There are limited data on the cost-effectiveness of smoking cessation interventions in lung cancer screening settings. We conducted an economic analysis embedded in a national randomized trial of 2 telephone counseling cessation interventions.We used a societal perspective to compare the short-term cost per 6-month bio-verified quit and long-term cost-effectiveness of the interventions. Trial data were used to micro-cost intervention delivery, and the data were extended to a lifetime horizon using an established Cancer Intervention Surveillance and Modeling Network lung cancer model. We modeled the impact of screening accompanied by 8 weeks vs 3 weeks of telephone counseling (plus nicotine replacement) vs screening alone based on 2021 screening eligibility. Lifetime downstream costs (2021 dollars) and effects  saved were discounted at 3%. Sensitivity analyses tested the effects of varying quit rates and costs; all analyses assumed nonrelapse after quitting.The costs for delivery of the 8-week vs 3-week protocol were $380.23 vs $144.93 per person, and quit rates were 7.14% vs 5.96%, respectively. The least costly strategy was a 3-week counseling approach. An 8-week (vs 3-week) counseling approach increased costs but gained QALYs for an incremental cost-effectiveness ratio of $4029 per QALY. Screening alone cost more and saved fewer QALYs than either counseling strategy. Conclusions were robust in sensitivity analyses.Telephone-based cessation interventions with nicotine replacement are considered cost-effective in the lung screening setting. Integrating smoking cessation interventions with lung screening programs has the potential to maximize long-term health benefits at reasonable costs. Lung cancer is the leading cause of cancer death in the United States . Lung caScreening can be a teachable moment, providing the opportunity to motivate individuals who currently smoke to quit . SmokingPrior studies have suggested that smoking cessation interventions offered at the point of screening could be cost-effective ,33–35. HWe used data from a multisite national randomized trial ,37 conduThis trial evaluated the impact of an 8-week vs 3-week telephone-based counseling intervention with nicotine replacement on smoking cessation among participants recruited between May 2017 and January 2021 from 8 US lung cancer screening facilities. Study procedures were approved by the Georgetown University and Lahey Hospital and Medical Center institutional review boards. Details of the trial have been published elsewhere .Briefly, participants were smokers  who met the National Comprehensive Cancer Network’s eligibility criteria for lung cancer screening (aged 50-80 years with 20+ pack-years) in place of the start of the trial . All parWe used the CISNET University of Michigan lung cancer smoking and screening model ,38 and tWe simulated the smoking histories of 1 million men and 1 million women from the 1960 US birth cohort from age 45 to 90 years. We selected this cohort because they have smoking patterns representative of the current US population eligible for lung cancer screening. We started the simulations at age 45 years to generate the population alive at the time of first eligibility for lung screening. Whereas the model follows each person until death, we report results through age 90 years because few cases or smokers are alive beyond 90 years. Smoking history for each person includes age at starting smoking, age at quitting smoking (if quit), and the number of daily cigarettes smoked at each age while smoking. These data are used to determine lung cancer screening eligibility. We modeled 100% screening uptake and adherence under screening eligibility based on USPSTF 2021 guidelines  included fixed costs and variable costs per participant  and wholesale costs of nicotine replacement therapy . Time coLung cancer screening and diagnostic costs included Medicare reimbursement rates for screening and follow-up procedures 47). Costs of lung cancer treatments by age, stage, histology, and phase of care  were based on Surveillance, Epidemiology, and End Results Program–Medicare data for smokers, with costs inflated to 2021 US dollars using a 3% annual inflation rate . Costs o. We usedThe age- and sex-specific utilities of smokers without lung cancer were based on a recent study . Among tWe compared the average and incremental short-term costs per 6-month bio-verified quit rate for the 8-week vs 3-week telephone counseling strategy. In sensitivity analyses, we varied quit rates across the upper and lower bounds of intervention efficacy, combinations of cost ranges and efficacy , and bio-verified quit rates at 12-month follow-up. Additionally, we varied the background no-intervention quit rates in the Tobacco Use Supplement to the Current Population Survey over its 95% confidence interval.The simulation model extended the trial results to evaluate the impact of the trial’s cessation interventions over a lifetime horizon. The results were compared across the 3 strategies using incremental cost-effectiveness ratios, where results are arrayed from the least to the costliest strategies and then the added quality-adjusted life-years (QALYs) gained in the next-most costly strategy is divided by its added costs. If a strategy costs more and has fewer QALYs, it is considered dominated because it would not be a recommended approach. If a strategy costs less and gains more life-years, it is considered cost-saving. We used the results of the sensitivity analysis of short-term costs per quit per trial arm to test the impact of these different assumptions on conclusions about the incremental ranking and magnitude of costs per QALY.An 8-week telephone counseling intervention accompanied by nicotine replacement therapy in the setting of lung screening was 2.6 times more costly per person than a 3-week regimen with nicotine replacement therapy . The timWhen the costs and quit results of the 2 counseling intervention strategies delivered in the setting of lung cancer screening were extended over the lifetime and compared with screening alone, both 3-week and 8-week strategies cost less and saved more QALYs than screening alone . CompareThis is the first study, to our knowledge, to conduct an economic evaluation of a large, national clinical trial of a telephone counseling intervention for smokers at the time of lung cancer screening and to use those data to project the lifetime costs and effects. The costs of the intervention program were modest and primarily driven by counselor time costs and provision of nicotine replacement therapy to smokers at no cost. Combined with screening, the modest quit rates seen in either a 3-week or 8-week telephone counseling program with nicotine replacement led to lower costs and more QALYs than screening alone. The added costs of using an 8-week (vs 3-week) counseling regimen was largely offset by higher quit rates, leading to very low incremental costs per QALY saved over a lifetime. These results provide important evidence for the value of smoking cessation in the setting of lung cancer screening and underscore the need to maintain reimbursement policies supporting this approach for the millions of screening-eligible smokers in the United States.Previous economic analyses of smoking cessation interventions conducted in health care or community settings have reported costs per quit of less than $4200 . We exteThe cessation rates observed in the trial were modest and somewhat lower than seen in other trials in the health care setting ,53. ThisThe costs per QALY for tobacco cessation and lung screening were similar to or lower than many other cancer screening modalities. For example, the costs per LYS used to support mammography screening as a covered Medicare benefit was $34 000 to $88 000 in 1989 dollars . The cosSeveral of our past modeling studies have assessed the potential impact of hypothetical cessation interventions in the lung screening setting, and the results suggested that adding a cessation intervention to lung cancer screening could extend the benefits of screening alone ,33–35. TAlthough these results highlight the value of considering long-term health outcomes when investing in implementation of smoking cessation interventions, they also underscore challenging issues in implementation of effective cessation strategies. For instance, the costs of delivering telephone counseling will be borne by health systems, clinics, and quitlines, with a potential negative impact on their budgets, whereas the benefits and cost offset occur downstream in other sectors of the health care system . New polThis study has several important strengths. We used data collected from a randomized clinical trial in the lung cancer screening setting. One-half of the trial participants were not ready to quit within the next 30 days , similarDespite these strengths, several caveats should be considered in evaluating our results. Because of the model structure and purpose of modeling lung cancer, other tobacco-related conditions are not modeled directly but are captured in other-cause competing mortality by age, sex, and smoking history. Therefore, we cannot readily incorporate quality of life and costs associated with other diseases. Because smoking cessation leads to lower tobacco-related disease incidence , higher Overall, this study demonstrates that even modest cessation rates achieved with telephone-based cessation interventions with nicotine replacement for smokers attending lung screening can lead to savings in costs and lives compared with screening alone and would be considered very cost-effective. Moving forward, it will be critical to test the impact of reimbursement policies that maximize delivery of smoking cessation and conduct dissemination and implementation trials to determine the most feasible and cost-effective smoking cessation interventions in the lung screening setting.This study was supported by the National Cancer Institute at the National Institutes of Health (grant numbers R01CA207228 and R01CA207228-S1). Financial support for this study was also provided in part by the National Cancer Institute Grants U01CA199284 and U01CA253858 to Drs Cao, Jeon and Meza and National Cancer Institute of the National Institutes of Health under Award Number K99CA241397 to Dr Jayasekera and R35CA197289, UO1CA152958, and UO1CA199218 to Dr Mandelblatt.Role of the funder: The funders had no role in the design of the study, the collection, analysis, and interpretation of the data, the writing of the manuscript, and the decision to submit the manuscript for publication.Disclosures: The authors have no conflict of interest to disclose.Author contributions: Conceptualization—PC, JSM, J Jeon, KLT, RM, J Jayasekera. Data curation—PC, LS. Formal analysis—PC, LS, J Jayasekera. Funding acquisition—JSM, KLT, DTL, RM. Investigation—PC, LS. Methodology—PC, JSM, J Jeon, RM, J Jayasekera. Project administration—PC, LS, KLT, AZ. Supervision—JSM, KLT, DTL, RM, J Jayasekera. Validation—PC, LS, J Jeon, RM, J Jayasekera. Visualization—PC, LS. Writing—original draft: PC, LS, JSM, J Jeon, RM, J Jayasekera. Writing: review & editing: PC, LS, JSM, J Jeon, KLT, AZ, DTL, RMW, RM, J Jayasekera.pkac048_Supplementary_DataClick here for additional data file.


