Data on seroconversion rates after SARS-CoV-2 infection in young children (<6 years) is scarce. The present study compares seroconversion rates between young children and adults and identifies associated factors.The COALA study (“Corona-outbreak-related examinations in daycare centers”) investigated transmission dynamics of SARS-CoV-2 in daycare centers and associated households (10/2020-06/2021). 114 individuals tested positive for SARS-CoV-2 through PCR either prior to the study period by health authorities or in PCR testing during the study period. Two capillary blood samples were obtained within five weeks consecutively and tested for SARS-CoV-2 IgG-antibodies (second sampling depending on positive PCR). Results from 91 participants  were included in the analyses.Seroconversion rate in young children is significantly higher than in adults (97.4% versus 66%). High viral load and longer time interval between the probable date of infection and antibody testing are associated with seroconversion.Our findings depict substantial development of specific antibodies in young children after SARS-CoV-2 infection. This may provide temporary protection from re-infection for young children or severe disease for this age group. When fighting an infectious disease, the immune system often produces antibodies. These proteins circulate in the blood, where they help to clear the infection and generally remain present for several months after recovery. Little is known about how often children younger than 6 years develop antibodies after SARS-CoV-2 infection. The aim of our study was to compare antibody development of young children and adults. We examined blood samples from young children and adults after SARS-CoV-2 outbreaks in daycare centers during the early pandemic (10/2020–06/2021) in Germany. Young children and adults who tested positive for SARS-CoV-2 had two blood samples taken at an interval of five weeks. We found that young children are more likely to develop antibodies after SARS-CoV-2 infection than adults. These findings indicate that young children may be—at least temporarily—protected from re-infection or from a severe course of the disease. Kubisch et al. examine antibody seroconversion rates after SARS-CoV-2 infection in children attending daycare centers and adults in Germany. They found higher seroconversion rates in children compared with adults, which may have an influence on reinfection severity in the younger population. Studies concerning children, however, show a greater variety in their results regarding seroconversion duration and rates. For children and teenagers (0–18 years), seroconversion rates from 37% to 100% have been described, based on blood samples taken 14 days (and up to 172 days) after positive PCR testing or symptom onset9. This heterogeneity highlights the need for further investigation. For example, children under 6 years of age present an understudied, albeit relevant population group. Children under the age of 6 years are often enrolled in daycare programs, where SARS-CoV-2 transmission, due to close contact among young children, cannot be fully prevented. Vaccines for children were not approved in the European Union, and thus also in Germany, until November 2021 (5–11-year-olds) and October 2022 (6 months to 4-year-olds). At the same time, in both cases, the STIKO (Standing Committee on Vaccination) did not issue a general vaccination recommendation for the respective age groups11.Seroconversion rate describes the proportion of individuals developing antibodies after antigen contact via an infection or vaccination in a given time period. Determining seroconversion rates after natural infection is important to assess immune protection and to develop and adjust vaccination strategies. Seroconversion after SARS-CoV-2 infection in adults has been described to occur as early as 7 days after symptom onset with seroconversion rates from 50% to 58.8%12. Most of the studies focusing on this age group assess children in clinical settings12. Only little data is available for young children in non-clinical community settings13. Seroconversion rates might also vary between different viral variants14, which requires a continuous updating of seroconversion rates throughout the COVID-19 pandemic.Generally, data regarding seroconversion after SARS-CoV-2 infection in young children is scarce. The few studies that exist describe seroconversion rates as low as 37.5% one to two months after positive SARS-CoV-2 PCR testing12.In addition to age, other potential determinants of seroconversion have been identified, such as sex, time interval between symptom onset or a positive PCR testing and antibody testing, severity of disease, and viral loadIn order to better understand seroconversion in young children, we addressed the following research questions: (a) To what extent do seroconversion rates differ between young children and adults? (b) What factors are associated with seroconversion in young children and adults?Our results show a significantly higher seroconversion rate in young children than in adults. The analyses depict that seroconversion is associated with viral load , time interval between the probable date of infection and antibody testing at follow-up and older age. These results may indicate temporary protection of young children from re-infection or severe disease in the event of re-infection.Data used for this analysis were derived from the COALA study and collected between October 2020 and June 2021, when the SARS-CoV-2 ancestral and alpha-variant were predominant in Germany.15. In brief, the COALA study (“Corona-outbreak-related examinations in daycare centers”) was a prospective, longitudinal study, investigating the transmission dynamics of SARS-CoV-2 in 30 child daycare centers and connected households. Therefore, the selection of participants was related to SARS-CoV-2 outbreaks in daycare centers all over Germany. Infection trajectories of affected children and their contacts were monitored via repeated PCR testing, symptom recording, and capillary blood sampling as well as via a written questionnaire about previous SARS-CoV-2 infections and exposure to SARS-CoV-2-infected individuals. The Ethics Committee of the Berlin Medical Association has reviewed the COALA study and approved the implementation of the study (Eth-39/20). Participation in the study was voluntary and all participants were informed about the objectives and contents of the study as well as data protection and gave their written informed consent. Every participant was assigned to a sequential study number (ANR) in order to ensure pseudonymization of the study documents and material. COALA is registered in the German Clinical Trial Register (DRKS-ID = DRKS00023501).Methods of the underlying COALA study are described elsewhere16).In the COALA study, 114 individuals tested positive for SARS-CoV-2 via PCR . Antibody results were available for 97 participants . For 17 participants  no measurement of antibodies was performed because no appointment for the blood sampling could be made, blood sampling was refused, or because the sample quality of the blood specimen was insufficient. Participants originated from a non-clinical community setting and were asymptomatic or had mild symptoms of COVID-19 , 4 to 6 days after the testing date of the index case. The index case was defined as the first positive case within the daycare group that was diagnosed with SARS-CoV-2 via PCR and reported to the local health authority. The second blood sample (=follow-up sampling) was taken approximately five weeks after the baseline sampling visit according to the study protocol if the participating individual tested positive for SARS-CoV-2 through PCR either prior to the study period by health authorities or in PCR testing during the study period.  or negative (<0.94 ratio) without quantification in terms of titer determination.Dried blood samples (DBS) were analyzed for SARS-CoV-2 IgG antibodies using the commercial qualitative antibody assay “Anti-SARS-CoV-2 ELISA (IgG)”  by the manufacturer Euroimmun . The test detects antibodies directed at the spike antigen (S1 antigen). A validation study was performed to verify the applicability of the test for dried blood samples. After evaluation of the results, the ratio threshold for seropositivity provided by the manufacturer (≥1.1) was adapted to ≥0.9418.The test quality of the PCR measurements was ensured by measuring human c-myc19). Due to our study design, the timing of the first positive PCR within the course of the infection varied among study participants. As described in other publications, symptoms and symptom onset are no reliable parameters for the onset of SARS-CoV-2 infections in children21. Therefore, we decided to determine the individual probable dates of infection as the starting points for analyses of individual infection courses.To establish comparability between the seroconversion rates in young children and adults, we determined the probable date of infection for each participant, considering information on exposure and symptom onset (if any), antibody status at first blood draw, and date of first positive PCR test (for further information see ref. 22) of each participant for the analyses.To assess a potential association between seroconversion and viral load, we selected the PCR results with the highest viral load  for continuous variables, and as % for categorical variables.A backward stepwise logistic regression approach was used to identify possible predictors of the outcome seroconversion at testing at follow up. The following variables were taken into consideration: sex, a variable indicating whether the person tested was an adult or a child, viral load , presence of symptoms, and the time interval between the probable date of infection and antibody testing at follow-up.Regardless of the criterion used to determine which variables to exclude, viral load , the time interval between the probable date of infection and antibody testing at follow-up, and a variable indicating the person tested being an adult were selected for further analysis, while sex and symptoms were dismissed.23. This correction was used to account for the small sample size in some categories (only one seronegative case in the group of young children). This small sample size also is the reason for presenting results as odds ratios, based on a logistic regression model, and not as relative risks, since the odds ratio estimate is less subject to small sample bias than the risk ratio estimate.The final model presented in the text was re-run with Firth bias correctionIn addition, the multivariable logistic regression analysis was corrected for the unequal distribution of antibody-positive individuals in young children and adults. For this purpose, the indicator variable indicating whether the tested person is an adult was inserted.Analyses were performed using STATA/SE 17 , as well as R version 4.1.2 (2021-11-01) “Bird Hippie” Copyright (C) 2021 for stepwise backward logistic regression package ‘MASS’ version 7.3-58.3 and for regression analysis with Firth bias correction package ‘logistf’ version 1.24.1.Further information on research design is available in the Data from 91 participants  was included in the analyses. Six participants had to be excluded due to detectable antibodies at the baseline sampling: for four participants, the questionnaire data indicated that these persons were vaccinated against COVID-19. For the other two participants, no information was given in the questionnaire about a previous infection. We assume that these participants had an undetected SARS-CoV-2 infection in the past.The basic characteristics of the study population  and the seropositive subgroup are described in Table More than 90% of both young children and adults had a positive PCR test during the study period. Five cases were reported as positive by national health authorities using official PCR testing, but possessed negative PCR test results during study sampling afterwards.In those cases, no Ct values were available. Viral