Cox regression with time-varying exposure was used to estimate weekly/monthly VE against COVID-19 outcomes from 27 December 2020 to 31 January 2022. The analyses were stratified by age, sex, and vaccine type . Results: Two vaccine doses offered good long-lasting protection against infection before Omicron  but limited protection against Omicron infection (dropped to 43% by week four and no protection by week 14). For severe COVID-19 outcomes, higher VE was observed during the entire follow-up period. Among individuals above age 65, the mRNA vaccines showed better VE against infection than AZD1222 but similar high VE against hospitalization. Conclusions: Our findings provide strong evidence for long-term maintained protection against severe COVID-19 by the basic two-dose schedule, supporting more efforts to encourage unvaccinated persons to get the basic two doses, and encourage vaccinated persons to get a booster to ensure better population-level protection.Background: We estimated real-world vaccine effectiveness (VE) against COVID-19 infection, hospitalization, ICU admission, and death up to 13 months after vaccination. VE before and after the emergence of Omicron was investigated. Methods: We used registered data from the entire Swedish population above age 12 ( With the rapid evolution of SARS-CoV-2 and vaccine approvals ,2, conceIn Sweden, vaccination was initiated in the elderly population on 27 December 2020 and reached larger and younger populations in 2021 and 2022 . We usedThis study is part of the RECOVAC  study within the larger SCIFI-PEARL (Swedish COVID-19 Investigation for Future Insights—a Population Epidemiology Approach using Register Linkage) project with regularly updated data from various National Registers . The curWe obtained data from multiple National Registers. Vaccination data came from the National Vaccination Register (NVR). All individuals with their first positive SARS-CoV-2 PCR test were identified from SmiNet, the national register of notifiable communicable diseases. PCR testing was introduced in 2020, and large-scale testing started in mid-2020. All individuals with symptoms of COVID-19 were encouraged to get tested, free of cost, until February 2022. COVID-19 diagnoses from outpatient specialist visits and inpatient care records were obtained from the Swedish National Patient Registry (NPR). COVID-19-related ICU data came from the Swedish Intensive care Register (SIR). The date and cause of death were obtained from the Register of Total Population (RTB) and the National Cause-of-Death Register (NCDR).A complete medical history from 2015 was obtained from NPR and drug history for prescription drugs from 2018 from the National Prescribed Drug Register (NPDR). Sociodemographic data, including education, family situation, income, and occupation from 2015, were obtained from Statistics Sweden (SCB). Information on elderly subjects living at special care facilities and/or receiving home care services was obtained from the National Social Service Register.The exposure variables were vaccination status , time intervals after each vaccination, and different vaccines , based on data from NVR. The first dose was defined as each individual’s first record in NVR. The second and third doses were defined as the following records with a predefined minimum time gap between doses  from NPR, an ICU admission from SIR, or death due to COVID-19 (underlying or contributing cause of death) from NCDR. Most COVID-19 infection cases (98.4%) were defined by positive PCR tests. The onset date of infection was set to two days before the registered date of any component event based on an estimated minimum incubation time . For hosThe procedure of covariate selection was performed in 10% random samples of the data due to computational challenges related to the large population and dataset . We inclWe studied the first occurrence of each outcome during the pandemic; therefore, the VE estimates apply to the first occurrence of an outcome event after vaccination, compared to unvaccinated individuals, in individuals previously free of this event.Cox proportional hazard models with time-varying exposure were used . In the This paper focused on the VE for two doses of vaccine. In this analysis, the time period under the first dose was treated as a loss to follow-up, meaning subjects were observed during the unvaccinated period (since the study started to the time when receiving their first dose) and then again observed when receiving their second dose to the end of follow up, which occurred at an outcome event or censoring that is defined as third dose, emigration, death, or study ended, whichever came first. We also estimated the VE for one dose and reported results in the To explore and illustrate VE trends in a smoother manner, we used an unrestricted cubic spline with five selected knots in extended Cox regression. We chose an unrestricted cubic spline for more flexibility in the two tails to adequately reflect the real-world data trends and ensure a better model fit. To cover the follow-up time since vaccination well, the five knots were selected at the 20th, 35th, 65th, 95th, and 99th percentiles.We estimated time-varying VE for the entire follow-up, for the period before Omicron (until 30 November 2021) and for the Omicron period, respectively. For analysis of the Omicron period, we modeled the entire follow-up period, but only events after 1 December 2021 were considered as incident cases for estimation, and individuals with earlier events were censored at their event.Additionally, stratified analyses were performed for COVID-19 infection and hospitalization by sex or age group  and in subjects who received two doses of homologous BNT162b2, mRNA-1273, or AZD1222. As AZD1222 was primarily used in older individuals, stratified analyses were restricted to those aged 65 and older.p values < 0.0001. All analyses were performed in STATA MP 17 (StataCorp. 2021. Stata: Release 17).From the estimated hazard ratios (HR), results were presented as VE with 95% confidence intervals (CI), with VE calculated as 100 × (1 − HR). Chi-square tests were used to check the model fit, and all models showed Among 9,153,456 study individuals, 15% remained unvaccinated during the entire study period, 85% had at least one dose of vaccine, 82% had two doses, and 45% had ≥three doses as of 31 January 2022 . Most inFrom 1 January 2020 to 31 January 2022, 2,002,024 first-time COVID-19 infections were reported , represeThe initial analysis was performed for the entire follow-up (13 months). After two doses of any vaccine, VE against COVID-19 infection peaked at week three at 72.0% (95%CI 71.0–73.0%) but then dropped quickly to 19.5% (18.8–20.2%) by weeks 14–17 and showed no protection from week 18 . VE agaiThe more fast-waning VE observed for the entire follow-up than in other earlier published data appeared to be related to the emergence of Omicron. It is, therefore, important to analyze the pre-Omicron and Omicron periods separately. There was a large difference in VE against infection before and after the emergence of Omicron. VE was above 85% before Omicron in most time intervals , whereasA difference in the VE between the pre-Omicron and Omicron period was also observed for hospitalization, but it was not as large as for infection . Before The overall VE trends against infection were relatively similar across age groups, although VE against infection was somewhat higher in older age groups . VE agaiThis study examined the time-varying VE against infection, hospitalization, and severe COVID-19 outcomes over 13 months, including the emergence of the Omicron variant in Sweden. We modeled exposure over time with high granularity  to provide a more detailed VE investigation than the previous vaccine studies. The most important finding was a difference in the pattern of VE during the pre-Omicron and Omicron periods. We found a high and stable VE (in the range of 85–95%) against infection when restricting the analysis to the pre-Omicron period, which was similar to a US study with over 10 million North Carolina residents and a nine-month follow-up until September 2021. They estimated monthly VE after two doses of the two mRNA vaccines, with a peak VE of about 95% at two months after the first dose, decreasing to 70–80% at seven months . HoweverWe observed, however, very rapidly waning effectiveness during the Omicron period, which dropped to zero protection by week 14. Several studies have also reported lower and more rapid VE waning with Omicron ,10,18,19Our data showed negative VE against Omicron infection from week 14, indicating that vaccinated individuals experienced a higher risk of infection than those unvaccinated. This is likely to be due to unbalanced susceptibility among vaccinated and unvaccinated individuals who had not developed COVID-19 by that time. In time-to-event analyses such as in this study, when an event is inevitable or very common, a protective effect in one group in the short term would be expected to reverse to increased risk at later time points as a reflection of the delay in disease onset in the protected group. The sudden emergence of the highly infective and less severe Omicron variant, with vaccine escape properties , represeOur analysis for the entire follow-up showed an overall lower peak VE than in previous studies, including phase-three trials ,25,26,27In line with previous trials and real-world observational studies showing lower VE of AZD1222 than mRNA-1273 or BNT162b2 ,28,30, wThe analysis in this study focused on two doses, as this was originally recommended basic vaccination schedule we wished to study. Despite the introduction of a third booster dose, some individuals and groups have considered themselves adequately covered by two doses, and the coverage of three vaccine doses has been relatively low to date. In addition, dose three had substantially fewer outcomes and a shorter follow-up period, which precludes a detailed time-related analysis such as the one we conducted here for two doses.The population-based character of our study reduces the potential for selection bias. Additionally, we used Cox regression, considering both time-varying exposure (from unvaccinated to one dose and then two doses) and time-varying effects (period effects for each dose). This approach avoids difficult assumptions about the interval between doses, as in the mentioned US study . HoweverThis study provides more detailed long-term data on time-varying VE against COVID-19 in a complete general population. The progressively waning protection against Omicron infection after two vaccine doses underscores the need for additional efforts to encourage people to get a booster dose to ensure better population-level protection. With respect to hospitalization and severe COVID-19, although waning was stronger during the Omicron period than the pre-Omicron period, two doses of vaccine continued to provide very good and long-lasting protection, emphasizing the need to continue to encourage persons not yet vaccinated to receive their basic two-dose vaccination.