Hand hygiene is at the core of effective infection prevention and control (IPC) programmes. 10 years after the development of the WHO Multimodal Hand Hygiene Improvement Strategy, we aimed to ascertain the level of hand hygiene implementation and its drivers in health-care facilities through a global WHO survey.From Jan 16 to Dec 31, 2019, IPC professionals were invited through email and campaigns to complete the online Hand Hygiene Self-Assessment Framework (HHSAF). A geospatial clustering algorithm selected unique health-care facilities responses and post-stratification weighting was applied to improve representativeness. Weighted median HHSAF scores and IQR were reported. Drivers of the HHSAF score were determined through a generalised estimation equation.3206 unique responses from 90 countries (46% WHO Member States) were included. The HHSAF score indicated an intermediate hand hygiene implementation level , which was positively associated with country income level and health-care facility funding structure. System Change had the highest score , whereby alcohol-based hand rub at the point of care has become standard practice in many health-care facilities, especially in high-income countries. Institutional Safety Climate had the lowest score . From 2015 to 2019, the median HHSAF score in health-care facilities participating in both HHSAF surveys (n=190) stagnated.Most health-care facilities had an intermediate level of hand hygiene implementation or higher, for which health-care facility funding and country income level were important drivers. Availability of resources, leadership, and organisational support are key elements to further improve quality of care and provide access to safe care for all.WHO, Geneva University Hospitals and Faculty of Medicine, and WHO Collaborating Center on Patient Safety, Geneva, Switzerland. Health-care-associated infections (HAIs) affect the quality of health-care services, jeopardising patient safety and increasing health-care costs. Up to 2·6 million HAIs occur every year in the EU and European Economic Area (approximately 500 per 100 000 inhabitants), resulting in more than 91 000 deaths.Improvement in hand hygiene compliance has been highlighted as the most effective measure to reduce transmission of pathogenic microorganisms in health care and lower the incidence of HAI in health-care settings,In response to these challenges, WHO has placed strong emphasis on improving hand hygiene practices globally. In 2009, WHO launched the Multimodal Hand Hygiene Improvement Strategy (MMIS) together with the Implementation Toolkit, which includes the Hand Hygiene Self-Assessment Framework (HHSAF),Evidence before this studymedRxiv, bioRxiv, arXiv, and WHO Global Health Databases for peer-reviewed articles or preprints in English, French, and Spanish that reported international assessments of hand hygiene programme implementation at the health-care facility level according to the WHO validated Hand Hygiene Self-Assessment Framework, between Jan 1, 2010, and April 30, 2021. The search strategy used the terms “healthcare-associated infection”, “infection prevention”, “hand hygiene”, “multimodal strategy”, “alcohol-based hand rub”, “hand hygiene self-assessment framework”, “organizational culture”, and similar terms. We identified one international report, from WHO, using the Hand Hygiene Self-Assessment Framework, which reported data from surveys in 2011  and 2015 , focused on the status of hand hygiene programmes and the progress of hand hygiene implementation over time of only 86 health-care facilities. The study found that 68 of 86 participating health-care facilities had an intermediate or advanced level of hand hygiene implementation, which had increased significantly between 2011 and 2015. System Change was the highest scoring element, while Institutional Safety Climate was the lowest. In addition, a number of reports are available from the regional, national, or health-care facility level, including the following WHO regions; Americas (USA), Europe , Eastern Mediterranean (Pakistan), Africa (South Africa), and the Western Pacific . These studies reported an intermediate or advanced level of hand hygiene implementation in health-care facilities of high-income countries, but a basic or inadequate level in health-care facilities of low-income countries. Overall, System Change was the element that scored the highest while Institutional Safety Climate scored the lowest.We searched PubMed, Added value of this studyTo our knowledge, this is the largest global survey  assessing the characteristics, implementation, and progress of hand hygiene programmes worldwide using a validated tool based on the WHO Multimodal Hand Hygiene Improvement Strategy. Compared with earlier efforts, it includes over a quarter of all low-income and lower-middle income countries and provides a global situation analysis of hand hygiene implementation across all income levels in the six WHO regions . Moreover, representativeness of results was optimised through rigorous methodology; ministries of health were actively approached to increase response rates, unique responses per health-care facility were selected, a threshold was applied to exclude countries with low (and possibly biased) response rates, and post-stratification weighting was applied for important factors that could influence scores. In addition, multivariable models were applied to identify the most important drivers of hand hygiene implementation. In this 2019 global survey, we observed large hand hygiene implementation differences across country income levels, and across health-care facility's funding structure (private versus public funding); availability of more resources was associated with significantly higher scores. Through a geospatial clustering algorithm, we were also able to identify and compare results of 190 health-care facilities that participated in the survey in 2015 and 2019, showing that the level of hand hygiene implementation has stagnated.Implications of all the available evidencevs private funding). A quarter of health-care facilities, mostly from low-income countries, have a basic or inadequate level of hand hygiene implementation with lack of resources hampering improvement. Our findings highlight that, in low-income countries, Evaluation and Feedback  is the weakest element of hand hygiene implementation; in these regions immediate and systematic feedback on hand hygiene performance needs strengthening, and alcohol-based handrub consumption needs to increase. In high-income countries, Institutional Safety Climate was still the lowest scoring element, indicating that leadership engagement and assignment of hand hygiene leaders is a complex hurdle. This finding is further underlined by the fact that the level of hand hygiene implementation has stagnated in the 190 health-care facilities participating in the 2015 and 2019 survey. Further improvement is needed if health systems worldwide are to provide safe health care, with the ability to prevent and control outbreaks, particularly in low-income settings and publicly funded health-care facilities. The call for a stronger implementation of effective hand hygiene programmes is urgent, as demonstrated by the COVID-19 pandemic, which underlined how infection prevention and control is our first, and most important line of defence. The WHO Hand Hygiene Self-Assessment Framework is a useful model to set-up, evaluate, and improve the organisation of hand hygiene promotion strategies, as a fundamental aspect of an effective infection prevention and control programme, to reduce pathogen transmission, and provide safe health-care for all.This study shows that most participating health-care facilities have an intermediate level of hand hygiene implementation or higher, although this was positively associated with country income level and health-care facility funding structure . Local ethics was not sought or needed because no personal information was collected.Between Jan 16 and Dec 31, 2019, WHO implemented a worldwide, cross-sectional survey for health-care facilities, focusing on the HHSAF, as well as the Infection Prevention and Control (IPC) assessment framework.SAVE LIVES: Clean Your Hands campaign, and promotion through the WHO website, social media, WHO Global IPC Network, WHO Partnership for Patient Safety, and IPC conferences. WHO Headquarters approached WHO regional and country offices to promote participation at national level. In 38 of 194 WHO Member States, ministries of health expressed interest to implement a nationally coordinated data collection.Since national health-care facility registries are not systematically available, a convenience sample of health-care facilities was accepted per country. For global representativeness, we aimed for a minimum participation of 25% of WHO Member States from each WHO region, and each World Bank income level, as a balance between improving respresentativeness compared with the previous HHSAF survey (19% participation from low-income countries) and being realistic. Health-care facilities were invited to participate through email sent from the global WHO IPC team to 22 144 IPC contact persons from 182 WHO Member States registered to the After database lock , three selection steps were applied to the received survey responses. In step 1, data from all surveys that fully completed at least one HHSAF element were selected. In step 2, multiple responses per health-care facility were identified through a geospatial clustering algorithm, and a single response per health-care facility was selected using a predefined selection strategy. Two strategies were applied to improve global representativeness: responses from countries with a ratio of number of survey responses per capita in the lowest ventile were excluded from the analysis (selection step 3), and post-stratification weighting was applied through raking, including country, World Bank country income level,All reported medians are weighted. Descriptive, absolute frequencies and proportions were reported ie, n in , as wellTo determine the independent association between factors considered as strata and the total HHSAF score, a generalised estimating equation (GEE) model was applied with robust standard errors  to accommodate clustering at country level, as well as post-stratification weights. The Box-Cox log-likelihood test was used to assess appropriateness of linear regression. No variable selection procedure was applied, as all available factors were considered relevant.The previous HHSAF global survey was conducted from June, 2015, to January, 2016 (2015 survey);All analyses were carried out using R, version 3.6.1. p values less than 0·05 were considered statistically significant.WHO with support from the WHO Collaborating Centre on Patient Safety conceived the study design and carried out data collection, data analysis, data interpretation, and writing of the Article. All authors had full access to the study data and accept responsibility to submit for publication.3982 HHSAF survey responses with at least one fully completed survey element were received from 109 countries, 34 of which had a nationally coordinated data collection initiative. After geospatial clustering, 3372 unique health-care facilities were identified. One to 504 responses per country were received, for which the ratio of responses per capita ranged from 4·849 per 100 000 to 0·001 per 100 000 and the lowest ventile cut-off was 0·012 per 100 000. After application of the threshold, 3206 unique responses remained from 90 countries (46% of WHO Member States), 3048 of which were complete surveys, and 158 of which had between one and four elements completed .The Americas (22 [63%] of 35) and the Eastern Mediterranean region (13 [62%] of 21) had the highest proportions of countries responding, followed by