bengal. For this approach, COVID-19 data have been compiled from several sources, including the WHO, Ministry of Health and Family Welfare (MoHFW), and demographic data from Census of India (2011). This analytical study was conducted based on detailed data from 23 districts of West Bengal from May 31, 2020, till December 31, 2021. We used ArcGIS Software for map-making and different formulas to measure Incidence, CFR, and CRR, considering all possible scenarios. Up to December 31, 2021, Kolkata, the origin of the COVID-19 epidemic, had reported 337767 COVID-19 cases, while the confirmed cases in the surrounding districts North 24 Parganas, South 24 Parganas, and Howrah were 337091, 104268, and 102048, respectively. The top five districts with the highest incidence were Kolkata (7.51%), Darjeeling (3.66%), North 24 Parganas (3.36%), Kalimpong (2.85%), and Jalpaiguri (1.79%), had high risks of COVID-19. Therefore, identification of the case fatality, recovery rates, and spatiotemporal trends should be the first step to evaluate disease severity and develop effective policies to manage and control any new epidemic. These results are informative locally and useful for the rest of the world. © 2020 The author (s). Published by Zagazig University. This is an open access article under the CC BY 4.0 license https://creativecommons.org/licenses/by/4.0/.

Spatial Autocorrelation Test on the Average Unemployment in Indonesia

According to World Bank statistics in 2019, Indonesia ranked two in the average unemployment rate with 5.28% in South East Asia. Although the unemployment rate can be reduced by an equitable distribution of human resource empowerment and national development, the global pandemic COVID-19 made a major impact on increasing the rate of unemployment. This paper tests the spatial autocorrelation on the average unemployment in Indonesia using Ordinary Least Squares (OLS) and Moran's I. The OLS method was used to examine the effects that affect the unemployment rate using an independent variable. In contrast, the Moran's I used to prove the existence of spatial effect on the level of movement in Indonesia. From the experiment, there are four variables that influence the unemployment rate by using the OLS modeling method. The Moran's I test showed a p-value = 0.006 with α = 0.05. Therefore, there is a spatial autocorrelation between provinces in Indonesia. In addition, the model is tested using the Variance Inflation Factor. The model showed a VIF score ¡10, therefore there is no collinearity and the assumption is fulfilled. The model is also being tested using dwtest, bptest, and Lilliefors test. The result showed p-value = 0.6231 for dwtest, p-value = 0.932 for bptest, and p-value = 0.08438 for Lilliefors test.. © 2022 IEEE.

Dangerous liaisons? Applying the social harm perspective to the social inequality, housing and health trifecta during the Covid-19 pandemic

Global rates of excess mortality attributable to the Covid-19 pandemic provide a fresh impetus to make sense of the associations between income inequality, housing inequality and the social gradient in health, suggesting new questions about the ways in which housing and health are treated in the framing and development of public policy. The first half of the paper uses a social harm lens to examine the threefold associations of the social inequality, housing and health trifecta and offers new insights for policy analysis which foregrounds the production, transmission, and experience of various types of harm which occur within the home. The main body of the paper then draws upon the outcomes of an international systematic literature mapping review of 213 Covid-19 research papers to demonstrate three specific harms associated with stay-at-home lockdowns: (i) intimate partner and domestic violence, (ii) poor mental health and (iii) health harming behaviours. The reported findings are interpreted using a social harm perspective and some implications for policy analysis are illustrated. The paper concludes with a reflection on the efficacy of social harm as a lens for policy analysis and suggests directions for further research in housing studies and zemiology.