load and interval between the probable date of infection and antibody testing at follow-up were comparable between adults and young children. Symptoms were more frequent in adults than in young children, both in the entire cohort and in the seropositive subgroup.n = 37) of the young children and 66% (n = 35) of the adults had detectable S-antigen IgG antibodies at the follow-up sampling about seven weeks after the probable date of infection (p < 0.001).The seroconversion rate in young children was significantly higher than in adults: 97.4% . That was to be expected, however, as we sampled all participants according to study protocol at similar times.9, and Ruedas-Lopez et al. in 37.5%12 of tested children. One explanation for the substantial differences to our study results may be the level of viral load. Young children in our study population had relatively high viral loads . In accordance with our results, Toh et al. concluded from a subgroup of their study sample that high viral loads (Ct < 26) may be associated with higher seroconversion rates9. Also, Walker et al. found that participants (≥16 years) with higher viral load (Ct < 25) showed seroconversion significantly more frequently than participants with lower viral load (Ct > 25)24.Interestingly, other studies found a considerably lower seroconversion rate among young children, compared to our results. Whereas 97% of young children in our study had developed SARS-CoV-2 antibodies, Toh et al. detected antibodies in only 37%25.A positive association between high viral load and seroconversion, regardless of age, was also shown in our analysis. We hypothesize that young children were more likely to have developed antibodies following a SARS-CoV-2 infection than adults due to their fast and strong immune system27.Our findings on seroconversion in adults (66%) are comparable with seroconversion rates also assessed by S-antigen antibodies against SARS-CoV-2 reported in literature ranging from 57.7% to 76.6% to 90%29.With respect to the time interval between the probable date of infection and antibody testing at follow-up, we can assume that we have captured the seroconversion of all individuals. As studies have shown that IgG antibodies against spike protein are reliably detectable in blood samples after more than 14 days and up to 4 to 5 months after the first positive PCR or symptom onsetStrengths of our study are the assessment of young children in a non-clinical, community setting, the prospective study design including the collection of biological samples , and close monitoring of symptoms.12. Although participants were selected in the context of SARS-CoV-2 outbreaks in daycare centers all over Germany, the sample of daycare centers was not randomized and thus may not be representative. Overall, 59% of the invited young children and adults of a daycare group and their close contacts participated. Reasons for non-participation were not evaluated.Nevertheless, some limitations should be considered. Our study has a relatively small sample size that limits statistical power. However, comparable studies have similarly small numbers of participants, presumably due to the young age of the study subjectsFurthermore, the results of the indicator variable suggest that young children seroconvert more frequently than adults. However, the number of cases in our study is too small to estimate this precisely. Therefore, we do not discuss age as an associating factor for seroconversion.Concerning the laboratory testing of antibodies, the results are semi-quantitative. Since the aim of the study was to investigate seroconversion, not the strength of the immune response, we did not perform quantitative measurements of the antibody concentrations. Overall, it is possible that under-reporting of infected individuals may have occurred due to false-negative PCR samples. Prolonged unrefrigerated transport in hot temperatures can lead to false-negative PCR results. However, the majority of our samples were collected in winter and spring. We, therefore, consider under-reporting, in the sense of an increased number of false-negative samples, to be very unlikely. In addition, all samples arrived at the laboratory within the time frame specified by the study.n = 35/35 children) and 81% for the Omicron variant (n = 13/16 children)14. These results are compatible with ours but differ from previously reported results for this age group.Viral variants of SARS-CoV-2 may induce different immunological responses. Our findings are derived from the ancestral and alpha SARS-CoV-2 variants that were predominant at the time of data collection in 2020/2021 in Germany. The results may therefore be not transferable to subsequent variants. Recent analyses in Australian children after infection with Delta- or Omicron SARS-CoV-2 variants show that seroconversion rates in young children  one month after a positive PCR were 100% for the Delta variant (Our findings contribute to the overall picture of published but still scarce data on seroconversion after infection with SARS-CoV-2 in young children.Supplementary InformationReporting Summary


The COVID-19 pandemic has challenged public health efforts globally. Timely population-based surveillance is crucial to support public health programs and policies to limit the spread of COVID-19. The South Carolina (SC) Sampling and Testing Representative Outreach for Novel Coronavirus Guidance (SC STRONG) statewide initiative was established to estimate population-level prevalence and immunity and characterize the transmission dynamics of SARS-CoV-2 using community testing and online surveys.This paper aimed to leverage the survey data collected as part of the initiative to understand risk perceptions, testing practices, and preventive behaviors and identify risk factors for COVID-19 test positivity in SC over time.Probability proportionate to size cluster random sampling was used to select SC residents to participate in testing for COVID-19 infection and antibodies and to complete an online survey. This paper focuses on data from the online surveys completed between November 2020 and June 2021. Descriptive statistics were used to describe risk perceptions, attitudes and behaviors, and associated changes over time. Univariate and multivariate logistic regression models were used to identify factors associated with self-reported COVID-19 test positivity.Among the 7170 online survey respondents, 58.7% (4213/7170) self-reported ever testing for COVID-19. The most commonly cited barriers to testing were inconvenient dates, time, and location, as well as discomfort. Overall, 18.7% (790/7170) of respondents reported a history of COVID-19 test positivity. Multivariate logistic regression results indicated that individuals who were aged 50 years or older, self-identified as Black/African American, were obese, and were employed as frontline health care workers or nursing home staff were more likely to self-report COVID-19 test positivity. By contrast, there was a decreased likelihood of test positivity among respondents who were concerned about the burden of COVID-19 in their community and about being infected.Strategies to remove testing barriers should be implemented to improve access. Our findings provide insights on statewide testing patterns, adoption of prevention behaviors, and risk factors for infection and may inform public health strategies to curb transmission. The year 2020 brought new public health challenges and highlighted the need for data to inform ongoing public health response efforts. In South Carolina (SC), the first case of COVID-19 was detected on March 4, 2020 ,4.The adoption of preventive behaviors in part depends on the perceived severity of disease, perceived susceptibility to disease, benefits of compliance, and removal of barriers to the adoption of protective behaviors . KnowledThe objectives of this analysis were 2-fold: first, to understand SC respondents’ testing patterns, risk perceptions, and preventive behaviors and associated temporal changes and second, to identify risk factors for COVID-19 test positivity to inform public health policies and programs in SC. Understanding the population’s perceived risk allows for the targeting of public health interventions to reduce the burden of COVID-19 while addressing health inequities . This paSC has a population of approximately 4.86 million inhabitants aged 5 years and older . The staThe online survey was administered via REDCap (Research Electronic Data Capture) and hosted by Health Sciences South Carolina. REDCap is a secure web-based software platform designed to support data capture for research studies . A standDescriptive statistics and visualizations were used to describe the sample population and chi-square statistics were used to compare sociodemographic characteristics of the SC general population, survey respondents, and respondents who reported ever testing for COVID-19. Data on the SC general population were obtained from the 2019 American Community Survey . TestingP value less than 0.1 in the univariate logistic regression models were included in the initial multivariable regression model. A stepwise backwards elimination procedure was used to obtain the final multivariable model. Crude and adjusted odds ratios (aORs) from the logistic regression analyses are reported. Checks for multicollinearity were performed using variance inflation factors (VIFs). Covariates with a VIF greater than 8 were dropped. A P value less than .05 was considered statistically significant. Analyses were conducted using STATA/SE  and SAS Studio .History of testing was derived from 2 survey questions: “Have you ever been tested for active coronavirus infection ?” and “Have you ever been tested for coronavirus antibodies (blood test)?” As the goal was to identify risk factors for a positive COVID-19 test among those with history of testing, individuals with no prior testing for active coronavirus infection or antibodies were excluded from this analysis. The outcome of interest was based on self-reported diagnosis of coronavirus antibodies or active coronavirus infection derived from the following survey questions: “Have you ever tested positive for active coronavirus infection?” and “Have you ever tested positive for coronavirus antibodies?” Respondents who answered yes to either question were categorized as having ever tested positive for COVID-19. The likelihood of COVID-19 test positivity was assessed with univariate multivariable logistic regression. Explanatory variables of interest were selected based on previous studies assessing risk factors for COVID-19. To account for likely racial inequalities, “Black” and “Hispanic” were added as indicator variables. Household income was added as a proxy for socioeconomic status. Other explanatory variables considered were preexisting medical conditions, number of household members, living or working in congregate settings, testing behaviors, risk perceptions, and knowledge of COVID-19 preventive measures. To account for differences in risk of COVID-19 over time, models adjusted for time in weeks since recruitment for SC STRONG began. In addition to age, gender, income, and race or ethnicity, which were specified a priori, variables with a Human-subjects ethics approval was sought for the SC STRONG project. The institutional review boards at the University of South Carolina and the SC DHEC both determined that SC STRONG project activities did not constitute human-subjects research under the auspices of public health surveillance (102072). Participation in testing and survey completion was voluntary.From October 26, 2020, to June 16, 2021, 3 rounds of data collection were conducted, and 450,063 invitation letters were mailed out . The firA total of 8200 surveys were attempted across the 3 rounds, and 1030 surveys were excluded due to incomplete or inconsistent responses; therefore, a total of 7170 completed surveys was used for the present analysis . The resMost survey respondents were aged 60 years or older , female , and Caucasian  reported becoming unemployed during