South-East Asia (six [55%] of 11), Europe (22 [42%] of 53), Africa (18 [38%] of 47), and the Western Pacific region (nine [33%] of 27). The proportion of participating countries was highest for upper-middle-income categories (31 [57%] of 54) and high-income categories (34 [55%] of 62). Country participation was eight (28%) of 29 for low-income categories and 17 (35%) of 49 for lower-middle-income categories .Figure 2The overall, weighted median HHSAF score was 350 points (IQR 248–430), which reflects an intermediate hand hygiene level, ranging from 233  in low-income countries to 395  in high-income countries . OverallThe multivariable regression analysis confirmed a significant, positive association between total HHSAF score and country income level, with a difference of −137·9 points (95% CI −79·9 to −195·9) between health-care facilities from low-income and high-income countries. For health-care facilities from lower-middle-income countries, this difference was smaller (−75·7 points [–134·6 to −16·7]), and no significant difference was found between health-care facilities from upper-middle-income or high-income countries. Private health-care facilities also scored significantly higher than public health-care facilities  for System Change to 55 (35–75) for Institutional Safety Climate, whereby the positive association with country income level was reflected as well .The weighted score for System Change ranged from 100 (IQR 85–100) in high-income countries to 45 (30–71) in low-income countries . Large dTraining and Education had a weighted median score of 75 (IQR 45–90), ranging from 85 (65–95) in high-income countries to 40 (32–60) in low-income countries . A largeThe weighted median score for Evaluation and Feedback was 70 (IQR 40–85), ranging from 75 (55–90) in high-income countries to 34 (15–65) in low-income countries, the lowest scoring element in the low-income stratum . Hand hyvs 759 [72·0%] of 1054 systematic feedback and 801 [75·9%] of 1054 for facility leadership in high-income countries). 960 (31·3%) of 3068 health-care facilities reported having the recommended ABHR consumption of at least 20 L per 1000 patient days, with 28 (9%) of 331 health-care facilities in low-income versus 380 (36·1%) of 1054 in high-income countries . 2166 (70·6%) of 3068 health-care facilities provide immediate feedback to health-care workers at the end of each observation session; however, just over 60% of health-care facilities provide systematic feedback to health-care workers (nw 1904 [62·1%] of 3068) or facility leadership (nw 1972 [64·%] of 3068). In low-income countries these proportions were almost half those of high-income countries (127 [42·8%] of 331 for systematic feedback and 113 [34%] of 331 for facility leadership in low-income countries ountries .The weighted median score for Reminders in the Workplace was 70 (IQR 50–95), ranging from 50 (30–70) in low-income countries to 75 (60–95) in high-income countries . More thvs 44 [14%] of 314 in low-income countries). More than half of health-care facilities (1796 [58·7%] of 3059) had established hand hygiene institutional targets (731 [70·4%] of 1038 in high-income countries vs 50 [16%] of 314 in low-income countries). Less than 40% of health-care facilities had hand hygiene role models (1111 [36·3%] of 3059) or champions (1196 [39·1%] of 3059). This proportion ranged from 26 (8%) of 314 for hand hygiene role models and 62 (20%) of 314 for hand hygiene champions in low-income countries up to 431 (41·5%) of 1038 for hand hygiene role models and 489 (47·1%) of 1038 for hand hygiene champions in high-income countries. A similar trend was seen for public versus private health-care facilities  with no large differences in high-income (65 [45–85]) versus low-income (50 [30–59]) countries. 916 [29·9%] of 3059 health-care facilities had implemented a formalised programme of patient engagement  in 2015 and 430 (385–460) in 2019. This corresponded to a non-significant, paired median difference in total HHSAF score of −1 (−25 to −35). The total HHSAF score increased significantly for high-income countries by 18 points p=0·026; . A signivs public). About a quarter of health-care facilities, mainly from low-income countries, reported basic or inadequate levels of hand hygiene implementation. The lowest scoring HHSAF element was Institutional Safety Climate, where lack of patient engagement and absence of hand hygiene leaders were identified. For low-income countries, Evaluation and Feedback had the lowest score due to infrequent immediate and systematic feedback of hand hygiene performance, and low or unknown rates of ABHR consumption.10 years after the development and first application of the HHSAF tool based on the WHO MMIS, this global survey provides a snapshot of the hand hygiene implementation level in 3206 health-care facilities from 90 countries. According to the survey, more than half of the health-care facilities have achieved an intermediate hand hygiene level (median 350 points [IQR 248–430]), although this varied widely with country income level and health-care facility funding structure  in 2015, indicating that only a selected number of improvements could still increase the score. This is underlined by the fact that the only HHSAF element with a significant increase was Institutional Safety Climate, the lowest scoring element.Based on the survey, the level of hand hygiene implementation seems highly dependent on available resources; lower scores across all elements were observed for health-care facilities from low-income versus high-income countries, but also from public versus private health-care facilities, with little variation. This included scores for indicators that could be proxies for availability of resources, like a dedicated budget for ABHR, or continuous availability of hand hygiene supplies. Lack of dedicated IPC budget in health-care facilities increases the risk of HAIs. Data from the International Nosocomial Control Consortium (INICC) surveillance programme have also shown that device-associated infection rates were strongly associated with socioeconomic levels of participating countries.Increasing funds alone will not be sufficient to improve hand hygiene implementation. The HHSAF element with the lowest overall score was the Institutional Safety Climate, among which indicators like hand hygiene champions and role models, and patient engagement are most frequently not fulfilled, especially in low-income countries. For these countries, systematic feedback to leadership, in the element Evaluation and Feedback, also scored low. While this finding might partly be related to resources, it also indicates that leadership engagement, and organisational support are important elements that could further enhance hand hygiene implementation. INICC identified a link between low-income and lack of legally enforceable IPC regulations and absence of hospital accreditation. In line with our findings, private institutions, for example, had stronger accreditation systems, which included funding for IPC programmes.The element Evaluation and Feedback for health-care facilities from low-income countries marked the lowest element-specific and stratum-specific score. Similarly, other reports have shown low levels of surveillance and monitoring of IPC-related indicators in low-income countries, including hand hygiene compliance and ABHR consumption.Implementation of elements of the WHO MMIS, such as Training and Education, Reminders in the Workplace, and System Change have already reached high levels in the majority of health-care facilities. Indicators like ABHR availability at the point of care, regular hand hygiene training, or posters in wards seem to have become part of routine care in many health-care facilities, especially in high-income countries. These might represent the most easily achievable indicators, because they do not require important organisational changes, and are visible modifications. Elements or indicators with generally lower scores, such as Institutional Safety Climate or patient engagement, depend more on culture change and excellent practice levels throughout the organisation.The WHO Save Lives: Clean Your Hands campaign is annually celebrated on May 5, and provides an opportunity for IPC teams to actively participate and engage leaders and policy makers in hand hygiene promotional activities. In the context of the current COVID-19 global pandemic, there has been an increased focus by WHO and other global partners to support hand hygiene implementation progress worldwide,Global health security relies on effective IPC, of which hand hygiene is an important element. Globally, a number of initiatives have been launched to support improvement of hand hygiene implementation with a clear focus on local solutions. Our study has some limitations. It was not possible to apply a random sampling approach because a complete list of health-care facilities is not systematically available at a global, or national level. Therefore, this was a self-selecting survey, risking attracting only those health-care facilities that are motivated to invest in hand hygiene practices, possibly resulting in an overestimation of the global HHSAF score. This possibility is partly corroborated by the finding that 81% of participating health-care facilities reported regular hand hygiene training in their facility. Nevertheless, comparisons across strata and over time would still be valid if the impact of selection bias has the same size and direction. If multiple responses from the same health-care facility were available, a single response per health-care facility was selected using a predefined selection strategy. This selection could have reduced representativeness of the response, but it also prevented double counting. Moreover, averaging of categorical question answers would not have been meaningful, and in most cases a more complete survey response was selected. In addition, the overall response rate, and country participation from certain regions (Western Pacific) and income levels (low-income countries) was low, which could have reduced global representativeness. Although it is difficult to estimate the overall response rate, it could be approximately 20%, taking the number of contacts in the SAVE LIVES: Clean Your Hands database as an approximate basis . This percentage is higher than in previous HHSAF surveysThis global HHSAF survey included data from 3206 health-care facilities in 46% of all WHO Member States worldwide. Representativeness of results was improved by selecting unique health-care facility responses only, and by applying a threshold to exclude countries with low response rates and post-stratification weights. The reported findings are based on a validated tool, providing the possibility of benchmarking hand hygiene implementation over time and place. This global survey shows that most participating health-care facilities have an intermediate or higher level of hand hygiene implementation, but the level is still basic or below in a quarter of health-care facilities, mostly from low-income countries. The Institutional Safety Climate is one of the most difficult areas to implement, and for low-income countries Hand Hygiene Evaluation and Feedback has most room for improvement. Availability of resources, leadership, and organisational support are key elements to further improve quality of care worldwide and provide access to safe care for all.The research protocol for this study included a commitment by WHO to restrain from publicly sharing results from individual health-care facilities, or results per country, to improve participation and minimise social desirability bias. Since aggregated results  are already available from the tables in the Article and in the appendix, no other data will be shared.All authors declare no competing interests.