DETECTION OF SARS COV-2 ANTIBODIES FOLLOWING VACCINATION IN PATIENTS WITH RHEUMATIC MUSCULOSKELETAL DISEASE (DECODIR) – A REPORT FROM A DANISH PROSPECTIVE COHORT STUDY

BackgroundThe COVID-19 pandemic caused concerns whether patients with rheumatic musculoskeletal disease (RMD) treated with conventional (cs) or biologic (b) disease modifying drugs (DMARDs) and/or prednisolone exhibit an adequate immune response to the applied SARS-CoV2 vaccines.ObjectivesWe established the DECODIR study to assess and compare the efficacy of the SARS-CoV2 vaccines administered as part of the national vaccine roll-out: BNT162b2 vaccine (Pfizer/BioNTech) and mRNA-1273 vaccine (Moderna). The vaccines were offered as two doses four weeks apart;followed by a booster vaccination six months later. This national regimen included inflammatory rheumatic patients regardless of their respective anti-inflammatory treatment. We used patients' SARS-CoV2 IgG serum level as proxy for vaccination response (1).MethodsThe study was conducted as a longitudinal prospective cohort study. Patients with rheumatoid arthritis (RA), spondyloarthropathies (SpA) or psoriatic arthritis (PsA) receiving their outpatient treatment at the Danish Hospital for Rheumatic Diseases, Sonderborg, and monitored in the Danish DANBIO registry, were included.Blood samples, Disease activity and treatment information (cs/bDMARD, prednisolone) were collected at baseline (i.e. prior to vaccination), after six weeks, six and twelve months. SARS-CoV-2 IgG levels in serum were assessed by ELISA (Thermo-Fischer), and manufacturer's cut-off (>=10 EliA U/mL) selected as definition of sufficient IgG response. Antibody response was measured and compared at all four time points.Associations between antibody response, age, gender, disease (RA/PsA/SpA), treatment (none, cs/bDMARD or prednisolone) and disease activity were tested using proportional odds regression and bootstrapped tests of medians. Results were reported using mean, median (IqR) and bootstrapped 95% confidence interval (CI) of the median.ResultsA total of 243 patients were included at baseline and all were followed-up after six weeks;data from 233 patients were available at six months and for 229 patients at twelve months' follow-up. Those 229 patients had completed the national vaccination programme.The measurements performed 6 months after baseline demonstrated a per se decrease of IgG levels for the whole study population (median of 2.08 EliA U/mL at 6 months vs. 16 EliA U/mL at 6 weeks). The final measurements performed after twelve months demonstrated a significant increase of IgG levels. Thus, the completed vaccination programme, was followed by a significant increase in IgG levels (median of 100 EliA U/mL at twelve months vs. 16.5 EliA U/mL at six months, p < 0.001).Sufficient response rates were now recorded in all treatment scenarios, also in patients treated with prednisolone or combination of csDMARD and bDMARD. These two groups were at 6 months characterized by significant lower response rates, when compared with patients without any DMARD treatment.ConclusionCompleted vaccination programme defined as two doses plus booster vaccination resulted in a sufficient vaccination response as measured by IgG levels regardless of RA treatment.It is noteworthy that IgG levels increased markedly in patients treated with a combination of cs/bDMARD or oral prednisolone, who had low IgG levels (below manufacturer's cut-off >=10 EliA U/mL) after 6 months. Our results strongly support the efficacy of the complete vaccination programme including the 3rd booster vaccine in patients with inflammatory rheumatic diseases.Figure 1.Serum IgG-levels at baseline, 6 weeks, 6 months and 12 months;stratified by antirheumatic treatment. (Box plot showing median and interquartile range).[Figure omitted. See PDF]Reference[1]Schreiber K. et al. Reduced Humoral Response of SARS-CoV-2 Antibodies following Vaccination in Patients with Inflammatory Rheumatic Diseases— an Interim Report from a Danish Prospective Cohort Study. Vaccines 2022, 10(1), 35;https://doi.org/10.3390/vaccines10010035AcknowledgementsWe acknowledge all patients contributing to the DANBIO registry.The Danish Rheumatologic Biobank is a knowledged for handling and storage of biological material.Lab chieftechnician Charlotte Drachmann is acknowledged for her assistance.Disclosure of InterestsChristine Graversgaard: None declared, Karen Schreiber Speakers bureau: Lilly, UCB, Henning Jakobsen: None declared, Randi Petersen: None declared, Anders Bo Bojesen: None declared, Niels Steen Krogh: None declared, Bente Glintborg Grant/research support from: Pfizer, AbbVie, BMS, Sandoz, Merete Lund Hetland: None declared, Oliver Hendricks Speakers bureau: Pfizer, Lilly, Novartis.