the pandemic, 10.6% (760/7170) reported reductions in income or pay, and 7.8% (562/7170) reported working fewer hours than normal . In termOf the 7170 survey respondents, 58.7% (4213/7170) reported ever testing for COVID-19 active infection or antibodies, and there was a 3% increased likelihood of testing for COVID-19 with every week that passed . The mosP<.001). Overall, 45.8% (2498/5460) of respondents reported being vaccinated against COVID-19. An assessment of attitudes toward the vaccine indicated that 72.9% (3982/5460) of respondents thought the COVID-19 vaccines were safe, and 41.6% (2270/5460) planned to get vaccinated at some point. Among those who reported not planning to get the vaccine, the main reason was discomfort with being one of the first to be vaccinated . Overall, there was an increased likelihood of vaccination (20%) and positive attitudes toward the vaccines per week.Most respondents reported social distancing  and wearing face masks  in the previous 2 weeks, and fewer reported self-isolation  or quarantining . The likIn the multivariable logistic regression analysis, several factors were significantly associated with COVID-19 test positivity . The likUsing serial population-based surveys conducted between November 2020 and June 2021, this paper sought to describe COVID-19 risk perceptions, testing patterns, and preventive behaviors and identify factors associated with test positivity among a large random sample of SC residents. Overall, 58.7% (4213/7170) of respondents reported ever testing for COVID-19, and over time, there was a decrease in barriers to testing. This finding is consistent with the increased availability of testing locations across the state over time. Moreover, our results suggested an increased likelihood of testing over time, an increased likelihood of positivity based on symptoms and exposure, and a decreased likelihood of positivity based on individual concern. Despite increased testing, there were decreases in practicing preventive behaviors such as mask-wearing and social distancing, indicating fatigue. Previous research has shown that states with no mask policy and low mask adherence reported high COVID-19 infection rates, while states with high mask adherence and strict mask-wearing policies showed decreased infection rates .In this paper, 18.7% (790/7170) of respondents self-reported ever testing positive for COVID-19, which is comparable to the estimated seroprevalence of 16.4% for SC residents aged 5 years or older . Our resThe findings presented in this paper should be interpreted in light of several limitations. First, given the cross-sectional survey design, the temporal relationship between outcome and exposures cannot be ascertained and causality cannot be determined. Second, the representativeness of the sample is limited due to the low response rate; the lack of partner clinics in some areas of the state may have hindered participation in the community testing and online survey. Third, the sampling frame was based on a purchased marketing listserve that may not be up-to-date. Fourth, COVID-19 test positivity was based on self-reports, which are prone to bias. Fifth, comparison of sociodemographic characteristics by history of testing indicated that the respondents who sought testing were more likely to be older, female, Caucasian, and have a higher income than the general population, potentially limiting the generalizability of our results. Lastly, although an electronic survey was selected to ensure respondent safety and minimize costs of survey implementation, only about 81.8% of households in SC have access to the internet . Staff wPopulation-based surveys are useful tools in understanding attitudes, behaviors, and practices and can inform public health responses and the adaptation of strategies to reach high-risk populations. Despite its limitations, the SC STRONG initiative represents a statewide outreach and partnership program with local clinics and health care providers. The findings presented here provide insights to understand risk perceptions and testing behaviors and have important implications given that Southern states have historically struggled with compliance and implementation of preventive public health measures.


Compared to no vaccination, all two-dose UVV strategies reduced varicella cases by 94%-96%, hospitalizations by 93%-94%, and deaths by 91%-92% over 50 years; herpes zoster cases were also reduced by 9%. There was a decline in the total number of annual varicella cases in all age groups including adolescents and adults. All UVV strategies were cost-effective compared to no vaccination, with ICER values ranging from €18,228-€20,263/QALY (payer perspective) and €3,746-€5,937/QALY . The frontier analysis showed that a two-dose strategy with V-MSD (15 months) and MMRV-MSD (48 months) dominated all other strategies and was the most cost-effective. In conclusion, all modeled two-dose UVV strategies were projected to substantially reduce the clinical and economic burden of varicella disease in Denmark compared to the current no vaccination strategy, with declines in both varicella and zoster incidence for all age groups over a 50-year time horizon.We modeled the long-term clinical and economic impact of two-dose universal varicella vaccination (UVV) strategies in Denmark using a dynamic transmission model. The cost-effectiveness of UVV was evaluated along with the impact on varicella (including age-shift) and herpes zoster burden. Six two-dose UVV strategies were compared to no vaccination, at either short (12/15 months) or medium (15/48 months) intervals. Monovalent vaccines (V-MSD or V-GSK) for the 1 Varicella (chickenpox) is an acute and highly infectious disease caused by the varicella zoster virus. Varicella infections commonly present as a generalized pruritic vesicular rash with fever and malaise. The infections are generally mild and self-limiting, but can sometimes result in complications  or, rarely, death –3. The rUniversal varicella vaccination (UVV) is not currently part of the Danish National Immunization Program . In the Varicella vaccines have been proven to be safe and effective against varicella, with UVV programs leading to significant declines in varicella morbidity and mortality , 3, 7–11The objective of the present study was to model the long-term public health impact and cost-effectiveness of universal childhood varicella vaccination strategies and assess their impact on varicella age-shift and HZ incidence in Denmark over a 50-year time horizon. These results can help inform decision-making around the introduction of a UVV program in Denmark.This is a dynamic transmission model that modeled for varicella-related outcomes and costs for the whole population in Denmark. No primary or secondary data was collected as part of this study. All inputs were from published literature and included only anonymized data. Our study did not involve the collection, use, or transmittal of individually identifiable data. Hence, our modeling study is out of scope for both patient IRB/EC review or patient informed consent.Maternal-Susceptible-Exposed-Infected-Recovered-Vaccinated) structure commonly used to evaluate vaccination programs  for UVV, each involving two doses of varicella vaccine, were compared to the no vaccination strategy over a 50-year time horizon . Four vaEpidemiological and clinical outcomes included: the annual incidence rates of natural varicella, breakthrough varicella, and HZ; the cumulative numbers of varicella cases, outpatient visits, hospitalizations, and deaths over 50 years; and the cumulative numbers of wild  and vaccine-related HZ cases and deaths. Age-stratified annual varicella incidence rates and the age distribution of cases were also reported to evaluate if there is a potential risk of age-shift of varicella infection to older populations.This study also assessed the impact of UVV on exogenous boosting and HZ incidence. The current model included assumptions for exogenous boosting, modeled from a recent real-world study with 20 years of follow-up conducted in the UK .Cost outcomes for all strategies were calculated from both payer (direct costs) and societal (direct and indirect costs) perspectives, along with incremental cost-effectiveness ratios (ICERs) for each UVV strategy compared to no vaccination. In addition, a frontier analysis was conducted to compare the effect and cost associated with the various UVV strategies, with comparisons made between strategies lying on the effectiveness frontier in the cost-effectiveness plane. All prices were updated to 2020 Euros. Discounting for costs and quality-adjusted life years (QALYs) followed the time-variable discount rate prescribed by the Danish Ministry of Finance of 3.5% for years 0–35, 2.5% for years 36–70, and 1.5% after 70 years .In the absence of a formal cost-effectiveness threshold for Denmark, we used the per capita gross domestic product (GDP) approach recommended by the World Health Organization and also compared results to the cost-effectiveness threshold recommended by UK’s Joint Committee on Vaccination and Immunisation , which is much lower than Denmark’s per capita gross domestic product  , 26.Additional scenario analyses explored outcomes at 25- and 100-year time horizons and with 3% or 5% annual discount rates . AdditioAll six vaccination strategies were projected to significantly reduce the annual incidence of varicella from 1,161 to 16–35 per 100,000 persons over 50 years , with MSIn the absence of vaccination, annual varicella incidence was highest among children aged 1–5 years  followed by incidence among children aged 5–10 years . Immediately following the introduction of UVV, total varicella incidence declined significantly in all age groups including adolescents and adults . After tAll six UVV strategies projected a decline in HZ incidence of 25%-26% at 50 years post-UVV, compared to no vaccination . The modIn the absence of varicella vaccination, the cumulative cost to manage varicella and HZ disease in Denmark over 50 years was projected to be €1.1 billion from the payer perspective and €1.2 billion from the societal perspective . CompareIn the one-way deterministic sensitivity analysis, the cost-effectiveness ratios compared with no vaccination remained relatively stable to parameter variation, with values ranging between €15,975 and €20,481/QALY from the payer perspective for Strategy E . The mosUnder the assumption of no exogenous boosting, there was no transient increase in HZ cases following introduction of varicella vaccination as observed in the base case. All strategies resulted in slightly greater reductions in HZ cases (10.4%-10.9%) and deaths (2.0%) without exogenous bosting than when exogenous boosting was included. Strategy E remained the most cost-effective vaccination strategy, with ICER values of €17,852 and €3,465/QALY gained from payer and societal perspectives, respectively  compared to the GSK vaccines (61.7%) , and is D: 94.4% ; V-GSK: D: 94.4% ).All six short and medium interval strategies were cost-effective compared to no vaccination. The cost-effectiveness frontier analysis further provided information on which strategy was cost-effective among all strategies under consideration. However, the medium interval Strategy E (V-MSD at 15 months and MMRV-MSD at 48 months) was the only UVV strategy on the cost-effectiveness frontier and deemed to be cost-effective at a threshold of 1 x GDP per capita from both payer and societal perspectives. The medium interval strategies align with the current MMR schedule in Denmark, thus possibly giving these strategies greater weight when considering adopting UVV in Denmark.This study assessed whether an age-shift in varicella cases would occur following the introduction of UVV, a common concern of implementing UVV in many countries , 10, 17.Some previously published mathematical models, including those by Brisson et al and van Hoek et al, had predicted that UVV would lead to an increase in the burden of HZ due to assumed pronounced exogenous boosting effect, and