Logistic regression analyses assessed the association between their perceived risk of having COVID when smoking LCCs and pandemic-related behavioral changes in CAI and CAB use . Findings showed that users with a higher perceived risk of getting COVID-19 when smoking LCCs were more likely to endorse trying to quit CAI and CAB during the pandemic. Compared to the non-Hispanic White population, the non-Hispanic Black population were less likely to endorse smoking less CAI and trying to quit CAB during the pandemic. Dual users of CAI and CAB and females were more likely to endorse smoking more CAB compared to CAB-only users and males, respectively. Tailored cessation strategies are needed for dual users, non-Hispanic Black young adults, and young women. Raising awareness about the risks of LCC use can be an effective strategy for LCC smoking cessation.We examined the smoking behaviors of U.S. young adults ages 18–36 regarding little cigars and cigarillos (LCCs) during the COVID-19 pandemic. Survey data were collected from a nationally representative sample of young adults between October and November 2020. Respondents who reported using LCCs with tobacco (CAI) and/or with marijuana (CAB) within the past 6 months prior to the survey ( Respondents were included in the study only if they reported having used CAI or CAB within the past 6 months or less.Based on the findings from a previous focus group study in which LCC users indicated that they most often smoke a part of LCC product occasionally,  respondeRespondents were asked individual questions about changes in their CAI and/or CAB smoking behaviors because of the COVID-19 pandemic. This was measured based on the questions, “Have any of the following happened to your little cigar or cigarillo smoking [as sold/as blunt] behavior because of COVID-19?” Respondents rated their level of agreement to the following items: smoked CAI/CAB more than usual, smoked CAI/CAB less than usual, tried to stop smoking CAI/CAB, and worried about smoking CAI/CAB because of the risk associated with COVID-19. Dual users responded to both CAI and CAB-specific questions. The responses were measured on a 7 point scale ranging from 1 (strongly disagree) to 7 (strongly agree).Respondents were asked about using other tobacco products, including cigarette, large cigars, hookah, and e-cigarette (with and without marijuana) in the past 6 months prior to the survey. This was measured based on the questions, “Have you ever used a [cigarette/large cigars/hookah/e-cigarette (with and/or without marijuana)] at least once, in your lifetime? If yes, when was the most recent time you used a [cigarette/large cigars/hookah/e-cigarette (with and/or without marijuana)] even one or two puffs?” Pictures and examples of brand names of these tobacco products were shown in the survey to help respondents accurately answer the survey questions.All analyses were weighted to the general U.S. young adult population and were performed using SAS version 9.4 . Chi-square tests were used to examine the differences in demographic characteristics among the user groups. Logistic regression was used to examine COVID-19 risk perception and subsequent behavioral changes specific to CAI and CAB use, controlling for demographic-level factors  and user type . The responses for the behavioral changes in LCC use during the COVID-19 pandemic were categorized as: those who “agreed” , “disagreed” , and “neither agree nor disagree”  to experiencing changes in LCC use behaviors. Those who neither agreed nor disagreed were excluded from the regression analysis.p < 0.01).Among 399 past 6 month LCC users in our sample, 72 were CAI-only users (14.5%), 160 were CAB-only users (38.0%), and 167 were dual users (47.4%) . The meaPast 6 month CAI-only users were 68.4% male, 86.8% heterosexual, 42.4% Hispanic, and 73.9% had some college experience. Past 6 month CAB-only users were 56.4% female, 70.4% heterosexual, 43.7% non-Hispanic White, and 63.4% had some college experience. Past 6 month dual users were 63.6% male, 79.9% heterosexual, 35.7% non-Hispanic White, and 57.6% had high school or less education.p < 0.01).Consistent with previous studies ,25, polyp < 0.01) between CAB-only and dual users regarding worrying about smoking CAB, trying to quit CAB, and smoking less CAB during the pandemic.Overall, respondents did not report experiencing substantial changes in CAI and CAB use during the first several months of the COVID-19 pandemic . Among CCompared to the non-Hispanic White population, the non-Hispanic Black population were more likely to endorse worrying about CAI use because of the risk associated with COVID-19 , controlling for other sociodemographic characteristics and risk perceptions . Those wCompared to CAB-only users, dual users were less likely to endorse worrying about CAB use because of the risk related to COVID-19  and smoking less CAB during the pandemic  after controlling for other characteristics. They were also more likely to endorse smoking more CAB  than CAB-only users. Those with greater perceived risk of having COVID-19 if using LCCs were more likely to endorse worrying about CAB  and trying to quit CAB  during the pandemic. The non-Hispanic Black population were less likely to endorse trying to quit CAB  compared to non-Hispanic White population. Moreover, compared to males, females were more likely to endorse smoking more CAB  during the pandemic.The current study examined factors that are associated with the reported behavioral changes in LCC use during the first several months of the COVID-19 pandemic among young adult users, specified by the modality of use , LCC use with marijuana as blunts (CAB) and user group status . Descriptive analysis of our sample indicated that dual use of CAI and CAB was the most prevalent (47.4%), compared to exclusive CAB (38.0%) and CAI use (14.5%). Such a finding is in line with emerging studies that reported increased sales of cannabis in some U.S. states  and incrRespondents in our sample did not report drastic changes in LCC use during the pandemic. Still, we observed racial and ethnic differences in CAI and CAB use behaviors. Compared to the non-Hispanic White population, the non-Hispanic Black population were less likely to endorse trying to quit CAB and smoking less CAI during the pandemic, although they were more likely to endorse worrying about smoking CAI because of risk related to COVID-19. The heightened concern about smoking CAI may be due to the disproportionate impact of COVID-19 on communities of color . Yet, thIn addition, females were less likely to endorse smoking less CAI and more likely to endorse smoking more CAB than males during the pandemic. One likely explanation for the observed racial/ethical and gender differences is that pandemic-induced stressors, such as financial concerns, death of loved ones, feeling isolated ,32, and We also found that user type  had an impact on COVID-related behavioral changes in LCC use. Compared to CAB-only users, dual users were more likely to endorse smoking more CAB and less likely to endorse smoking less CAB during the pandemic. Because dual users use multiple tobacco products and consume both tobacco and marijuana, they may experience greater nicotine dependence ,37,38 thOur findings showed that users with a higher perceived risk of having COVID-19 when smoking LCCs were more likely to endorse trying to quit both CAI and CAB during the pandemic. Nevertheless, the perceived risk of having COVID-19 when smoking LCCs was low in our sample. This finding builds upon previous research that reported cigar smokers, including LCC users, tend to underestimate its health risks compared to cigarettes ,39,40. MPreliminary research shows that tobacco use is associated with more severe symptoms and progression of COVID-19 ,42. In aThis study has a few limitations. The behavioral changes in LCC use during the COVID-19 pandemic were self-reported and were not directly measured using biological measures. There is also a potential recall bias in reporting past LCC use. Moreover, while this study is representative of the young adult population in the U.S., it is important to note the limited sample size included in the logistic regression analysis, particularly for the CAI-only users.We examined young adult LCC users’ perceived risk of COVID-19 and their behavioral changes in LCC use in response to the COVID-19 pandemic. Our findings showed that young adult LCC users, particularly the non-Hispanic Black population, dual users, and females, are at higher risk of increased LCC use during the pandemic. Smoking behaviors formed during the first few waves of the COVID-19 pandemic, as well as during the repeated outbreaks caused by new variants and subvariants of the virus, will continue to affect the health of LCC users. Furthermore, because the Food and Drug Administration (FDA) is considering stricter regulations on tobacco products, such as a JUUL e-cigarette ban and reducing allowable nicotine levels in cigarettes, it is possible that users of these tobacco products switch to using LCCs. Therefore, our findings may inform the development of tobacco prevention and cessation interventions. Programs seeking to reduce LCC use must have focused efforts to reduce all modalities of LCC use, including LCC use with tobacco (CAI) and with marijuana as blunts (CAB), among vulnerable sub-populations of young adult LCC users.