The study aims to investigate the impact of COVID-19 pandemic on physical activity and frequency of implantable cardioverter-defibrillator (ICD) therapies of patients with cardiac implantable electronic devices.Physical activity, heart rate and ICD-therapies were assessed via routine remote monitoring over two years. We focussed on a 338-day period during COVID-19 pandemic that was divided in 6 time-intervals defined by public health interventions and compared to the previous regular year. Paired nonparametric longitudinal analysis was performed to detect differences between time-intervals. To model effects of age, sex and time we applied a nonparametric ANOVA-type-statistic. 147 patients with cardiac implantable electronic devices were analysed. Longitudinal analysis of physical activity in 2019 and 2020 showed a specific weekly and seasonal pattern. Physical activity was reduced during the pandemic  with the strongest reductions  during the two lockdown-periods. In older patients (>70 years), physical activity was decreased in every time-interval of the year 2020. In time-intervals of eased restrictions, physical activity of younger patients (≤70 years) was not different compared to 2019. No variation in mean heart rate, arrhythmia-burden and count of ICD-therapies was found.Physical activity shows fluctuations dependent on days of the week and time of the year. During the pandemic, physical activity was reduced in patients with cardiac implantable electronic devices with the strongest reductions during lockdown-periods. Younger patients resumed former levels of physical activity in times of eased restrictions while older patients remained less active. Thus, activation of the elderly population is important to prevent long-term health impairments due to the pandemic. Furt in 2020 , 6. The  in 2020 . The objThis single centre study included all CIED patients from the University Hospital Würzburg receiving remote monitoring . CIED encompassed pacemakers , ICDs , cardiac resynchronisation therapy devices with defibrillator function (CRT-D) and implantable loop recorders (ILR). Device implantation after 01/01/2019 or incomplete data-transfer, defined as missing data transfer in any of the prespecified 12 time intervals within the period analysed (01/01/2019–12/31/2020) led to exclusion from analysis. The study was performed in line with the principles of the Declaration of Helsinki and its later amendments and approval was granted by the Ethics Committee of the University Hospital Würzburg (# 2021-07-1501).https://github.com/AlexGa/Impact-of-COVID-19-pandemic-on-physical-activity-of-CIED-patients).General information , indication for device implantation and the type of CIED implanted were taken from the hospital’s documentation system (SAP). Device programming, device derived mean daily physical activity, daily mean heart rate (HR), mean resting HR (rHR), occurrence of arrhythmias  and ICD-therapies (anti-tachycardia pacing (ATP), shock-therapy) as well as thoracic impedance (TI) were retrospectively assessed via the remote monitoring platform . The anonymised dataset is available online  and potentially associated variations of arrhythmia burden ). As detection and treatment of VT and VF depend on thresholds programmed at the discretion of the treating cardiologist, only VTs/VF triggering ICD therapies such as ATP or ICD-shocks were included in the analysis to ensure comparability. To investigate age-dependent variations and due to the non-uniform distribution of age, the study population was divided into two subgroups: age group 1  and age group 2 .The period analysed comprised 01/01/2019 until 12/31/2020 to cover two full calendar years. According to important decisions and events related to the COVID-19 pandemic, we defined six time intervals (A20-F20). Prominent news such as the first confirmed case of SARS-CoV-2 in Germany as well as public health interventions of exceptional political and social significance like the imposition of curfews were considered. Time intervals were set as follows: A20: 01/01/20–03/20/20 , B20: 03/21/20–05/06/20 , C20: 05/07/2020–08/04/20 and D20: 08/05/20–11/03/20 , E20: 11/04/20–12/16/20  and F20: 12/17/20–12/31/20 . To comprise seasonal variations and to allow valid cross comparisons to a regular calendar year, time intervals A20-F20 were identically assigned to the year 2019 (A19-F19).The primary outcomes of the study were physical activity in CIED patients during the 2020 COVID-19 pandemic and the impact of the pandemic on physical activity determined by cross-comparison with corresponding control-periods in 2019. We hypothesized a decline of physical activity in the year of the pandemic. The secondary outcomes were ventricular arrhythmia burden and ICD-therapies in the ICD-subgroup of the study population. An increase of ventricular arrhythmia burden during the 2020 COVID-19 pandemic was expected due to impaired outpatient cardiology care and elevated psychological distress.https://github.com/AlexGa/Impact-of-COVID-19-pandemic-on-physical-activity-of-CIED-patients).The values of each parameter  for each patient in each time interval were calculated as the arithmetic mean over all days per respective time interval. Since we focussed on patients’ physical activities, we only included patients’ data providing at least one day of activity in each time interval. Thus, the basic data set contains information from 147 patients. For the analysis of further parameters, we additionally removed subjects if they did not contain at least one value in each time interval for that particular parameter. To analyse the physical activity within an average week of each time interval, we calculated for each patient the arithmetic means of physical activity at each weekday for all time intervals. We refer to this measure as the mean daily activity (MDA). Calculating the MDA, we considered only subjects who transmitted activity values on each weekday in each time interval. Detecting statistically significant differences between the different time intervals, a paired nonparametric longitudinal data analysis was performed with the R package nparLD . Since t248 patients with active home monitoring  were considered eligible for inclusion in the current study. 101 patients were excluded from the study population due to device implantation after 01/01/2019, missing data-transfer in any of the 12 time intervals or missing demographic data. The final study cohort consisted of 147 patients. Mean recording of remote monitoring was 660 days. Median age was 69 years ± 11.9 years (Median absolute deviation) and most patients were male . 92.5% (n = 136) of patients carried an ICD , 5.4% (n = 8) a PM  and 2.0% (n = 3) an ILR. Within the ICD subgroup, indication for ICD implantation was primary prevention in 58.1% (n = 79) and secondary prevention in 41.9% (n = 57). Further demographic and clinical data are displayed in Longitudinal analysis of physical activity of the reference year 2019 shows statistically significant variations. Increase and reduction of physical activity levels in this German study population correlate with changing seasons: As displayed in Importantly, mean device derived physical activity was significantly lower in 2020 compared to 2019 . No gender-dependent variation of mean device derived physical activity was observed neither in 2019 nor in 2020 .As displayed in Physical activity and patients’ response to changing social and occupational conditions may be age dependent. Due to the non-uniform distribution of age, the study cohort was subdivided in age group 1 (≤ 70 years) and age group 2 (> 70 years). Comparing age group 1 and 2, mean physical activity  was significantly higher in the younger subgroup in 2019 as well as in 2020.Comparing the corresponding time intervals of 2019 and 2020 after subdivision into age categories, physical activity was significantly decreased in every time interval of the year 2020 in older patients (age group 2) as displayed in Physical exertion leads to an increase in HR in chronotropic competent patients as well as in chronotropic incompetent patients with activated rate-adaptive pacing-modes. Furthermore, rHR is affected by training condition. Therefore, we additionally analysed mean HR and mean rHR in the respective time intervals of both years. Contrary to our assumption, no significant variation of mean HR and mean rHR was found comparing 2019 and 2020 . Comparing corresponding time intervals of 2019 and 2020, a tendency towards numerically lower mean HR and mean rHR in time intervals A20 –F20 was observed. However, even after subdivision into age categories, no significant differences between intervals of mean HR and mean rHR was detected. Additional evaluation of atrial arrhythmia burden and thoracic impedance revealed no significant differences across years and age groups.As variation of physical activity levels may translate into changes of electric stability in ICD carriers, we investigated the frequency of ICD therapies in the respective time intervals. Within the ICD subgroup a cumulative number of 99 ICD therapies were observed in 26 patients in 2019 . In 2020, a cumulative number of 36 ICD therapies  were recorded in 25 patients. Despite numerically differences, no significant variation in ICD therapies  comparing time intervals in 2020 and 2019 was detected.This is a retrospective, observational cohort study assessing physical activity and ventricular arrhythmia burden in CIED patients via remote monitoring over a nearly two-year period in Germany. The following key observations could be made: 1) In this cohort of 147 CIED-patients, changes of physical activity showed a distinct weekly periodicity and seasonal pattern. 2) During COVID-19 pandemic, physical activity was significantly reduced with the most pronounced reduction during the two lockdown periods. 3) Younger CIED patients resumed their former level of physical activity in times of eased public health restrictions whereas 4) older patients remained less active even after lockdown periods. 5) Despite numeric reductions, no statistically significant decrease in ICD therapies was observed during COVID-19 pandemic.Longitudinal analysis of physical activity revealed a weekly recurring reduction of physical activity on Sundays. This finding indicates that patients in this CIED-cohort do not use leisure time for physically strenuous activities. Furthermore, levels of physical activity change throughout the year in a typical pattern that correlates with different seasons: Physical activity levels peak in early spring and are lowest in winter months. Thus, regardless of a pandemic, patients are at greater risk of losing their physical condition in the winter months.We show a relative reduction of physical activity of 11.5% during the first lockdown period in comparison to the 2019 control-interval and constant levels of physical activity in comparison to the 2020 pre-lockdown time interval. Thus, the usual increase in physical activity in spring was blunted in 2020. Comparing corresponding time intervals of 2019 and 2020, physical activity was decreased in every time interval of the year 2020 with the greatest differences in physical activity in time intervals of strictest public health interventions. These results stand in line with two observational remote-monitoring trials from Italy. Whereas the first study detected a relative reduction of physical activity of 21.6% in 180 ICD/CRT patients during two consecutive intervals pre and post imposition of curfews , the secOur study shows distinct age-dependent differences in activity levels: During the pandemic, younger CIED patients quickly resumed their former level of physical activity in times of eased restrictions, whereas older patients remained less active even after lockdown periods. These results are interesting as they contradict a previous study performed in Italy that reported a more prominent and more consistent reduction of physical activity in patients aged less than 70 years compared to older patients. We attribute the differences to the fact that governmental restrictions in Italy were stricter with regard to employed people. In line with this, the Stringency index, objectifying the severity of governmental containment measures, consistently showed lower scores for Germany compared to Italy . We attrThe seemingly paradoxical detrimental and/or beneficial effects of exercise training on ICD-therapies are well described: While physical exertion can acutely trigger arrhythmias during a hyperadrenergic state, it is known to reduce mortality and ICD-therapies in the long-term –19. TherWhether reduced levels of physical activity and a reduced number of ICD therapies are coincidental or causally related cannot be dissolved yet. It is important, however, that exercise training has proven to reduce ICD shocks in the long term . LikewisOur study shows that CIED patients in general are at increased risk of losing their physical fitness level during the pandemic. Given the persistent and strongest reduction of physical activity levels among the elderly, motivation and guided activation of this vulnerable population are paramount to prevent further health impairments during the ongoing pandemic. Treating physicians should encourage patients to stay active and help them find ways to perform physical activities that are compatible with social restrictions and curfews. Furthermore, future studies are necessary to investigate long-term effects of physical inactivity during the pandemic on cardiovascular health in general and arrhythmia risk in particular.Our results should be considered in light of two limitations. First, the study comprises a heterogeneous population of patients as carriers of different types of CIEDs  were enrolled. A differentiated analysis of the different subgroups (defibrillator vs. PM vs. ILR) was not feasible due to the small number of patients in the PM- and ILR-subgroup. Thus, we cannot exclude an impact of the type of CIED on the study’s results. Second, in this remote-monitoring study, important baseline characteristic such as ejection fraction and NYHA functional class were not reported. Therefore, an impact of these variables on the study’s results cannot be excluded.