further concluded that UVV may not be a cost-effective strategy , 32, 33.The results of our model are robust to variation in input parameters, and are consistent with recently published results for Turkey, Italy, Switzerland, UK and Norway, which showed significant reductions in the clinical and economic burden of disease after the introduction of UVV , 36–39. Modeling studies incorporate various assumptions about which there is uncertainty. We used proxy data from Norway and Sweden when recent Danish data were unavailable, though the similarity between these Nordic countries suggests they can be appropriate substitutes. Since the list prices for MMRV formulations in Denmark were not available, we estimated list prices from the International Reference Pricing basket using an approach recommended by the Danish Ministry of Health . List prAll modeled two-dose strategies of universal childhood varicella vaccination were projected to substantially reduce the clinical burden of varicella and were cost-effective compared to no vaccination in Denmark. Our model also showed no significant age-shift, and varicella incidence was substantially reduced across all age groups including adolescents and adults compared to no vaccination. Although there was a small increase in HZ incidence within the first few years of UVV introduction, overall HZ incidence decreased over 50-year time horizon. While all vaccination strategies were cost-effective compared to no vaccination, the medium interval strategy, V-MSD at 15 months and MMRV-MSD at 48 months, was the most cost-effective from payer and societal perspectives. These results provide evidence to support the introduction of universal childhood two-dose varicella vaccination in Denmark.S1 Text(DOCX)Click here for additional data file.S2 Text(DOCX)Click here for additional data file.S3 Text(DOCX)Click here for additional data file.


Frailty also correlated with MUST , MNA-SF , and hospitalization length . Delirium, malnutrition, and frailty are correlated in COVID-19 survivors. Screening for these geriatric syndromes should be incorporated in routine clinical practice.Older individuals face an elevated risk of developing geriatric syndromes when confronted with acute stressors like COVID-19. We assessed the connection between in-hospital delirium, malnutrition, and frailty in a cohort of COVID-19 survivors. Patients aged ≥65, hospitalized in a tertiary hospital in Milan for SARS-CoV-2 pneumonia, were enrolled and screened for in-hospital delirium with the 4 ‘A’s Test (4AT) performed twice daily (morning and evening) during hospital stay. Malnutrition was assessed with the malnutrition universal screening tool (MUST) at hospital admission and with the mini-nutritional assessment short-form (MNA-SF) one month after hospital discharge. Frailty was computed with the frailty index one month after hospital discharge. Fifty patients  were enrolled. At hospital admission, 10% were malnourished. The 13 patients (26%) who developed delirium were frailer (7 vs. 4), experienced a higher in-hospital mortality (5 vs. 3), and were more malnourished one month after discharge (3 of the 4 patients with delirium vs. 6 of the 28 patients without delirium who presented at follow up). The 4AT scores correlated with the MNA-SF scores (r = −0.55,  Older people, especially the frailer ones, are at high risk of developing geriatric syndromes while facing acute stressors such as hospitalization . GeriatrAbout 25% of people aged 65 years and older are malnourished or at risk of malnutrition ,8,9.Delirium is an acute decline of cognitive function underpinned by predisposing chronic conditions and triggered by precipitating events. Delirium can be considered as a marker of brain vulnerability and has been proposed to be a sort of vital parameter in older people ,5. One tSince the brain has elevated metabolic requirements, inadequate nutritional supply impairs the brain’s ability to work properly and predisposes to delirium ,13. On the other hand, people with a hyper- or hypoactive cognitive status, or excessively sedated, are at risk of not eating properly ,15. MoreIt should be also taken into account that the acute illnesses that can cause delirium may also impair the patients’ ability to eat by the oral route. For example, patients admitted for respiratory diseases often manifest dyspnea and are supplied with oxygen using masks that interfere with food intake. Despite the elevated metabolic requirements, enteral or parenteral nutritional supply is seldom administered during the first days of hospital stay, and, indeed, hospitalized individuals experience long fasting periods . Moreover, the focus of physicians on the treatment of the acute pathologic conditions responsible for the hospitalizations, the scarce awareness of the importance of malnutrition and delirium, and the lack of time and personnel to perform multidimensional evaluations lead to an underdiagnosis of these dangerous geriatric syndromes. As a consequence, there is a dramatic increase in patients’ morbidity and mortality related to potentially preventable conditions ,4,20,21.COVID-19 has been associated with both an elevated risk of developing delirium and malnutrition ,23,24,25An acute alteration in mental status has been reported in about 20–30% of COVID-19 hospitalized patients, with percentages up to 60–70% in severely ill ones .Malnutrition  criteria) has been described in 42% of COVID-19 hospitalized patients . Other sMalnutrition is also a common complaint in COVID-19 survivors. Two French studies which evaluated malnutrition in terms of percentage of weight loss or reduced body mass index (BMI) identified a prevalence of malnutrition of 47%  and 33% We aimed at assessing the link between in-hospital delirium and post-discharge malnutrition in a sample of COVID-19 survivors hospitalized for SARS-CoV-2 pneumonia during the third wave of the COVID-19 pandemic.This prospective observational study was part of the COVID-BioB study NCT04318366) [8366 [31]All patients hospitalized between January and May 2021, during the third wave of the COVID-19 pandemic, for SARS-CoV-2 pneumonia were invited to a dedicated out-patient clinic, for a follow-up visit one month after hospital discharge. The data presented in this study refer to a sample of older patients (aged ≥65 years), who were screened for the presence of delirium during hospital stay in COVID-19 medical wards.The 4 ‘A’s Test (4AT) was administered twice daily (morning and evening) by trained emergency and internal medicine residents, and was used to screen delirium. The diagnosis of delirium was confirmed by the clinical evaluation of a senior geriatrician (SD). Patients with any 4AT score ≥ 4 during hospital stay and a clinical evaluation of a geriatrician consistent with a diagnosis of delirium were included in the delirium group. The 4AT scale has been widely validated for the screening of delirium . It is cAt hospital admission, the patients were also screened for the presence of malnutrition by their attending physicians, though the administration of the malnutrition universal screening tool (MUST) questionnaire . MUST isModified GLIM criteria  were alsThe length of hospital stay and in-hospital mortality were retrieved from the consultation of medical records.Follow-up visits took place one month after hospital discharges, from March to June 2021. During the visits, multidimensional geriatric evaluations were performed. These included anamnesis (with a particular attention on the presence of persistent or de novo COVID-19 symptoms), physical examination, measurement of the anthropometric parameters  and screening for malnutrition  questionnaire . Total MMoreover, we screened for the presence of sarcopenia though the Strength, Assistance with walking, Rising from a chair, Climbing stairs, and Falls (SARC-F) questionnaire  and we a2/FiO2) during hospital stay (taking into account the complexity and the severity of COVID-19 disease), social information , and the presence of polytherapy and of the anticholinergic burden of chronic therapies computed through the anticholinergic cognitive burden (ACB) scale. The ACB score captures the severity of the accumulative anticholinergic cognitive burden due to the total medications taken by older adults [To synthetize the patients’ clinical complexity and vulnerability to stressors, we computed a frailty index (FI) according to the criteria described by Searl et al.  Table S. The varr adults . Each der adults . The parr adults . The scor adults .Descriptive statistics were used to illustrate the baseline characteristics of the study population. Continuous variables were presented as mean and standard deviation (SD), when normally distributed, or with median and interquartile range (IQR), when data had a skewed distribution. Dichotomous variables were presented as number (N) and percentage (%). Comparisons of the baseline characteristics between patients with in-hospital delirium and without in-hospital delirium were performed with the t test or U Mann–Whitney test for continuous variables, and with the chi-squared test for categorical variables. Spearman’s correlations were used to explore the association among 4AT scores during hospital stay, frailty index score, MUST, and MNA-SF one month after hospital discharge. Cohen’s kappa coefficients were used to compare the agreement among the different tools used to detect malnutrition.All statistical analyses were performed with IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY, USA: IBM Corp.In this prospective observation study, we enrolled 50 older patients  hospitalized for SARS-CoV-2 pneumonia. No patient had received the SARS-CoV-2 vaccine before being infected because vaccination was not yet available for older people in that period. No patient received invasive mechanical ventilation but four patients (8%) were supported with non-invasive mechanical ventilation. A total of 30 patients (60%) were treated with steroids, 27 (54%) with heparin, and 15 (30%) with antiviral therapies. Nine patients (18%) received biological drugs and fourteen (28%) antibiotics.p = 0.001) compared to patients who did not develop an acute brain failure. p = 0.046), polytherapy, or nutritional status before hospital admission. On the other hand, the patients who experienced delirium during hospitalization had a lower pre COVID-19 weight compared to the patients who did not have delirium . According to the MUST criteria, only five patients, 10% of the sample, were malnourished before hospital admission .Thirteen (26%) experienced delirium during hospital stay. These patients were frailer, .By using the modified GLIM criteria, eight patients (16%) were classified as malnourished at hospital admission. Weight loss and the nadir of body weight during hospitalization were not statistically different in patients with and without delirium. The median weight loss during hospital stay was 4 kg. For 54% of the sample, the amount of weight loss was greater than 5% of their body weight before hospital admission. Liver enzymes (transaminases) at hospital admission were higher in the patients who experienced delirium compared to the patients who did not .Eight patients died during hospital stay. In-hospital mortality was higher in the patients with delirium compared to the patients without delirium .p = 0.87; between MNA-SF and modified GLIM criteria: κ = 0.003, p = 0.98).The concordance among the different malnutrition screening tools was poor . One month after hospital discharge, 7 patients (22%) were at risk of sarcopenia (SARC-F score ≥ 4) and 23 patients (72%) were pre-sarcopenic . The prevalence of pre-sarcopenia and the risk of sarcopenia were not different among the patients who experienced delirium and those who did not experience it.The people who experienced delirium during hospitalization were more malnourished (MNA-SF ≤ 7) at the one-month follow-up visits compared to the people who did not have delirium . On the other hand, the length of hospital stay was also correlated with malnutrition after hospital discharge . We