Transdiagnostic internet-delivered cognitive behavioral therapy (iCBT) for emotional disorders has been shown to be effective in specialized care in the short term. However, less is known about its long-term effects in this specific setting. In addition, predictors of long-term effectiveness may help to identify what treatments are more suitable for certain individuals.This study aimed to analyze the long-term effectiveness of transdiagnostic iCBT compared with that of treatment as usual (TAU) in specialized care and explore predictors of long-term effectiveness.Mixed models were performed to analyze the long-term effectiveness and predictors of transdiagnostic iCBT (n=99) versus TAU (n=101) in public specialized mental health care. Outcomes included symptoms of depression and anxiety, health-related quality of life (QoL), behavioral inhibition and behavioral activation, comorbidity, and diagnostic status  from baseline to 1-year follow-up. Sociodemographic characteristics  and clinical variables  were selected as predictors of long-term changes.b=–4.16, SE 1.80, 95% CI –7.68 to –0.67), health-related QoL , diagnostic status , and comorbidity at 1-year follow-up . From pretreatment assessment to follow-up, anxiety symptoms improved in both transdiagnostic iCBT and TAU groups, but no significant differences were found between the groups. Regarding the predictors of the long-term effectiveness of transdiagnostic iCBT compared with that of TAU, higher health-related QoL at follow-up was predicted by a baseline diagnosis of anxiety, male sex, and the use of psychiatric medication; fewer comorbid disorders at follow-up were predicted by older age and higher baseline scores on health-related QoL; and fewer depressive symptoms at follow-up were predicted by baseline diagnosis of depression. However, this pattern was not observed for baseline anxiety diagnoses and anxiety symptoms.Compared with baseline, transdiagnostic iCBT was more effective than TAU in improving symptoms of depression (The results suggest that transdiagnostic iCBT is more effective than TAU to target depressive symptoms among patients with emotional disorders. Anxiety symptoms remained stable at 1-year follow-up, with no differences between the groups. Results on predictors suggest that some groups of patients may obtain specific gains after transdiagnostic iCBT. Specifically, and consistent with the literature, patients with baseline depression improved their depression scores at follow-up. However, this pattern was not found for baseline anxiety disorders. More studies on the predictor role of sociodemographic and clinical variables in long-term outcomes of transdiagnostic iCBT are warranted. Future studies should focus on studying the implementation of transdiagnostic iCBT in Spanish public specialized mental health care.ClinicalTrials.gov NCT02345668; https://clinicaltrials.gov/ct2/show/NCT02345668 In the past 20 years, epidemiological studies have systematically shown the high prevalence of emotional disorders  and the disability and costs associated with these disorders -3. The cIn the field of internet-delivered interventions, most of the research has focused on internet-delivered cognitive behavioral therapy (iCBT). The literature has shown that iCBT for emotional disorders is accessible , easy tog) in the medium-to-large range for overall measures of anxiety (0.78-0.82), depression (0.79-0.84), and quality of life  after treatment [Among the range of iCBT programs, transdiagnostic iCBT for emotional disorders has shown its efficacy and effectiveness in a growing number of RCTs. Several meta-analyses have shown that transdiagnostic iCBT is effective in the short term, with pooled effect sizes  ,19 and sd=0.35), depression (Cohen d=0.41), and health-related QoL (Cohen d=–0.45) after treatment. However, the effects of this intervention in the long term (1-year follow-up) have not yet been analyzed. Therefore, the aim of this investigation was 2-fold: to analyze the long-term outcomes of transdiagnostic iCBT for emotional disorders compared with those of TAU and to analyze potential predictors of the long-term effectiveness of transdiagnostic iCBT versus TAU.To the best of our knowledge, studies on the long-term effectiveness of iCBT in public specialized mental health care are scarce in the literature. However, the high demand for mental health resources as well as the lack of resources in this specific setting ,25 highlThis study analyzes long-term data (1-year follow-up) from a previously published RCT that compared transdiagnostic iCBT with TAU in public specialized mental health care services . The proThis study was granted ethics approval by the ethics committee of Universitat Jaume I  and the clinical research ethics committees of the 3 hospitals that participated in the trial .Participants were adults who attended public mental health units in Spain to seek psychological or psychiatric help between July 2015 and June 2019. Potential participants were identified by the psychiatrists and psychologists at these centers and referred to the study researchers for eligibility assessment  and the Spanish version (Cronbach α=.87-.89) [α value for the BDI-II in this study at baseline was .90.The Beck Depression Inventory, Second Edition (BDI-II)  is a sel.87-.89) . The Croα between .85 and .94) and convergent and divergent validity. The Spanish version of the BAI has demonstrated high internal consistency (Cronbach α=.93) [α value for the BAI in this study at baseline was .92.The Beck Anxiety Inventory (BAI)  is a 21-h α=.93) . The CroThe EQ-5D-3L  is a genα=.73-.92) and good convergent and divergent validity as indicators of temperament. The internal consistency of the Spanish version ranges between Cronbach α=.65 and Cronbach α=.82. The Cronbach α values for the BIS and BAS subscales in this study at baseline were .61 and .80, respectively.The behavioral inhibition system (BIS) and behavioral activation system (BAS) scales ,35 contaAll participants received a 12-module transdiagnostic iCBT protocol through the web platform designed by our research group . The cor0. Welcome module: gives information about the protocol  and recommendations about how to use the treatment protocol1. Introduction to treatment: provides a framework on the role of emotion regulation in emotional disorders2. Motivation for change: promotes patients’ motivation for change by analyzing the pros and cons of changing and trains them in goal setting3. Understanding the role of emotions: provides psychoeducation on the nature, role, and functions of emotions and trains the patient in identifying emotion components4. Nonjudgmental emotional awareness and acceptance of emotional experiences: trains the patient in nonjudgmental emotional awareness and the acceptance of emotional experiences5. Practicing present-focused awareness: provides additional training in nonjudgmental present-focused awareness in different domains, namely physical sensations, thoughts, emotions, and daily activities6. Learning to be flexible: teaches the patient how to identify maladaptive cognitive patterns 7. Practicing cognitive flexibility: trains the patient in modifying maladaptive cognitive patterns  and also provides psychoeducation to the patient about intrusive thoughts and how to manage them8. Emotional avoidance: trains the patient to understand and identify maladaptive emotion avoidance patterns9. Emotion-driven behaviors (EDBs): patients learn the concept of EDB and to replace maladaptive EDB with other more adaptive behaviors 10. Accepting and facing physical sensations: teaches patients about the role of physical sensations in emotional response and trains them in interoceptive exposure11. Facing emotions in the contexts where they occur: trains the patient to face situation-elicited avoided emotions through exposure procedures12. Relapse prevention: patients review what they have learned throughout the program, schedule future practice of skills, and learn how to cope with high-risk situationsTAU was delivered by psychiatrists and clinical psychologists at mental health units in Spain. TAU in this study was provided by 3 hospitals: Consorcio Hospitalario Provincial de Castellón (Castellón de la Plana), Hospital Universitario de la Ribera , and Hospital Universitario Vall d’Hebron (Barcelona). To maximize the external validity of this RCT, participants in this condition were allowed to receive psychiatric treatment , psychological treatment , or a combination of the two. The frequency of visits during the 18-week treatment period varied depending on the type of treatment  provided to the participant. Patients in the TAU condition who were already receiving any of the aforementioned treatments at the time of enrollment were informed that they would continue to receive these services during the treatment period. Furthermore, participants who were receiving any treatment other than those provided at the mental health unit were excluded from the trial.t tests for continuous data and chi-square tests for categorical data were performed to confirm that there were no demographic or clinical differences between transdiagnostic iCBT and TAU at baseline. Missing data were explored, and we concluded that 33.63% of the values were missing overall at the construct level. The Little missing completely at random test showed that data were missing completely at random . Furthermore, completers did not differ significantly from dropout cases on the baseline variables: age , sex , marital status , and education . Multiple imputation of missing values is not necessary before performing longitudinal mixed model analysis [First, independent samples 2-tailed analysis .lmer function from the lme4 R package [d) were calculated by dividing the differences in means by the pooled SD. Effect sizes were interpreted according to the Cohen convention: effect sizes of 0.20 are considered low, effect sizes of 0.50 are considered medium, and effect sizes of ≥0.80 are considered large [Mixed effects models were conducted to analyze the long-term effects and predictors of transdiagnostic iCBT using the  package  with R s package . Analyse package ,44. In c package ,45,46. Ted large .Mixed effects models were performed on 5 dependent variables (DVs): depression , anxiety , BIS , BAS , health-related QoL , comorbidity , and diagnostic status . The models had 2 main components: fixed effects and random effects. First, variance across participants and hospitals was modeled as random effects in the model . Second, group  and time  were modeled as fixed effects. Furthermore, we selected different baseline variables as potential predictors of long-term outcomes, including demographic variables , clinical status , dispositional traits , and symptomatology . Given