Good outcomes (mRS 0–2) were significantly associated with COVID-19 vaccination before AIS . Conclusions: The observation that COVID-19 vaccination can influence the outcome of AIS provides support for further studies investigating the role of immunity in ischemic brain damage.Background: It is unclear whether and how COVID-19 vaccination may affect the outcome of patients with acute ischemic stroke (AIS). We investigated this potential association in a retrospective study by comparing previously vaccinated (VAX) versus unvaccinated (NoVAX) stroke patients. Methods: We collected clinical reports for all consecutive AIS patients admitted to our hospital and evaluated the outcome predictors in VAX and NoVAX groups. Adjustments were made for possible confounders in multivariable logistic regression analysis, and adjusted hazard ratios were calculated. Results: A total of 466 AIS patients (287 VAX and 179 NoVAX) were included in this study. The NIHSS score at discharge and mRS score at a 3-month follow-up visit were significantly lower in VAX patients compared to NoVAX patients ( Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, the national healthcare system and economic markets have suffered a heavy backlash. The rapid growth of infected and deceased patients due to SARS-CoV-2 and the consequent efforts of pharmaceutical companies resulted in the rapid development of vaccines against SARS-CoV-2, such as Comirnaty , Spikevax mRNA-1273 , Vaxzevria  and a recombinant adenovirus type 26 vector encoding SARS-CoV-2 spike protein , with more than 11 billion vaccines administered up until September 2022 ,5,6,7. SIn this retrospective observational study, we included consecutive adult patients with a primary diagnosis of AIS who were admitted to our hospital between 1 January 2021 and 31 December 2021. We excluded patients with a diagnosis of intracranial hemorrhage or subdural hemorrhage, subarachnoid hemorrhage and patients with ongoing SARS-CoV-2 infection, and any patients aged <18 years. This study was approved by the local ethics committee .All patients underwent baseline neurological examination using the National Institutes of Health Stroke Scale (NIHSS) , laboratU-test, with exact significance, was used for the non-normal distribution of data. An independent t-test was chosen for the analysis of continuous variables between two sets of normally distributed data. Dichotomous variables were compared using Fisher’s exact and chi-squared (χ2) tests. Ordinal variables were analyzed with the Wilcoxon signed-rank test, utilizing the exact test if necessary. All data obtained in this study were regularly registered. The study population was divided into two subgroups: COVID-19-vaccinated patients (VAX) and non-vaccinated patients (NoVAX). In the univariate analysis, all variables were compared between the subgroups VAX and NoVAX. Successively, to adjust the effect size for potential confounders, a multivariate analysis was performed. Variables used in the univariate analysis were entered into a multivariate logistic regression analysis to determine adjusted odds ratios. The multivariate model was built by selecting variables for their significance in the univariate comparison  and for their clinical relevance ). The level of significance was set at p < 0.05. All statistical analyses were performed using the Statistical Package for Social Sciences (SPSS®) software version 22 .Continuous data were summarized using the mean and standard deviation (SD) and median and interquartile range (IQR); categorical data were summarized using counts and percentages. The distribution was studied using the Kolmogorov–Smirnov test. The Mann–Whitney From 1 January 2021 to 31 May 2022, 1324 patients discharged from the stroke unit and the emergency neurology department with a diagnosis of AIS were assessed for study eligibility. According to the exclusion criteria, 275 patients were excluded due to hemorrhagic pathogenesis of stroke, 489 due to appropriate coding of acute stroke, 35 due to acute phase COVID-19 and 59 lost due to lack of response to the follow-up questionnaire three months after acute ischemic event. Hence, 466 AIS patients were included in this study: 179 NoVAX and 287 VAX. A flow diagram showing the enrolment process is represented in As expected, VAX patients were slightly older than NoVAX patients, with a similar male/female ratio. Hypertension and COPD were significantly more frequent in the VAX than in the NoVAX group, while coagulopathy was slightly more frequent in the NoVAX group. Most patients in the VAX group had been vaccinated with BNT162b2 (Pfizer), followed by ChAdOx1-S (AstraZeneca), Spikevax (Moderna) and Ad26.COV-2.S (Johnson & Johnson/Janssen) vaccine. Most of the patients  experienced the ischemic event after the second administration of the COVID-19 vaccination, with a mean timelapse of 101 days (SD = 68).Thirty-four patients (15%) had a stroke after the first dose of the COVID-19 vaccine, in a mean time of 43 days. Five patients (3%) experienced an ischemic event after the third dose of the vaccine, at 14 days after administration. Stroke severity on admission, assessed by NIHSS, was similar between the two groups.p < 0.001) and mRS scores at 3-month follow-up evaluations (p < 0.001). After mRS dichotomization, the number of patients with poor prognosis (mRS 3–6) in the NoVAX group was higher than in the VAX group (p = 0.007). Finally, the death rate was lower in the VAX group compared to the NoVAX group (p < 0.001). During the 3-month follow-up period, 14 (7%) of VAX patients and 11 (9%) of NoVAX patients were infected with SARS-CoV-2. As expected, the majority of NoVAX patients (54%) and a minority (14%) of VAX patients required hospitalization for COVID-19. No patients in either group died from COVID-19-related causes. The demographics, risk factors and procedural data of the VAX and NoVAX groups are detailed in Despite being older and with more comorbidities, VAX patients had significantly lower NIHSS scores at discharge (p = 0.004). Patients who received a COVID-19 vaccination prior to the cerebral ischemic event had a better prognosis than unvaccinated stroke patients (p = 0.006). Finally, while the COVID-19 prevalence was similar, hospitalization for COVID-19 was significantly more prevalent in patients with a worse outcome than in functionally independent patients. The demographics, risk factors and procedural data of patients with functional independence (mRS 0–2) versus patients with a poor outcome (mRS 3–6) are detailed in Among risk factors, hypertension was significantly more frequent in patients with poor prognoses than those without . On the contrary, a poor outcome (mRS 3–6) was associated with age > 85 years , NIHSS score > 4 after discharge  or previous stroke . Detailed results of the multivariate analysis are displayed in In this observational study, we observed better short-term (NIHSS at discharge) and long-term outcomes (mRS at 3 months) for acute stroke patients vaccinated against COVID-19 before an ischemic event than a non-vaccinated stroke population. These data were even more significant considering the highest comorbidities, such as hypertension and COPD, in the VAX group. The old age of VAX patients is due to the COVID-19 vaccination campaigns, which prioritized the most fragile subjects, such as the elderly or patients with multiple comorbidities. In contrast, the mild prevalence of coagulopathies in NoVAX patients is justified by the initial exclusion of these patients from COVID-19 vaccination due to the increased risk of venous thrombosis or thromboembolic complications.Considering the outcome predictors, we observed that an age above 85 years, NIHSS > 4 after discharge and previous stroke are significantly associated with a worse prognosis and a greater functional dependence of the patient. Conversely, the presence of an active vaccination against COVID-19 at the time of onset of an ischemic stroke constitutes the only predictor of a favorable prognosis and functional independence, regardless of SARS-CoV-2 infection or hospitalization for COVID-19 in the 3-month follow-up period.While age and stroke severity are well-known outcome predictors , the samCOVID-19 RNA vaccines are able to induce balanced B- and T-cell responses, which are further increased by a second booster dose ,16,17. ICompelling evidence suggests a key role of the immune system, too, in the development, progression and, consequently, prognosis of ischemic stroke ,26. BraiAnother suggestion comes from the observation that in ischemic preconditioning, one or more short episodes of sublethal ischemia protect the brain against subsequent severe ischemic attacks . These dAs a result, the high percentage of circulating B cells observed in subjects undergoing COVID-19 vaccination may reveal the deleterious effects of the proinflammatory response caused by ischemic brain damage.The main limitations of this study are the small sample size (at least partly due to the monocentric nature of this study), the impossibility of ruling out a selection bias and the duration of the follow-up having been limited to 3 months. Moreover, we did not evaluate previous SARS-CoV-2 infections before participants’ inclusion in the study.Despite the small sample size and the short duration of the follow-up, the observation that the COVID-19 vaccination can influence the outcome of AIS provides support for further studies investigating the role of immunity in ischemic brain damage. In particular, it will be challenging to develop vaccines that can modulate the regulatory and B-mediated immune response to improve the prognosis for AIS patients.