found a significant correlation among various 4AT scores during hospital stay and MNA-SF one month after hospital discharge  was significantly correlated with the MNA-SF scores one month after hospital discharge. Transaminases at hospital admission were higher in the patients who developed delirium during hospital stay compared to the patients who did not have delirium. Finally, we detected correlations among frailty, malnutrition , length of hospital stay, and delirium.Our results are in line with the findings of a recent Danish study, conducted by Rosted et al., among 612 old COVID-19 patients hospitalized in a geriatric unit [2 plus loss of appetite and weight during hospital stay, or a weight loss of more than 5 kg within three months prior to admission. If we compare the percentage of the patients who experienced a weight loss ≥ 5% during hospital stay in our study, (54%) and the patients defined as malnourished by Rosted et al. (57%), they are very similar.In the study by Rosted et al., malnutrition was defined as a BMI below 25 kg/mCompared to the study by Pironi et al. , who assCompared to the previous data of our group , collected during the first wave of the COVID-19 pandemic , the preWe confirmed the link between delirium and malnutrition, as previously detected among SARS-CoV-2 patients, both in the hospital  and nursFrailty, being characterized by a reduction of physiological reserves, increases the risk of developing adverse outcomes ,54 incluThe prevalence of pre-sarcopenia one month after hospital discharge was high in this study (46%). Unfortunately, we do not have data referring to muscle function in the pre-infection period. Thus, we cannot be sure that these people were not pre-sarcopenic before SARS-CoV-2 infection. However, acute sarcopenia during COVID-19 is common ,56, and The concordance among the different malnutrition definitions was poor. This is a problem already described in the literature ,59, and,2) and weight loss (5–10%) have to be present. Alternatively, a severe reduction in BMI (<18.5 kg/m2) or weight (loss > 10% in the previous months), or the presence of an acute disease, with a reduction of nutritional intake, for at least five days, is required for being classified at high risk of malnutrition. Instead, according to the GLIM criteria, just a phenotypic criterium  plus an etiologic criterium (reduced food intake/assimilation or disease burden/inflammation) are sufficient to diagnose malnutrition.At hospital admission, eight patients (16% of the study sample) were malnourished, according to the GLIM criteria, and five individuals (10% of the study population) had an elevated risk of malnutrition, according to the MUST score. Indeed, for being classified at an elevated risk of malnutrition with the MUST tool, at least two criteria among low BMI  compared to the MNA-SF (28%).However, all COVID-19 survivors had a recent acute disease (and thus a recent acute inflammatory burden), and most of them experienced a weight loss during the acute phase of the SARS-CoV-2 infection. MNA-SF is a more multidimensional tool, and was the only one which detected a difference in the prevalence of malnutrition in the patients who experienced delirium during hospital stay and those who did not. Therefore, even if there is no consensus on which tool is better to assess nutritional status in COVID-19 patients, in this population, MNA-SF was found to be the most suitable tool to study malnutrition in COVID-19 survivors.We found that transaminases at hospital admission were higher in the patients who developed delirium compared to the patients who did not. A recent prospective, multicenter, cohort study by Zhu et al. , who enrSome limits of this study deserve to be mentioned. First, we should note the reduced sample size, due to the emergency care setting of the COVID-19 pandemic, and the monocentric nature of the study that limits the generalizability of our results. However, our findings are in line with previous European studies with wider sample sizes. Another potential limitation is the underreporting of pre-existing dementia, which can have been complicated by behavioral disorders, misinterpreted as delirium during hospitalization. Last but not least, the weight at hospital admission was self-reported, and this could have introduced a recall bias in our study.In-hospital delirium and post-discharge malnutrition are correlated in COVID-19 survivors. Screening for these geriatric syndromes should become part of the routine clinical practice to improve patients’ well-being and potentially reduce the burden of adverse outcomes linked to these conditions.


Improving access to facility-based delivery care has the potential to reduce maternal and newborn deaths across settings. Yet, the access to a health facility for childbirth remains low especially in low-income settings. To inform evidence-based interventions, more evidence is needed especially accounting for demand- and supply-side factors influencing access to facility-based delivery care. We aimed to fill this knowledge gap using data from Tanzania.We used data from a cross-sectional survey (conducted in January 2012) of 150 health facilities, 1494 patients and 2846 households with women who had given births in the last 12 months before the survey across 11 districts in three regions in Tanzania. The main outcome was the place of delivery , while explanatory variables were measured at the individual woman and facility level. Given the hierarchical structure of the data and variance in demand across facilities, we used a multilevel mixed-effect logistic regression to explore the determinants of facility-based delivery care.Eighty-six percent of 2846 women gave birth in a health facility. Demand for facility-based delivery care was influenced more by demand-side factors (76%) than supply-side factors (24%). On demand-side factors, facility births were more common among women who were educated, Muslim, wealthier, with their first childbirth, and those who had at least four antenatal care visits. On supply-side factors, facility births were more common in facilities offering outreach services, longer consultation times and higher interpersonal quality. In contrast, facilities with longer average waiting times, longer travel times and higher chances of charging delivery fees had few facility births.Policy responses should aim for strategies to improve demand like health education to raise awareness towards care seeking among less educated groups and those with higher parity, reduce financial barriers to access (including time costs to reach and access care), and policy interventions to enhance interpersonal quality in service provision.The online version contains supplementary material available at 10.1186/s13561-023-00468-1. Maternal mortality remains a global health challenge , 2. OverDemand for health care  is influenced by demand-side and supply-side factors. Demand-side determinants are factors influencing the ability to use health services at individual, household or community levels . Supply-side determinants are aspects inherent to the health system particularly at the point of service delivery  which may influence service delivery and uptake by individuals, households or the community –13. PoliThis study aimed to broaden the understanding of the factors associated with facility-based delivery care and relative influence of these factors on how they associate with the demand for facility-based delivery care in the Tanzanian context. We used multiple sources of primary data  which offers a wider range of demand- and supply-side factors than available through routine surveys , service provision assessment (SPA)) , 22.This study was conducted in three regions of Tanzania: Pwani, Morogoro and Lindi, with the population size of 2 million, 3.2 million, and 1.2 million in 2022, respectively . The stuGrossman model reflecting on the human capital model of the demand for health . The odds of a facility-based delivery increased by 4% [OR = 1.04 (CI: 1.01 to 1.07)] for every additional minute of consultation time for facility outpatients; and, for each unit increase in facility-level interpersonal quality score, the odds of facility-based delivery increased five times on average [OR = 5.11 (CI: 1.12 to 23.4)].On the other hand, longer waiting times, longer travel time to reach the facility, and a higher chance of charging delivery fee significantly associated with the reduced demand for facility-based delivery. The odds of facility-based delivery were reduced by 1% for each additional minute of travel time to health facility [OR = 0.99 (CI: 0.98 to 0.99)] and for each additional minute of waiting time for outpatient care [OR = 0.99 (CI: 0.98 to 1.00)]. For each percent increase in charging delivery fee at the facility associated with the reduced odds of facility-based delivery by 76% on average [OR = 0.24 (CI: 0.09 to 0.63)].Overall, demand-side factors had stronger association with the demand for facility-based delivery care than supply-side factors Table . The demThe reduced model revealed almost similar significant factors associated with the demand for facility-based delivery  . It is therefore important for routine facility surveys such as SPA to collect patient level information at the point of service delivery.Our study had some limitations. First, we relied on only 20 women from each facility’s catchment area to estimate the rate of facility-based delivery and ascertain associated demand-side determinants. This may not reflect the actual status of all reproductive women in the catchment area. Second, we used women’s nearest facility to characterise the supply-side determinants, but due to bypassing tendency especially for primary healthcare facility in Tanzania , not allWe highlight important implications for improving demand for facility-based delivery care services in Tanzania. The predominance of demand-side influences indicates the need for targeted health education programmes in the community to improve awareness on care seeking among those with lower levels of education and higher parity. Lower demand among poorer households together with the large effect of delivery care costs on decision making, highlight the importance of interventions to increase the affordability of care especially for poorer groups . The lack of effect of health insurance on demand, suggests that the benefit package may not be sufficient to offset financial barriers beyond user charges . It is sMoreover, we underscore the importance of antenatal care as a channel through which to encourage facility-based deliveries, suggesting that policy initiatives that enhance access to regular antenatal care are likely to increase the rate of institutional deliveries. Equally, our study highlights that local facility reputation characterised in our study by the length of outpatient consultations and the quality of patient-provider interactions has a substantial influence over decision-making for where a women would go for delivery. For instance, poor quality of services at local or nearest facility influences clients to bypass towards other or even a higher-level facility , 64. In This study shows the importance of assessing both demand- and supply-side factors associated with the demand for facility-based delivery care in order to inform evidence-based interventions. Interestingly, we found the demand for facility-based delivery care was influenced more by demand-side factors than supply-side factors. This finding reinforces the need for policy makers to design interventions or strategies to improve healthcare demand like health education to raise awareness towards care seeking among less educated groups and those with higher parity, reduce financial barriers to access (including time costs to reach and access care), and policy interventions to enhance interpersonal quality in service provision.Additional file 1: Appendix Table 1. Random effects parameters for random intercept only model (empty model). Appendix Table 2. Reduced model by using logistic backward stepwise regression.