that we were interested in long-term changes , we focused on group1×time4 interactions  to analyze the long-term effects and predictors .P<.05) were entered simultaneously into the multivariate mixed models using backward deletion . This procedure helped us to obtain a final adjusted model for each DV.Specification of the random effect structure was modeled following the recommendations by Barr et al ,50. FirsEffect sizes for each model are presented as the model-derived fixed-effect parameter regression weights ,54. ModeThe participants (N=200) had a mean age of 38.64  years, and the majority were female . b=–5.85, SE 1.26, 95% CI –8.30 to –3.40), indicating that BDI-II scores were, on average, 5.85 points greater at baseline than at 1-year follow-up when the other variables in the model were kept constant. The main effect of group was not significant ; thus, both groups showed similar BDI-II scores on average. The results also revealed a significant group×time interaction . Tukey-corrected post hoc comparisons indicated that both groups significantly reduced their depressive symptoms at 1-year follow-up; however, these reductions were larger in the transdiagnostic iCBT group.Regarding depressive symptoms, there was a significant main effect of time , indicating that BAI scores were, on average, 5.37 points greater at baseline than at 1-year follow-up when the other variables in the model were kept constant. The main effect of group was not significant ; thus, both groups showed similar BAI scores on average. The group×time interaction was not significant . Tukey-corrected post hoc comparisons indicated that both groups similarly reduced their anxiety symptoms at 1-year follow-up.Regarding anxiety symptoms, there was a significant main effect of time , indicating that EQ-5D-3L scores were, on average, 6.19 points greater at 1-year follow-up than at baseline when the other variables in the model were kept constant. The main effect of group was not significant ; thus, both groups showed similar EQ-5D-3L scores on average. The results also revealed a significant group×time interaction . Tukey-corrected post hoc comparisons indicated that only transdiagnostic iCBT participants significantly improved health-related QoL at 1-year follow-up, whereas no changes were found in the TAU group.Regarding health-related QoL, there was a significant main effect of time  and group main effect  were not significant. The results revealed a significant group×time interaction . Tukey-corrected post hoc comparisons indicated that only participants in the transdiagnostic iCBT group significantly reduced BIS scale scores at 1-year follow-up, whereas no changes were found in the TAU group. Regarding the BAS scale, the time main effect , group main effect , and group×time interaction  were not significant.Regarding the BIS scale, the time main effect , indicating that changes in diagnostic status were significant at 1-year follow-up compared with baseline when the other variables in the model were kept constant. However, the main effect of group was not significant ; thus, both groups showed similar changes in diagnostic status. The results also revealed a significant group×time interaction . Tukey-corrected post hoc comparisons indicated that patients in both groups significantly improved their diagnostic status at 1-year follow-up; however, these changes were significantly higher in the transdiagnostic iCBT group. Specifically, 22% (22/99) of the patients in the transdiagnostic iCBT group met the diagnostic criteria at 1-year follow-up versus 45.5% (46/101) of the patients in the TAU group.Regarding diagnostic status , there was a significant main effect of time , indicating that comorbidity was, on average, 0.53 points greater at baseline than at 1-year follow-up when the remaining variables in the model were kept constant. However, the main effect of group was not significant ; thus, both groups showed similar comorbidity on average. The results also revealed a significant group×time interaction . Tukey-corrected post hoc comparisons indicated that both groups significantly reduced comorbidity at 1-year follow-up; however, these reductions were larger in the transdiagnostic iCBT group. Specifically, comorbidity decreased from 1.78 (SE 0.1) diagnoses at baseline in the transdiagnostic iCBT group to 0.70 (SE 0.1) at 1-year follow-up. By contrast, comorbidity in the TAU group changed from 1.88 (SE 0.1) diagnoses at baseline to 1.38 (SE 0.2) at 1-year follow-up.Finally, regarding comorbidity , there was a significant main effect of time  and comorbidity  at baseline predicted changes in depressive symptoms at 1-year follow-up. However, none of the predictors were significantly related to changes in anxiety symptoms, behavioral inhibition, or behavioral activation. Post hoc comparisons of the principal diagnosis at baseline showed the following results: (1) there were significant differences in depressive symptoms at follow-up between transdiagnostic iCBT and TAU only in participants with depressive disorders at baseline and not in participants with anxiety and OCD diagnoses. Specifically, participants with depressive disorders showed significant decreases in depressive symptoms at follow-up in the transdiagnostic iCBT group, whereas no changes were found in the TAU group; (2) participants with anxiety disorders showed significant reductions in depressive symptoms at 1-year follow-up in both transdiagnostic iCBT and TAU groups; (3) however, no changes in depressive symptoms were found in participants with OCD; and (4) participants with depressive disorders in the transdiagnostic iCBT group showed greater depressive symptoms at baseline than participants with anxiety and OCD diagnoses, but these differences disappeared at follow-up. However, these differences were not found in the TAU group. By contrast, transdiagnostic iCBT participants showed a significant positive association between comorbidity and depressive symptoms both at baseline and at 1-year follow-up , but this association was significantly stronger in the transdiagnostic iCBT group at follow-up, and it was not significant in the TAU group. Finally, when these predictors were entered simultaneously into the mixed model, a principal diagnosis of anxiety at baseline  and comorbidity  remained significant, whereas a principal diagnosis of OCD at baseline was no longer significant . The final model significantly improved the model fit .The principal diagnosis , medication , and a principal diagnosis of anxiety at baseline  predicted changes in health-related QoL at 1-year follow-up. Post hoc comparisons showed that there were significant differences in health-related QoL at follow-up between the transdiagnostic iCBT group and the TAU group in male participants but not in female participants. Specifically, significant health-related QoL improvements at follow-up were found in the male participants in the transdiagnostic iCBT group but not in the male participants in the TAU group. Similarly, significant differences in health-related QoL between transdiagnostic iCBT and TAU were found only in participants who were taking medications at baseline. Specifically, significant health-related QoL improvements at follow-up were found in participants taking medications in the transdiagnostic iCBT group but not in the TAU group. Finally, post hoc comparisons showed that only participants with an anxiety diagnosis in the transdiagnostic iCBT group experienced significant increases in health-related QoL at follow-up, whereas no changes were found in the TAU group. Moreover, when these predictors were entered simultaneously into the mixed model, sex  and a principal diagnosis of anxiety at baseline  remained significant, whereas medication was no longer significant . The final model significantly improved the model fit .Sex  and health-related QoL  predicted changes in diagnostic status at follow-up. First, there was a negative association between age and diagnostic status in the transdiagnostic iCBT group at follow-up , whereas this relationship was positive in the TAU group . Second, there was a significant negative association between health-related QoL and diagnostic status at follow-up in the transdiagnostic iCBT group . However, this association was not significant in the TAU group. Finally, when these predictors were entered simultaneously into the mixed model, health-related QoL remained significant , whereas age was no longer significant . The final model significantly improved the model fit .Age , education , and health-related QoL  were marginally significant predictors of comorbidity changes at 1-year follow-up. First, age and comorbidity at follow-up were negatively associated in the transdiagnostic iCBT group but not in the TAU group . Second, no significant differences in comorbidity were found between participants with nonuniversity and university education in the transdiagnostic iCBT group; that is, both participants with nonuniversity and university education significantly reduced their comorbidity in this group. Moreover, participants with nonuniversity education in the transdiagnostic iCBT group had less comorbidity than participants with nonuniversity education in the TAU group at follow-up. Third, a significant negative association between comorbidity and health-related QoL was found in the transdiagnostic iCBT group at follow-up , but this association was not significant in the TAU group. Finally, when these predictors were entered simultaneously into the mixed model, only health-related QoL remained significant , whereas age  and education  were no longer significant. The final model significantly improved the model fit .Age , whereas no differences were found for behavioral activation. The study of how these and other related dimensions  change after treatment is of paramount importance in the context of transdiagnostic treatments that target shared psychopathological processes [Taken together, transdiagnostic iCBT was shown to be more effective than TAU at 1-year follow-up for the treatment of emotional disorders in specialized care. Specifically, in comparison with TAU, transdiagnostic iCBT was more effective in reducing symptoms of depression and improving health-related QoL at 1-year follow-up, with effect sizes in the small-to-moderate range. Furthermore, participants in the transdiagnostic iCBT group had a better diagnostic status  and showed less comorbidity (0.7 diagnoses in the transdiagnostic iCBT group vs 1.38 diagnoses in the TAU group). Regarding anxiety, both groups improved their scores from pretreatment assessment to 1-year follow-up, without significant differences between the groups. These results suggest that transdiagnostic iCBT was at least as effective as TAU for anxiety symptoms in the