Greater affective inertia during the day (higher carry-over effects of prior affect to the current moment) is associated with depression and its development. However, the role of overnight affective inertia (from evening to morning) in depression, and the role of sleep therein, has been scarcely studied.We examined i) the difference in overnight inertia for positive (PA) and negative affect (NA) between individuals with past depression, current depression, and no depression; ii) how sleep duration and quality influence overnight affective inertia in these groups, and iii) whether overnight affective inertia predicts depression development.t-1 on affectt. Linear mixed-effect models were used to test the hypotheses.We used data of 579 women from the East-Flanders Prospective Twin Survey. First, individuals with past (n=82), current (n=26), and no depression (n=471) at baseline were examined, and then individuals who did (n=58) and did not (n=319) develop depression at 12-months follow-up. Affect was assessed 10 times a day for 5 days. Sleep was assessed with sleep diaries. Affective inertia was operationalized as the influence of affectOvernight affective inertia was not associated with depression, neither was it differently associated with sleep characteristics in the depression groups. However, sleep characteristics were more negatively associated with morning NA in both depression groups compared to the non-depressed group. Overnight affective inertia did not predict the development of depression at follow-up.Depression and sleep characteristics might be more related to mean affect levels rather than to more complex emotion dynamics measures. Replication of these findings with longer time-series is needed.


COVID-19 was first identified in Wuhan, China, in December 2019, spreading to the rest of the globe, becoming a pandemic. Some studies have shown an association between pregnancy status and severe COVID-19 with a cytokine storm, whereas others have shown contrasting results.The aim of this study was to examine the relationship between pregnancy status and the clinical COVID-19 severity characterized by the cytokine storm through a systematic review and meta-analysis.We searched the Google Scholar, PubMed, Scopus, Web of Science, and Embase databases to identify clinical studies suitable for inclusion in this meta-analysis. Studies reporting pregnancy status and comparing the COVID-19 severity cytokine storm outcome were included. COVID-19 severity characterized by a cytokine storm was described using parameters such as intensive care unit admission, invasive mechanical ventilation, mechanical ventilation, hospital admission, pro- and anti-inflammatory cytokine levels, consolidation on chest computed tomography scan, pulmonary infiltration, extreme fevers as characteristic of a cytokine storm, syndromic severity, higher neutrophil count indicative of a cytokine storm, and severe COVID-19 presentation.P<.001), with a cumulative incidence of 6432 (14.1%) and 24,352 (3.1%) among pregnant and nonpregnant women with COVID-19, respectively. The fixed-effects model also showed a correlation between pregnancy status and severe COVID-19 with a cytokine storm . Considerable heterogeneity was found among all pooled studies . Furthermore, the updated analysis showed substantially low heterogeneity , and the funnel plot revealed no publication bias. The subanalysis between single-center and multicenter studies demonstrated similar heterogeneity . Sensitivity analysis on each subgroup revealed that pregnancy was significantly related to severe COVID-19 with a cytokine storm from single-center studies  with very low heterogeneity .A total of 17 articles including data for 840,332 women with COVID-19 were included. This meta-analysis revealed a correlation between positive pregnancy status and severe COVID-19 with a cytokine storm (random-effects model odds ratio [OR] 2.47, 95% CI 1.63-3.73; Being pregnant is clearly associated with experiencing a severe course of COVID-19 characterized by a cytokine storm. The COVID-19 pandemic should serve as an impetus for further research on pregnant women diagnosed with COVID-19 to map out the salient risk factors associated with its severity.PROSPERO CRD42021242011; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=242011. Once considered to be an “immunosuppressed” state, pregnancy is associated with an immunological transformation, where the immune system is required to promote and support the pregnancy and growing fetus. When this protection is breached, as in a viral infection, this security is weakened and infection with microorganisms can then propagate and lead to negative outcomes such as preterm labor .Pregnancy is considered a high-risk condition for COVID-19. Pregnant women are more likely to have an asymptomatic infection, accounting for 75% of SARS-CoV-2 infections during the pandemic. Even among those with symptoms, cough and fever are the main symptoms in 40% of cases, with breathing difficulty and myalgia being present in 21% and 19% of pregnant women, respectively. Severe COVID-19 usually occurs with infection in the second half of pregnancy, especially toward the end of the second trimester onward. Those at greatest risk of severe COVID-19 include women who have a higher-than-ideal BMI, those over the age of 35 years, and those who have chronic underlying conditions [COVID-19 is an infectious disease caused by a newly discovered coronavirus (SAR2-CoV-2) that was first identified in Wuhan, China, in December 2019 . COVID-1Most people infected with the SARS-CoV-2 will experience mild to moderate respiratory illness and recover without requiring special treatment. Older people above the age of 58 years and those with underlying medical conditions such as cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illnesses .Pregnant women who have COVID-19 appear more likely to develop respiratory complications requiring intensive care than women who are not pregnant . PregnanPregnant women are a potentially highly vulnerable population due to anatomical, physiological, and immunological changes under the COVID-19 pandemic. Issues related to pregnancy with COVID-19 attracted widespread attention from researchers. A large number of articles were published aiming to elaborate on the clinical characteristics and outcomes of pregnant women infected with COVID-19 to provide evidence for management . The exiThere are many unknowns for pregnant women during the COVID-19 pandemic. Clinical experience of pregnancies complicated with infection by other coronaviruses such as severe acute respiratory syndrome (SARS) and Middle Eastern respiratory syndrome (MERS) indicated that pregnant woman should be considered to be particularly vulnerable to severe SARS-CoV-2 infection. Physiological changes during pregnancy have a significant impact on the immune system, respiratory system, cardiovascular function, and coagulation .Given divergent findings in the existing literature, we systematically reviewed English-language studies to investigate whether pregnancy was associated with a more severe clinical course of COVID-19. Specifically, the aim of this study was to establish if pregnancy status is associated with COVID-19 severity characterized by a cytokine storm.All guidelines listed in the PRISMA  statement were followed in performing this meta-analysis . For thiWe performed a simple search in the Google Scholar, PubMed, Scopus, Web of Science, and Embase databases to identify observational studies suitable for inclusion with the following search terms: “COVID-19” OR “SARS-COV-2” OR “novel coronavirus (CoV)” AND “pregnant” OR “gestation” AND “clinical features” OR “characteristic” AND “severity” OR “severe.” Studies were restricted to those published in English from March 2020 to March 2021.Inclusion criteria were as follows: (1) studies that examined women within reproductive age and diagnosed with COVID-19 according to World Health Organization (WHO) criteria; (2) observational, cross-sectional, prospective, or retrospective studies; (3) studies that compared pregnant women to nonpregnant women with severe COVID-19 characterized by a cytokine storm; (4) studies evaluating the clinical prognosis in pregnancy and the immunological profile at any gestation stage, examining the proinflammatory response in COVID-19 and a severe cytokine storm as the hallmark outcome.Exclusion criteria were as follows: (1) unrelated, duplicated, and missing information answering our research question; (2) non-English-language studies; (3) case reports/series; (4) reviews; (5) editorials; (6) studies lacking a full text (unavailable or not yet published); (7) articles without a DOI; and (8) studies with small sample sizes (<50 patients) because of low statistical power.Notably, we included preliminary findings published as preprints given that the phenomenon in question remains very grey in the public domain and thus we presumed inclusion of such reports would be of value in converging relevant data and information.Both adjusted and nonadjusted data among pregnant versus nonpregnant cases were extracted to identify the most relevant confounding factors to be used in the analysis by subsequent pooling. One reviewer (JM) scanned study titles and abstracts obtained via an initial database search and included relevant articles in a secondary pool. Next, two independent reviewers (MK and KO) evaluated the full texts of these articles to determine whether they met the study inclusion criteria. Any disputes were resolved by discussion and negotiation with a fourth reviewer (EN). Only studies agreed upon by all reviewers were included in the final analysis.The following data were obtained from all studies: title, first author, publication year, location, sample size, age (median), pregnancy status (pregnant or nonpregnant), and severe COVID-19 cytokine storm presentation. The analysis was then performed to determine whether the pregnant group was more likely to develop severe COVID-19 characterized