Background: Moderate- to high-certainty evidence supports the benefits of pre- and post-operative exercise for people undergoing surgical resection for lung cancer. Despite this, exercise programs are not commonly provided. Previous data regarding exercise practices are a decade old. Therefore, this study aimed to understand current exercise practices in surgical lung cancer care in Australia and New Zealand. Methods: An online cross-sectional survey of Australian and New Zealand allied health professionals specialising in exercise-based interventions was carried out. Survey development and reporting adhered to CHERRIES and CROSS checklists. Institutions with thoracic surgery departments were invited to participate via email, and additional responses were sought via snowballing. Results: The response rate was 81%, with a total of 70 health services responding. A total of 18 (26%) pre-operative services, 59 (84%) inpatient post-operative services, and 39 (55%) community/outpatient post-operative services were identified. Only eight (11%) services provided a pre-operative exercise program. Half of the respondents referred less than 25% of patients to community/outpatient exercise programs on hospital discharge. Respondents reported that their clinical management was predominantly influenced by established workplace practices and personal experience rather than evidence. Conclusions: The availability and uptake of pre- and post-operative exercise remain low, and work should continue to make pre/post-operative exercise training usual practice.  The optimal curative treatment for early-stage lung cancer is surgical lung resection . In the Despite this, it is uncommon in Australia, New Zealand, and elsewhere in the world for people undergoing lung cancer surgery to be referred to pre- or post-operative exercise programs . TypicalIt is pertinent now to update our understanding of current exercise health professional practice in this area, given the newly available breadth of evidence, improved patient survival rates , and chaThe aims of our study were to (1) identify the current role of exercise health professionals/clinicians in the management of people undergoing surgical resection for lung cancer across the continuum of care  in Australia and New Zealand and (2) investigate the success in implementing the evidence base into routine clinical practice over the last decade. A purpose-built, cross-sectional, open online survey was developed using the online survey platform Qualtrics  and pre-The survey was developed by adapting the questionnaire by Cavalheri and colleagues from 2013, which examined management from the perspective of physiotherapists only across Australia and New Zealand . The surRespondents could self-select their primary area(s) of clinical practice and were then directed to complete the relevant section(s) using branching logic. This enabled adaptive questioning whereby only certain items or sections were displayed based on responses to other items, reducing the number and complexity of questions. Closed question formats included drop-down responses, binary yes–no, multiple choice, and 5-point Likert scales. Some questions allowed the selection of multiple responses . Additionally, participants were asked to rate the factors that influenced their assessment and treatment of people with lung cancer undergoing surgical resection. These factors were based on the initial 2013 survey . All iteA three-stage testing process was implemented prior to dissemination to confirm the face validity, usability, and technical function of the electronic survey; one author (GWW) designed the online survey, following which the remaining authors reviewed and refined the survey until consensus was reached. The survey was then tested twice with a representative pilot sample of clinician researchers  who provided feedback on the survey clarity, ease of completion, and content to confirm the face validity and usability of the survey. This step was crucial to enhance validity, improve responder reliability, and help reduce potential measurement error or non-response errors in the live survey. Minor revisions to wording for clarification and technical elements  were made to the survey based on feedback from pilot testing. Data were stored in a secure, password-protected, University-hosted server accessible only to the research team. Data were de-identified prior to analysis, and all re-identifiable data were stored separately from the main data file. The survey was open for five weeks between June and July 2022. Incomplete surveys were automatically submitted after survey closure. As an incentive to participate in the study, respondents were eligible to enter a draw to win a new Apple iPad upon completing the survey. This was facilitated through a separate process that could not be linked to their survey answers. The development and reporting of the survey were informed by the Checklist for Reporting Results of Internet E-Surveys (CHERRIES) and Checklist for Reporting of Survey Studies (CROSS)  11,12].,12.11,12Allied health professionals specialising in exercise-based interventions ;Currently working in a setting that manages people undergoing surgical resection for lung cancer, in any setting across the continuum  to ensure data collected were reflective of current practices;Based in Australia or New Zealand.Eligibility was assessed in the opening pages of the survey. Clinicians were eligible to participate if they were: Respondents were excluded if they did not progress beyond the demographics section . No further exclusion criteria were defined. Respondents were asked to nominate their name, workplace, and professional email address to ensure response accountability and prevent multiple entries from the same individual. Hospitals with thoracic surgery services were identified as the main target for dissemination. A list of hospitals in Australia and New Zealand that likely provided thoracic surgery services was created through interstate and international clinical networks of investigators and through Internet searches and was cross-checked against the list used in a previous survey . The phyAdditional responses were sought via online snowball sampling. The survey was advertised via multiple avenues including collaborative networks and social media . An examWhere duplicate responses were received , a hierarchy was used to determine which response would be included in the analysis of clinical practice data. Firstly, the most complete response was prioritised. If all duplicate responses were complete, the response from the most senior clinician as determined by years of clinical experience was included in the analysis. Duplicate responses were included in the analysis of demographic data to avoid over-estimating the seniority of clinicians working in this area. Incomplete responses were included in the data analysis. Ineligible responses (responses that did not meet eligibility criteria) were removed prior to analysis.De-identified survey data were exported, coded, and analysed descriptively using Statistical Package for Social Sciences (SPSS) Version 28. Demographic and clinical practice data are displayed as either n (%) for categorical variables, median [IQR], or mean (SD) depending on the distribution of the data using the Kolmogorov–Smirnov test of normality. Responder rurality was coded according to the Australian Statistical Geography Standard (ASGS)  and the The response rate was defined as the number of institutions returning a response, divided by the number of institutions invited to participate via email. The completion rate was defined as the number of submitted surveys (irrespective of completeness), divided by the number of users who consented to participate  . The comBetween June and July 2022, 97 potentially eligible health services were identified, and contact was attempted . The surp = 0.030). No difference was observed in the proportion of rural/remote health services between the groups (non-responders n = 3/13 (23%) versus responders n = 3/57 (5%), p = 0.073). A comparison of responder (n = 57) and non-responder (n = 13) demographics revealed a higher proportion of private health services within the non-responder group (n = 9/13 (69%) versus responders n = 22/57 (39%), Thirteen additional health services were identified via snowballing, all of which were eligible to participate and responded. Overall, from across the 70 responding health services, a total of 132 participants consented to participate in the survey, and 102 valid responses were received. The flow of survey responses from health services is summarised in The survey completion rate was 77% (n = 102/132) . Of those who progressed past the informed consent stage, the survey completeness rate was 85% (n = 87/102) . No responses were excluded based on completeness. Nine duplicate responses were excluded from the analysis of clinical data. One hundred and two clinicians responded to the survey . Most  of people undergoing surgical resection for lung cancer is summarised in Eighteen (26%) health services had a pre-operative exercise service for people awaiting surgical resection for lung cancer. Two (11%) of the pre-operative services were in rural/remote areas. The most common discipline who responded that they were working in the pre-operative setting was physiotherapists , and the remaining four (22%) respondents working in this setting were exercise physiologists. Of the 18 identified services, 6 (33%) were exercise/prehabilitation programs, 6 (33%) were exercise services based within pre-admission clinics, 5 were inpatient ward-based exercise services (28%), and 1 (6%) was exclusively a pre-operative exercise capacity assessment.Six (33%) and five (29%) services reported that ‘most’ or ‘some’ patients were assessed by exercise clinicians prior to surgery, respectively. Twelve services (67%) reported patient-related factors associated with triggering pre-operative assessment. The most common triggers were respiratory comorbidity , frailty , advanced age , and poor performance in functional tests . Other factors influencing whether a pre-operative assessment occurred included referral processes , staff availability , and patient availability . The parameters assessed by clinicians pre-operatively are summarised in All services provided pre-operative