long term. In this regard, it should be noted that participants in the TAU group were also undergoing some treatment  provided by clinicians of the national health system, that is, patients in the TAU group received an active treatment. As a recent meta-analysis on the efficacy of the unified protocol shows, studies that used active control conditions (including TAU) as comparison groups exhibited lower effect sizes than those that used passive control groups  . Moreoverocesses ,56. Neverocesses . The patrocesses . With throcesses , to our Overall, the results support the long-term effectiveness of transdiagnostic iCBT in comparison with that of TAU for a number of measures, including depression, health-related QoL, behavioral inhibition, diagnostic status, and comorbidity. These results add to those of our previously published RCT, which showed that transdiagnostic iCBT in public specialized care was both effective and safe in the short term for individuals with emotional disorders . ImproveTo our knowledge, this is the first study to report on the long-term effectiveness of transdiagnostic iCBT for emotional disorders in public specialized mental health care and support its application in this setting. On the basis of our results, we believe that the implementation of iCBT in public specialized mental health care might help to deal with long-lasting barriers  and the difficulties arising from the current COVID-19 pandemic by increasing dissemination of, and access to, evidence-based interventions.The second goal of the study was to analyze potential predictors of long-term effectiveness. To do so, we explored the predictor role of different baseline variables, including sociodemographic characteristics  and clinical variables  in the following outcomes at 1-year follow-up: depressive symptoms, anxiety symptoms, health-related QoL, behavioral inhibition and behavioral activation, comorbidity , and diagnostic status . Overall, a heterogeneous pattern of predictors of long-term outcomes was observed, but we found several relationships that are worth discussing.We found that participants in the transdiagnostic iCBT group with a baseline diagnosis of depression had fewer depressive symptoms at follow-up. In addition, participants with depressive disorders at baseline in the transdiagnostic iCBT group showed more severe depressive symptoms than patients with baseline anxiety and OCD. These results are in line with research showing that individuals with more severe depression improve more than patients with mild depression after receiving psychotherapy ,19,59. BA baseline principal diagnosis of anxiety predicted better health-related QoL at follow-up after transdiagnostic iCBT but not after TAU, which suggests that transdiagnostic iCBT might be particularly beneficial for health-related QoL in patients with primary anxiety disorders. In addition, being male and being on medication at baseline predicted improvements in health-related QoL at follow-up after transdiagnostic iCBT but not after TAU. With regard to being male, sex has not usually been reported as a predictor of outcomes in the cognitive behavioral therapy literature ,61.Age at baseline predicted changes in diagnostic status at follow-up. Specifically, older participants were more likely to not meet the diagnostic criteria for their principal baseline diagnosis at follow-up after transdiagnostic iCBT, whereas an opposite pattern was observed for older participants in the TAU group. Moreover, older individuals who received transdiagnostic iCBT also showed less comorbidity at follow-up than older individuals who received TAU. Taken together, these findings suggest that older individuals with emotional disorders are adequate targets for transdiagnostic iCBT in specialized care, at least in terms of improving their diagnostic status and comorbidity. Likewise, participants with nonuniversity and university education had significantly less comorbidity at follow-up. Moreover, participants with nonuniversity education in the transdiagnostic iCBT group had significantly less comorbidity at follow-up than participants with nonuniversity education in the TAU group. These results support the notion that, regardless of sociodemographic variables such as age and educational level, iCBT probably works equally well .The results should be interpreted in the context of some limitations. First, attrition was high in both conditions at 1-year follow-up. However, it should be noted that data were missing completely at random. Moreover, dropout rates in internet interventions are high even at short-term follow-ups . Second,The findings show that transdiagnostic iCBT in public specialized care is in general effective for the treatment of emotional disorders in the long term. Together with our previously published RCT, we have provided research that shows both the short- and long-term effectiveness of transdiagnostic iCBT in public specialized mental health care. In addition, it should be highlighted that specialized mental health care in Spain is provided by psychiatrists and clinical psychologists with the highest degree of specialization in the treatment of psychological disorders in the national public health care system, which the authors believe adds value to the findings of this study. On the basis of these results, we encourage other researchers to conduct studies with a specific focus on implementation in this setting to achieve widespread integration of iCBT in the Spanish national public health care system, which has also been deeply affected by the consequences of the COVID-19 pandemic. In our analysis of predictors, the results showed trends suggesting that transdiagnostic iCBT predicts improvements in depression for patients with baseline depression. However, we did not find such patterns for baseline anxiety disorders and anxiety symptoms. As this is, to our knowledge, the first study to explore predictors of long-term outcomes in transdiagnostic iCBT, further studies should be conducted to shed light on the predictor role of sociodemographic and clinical variables for these interventions. Finally, in addition to sociodemographic and clinical variables, other variables specific to iCBT, such as the association between program use  and outcome in iCBT, should be further studied. Although research exists on the association among these variables, the findings mostly refer to posttreatment results -65. Henc


Repeated rotation of empiric antibiotic treatment strategies is hypothesized to reduce antibiotic resistance. Clinical rotation studies failed to change unit-wide prevalence of antibiotic resistant bacteria (ARB) carriage, including an international cluster-randomized crossover study. Unit-wide effects may differ from individual effects due to “ecological fallacy”. This post-hoc analysis of a cluster-randomized crossover study assesses differences between cycling and mixing rotation strategies in acquisition of carriage with Gram-negative ARB in individual patients.Pseudomonas aeruginosa with reduced susceptibility for piperacillin-tazobactam or carbapenems. Cycling , to mixing (changing antibiotic type every empiric antibiotic course). Rotated antibiotics were third- or fourth generation cephalosporins, piperacillin-tazobactam and carbapenems.This was a controlled cluster-randomized crossover study in 7 ICUs in 5 European countries. Clinical cultures taken as routine care were used for endpoint assessment. Patients with a first negative culture and at least one culture collected in total were included. Community acquisitions (2 days of admission or less) were excluded. Primary outcome was ICU-acquisition of Enterobacterales species with reduced susceptibility to: third- or fourth generation cephalosporins or piperacillin-tazobactam, and Acinetobacter species and Pseudomonas aeruginosa (n = 38) and Acinetobacter species (n = 20), with no statistically significant differences between interventions.For this analysis 1,613 admissions were eligible , with 16,437 microbiological cultures obtained. Incidences of acquisition with ARB during ICU-stay were 7.3% (n = 62) and 5.1% (n = 39) during cycling and mixing, respectively , after a mean of 17.7 (median 15) and 20.8 (median 13) days. Adjusted odds ratio for acquisition of ARB carriage during mixing was 0.62 (95% CI 0.38 to 1.00). Acquired carriage with ARB were Enterobacterales species (n = 61), There was no statistically significant difference in individual patients’ risk of acquiring carriage with Gram-negative ARB during cycling and mixing. These findings substantiate the absence of difference between cycling and mixing on the epidemiology of Gram-negative ARB in ICU.NCT01293071.This trial is registered with ClinicalTrials.gov, registered 10 January 2011,  Treatment of critically ill patients admitted in Intensive Care Units (ICU) is frequently complicated by infections caused by antibiotic resistant Gram-negative bacteria. Patients in ICU often receive broad spectrum antibiotics which increases antibiotic resistance selective pressure and the chance of acquiring colonization with Gram-negative antibiotic resistant bacteria (ARB). To reduce this selective pressure, unit-wide antibiotic stewardship programs (ASP) have been advocated, sometimes advocating but also discouraging antibiotic rotation strategies –3. ThesePrevious observational and quasi-experimental studies, however, yielded non-conclusive results for different pathogens, rotation schedules and outcomes –26. In aThis was a post-hoc nested cohort analysis of a cluster-randomized cross-over study . Four moPatients were included if a first culture was negative for Gram-negative ARB in cultures from the respiratory- or gastrointestinal tract .Enrolment of patients was preceded by approvement of the study and a waiver for individual informed consent, by all local Interne Review Boards of each participating center. This trial is registered with ClinicalTrials.gov, number NCT01293071. There were no changes to the study design after trial commencement.rd- or 4th generation cephalosporins and piperacillin-tazobactam in Enterobacterales species, and piperacillin-tazobactam or- meropenem resistance in Pseudomonas aeruginosa and Acinetobacter species. For the current analysis patients were eligible if microbiological cultures had been obtained from either the respiratory tract or gastrointestinal tract. Admissions with detected Gram-negative ARB carriage in the first two days of admission were excluded, as were admissions with positive endpoints on the same day as a first negative culture. Acquired carriage with Gram-negative ARB after day 2 of admission was assumed to be permanent for the duration of the study, i.e., readmissions of these patients were excluded from the analysis.The primary endpoint was