by a cytokine storm.The National Institutes of Health tool for observational and cross-sectional studies  was usedReview Manager 5.4.1 was used to calculate odds ratios (ORs) with 95% CIs, which are depicted using forest plots. Quantitative variables are summarized in terms total numbers and percentages. The OR of a severe COVID-19 cytokine storm among pregnant and nonpregnant women was calculated. Heterogeneity was evaluated with the Cochran Q statistic and Higgins test. The Higgins test uses a fixed-effects model when the heterogeneity is <50% and a random-effects model when the heterogeneity is >50%. When heterogeneity was detected, a sensitivity adjustment was made to determine its source. This procedure was performed by leaving a study out of the analysis one at a time, with the fixed-effects model applied after excluding heterogeneity. Subgroup, cumulative analyses, and metaregression were used to test whether or not the results are consistent and to investigate the effect of confounders on the outcome (cytokine storm) and elucidate the best predictors in pregnancy status among women with COVID-19. Publication bias was evaluated using the Cochrane Risk of Bias tool.The initial search of international databases using the keywords described above yielded 221 articles. After excluding 70 duplicate articles, 151 articles remained. When article titles and abstracts were evaluated for appropriateness, 29 articles ultimately met the inclusion criteria. In addition, 12 articles not meeting the inclusion criteria were excluded after full-text review. A total of 17 articles met the inclusion criteria -29. MultThe 17 included studies provided data for 840,417 women with COVID-19 -29 Tabl. AccordiThe main outcome of this meta-analysis was the possible association of pregnancy with severe COVID-19 characterized by a cytokine storm, which was indicated by a specific prognosis and event. The parameters used for assessment of COVID-19 severity were intensive care unit (ICU) admission in three studies; ICU plus mechanical ventilation in two studies; higher levels of inflammatory response markers in three studies; severe COVID-19 presentation in two studies; and consolidation on chest computed tomography scan, pulmonary infiltration, extreme fever as a characteristic of a cytokine storm, syndromic severity, hospital admission, invasive mechanical ventilation, and higher neutrophil count indicative of a cytokine storm in one study each. The study designs included retrospective  and prospective . A summary of the studies included in the meta-analysis is provided in We assessed the quality of the included observational studies based on a modified version of the Newcastle-Ottawa Scale (NOS), which consists of 8 items with 3 subscales, and the total maximum score of these 3 subsets is 9. We considered a study that scored ≥7 to be a high-quality study since a standard criterion for what constitutes a high-quality study has not yet been universally established. The 17 studies assessed generated a mean value of 6.47, indicating that the overall quality was moderate (NOS score range 5-8), as detailed in P<.001) to the fixed-effects model , revealing that pregnancy was significantly associated with severe COVID-19 characterized by a cytokine storm. Furthermore, this updated analysis showed substantially low heterogeneity . I²=98%, P<.001). The meta-analysis revealed a significant association between pregnancy status and severe COVID-19 characterized by a cytokine storm . A sensi2I=72%). The test for the overall effect for single-center studies  and multicenter studies  showed no significance difference . This prompted further sensitivity analysis on each subgroup to ascertain the group that was most strongly associated with heterogeneity.Heterogeneity in the pooled effect estimates was considerably high for all 17 studies, contributed by 748,058 out of 840,332 (89.02%) evaluated subjects, and thus it was necessary to perform subgroup analyses to identify possible variables or characteristics moderating the results obtained. Subgroup analysis was performed according to whether it was a multicenter study, including 879,556 patients, or a single-center study with 776 patients. P<.001), with this updated analysis showing substantially low heterogeneity . In multicenter studies, subsequent removal of any one study did not change the heterogeneity from its original value , demonstrating that multicenter studies were the main cause of heterogeneity and this was similar to the overall heterogeneity of the combined groups , with the test for subgroup differences being insignificant . This review established that pregnancy is associated with an experience of severe COVID-19 characterized by a cytokine storm. Heterogeneity analysis revealed that the pooled effect estimate was considerably high considering all 17 included studies, contributed by 89% of the total patients evaluated. Further, sensitivity analysis on each subgroup indicated that single-center studies were more homogeneous in comparison to multicenter studies.This meta-analysis included 17 studies and revealed that pregnant women had a significantly increased risk for severe COVID-19 characterized by a cytokine storm. Previous research has indicated a similar association ,33. AddiP<.001) more frequent in pregnant women. Some previous studies, including some meta-analyses [Prior studies have reported results that contrast with those presented here, namely a lack of significant difference between pregnant and nonpregnant women diagnosed with COVID-19 in terms of disease severity ,36. In aanalyses ,42-46, sA recent meta-analysis revealed that SARS-CoV-2 infection may not manifest as mild symptoms during pregnancy . InteresThe association between pregnancy and illness severity due to other respiratory viruses such as MERS has been investigated previously. In one study, the case fatality (25%), ICU admission (50%), and mechanical ventilation (33%) rates were increased in the pregnant population compared with those of the nonpregnant population (20%) , which mThe novel SARS-CoV-2 virus uses angiotensin-converting enzyme 2 (ACE2) receptor in the lungs to enter cells and cause infection. ACE2 expression and activity are enhanced during pregnancy, and transient ACE2 overexpression and its increased activity during pregnancy may be important in modulating systemic as well as local hemodynamics in the uteroplacental unit ,61. ACE2ACE2 gene expression was found to be upregulated in cells specific to the maternal-fetal interface [In one recent study, nterface , therebynterface . Thus, tnterface .The cytokine storm phenomenon has received substantial research attention recently because of the COVID-19 pandemic. Although more and more information is accumulating daily, the cytokine storm seems to be at least part of the reason that some people develop life-threatening symptoms from COVID-19. Hyperinflammatory cytokine storms in many patients with severe symptomatic cases of COVID-19 may be rooted in an atypical response to SARS-CoV-2 by dysfunctional mast cells, in a condition known as mast cell activation syndrome, rather than the typical response by normal mast cells . This maAnother critical area of concern is that the cytokine storm is a critical contributor to mortality in some patients with severe COVID-19. In these patients, the levels of proinflammatory cytokines such as interleukin (IL)-1, IL-2, IL-6, IL-8, IL-17, interferon (IFN)-γ, and tumor necrosis factor (TNF)-α are elevated, which affect the patient’s clinical symptoms and severity in the general population . In pregSeveral factors limit the interpretation of the present study. First, the vast majority of studies included in the meta-analysis were retrospective epidemiological studies conducted in the United States and China, with limited studies from other regions. Second, some of the included studies did not distinguish the age range of the participants as well as the stage of the gestation period. Third, COVID-19 severity as assumed to be characterized by a cytokine storm relied on different parameters of clinical implications such as the levels of inflammatory cytokines, invasive mechanical ventilation, and ICU admission. Given these limitations, caution should be exercised when interpreting the current findings for more valid clinical practice. Future studies may respond to these issues by defining disease severity more clearly and by obtaining more detailed information on the associated inflammatory cytokines defining the COVID-19 cytokine storm.Multiple factors are responsible for recurrent pregnancy loss, although an altered cytokine profile is known to be a major contributor, especially in the early stages of gestation. Similarly, exposure to high maternal proinflammatory cytokine concentrations in early pregnancy might play a role in several adverse effects for either the woman or infant. Thus, women expecting a pregnancy should be screened to assess the cytokine profile even prior to conception whenever possible to avoid pregnancy loss and to improve their health and social well-being, as abnormal cytokine levels could aggravate COVID-19 severity.Finally, the interactions between the inherent inflammatory cytokines and cytokine storm due to COVID-19 should also be further examined and clarified. In addition, clinicians should pay more attention to the history of pregnancy-related altered immune responses of COVID-19 patients. Further research may aim to determine the mechanisms that drive or decrease this risk of severity by a within–pregnant population study approach.This meta-analysis revealed that pregnancy is significantly associated with increased COVID-19 symptom severity defined by a cytokine storm. The SARS-CoV-2 epidemic should serve as an impetus for further research on pregnant women diagnosed with COVID-19, and to map out salient risk factors associated with its severity with an aim of maintaining a good pregnancy outcome and possibly evading an adverse COVID-19 clinical prognosis.