education delivered by an exercise health professional, with nine (50%) services providing education to ‘all’ or ‘most’ patients. Education topics are summarised in Eight services (44%) reported that at least ‘a few’ patients participate in pre-operative exercise, inclusive of five prehabilitation services, one pre-admission clinic service, and two inpatient ward-based services. No rural/remote pre-operative services offered exercise programs. Pre-operative exercise programs ran for a median of 3 [1.5–4] weeks and provided three [2–5] sessions per week, and individual sessions ran for a median of 60 [30–60] min. Characteristics and components of exercise programs are summarised in The most common non-exercise components of pre-operative services included nutritional counselling , smoking cessation , and psychological support .Fifty-nine (84%) health services had an inpatient post-operative exercise service for people undergoing surgical resection for lung cancer. Four (7%) of the inpatient post-operative services were in rural/remote areas. All primary respondents were physiotherapists, with one duplicate response from an allied health assistant. Thirty-one (53%) respondents reported that, of the patients they are involved with post-operatively, no patients participated in pre-operative exercise, with 15 (25%) and three (5%) reporting that ‘a few’ patients or ‘some’ patients participated in pre-operative exercise, respectively.Most respondents  reported the presence of a standard clinical pathway in the post-operative management of these patients, and most included early post-operative physiotherapy assessment and early mobilisation. Forty-three (73%) services reported a blanket referral system for physiotherapy for all patients with lung cancer undergoing thoracic surgery. In health services without blanket referrals, the most common triggers for physiotherapy referral were open surgery  , risk of post-operative pulmonary complication , and concerns regarding mobility/safety on discharge .Post-operative physiotherapy input usually commenced on the first post-operative day , with two (3%) reporting commencing input on the day of surgery. Across health services, ‘most’  or ‘all’ patients  were assessed post-operatively by physiotherapists. The most common parameters assessed by physiotherapists were cardiorespiratory status , mobility/physical function , and pain . Assessments utilised by physiotherapists at this timepoint are summarised in Most respondents reported that ‘all’  or ‘most’  patients received some form of post-operative intervention from clinicians, including post-operative education and/or exercise-based interventions. Education topics and exercise interventions commonly prescribed are summarised in Referral rates to post-operative community/outpatient exercise programs on discharge from hospital were low . Most reThirty-four (49%) health services had a community/outpatient post-operative exercise service for people after surgical resection for lung cancer. Five health services (7%)  reported having multiple community/outpatient post-operative services . Four (12%) community/outpatient post-operative services were in rural/remote areas. A total of 77% (n = 30) of respondents for this timepoint were physiotherapists, and the remaining 23% (n = 9) were exercise physiologists. This timepoint, therefore, represents the most common timepoint of exercise physiology involvement. The most common community/outpatient exercise service type was pulmonary rehabilitation  followed by oncology rehabilitation .The time between hospital discharge and commencing in community/outpatient exercise services varied. Most commonly, patients commenced in programs after longer than 8 weeks . Six (15%) services reported patients commencing within 4 weeks of hospital discharge. One hundred percent (n = 4) of rural/remote services reported wait times longer than 8 weeks. Wait times did not differ based on the type of program.The parameters assessed by clinicians at the community/outpatient post-operative timepoint are summarised in Seventeen (45%) and eleven (29%) services reported that ‘all’ or ‘most’ patients receive post-operative education from an exercise health professional, respectively. Education topics are summarised in Most respondents reported that ‘all’  or ‘most’  patients participate in an exercise program during this timepoint. Post-operative exercise programs ran for a median of eight [8-8] weeks, provided two [2-2] sessions per week, and ran for 60 [45–60] min. The characteristics and components of exercise programs are summarised in Referral rates to longer-term maintenance exercise programs on completion of post-operative community/outpatient programs were variable. Eight (24%) and five (15%) respondents reported that either ‘less than 25%’ or ‘no’ patients were referred onwards on completion, respectively, whereas six (18%) and two (6%) respondents reported referring either ‘more than 75%’ or ‘100%’ of patients, respectively.This study provides in-depth insight into the current exercise management of people undergoing surgical resection for lung cancer. In line with previous studies, our results suggest that most exercise professional input occurs in the initial inpatient post-operative phase. Other key findings of this survey include the following: (1) only 18 (26%) health services reported having a pre-operative service, 8 (44%) of which included an exercise program; (2) half of the respondents working in the inpatient post-operative setting reported referring less than 25% of patients to exercise programs on hospital discharge; and (3) only 34 (49%) health services reported having a community/outpatient post-operative exercise service.Despite moderate- to high-certainty evidence supporting the efficacy of pre- and post-operative exercise for patients with lung cancer, such programs are currently scarcely available across Australia and New Zealand , and referral to programs is still not routine practice. Our findings suggest a potential small improvement in the availability of, and referral to, post-operative exercise services in comparison to the 2013 study, which reported that 72% (compared to 50% in the present study) of respondents referred less than 25% of patients to post-operative exercise services ,16,17, iOur study identified a number of potential barriers to the implementation of exercise into routine lung cancer care, some of which have been identified previously in the literature, including clinician knowledge, workplace culture, constraints of the health care system , and patient appointment burden ,21,22.Regarding the implementation of pre-operative exercise, several potentially modifiable barriers were identified. Anecdotally, professional input about exercise during the pre-admission clinic visit is often ad hoc and reliant on referrals from medical staff. Traditionally, patients are scheduled to see multiple clinicians on the day of the pre-admission clinic visit , and rarely are exercise health assessments pre-planned. The ad hoc nature of these appointments presents logistical barriers such as referral processes and staff/patient availability. By pre-scheduling exercise professional assessments during lung cancer pre-admission clinic visits, as is routinely carried out for other disciplines and has been shown to be beneficial in other surgical populations , these cAside from early mobility , clinicians working in the post-operative inpatient timepoint reported relatively low rates of both exercise prescription and education regarding exercise guidelines. Given that in 63% of health services, post-operative exercise management followed a standard post-operative clinical pathway, it is likely that existing pathways/protocols do not routinely include exercise prescription or education. Given these findings, and the strong influence of personal experience and established workplace practice on clinical decision making, it may be warranted to conduct further research into both (1) the contents and ongoing validity of current post-operative clinical pathways and (2) individual clinicians’ knowledge, attitudes, and beliefs regarding exercise prescription for patients undergoing lung cancer surgery to identify any further barriers to implementation.Additionally, while no research exists suggesting that subgroups of people with lung cancer benefit more from exercise programs, our findings suggest that patient factors such as respiratory comorbidity  and impairment  are commonly used by clinicians as indicators for referrals to post-operative exercise. These findings are likely explained by the longstanding routine practice of referring patients with COPD to pulmonary rehabilitation programs and those with post-operative impairments to community rehabilitation programs. Given the relatively new breadth of evidence supporting exercise for all people undergoing surgical resection for lung cancer, interventions targeting clinician awareness of the evidence may be warranted.Clearly, the implementation of exercise into routine clinical practice would be helped by increasing the number of services available and by ensuring the availability of remote  delivery alongside face-to-face models, particularly in rural and remote areas where service availability is significantly lower and lung cancer incidence is higher . Aside fSome strengths of our study include the high response rate (81%) with representation across all states, territories, and regions in Australia and New Zealand. This means we have strong confidence that the results are reflective of current practices amongst exercise health services across Australia and New Zealand. However, as in the 2013 survey , we obsThe results of this study show a clear, persistent gap between research evidence and exercise professional clinical practice in surgical lung cancer care. Despite moderate- to high-certainty evidence supporting the routine use of pre- and post-operative exercise, such services remain scarce, and referral rates remain low. Work should continue to integrate exercise into the pathway of lung cancer surgical care, including further research to understand clinician knowledge and beliefs relating to exercise in this population and the implementation enablers and barriers they may face when attempting to translate research into practice.