the first clinical culture with Gram-negative ARB, defined as non-susceptibility to 3All clinical cultures taken during the study periods were included for endpoint analysis. If the primary endpoint was reached in an individual patient, subsequent culture results were excluded. Microbiological procedures were performed according to local laboratory practices, including local Minimum Inhibitory Concentration cutoff values. Participating microbiological laboratories did not change protocols for antibiotic susceptibility testing during the study. Likewise, infection prevention- and control measures did not change during the study period.Systemic antibiotic use was collected at the individual or aggregate level. Consumed quantities of antibiotics were converted to WHO Defined Daily Dose (DDD). Use of different antibiotic groups were represented as subdivided 6-week periods for cycling, and divided over the 9-month intervention period for mixing.The study protocol was approved by each local Institutional Review Board (IRB) and all centers obtained a waiver for individual patient written informed consent.Demographic- and infection prevention variables comparison between interventions were performed using bivariate tests. Dichotomous endpoints were tested using Pearsons’ chi-square test and for continuous outcomes using Students’ t-test. The primary outcome analysis was performed using chi-square test for binary endpoints. Odds ratios for acquisition between the two intervention periods (cycling and mixing) were calculated using mixed effects logistic regression modelling, with adjustment for clustering within each hospital and for confounders age, gender, length of stay, previous admission, origin of transfer to ICU and survival at ICU-discharge). Independent variables for this model were chosen based on potential correlation with the intervention and endpoint and being reasonably objectifiable.Additionally, detection bias between intervention periods from clinical cultures was assessed by modeling the probability of having a culture taken during admission using a mixed effects model correcting for clustering within individual ICU. This analysis was performed on all admissions during the intervention period, including patients without clinical cultures obtained. To assess competing events bias, mean and median length of stay was calculated and linear regression modelling of the effect of intervention type on length of stay.Carryover effects between first and second intervention period were assessed using the mixed effects model with an additional interaction term between intervention type and the sequence of performing mixing-then-cycling or vice-versa.pwr package, an effect size of 0.1 based on an arbitrary ‘small’ effect, significance level of 5% and assumed a two-sided alternative outcome. The calculated power to find a relevant difference was 88%. Analyses were performed using R software [Post-hoc power calculations were performed using the software .During the cycling and mixing intervention periods, there were 8,267 admissions overall in 7 ICUs in 5 countries . For thiEnterobacterales, most were Escherichia coli (22.5%), Pseudomonas species (21.2%), Klebsiella species (18.8%) and Enterobacter species ; upper- or lower respiratory materials (%), blood- or intravascular cultures (34.8%), enteric cultures  and urine (12.2%) (Appendix Table 1 in Pseudomonas aeruginosa (n = 38) and Acinetobacter species (n = 20), without statistically significant differences between interventions  and in 39 (5.1%) patients during cycling and mixing, respectively, , after a mean of 17.7 (median 15) and 20.8 (median 13) days, in cycling and mixing respectively . Distributions of endpoint defined micro-organisms were: Enterobacterales species (n = 61), ventions .Antibiotic use per interventions were similar, as expected by the protocol of equal use of each antibiotic (group) type over time (Appendix Table 4 in In mixed effect logistic regression modelling the unadjusted odds ratio for acquiring Gram-negative ARB was 0.72 (CI 95% 0.47 to 1.10) during mixing compared to cycling . After aThe culture probability OR was used for a sensitivity analysis. Here we assume the mixing population was indeed cultured less with missed endpoints. We assumed the size of this hypothetical mixing population was equal to the ‘real’ cultured cycling population. With an OR of 0.83, the mixing hypothetical population is roughly 120% of the real mixing population. To meet statistical significance in a comparison of the hypothetical mixing population and the real cycling population, the incidence in this missed group would need to be 17%. This is more than two times the prevalence found in the real mixing study population.In this patient-level analysis of a randomized cluster design multi-center study, we found no difference in effect of two antibiotic rotation strategies on the individual risk of acquiring colonization with Gram-negative ARB. These findings are in line with the results of the previously reported ecological analysis of this trial .Cycling and mixing are strategies that create opposite extremes of antibiotic diversity: Cycling maximizes homogeneity during a 6-week period, and mixing maximizes heterogeneity.The cluster randomized, crossover design allows comparison of unit-wide interventions in ICUs. As compared to cohort studies it reduces the likelihood of differences in clinical practice between ICUs, and other differences such as case-mix that may affect endpoint risk estimates. Compared to individual randomization, cluster randomization prevents non-adherence to protocol in individual patients, but provides an estimate of intervention effect at the ward level.With respect to patient characteristics there were statistically significant differences in mortality and illness severity scores. For mortality this was assumed to be a chance finding, as both intervention arms did not aim to influence mortality, and both arms received the same amounts and types of antibiotics. This difference is also reflected in the mortality scores APACHE II, SAPS II, TIS-28, which do not represent relevant differences: 2, 4.1 and 4.8 respectively points on these scores with maximum scores being from 55, 78 to 163.It was not possible to compare the interventions to a non-intervention period due to high baseline variability of antibiotic use between ICUs. More specific, there is no parameter to measure antibiotic diversity, therefore it is not possible to quantitatively compare baseline periods, whether these are alike -or different- to their corresponding mixing or cycling periods.This post-hoc analysis was motivated by previous findings of different results from ecological- and individual-based analyses . This stEcological fallacy occurs when group-averaged risks lead to incorrect associations between exposure and outcome. For instance, when the exposed patients are not the same patients that acquire the endpoint. Or, when group analyses overlook that the outcome might have occurred before the exposure, distorting causal inference. Or when individual baseline and longitudinal risks differ over time, creating different subgroup risk profiles. For instance, when in time, enrolled patients have incrementally higher baseline-risk for adverse outcome at admission, , but individual treatment outcomes during admission improve over time. A mechanism that has been termed Simpsons Paradox . The firFor this analysis, available clinical cultures were used as endpoint determinant. The inclusion criterion of having a first negative enteric or respiratory culture ascertained acquisition of colonization with Gram-negative antibiotic resistant bacteria. The intervention is still ecological, but the analysis aims to provide individual risk estimates for colonization in a ward where antibiotic rotation is applied.The restricted availability of these cultures prompted us to assess the presence and size of detection bias, which could have resulted from differential inclusion between intervention arms, due to the absence of individual randomization. We found no indication that baseline demographics or confounders were different between interventions, and adjustment for this in regression analysis did not change effect estimates. Alternatively, there could be a direct effect of the intervention on diagnostic culture practices. We used the source population  to perform two additional analyses to assess inclusion bias or an intervention effect on culture rates: 1) Assessing the differences between interventions of the probability of a culture being taken by regression analysis, and 2) a sensitivity analysis to contextualize the size of the potential intervention-effect on detection bias and outcome.The probability of having a culture taken was lower during the mixing period, but there was no evidence that this was due to differences in patient characteristics, nor were there differences in intervention effects after adjusting for confounders in the primary analysis. Sensitivity analysis yielded that detection bias would have needed to have caused a relative decrease in incidence of ARB acquisition with 29.2% (absolute 2.8%) to achieve statistical significance of the primary analysis. Or, in other words, in the uncultured patients in the mixing population that should have been cultured, based on our model, endpoint incidence would have to have been 16.6%, compared to 7.3% in the included mixing population, to achieve statistical significance. We consider this unlikely and therefore conclude that detection bias did not substantially reduce the validity of our analysis.Naturally, generalizability of our findings is shifted towards patients with a clinical culture taken, and thus patients with a relatively long stay in ICU. In fact, despite generally a low threshold for collecting clinical cultures in ICU patients in the participating ICUs, excluded patients were mostly short-stay patients, discharged after a median 2 days of admission. Furthermore, results would not be generalizable to ICUs with higher endemic prevalence of antibiotic resistant bacteria, where the antibiotics that were used in this study cannot be used for empiric therapy. These results however, are generalizable to most European ICUs, and any non-European ICU with similar technical capacities, staffing and resistance prevalence.Our findings do not support superiority of effects of cycling over mixing or vice versa on acquisition of ARB in the participating ICUs. Based on current scientific evidence, including our study, antibiotic rotation should, therefore, not be recommended as standard care –27. ThisS1 Checklist(DOC)Click here for additional data file.S1 Appendix(DOCX)Click here for additional data file.S1 Protocol(DOC)Click here for additional data file.