Circulating human papillomavirus DNA is a promising tool in the management of HPV-positive oropharyngeal cancer. The potential use of this tool will substantially change the way we treat, diagnose, and follow-up these patients.There has been a rising trend in HPV-induced head and neck cancers in the last several decades. This subgroup of squamous cell carcinoma is mostly located in the oropharynx and comprises a subset of patients who are typically younger and without the usual risk factors of smoking and alcohol use. As the prognosis of HPV-induced OPC is more favorable, there is a desire to properly select these patients for de-intensification protocols while identifying individuals who may suffer treatment failure. Here, we describe recent developments in circulating tumor HPV DNA as a marker of HPV-positive oropharyngeal cancer that can potentially be used as a diagnostic tool to stratify patients for de-escalation strategies and to survey for recurrence. Human papillomavirus (HPV)-induced oropharyngeal cancer (OPC) is a distinct entity with a rising incidence in the US and across the world . This suCirculating cell-free tumor DNA, also called liquid biopsy, is being used in several fields and can serve as a potential marker in cancers attributed to viruses ,7. SeverCirculating cell-free DNA (cfDNA) has been utilized in several fields of medicine since its advent in the mid-twentieth century. Cell-free DNA is fragmented DNA with an average size of 160–200 bp, and it is released into the blood and other body fluids through the apoptosis or necrosis of cells . It has The most common target for detecting ctDNA for HPV-OPC is the E6 or E7 open reading frame . One areIn addition to target variation, three different methods of assaying for ctDNA have been employed to date, most commonly quantitative PCR (qPCR), next-generation sequencing (NGS), and the more novel digital droplet PCR (ddPCR) ,21. AlthAn earlier stage at diagnosis correlates with improved survival in HPV-related oropharynx squamous cell carcinoma ,24. In tThe current gold standard for the diagnosis of HPV-OPC is tissue diagnosis on either the biopsy of the primary site or the fine needle aspiration of a metastatic lymph node, which is not without limitations. Clinical facilities in low-resource settings may not be well-suited to obtain, store, and process specimens for off-site histologic diagnosis. In addition, fine needle aspiration may yield insufficient cells for p16-immunohistochemistry, so additional biopsies may be necessary to obtain more tissue for determining p16 status. P16 status is critical to diagnostic work-up as it bears a strong prognostic indicator related to HPV-16 status . In thisTo date, the detection of ctDNA in HPV-OPC has been predicated on detecting circulating HPV DNA. This premise assumes that circulating HPV DNA is never observed in patients with either (1) Pnon-invasive HPV-associated lesions or (2) HPV infection without carcinogenesis. Indeed, Jeannot et al. detected no circulating HPV DNA in 18 patients with cervical intraepithelial neoplasia . HoweverFor diagnostic purposes, sensitivity is a measure of a screening tool’s efficacy. A high sensitivity ensures that few, if any, patients with HPV-OPC would test negative for ctDNA. For patients with a high pre-test probability of HPV-OPC in whom a false-negative ctDNA result is suspected, traditional tissue biopsy has a vital role.Siravegna et al. utilized ddPCR assays for the HPV E7 oncoprotein in HPV 16, 18, 33, 35, and 45 in 70 cases of HPV-OPC and 70 controls (HPV-negative (45) and non-cancer 25)) . With on) . With In a small study of 17 patients with HPV-related carcinoma of the cervix, Leung et al. tested the utility of a novel detection method called HPVseq, which utilizes 150 bp long “bait” probes to detect E6 and E7, as well as full-length HPV 16, 18, 33, 45, 31, 33, and 35. They reported a 100% sensitivity, as all patients had detectable ctDNA down to 0.03 copies/mL [No treatment modality for head and neck cancer is free of adverse effects. Surgery, radiation, and cytotoxic chemotherapeutics all bear permanent side effects such that the treatment of false positives is impermissible. Consequently, it is critical that diagnostic pathways incorporating ctDNA never produce a false positive.Ahn et al. performed qPCR using probes for HPV16 E6 and E7 and found a specificity of 100% in nine patients with HPV-negative tumors . Rettig Most methods for detecting ctDNA in HPV-OPC demonstrate sensitivity rates of 70–80%, although sensitivity can be improved depending on the used PCR technique. Increasing the number of targets  would likely increase sensitivity rates and diagnostic utility. However, increasing the number of targets may also introduce the added possibility of false positives and thereby decrease the specificity rate. As this area continues to expand, distinct screening and confirmation probe sets may be necessary.Pretreatment levels of circulating HPV DNA as a prognostic tool.As an early recurrence detection tool for follow-up after definitive treatment.As a tool to define the treatment strategy and implementation of deintensification therapies.Currently, surveillance is typically predicated on physical exams at regular intervals and post-treatment PET–CT scans. Unfortunately, even with state-of-the-art treatment, recurrence is not uncommon, reaching 26% (9–26%) in 5 years and 36% in 8 years ,38,39. CAlthough HPV-positivity is associated with a favorable prognosis, up to 35% of patients develop an aggressive disease . Currentp < 0.03). Furthermore, there was a significantly lower risk of tumor progression in patients with a significant increase in ctDNA two weeks into chemoradiation . Notably, smoking status did not predict freedom from persistence in this cohort.Emerging data suggest that ctHPV DNA bears promise as a prognostic tool; however, the timing of the ctDNA assessment and assay technique are critical. In a recent study by Cao et al. where HPV-OPC patients were all of the same stage (AJCC Stage III), both pretreatment ctDNA values and ctDNA two weeks into chemoradiation demonstrated prognostic value . Higher The timing of ctDNA measurement is important, as Dahlstrom et al. did not observe that patients without detectable pre-treatment ctDNA had a more favorable disease course than patients with detected ctDNA . One majThe importance of the assay technique was also highlighted in a study by Veyer et al. on 66 patients with HPV-OPC . Pre-treIn summary, higher levels of detectable ctDNA are associated with a worse prognosis. This finding appears most convincing when ddPCR is utilized instead of qPCR. As such, despite challenges, pre-treatment levels can potentially be considered prognostic for cases with classic HPV and oropharyngeal cancer with no or minimal other concurrent risk factors.Unlike HPV-negative head and neck cancer, HPV-OPC patients with recurrence can achieve high survival rates with salvage therapy. Surgery for locoregional recurrence has been shown to have two-year overall survival rate approaching 80%, and re-irradiation with either IMRT or protons can lead to a two-year overall survival rate of up to 70% . IncorpoSeveral studies have shown that detectable post-treatment ctDNA indicates recurrence and may precede imaging or exam findings . Cao et Rutkowski et al. explored the usage of post-treatment ctHPV DNA in 66 patients with HPV-OPC. An incomplete response  was associated with detectable ctDNA 12 weeks after treatment, as ctDNA was detected in 28% of patients with persistence and only in 4% of patients with a complete response. No patients showed negative ctDNA developed recurrence.Chera et al. studied 115 HPV-OPC patients treated with definitive CRT , and theRecently, the largest study of ctDNA for the detection of the occult occurrence of HPV-OPC was conducted by Berger et al. Digital droplet PCR was utilized as the assay technique. Of 1076 patients, post-treatment ctDNA was detected in 80 patients (7.4%). Only 2 of 80 patients with detectable ctDNA had no evidence of disease on the last follow-up. The remaining 98% either had recurrence at first follow-up or were subsequently found to have a biopsy-proven recurrence.In summary, the absence of detectable ctDNA after treatment for HPV-OPC portends a low risk of recurrence, and depending on the technique used, a positive assay for ctDNA appears to be associated with a more aggressive course. Larger prospective studies are needed to shed light on cases with several risk factors, potential HPV-negative second primaries, and the possibility of early intervention solely based on ctHPV DNA levels. In fact, several large prospective clinical trials are recruiting patients to answer these questions, with the targeted recruitment of several thousand participants, and results are expected in 2024–2025 .Several different strategies to de-escalate treatment are being studied in dozens of trials throughout the world. Such trials are attempting to reduce definitive radiotherapy dose, perform upfront surgery with modified adjuvant treatment, and/or use immunotherapy. Many of them face the challenge of selecting an appropriate patient population that will benefit from de-intensification while preserving oncologic outcomes. Potentially, ctHPV DNA can become such a marker. There are several ongoing clinical trials underway. The NCT05307939 trial being conducted at the Memorial Sloan Kettering Cancer Center uses ctHPV DNA levels to stratify patients for either observation or adjuvant radiation after surgery. If ctDNA is undetectable after transoral robotic surgery, the patient is stratified to a close observation group without any adjuvant therapy. The SIRS 2.0 trial uses a similar design but stratifies patients with undetectable levels of ctDNA into low and intermediate-risk groups based on final pathology (NCT05419089). The low-risk group receives close follow-up, and the intermediate-risk group receives de-intensified adjuvant RT. Investigators from the ReACT (NCT04900623) study set 40–50 Gy for the low-risk group and 50–60 Gy for the intermediate-risk group (risk group based on ctDNA levels).DART 2.0 (NCT05541016) is a study evaluating different de-intensification strategies using the ctDNA levels as a stratification variable. The low-risk group receives observation alone, while two other groups receive adjuvant radiation based on ctDNA levels.An emerging body of evidence suggests an evolving role for measuring ctDNA as an adjunct to standard care pathways for HPV-OPC. In the diagnostic setting, ctDNA  has a sensitivity in the 90–98% range and a specificity approaching 100%. On occasion, squamous cell carcinoma is diagnosed but with insufficient tissue for p16 staining (often on fine-needle aspiration samples). In such a situation, plasma ctDNA may help differentiate between HPV-negative and HPV-positive disease in lieu of repeating the biopsy. Moreover, ctDNA levels before treatment and in the initial phase of treatment have been shown to predict therapeutic responses. Furthermore, the detection of ctDNA after treatment has been shown to precede biopsy-proven recurrences by up to 4 months. Finally, ctDNA may have a role in cost-effectiveness. In a review, Haring et al. calculated the 2021 CMS costs for 10-year surveillance in OPSCC to be $17,381.68 . KowalchOngoing clinical trials are also exploring the role of ctDNA in selecting patients for de-intensification strategies. As new data emerge, the possibility of better care pathways heralds improved outcomes for patients with HPV-OPC.