Segmentation of liver vessels from CT images is indispensable prior to surgical planning and aroused a broad range of interest in the medical image analysis community. Due to the complex structure and low-contrast background, automatic liver vessel segmentation remains particularly challenging. Most of the related researches adopt FCN, U-net, and V-net variants as a backbone. However, these methods mainly focus on capturing multi-scale local features which may produce misclassified voxels due to the convolutional operator’s limited locality reception field.We propose a robust end-to-end vessel segmentation network called Inductive BIased Multi-Head Attention Vessel Net(IBIMHAV-Net) by expanding swin transformer to 3D and employing an effective combination of convolution and self-attention. In practice, we introduce voxel-wise embedding rather than patch-wise embedding to locate precise liver vessel voxels and adopt multi-scale convolutional operators to gain local spatial information. On the other hand, we propose the inductive biased multi-head self-attention which learns inductively biased relative positional embedding from initialized absolute position embedding. Based on this, we can gain more reliable queries and key matrices.We conducted experiments on the 3DIRCADb dataset. The average dice and sensitivity of the four tested cases were 74.8The proposed model IBIMHAV-Net provides an automatic, accurate 3D liver vessel segmentation with an interleaved architecture that better utilizes both global and local spatial features in CT volumes. It can be further extended for other clinical data. CT liver vessel segmentation is essential for 3D visualization, path planning , and guidance in interventional liver surgery , 29. HowTherefore, automatic vessel segmentation has triggered a broad discussion in the community. Even though some deep learning methods achieved big success on organ segmentation tasks, they cannot perform well in vessel segmentation due to the considerable variations of vessel structure and unbalance between backgrounds and vessels. Most recent work are designed based on FCN , U-net , and V-nCurrent liver vessel segmentation methods can be roughly classified into traditional region-based methods, edge-based segmentation methods and deep learning-based methods. As region-based methods do not perform well in vessel segmentation, we review most related work in the latter two categories. Since we use the transformer model as our backbone, we also review the newest work related to the transformer model. A more comprehensive literature survey can refer to .Traditional methodsEdge-based methods can be further classified into image filtering and enhancement algorithms, tracking-based algorithms . Filter Deep learning-based methodsMost deep learning-based liver vessel segmentation work rely on CNN-based architecture, specifically, U-net  and its Vision transformers and 2D swin transformerThe self-attention mechanism allows transformers to dynamically extract the important features of word sequences and learn their long-range dependencies. This notion has recently been extended to computer vision by defining the vision transformer , which aMotivated by existing 2D swin-transformer , 19 and 1. We propose a network architecture by expanding swin transformer to 3D and combining convolution and self-attention to play their strengths. For self-attention, the global spatial information has been encoded by embedding, and long dependencies have been entangled by our designed 3D transformer block. For convolution, multi-scale convolutions in the local feature path and downsampling/upsampling layers help to encode precise local information and capture hierarchical resolution features.2. We introduce the voxel-wise rather than patch-wise embedding as the initial transformer input to fully utilize volumetric information, which transforms volumetric prediction to the sequence-to-sequence prediction in hierarchical resolution features.3. We propose the Inductive Biased multi-head attention(IB-MSA) which changes the positional embedding way that learns biased positional embedding with initialization of absolute 1-dimensional embedding in the transformer blocks. Thus dramatically improving liver vessel segmentation results.The proposed method starts with dataset preprocessing. Then we introduce the architecture of our framework, namely Inductive BIased Multi-Head Attention Vessel Net(IBIMHAV-Net), including the details of our 3D transformer design and inductive biased multi-head attention mechanism. Finally, we describe post-processing which reduces some discrete inaccurate results.Preprocessing plays an essential role and affects the segmentation results significantly , 13. ForThe overall proposed architecture is shown in Fig. Past vision transformer work like , 8, 19 hThe patch embedding block The linear embedding part is essential in the original swin transformer design [r design , the SwiDown-sampling layer The swin transformer blocks used neighboring concatenate operations in past 2D tasks [2D tasks , 19. HowAfter passing patch embedding block’s, the high dimensional sequence tensor 3D transformer block In the tail of embedding block, the sequence is transformed to the high-dimensional tensor in swin transformer blocks. The main idea is to fully mix the captured long-term dependencies with hierarchical object concepts at various scales by following down-sampling convolution and global spatial information from the beginning embedding block.N is the number of tiny local volumes, In order to represent the workflow in our design, let the high-dimensional tensor Following the baseline , we presl expresses the layer number, MLP represents multi-layer perceptron. IB-MSA is our bias multi-head attention and it has the 3D shifted version.Here, The 3D shifted window partitioning For an efficient self-attention model, we propose the method within local 3D windows. The windows are arranged to evenly partition the image in a non-overlapping style and background classes(liver) often cause predictive deviation and bias the classification to the background with more voxels. Traditional dice coefficient calculated as:mentclasspt{minimai belongs to the foreground (liver vessel) and the background (liver), respectively in the softmax layer output result. i in the annotated data for liver vessels or liver with values 0 or 1, respectively.Since our task has 2 class labels, we can take foreground and background as the first and second classes, respectively. Then Eq.  becomes:From Huang et al. ’s studieDue to limitations of the GPU’s memory, we cannot put full-size volumes into our model. It can cause residual errors in the patch edges. Therefore, connected component analysis is performed on the vessels after being trained by IBIMHAV-Net. To remove some noises caused by classification, regions with small partitions based on hand selection in each experiment. For the 16 training sets, we have to apply some image amplification methods for increasing our training set. For a sample in the training case, the fixed rotation set for 60Our proposed method was implemented using Python 3.8 and PyTorch 1.9.0. All experiments were conducted on an Nvidia A6000 GPU with 48GB memories. Input image size after preprocessing is set as 256x256x192. The crop size based on our network is 128x128x96 with overlapping stride 24 in the test result. The batch size is set to 2, and the learning rate was set as 3e-5, as far as the initial work tested [In this subsection, we compare the proposed model with other state-of-art methods on 3DIRCAD dataset work. CNN-based methods including UNet , VNet 22, Huang Quantitative Results To compare with other state-of-art methods in an equitable way, we only focus on original volume 3DIRCADb dataset. Our results are reported in Table For the other three indexes which measure voxel results, our method exceeds other methods significantly in Dice and precision. The weighted loss function balanced segmented classes which avoid single voxel obtaining multiple labels, which leads to the larger sensitive index and prevents the over-segmentation.Moreover, in order to achieve higher precision, our structure costs much more time and storage than the pure CNN-based methods. Here is a comparison of different models for this trade-off.Visualization ResultsFigure To validate the generalization of our method, we conduct 4 test cases with hard cases and simple cases to show the result in Table To explore the influence of our design on the model performance, we conducted a series of ablation studies on 3Dircadb dataset.Influences of inductive biased positional embedding and IB-MSA Table Influences of more skip and transformer blocks(bottleneck)In our network architecture, the skip connections are connected after the down-sampling block and before the up-sampling block to unify the feature dimensions. Because the transformer has a different convergence rule compared to CNNs, which needs more discussion , 30. In Effect of downsampling strategies Patch merging is the down-sampling strategy used in the original swin transformer and the main idea is to concatenate the neighboring patches [4 patches , 19. We Effect of up-sampling strategies The original swin transformer chooses the patch expanding layer in the encoder based on resize [n resize , which rn resize ) on IRCAEffect of local feature extraction block The local feature extraction block includes some large kernel convolution layers. We have tried adding other feature extraction residual blocks in deeper places. The experiment shows that the CNNs can only perform well in high-resolution parts. The main reason may be that the CNNs do not have enough spatial invariant properties, which can supplement precise local features for another swin-transformer path. When we dropped this design, the accuracy, and Dice coefficient were reduced by 12% and 7.5%, respectively.Rolling of cropping patch size The testing results of the proposed IBIMHAV-Net with 224 x 224 x 96, 128 x 128 x 96 input resolutions as input are presented in Table Effect of the weighted loss function and post-processing The testing results of the proposed IBIMHAV-Net structure have been discussed above. Here we design this ablation study to evaluate the necessity of these two processes. From Table This paper designs a liver vessels segmentation method from CT images using the transformer-based network. Swin transformer has been expanding to 3D as the backbone which interleaved with convolutions and expanding for 3D volumes. In specific, the small stride convolution in both local feature block path and up/down-sampling blocks keep the spatial information hierarchically for two successive swin transformer blocks. A new pixel-wised embedding method has been used for our few samples task with variant structures. A new type of bias positional embedding in our transformer is proposed. Numerical Evaluation and visualization based on different benchmarks proved the validity of this deep learning method. Our method has been trained and tested on 3D-IRCADb-01 dataset. In the future, we would further improve segmentation accuracy by introducing more precise datasets and trying multi-task methods to reduce the negative effects of liver tumors.