We assessed the frequency and correlates of COVID-19 vaccine hesitancy before Canada’s vaccine rollout. A cross-sectional vaccine hesitancy survey was completed by consecutive patients/family members/staff who received the influenza vaccine at McGill University affiliated hospitals. Based on the self-reported likelihood of receiving a future vaccine , the following three groups were defined: non-hesitant (score 10), mildly hesitant (7.1–9.9), and significantly hesitant (0–7). Factors associated with vaccine hesitancy were assessed with multivariate logistic regression analyses and binomial logistic regression machine learning modelling. The survey was completed by 1793 people. Thirty-seven percent of participants (n = 669) were hesitant (mildly: 315 (17.6%); significantly: 354 (19.7%)). Lower education levels, opposition and uncertainty about vaccines being mandatory, feelings of not receiving enough information about COVID-19 prevention, perceived social pressure to get a future vaccine, vaccine safety concerns, uncertainty regarding the vaccine risk-benefit ratio, and distrust towards pharmaceutical companies were factors associated with vaccine hesitancy. Vaccine safety concerns and opposition to mandatory vaccinations were the strongest correlates of vaccine hesitancy in both the logistic regressions and the machine learning model. In conclusion, in this study, over a third of people immunized for influenza before the COVID-19 vaccine rollout expressed some degree of vaccine hesitancy. Effectively addressing COVID-19 vaccine safety concerns may enhance vaccine uptake. The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has profoundly impacted our society ,4,5. At Vaccine hesitancy is defined as refusal or a delay in acceptance of vaccination, despite the availability of immunization services . This coSince October 2020, a sharp increase in positive tests for COVID-19 occurred in Quebec and on 12 November 2020, the province announced social gathering restrictions as part of a public health emergency plan . The first COVID-19 vaccines to receive authorization for use in Canada were Pfizer/BioNTech on 9 December and Moderna on 23 December 2020.Individuals (n = 2491) who voluntarily agreed, between 2 November and 11 December 2020 (pre-vaccine rollout), to receive the seasonal influenza vaccine to one of the two major adult hospitals of the McGill University Health Centre (MUHC), Montreal General Hospital or Glen Site, were approached to complete the survey. The vaccine clinic was open to all members of the community. Patients were informed about the study by the vaccinating nurse at the time they received the influenza vaccine. No remuneration was offered for completing the survey.Adults who consented to this study completed a paper survey in their language of preference (English or French) during the 15 min post-vaccination observation period. The survey was anonymous.Demographics, including age, sex, race/ethnic minorities associated with increased COVID-19 infection/mortality, role , employment status, household members, smoking status, and risk factors for severe illness from COVID-19 infection .Previous seasonal influenza vaccination.Previous COVID-19 infection and perception of amount of COVID-19 preventative information received.Likelihood of receiving a future vaccine and causes of vaccine hesitancy, using questions proposed by the WHO-SAGE working group adapted to the COVID-19 vaccine .Likelihood of engaging in risk-associated behaviors based on the adult Domain-Specific Risk-Taking (DOSPERT) health and safety subscale, and medical risk domain add-on ,12. A riWe assessed the willingness to receive a future COVID-19 vaccine, as well as factors potentially associated with COVID-19 vaccine hesitancy. Those factors were selected from the framework proposed by the World Health Organization (WHO) Strategic Advisory Group of Experts on Immunization (SAGE) working group . In addiWe summarized the population characteristics and distributions of responses using frequency distributions for categorical variables and means and standard deviations for continuous variables. Multiple imputation was used for the variables with missing data. The following three groups were defined based upon the frequency distribution of self-reported likelihood of getting a future COVID-19 vaccine: (a) significantly hesitant , (b) mildly hesitant , and (c) non-hesitant . Two multivariate multinomial logistic regression models were built to identify factors associated with significant or mild hesitancy, using non-hesitancy as the reference group. One model included all covariates (saturated model), and another one  featured stepwise backward elimination of the non-significant variables.A multinomial logistic regression machine learning model was initially built to evaluate how well the survey responses predicted the outcome of significant and mild hesitancy. As a result, binary logistic regression models were built to predict any hesitancy . The models were trained using 200 replications of bootstrap sampling with replacement, and performance of each model was evaluated on the unused (out-of-sample or out-of-bag) data and corrected for optimism/overfitting ,14. LassAll analyses were conducted using SPSS version 24.0  and Matlab 2019b .A total of 1793 out of 2491 eligible individuals (72%) agreed to complete the survey. The median age of the study participants was 52.2 years (range 18–97 years)  were hesitant, about half (354/669) of whom were significantly hesitant. Most participants trusted the government to make decisions in society’s best interest, while just over half considered that COVID-19 vaccines should be mandatory and had confidence that pharmaceutical companies would provide safe and effective COVID-19 vaccines. Almost a third of participants believed that barriers related to waiting time (at the vaccine provider) and travel  might limit their access to a COVID-19 vaccine. Although most participants indicated they were ‘a little’ concerned about the safety of COVID-19 vaccines, over three-quarters considered that in general, vaccine benefits outweigh risks. Only 20% of the individuals anticipated any social pressure to receive a future COVID-19 vaccine. The proportion of participants with high risk-taking behaviors was similar in all hesitancy groups and did not differ between patients and health care professionals.The results of the full  and parsThe full binomial logistic regression machine learning model, including all available variables, showed excellent discriminative performance in terms of being associated with overall hesitancy  a, and thThis cross-sectional survey conducted prior to the COVID-19 vaccine rollout in Canada revealed that 20% of people from a convenience sample who voluntarily agreed to influenza vaccination  had significant vaccine hesitancy. The strongest factors associated with significant hesitancy included opposition/uncertainty regarding mandatory vaccination, concerns about vaccine safety, uncertainty about the vaccine risk: benefit ratio, lack of trust in pharmaceutical companies, low level of education, and low risk-taking behavior.The proportion of participants ‘not likely at all’ (score of 0) to receive a COVID-19 vaccine in our study was 2%, while those who were highly likely to accept it (score of 10) represented 63%. In a previous survey that assessed the likelihood of vaccine acceptance in 19 countries, including Canada, 8% of the participants reported they would not accept a future COVID-19 vaccine , while 6Over half of the participants in this survey had a university education and were employed. The association between level of education or employment and COVID-19 vaccine hesitancy was inconsistent in previous work. While some studies suggested that employed individuals are more likely to accept a COVID-19 vaccine, others suggest the opposite may be true ,21,22. TIn the current study, individuals accepting vaccines were more likely to agree with the idea that vaccines should be mandatory, trusted the government to make decisions in society’s best interest, and were confident that pharmaceutical companies would provide safe and effective COVID-19 vaccines. The concept of trust or confidence in the need for/value of vaccines has the strongest association with vaccine uptake . SimilarIn this study, vaccine hesitancy was associated with the participant’s impression of not receiving enough information about COVID-19 prevention, disagreement/uncertainty regarding vaccine benefits and risks, and safety related-concerns. This highlights the relevance of developing a multifaceted program of intervention with the sharing of information by trusted sources at its core. Vaccine development and use depend on data-driven assessments of benefits and risks, first by regulatory bodies, and then by individual physicians and patients. Recognizing individual concerns and tailoring the message to specific groups is crucial to positively influence vaccine acceptance ,30. We aSignificant COVID-19 vaccine hesitancy was associated with low risk-taking behaviors. “Risk taking” involves perceptions of expected benefits or harm, with respect to the risk behavior. One’s preference for risky options is assumed to reflect a trade-off between expected benefit or value, and its riskiness . To the best of our knowledge, this is the first study to report this finding.This study has several limitations, which include the following: (1) selection bias, as we only surveyed people who were generally unopposed to vaccines ; (2) the fact that it was limited to two large hospitals from a single university center in an urban area; (3) the lack of validation of the intention to accept or refuse the COVID-19 vaccine with the actual vaccination status; and, (4) the risk of reporting bias, given that the data were self-reported. Despite the study’s limitations, the fact that these data were collected in a ‘vaccine-favorable environment’ not only highlights the magnitude of the problem but, by identifying the strongest correlates of vaccine hesitancy, the findings provide potentially actionable targets for interventions to enhance vaccine uptake. Our findings are also interesting, as they included both healthcare workers and patients.We found significant COVID-19 vaccine hesitancy to be present even among a group of people who sought out the influenza vaccine. Multiple factors were associated with this phenomenon, but those most strongly associated were disagreement/uncertainty that COVID-19 vaccines should be mandatory, safety concerns, doubts about benefits versus risks, and not trusting pharmaceutical companies. Carefully targeted educational interventions, focusing on the advantages and safety of COVID-19 vaccines, delivered by individuals and organizations most trusted by people  may promote vaccine uptake and reduce COVID-19 vaccine hesitancy.


There are limited data on risk factors for serious outcomes and death from COVID-19 among patients representative of the U.S. population. The objective of this study was to determine risk factors for critical care, ventilation, and death among hospitalized patients with COVID-19.This was a cohort study using data from Optum’s longitudinal COVID-19 electronic health record database derived from a network of healthcare provider organizations across the US. The study included patients with confirmed COVID-19 (presence of ICD-10-CM code U07.1 and/or positive SARS-CoV-2 test) between January 2020 and November 2020. Patient characteristics and clinical variables at start of hospitalization were evaluated for their association with subsequent serious outcomes  using odds ratios (OR) and 95% confidence intervals (CI) from logistic regression, adjusted for demographic variables.Among 56,996 hospitalized COVID-19 patients , 11,967 received critical care, 9136 received mechanical ventilation, and 8526 died. The median duration of hospitalization was 6 days , and this was longer among patients that experienced an outcome: 11 days  for critical care, 15 days  for mechanical ventilation, and 10 days  for death. Dyspnea and hypoxemia were the most prevalent symptoms and both were associated with serious outcomes in adjusted models. Additionally, temperature, C-reactive protein, ferritin, lactate dehydrogenase, D-dimer, and oxygen saturation measured during hospitalization were predictors of serious outcomes as were several in-hospital diagnoses. The strongest associations were observed for acute respiratory failure  and sepsis . Treatment with angiotensin-converting enzyme inhibitors/angiotensin receptor blockers during hospitalization were inversely associated with death .We identified several clinical characteristics associated with receipt of critical care, mechanical ventilation, and death among COVID-19 patients. Future studies into the mechanisms that lead to severe COVID-19 disease are warranted.The online version contains supplementary material available at 10.1186/s12879-022-07383-6. Since coronavirus disease 2019 (COVID-19) first emerged in China in December 2019, there have been over 270 million confirmed cases and 5.3 million deaths worldwide due to COVID-19 as of 15 December 2021, according to the World Health Organization [There is significant heterogeneity in the clinical presentation of COVID-19 infection, ranging from patients who are asymptomatic to those with severe disease –4. It isThe serious outcomes of COVID-19  and their preceding risk factors have been identified previously –10. CommThe purpose of this study was to apply an exploratory, data-driven approach to the identification of potential risk factors for serious outcomes among patients with COVID-19 in order to inform clinicians and researchers of characteristics that may be integral to identifying high risk patients. Thus, the objective was to determine demographic and clinical predictors associated with critical care, mechanical ventilation, and death among hospitalized COVID-19 patients in a large electronic health record (EHR) database that is representative of a geographically diverse U.S. population.th Revision, Clinical Modification (ICD-10-CM) diagnosis code (U07.1) and/or a positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral test. The date of confirmed infection was the earlier of the date of diagnosis or the date of a positive test. The cohort entry date was the earliest date that the patient met both of the following criteria: confirmed COVID-19 infection and admission to the hospital. For patients who were hospitalized prior to contracting COVID-19, the date of cohort entry was the date of confirmed infection. For patients who were confirmed to have COVID-19 before they were admitted to the hospital, the date of cohort entry was date of hospital admission. This approach for assigning cohort entry date allows for the description of clinical characteristics at the time patients were first hospitalized with COVID-19. For clinical characteristics other than death, patients were followed from cohort entry to discharge or 30 days after cohort entry, whichever came first. Deaths were identified in all follow-up available, including during and after hospitalization.This was a retrospective cohort study that included patients confirmed with COVID-19 infection between January 2020 and November 2020. Among these patients, a subset of patients hospitalized with COVID-19 was identified from those with an inpatient health care encounter. Confirmation of COVID-19 infection was based on presence of a specific International Classification of Diseases, 10Patients were identified from Optum’s longitudinal COVID-19 EHR database derived from a network of healthcare provider organizations across the U.S. The COVID-19 EHR database consists of a subset of patients from Optum’s EHR database, which represents a geographically diverse U.S. patient population with more than 85 million patients from 2007 through 2019. Optum’s EHR database includes data collected from tens of thousands of providers and hundreds of hospitals representing more than 60 electronic medical record (EMR)-based provider/hospital networks across the U.S. This database incorporates clinical and medical administrative data from both inpatient and ambulatory EMRs, practice management systems, and numerous other internal systems. Information is processed from across the continuum of care, including acute inpatient stays and outpatient visits. The COVID-19 data captures diagnostics specific to the COVID-19 patient during initial presentation at hospital admission, acute illness, and convalescence with over 500 mapped labs and bedside observations, including COVID-19 specific testing. The data are incorporated into the underlying database on a biweekly basis, allowing for near real-time analysis and assessment of the COVID-19 clinical landscape. The database is certified as de-identified by an independent statistical expert following Health Insurance Portability and Accountability Act statistical de-identification rules.Demographic characteristics were assessed on the date of cohort entry. Comorbidities and medication use were assessed in the 21 days prior to cohort entry. Comorbidities were identified by ICD-10-CM diagnosis code with a diagnosis status of “history of” and medications were mapped according to the Anatomical Therapeutic Chemical (ATC) classification scheme.Additionally, vital signs, laboratory results, symptoms, diagnoses, and treatments during hospitalization were assessed. For vital signs and laboratory results, the first measurement on or after the date of cohort entry was selected if the patient had multiple measurements. Symptoms and diagnoses were identified by ICD-10-CM diagnosis codes.The primary outcomes of interest were receipt of critical care, mechanical ventilation, and death. Patients were classified according to the presence or absence of an outcome, and outcome groups were not mutually exclusive.Receipt of critical care was identified using Current Procedural Terminology, 4th Edition (CPT-4) codes. Receipt of mechanical ventilation was identified by CPT-4 and International Classification of Diseases, 10th Revision, Procedural Coding System (ICD-10-PCS) codes and included intubation, mechanical ventilation, and extracorporeal membrane oxygenation (ECMO). Receipt of critical care and mechanical ventilation were ascertained during hospitalization.Death was ascertained via linkage to the Social Security Administration’s Death Master File and/or the presence of a death indicator in the structured EHR data within all data available during and after hospitalization.All analyses were conducted using SAS version 9.4 . Baseline characteristics were examined overall and according to outcome . Categorical variables were summarized using frequency and percent while continuous variables were summarized using median and interquartile range (IQR).For the association analyses, vital signs and laboratory values were transformed into categorical variables. Dichotomous variables were created based on clinically-relevant cutpoints. Additionally, categories based on quintiles were generated to examine the shape of the dose–response relationship of vital signs and laboratory values with each outcome. Quintiles were determined based on the distribution of each biomarker among hospitalized patients overall. Tests for linear trend were computed by using the medians of the quintiles modeled as a continuous variable.Logistic regression models were used to estimate unadjusted and adjusted odds ratios (OR) and corresponding 95% confidence intervals (CI) for associations between the covariates and each outcome. Adjusted models included age, gender, region, race, and week of cohort entry. Week of cohort entry was included as a covariate to adjust for any potential changes in patient characteristics or treatments over time.A total of 56,996 hospitalized patients with COVID-19 between January 2020 and November 2020 were identified , kidney disease (31.0%), diabetes (29.5%), and obesity (23.8%)  and hypoxemia (25.8%)   among 56,996 hospitalized patients with COVID-19 within a large, EHR database. We conducted an evaluation of the association of many demographic and clinical characteristics with these outcomes in order to identify potential signals for experiencing a serious outcome. As was observed in prior studies –10, oldeIn this study of hospitalized patients with COVID-19, we found that African-Americans and women were at lower risk of experiencing a serious outcome compared to Caucasians and men, respectively. Since the start of the pandemic, African-Americans have been more likely than Caucasians to contract and be hospitalized with COVID-19 . NonetheHypertension was the most prevalent comorbidity among hospitalized COVID-19 patients in this study and its prevalence was even higher among patients with a serious outcome. Hypertension was positively associated with receipt of critical care and mechanical ventilation, but inversely associated with mortality after adjusting for age and other demographics. Findings in this study are consistent with previous studies that have found hypertension to be common among adults diagnosed with COVID-19, but not associated with mortality after adjusting for covariates , 17. ThuIn the current study, treatment with ACEs/ARBs during hospitalization was associated with lower odds of critical care and mortality. It is recommended that patients who are prescribed ACEs/ARBs for cardiovascular disease continue taking these medications if hospitalized with COVID-19 , 19. WitSeveral vital signs and laboratory results were associated with serious outcomes in COVID-19 patients in this study, with the strongest associations observed for LDH, D-dimer, and CRP. LDH is an enzyme found within cells in almost all organ systems . Its levThe current study was based on an analysis of EHR data, which are valuable for the examination of clinical health outcomes and treatment patterns. Nonetheless, all EHR databases have inherent limitations because the data are collected for the purpose of clinical patient management, not research. Unlike in clinical trials, where the collection of clinical and laboratory measures is standardized, the Optum EHR includes real-world clinical data obtained from multiple medical and laboratory settings used for patient care. Because data are not collected in a systematic way, clinical measurements  were not available for all patients.Additionally, the presence of a diagnosis code in EHR data may not represent the actual presence of disease, as the diagnosis code may be incorrectly coded, or included as rule-out criteria rather than actual disease. We assumed the absence of a diagnosis code meant the patient did not have the disease. This assumption may be a reason why we observed an inverse association between cough and serious outcomes; in a patient who is very ill, severe symptoms like dyspnea and hypoxemia may be more likely to be recorded than minor symptoms such as cough. Furthermore, it is possible that some comorbidities and medications may not have been captured as health care encounters with medical providers who do not contract with Optum would not be observed.The median duration of hospitalization among patients who died was 10 days. However, deaths were identified within all data available, not only during hospitalization. As such, it is possible that duration of hospitalization may have been shorter among patients that died during hospitalization. An additional limitation of the EHR database is the data lag at the time of data extraction, which likely resulted in an underestimation of the number of deaths. Finally, residual confounding is a concern as we only adjusted for demographic covariates.In summary, we utilized an exploratory, data-driven approach to identify many clinical characteristics that were associated with receipt of critical care, mechanical ventilation, and death among patients hospitalized with COVID-19. As more continues to be learned about COVID-19 by clinicians and researchers, future studies should move toward causal inference and focus on identifying the etiologic factors and mechanisms responsible for some patients experiencing more severe COVID-19 disease.Additional file 1. Supplemental tables.